We propose a method of detecting and tracking occluded illegally parked vehicles. The method used a deep learning framework that can detect and track moving vehicles. To obtain the long term tracking of stationary vehicles the process must be capable of withstanding large changes in lighting, weather and large amounts of occlusion from passing vehicles. A modified dense SIFT descriptor algorithm has been developed. This compares the current frame with the background and removes objects in motion. The tracking of the occluded illegally parked vehicle is achieved by YOLO version 3 algorithm, combined with a predictive filter. For each illegally parked vehicle, the occluded portion is not used for feature point matching. Based on the matching result, the occluded illegal vehicle can be tracked. Our approach tested performance on a public database(i-LIDS) and the results indicate the method produces a very high accuracy compared to other published work.

A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

Illegally parked vehicle detection systems are considered crucial elements in the development of any video-surveillance based traffic-management system. The major challenges in this task lie in making the end solution real time, illumination invariant and occlusion tolerant. A two-stage application framework is presented which efficiently identifies vehicles parked illegally in restricted parking- zones. A real-time approach has been followed and an improved foreground segmentation method based on Segmentation History Images (SHI) is developed to identify stationary objects. A three step pixel based classification method is applied on the background segmentation output to segment adjacent moving pixels that become stationary for certain periods of time. The process then locks on to all identified stationary pixel patches, parts of which overlap with the regions of interest marked interactively a priori. The second stage of the process is applied subsequently to track all the stationary pixel patches detected during the first stage using an adaptive edge orientation based tracking method. Experimental results show that the tracking technique gives more than a 90% detection success rate, even if objects are partially occluded. The technique has been tested on the UK Home Office i-LIDS Parked Vehicle video sequences along with the University of Sussex Traffic Dataset and results are compared with other available state of the art methods. © ICROS, KIEE and Springer 2012.

Deep learning object detection within images has revolutionised AI in the past few years. However, the growing energy requirements to power the computer servers is becoming a major problem. Optical computation has long been a possible solution to this, it has the capabilities of calculating at the speed of light! A number of technological challenges remain. This talk will discuss the current state of the art.

In this paper, we present a method for identifying and tracking illegally parked vehicles. This approach is based on deep learning for vehicles detection and hand crafted descriptors for the tracking which are designed to cope with occlusions. The tracking of the parked vehicle is achieved by key-point extraction of the detected vehicles and feature point matching. For each frame, a bounding box was generated to represent the vehicle and feature points extracted in that area. All parked vehicles have a unique ID which was generated by the Hungarian algorithm and Kalman filter, and the parked vehicle with the same ID was matched frame by frame. Based on this matching result, the stationary vehicles in the forbidden area can be tracked. Our approach tested efficiency and robustness on a public database and is shown to produce state of the art results.

With the advances in Body Sensor Networks (BSNs) and textile-integrated sensing, more sensor data becomes available from which human activities are recognised. However, some sensors may become unavailable unexpectedly in practice. Previous work proposed to complement the features of a missing sensor with regression-based methods but considered only up to one sensor missing and thus lacked a mechanism for selecting relevant sensors when multiple sensors were missing. The number of unique combinations of missing sensors increases exponentially when multiple sensors may be missing. To handle this, we propose a Hierarchical Feature Recovery (HFR) approach. We first assess the dependencies between sensors by comparing the feature mapping accuracy between each sensor and then evaluate the HFR approach on a dataset of activities of daily living with 17 gestures using 14 motion sensors. Our HFR method can alleviate classification performance drop by up to 8.3 pp compared to a baseline method.

Deep learning object detection has revolutionised AI in the past ten years. However, computer servers have huge, and growing, energy requirements. The best in class compute training requirements are currently doubling every 3.4 months. For object detection networks the vast bulk of the computation is taken up by convolutional layers. Since a lens can perform a two dimensional Fourier transform of the incoming optical wavefront, correlation and convolution operations have been successfully demonstrated in the past with a 4-f based optical system. These have the potential of a very large computational bandwidth. The power requirements are then from the coherent light source, spatial light modulators and sensors. This is typically dominated by the camera but still only about 5W.

However, network designs such as ResNet, VGG and Inception do not map well onto a pure optical domain. Convolutional layers are not just a single operation but the sum of several convolutions, with an additional bias and a non-linear activation function. In this paper we develop a Python framework for simulating optical deep learning using Pytorch. This is used to develop and demonstrate optical designs that take a hybrid approach to the network by combining low-powered electronic signal processing with parallel optical Fourier transforms, the latter performing the majority of the calculations. We introduce a novel activation function and demonstrate that the performance remains intact. This allows for arbitrary network designs to be introduced.

Coherent optical processing methods however suffer from various noise sources of which laser speckle is a major source. We model speckle effects in a simple four layer convolutional network which was able to achieve a 73% accuracy, but this drops off rapidly to 10% with a speckle contrast of 0.5. Various speckle reduction methods have been considered and shown to restore the accuracy.

This is ongoing interdisciplinary research drawing inspiration from the rapidly growing field of evolutionary developmental biology, i.e., the so-called “Evo-Devo” paradigm. We not only aim to investigate the similarities but importantly, also the differences between the synthesis of a biological organism and an engineering system. A bespoke algorithm will be developed using an Evolutionary Algorithm to generate fractal patterns observed in nature. The success of this method will allow us to investigate its possible usage in designing an engineering system.

Performing deep learning in the optical domain is attractive due to the very low electrical power requirements when compared to running networks on a GPU. Since a single positive lens can perform a Fourier transform, correlation operations are relatively simple to implement and they have the potential of a very large computational bandwidth. However, many of the current designs of deep learning networks are not easily implemented in the optical domain. In this paper we develop a python framework for simulating optical deep learning using Pytorch. This allows the discovery of the optimal weights by calculating them on a realistic optical system. Noise sources, speckle models, and calibration errors can be accounted for. The effect of readily realisable filters such as nematic liquid crystal phase only spatial light modulators is investigated. Key differences still exist such as activation functions and the ability to modulate the signal is limited.

Deep learning for object detection offers the advantage of very low electrical power requirements but with the potential of a very large computation bandwidth due to the ability of Fraunhofer diffraction to perform correlation operations. However, many of the current designs of deep learning networks are not easily implemented in the optical domain. In this paper we develop a modified version of the deep learning library, Keras, that can accurately model optical systems. This allows the discovery of the optimal weights by calculating them on a realistic optical system. Noise sources, speckle models, and calibration errors can be accounted for. The effect of using readily realisable filters such as nematic liquid crystal phase only spatial light modulators is investigated. The effect of multiplexing a number of correlations in order to replicate the Conv2D multiple channel input is assessed. The effect of an optically implementable bias and activation functions are examined and compared to the state-of-the-art software implementations. We show that object recognition can be achieved using spatial light modulator technology and give comparable results to digital implementations.

We present the first work that investigates the potential of improving the performance of transportation mode recognition through fusing multimodal data from wearable sensors: motion, sound and vision. We first train three independent deep neural network (DNN) classifiers, which work with the three types of sensors, respectively. We then propose two schemes that fuse the classification results from the three mono-modal classifiers. The first scheme makes an ensemble decision with fixed rules including Sum, Product, Majority Voting, and Borda Count. The second scheme is an adaptive fuser built as another classifier (including Naive Bayes, Decision Tree, Random Forest and Neural Network) that learns enhanced predictions by combining the outputs from the three mono-modal classifiers. We verify the advantage of the proposed method with the state-of-the-art Sussex-Huawei Locomotion and Transportation (SHL) dataset recognizing the eight transportation activities: Still, Walk, Run, Bike, Bus, Car, Train and Subway. We achieve F1 scores of 79.4%, 82.1% and 72.8% with the mono-modal motion, sound and vision classifiers, respectively. The F1 score is remarkably improved to 94.5% and 95.5% by the two data fusion schemes, respectively. The recognition performance can be further improved with a post-processing scheme that exploits the temporal continuity of transportation. When assessing generalization of the model to unseen data, we show that while performance is reduced - as expected - for each individual classifier, the benefits of fusion are retained with performance improved by 15 percentage points. Besides the actual performance increase, this work, most importantly, opens up the possibility for dynamically fusing modalities to achieve distinct power-performance trade-off at run time.

Computer vision techniques applied on images opportunistically captured from body-worn cameras or mobile phones offer tremendous potential for vision-based context awareness. In this paper, we evaluate the potential to recognise the modes of locomotion and transportation of mobile users, by analysing single images captured by body-worn cameras. We evaluate this with the publicly available Sussex-Huawei Locomotion and Transportation Dataset, which includes 8 transportation and locomotion modes performed over 7 months by 3 users. We present a baseline performance obtained through crowd sourcing using Amazon Mechanical Turk. Humans infered the correct modes of transportations from images with an F1-score of 52%. The performance obtained by five state-of-the-art Deep Neural Networks (VGG16, VGG19, ResNet50, MobileNet and DenseNet169) on the same task was always above 71.3% F1-score. We characterise the effect of partitioning the training data to fine-tune different number of blocks of the deep networks and provide recommendations for mobile implementations.

The market for electric vehicles is growing day by day and electric car chargers can be seen often on pavements of the major cities and towns. With this growing market, industry is already looking for another breakthrough, i.e. wireless vehicle charging. This is much like charging smart phones using wireless charging pads instead of plugging the vehicle in. Industry is exploring ways to charge Electric vehicles wirelessly when the car is parked over a charger on the ground beneath it. For the wireless charging to work, both elements must be well aligned. This paper explores using vision based approaches to provide the automatic recognition, localisation and tracking of an inductive plate for wireless car charging. Visual feedback is provided to a motion control system for accurate charger alignment.

