Recent research has shown that a glial cell of astrocyte underpins a self-repair mechanism in the human brain, where spiking neurons provide direct and indirect feedbacks to presynaptic terminals. These feedbacks modulate the synaptic transmission probability of release (PR). When synaptic faults occur, the neuron becomes silent or near silent due to the low PR of synapses; whereby the PRs of remaining healthy synapses are then increased by the indirect feedback from the astrocyte cell. In this paper, a novel hardware architecture of Self-rePAiring spiking Neural NEtwoRk (SPANNER) is proposed, which mimics this self-repairing capability in the human brain. This paper demonstrates that the hardware can self-detect and self-repair synaptic faults without the conventional components for the fault detection and fault repairing. Experimental results show that SPANNER can maintain the system performance with fault densities of up to 40%, and more importantly SPANNER has only a 20% performance degradation when the self-repairing architecture is significantly damaged at a fault density of 80%.

There is a biological evidence to prove information is coded through precise timing of spikes in the brain. However, training a population of spiking neurons in a multilayer network to fire at multiple precise times remains a challenging task. Delay learning and the effect of a delay on weight learning in a spiking neural network (SNN) have not been investigated thoroughly. This paper proposes a novel biologically plausible supervised learning algorithm for learning precisely timed multiple spikes in a multilayer SNNs. Based on the spike-timing-dependent plasticity learning rule, the proposed learning method trains an SNN through the synergy between weight and delay learning. The weights of the hidden and output neurons are adjusted in parallel. The proposed learning method captures the contribution of synaptic delays to the learning of synaptic weights. Interaction between different layers of the network is realized through biofeedback signals sent by the output neurons. The trained SNN is used for the classification of spatiotemporal input patterns. The proposed learning method also trains the spiking network not to fire spikes at undesired times which contribute to misclassification. Experimental evaluation on benchmark data sets from the UCI machine learning repository shows that the proposed method has comparable results with classical rate-based methods such as deep belief network and the autoencoder models. Moreover, the proposed method can achieve higher classification accuracies than single layer and a similar multilayer SNN.

This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

A Self-rePAiring spiking Neural NEtwoRk (SPANNER) hardware architecture is presented in this paper. It is based on a software model of an astrocyte-neuron network which previously demonstrated the ability to self-detect faults and self-repair autonomously. Experimental results in this paper show that when faults occur at the synapse, remaining healthy synapses of the same neuron are enhanced by the feedback from the astrocyte, which enables system functionality to be maintained. This is the first time that astrocyte cells merged within spiking neurons demonstrate a self-repairing capability in hardware. This repair capability achieves a much more finegrained repair capability in hardware compared to the conventional fault tolerance techniques.

Fault tolerance and adaptive capabilities are challenges for modern networks-on-chip (NoC) due to the increase in physical defects in advanced manufacturing processes. Two novel adaptive routing algorithms, namely coarse and fine-grained (FG) look-ahead algorithms, are proposed in this paper to enhance 2-D mesh/torus NoC system fault-tolerant capabilities. These strategies use fault flag codes from neighboring nodes to obtain the status or conditions of real-time traffic in an NoC region, then calculate the path weights and choose the route to forward packets. This approach enables the router to minimize congestion for the adjacent connected channels and also to bypass a path with faulty channels by looking ahead at distant neighboring router paths. The novelty of the proposed routing algorithms is the weighted path selection strategies, which make near-optimal routing decisions to maintain the NoC system performance under high fault rates. Results show that the proposed routing algorithms can achieve performance improvement compared to other state of the art works under various traffic loads and high fault rates. The routing algorithm with FG look-ahead capability achieves a higher throughput compared with the coarse-grained approach under complex fault patterns. The hardware area/power overheads of both routing approaches are relatively low which does not prohibit scalability for large-scale NoC implementations.

