Efforts to reduce energy consumption are being made across all disciplines. ICT's contribution to global energy consumption and by-products such as C02 emissions continues to grow, making it an increasingly significant area in which improvements must be made. This thesis focuses on software as a means to reducing energy consumption. It presents methods for profiling and modelling a multi-threaded, multi-core embedded processor at the instruction set level, establishing links between the software and the energy consumed by the underlying hardware. A framework is presented that profiles the energy consumption characteristics of a multi-threaded processor core, associating energy consumption with the instruction set and parallelism present in a multi-threaded program. This profiling data is used to build a model of the processor that allows instruction set simulations to be used to estimate the energy that programs will consume, with an average of 2.67 % error. The profiling and modelling is then raised to the multi-core level, examining a channel based message passing system formed of a network of embedded multi-threaded processors. Additional profiling is presented that determines network communication costs as well as giving consideration towards system level properties such as power supply efficiency. Then, this is used to form a system level energy model that can estimate consumption using simulations of multi-core programs. The system level model combines multiple instances of a core energy model with a network level communication cost model. The broader implications of this work are explored in the context of other embedded and multi-core processor architectures, identifying opportunities for expanding or transferring the models. The models in this thesis are formed at the instruction set level, but have been demonstrated to be effective at higher-levels of abstraction than instruction set simulation, through their support of further work carried out externally. This work is enabled by several pieces of development effort, including a profiling framework for taking power measurements-of the devices under investigation, tools for programming, routing and debugging software on a multi-core hardware platform called Swallow, and enhancements to an instruction set simulator for the simulation of this multi-core system. Through the work of this thesis, an embedded software developer for multi-threaded and multi-core systems is equipped with tools, techniques and new understanding that can help them in determining how their software consumes energy. This raises the status of energy efficiency in the software development cycle as we

Over the last decade, there has been a trend where water utility companies aim to make water distribution networks more intelligent in order to improve their quality of service, reduce water and energy waste, minimize maintenance costs etc., by incorporating Information and Communications Technologies (ICT). Current state of the art solutions use expensive power hungry deployments to monitor and transmit water network states periodically in order to detect anomalous behaviors, such as water leakage and bursts, and control water network assets. However, more than 97% of water network assets are found in remote areas, away from power and are often in geographically remote underpopulated areas; facts that make current approaches unsuitable for next generation more dynamic adaptive water networks. Battery-driven wireless sensor/actuator-based solutions are theoretically the perfect choice to support next generation cyber-physical water distribution systems. In this context, this thesis answers the question: "How can the communication be optimized to achieve sustainable Cyber-Physical Systems (CPS) deployed in such harsh environments exploiting limited resources by combining Information, Control, and Communication theory (I2C)? " In order to efficiently utilize underground wireless sensor and actuator network infrastructures, the concepts of edge data processing, anomaly detection and localization, based on compression, stream analyses and graph theory, are introduced. Furthermore, energy optimization and network sustainability by exploiting data-rate and communication scheduling adaptation, based on Lyapunov optimization, is proposed; while the benefits of aperiodic communication are investigated by accommodating event-triggered control technique into smart water networks. In addition to simulations based on real data, WaterBox and BentoBox evaluation platforms were developed to evaluate the proposed algorithms and prove the benefits of event-triggered control and Low Power Wide Area (LPWA) communication technologies against the state-of-the-art solutions. Through theoretical analysis, simulations, and real testbed experiments, the proposed algorithms and systems are shown to outperform contemporary solutions by achieving communication and actuation optimization, data reliability enhancement, while ensuring the sustainable operation of smart water networks. The work presented in this thesis should be of interest to researchers in the emerging areas Cyber-Physical Systems (CPS), Internet of Things (IoT), and Information and Communications Technology (ICT) for smart sustainable cites.

This thesis investigates multipath interference and the implication of this phenomenon for the design of future radio frequency identification devices, or RFID tags. The future radio frequency identification market and the design of appropriate tags is discussed. In particular utilising microwave carrier frequencies to extend tag-to-reader range and increase product functionality is considered. It is demonstrated that whilst the broader bandwidth inherent in microwave frequencies does allow an increase in RFID product functionality multipath interference effects will severely limit the range and reliability of microwave RFID devices. Through the development of a new mathematical framework describing multipath interference phenomena a range of models of the multipath propagation environment are constructed that allow the effects of multipath interference upon range and functionality of RFID devices to be investigated. From this fuller understanding of the effect of multipath interference upon RFID tag design a novel communications protocol is postulated that mitigates multipath effects and thus extends the range and reliability of low-microwave, radio frequency identification devices.

