Shuttle orbiter Ku-band radar/communications system design evaluation

Tasks performed in an examination and critique of a Ku-band radar communications system for the shuttle orbiter are reported. Topics cover: (1) Ku-band high gain antenna/widebeam horn design evaluation; (2) evaluation of the Ku-band SPA and EA-1 LRU software; (3) system test evaluation; (4) critical design review and development test evaluation; (5) Ku-band bent pipe channel performance evaluation; (6) Ku-band LRU interchangeability analysis; and (7) deliverable test equipment evaluation. Where discrepancies were found, modifications and improvements to the Ku-band system and the associated test procedures are suggested

Imaging through obscurants using time-correlated single-photon counting in the short-wave infrared

Single-photon time-of-flight (ToF) light detection and ranging (LiDAR) systems have emerged in recent years as a candidate technology for high-resolution depth imaging in challenging environments, such as long-range imaging and imaging in scattering media. This Thesis investigates the potential of two ToF single-photon depth imaging systems based on the time-correlated single-photon (TCSPC) technique for imaging targets in highly scattering environments. The high sensitivity and picosecond timing resolution afforded by the TCSPC technique offers high-resolution depth profiling of remote targets while maintaining low optical power levels. Both systems comprised a pulsed picosecond laser source with an operating wavelength of 1550 nm, and employed InGaAs/InP SPAD detectors. The main benefits of operating in the shortwave infrared (SWIR) band include improved atmospheric transmission, reduced solar background, as well as increased laser eye-safety thresholds over visible band sensors. Firstly, a monostatic scanning transceiver unit was used in conjunction with a single-element Peltier-cooled InGaAs/InP SPAD detector to attain sub-centimetre resolution three-dimensional images of long-range targets obscured by camouflage netting or in high levels of scattering media. Secondly, a bistatic system, which employed a 32 × 32 pixel format InGaAs/InP SPAD array was used to obtain rapid depth profiles of targets which were flood-illuminated by a higher power pulsed laser source. The performance of this system was assessed in indoor and outdoor scenarios in the presence of obscurants and high ambient background levels. Bespoke image processing algorithms were developed to reconstruct both the depth and intensity images for data with very low signal returns and short data acquisition times, illustrating the practicality of TCSPC-based LiDAR systems for real-time image acquisition in the SWIR wavelength region - even in the photon-starved regime.The Defence Science and Technology Laboratory ( Dstl) National PhD Schem

Novel implementation technique for a wavelet-based broadband signal detection system

This thesis reports on the design, simulation and implementation of a novel Implementation for a Wavelet-based Broadband Signal Detection System. There is a strong interest in methods of increasing the resolution of sonar systems for the detection of targets at sea. A novel implementation of a wideband active sonar signal detection system is proposed in this project. In the system the Continuous Wavelet Transform is used for target motion estimation and an Adaptive-Network-based Fuzzy inference System (ANFIS) is adopted to minimize the noise effect on target detection. A local optimum search algorithm is introduced in this project to reduce the computation load of the Continuous Wavelet Transform and make it suitable for practical applications. The proposed system is realized on a Xilinx University Program Virtex-II Pro Development System which contains a Virtex II pro XC2VP30 FPGA chip with 2 powerPC 405 cores. Testing for single target detection and multiple target detection shows the proposed system is able to accurately locate targets under reverberation-limited underwater environment with a Signal-Noise-Ratio of up to -30db, with location error less than 10 meters and velocity estimation error less than 1 knot. In the proposed system the combination of CWT and local optimum search algorithm significantly saves the computation time for CWT and make it more practical to real applications. Also the implementation of ANFIS on the FPGA board indicates in the future a real-time ANFIS operation with VLSI implementation would be possible