Spikes are an important part of information transmission between neurons in the biological brain. Biological evidence shows that information is carried in the timing of individual action potentials, rather than only the firing rate. Spiking neural networks are devised to capture more biological characteristics of the brain to construct more powerful intelligent systems. In this paper, we extend our newly proposed supervised learning algorithm called DL-ReSuMe (Delay Learning Remote Supervised Method) to train multiple neurons to classify spatiotemporal spiking patterns. In this method, a number of neurons instead of a single neuron is trained to perform the classification task. The simulation results show that a population of neurons has significantly higher processing ability compared to a single neuron. It is also shown that the performance of Multi-DL-ReSuMe (Multiple DL-ReSuMe) is increased when the number of desired spikes is increased in the desired spike trains to an appropriate number.

This paper presents a novel RBF-like fast dynamically Evolving Spiking Neural classifier (ESNC). The trained feed-forward SNN consists of three layers of spiking neurons: an encoding layer which temporally encodes real valued features into spatio-temporal spike patterns, a hidden layer of dynamically grown and pruned neurons which perform spatiotemporal clustering, and an evolving output layer for classification. Both the structure and weights of the SNN are learned dynamically through a combination of unsupervised and supervised learning paradigms. An unsupervised clustering method is implemented by the hidden layer for adjusting the synaptic weights of the hidden neurons afferent connections. The centre of each hidden RBF neuron is represented by a vector of temporal distances between the first spike of the hidden neuron and the presynaptic spikes. In addition, strategies are proposed to adjust the structure of the hidden and output layers as inputs are presented to the SNN, and classification at the output layer is achieved through supervised learning where a learning window is used to adjust the weights of the output neurons afferent connections. The proposed learning algorithm is demonstrated on several benchmark datasets from the UCL machine learning repository. The results show comparable performance with existing machine learning algorithms and demonstrate the ability of the proposed algorithm to learn incoming data samples in a hybrid way and in one epoch only.

This paper presents a level set boundary description (LSBD) approach for novelty detection that treats the nonlinear boundary directly in the input space. The proposed approach consists of level set function (LSF) construction, boundary evolution, and termination of the training process. It employs kernel density estimation to construct the LSF of the initial boundary for the training data set. Then, a sign of the LSF-based algorithm is proposed to evolve the boundary and make it fit more tightly in the data distribution. The training process terminates when an expected fraction of rejected normal data is reached. The evolution process utilizes the signs of the LSF values at all training data points to decide whether to expand or shrink the boundary. Extensive experiments are conducted on benchmark data sets to evaluate the proposed LSBD method and compare it against four representative novelty detection methods. The experimental results demonstrate that the novelty detector modeled with the proposed LSBD can effectively detect anomalies.

Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

This paper proposes a new locally adaptive boundary evolution algorithm for level set methods (LSM)-based novelty detection. The proposed approach consists of level set function construction, boundary evolution, and evolution termination. It utilises the exterior data points lying outside the decision boundary to effect the segments of the boundary that need to be locally evolved in order to make the boundary better fit the data distribution, so it can evolve boundary locally without requiring knowing explicitly the decision boundary. The experimental results demonstrate that the proposed approach can effectively detect novel events as compared to the reported LSM-based novelty detection method with global boundary evolution scheme and four representative novelty detection methods when there is an exacting error requirement on normal events.

A key requirement for modern Networks-on-Chip (NoC) is the ability to detect and diagnose faults and failures. This paper addresses the challenge of fault diagnosis using online testing where the interruption of the runtime operation (performance) under diagnosis is minimised. A novel Monitor Module (MM) is proposed to detect NoC interconnect faults which minimise the intrusion of the regular NoC traffic throughput by (1) using a channel tester which only examines NoC channels when they are idle; and (2) using a testing interval parameter based on the Binary Exponential Back off algorithm to dynamically balance the level of testing when recovering from temporary faults. The paper presents results on the minimal impact on NoC throughput for a range of testing conditions and also highlights the minimal area overhead of the MM (11.56%) compared with an adaptive NoC router implemented on FPGA hardware. Simulation results demonstrate non-intrusion of the NoC runtime traffic throughput when channel are fault free, and also how throughput loss is minimised when faults are identified.