This thesis is concerned with understanding the issues in generating cost estimates at the conceptual design stage for Embedded Systems Design and Development (ES D&D), based on specifications. The research examines if there are any relationships between the system’s specifications and the system’s cost, and if these relationships can be formalised. The aim is to develop a framework that will structure, formalise and improve the ES D&D Cost Estimating process. Literature review examines current situation regarding ES D&D Cost Estimating and the information requirements for generating cost estimates. The review identifies that research concentrates on Embedded Systems manufacturing cost estimation, there is a lack of research regarding D&D cost estimation, as well as on the information requirements for generating D&D cost estimates. By conducting an industrial survey, the author identifies the internal practice on ES D&D Cost Estimating for the automotive and aerospace industries and identifies trends, commonalties and differences within and between them. The survey establishes that in order to improve the ES D&D Cost Estimating process, it is essential to establish a data infrastructure that will avoid issues with shortage of information imposed by suppliers and will link the Embedded System’s specifications with the system’s actual implementation and expected functionality. Using a case study approach, the author also establishes that it is essential to analyse the product functionality in such a way that will enable the development of a detailed cost estimating framework at the specification’s design stage. The framework is developed in three parts for hardware, software and integration and reuse. The ES hardware design and development effort is predicted using a complexity based cost estimating approach. The research has demonstrated that Use Case Points can be used to predict software development effort for ES software development when the specification is expressed as use cases. In case of statechart based specifications, the development effort is predicted, like in the case of Hardware, using a complexity based cost estimating approach. The study then investigates factors that affect Integration and Reuse effort for ES D&D. The Integration and Reuse effort is predicted using a expert judgement based methodology. The developed results provide automotive industry with a structured, consistent approach to develop cost estimates for the ES D&D Cost at the specifications design stage. The approach contributes towards improvement of the cost estimating practice within the automotive industry.

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Nowadays embedded devices have the need to be portable, battery powered and high performance. This need for high performance makes power management a matter of critical priority. Power management algorithms exist, but most of the approaches focus on an energy-performance trade-off oblivious to the applications running on the system. Others are application-specific and their solution cannot be applied to other applications. This work proposes Shepherd, a cross-layer runtime management system for reduction of energy consumption whilst offering soft real-time performance. It is cross-layer because it takes the performance requirements from the application, and learns to adjust the power management knobs to provide the expected performance at the minimum cost of energy. Shepherd is implemented as a Linux governor running at OS level, this layer offers a low-overhead interface to change the CPU voltage and frequency dynamically. As opposed to the reactive behaviour of Linux Governors, Shepherd adapts to the application-specific performance requirements dynamically, and proactively selects the power state that fulfils these requirements while consuming the least power. Proactiveness is achieved by using AEWMA for adapting to the upcoming workload. These adaptations are facilitated using a model-free reinforcement learning algorithm, that once it learns the optimal decisions it starts exploiting them. This work enables Shep-herd to work with different applications. A programming framework was designed to allow programmers to develop their applications to be power-aware, by enabling them to send their performance requirements and annotations to Shepherd and provide the cross-layer soft real-time performance desired. Shepherd is implemented within the Linux Kernel 3.7.10, interfacing with the application and hardware to select an appropriate voltage-frequency control for the executing application. The performance of Shepherd is demonstrated on an ARM Cortex-A8 pro-cessor. Experiments conducted with multimedia applications demonstrate that Shep-herd minimises energy consumption by up to 30% against existing Governors. Also, the framework has been used to adapt example applications to work with Shepherd, achieving 60% energy savings compared to the existing approaches.

No description available.

The goal of face detection is to determine whether or not an image or video frame contains faces and, if present, return the number of instances of each face object and their location within an image space. Face detection is an important computer vision task as it is the building block for more sophisticated face processing algorithms such as face recognition and facial expression tracking. However, robust and reliable face detection in completely unconstrained settings remains a very challenging task. For example, while the human brain performs face detection and recognition robustly and with apparent ease, computer algorithms continue to find this a difficult task due to the huge variation of facial appearance in still images and video sequences. The existing literature documents extensive work on face detection utilising different classical machine learning and traditional algorithmic techniques. Given that challenges such as invariance to facial pose still remain with these traditional machine learning approaches, an exploration of biologically representative solutions that behave adaptively and autonomously through learning may help account for the well documented superior human and primate detection performance. In an effort to implement a more biologically plausible approach to invariant multi-view face detection, this thesis presents a novel hierarchical Spiking Neural Network (SNN) framework that adopts a hybrid approach to learning. This is achieved by combining a bottom-up unsupervised Spike-Timing Dependent Plasticity (STDP) feature extraction and filtering phase with a supervised feature selection process that provides feedback to the framework in an effort to select the most diagnostic neurons for accurate face detection. The detection accuracy of the hybrid system is further enhanced through two biologically plausible mechanisms of error control; namely threshold potential adaptation and spike latency thresholding. The broadly tuned behaviour of the neurons allows for a small but expressive set of multi­view neurons to achieve efficient and robust detection for multi-view face poses. The merged, multi-view face detection system is further adapted through a competitive lateral inhibition mechanism to achieve accurate in-plane and out-of-plane face pose estimation.

