9,935 research outputs found

    Towards A Practical High-Assurance Systems Programming Language

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    Writing correct and performant low-level systems code is a notoriously demanding job, even for experienced developers. To make the matter worse, formally reasoning about their correctness properties introduces yet another level of complexity to the task. It requires considerable expertise in both systems programming and formal verification. The development can be extremely costly due to the sheer complexity of the systems and the nuances in them, if not assisted with appropriate tools that provide abstraction and automation. Cogent is designed to alleviate the burden on developers when writing and verifying systems code. It is a high-level functional language with a certifying compiler, which automatically proves the correctness of the compiled code and also provides a purely functional abstraction of the low-level program to the developer. Equational reasoning techniques can then be used to prove functional correctness properties of the program on top of this abstract semantics, which is notably less laborious than directly verifying the C code. To make Cogent a more approachable and effective tool for developing real-world systems, we further strengthen the framework by extending the core language and its ecosystem. Specifically, we enrich the language to allow users to control the memory representation of algebraic data types, while retaining the automatic proof with a data layout refinement calculus. We repurpose existing tools in a novel way and develop an intuitive foreign function interface, which provides users a seamless experience when using Cogent in conjunction with native C. We augment the Cogent ecosystem with a property-based testing framework, which helps developers better understand the impact formal verification has on their programs and enables a progressive approach to producing high-assurance systems. Finally we explore refinement type systems, which we plan to incorporate into Cogent for more expressiveness and better integration of systems programmers with the verification process

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Implementation of a real time Hough transform using FPGA technology

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    This thesis is concerned with the modelling, design and implementation of efficient architectures for performing the Hough Transform (HT) on mega-pixel resolution real-time images using Field Programmable Gate Array (FPGA) technology. Although the HT has been around for many years and a number of algorithms have been developed it still remains a significant bottleneck in many image processing applications. Even though, the basic idea of the HT is to locate curves in an image that can be parameterized: e.g. straight lines, polynomials or circles, in a suitable parameter space, the research presented in this thesis will focus only on location of straight lines on binary images. The HT algorithm uses an accumulator array (accumulator bins) to detect the existence of a straight line on an image. As the image needs to be binarized, a novel generic synchronization circuit for windowing operations was designed to perform edge detection. An edge detection method of special interest, the canny method, is used and the design and implementation of it in hardware is achieved in this thesis. As each image pixel can be implemented independently, parallel processing can be performed. However, the main disadvantage of the HT is the large storage and computational requirements. This thesis presents new and state-of-the-art hardware implementations for the minimization of the computational cost, using the Hybrid-Logarithmic Number System (Hybrid-LNS) for calculating the HT for fixed bit-width architectures. It is shown that using the Hybrid-LNS the computational cost is minimized, while the precision of the HT algorithm is maintained. Advances in FPGA technology now make it possible to implement functions as the HT in reconfigurable fabrics. Methods for storing large arrays on FPGA’s are presented, where data from a 1024 x 1024 pixel camera at a rate of up to 25 frames per second are processed

    Characterising Shape Variation in the Human Right Ventricle Using Statistical Shape Analysis: Preliminary Outcomes and Potential for Predicting Hypertension in a Clinical Setting

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    Variations in the shape of the human right ventricle (RV) have previously been shown to be predictive of heart function and long term prognosis in Pulmonary Hypertension (PH), a deadly disease characterised by high blood pressure in the pulmonary arteries. The extent to which ventricular shape is also affected by non-pathological features such as sex, body mass index (BMI) and age is explored in this thesis. If fundamental differences in the shape of a structurally normal RV exist, these might also impact the success of a predictive model. This thesis evaluates the extent to which non-pathological features affect the shape of the RV and determines the best ways, in terms of procedure and analysis, to adapt the model to consistently predict PH. It also identifies areas where the statistical shape analysis procedure is robust, and considers the extent to which specific, non-pathological, characteristics impact the diagnostic potential of the statistical shape model. Finally, recommendations are made on next steps in the development of a classification procedure for PH. The dataset was composed of clinically-obtained, cardiovascular magnetic resonance images (CMR) from two independent sources; The University of Pittsburgh Medical Center and Newcastle University. Shape change is assessed using a 3D statistical shape analysis technique, which topologically maps heart meshes through an harmonic mapping approach to create a unique shape function for each shape. Proper Orthogonal Decomposition (POD) was applied to the complete set of shape functions in order to determine and rank a set of shape features (i.e. modes and corresponding coefficients from the decomposition). MRI scanning protocol produced the most significant difference in shape; a shape mode associated with detail at the RV apex and ventricular length from apex to base strongly correlated with the MRI sequence used to record each subject. Qualitatively, a protocol which skipped slices produced a shorter RV with less detail at the apex. Decomposition of sex, age and BMI also derives unique RV shape descriptors which correspond to anatomically meaningful features. The shape features are shown to be able to predict presence of PH. The predictive model can be improved by including BMI as a factor, but these improvements are mainly concentrated in identification of healthy subjects