A self-organising fuzzy-neural network (SOFNN) adapts its structure based on variations of the input data. Conventionally in such self-organising networks, the number of inputs providing the data is fixed. In this paper, we consider the situation where the number of inputs to a network changes dynamically during its online operation. We extend our existing work on a SOFNN such that the SOFNN can self-organise its structure based not only on its input data, but also according to the changes in the number of its inputs. We apply the approach to a smart home application, where there are certain situations when some of the existing events may be removed or new events emerge, and illustrate that our approach enhances cognitive reasoningin a dynamic smart home environment. In this case, the network identifies the removed and/or added events from the received information over time, and reconfigures its structure dynamically. We present results for different combinations of training and testing phases of the dynamic reconfigurable SOFNN using a set of realistic synthesized data. The results show the potential of the proposed method.

This paper proposes a locally adaptive level set boundary description (LALSBD) method for novelty detection. The proposed method adjusts the nonlinear boundary directly in the input space and consists of a number of processes including level set function (LSF) construction, local boundary evolution and termination. It employs kernel density estimation (KDE) to construct the LSF and form the initial boundary surrounding the training data. In order to make the boundary better fit the data distribution, a data-driven based local expanding/shrinking evolution method is proposed instead of the global evolution approach reported in our previous level set boundary description (LSBD) method. The proposed LALSBD is compared with LSBD and other four representative novelty detection methods. The experimental results demonstrate that LALSBD can detect novel events more accurately, especially for applications which demand very high classification accuracy for normal events.

A reliable novelty detector employs a model that encloses the normal dataset tightly. As nonparametric probability density function estimation methods make no assumptions about the probability distribution of a dataset, this paper applies kernel density estimation to construct the initial boundaries surrounding the normal data points. Afterwards, the level set method makes the initial boundaries shrink or expand to better fit the normal data distribution and optimize the boundary surfaces. The proposed method is able to smooth the boundary's evolution automatically while merging or splitting happens. The boundary motion is governed by partial differential equations which formulate the dynamics of the level set method. The proposed novelty detection method is compared with four representative existing methods: support vector data description, nearest neighbours data description, mixture of Gaussian and k-means. The experimental results illustrate that the proposed level set based method presents a comparable performance as mixture of Gaussian, which performs best in terms of false negative and false positive rates.

In this paper, a spiking neural network (SNN) architecture to simulate the sound localization ability of the mammalian auditory pathways using the interaural intensity difference cue is presented. The lateral superior olive was the inspiration for the architecture, which required the integration of an auditory periphery (cochlea) model and a model of the medial nucleus of the trapezoid body. The SNN uses leaky integrate-and-fire excitatory and inhibitory spiking neurons, facilitating synapses and receptive fields. Experimentally derived head-related transfer function (HRTF) acoustical data from adult domestic cats were employed to train and validate the localization ability of the architecture, training used the supervised learning algorithm called the remote supervision method to determine the azimuthal angles. The experimental results demonstrate that the architecture performs best when it is localizing high-frequency sound data in agreement with the biology, and also shows a high degree of robustness when the HRTF acoustical data is corrupted by noise.

This paper investigates how layers of spiking neurons can be connected using lateral inhibition in different ways to bring about synchrony, reduce noise, and extract or enhance features. To illustrate the effects of the various connectivity regimes spectro-temporal speech data in the form of isolated digits is employed. The speech samples are preprocessed using the Lyon's Passive Ear cochlear model, and then encoded into tonotopically arranged spike arrays using the BSA spiker algorithm. The spike arrays are then subjected to various lateral inhibitory connectivity regimes configured by two connectivity parameters, namely connection length and neighbourhood size. The combination of these parameters are demonstrated to produce various effects such as transient synchrony, reduction of noisy spikes, and sharpening of spectro-temporal features.