A spatial domain optimal trade-off Maximum Average Correlation Height (SPOT-MACH) filter has been shown to have advantages over frequency domain implementations of the Optimal Trade-Off Maximum Average Correlation Height (OR-MACH) filter as it can be made locally adaptive to spatial variations in the input image background clutter and normalized for local intensity changes. This enables the spatial domain implementation to be resistant to illumination changes. The Affine Scale Invariant Feature Transform (ASIFT) is an extension of previous feature transform algorithms; its features are invariant to six affine parameters which are translation (2 parameters), zoom, rotation and two camera axis orientations. This results in it accurately matching increased numbers of key points which can then be used for matching between different images of the object being tested. In this paper a novel approach will be adopted for enhancing the performance of the spatial correlation filter (SPOT MACH filter) using ASIFT in a pre-processing stage enabling fully invariant object detection and recognition in images with geometric distortions. An optimization criterion is also be developed to overcome the temporal overhead of the spatial domain approach. In order to evaluate effectiveness of algorithm, experiments were conducted on two different data sets. Several test cases were created based on illumination, rotational and scale changes in the target object. The performance of correlation algorithms was also tested against composite images as references and it was found that this results in a well-trained filter with better detection ability even when the target object has gone through large rotational changes.

Considering the relatively poor robustness of quality scores for different types of distortion and the lack of mechanism for determining distortion types, a no-reference image quality assessment (NR-IQA) method based on the AdaBoost BP Neural Network in Wavelet domain (WABNN) is proposed. A 36-dimensional image feature vector is constructed by extracting natural scene statistics (NSS) features and local information entropy features of the distorted image wavelet sub-band coefficients in three scales. The ABNN classifier is obtained by learning the relationship between image features and distortion types. The ABNN scorer is obtained by learning the relationship between image features and image quality scores. A series of contrast experiments are carried out in the LIVE database and TID2013 database. Experimental results show the high accuracy of the distinguishing distortion type, the high consistency with subjective scores and the high robustness of the method for distorted images. Experiment results also show the independence for the database and the relatively high operation efficiency of this method.

In order to solve problems of occlusion and fast motion of small targets in UAV (Unmanned Aerial Vehicle) target tracking , an adaptive algorithm which fuses the improved color histogram tracking response and the correlation filter tracking response based on multi-channel HOG features is proposed to realize small target tracking with high accuracy. The state judgment index is used to determine whether the target is in a fast motion or an occlusion state. In the fast motion state, the search area is enlarged, and the color optimal model which suppresses the suspected area is used for rough detection. Then, re-detection in the location of multiple peaks in the rough detection response is carried out using the correlation filter to accurately locate the target. In an occlusion state, the model stops updating, the search area is expanded, and the current color model is used for rough detection. Then, re-detection in the place of multiple peaks in the rough detection response is carried out using the correlation filter to accurately locate the target. Experimental results show that the proposed method can track small targets accurately. The frame rate of the proposed method is 40.23 frames/second, indicating usable real-time performance.

A multiple view polarimetric camera is developed. The system uses four separate action cameras and software is employed to map the images onto each other in order to generate the Stokes vectors, the degree of linear polarisation and angle images. To ensure robustness, an automated calibration system has been developed that ensures the pixels are correctly mapped. Video frame synchronisation is also developed.

The Shor quantum factorization algorithm allows the factorization or large integers in logarithmic squared time whereas classical algorithms require an exponential time increase with the bit length of the number to be factored. The hardware implementation of the Shor algorithm would thus allow the factorization of the very large integers employed by commercial encryption methods. We propose some modifications of the algorithm by employing some simplification to the stage employing the quantum Fourier transform. The quantum Hadamard transform may be used to replace the quantum Fourier transform in certain cases. This would reduce the hardware complexity of implementation since phase rotation gates with only two states of 0 and π would be required.

The surveillance industry has traditionally focused on the use of colour intensity images and then used computer vision methods to extract information. Deep learning methods have been demonstrated successfully but require significant computational resources. Fog and rain still present a problem to these methods. Other non-optical imaging technologies are available but the applications can be cost sensitive. Polarimetric cameras offer a solution to some of these problems. This paper presents a practical and low cost design that uses between two and four HD cameras with a wide field of view. This system has an automatic calibration stage that ensures the video frames are synchronised in time. To produce the Stoke parameters each pixel from one camera must be mapped to the others. To perform this, a homography matrix for each camera is automatically discovered and maps each video stream into the correct spatial coordinates. This attempts to use SIFT keypoint mapping but since each input image is a different polarisation state there are potentially a low number of keypoints so an additional check stage is introduced. Calibration results are presented along with example images, post process methods and feature extraction results.

A city Resource Directory (CRD) for the smart video surveillance system offers enhanced security for citizens. With a growing need for public security and safety, video enabled devices are a common sight in private and public locations around the world. They are however isolated systems, and unless owned by the government, there is no means to intelligently confirm their existence or benefit from their activities, at the public level. We propose a citywide surveillance directory system to support and facilitate the cataloguing, accessing, managing and administration of the video surveillance networks. To demonstrate the our work, we developed a simulation project in which resource directories designated for specific cities were used to administer local surveillance systems and we demonstrate how a surveillance system registers with only the appropriate CRD in its local city. We demonstrated how our solution manages failed nodes during the registration process of cameras joining the CRD.

A city Resource Directory (CRD) for the smart video surveillance system offers enhanced security for citizens. With a growing need for public security and safety, video enabled devices are a common sight in private and public locations around the world. They are however isolated systems, and unless owned by the government, there is no means to intelligently confirm their existence or benefit from their activities, at the public level. We propose a citywide surveillance directory system to support and facilitate the cataloguing, accessing, managing and administration of the video surveillance networks. To demonstrate the our work, we developed a simulation project in which resource directories designated for specific cities were used to administer local surveillance systems and we demonstrate how a surveillance system registers with only the appropriate CRD in its local city. We demonstrated how our solution manages failed nodes during the registration process of cameras joining the CRD.

There are an estimated 6 million CCTV cameras now in the UK. Many of these are still analogue and many are completely unwatched, however, this is changing with new technology. Whether you think this is vital for crime prevention or a gross violation of civil liberties new systems will soon enable computers to track you like never before. This talk will review the state of the art of IP cameras, tracking and deep learning methods.

One of the key challenges in the design of a fibre optics system that degrades the system performance is the presence of bending, which should be carefully considered to ensure a reduction of losses in order to further increase the total carrying capacity. In this work, we present numerical simulations to examine bending loss in a newly designed high-capacity fibre with a large-core diameter of 200 µm and graded index profile (GI200 fibre) supporting a multiplicity of communication channels at a wavelength of 1.55 µm. In addition, we investigate the effect of bending on the periodic reconstruction phenomenon when four input channels are multiplexed into both a single bend and multiple bends of fibre under various bending conditions.

An obstacle detection algorithm is introduced for aiding the navigation of unmanned ground vehicles (UGV). Coloured obstacles are placed randomly in an indoor environment. The coloured obstacles are detected, analysed and processed using a proposed monocular vision algorithm. A camera calibration is conducted to determine the relative position and orientation of the UGV with respect to the obstacles based on intrinsic and extrinsic matrices to form a perspective projection matrix. The field geometry is used to obtain a mapped environment in the world coordinates. Our obstacle detection algorithm is proposed to identify the existence of the obstacles in the field. Using bounding boxes around the detected obstacle allows the determination of the obstacles locations in a pixel coordinate frame. Thus, the depth perception is determined by using the pixel coordinates and the camera projection matrix. Real-time experiments are carried out to demonstrate the validity and efficiency of the proposed algorithm.

The presentation covers some history on lasers, then goes on to describe the the USA Airborne Laser Testbed and its Chemical Oxygen Iodine Laser (COIL). The significance of laser modes for beam propagation is explained. Fibre lasers and their applications and construction is described. The US Navy 33kW fibre laser is reported, then the Lockheed Martin 30kW and 60kW systems are described; UK Dragonfire is reported followed by the US Textron 150kW liquid laser. Some conclusions are drawn.

The motion control of unmanned ground vehicles is essential in the industry of automation. In this paper, the sensors of a fuzzy inference system that is based on a navigation technique for an unmanned ground vehicle are formulated in a cluttered dynamic environment. This fuzzy inference system consists of two controllers. The first controller uses three sensors based on the distances from the front, the right and the left. The second controller employs the angle difference between the heading of the vehicle and the targeted angle to choose the optimal route based on the dynamic environment and reach the desired destination with minimum running power and time. Experimental tests have been carried out in three different case studies to investigate the validation and effectiveness of the introduced controllers of the fuzzy inference system. The reported simulation results are conducted using MATLAB software package. The results show that the controllers of the fuzzy inference system consistently perform the maneuvering task and route planning efficiently even in a complex environment with populated dynamic obstacles.

The quantum Fourier transform (QFT);Similarities and differences of coherent optical FFT to the QFT. Grover’s search algorithm implemented with a coherent optical correlator. Quantum algorithms requiring bit entanglement -Shor’s algorithm for large number factorisation. Spatial Light Modulator (SLM) pixels placed in a binary superposition state. Addressed with an “interaction free” measurement. Allows exponential increase in processing power. Quantum search algorithm for a decryption problem based on superposition state of coherent wavefront.

Calcium sensitive dyes are valuable tools in the study of intracellular Ca+ signalling. But application of these sensors using conventional wide field fluorescence intensity imaging is limited in model organisms, embryos and other moderately thick tissues by blurring due to detection of emitted light from outside the focal plane, unproductive photobleaching above and below the focal plane as well as depth-dependent variation in intensity. In order to overcome these difficulties we are combining wide field fluorescence lifetime imaging which is intensity-independent with planar illumination orthogonal to the axis of detection. We will demonstrate the accurate calibration of Ca+ sensitivity of the fluorescence lifetime of Oregon Green Bapta1 in buffer systems and its utility in detection of rapid Ca+ dynamics in vivo. In this implementation of our Single Plane Illumination Microscope (SPIM), we are testing a novel detector, the Capacitive Division Imaging Readout (CDIR) detector, consisting of a Microchannel Plate (MCP) vacuum detector with spatial resolution (102 pixels) and timing resolution of the order of 100 ps. The CDIR detector, capacitively couples the charge output of the MCP between four readout nodes. The charge collected at each node is then measured using the NINO ASIC, a charge sensitive amplifier/discriminator, and the HPTDC ASIC, a high time resolution time-to-digital convertor, allowing the charge position to be calculated using a centroiding imaging algorithm.