The continuous miniaturisation of sensing and processing technologies is increasingly offering a variety of embedded platforms, enabling the accomplishment of a broad range of tasks using such systems. Motivated by these advances, this thesis investigates embedded detection and localisation solutions using vision and acoustic sensors. Focus is particularly placed on surveillance applications using sensor networks. Existing vision-based detection solutions for embedded systems suffer from the sensitivity to environmental conditions. In the literature, there seems to be no algorithm able to simultaneously tackle all the challenges inherent to real-world videos. Regarding the acoustic modality, many research works have investigated acoustic source localisation solutions in distributed sensor networks. Nevertheless, it is still a challenging task to develop an ecient algorithm that deals with the experimental issues, to approach the performance required by these systems and to perform the data processing in a distributed and robust manner. The movement of scene objects is generally accompanied with sound emissions with features that vary from an environment to another. Therefore, considering the combination of the visual and acoustic modalities would offer a significant opportunity for improving the detection and/or localisation using the described platforms. In the light of the described framework, we investigate in the first part of the thesis the use of a cost-effective visual based method that can deal robustly with the issue of motion detection in static, dynamic and moving background conditions. For motion detection in static and dynamic backgrounds, we present the development and the performance analysis of a spatio- temporal form of the Gaussian mixture model. On the other hand, the problem of motion detection in moving backgrounds is addressed by accounting for registration errors in the captured images. By adopting a robust optimisation technique that takes into account the uncertainty about the visual measurements, we show that high detection accuracy can be achieved. In the second part of this thesis, we investigate solutions to the problem of acoustic source localisation using a trust region based optimisation technique. The proposed method shows an overall higher accuracy and convergence improvement compared to a linear-search based method. More importantly, we show that through characterising the errors in measurements, which is a common problem for such platforms, higher accuracy in the localisation can be attained. The last part of this work studies the different possibilities of combining visual and acoustic information in a distributed sensors network. In this context, we first propose to include the acoustic information in the visual model. The obtained new augmented model provides promising improvements in the detection and localisation processes. The second investigated solution consists in the fusion of the measurements coming from the different sensors. An evaluation of the accuracy of localisation and tracking using a centralised/decentralised architecture is conducted in various scenarios and experimental conditions. Results have shown the capability of this fusion approach to yield higher accuracy in the localisation and tracking of an active acoustic source than by using a single type of data.

This thesis consists of two independent contributions. The rst deals with model checking of reference passing systems, and the second considers diagnosability under the weak fairness assumption. Reference passing systems, like mobile and recon gurable systems are everywhere nowadays. The common feature of such systems is the possibility to form dynamic logical connections between the individual modules. However, such systems are very di cult to verify, as their logical structure is dynamic. Traditionally, decidable fragments of -calculus, e.g. the well-known Finite Control Processes (FCP), are used for formal modelling of reference passing systems. Unfortunately, FCPs allow only `global' concurrency between processes, and thus cannot naturally express scenarios involving `local' concurrency inside a process. This thesis proposes Extended Finite Control Processes (EFCP), which are more convenient for practical modelling. Moreover, an almost linear translation of EFCPs to FCPs is developed, which enables e cient model checking of EFCPs. In partially observed systems, diagnosis is the task of detecting whether or not the given sequence of observed labels indicates that some unobservable fault has occurred. Diagnosability is an associated property, stating that in any possible execution an occurrence of a fault can eventually be diagnosed. In this thesis, diagnosability is considered under the weak fairness (WF) assumption, which intuitively states that no transition from a given set can stay enabled forever - it must eventually either re or be disabled. A major aw in a previous approach to WF-diagnosability in the literature is identi ed and corrected, and an e cient method for verifying WF-diagnosability based on a reduction to LTL-X model checking is presented.