This paper outlines the development of a cross-correlation algorithm and a spiking neural network (SNN) for sound localisation based on real sound recorded in a noisy and dynamic environment by a mobile robot. The SNN architecture aims to simulate the sound localisation ability of the mammalian auditory pathways by exploiting the binaural cue of interaural time difference (ITD). The medial superior olive was the inspiration for the SNN architecture which required the integration of an encoding layer which produced biologically realistic spike trains, a model of the bushy cells found in the cochlear nucleus and a supervised learning algorithm. The experimental results demonstrate that biologically inspired sound localisation achieved using a SNN can compare favourably to the more classical technique of cross-correlation.

Pattern selection methods have been traditionally developed with a dependency on a specific classifier. In contrast, this paper presents a method that selects critical patterns deemed to carry essential information applicable to train those types of classifiers which require spatial information of the training data set. Critical patterns include those edge patterns that define the boundary and those border patterns that separate classes. The proposed method selects patterns from a new perspective, primarily based on their location in input space. It determines class edge patterns with the assistance of the approximated tangent hyperplane of a class surface. It also identifies border patterns between classes using local probability. The proposed method is evaluated on benchmark problems using popular classifiers, including multilayer perceptrons, radial basis functions, support vector machines, and nearest neighbors. The proposed approach is also compared with four state-of-the-art approaches and it is shown to provide similar but more consistent accuracy from a reduced data set. Experimental results demonstrate that it selects patterns sufficient to represent class boundary and to preserve the decision surface.

In this paper a model selection algorithm for a nonlinear system identification method is proposed to study functional magnetic resonance imaging (fMRI) effective connectivity. Unlike most other methods, this method does not need a pre-defined structure/model for effective connectivity analysis. Instead, it relies on selecting significant nonlinear or linear covariates for the differential equations to describe the mapping relationship between brain output (fMRI response) and input (experiment design). These covariates, as well as their coefficients, are estimated based on a least angle regression (LARS) method. In the implementation of the LARS method, Akaike's information criterion corrected (AICc) algorithm and the leave-one-out (LOO) cross-validation method were employed and compared for model selection. Simulation comparison between the dynamic causal model (DCM), nonlinear identification method, and model selection method for modelling the single-input-single-output (SISO) and multiple-input multiple-output (MIMO) systems were conducted. Results show that the LARS model selection method is faster than DCM and achieves a compact and economic nonlinear model simultaneously. To verify the efficacy of the proposed approach, an analysis of the dorsal and ventral visual pathway networks was carried out based on three real datasets. The results show that LARS can be used for model selection in an fMRI effective connectivity study with phase-encoded, standard block, and random block designs. It is also shown that the LOO cross-validation method for nonlinear model selection has less residual sum squares than the AICc algorithm for the study.

This study investigates the computing capabilities and potential applications of neural oscillators to grey scale and colour image segmentation, an important task in image understanding and object recognition. A proposed neural system that combines the synergy between neural oscillators and Kohonen self-organising maps (SOM) is presented. Colour image segmentation is achieved through temporal synchronisation of neural oscillators that are mapped to pixels of the same object. Neurons are organised in a two-dimensional grid and are locally connected through excitatory connections and globally connected to a common inhibitor. Self-organising maps form the basis of a colour reduction system whose output is fed to a 2D grid of neural oscillators such as each neuron is mapped to a pixel of the input image. Both chromatic and local spatial features are used. The proposed system is simulated in Matlab and its demonstration on real world colour images shows promising results and the emergence of a new bio-inspired approach for colour image segmentation.

This paper describes a study of the effects of variations in the thalamocortical synaptic activity on alpha rhythms (8 - 13 Hz) using a computational model. The study aims to investigate alpha rhythm slowing associated with Alzheimer's Disease. It is observed that for a certain range of values of the input, an increase in inhibitory activity results in an increase of the lower alpha band (8 - 10 Hz) power and a corresponding decrease in the upper alpha band (11 - 13 Hz) power, thus indicating a slowing of the alpha rhythms. On the other hand, an increase in the excitatory synaptic activity results in an overall shift of the peak power in the output signal from the lower alpha band to the upper alpha band. However, for values of input outside this range, the output signal shows a bifurcation in behaviour and enters a limit cycle mode. In this state, the output power lies dominantly in the lower alpha band. Variation in the inhibitory or excitatory synaptic parameters has little or no effect on the frequency band of the output power.