Gyrotrons are capable of producing Megawatt power radiation at millimeter and sub-millimeter wavelengths , they are very efficient, circa 60% and can be made to chirp over wide frequency ranges. Gryratrons offer a very effective means of defence against advanced weaponry that rely on embedded electronics and multiple communication systems. Electronics, sensors and communication systems can be damaged from a distance of 40 kM. The ability to deliver directed beams is straightforward; Russia already has this technology to target Drones.

Fluorescent microscopy is one of the most widely used tools in the life-sciences. The ability to tag molecules of interest and then localise their positions within two or even three dimensions provides a great deal of information about the underlying chemical and biological processes. However, many microscope are high cost, with expensive imaging optics and lasers illumination systems. We report on the development of the a novel low cost microscope designed for fluorescent imaging of tagged markers within live cells. The microscope is designed using low cost components and aims to give diffraction limited imaging.
One of the major costs in a microscope its the optics, especially the objective lens that must provide a flat diffraction limited image. There have been a number of low cost microscopes developed in recent years that completely remove the objective and either directly cast a shadow of the object directly onto the CCD sensor or uses some diffractive process. However, these tend to be bright-field illumination microscopes. Lensless fluorescent microscopes are more difficult since a bandpass filter must be incorporated into the system. The physical size of the filter means the object will be too far away from the CCD sensor to achieve a good image. To overcome this we place a one to one imaging lens within the optical path, which then allows room for the filter. Since one to one lenses are symmetric, the aberrations tend balance out meaning they can provide good image quality with a simple low cost design. However, to achieve sub-micron resolution we introduce a pinhole array into the system which, by scanning can be shown to provide super-resolution images. The excitation illumination is provided by a high powered LED and the system imaged onto a standard CCD camera.

Fluorescence lifetime imaging microscopy (FLIM) is a tool that has become of important use to many biologists and medical researchers. It allows for the imaging of fluorescent markers within cells and as well their decay lifetime information. This lifetime information yields useful information about the chemical properties of the local region surrounding the fluorophore marker. It is therefore desirable to be able to measure this information and localise it to produce either a 2D or 3D image stack of the lifetimes and intensity information. Two major problems must be overcome to achieve good resolution, both spatially and temporally. There must be a method to provide optical depth sectioning and the imaging must be fast. Sectioning can be done with confocal or two photon microscopes however, these are point scanning devices and are relatively slow.
We will detail a new microscope system based on light sheet illumination that uses a micro channel plate (MCP) device called a Capacitive Division Imaging Readout (CDIR) which has been developed by Photek Ltd. The device uses an array of capacitors to move the charge site from the MCP to four pre-amplifiers and time- over-threshold discriminators. This camera has the ability to image photons as well as measure the arrival time, enabling high speed FLIM imaging of biological samples. The optical sectioning is achieved by using a diffraction limited sheet of light, that illuminates the sample from the side in the plane orthogonal to the objectives optical axis.
We will present the first results taken from the microscope.

Measuring the spatial position and lifetime distribution of the fluorophores enables users to discern vital information about the chemical environment within a biological system. It however, remains challenging since the photon count is usually extremely limited, requiring a high degree of efficiency within the system. The timing precision to measure the lifetimes are of the order of picoseconds. Traditional methods to achieve two dimensional images involve point scanning with a confocal or two photon microscope and collecting the photon timings with a point collector. This has the downside of being comparatively slow. High speed, photon timing counters with spatial imaging capabilities therefore offer a desirable option to decrease measurement times.
We have developed a new solution to this problem. Our system side illuminated our sample with a sheet of excitation light produced by a pulsed laser diode at 470nm. The imaging path is then performed by two cameras: a high resolution EMCCD captures an intensity image allowing for alignment of the sample. The second path contains a multichannel plate (MCP) photomultiplier.
To read the charge, time and position from the MCP remains a challenge. We have demonstrated an alternative technology; a Capacitive Division Imaging Readout (C-DIR) which has been developed by Photek Ltd. The device uses an array of capacitors to move the charge site from the MCP to four preamplifiers and time-over-threshold discriminators.
The work here will present data on the measured frame rate,point spread function, and spatial and temporal resolution of the device. The microscope shows a excellent degree of time resolution and temporal repeatability. The spatial imaging exhibits some degree of geometric distortion and post measurement correction methods have been implemented to reduce this. Images of biological samples will be presented showing the two dimensional life time imaging of the system along with measurements of FRET.

Trajectory tracking is an essential capability of robotics operation in industrial automation. In this article, an artificial neural controller is proposed to tackle trajectory-tracking problem of an autonomous ground vehicle (AGV). The controller is implemented based on fractional order proportional integral derivative (FOPID) control that was already designed in an earlier work. A non-holonomic model type of AGV is analysed and presented. The model includes the kinematic, dynamic characteristics and the actuation system of the VGA. The artificial neural controller consists of two artificial neural networks (ANNs) that are designed to control the inputs of the AGV. In order to train the two artificial neural networks,
Levenberg-Marquardt (LM) algorithm was used to obtain the parameters of the ANNs. The validation of the proposed controller has been verified through a given reference trajectory. The obtained results show a considerable improvement in term of minimising trajectory tracking error
over the FOPID controller.

The hidden Markov model (HMM) is commonly employed in automatic speech recognition (ASR). The HMM has been shown to have a good performance in many applications, although it has some well-known limitations in modelling speech. Therefore, the standard HMM topology has been modified in a variety of ways to reduce errors, including factorization of the HMM into multiple-streams. However, the gap between the theoretical representation of speech and its acoustic representation can be further reduced. This paper describes a new method of correcting the HMM based on matching two dimensional templates of word time-frequency patterns to assist in low level speech recognition. This is shown to be a promising method to enhance speech recognition performance.

Previously we have examined the similarities of the quantum Fourier transform to the classical coherent optical implementation of the Fourier transform (R. Young et al, Proc SPIE Vol 87480, 874806-1, -11). In this paper, we further consider how superposition states can be generated on coherent optical wave fronts, potentially allowing coherent optical processing hardware architectures to be extended into the quantum computing regime. In particular, we propose placing the pixels of a Spatial Light Modulator (SLM) individually in a binary superposition state and illuminating them with a coherent wave front from a conventional (but low intensity) laser source in order to make a so-called ‘interaction free’ measurement. In this way, the quantum object, i.e. the individual pixels of the SLM in their superposition states, and the illuminating wavefront would become entangled. We show that if this were possible, it would allow the extension of coherent processing architectures into the quantum computing regime and we give an example of such a processor configured to recover one of a known set of images encrypted using the well-known coherent optical processing technique of employing a random Fourier plane phase encryption mask which classically requires knowledge of the corresponding phase conjugate key to decrypt the image. A quantum optical computer would allow interrogation of all possible phase masks in parallel and so immediate decryption.

Fluorescence lifetime imaging microscopy (FLIM) has allowed scientists to discern information about the chemical properties of biological processes and has become a vital tool in the life sciences and medical research communities. Measuring the spatial lifetime distribution of the fluorophores as well as the intensity distribution enables users to discern vital information about the chemical environment. It however, remains challenging and often involves slow scanning. We present a new microscope system based on light sheet illumination that uses a micro channel plate (MCP) device called a Capacitive Division Imaging Readout (CDIR) which has been developed by Photek Ltd. The device uses an array of capacitors to move the charge site from the MCP to four pre-amplifiers and time-over-threshold discriminators. This camera has the ability to image photons as well as measure the arrival time, enabling high speed FLIM imaging of biological samples.

Since the first spacecraft was launched in 1957 a great number of spacecraft have been put into orbit and a significant fraction of these are still orbiting the Earth as inert vehicles or space debris. Major collision events between large satellites in Earth orbits have broken-up spacecraft systems for about half century; this has created a massive quantity of space junk – most of which are small particles. The number and quantity of debris items is increasing and as a result the probability of catastrophic collisions is growing progressively.

Objects in space, whatever their size, are potentially hazardous and can cause considerable damage, which may disable a space system and producing numerous secondary fragments as a result. Low Earth Orbit (LEO) requires particular attention because this band contains large masses of material orbiting at high relative velocities, up to 14 km/s. At this hypervelocity, even small debris, 5-10 cm, can produce extensive damage to any operation satellite and destroy any small satellites. Collision with smaller debris, 1-5 cm, could disable any space system. Therefore, our space assets in LEO are threatened by this large quantity of space junk, which may lead to collision cascading in the future.

Small orbital debris stays in LEO for a very long time (100s years) before re-entering the atmosphere, so it poses a great threat to any operational spacecraft. However, the debris lifetime can be reduced significantly by slowing the debris velocity slightly and lowering its perigee. This can be achieved by using the unique property of laser propulsion to generate thrust remotely on the orbital debris by beaming the necessary power from the ground. So, this paper assesses and simulates the engagement of laser beam pulses with space debris. It also calculates and simulates the required time and number of interactions for de-orbit and also simulates the required change in orbital velocity (∆V) of the debris to lower its altitude and cause it to change orbit and eventually fall into the upper atmosphere, where it will burn up.

In conclusion, space debris mitigation is now essential to protect existing space systems and maintain the sustainable use of outer space. That is why the space debris problem is now a very significant environmental issue. As this technique does not require launching space vehicles, we believe that this clearing strategy is an achievable and cost effective method to deflect and mitigate the effect of space debris.

The fragmentation in Earth orbits and the major collision events between large spacecraft systems in addition to the poor housekeeping in space for more than five decades has generated millions pieces of orbital debris at different altitude above the Earth. Space debris in Low Earth Orbit (LEO) are now sufficiently dense that it threatens our space assets in this band by runaway collision cascading. The critical density in this band has now reached the point where collisions between debris are the most dominant debris-generating mechanism; the number of orbital junk in this band is increasing exponentially with actual and potential collision events.

The average relative velocity between orbiting objects in LEO is very high, between 9-10 km/s, with maximum values around 14 km/s and the number of debris with a size range bigger than 1 cm in this band is large. Therefore, due to their hypervelocity and the massive number of debris, the space debris situation requires significant mitigating measures to protect our assets in space as the debris can cause deadly harm to both manned and unmanned space missions in orbit and produce more space junk, which renders this very useful band difficult to use.