    Developing a novel and versatile approach to study populations of microbes on surfaces

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    Spatial structure, for example regarding antibiotic gradients, is an important topic of investigation in microbial ecology and evolution. Experiments investigating pop- ulation dynamics in spatially-structured environments are often performed on agar plates. Whilst inexpensive and straightforward, these provide only rudimentary temporal and spatial control of environmental conditions. In chemostats and microfluidic devices, for well-mixed and micrometre-scale environments, respectively, regulating media inflow and outflow enables environ- mental control. We combine proven use of agar surfaces with such flow-enabled control in a novel, low-cost fluidic device; the device comprises an elastomer supporting base with a thin agar sheet on top on which microbes grow. Indented channels in the base allow flow of media/antibiotics below the agar surface. A Raspberry-Pi-operated camera allows for time-lapse imaging suitable for quantita- tive image analysis. As a proof of principle, we used our device for extended and robust growth of non-motile E. coli and motile P. aeruginosa maintaining the initial speed with which colonies propagate over three days, whilst a continual speed decrease occurred on agar plates. Guided by simulations of flow and diffusion, we then used the device to create stable antibiotic gradients within the agar. Along these gradients, we found P. aeruginosa exhibit unique microbial growth patterns with local adaptations. Because flow below the agar surface can be controlled spatially and temporally, the device promises a range of applications for studying microbial ecology and evolution in spatially continuous environments at a substrate-air interface.Engineering and Physical Sciences Research Council (EPSRC

    Number Systems for Deep Neural Network Architectures: A Survey

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    Deep neural networks (DNNs) have become an enabling component for a myriad of artificial intelligence applications. DNNs have shown sometimes superior performance, even compared to humans, in cases such as self-driving, health applications, etc. Because of their computational complexity, deploying DNNs in resource-constrained devices still faces many challenges related to computing complexity, energy efficiency, latency, and cost. To this end, several research directions are being pursued by both academia and industry to accelerate and efficiently implement DNNs. One important direction is determining the appropriate data representation for the massive amount of data involved in DNN processing. Using conventional number systems has been found to be sub-optimal for DNNs. Alternatively, a great body of research focuses on exploring suitable number systems. This article aims to provide a comprehensive survey and discussion about alternative number systems for more efficient representations of DNN data. Various number systems (conventional/unconventional) exploited for DNNs are discussed. The impact of these number systems on the performance and hardware design of DNNs is considered. In addition, this paper highlights the challenges associated with each number system and various solutions that are proposed for addressing them. The reader will be able to understand the importance of an efficient number system for DNN, learn about the widely used number systems for DNN, understand the trade-offs between various number systems, and consider various design aspects that affect the impact of number systems on DNN performance. In addition, the recent trends and related research opportunities will be highlightedComment: 28 page

    Insect neuroethology of reinforcement learning

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    Historically, reinforcement learning is a branch of machine learning founded on observations of how animals learn. This involved collaboration between the fields of biology and artificial intelligence that was beneficial to both fields, creating smarter artificial agents and improving the understanding of how biological systems function. The evolution of reinforcement learning during the past few years was rapid but substantially diverged from providing insights into how biological systems work, opening a gap between reinforcement learning and biology. In an attempt to close this gap, this thesis studied the insect neuroethology of reinforcement learning, that is, the neural circuits that underlie reinforcement-learning-related behaviours in insects. The goal was to extract a biologically plausible plasticity function from insect-neuronal data, use this to explain biological findings and compare it to more standard reinforcement learning models. Consequently, a novel dopaminergic plasticity rule was developed to approximate the function of dopamine as the plasticity mechanism between neurons in the insect brain. This allowed a range of observed learning phenomena to happen in parallel, like memory depression, potentiation, recovery, and saturation. In addition, by using anatomical data of connections between neurons in the mushroom body neuropils of the insect brain, the neural incentive circuit of dopaminergic and output neurons was also explored. This, together with the dopaminergic plasticity rule, allowed for dynamic collaboration amongst parallel memory functions, such as acquisition, transfer, and forgetting. When tested on olfactory conditioning paradigms, the model reproduced the observed changes in the activity of the identified neurons in fruit flies. It also replicated the observed behaviour of the animals and it allowed for flexible behavioural control. Inspired by the visual navigation system of desert ants, the model was further challenged in the visual place recognition task. Although a relatively simple encoding of the olfactory information was sufficient to explain odour learning, a more sophisticated encoding of the visual input was required to increase the separability among the visual inputs and enable visual place recognition. Signal whitening and sparse combinatorial encoding were sufficient to boost the performance of the system in this task. The incentive circuit enabled the encoding of increasing familiarity along a known route, which dropped proportionally to the distance of the animal from that route. Finally, the proposed model was challenged in delayed reinforcement tasks, suggesting that it might take the role of an adaptive critic in the context of reinforcement learning
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