This paper presents a spiking neural network-based investigation of the issues associated with extraction of onset, offset, and coincidental firing features from spectro-temporal data. Speech samples containing spoken isolated digits from the TI46 database are employed to demonstrate the way in which these features can be extracted using leaky integrate-and-fire spiking neurons with dynamic synapses. The flexibility that the additional synaptic parameters in the neuron model provides, is demonstrated to be essential for onset, offset and coincidental firing extraction. Recurrency and the interaction between excitation and inhibition together with latency is demonstrated to be a viable means of extracting offset features. The effects of lateral inhibition and in particular its ability to induce transient synchrony in spike firing is evaluated. In particular, by defining a connection length parameter, and hence a neighbourhood size, synchronous firing is shown to gradually develop as connection length and neighbourhood size increases. Finally, the implications for this connectivity in spiking neural networks and its potential for learning spectral and spatio-temporal patterns via the formation of receptive fields is discussed.

This paper presents an approach that permits the effective hardware realization of a novel Evolvable Spiking Neural Network (ESNN) paradigm on Field Programmable Gate Arrays (FPGAs). The ESNN possesses a hybrid learning algorithm that consists of a Spike Timing Dependent Plasticity (STDP) mechanism fused with a Genetic Algorithm (GA). The design and implementation direction utilizes the latest advancements in FPGA technology to provide a partitioned hardware/software co-design solution. The approach achieves the maximum FPGA flexibility obtainable for the ESNN paradigm. The algorithm was applied as an embedded intelligent system robotic controller to solve an autonomous navigation and obstacle avoidance problem.

This paper proposes a supervised training algorithm for Spiking Neural Networks (SNNs) which modifies the Spike Timing Dependent Plasticity (STDP)learning rule to support both local and network level training with multiple synaptic connections and axonal delays. The training algorithm applies the rule to two and three layer SNNs, and is benchmarked using the Iris and Wisconsin Breast Cancer (WBC) data sets. The effectiveness of hidden layer dynamic threshold neurons is also investigated and results are presented.

Spiking neural networks (SNNs) are an emerging computing paradigm that attempt to model the biological functions of the human brain. However, as networks approach the biological scale with significantly large numbers of neurons, software simulations face the problem of scalability and increasing computation times. Thus, numerous researchers have targeted hardware implementations in an attempt to more closely replicate the parallel processing capabilities of biological networks. Reconfigurable hardware is seen as a particularly viable platform for attempting to replicate to some degree the natural plasticity and flexibility of the human brain. This paper presents a scalable FPGA based implementation approach that facilitates the accelerated emulation of large-scale SNNs. The approach is validated using a SNN-based edge detection application where an order of magnitude speed performance increase was observed in comparison to a software equivalent implementation.

Sound localisation is defined as the ability to identify the position of a sound source. The brain employs two cues to achieve this functionality for the horizontal plane, interaural time difference (ITD) by means of neurons in the medial superior olive (MSO) and interaural intensity difference (IID) by neurons of the lateral superior olive (LSO), both located in the superior olivary complex of the auditory pathway. This paper presents spiking neuron architectures of the MSO and LSO. An implementation of the Jeffress model using spiking neurons is presented as a representation of the MSO, while a spiking neuron architecture showing how neurons of the medial nucleus of the trapezoid body interact with LSO neurons to determine the azimuthal angle is discussed. Experimental results to support this work are presented.