The length of time small debris stays in, for example, 1000 km altitude orbit before re-entering the atmosphere is of the order of 100 years. Therefore, reducing the altitude of the orbital debris will reduce its lifetime in orbit. Recent studies have suggested a proposed solution uses a repetitively pulsed ground based laser to target space debris using the laser beam to slow them slightly and lower their perigee, by the amount necessary, to cause them to re-enter the atmosphere where they will burn up. However, only very few studies have been done using this approach. In this paper, we discuss and assess the treatment of space debris and the engagement between the laser pulses and different shapes and sizes of debris. Beyond that, we believe that understanding the most common on-orbit debris materials will help the design of the laser beam system and give a more accurate picture of this debris clearing strategy.

Using a pulsed laser to mitigate orbital debris is a most feasible and cost-effective method to clean up space without adding extra debris. No other solutions have been suggested to solve this debris dilemma without launching a new satellite, which without doubt would be costly and produce new debris itself.

There is a large amount of space debris in the size range of 1 to 10 cm that is orbiting the Earth at a very high velocity which could do tremendous damage to any space mission if it were to collide. This orbital debris has been generated from collision events between objects and fragmentations of objects in Earth orbits. The problem is that the amount of space debris is increasing exponentially with every major collision in space. To limit the probability of this happening, the rate of growth of space debris in orbit is being reduced through various design techniques employed in new space missions. However, the orbital debris already in space will require to be mitigated as well because in some of the bands, such as Low Earth Orbit (LEO), the density of space debris is very high. It is posing a threat to any operational satellite in orbit and the safety of spacecraft flights LEO is rather urgent.

The technique of using high power pulsed lasers on the ground has been viewed as the most feasible method to mitigate small space junk in the LEO band. It is one of the most viable solutions to mitigate the existing space debris in LEO. However, in order to design an effective orbital debris removal technique, the first logical step would be to gather as much information as possible on space debris that is known to exist within the orbital band of interest, like the two-line element of the space debris and its materials. This will help assess the design of the laser beam system and help give a more accurate picture of this de-orbiting strategy.

The objective of this poster is to provide a summary of the developed novel techniques for this de-orbiting model. It also presents the calculation and simulation of the required power that is necessary for the laser beam in order to slow down the orbital velocity of the space debris and also reduce its perigee. Reducing the space debris altitude by the amount necessary will significantly reduce its lifetime in Earth orbit and eventually cause it to re-enter the atmosphere where it will burn up.

Some laser history;
Airborne Laser Testbed & Chemical Oxygen Iodine Laser (COIL);
Laser modes and beam propagation;
Fibre lasers and applications;
US Navy Laser system – NRL 33kW fibre laser;
Lockheed Martin 30kW fibre laser;
Conclusions

Bandwidth management and availability is going to improve greatly.The Cloud will become increasingly important for security and computer vision. Integration of Satellite, Fibre, Wireless. Impacts where you do the Computer Vision

Free space optical communication has the potential to transmit information with both high speed and security. However, since it is unguided it suffers from losses due to atmospheric turbulence and diffraction. To overcome the diffraction limits the long distance propagation of Bessel beams is considered and compared against Gaussian beam properties. Bessel beams are shown to have a number of benefits over Gaussian beams when propagating through atmospheric turbulence

A robust control technique is proposed to address the problem of trajectory tracking of an autonomous ground vehicle (AGV). This technique utilizes a fractional-order proportional integral derivative (FOPID) controller to control a non-holonomic autonomous ground vehicle to track the behaviour of the predefined reference path. Two FOPID controllers are designed to control the AGV’s inputs. These inputs represent the torques that are used in order to manipulate the implemented model of the vehicle to obtain the actual path. The implemented model of the non-holonomic autonomous ground vehicle takes into consideration both of the kinematic and dynamic models. In additional, a particle swarm optimization (PSO) algorithm is used to optimize the FOPID controllers’ parameters. These optimal tuned parameters of FOPID controllers minimize the cost function used in the algorithm. The effectiveness and validation of the proposed method have been verified through different patterns of reference paths using MATLAB–Simulink software package. The stability of fractional-order system is analysed. Also, the robustness of the system is conducted by adding disturbances due to friction of wheels during the vehicle motion. The obtained results of FOPID controller show the advantage and the performance of the technique in terms of minimizing path tracking error and the complement of the path following.

In this paper, we present a four-step ortho-rectification procedure for real-time geo-referencing of video data from a low-cost UAV equipped with a multi-sensor system. The basic procedures for the real-time ortho-rectification are: (1) decompilation of the video stream into individual frames; (2) establishing the interior camera orientation parameters; (3) determining the relative orientation parameters for each video frame with respect to each other; (4) finding the absolute orientation parameters, using a self-calibration bundle and adjustment with the aid of a mathematical model. Each ortho-rectified video frame is then mosaicked together to produce a mosaic image of the test area, which is then merged with a well referenced existing digital map for the purpose of geo-referencing and aerial surveillance. A test field located in Abuja, Nigeria was used to evaluate our method. Video and telemetry data were collected for about fifteen minutes, and they were processed using the four-step ortho-rectification procedure. The results demonstrated that the geometric measurement of the control field from ortho-images is more accurate when compared with those from original perspective images when used to pin point the exact location of targets on the video imagery acquired by the UAV. The 2-D planimetric accuracy when compared with the 6 control points measured by a GPS receiver is between 3 to 5 metres.

Accurately tracking moving targets in a complex scene involving moving cameras, occlusions and targets embedded in noise is a very active research area in computer vision. In this paper, an optimal trade-off maximum correlation height (OT-MACH) filter has been designed and implemented as a robust tracker. The algorithm allows selection of different objects as a target, based on the operator’s requirements. The user interface is designed so as to allow the selection of a different target for tracking at any time. The filter is updated, at a frequency selected by the user, which makes the filter more resistant to progressive changes in the object’s orientation and scale. The tracker has been tested on both colour visible band as well as infra-red band video sequences acquired from the air by the Sussex County police helicopter. Initial testing has demonstrated the ability of the filter to maintain a stable track on vehicles despite changes of scale, orientation and lighting and the ability to re-acquire the track after short losses due to the vehicle passing behind occlusions.

Accurately tracking moving targets in a complex scene involving moving cameras, occlusions and targets embedded in noise is a very active research area in computer vision. In this paper, an optimal trade-off maximum correlation height (OT-MACH) filter has been designed and implemented as a robust tracker. The algorithm allows selection of different objects as a target, based on the operator’s requirements. The user interface is designed so as to allow the selection of a different target for tracking at any time. The filter is updated, at a frequency selected by the user, which makes the filter more resistant to progressive changes in the object’s orientation and scale. The tracker has been tested on both colour visible band as well as infra-red band video sequences acquired from the air by the Sussex County police helicopter. Initial testing has demonstrated the ability of the filter to maintain a stable track on vehicles despite changes of scale, orientation and lighting and the ability to re-acquire the track after short losses due to the vehicle passing behind occlusions.

A spatial domain optimal trade-off Maximum Average Correlation Height (OT-MACH) filter has been previously developed and shown to have advantages over frequency domain implementations in that it can be made locally adaptive to spatial variations in the input image background clutter and normalised for local intensity changes. In this paper we compare the performance of the spatial domain (SPOT-MACH) filter to the widely applied data driven technique known as the Scale Invariant Feature Transform (SIFT). The SPOT-MACH filter is shown to provide more robust recognition performance than the SIFT technique for demanding images such as scenes in which there are large illumination gradients. The SIFT method depends on reliable local edge-based feature detection over large regions of the image plane which is compromised in some of the demanding images we examined for this work. The disadvantage of the SPOTMACH filter is its numerically intensive nature since it is template based and is implemented in the spatial domain. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

An improvement to the Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter with the addition of a Rayleigh distribution filter has been used to detect humans in FLIR imagery scenes. The Rayleigh distribution filter is applied to the OT-MACH filter to provide a sharper low frequency cut-off which improves the OT-MACH filter performance in terms of target discrimination. The OT-MACH filter has been trained using a Computer Aided Design (CAD) model and tested on the corresponding real target object in high clutter environments acquired from a Forward Looking Infra Red (FLIR) sensor. Evaluation of the performance of the Rayleigh modified OT-MACH filter is reported for the recognition of humans present within the thermal infra-red image data set.

Optical structures to implement the discrete Fourier transform (DFT) and fast Fourier transform (FFT) algorithms for discretely sampled data sets are considered. In particular, the decomposition of the FFT algorithm into the basic Butterfly operations is described, as this allows the algorithm to be fully implemented by the successive coherent addition and subtraction of two wavefronts (the subtraction being performed after one has been appropriately phase shifted), so facilitating a simple and robust hardware implementation based on waveguided hybrid devices as employed in coherent optical detection modules. Further, a comparison is made to the optical structures proposed for the optical implementation of the quantum Fourier transform and they are shown to be very similar.

Optical structures to implement the discrete Fourier transform (DFT) and fast Fourier transform (FFT) algorithms for discretely sampled data sets are considered. In particular, the decomposition of the FFT algorithm into the basic Butterfly operations is described, as this allows the algorithm to be fully implemented by the successive coherent addition and subtraction of two wavefronts (the subtraction being performed after one has been appropriately phase shifted), so facilitating a simple and robust hardware implementation based on waveguided hybrid devices as employed in coherent optical detection modules. Further, a comparison is made to the optical structures proposed for the optical implementation of the quantum Fourier transform and they are shown to be very similar.

As Africa is lagging behind in global IT developments this lecture looks at the:

Challenges & cost of the Internet in Nigeria
Global Fibre Optic Backbone
African Sub-sea Optical Fibre Backbone
Satellite Option
Satellite & Wireless Systems
DWDM in Tanzania

This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD); right distance (RD) and left distance (LD) for the low-level motion control. Two heading controllers deploy the angle difference (AD) between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles.