Integrated helicopter survivability

A high level of survivability is important to protect military personnel and equipment and is central to UK defence policy. Integrated Survivability is the systems engineering methodology to achieve optimum survivability at an affordable cost, enabling a mission to be completed successfully in the face of a hostile environment. “Integrated Helicopter Survivability” is an emerging discipline that is applying this systems engineering approach within the helicopter domain. Philosophically the overall survivability objective is ‘zero attrition’, even though this is unobtainable in practice. The research question was: “How can helicopter survivability be assessed in an integrated way so that the best possible level of survivability can be achieved within the constraints and how will the associated methods support the acquisition process?” The research found that principles from safety management could be applied to the survivability problem, in particular reducing survivability risk to as low as reasonably practicable (ALARP). A survivability assessment process was developed to support this approach and was linked into the military helicopter life cycle. This process positioned the survivability assessment methods and associated input data derivation activities. The system influence diagram method was effective at defining the problem and capturing the wider survivability interactions, including those with the defence lines of development (DLOD). Influence diagrams and Quality Function Deployment (QFD) methods were effective visual tools to elicit stakeholder requirements and improve communication across organisational and domain boundaries. The semi-quantitative nature of the QFD method leads to numbers that are not real. These results are suitable for helping to prioritise requirements early in the helicopter life cycle, but they cannot provide the quantifiable estimate of risk needed to demonstrate ALARP. The probabilistic approach implemented within the Integrated Survivability Assessment Model (ISAM) was developed to provide a quantitative estimate of ‘risk’ to support the approach of reducing survivability risks to ALARP. Limitations in available input data for the rate of encountering threats leads to a probability of survival that is not a real number that can be used to assess actual loss rates. However, the method does support an assessment across platform options, provided that the ‘test environment’ remains consistent throughout the assessment. The survivability assessment process and ISAM have been applied to an acquisition programme, where they have been tested to support the survivability decision making and design process. The survivability ‘test environment’ is an essential element of the survivability assessment process and is required by integrated survivability tools such as ISAM. This test environment, comprising of threatening situations that span the complete spectrum of helicopter operations requires further development. The ‘test environment’ would be used throughout the helicopter life cycle from selection of design concepts through to test and evaluation of delivered solutions. It would be updated as part of the through life capability management (TLCM) process. A framework of survivability analysis tools requires development that can provide probabilistic input data into ISAM and allow derivation of confidence limits. This systems level framework would be capable of informing more detailed survivability design work later in the life cycle and could be enabled through a MATLAB® based approach. Survivability is an emerging system property that influences the whole system capability. There is a need for holistic capability level analysis tools that quantify survivability along with other influencing capabilities such as: mobility (payload / range), lethality, situational awareness, sustainability and other mission capabilities. It is recommended that an investigation of capability level analysis methods across defence should be undertaken to ensure a coherent and compliant approach to systems engineering that adopts best practice from across the domains. Systems dynamics techniques should be considered for further use by Dstl and the wider MOD, particularly within the survivability and operational analysis domains. This would improve understanding of the problem space, promote a more holistic approach and enable a better balance of capability, within which survivability is one essential element. There would be value in considering accidental losses within a more comprehensive ‘survivability’ analysis. This approach would enable a better balance to be struck between safety and survivability risk mitigations and would lead to an improved, more integrated overall design

Novel implementation technique for a wavelet-based broadband signal detection system

This thesis reports on the design, simulation and implementation of a novel Implementation for a Wavelet-based Broadband Signal Detection System. There is a strong interest in methods of increasing the resolution of sonar systems for the detection of targets at sea. A novel implementation of a wideband active sonar signal detection system is proposed in this project. In the system the Continuous Wavelet Transform is used for target motion estimation and an Adaptive-Network-based Fuzzy inference System (ANFIS) is adopted to minimize the noise effect on target detection. A local optimum search algorithm is introduced in this project to reduce the computation load of the Continuous Wavelet Transform and make it suitable for practical applications. The proposed system is realized on a Xilinx University Program Virtex-II Pro Development System which contains a Virtex II pro XC2VP30 FPGA chip with 2 powerPC 405 cores. Testing for single target detection and multiple target detection shows the proposed system is able to accurately locate targets under reverberation-limited underwater environment with a Signal-Noise-Ratio of up to -30db, with location error less than 10 meters and velocity estimation error less than 1 knot. In the proposed system the combination of CWT and local optimum search algorithm significantly saves the computation time for CWT and make it more practical to real applications. Also the implementation of ANFIS on the FPGA board indicates in the future a real-time ANFIS operation with VLSI implementation would be possible.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Exploring, evaluating and improving the development process for Military Load Carrying Equipment