In this paper an area efficient multiplier-less hardware architecture is proposed for the implementation of an integrate- and-fire SNN model. The proposed architecture is intended for large scale implementation on a single FPGA. A modular design is proposed in order to make it flexible. Synaptic multiplication is performed with a simple AND gate, and pulses from different synapses are added together at different times, replicating the accumulation of synaptic inputs for the membrane potential. In order to introduce non-linearity into the membrane potential a normalized random number is introduced to this state variable. The proposed architecture uses spike trains as an input much like those in real networks

This paper presents an extension of previous work whereby the Spike Timing Dependant Plasticity (STDP) rule was used to train a two layer Spiking Neural Network (SNN). In that work a supervised training algorithm was developed using an STDP based rule that affected weights both locally and at network level. This work extends the rule to a three layer network with multiple inter-neuron excitatory synaptic connections and associated delays. The network utilises dynamic thresholds to facilitate an association between spatial patterns in the input data and classes. The algorithm is benchmarked using nonlinearly separable classification problems and results show that the three layer network exhibits a significant improvement over the two layer.

In nano-architectures, transistor counts place extreme demands on interconnection resources. The chronic problem of interconnect area versus device area becomes more acute than it currently is. Even with multilayering of conductors there may still be attenuation and propagation delay issues which at the extremes of nano-architecture severely limit performance. When reconfigurable devices such as field programmable gate arrays (FPGAs) are considered the issue of interconnection resources becomes even more acute due to the inherent redundancy already apparent in existing devices. The authors propose the use of a multiple valued signal system to increase functional density whilst reducing the interconnection resource overhead in FPGA based nano-architectures.

An algorithm inspired by the biological phenomenon of symbiosis is presented in this paper. The genetic diversity obtained from the creation of symbiotic relationships is investigated and the symbiosis algorithm is applied to a robotic forward kinematics control problem. Compared with other evolutionary optimisation techniques, the symbiosis algorithm is shown to be an effective paradigm in discovering optimal solutions. Genetic diversity was found to be maintained in the absence of conventional genetic operators.

Threshold logic gates (TLGs) based on resonant tunneling diodes (RTDs) allow the implementation of simple McCulloch-Pitts (MCP) neuron models. Whilst they have been demonstrated implementing a wide range of binary logic circuits the gates are also theoretically capable of implementing artificial neural networks. The conventional operation of RTD based logic circuits with many cascaded stages has inherent disadvantages associated with the requirement of an evaluation period at each stage and the resultant use of multiple clocks. The authors propose that highly parallel structures similar to those found in artificial neural networks are better suited to RTD based TLGs and offer the way forward for future large scale designs.

Increasingly web based distance education engineering courses are on offer, augmented by the provision of remote experimentation laboratories facilitating distant access to campus based physical resources. The design and implementation of effective and usable remote experimentation facilities poses unique challenges given the inherent complexities of the learning environment and the constraints imposed by the delivery medium. Developments in recent years have addressed many of these issues. However autonomous learning environments by their very nature offer minimal educator assistance and from a students perspective it is inevitable that at some stage of the experimental process, context specific help will be required. This paper seeks to address this issue in the context of remote experimentation for embedded systems and presents a distributed communications application implemented using a web services/.NET Remoting framework for an adaptive intelligent learning environment.

Constant innovation and product evolution in the area of embedded systems necessitates educational institutions and other training providers to continuingly reassess the content and delivery of engineering curricula. Increasingly web based distance education courses are on offer, augmented by the provision of remote experimentation laboratories facilitating distant access to campus based physical resources. The design and implementation of effective and usable remote experimentation facilities poses unique challenges given the inherent complexities of the learning environment and the constraints imposed by the delivery medium. Developments in recent years have addressed many of these issues. However autonomous learning environments by their very nature offer minimal educator assistance. This paper seeks to address this issue in the context of remote experimentation for embedded systems and demonstrates a distributed communications application implemented using a web services/.NET Remoting framework for an adaptive intelligent learning environment for remote experimentation.

The hardware--software (HW--SW) partitioning of applications to dynamically reconfigurable embedded systems allows for customization of their hardware resources during run-time to meet the demands of executing applications. The run-time reconfiguration (RTR) of such systems can have an impact on the HW--SW partitioning strategy and the system performance. It is therefore important to consider approaches to optimally reduce the RTR overhead during the HW--SW partitioning stage. In order to examine potential benefits in performance, it is necessary to develop a method to model and evaluate the RTR. In this paper, a novel method of modeling and evaluating such RTR-reduced HW--SW partitions is presented. The techniques of computation-reconfiguration overlap and the retention of circuitry between reconfigurations are used within this model to explore the possibilities of RTR reduction. The integration of this model into the authors' current genetic-algorithm-driven HW--SW partitioner is also presented, with two applications used to illustrate the benefits of RTR-reduced exploration during HW--SW partitioning.