A high-speed hybrid optical–digital correlator system was designed, constructed, modeled, and demonstrated experimentally. This correlator is capable of operation at approximately 3000 correlations/s. The input scene is digitized at a resolution of 512 × 512 pixels and the phase information of the two-dimensional fast Fourier transform calculated and displayed in the correlator filter plane at normal video frame rates. High-fidelity reference template images are stored in a phase-conjugating optical memory placed at the nominal input plane of the correlator and reconstructed with a high-speed acousto-optic scanner; this allows for cross correlation of the entire reference data set with the input scene within one frame period. A high-speed CCD camera is used to capture the correlation-plane image, and rapid correlation-plane processing is achieved with a parallel processing architecture.

A new stereophotolithography technique utilizing a spatial light modulator ~SLM! to create threedimensional components with a planar, layer-by-layer process of exposure is described. With this procedure it is possible to build components with dimensions in the range of 50 mm–50 mm and feature sizes as small as 5 mm with a resolution of 1 mm. A polysilicon thin-film twisted nematic SVGA SLM is used as the dynamic photolithographic mask. The system consists of eight elements: a UV laser light source, an optical shutter, beam-conditioning optics, a SLM, a multielement reduction lens system, a high-resolution translation stage, a control system, and a computer-aided-design system. Each of these system components is briefly described. In addition, the optical characteristics of commercially available UV curable resins are investigated with nondegenerate four-wave mixing. Holographic gratings were written at a wavelength of 351.1 nm and read at 632.8 nm to compare the reactivity, curing speed, shrinkage, and resolution of the resins.

The presentation explores the NATO Space Surveillance Network and the recent developments in updating it with state of the art infrared sensors and high bandwidth communication satellites using triple junction solar panel arrays.

We model the propagation of Gaussian and Bessel beams from ground through 22km altitude of atmospheric turbulence. We observe the Bessel beam has better performance based on
RMS intensity error and the captured beam power

The speech signal of a word is a combination of frequencies which can produce specific transition frequency shapes. These can be regarded as a written text in some unknown ‘script’. Before attempting methods to read the speech spectrogram image using image processing techniques we need first to define the properties of the speech spectrogram image as well as the reduction of the clutter of the spectrogram image and the selection of the methods to be employed for image matching. Thus methods to convert the speech signal to a spectrogram image are initially employed, followed by reduction of the noise in the signal by capturing the energy associated with formants of the speech signal. This is followed by the normalisation of the size of the image and its resolution of in both the frequency and time axes. Finally, template matching methods are employed to recognise portions of text and isolated words. The paper describes the pre-processing methods employed and outlines the use of normalised grey-level correlation for the recognition of words. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

The mosquito is a major pest that transmits many dangerous diseases and seriously damages human health. Hence, the reduction of their population by the use of a sustainable control method is a primary objective of this research. This mosquito reduction method utilises biological predators and a fermentation traptosubstantially improve the general environment and provide in-door clearance of mosquitoes. The frequency of capturing the pest mosquitoes by the predators is determined using a Pascal distribution, whilst the insect life cycle mortality is modelled using a Weibull distribution. The results from the model show that by using insect predators, a significant reduction of the larva stage of the mosquito population is possible and the fermentation mosquito trap greatly reduces the adult mosquito population without the application of insecticide spray.

A new stereophotolithography technique utilizing a spatial light modulator ~SLM! to create threedimensional
components with a planar, layer-by-layer process of exposure is described. With this
procedure it is possible to build components with dimensions in the range of 50 mm–50 mm and feature
sizes as small as 5 mm with a resolution of 1 mm. A polysilicon thin-film twisted nematic SVGA SLM
is used as the dynamic photolithographic mask. The system consists of eight elements: a UV laser
light source, an optical shutter, beam-conditioning optics, a SLM, a multielement reduction lens system,
a high-resolution translation stage, a control system, and a computer-aided-design system. Each of
these system components is briefly described. In addition, the optical characteristics of commercially
available UV curable resins are investigated with nondegenerate four-wave mixing. Holographic gratings
were written at a wavelength of 351.1 nm and read at 632.8 nm to compare the reactivity, curing
speed, shrinkage, and resolution of the resins. These experiments were carried out to prove the suitability
of these photopolymerization systems for microstereolithography.

Scoliid and Voria Tachinidae parasitoid wasps are shown to be able to control the population of the Taro Scarab beetle (Papuanauninodis, Coleoptera: Scarabaeidae) larvae using a newly created continuous-time simulation model based on non-linear ordinary differential equations that track the populations of the beetle’s life cycle stages: egg, larva, pupa, adult and the populations of the two parasitoid wasps. Due to the fact that the scarab beetles are, relatively speaking, long lived it is challenging to drive down the adult population below the environmental carrying capacity. Mortality and predator/prey capture rates are modelled using the Weibull and Pascal probability distribution functions, respectively. We suggest the use of a virus or fungi to drive down the population of the adult beetles, the ambition being to avoid the use of pesticides so as to produce higher quality food that doesn’t damage human health via chemical residues.

A continuous time model of the interaction between crop insect pests and naturally beneficial pest enemies is created using a set of simultaneous, non-linear, ordinary differential equations incorporating natural death rates based on the Weibull distribution. The crop pest is present in all its life-cycle stages of: egg, larva, pupa and adult. The beneficial insects, parasitoid wasps, may be present in either or all parasitized: eggs, larva and pupa. Population modelling is used to estimate the quantity of the natural pest enemies that should be introduced into the pest infested environment to suppress the pest population density to an economically acceptable level within a prescribed number of days. The results obtained illustrate the effect of different combinations of parasitoid wasps, using the Pascal distribution to estimate their success in parasitizing different pest developmental stages, to deliver pest control to a sustainable level. Effective control, within a prescribed number of days, is established by the deployment of two or all three species of wasps, which partially destroy pest: egg, larvae and pupae stages. The selected scenarios demonstrate effective sustainable control of the pest in less than thirty days.

The particle filter technique has been used extensively over the past few years to track objects in challenging environments. Due to its nonlinear nature and the fact that it does not assume a Gaussian probability density function it tends to outperform other available tracking methods. A novel adaptive sample count particle filter (ASCPF) tracking method is presented in this paper for which the main motivation is to accurately track an object in crowded scenes using fewer particles and hence with reduced computational overhead. Instead of taking a fixed number of particles, a particle range technique is used where an upper and lower bound for the range is initially identified. Particles are made to switch between an active and inactive state within this identified range. The idea is to keep the number of active particles to a minimum and only to increase this as and when required. Active contours are also utilized to determine a precise area of support around the tracked object from which the color histograms used by the particle filter can be accurately calculated. This, together with the variable particle spread, allows a more accurate proposal distribution to be generated while using less computational resource. Experimental results show that the proposed method not only tracks the object with comparable accuracy to existing particle filter techniques but is up to five times faster.

Cities are in a constant state of evolution, and made up of many different system: energy, transport, healthcare, security, water treatment and so on. There is a significant push by governments around the world to integrate these services using the internet. This presentation looks at some history, current progress and opportunities.

The presentation gives a partial summary of some of the research conducted by the Industrial Informatics & Signal Processing Research Group over the last 20 years. This was to celebrate 50 years of Engineering at Sussex University; many of our past graduates attended. The conference was a great success and culminated in a very enjoyable dinner with all the delegates and presenters.

Caterpillars in their various forms: size, shape, and colour cause significant harm to crops and humans. This paper offers a solution for the detection and control of caterpillars through the use of a sustainable pest control system that does not require the application of chemical pesticides, which damage human health and destroy the naturally beneficial insects within the environment. The proposed system is capable of controlling 80% of the population of caterpillars in less than 65 days by deploying a controlled number of larval parasitoid wasps (Cotesia Flavipes, Cameron) into the crop environment. This is made possible by using a continuous time model of the interaction between the caterpillar and the Cotesia Flavipes (Cameron) wasps using a set of simultaneous, non-linear, ordinary differential equations incorporating natural death rates based on the Weibull probability distribution function. A negative binomial distribution is used to model the efficiency and the probability that the wasp will find and parasitize a host larva. The caterpillar is presented in all its life-cycle stages of: egg, larva, pupa and adult and the Cotesia Flavipes (Cameron) wasp is present as an adult larval parasitoid. Biological control modelling is used to estimate the quantity of the Cotesia Flavipes (Cameron) wasps that should be introduced into the caterpillar infested environment to suppress its population density to an economically acceptable level within a prescribed number of days.
Keywords:

A solution was sought for control of Spodoptera exempta in cereal crops. The proposed solution enhanced a previous scheme since it provided control of the pest eggs and larvae and improved the quality of crop products by replacing pesticides. The scheme consists of a surveillance and monitoring system to activate a measured response to pest invasion. In the control phase naturally beneficial insects (NBIs) were deployed via an unmanned aerial vehicle (UAV) system to control the pest population; parasite egg wasps (Trichogramma) were combined with a larval parasite Diptera (Tachinidae) to achieve greater control of the life cycle stages of the African Armyworm e Spodoptera exempta.

Taro Scarab beetles (Papuana uninodis, Coleoptera: Scarabaeidae) inflict severe damage on important root crops and plants such as Taro or Cocoyam, yam, sweet potatoes, oil palm and coffee tea plants across Africa and Asia resulting in economic hardship and starvation in some nations. Scoliid wasps and Metarhizium anisopliae fungus - bio-control agents; are shown to be able to control the population of Scarab beetle adults and larvae using a newly created simulation model based on non-linear ordinary differential equations that track the populations of the beetle life cycle stages: egg, larva, pupa, adult and the population of the scoliid parasitoid wasps, which attack beetle larvae. In spite of the challenge driven by the longevity of the scarab beetles, the combined effect of the larval wasps and the fungal bio-control agent is able to control and drive down the population of both the adult and the beetle eggs below the environmental carrying capacity within an interval of 120 days, offering the long term prospect of a stable and eco-friendly environment; where the population of scarab beetles is: regulated by parasitoid wasps and beneficial soil saprophytes.

