Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems

Automatic classification of power quality disturbances using optimal feature selection based algorithm

The development of renewable energy sources and power electronic converters in conventional power systems leads to Power Quality (PQ) disturbances. This research aims at automatic detection and classification of single and multiple PQ disturbances using a novel optimal feature selection based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). DWT is used for the extraction of useful features, which are used to distinguish among different PQ disturbances by an ANN classifier. The performance of the classifier solely depends on the feature vector used for the training. Therefore, this research is required for the constructive feature selection based classification system. In this study, an Artificial Bee Colony based Probabilistic Neural Network (ABCPNN) algorithm has been proposed for optimal feature selection. The most common types of single PQ disturbances include sag, swell, interruption, harmonics, oscillatory and impulsive transients, flicker, notch and spikes. Moreover, multiple disturbances consisting of combination of two disturbances are also considered. The DWT with multi-resolution analysis has been applied to decompose the PQ disturbance waveforms into detail and approximation coefficients at level eight using Daubechies wavelet family. Various types of statistical parameters of all the detail and approximation coefficients have been analysed for feature extraction, out of which the optimal features have been selected using ABC algorithm. The performance of the proposed algorithm has been analysed with different architectures of ANN such as multilayer perceptron and radial basis function neural network. The PNN has been found to be the most suitable classifier. The proposed algorithm is tested for both PQ disturbances obtained from the parametric equations and typical power distribution system models using MATLAB/Simulink and PSCAD/EMTDC. The PQ disturbances with uniformly distributed noise ranging from 20 to 50 dB have also been analysed. The experimental results show that the proposed ABC-PNN based approach is capable of efficiently eliminating unnecessary features to improve the accuracy and performance of the classifier

BDD, BNN, and FPGA on Fuzzy Techniques for Rapid System Analysis

This paper looks at techniques to simplify data analysis of large multivariate military sensor systems. The approach is illustrated using representative raw data from a video-scene analyzer. First, develop fuzzy neural net relations using Matlab. This represents the best fidelity fit to the data and will be used as reference for comparison. The data is then converted to Boolean, and using Boolean Decision Diagrams (BDD) techniques, to find similar relations between input vectors and output parameter. It will be shown that such Boolean techniques offer dramatic improvement in system analysis time, and with minor loss of fidelity. To further this study, Boolean Neural Net techniques (BNN) were employed to bridge the Fuzzy Neural Network (FNN) to BDD representations of the data. Neural network approaches give an estimation method for the complexity of Boolean Decision Diagrams, and this can be used to predict the complexity of digital circuits. The neural network model can be used for complexity estimation over a set of BDDs derived from Boolean logic expressions. Experimental results show good correlation with theoretical results and give insights to the complexity. The BNN representations can be useful as a means to FPGA implementation of the system relationships and can be used in embedded processor based multi-variate situations

LEARNING WITH MORE DATA AND BETTER MODELS FOR VISUAL SIMILARITY AND DIFFERENTIATION

This thesis studies machine learning problems involved in visual recognition on a variety of computer vision tasks. It attacks the challenge of scaling-up learning to efficiently handle more training data in object recognition, more noise in brain activation patterns, and learning more capable visual similarity models. For learning similarity models, one challenge is to capture from data the subtle correlations that preserve the notion of similarity relevant to the task. Most previous work focused on improving feature learning and metric learning separately. Instead, we propose a unified deep-learning modeling framework that jointly optimizes the two through back-propagation. We model the feature mapping using a convolutional neural network and the metric function using a multi-layer fully-connected network. Enabled by large datasets and a sampler to handle the intrinsic imbalance between positive and negative samples, we are able to learn such models efficiently. We apply this approach to patch-based image matching and cross-domain clothing-item matching. For analyzing activation patterns in images acquired using functional Magnetic Resonance Imaging (fMRI), a technology widely used in neuroscience to study human brain, challenges are small number of examples and high level of noise. The common ways of increasing the signal to noise ratio include adding more repetitions, averaging trials, and analyzing statistics maps solved based on a general linear model. In collaboration with neuroscientists, we developed a machine learning approach that allows us to analyze individual trials directly. This approach uses multi-voxel patterns over regions of interest as feature representation, and helps discover effects previous analyses missed. For multi-class object recognition, one challenge is learning a non-one-vs-all multi-class classifier with large numbers of categories each with large numbers of examples. A common approach is data parallelization in a synchronized fashion: evenly and randomly distribute the data into splits, learn a full model on each split and average the models. We reformulate the overall learning problem in a consensus optimization framework and propose a more principled synchronized approach to distributed training. Moreover, we develop an efficient algorithm for solving the sub-problem by reducing it to a standard problem with warm start.Doctor of Philosoph

Vision-based neural network classifiers and their applications

A thesis submitted for the degree of Doctor of Philosophy of University of LutonVisual inspection of defects is an important part of quality assurance in many fields of production. It plays a very useful role in industrial applications in order to relieve human inspectors and improve the inspection accuracy and hence increasing productivity. Research has previously been done in defect classification of wood veneers using techniques such as neural networks, and a certain degree of success has been achieved. However, to improve results in tenus of both classification accuracy and running time are necessary if the techniques are to be widely adopted in industry, which has motivated this research. This research presents a method using rough sets based neural network with fuzzy input (RNNFI). Variable precision rough set (VPRS) method is proposed to remove redundant features utilising the characteristics of VPRS for data analysis and processing. The reduced data is fuzzified to represent the feature data in a more suitable foml for input to an improved BP neural network classifier. The improved BP neural network classifier is improved in three aspects: additional momentum, self-adaptive learning rates and dynamic error segmenting. Finally, to further consummate the classifier, a uniform design CUD) approach is introduced to optimise the key parameters because UD can generate a minimal set of uniform and representative design points scattered within the experiment domain. Optimal factor settings are achieved using a response surface (RSM) model and the nonlinear quadratic programming algorithm (NLPQL). Experiments have shown that the hybrid method is capable of classifying the defects of wood veneers with a fast convergence speed and high classification accuracy, comparing with other methods such as a neural network with fuzzy input and a rough sets based neural network. The research has demonstrated a methodology for visual inspection of defects, especially for situations where there is a large amount of data and a fast running speed is required. It is expected that this method can be applied to automatic visual inspection for production lines of other products such as ceramic tiles and strip steel

Bioinformatics framework for genotyping microarray data analysis

Functional genomics is a flourishing science enabled by recent technological breakthroughs in high-throughput instrumentation and microarray data analysis. Genotyping microarrays establish the genotypes of DNA sequences containing single nucleotide polymorphisms (SNPs), and can help biologists probe the functions of different genes and/or construct complex gene interaction networks. The enormous amount of data from these experiments makes it infeasible to perform manual processing to obtain accurate and reliable results in daily routines. Advanced algorithms as well as an integrated software toolkit are needed to help perform reliable and fast data analysis. The author developed a MatlabTM based software package, called TIMDA (a Toolkit for Integrated Genotyping Microarray Data Analysis), for fully automatic, accurate and reliable genotyping microarray data analysis. The author also developed new algorithms for image processing and genotype-calling. The modular design of TIMDA allows satisfactory extensibility and maintainability. TIMDA is open source (URL: http://timda.SF.net and can be easily customized by users to meet their particular needs. The quality and reproducibility of results in image processing and genotype-calling and the ease of customization indicate that TIMDA is a useful package for genomics research