Embedded systems are an integral part of industrial and domestic electronics necessitating educational institutions and other training providers to offer advanced embedded systems courses. Effective teaching in this area requires an interdependent approach combining theoretical material underpinned by practical laboratory experiments and individualized tutoring. Increasingly the courses on offer include web based distance education packages augmented by the provision of remote experimentation laboratories facilitating distant access to campus based physical resources. The degree of functionality and level of user access to remote laboratories has evolved in recent years expedited by advances in web applications and technologies. This paper details our recent work in this area focusing on the development of architecture for an integrated remote experimentation laboratory designed to meet the unique requirements of a complex, dynamic learning environment.

There are several key attributes within the medical packaging industry, such as, anchorage, seal strength etc, which characterize the quality of the medical packaging product. This paper presents a new approach to determine the anchorage of the packaging material. The conventional approach to anchorage measurement involves a manual visual inspection procedure, which is very subjective and has led to inconsistencies in quality control. A new proposed method to improve this procedure involves image-processing technique, which eliminates operator dependencies. A fuzzy reasoning approach is then used to quantify the results generated from image processing which replaces the human decision process. This fusion of these techniques results in a more automated systematic approach to anchorage measurement. Experimental studies confirm that this novel approach leads to a more robust and improved quality control system for the medical packaging company.

The use of laboratory experiments is a critically important aspect of engineering education where experience has shown that a complementary approach combining theoretical and practical exercises is vital for effective learning. Increasingly, teaching institutions are offering Web based remote access to distant laboratories as part of an overall e-learning strategy. The design and implementation of effective and usable remote experimentation facilities poses unique challenges given the inherent complexities of the learning environment and the constraints imposed by the delivery medium. Developments in recent years have addressed many of these issues. However autonomous learning environments by their very nature offer minimal educator assistance and from a students perspective it is inevitable that at some stage of the experimental process, context specific help will be required. We address this issue in the context of remote experimentation for embedded systems and present an adaptive intelligent learning environment with intelligent user help.

The use of laboratory experiments is a critically important aspect of engineering education. Experience in teaching has shown that a complementary approach combining theoretical and practical exercises is vital for effective learning. Increasingly, teaching institutions are offering remote access to distant laboratories as part of an overall e-learning strategy. Remote experimentation provided as part of a Web-based learning approach affords a number of critical benefits, allowing flexible access to on-campus resources free of time or geographical constraints. In the context of distance learning it can be the only realistic method of performing experiments. However adapting and redeveloping existing software and hardware resources to this purpose is both time consuming and expensive. An innovative simple approach to this problem is presented in this paper. The remote desktop method offered here allows students to have full access to existing on-campus laboratory resources remotely but without substantial implementation and development overheads incurred by the providers. This is achieved through increased functionality and extended utilisation of existing assets.

In an increasingly competitive marketplace system complexity continues to grow, but time-to-market and lifecycle are reducing. The purpose of fault diagnosis is the isolation of faults on defective systems, a task requiring a high skill set. This has driven the need for automated diagnostic tools. Over the last two decades, automated diagnosis has been an active research area, but the industrial acceptance of these techniques, particularly in cost-sensitive areas, has not been high. This paper reviews this research, primarily covering rule-based, model-based, and case-based approaches and applications. Future research directions are finally examined, with a concentration on issues, which may lead to a greater acceptance of automated diagnosis.

The authors propose an evolutionary algorithm (EA) approach to hardware-software partitioning and performance estimation of dynamically reconfigurable embedded systems. A demonstrative application is used to show the effectiveness of the use of GAs to achieve hardware-software partitions with maximum speedup.