This work sought to explore, evaluate and then improve the process of development for personal Military Load Carriage Equipment (MLCE), such as rucksacks. It was suspected that current MLCE had a number of user interaction deficiencies which should have been addressed during development. Three research questions were posed to determine: the influences on MLCE development, what needed improvement in MLCE development and how MLCE development could be improved. The work was based on eight studies conducted in three phases: the first to explore MLCE development and the observed deficiencies, the second to evaluate MLCE development, and the third to improve it. The chosen research strategy was henomenological, using a grounded theory methodology within which phenomena could emerge. Grounded theory approaches were adopted for this research because they were the best way in which to access the design domain. The research was framed within cycles of reflective action research to enable the researcher to re-orientate the enquiry to make the best use of the research opportunities that arose from the organisational context in which the research was sited. An initial investigation into the development of in-service equipment was done via a comparative case study, using documentary analysis and interviews with authorities in the field. Through this investigation it became clear that MLCE development was based on heuristics and tacit knowledge of manufacturing techniques, and collaboration between professional groups, including: materials / manufacturing, human systems, project management and military personnel. Deficiencies within MLCE development, determined through the comparative study, were validated against current practice through a further case study and additional evaluations. A comparison of outputs from these studies was then reviewed in a grounded manner to gain a holistic understanding of MLCE development. The interaction and importance of the various influences on MLCE development was then better understood, in particular the inadequate understanding of MLCE user needs, and requirement specification. To refine the possible avenues and target audience for an improvement of MLCE development stakeholder interviews were undertaken to develop a better understanding of how military user needs were gathered and applied. Following the interview survey, a tool was developed to analyse video and audio data of soldiers operating with MLCE on current operations. The tool was then reviewed by a panel of MLCE developers and stakeholders. The panel thought that the tool had a number of benefits to MLCE development: improving understanding of soldier environments, improved quality and reliability of information used in development, and as a conduit for concept evaluation. The research has provided a novel perspective on MLCE development, and provided a number of avenues upon which subsequent research could focus. The research has been able to make original contributions to understanding, albeit in a manner limited by the methodologies used.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Studies of methods of pre-launch testing of satellite radar altimeters

The radar altimeter operating in a pulse-limited mode has been successful in charting the ocean surfaces of the Earth. The scientific community, in a drive to map rougher terrain, have adopted the same principle. However in order to overcome the problem of slope- induced error, the range window may be widened or narrowed in accordance with the surface roughness. The ERS-1 altimeter included a second range window for operation over ice, but which had to be controlled by macro-command from the ground. The Advanced Terrain-Tracking Altimeter is a prototype altimeter which has an on-board resolution-switching algorithm, allowing the range window to be changed appropriately. This thesis focuses on methods of pre-launch testing of advanced radar altimeters. The early chapters review some of the calibration and testing methods used for the ERS-1 altimeter, presenting a critical assessment of some of the pre-launch methods. The testing procedure for the Adaptive Terrain-Tracking Altimeter is significantly more complex because of the extra resolution-switching algorithm, and a return signal simulator is identified as an essential element in testing the adaptive resolution prior to launch. The core of the thesis therefore describes a novel method of return signal simulation in which sequences of realistic echoes, from all types of surface, are fed in real time to the prototype altimeter, at the appropriate resolution, with the appropriate fading characteristics, and at the appropriate instant in time. Such a simulator is feasible only if the simulated echo is modelled in the deramp domain (i.e range window space) rather than actual delay time. Then the Fourier Transforms of the echoes, rather than the echoes themselves, are calculated at the full pulse repetition frequency and are stored in a memory. The resolution may then be varied by altering the rate at which the echoes are read out of memory. A prototype Return Signal Simulator is built, tested and shown to be capable of testing the Adaptive Terrain-Tracking Altimeter. A test philosophy is defined to assist the testing of the prototype altimeter, which will be undertaken by British Aerospace. A preliminary analysis, using a software implementation of the return signal simulator and realistic echoes, demonstrated that the Model Free Tracker has a superior tracking performance than the generally preferred Offset Centre Of Gravity tracking algorithm. However both algorithms suffer from problems, and these problems are identified. Finally a new approach to the analysis of the effect of chirp phase errors is presented, which leads to a quantitative expression for the height error resulting from chirp phase distortion. Such an approach can be used to apply a correction to the height estimate, unlike previous approaches which could only be used to set a specification for altimeter design

Computer aided diagnosis system for breast cancer using deep learning.