There is increasing reliance on video surveillance systems for systematic derivation, analysis and interpretation of the data needed for predicting, planning, evaluating and implementing public safety. This is evident from the massive number of surveillance cameras deployed across public locations. For example, in July 2013, the British Security Industry Association (BSIA) reported that over 4 million CCTV cameras had been installed in Britain alone. The BSIA also reveal that only 1.5% of these are state owned. In this paper, we propose a framework that allows access to data from privately owned cameras, with the aim of increasing the efficiency and accuracy of public safety planning, security activities, and decision support systems that are based on video integrated surveillance systems. The accuracy of results obtained from government-owned public safety infrastructure would improve greatly if privately owned surveillance systems ‘expose’ relevant video-generated metadata events, such as triggered alerts and also permit query of a metadata repository. Subsequently, a police officer, for example, with an appropriate level of system permission can query unified video systems across a large geographical area such as a city or a country to predict the location of an interesting entity, such as a pedestrian or a vehicle. This becomes possible with our proposed novel hierarchical architecture, the Fused Video Surveillance Architecture (FVSA). At the high level, FVSA comprises of a hardware framework that is supported by a multi-layer abstraction software interface. It presents video surveillance systems as an adapted computational grid of intelligent services, which is integration-enabled to communicate with other compatible systems in the Internet of Things (IoT).

Histogram of Oriented Gradients (HOG) based methods for the detection of humans have become one of the most reliable methods of detecting pedestrians with a single passive imaging camera. However, they are not 100 percent reliable. This paper presents an improved tracker for the monitoring of pedestrians within images. The Parallel HOG and Correlation Tracking (PHACT) algorithm utilises self learning to overcome the drifting problem. A detection algorithm that utilises HOG features runs in parallel to an adaptive and stateful correlator. The combination of both acting in a cascade provides a much more robust tracker than the two components separately could produce. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

The main impact of mosquito pests is the transmission of many dangerous diseases and death. Hence, the reduction of their population by the use of a natural control method is a primary objective of this research. This mosquito reduction method utilises different species of predators (Odonata) and (Toxorhynchites) to substantially improve the environment. The frequency of capturing the pest mosquitoes by the predators is determined using a Pascal distribution, whilst insect mortality is modelled using a Weibull distribution. The results from the model show that by using insect predators, a significant reduction of the mosquito population is possible in less than eighty days.

Despite the widespread use of insecticides, community engagement programmes and preventive measures mosquito borne diseases are growing and new tools to prevent the spread of disease are urgently needed. An alternative control measure for the eradication of Anopheles mosquitoes is suggested by the use of a Sustainable Control Model, which demonstrates the capability of Odonata, a natural beneficial predator, to exercise control over Anopheles mosquitoes in less than 140 days.

Plant pest recognition and detection is vital for food security, quality of life and a stable agricultural economy. This research demonstrates the combination of the k-means clustering algorithm and the correspondence filter to achieve pest detection and recognition. The detection of the dataset is achieved by partitioning the data space into Voronoi cells, which tends to find clusters of comparable spatial extents, thereby separating the objects (pests) from the background (pest habitat). The detection is established by extracting the variant distinctive attributes between the pest and its habitat (leaf, stem) and using the correspondence filter to identify the plant pests to obtain correlation peak values for different datasets. This work further establishes that the recognition probability from the pest image is directly proportional to the height of the output signal and inversely proportional to the viewing angles, which further confirmed that the recognition of plant pests is a function of their position and viewing angle. It is encouraging to note that the correspondence filter can achieve rotational invariance of pests up to angles of 360 degrees, which proves the effectiveness of the algorithm for the detection and recognition of plant pests.

The tracking of humans in a video stream has become one of the most desirable computer vision tasks over the past few years. It remains however a difficult problem and the reliability of systems is often dependent on getting good lighting and clear video images. This paper reports on the development of our PHACT tracker: parallel HOG and correlation tracking. This system uses a cascade of tracking algorithms that enhances the reliability and robustness of the system as a whole when used in difficult conditions.

A real-time system for the detection and tracking of moving objects that becomes stationary in a restricted zone. A new pixel classification method based on the segmentation history image is used to identify stationary objects in the scene. These objects are then tracked using a novel adaptive edge orientation-based tracking method. Experimental results have shown that the tracking technique gives more than a 95% detection success rate, even if objects are partially occluded. The tracking results, together with the historic edge maps, are analysed to remove objects that are no longer stationary or are falsely identified as foreground regions because of sudden changes in the illumination conditions. The technique has been tested on over 7 h of video recorded at different locations and time of day, both outdoors and indoors. The results obtained are compared with other available state-of-the-art methods.

The particle filter technique has been used extensively over the past few years to track objects in challenging environments. Due to its nonlinear nature and the fact that it does not assume a Gaussian probability density function it tends to outperform other available tracking methods. A novel adaptive sample count particle filter (ASCPF) tracking method is presented in this paper for which the main motivation is to accurately track an object in crowded scenes using fewer particles and hence with reduced computational overhead. Instead of taking a fixed number of particles, a particle range technique is used where an upper and lower bound for the range is initially identified. Particles are made to switch between an active and inactive state within this identified range. The idea is to keep the number of active particles to a minimum and only to increase this as and when required. Active contours are also utilized to determine a precise area of support around the tracked object from which the color histograms used by the particle filter can be accurately calculated. This, together with the variable particle spread, allows a more accurate proposal distribution to be generated while using less computational resource. Experimental results show that the proposed method not only tracks the object with comparable accuracy to existing particle filter techniques but is up to five times faster.

With ongoing research assessment in higher education and the introduction of master’s‐level work in initial teacher education, the growing need for teacher educators to develop research identities is discussed in relation to mentoring and support in two universities. Twelve interviews—with three teacher educators and three research mentors from each university—were carried out, in order to identify effective mentoring practices and other forms of support, as well as any barriers or problems encountered in developing a research profile. An innovative aspect of the methodological approach is that beginning researchers from the teacher education faculty in both universities undertook the interviewing and co‐authored the article. The need for an entitlement to and protection of research time is stressed, as well as a range of supportive practices within an active research culture. It is argued that this aspect of teacher educators’ professional development requires as much attention as the pedagogical aspects of their role

Optical correlation has traditionally processed monochromatic grey scale images. This paper develops a new encoding mechanism that uses the chromaticy of the input signal. It is then not only possible to detect different coloured objects but the system is invariant to changes in the brightness of the lighting, including variations across the object.

Tracking objects in multi camera environments is an important requirement for video surveillance applications. A new active particle filter based tracking technique is presented, where objects are tracked across different cameras using a reduced number of particles. In order to cope with sudden colour and scale changes, a variable standard deviation value for spreading the particles is proposed. As the object moves from one scene to another, the number of particles along with the spread value is increased to minimize any effect of scale and colour change. The technique has been tested on live feeds from two different cameras and with scenes from the PETS dataset. The results have been compared with standard particle filtering techniques. It was found that not only did the proposed method result in almost similar tracking results but there is a 70% reduction in computational cost. © 2011 IEEE.

We present results of a correlation filter utilizing a computer-generated hologram using an analog ferroelectric liquid-crystal spatial light modulator (SLM). The SLM amplitude-modulates light and can induce a 0-p phase shift, which is equivalent to modulating along the real axis. Two pixels are combined into a macropixel using a phase-detour technique enabling full complex modulation. The method is used as the filter for a conventional optical correlator and in a digital-optical hybrid correlator. © 2002 Society of Photo-Optical Instrumentation Engineers.

The window unit in the design of the complex logarithmic r-¿ mapping for hybrid optical neural network filter can allow multiple objects of the same class to be detected within the input image. Additionally, the architecture of the neural network unit of the complex logarithmic r-¿ mapping for hybrid optical neural network filter becomes attractive for accommodating the recognition of multiple objects of different classes within the input image by modifying the output layer of the unit. We test the overall filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. Logarithmic r-¿ mapping for hybrid optical neural network filter is shown to exhibit with a single pass over the input data simultaneously in-plane rotation, out-of-plane rotation, scale, log r-¿ map translation and shift invariance, and good clutter tolerance by recognizing correctly the different objects within the cluttered scenes. We record in our results additional extracted information from the cluttered scenes about the objects' relative position, scale and in-plane rotation. © 2009 SPIE.

We present a simple computational model that works in the RGB colour space to detect moving shadow pixels in video sequences of indoor scenes, illuminated in each case by an incandescent source. A channel ratio test for shadows cast on some common indoor surfaces is proposed that can be appended to the developed scheme so as to reduce the otherwise high false detection rate. The core method, based on a Lambertian hypothesis, has been adapted to work well for near-matte surfaces by suppressing highlights. The results reported, based on an extensive data analysis conducted on some of the crucial parameters involved in the model, not only bring out the subtle details of the parameters, but also remove the ad hoc nature of the chosen thresholds to a certain extent. The method has been tested on various indoor video sequences; the results obtained indicate that it can be satisfactorily used to mark or eliminate the strong portion of the foreground shadow region.

We present in detail the recorded results of the modified-hybrid optical neural network (M-HONN) filter during a full series of tests to examine its robustness and overall performance for object recognition tasks. We test the M-HONN filter for its detectability and peak sharpness with within-class distortion of the input object, its discrimination ability between an in-class and out-of-class object, and its performance with cluttered images of the true-class object. The M-HONN filter is found to exhibit good detectability, an ability to maintain its correlation-peak sharpness throughout the recorded tests, good discrimination ability, and an ability to detect the true-class object within cluttered input images. Additionally we observe the M-HONN filter's performance within the tests in comparison with the constrained-hybrid optical neural network filter for the first three series of tests and the synthetic discriminant function-maximum average correlation height filter for the fourth set of tests.

We propose a novel space domain volume holographic correlator system. One of the limitations of conventional correlators is the bandwidth limits imposed by updating the filter and the readout speed of the CCD. The volume holographic correlator overcomes these by storing a large number of filters that can be interrogated simultaneously. By using angle multiplexing, the match can be read out onto a high speed linear array of sensors. A scanning window can be used to implement shift invariance, thus, making the system operate like a space domain correlator. The space domain correlation method offers an advantage over the frequency domain correlator in that the correlation filter no longer has shift invariance imposed on it since the kernel can be modified depending on its position. This maybe used for normalising the kernel or imposing some non-linearity in an attempt to improve performance. However, one of the key advantages of the frequency domain method is lost using this technique, namely the speed of the computation. A large kernel space-domain correlation, performed on a computer, will be very slow compared to what is achievable using a 4f optical correlator. We propose a method of implementing this using the scanning holographic memory based correlator. © 2009 SPIE.

The availability of high resolution spatial light modulators (SLMs) and modern digital signal processors (DSPs) allows a convenient hybridization of digital and optical image processing to produce a small and compact correlator system. The hybridization allows the optical Fourier transform lens system for a correlator to be compact, such that it will fit inside a conventional PC. We describe the design of the optical system. © 2002 Society of Photo-Optical Instrumentation Engineers.

The Difference of Gaussian (DOG) filter is widely used in optics and image processing as, among other things, an edge detection and correlation filter. It has important biological applications and appears to be part of the mammalian vision system. In this paper we analyse the filter and provide details of the full width half maximum, bandwidth and frequency response in order to aid the full characterisation of its performance. © 2010 Elsevier B.V. All rights reserved.

We combine the complex logarithmic r-theta mapping of a space-variant imaging sensor with the hybrid optical neural network filter for achieving an overall out-of-plane rotation, in-plane rotation, scale and projection invariance and is resistance to clutter. The resulted filter is called the complex logarithmic r-theta mapping for the hybrid optical neural network (L-HONN) filter. For restoring the shift invariance of the input images of the objects, lost by applying to the images the logarithmic mapping, we include in the filter's design a window-based unit. We assess the performance and record the results of the L-HONN filter with cluttered object images.

A robust human intrusion detection technique using hue-saturation histograms is presented in this paper. Initially a region of interest (ROI) is manually identified in the scene viewed by a single fixed CCTV camera. All objects in the ROI are automatically demarcated from the background using brightness and chromaticity distortion parameters. The segmented objects are then tracked using correlation between hue-saturation based bivariate distributions. The technique has been applied on all the 'Sterile Zone' sequences of the United Kingdom Home Office iLIDS dataset and its performance is evaluated with over 70% positive results.

Achieving phase only modulation from a spatial light modulator (SLM) is useful for many optical processing tasks. In this paper, we demonstrate a simple method of decoupling phase and amplitude modulation in a twisted nematic liquid crystal (TNLC) SLM using a double pass technique. A Jones calculus model is developed which matches our experimental data.

We propose a hybrid filter, which we call the hybrid optical neural network (HONN) filter. This filter combines the optical implementation and shift invariance of correlator-type filters with the nonlinear superposition capabilities of artificial neural network methods. The filter demonstrates good performance in maintaining high-quality correlation responses and resistance to clutter to nontraining in-class images at orientations intermediate to the training set poses. We present the design and implementation of the HONN filter architecture and assess its object recognition performance in clutter.

A general model of a CW-pumped passively Q-switched laser is presented which considers the pump power and excitation state of the absorber. The recovery time of the absorber is affected by the Q-switched laser pulse length and repetition rate, which results in changes of the Output power, peak power and pulse duration of a CW-pumped passively Q-switched solid-state laser. At low pump power, the final inversion population density alters slowly by a small amount and the effect of the absorber's finite recovery time on laser output energy can be ignored. At high pump power, the final inversion population density increases quickly with pump power increase and the output energy decreases greatly. Simulation results of a CW-pumped, Cr:YAG Q-switched, Yb:YAG laser are reported. The numerical simulation results agree with the theoretical analytical analysis. (c) 2009 Elsevier Ltd. All rights reserved.

We present the implementation of a clutter-tolerant f filter in a hybrid correlator system. Wiener filters were mapped with a complex encoding technique onto a smectic A* liquid-crystal spatial light modulator (SLM). The technique overcomes the problem of representing high-dynamic-range data on SLM¿s that have limited modulation capabilities. It also provides a compact image recognition system that is robust enough for many real-world applications. Experimental results are presented.

Full complex modulation in the Fourier plane is demonstrated using a 128 x 128 analogue ferroelectric liquid crystal spatial light modulator. The device can modulate along both the positive and negative real axes. Two pixels were used together as a macro pixel, one representing the real component and the other the imaginary component, phase shifted by pi/2 using a phase detour technique. Using this method, a fully complex Fourier spectrum was displayed on the SLM and an asymmetric reconstruction into the first diffraction order was produced. (

A moving space domain window is used to implement a Maximum Average Correlation Height (MACH) filter which can be locally modified depending upon its position in the input frame. This enables adaptation of the filter dependant on locally variant background clutter conditions and also enables the normalization of the filter energy levels at each step. Thus the spatial domain implementation of the MACH filter offers an advantage over its frequency domain implementation as shift invariance is not imposed upon it. The only drawback of the spatial domain implementation of the MACH filter is the amount of computational resource required for a fast implementation. Recently an optical correlator using a scanning holographic memory has been proposed by Birch et al [1] for the real-time implementation of space variant filters of this type. In this paper we describe the discrimination abilities against background clutter and tolerance to in-plane rotation, out of plane rotation and changes in scale of a MACH correlation filter implemented in the spatial domain. © 2010 SPIE.

Surveillance and its security applications have been critical subjects recently with various studies placing a high demand on robust computer vision solutions that can work effectively and efficiently in complex environments without human intervention. In this paper, an efficient illumination invariant template generation and tracking method to identify and track abandoned objects (bags) in public areas is described. Intensity and chromaticity distortion parameters are initially used to generate a binary mask containing all the moving objects in the scene. The binary blobs in the mask are tracked, and those found static through the use of a 'centroid-range' method are segregated. A Laplacian of Gaussian (LoG) filter is then applied to the parts of the current frame and the average background frame, encompassed by the static blobs, to pick up the high frequency components. The total energy is calculated for both the frames, current and background, covered by the detected edge map to ensure that illumination change has not resulted in false segmentation. Finally, the resultant edge-map is registered and tracked through the use of a correlation based matching process. The algorithm has been successfully tested on the iLIDs dataset, results being presented in this paper. © 2010 SPIE.

In this paper we present a hardware architecture for the Sum of Absolute Difference (SAD) technique. This paper also gives the design details and the implementation results for an FPGA based core that permits realisation of a high speed matching algorithm for real time image processing applications. The matching criterion chosen is the SAD algorithm The implementation provides the correct position of the target within the frame/image. The ease of implementation lies in the fact that the core is highly parameterized and therefore can cater effectively to the needs of different sizes and resolutions of images and filters. The high speed and the low area of silicon usage make it useful for a number of image processing applications. The paper also gives a review of different hardware architectures. © EuroJournals Publishing, Inc. 2009.

Two optical lenses are used to compose a surface position measuring system. A measurand is positioned near the front focal point of the first lens. At the output end of the other lens, a beam splitter is used to split the beam into two paths. One photodetector is positioned before the focal point of the lens in one path and another after the focal point in the other so that the intensities falling onto the two detectors can be adjusted to be equal when the first lens is in focus. This device provides a compact and high-accuracy surface sensor. In this paper, the design and experimental study of the sensor system is described. It is shown that such a sensor embodiment can lead to a resolution of 1 µm.

Moving shadow detection is an important step in automated robust surveillance systems in which a dynamic object is to be segmented and tracked. Rejection of the shadow region significantly reduces the erroneous tracking of non-target objects within the scene. A method to eliminate such shadows in indoor video sequences has been developed by the authors. The objective has been met through the use of a pixel-wise shadow search process that utilizes a computational model in the RGB colour space to demarcate the moving shadow regions from the background scene and the foreground objects. However, it has been observed that the robustness and efficiency of the method can be significantly enhanced through the deployment of a binary-mask based shadow search process. This, in turn, calls for the use of a prior foreground object segmentation technique. The authors have also automated a standard foreground object segmentation technique through the deployment of some popular statistical outlier-detection based strategies. The paper analyses the performance i.e. the effectiveness as a shadow detector, discrimination potential, and the processing time of the modified moving shadow elimination method on the basis of some standard evaluation metrics.

Since the introduction of SDF correlation filters vast amounts of work has been done on improving the filters¿ synthesis. Several different variations of classic SDF filter have been suggested. The objective is to create filters invariant to distortions from rotation, scaling, translation and inclusion of noise. Recently, the use of wavelets as a pre-processing stage of the images has been explored. Additionally, the application of non-linear operations as part of the pre-processing stages seems promising. This paper investigates those non-linear operations in filters like NLDOG SDF filter. We also discuss the newly introduced SDF-MACH filter in this paper. Advances in artificial neural network architectures emerged their wide availability in pattern recognition tasks. We have created a new non-linear neural model able to input analogue patterns. The paper focuses in exploring in detail how any non-linear operations are utilised in our model comparatively to correlation filters. We mathematically prove the non-linear superposition characteristics of the neural network. Finally simulation results are given to demonstrate both, the inside synthesis of SDF type correlation filters and the neural network design under the scope of those non-linear operations. We do not intend to provide an analytical comparison of correlation filters and neural networks rather to focus on the understanding of their underlying working functions.

Volume holographic correlators offer the ability to encode and compare thousands of templates in one operation. Angle multiplexing of each individual template means the position of the correlation spot in the output plane corresponds to the matching template. To be useful as a correlator the shift invariance must be restored by scanning the input image. This can be achieved by implementing the input signal modulation on a high speed SLM such as a MQW or DLP that is capable speeds in excess of 30kHz. The output correlation peak is read out using a high-speed linear CCD camera. The Bragg angle affects the number of templates that can be held on the hologram. However, this is not the same in both directions and this changes the correlator's shift invariance ability in different scan directions. In this paper we investigate this and how it affects the correlator's performance. This arrangement allows thousands of templates to be searched at video rate. The scanning nature allows space domain correlation to be implemented. The system we describe offers the ability to pre-filter the signal. We report on the results of a MACH filter implemented in a volume holographic correlator. The scanning window allows some interesting pre-filtering to be performed, such normalisation and non-linear optimisation.

A model is presented to show the effect of the absorber’s recovery time on selecting an output coupler and absorber pair to generate a shorter pulse width. The pump power pumping the laser system is kept constant as is the output energy. Results are presented which compare the model predictions with and without considering the Q-switch recovery time. Simulation results are presented by considering a CW pumped Cr: YAG passively Q-switched Nd:YAG laser.