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists and doctors for medical imaging analysis, which has remained the essence of the visual representation that is used to construct the final observation and diagnosis. Medical research in cancerology and oncology has been recently blended with the knowledge gained from computer engineering and data science experts. In this context, an automatic assistance or commonly known as Computer-aided Diagnosis (CAD) system has become a popular area of research and development in the last decades. As a result, the CAD systems have been developed using multidisciplinary knowledge and expertise and they have been used to analyze the patient information to assist clinicians and practitioners in their decision-making process. Treating and preventing cancer remains a crucial task that radiologists and oncologists face every day to detect and investigate abnormal tumors. Therefore, a CAD system could be developed to provide decision support for many applications in the cancer patient care processes, such as lesion detection, characterization, cancer staging, tumors assessment, recurrence, and prognosis prediction. Breast cancer has been considered one of the common types of cancers in females across the world. It was also considered the leading cause of mortality among women, and it has been increased drastically every year. Early detection and diagnosis of abnormalities in screened breasts has been acknowledged as the optimal solution to examine the risk of developing breast cancer and thus reduce the increasing mortality rate. Accordingly, this dissertation proposes a new state-of-the-art CAD system for breast cancer diagnosis that is based on deep learning technology and cutting-edge computer vision techniques. Mammography screening has been recognized as the most effective tool to early detect breast lesions for reducing the mortality rate. It helps reveal abnormalities in the breast such as Mass lesion, Architectural Distortion, Microcalcification. With the number of daily patients that were screened is continuously increasing, having a second reading tool or assistance system could leverage the process of breast cancer diagnosis. Mammograms could be obtained using different modalities such as X-ray scanner and Full-Field Digital mammography (FFDM) system. The quality of the mammograms, the characteristics of the breast (i.e., density, size) or/and the tumors (i.e., location, size, shape) could affect the final diagnosis. Therefore, radiologists could miss the lesions and consequently they could generate false detection and diagnosis. Therefore, this work was motivated to improve the reading of mammograms in order to increase the accuracy of the challenging tasks. The efforts presented in this work consists of new design and implementation of neural network models for a fully integrated CAD system dedicated to breast cancer diagnosis. The approach is designed to automatically detect and identify breast lesions from the entire mammograms at a first step using fusion models’ methodology. Then, the second step only focuses on the Mass lesions and thus the proposed system should segment the detected bounding boxes of the Mass lesions to mask their background. A new neural network architecture for mass segmentation was suggested that was integrated with a new data enhancement and augmentation technique. Finally, a third stage was conducted using a stacked ensemble of neural networks for classifying and diagnosing the pathology (i.e., malignant, or benign), the Breast Imaging Reporting and Data System (BI-RADS) assessment score (i.e., from 2 to 6), or/and the shape (i.e., round, oval, lobulated, irregular) of the segmented breast lesions. Another contribution was achieved by applying the first stage of the CAD system for a retrospective analysis and comparison of the model on Prior mammograms of a private dataset. The work was conducted by joining the learning of the detection and classification model with the image-to-image mapping between Prior and Current screening views. Each step presented in the CAD system was evaluated and tested on public and private datasets and consequently the results have been fairly compared with benchmark mammography datasets. The integrated framework for the CAD system was also tested for deployment and showcase. The performance of the CAD system for the detection and identification of breast masses reached an overall accuracy of 97%. The segmentation of breast masses was evaluated together with the previous stage and the approach achieved an overall performance of 92%. Finally, the classification and diagnosis step that defines the outcome of the CAD system reached an overall pathology classification accuracy of 96%, a BIRADS categorization accuracy of 93%, and a shape classification accuracy of 90%. Results given in this dissertation indicate that our suggested integrated framework might surpass the current deep learning approaches by using all the proposed automated steps. Limitations of the proposed work could occur on the long training time of the different methods which is due to the high computation of the developed neural networks that have a huge number of the trainable parameters. Future works can include new orientations of the methodologies by combining different mammography datasets and improving the long training of deep learning models. Moreover, motivations could upgrade the CAD system by using annotated datasets to integrate more breast cancer lesions such as Calcification and Architectural distortion. The proposed framework was first developed to help detect and identify suspicious breast lesions in X-ray mammograms. Next, the work focused only on Mass lesions and segment the detected ROIs to remove the tumor’s background and highlight the contours, the texture, and the shape of the lesions. Finally, the diagnostic decision was predicted to classify the pathology of the lesions and investigate other characteristics such as the tumors’ grading assessment and type of the shape. The dissertation presented a CAD system to assist doctors and experts to identify the risk of breast cancer presence. Overall, the proposed CAD method incorporates the advances of image processing, deep learning, and image-to-image translation for a biomedical application

Alternative approaches to combinational and sequential logic design.

SIGLEAvailable from British Library Document Supply Centre- DSC:D44792/83 / BLDSC - British Library Document Supply CentreGBUnited Kingdo