Digital watermarking is a vital process for protecting the copyright of images. This paper presents a method of embedding a private robust watermark into a digital image. The full complex form the Wiener filter is used to extract the signal from the watermarked image. This is shown to outperform the more conventional approximate notation. The results are shown to be extremely noise insensitive.

A method of producing an arbitrary complex field modulation by use of two pixels of an analog ferroelectric spatial light modulator (SLM) is demonstrated. The method uses the gray-scale modulation capabilities of a SLM to spatially encode the complex data on two pixels. A spatial filter is used to remove the carrier signal. This technique gives fast gray-level amplitude and phase modulation.

We propose a space variant Maximum Average Correlation Height (MACH) filter which can be locally modified depending upon its position in the input frame. This can be used to detect targets in an environment from varying ranges and in unpredictable weather conditions using thermal images. It enables adaptation of the filter dependant on background heat signature variances and also enables the normalization of the filter energy levels. The kernel can be normalized to remove a non-uniform brightness distribution if this occurs in different regions of the image. The main constraint in this implementation is the dependence on computational ability of the system. This can be minimized with the recent advances in optical correlators using scanning holographic memory, as proposed by Birch et al. [1] In this paper we describe the discrimination abilities of the MACH filter against background heat signature variances and tolerance to changes in scale and calculate the improvement in detection capabilities with the introduction of a nonlinearity. We propose a security detection system which exhibits a joint process where human and an automated pattern recognition system contribute to the overall solution for the detection of pre-defined targets. © 2010 SPIE.

Reliable ICT services reduce the influence of geographical obstacles in bringing people together, and improve social-economic development in any country. However in Tanzania, ICT services are limited because Tanzania doesn't have its own ICT backbone infrastructure. In order to close the digital divide with other countries and enhance sustainable socio-economic development and accelerate poverty reduction in the country, Tanzania must construct a high capacity internationally connected terrestrial ICT backbone. This can be achieved using optical DWDM network technology, which can provide virtually unlimited bandwidth with minimum costs, for all ranges of ICT services such as Internet, voice, videos and other multimedia interactions. This article explores the performance of a DWDM system utilizing erbium-doped fiber amplifiers (EDFA) with a dispersion compensating fiber (DCF) in a standard single mode fiber (SSMF) for deployment as the Tanzanian ICT backbone. Performance assessments at data rates of 2.5 Gb/s and 10 Gb/s per wavelength channel take into consideration the presence of each optical component. The simulation results show that a data transmission rate of 2.5 Gb/s can be successfully transmitted with an external transmitter modulator over a greater distance than 10 Gb/s with minimum system impairments. Operating at the lower data rate delivers a cost effective infrastructure with good system performance for the required ICT services. A forty-channel DWDM system will provide a bandwidth of 100 Gb/s. © EuroJournals Publishing, Inc. 2009.

Almost every nation today relies on space-based technology for communications, weather forecasting, satellite navigation and resource management, either through ndigenous
programs or through programs run by its allies. As such, it is safe to say that every country is a space-faring nation. However,when it comes to space science and technology, attention must be directed towards countries that possess the ability to launch payloads. Russia, Japan, China, ESA, India, Israel, Iran and the United States form an exclusive club of nations that not only possess launch capability but also dominate research and development in space sciences. With the exception of the ESA, whose space policy is dictated by its member states and primarily serves the EU, all other countries have national space programs governed by a dedicated space policy. However, all states including the EU have the following objectives in common:
1. Development and exploitation of space applications to serve the state’s public policy objectives,
2. Ensuring that the state’s national security and defense
needs are met with regards to space,
3. Securing unrestricted access to critical technologies allowing states to pursue independent applications, and
4. To further international collaboration between likeminded
nations through improved coordination of international activities and by setting in place a better mechanism for sharing of resources.
Whilst the above nations agree in principal to the UN Outer
Space Treaty, and they all consider space as a vital resource for ensuring national security. As such, any application or development of critical technologies is used primarily to bolster a state’s national defense capability. Current national space programs run by these countries cover various commercial, civilian and military aspects. This cross discipline research and development has led to an extremely integrated industrial base, where drawing a line between civilian and military programs is often impossible. This hazy line has often hindered technology transfer even in commercial applications as companies grapple with stringent export control regulations. With respect to the United States, whilst ITAR has ensured that critical technology is not transferred without the states consent, many in Washington agree that the regulation fails to meet its objectives and must be overhauled. It has also hindered US companies wanting to sell their products outside the United States, prompting other nations to develop indigenous technologies and market them as ITARfree.

Although it is safe to assume that research and development with regards to space will be dominated by the United States and its partners in Europe for the foreseeable future, the recent recession has caused financial strains on both sides of the Atlantic causing drastic changes with regards to space budgets and the future outlook for both NASA and ESA. This paper briefly discusses why emerging nations might consider investing in a national space program, and factors that would determine its success. It also focuses on how the establishment of a global space program could prove to be an innovative and cost-effective way of ensuring a robust space industry that serves the social and political objectives of member nations whilst promoting a global technological base that fosters innovation, growth and sustainable development.

Preprocessing of speech signals is considered a crucial step in the development of a robust and efficient speech or speaker recognition system. In this paper, we present some popular statistical outlier-detection based strategies to segregate the silence/unvoiced part of the speech signal from the voiced portion. The proposed methods are based on the utilization of the 3 σ edit rule, and the Hampel Identifier which are compared with the conventional techniques: (i) short-time energy (STE) based methods, and (ii) distribution based methods. The results obtained after applying the proposed strategies on some test voice signals are encouraging

Rocketry can be considered as the enabler of more or less all space technologies. It's ability to deliver satellites at reasonable costs has made us dependent on this delivery mechanism to fulfill our daily tasks, be it communications, satellite navigation, visual entertainment or weather forecasting. Scientifically, advances in rocketry have enabled us to view the ever-expanding universe. It's most significant impact however comes in the form of manned spaceflight vehicles such as the Space Shuttle, Soyuz and now Space Ship One. A significant development is greater commercialization of manned rocketry, helping provide widespread access to space. Most rocket designs use some form of chemical propellant as their dominant fuel source, primarily as combustion of these propellants provides ample power at limited costs. However, over the last decade as the price of chemical propellants continues to soar and their detrimental effect on our environment comes to light, there has been significant interest in developing an alternative propulsion mechanism to power a new generation of space vehicles. There is now, more than ever, the need for a greener technology, which is capable of providing a similar power output without compromising on safety and reliability. This paper proposes the use of magnetic levitation and propulsion within a vacuum chamber as that technology. It aims to prove that such a system, located strategically close to the equator, is capable of providing adequate thrust to future space vehicles, be it for space tourism or exploration. Although, inception and creation of such a system may seem radical and expensive, the long-term costs are relatively lower than current day systems.

Space has often been referred to as the final frontier. It is the curiosity of what lies beyond our planet that drives us to turn to the skies. This quest for knowledge and the chance of travelling to the heavens has compelled people to devote their lives to space science, innovation and analysis of our ever-expanding universe. Today the most significant impact of rocketry comes in the form of manned spaceflight. Vehicles like the Space Shuttle and Soyuz began the trend of greater commercialization of manned rocketry, enabling widespread access to space. Whilst the curiosity of what lies beyond may have propelled the development of the space tourism industry, its current operational cost is estimated as $20-$28 million per passenger per flight. Although the vision of providing low cost space travel still exists, its application is hindered by the costs associated with current space vehicles and mission operations. Furthermore, if we are to better understand our universe and are keen on commercializing space, we would require the space tourism industry to operate in a similar fashion to the aviation industry. As most current launch vehicles rely on chemical propulsion, the level of uncertainty in the market drives their fuel costs. In order to reduce the cost per flight, we must effectively increase the load factor per flight and operate multiple flights, enabling a greater number of paying passengers. In order to provide widespread access to space there needs to be a greater emphasis on the research and development of low cost Reusable Launch Vehicles (RLV) which predominantly rely on alternative fuel technologies, thereby reducing the overall cost per flight. Although progress would be slow, we would still be able to witness a boom in space tourism. This paper proposes the use of magnetic levitation and propulsion (Maglev) within a vacuum chamber as a viable low-cost propulsion technology. It aims to prove that such a system is capable of providing adequate thrust to future space vehicles. As Maglev systems allow for horizontal take-off and landing, such a launch system could be used in conjunction with current airports worldwide. Although the inception and creation of such a system may seem expensive, the long-term fiscal costs are relatively lower than current day systems. This is primarily because such a system relies on electrical power, whose supply and generation costs are much lower than that of chemical propellants. Also, the maintenance costs associated with the Maglev track are minimal, as during take-off there is no physical contact between the track and the launch vehicle. Similar to the aviation industry, the success of future space exploration programs and space tourism relies on international cooperation and alliances. This not only ensures that no one country dominates access to space, but also nurtures healthy competition by providing a level playing field. By implementing the afore mentioned system in politically stable developing nations, we ensure employment, innovation and motivation, all achieved through an international alliance. This system would not only ensure a faster urban development within these countries, but would also bring the vision of space science and exploration to a larger global audience. This paper discusses the overall cost analysis for a vacuum operated Maglev system, the various options available for the generation of power required by such a system and how the system’s long term costs can be aligned with the aviation industry.

Over the last century chemical propellants have been the dominant fuel source for the aviation and aerospace industry, primarily because they are able to provide ample power for a limited cost. However, over the last decade as the price of chemical propellant continues to soar and their detrimental effect on our environment comes to light, there has been a surge to develop an alternative propulsion system. This has driven us in the search for a greener technology, which is capable of providing similar power without compromising on safety and reliability. This paper proposes the use of magnetic levitation and propulsion within a vacuum chamber as an alternative technology. Since such a system relies on an external energy source, the amount of power that can be supplied to the track and craft is only limited by available technology and power sources. Although magnetic levitation has been used for high speed ground transport before, we shall break from tradition by trying to propose the development of an alternative propulsion system that could be used for commercial aircrafts as well as future space travel.

We present a method of producing a computer-generated hologram by use of a zero-twist linear nematic liquid-crystal spatial light modulator. A 2×1 macro pixel method is used; one pixel represents the real data, and one, the imaginary. A method is shown that produces both positive and negative analog amplitude modulation.