Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Physical layer security (PLS) has emerged as a new concept and powerful alternative that can complement and may even replace encryption-based approaches, which entail many hurdles and practical problems for future wireless systems. The basic idea of PLS is to exploit the characteristics of the wireless channel and its impairments including noise, fading, interference, dispersion, diversity, etc. in order to ensure the ability of the intended user to successfully perform data decoding while preventing eavesdroppers from doing so. Thus, the main design goal of PLS is to increase the performance difference between the link of the legitimate receiver and that of the eavesdropper by using well-designed transmission schemes. In this survey, we propose a conceptual, generic, and expandable framework for classifying the existing PLS techniques against wireless passive eavesdropping. In this flexible framework, the security techniques that we comprehensively review in this treatise are divided into two primary approaches: signal-to-interference-plus-noise ratio-based approach and complexity-based approach. The first approach is classified into three major categories: first, secrecy channel codes-based schemes; second, security techniques based on channel adaptation; third, schemes based on injecting interfering artificial (noise/jamming) signals along with the transmitted information signals. The second approach (complexity-based), which is associated with the mechanisms of extracting secret sequences from the shared channel, is classified into two main categories based on which layer the secret sequence obtained by channel quantization is applied on. The techniques belonging to each one of these categories are divided and classified into three main signal domains: time, frequency and space. For each one of these domains, several examples are given and illustrated along with the review of the state-of-the-art security advances in each domain. Moreover, the advantages and disadvantages of each approach alongside the lessons learned from existing research works are stated and discussed. The recent applications of PLS techniques to different emerging communication systems such as visible light communication, body area network, power line communication, Internet of Things, smart grid, mm-Wave, cognitive radio, vehicular ad-hoc network, unmanned aerial vehicle, ultra-wideband, device-to-device, radio-frequency identification, index modulation, and 5G non-orthogonal multiple access based-systems, are also reviewed and discussed. The paper is concluded with recommendations and future research directions for designing robust, efficient and strong security methods for current and future wireless systems.

Classifications and Applications of Physical Layer Security Techniques for Confidentiality: A Comprehensive Survey

We consider the numerical calculation of several matrix eigenvalue problems which require some manipulation before the standard algorithms may be used. This includes finding the stationary values of a quadratic form subject to linear constraints and determining the eigenvalues of a matrix which is modified by a matrix of rank one. We also consider several inverse eigenvalue problems. This includes the problem of determining the coefficients for the Gauss–Radau and Gauss–Lobatto quadrature rules. In addition, we study several eigenvalue problems which arise in least squares.

Some modified matrix eigenvalue problems.

Fault diagnosis in electronic circuits is an emerging area of research, where fully automated diagnosis systems are being developed for the investigation of the circuits. Developing test methods for the diagnosis of faults in analog circuits is still a complex task. Consequently, a technique for the fault diagnosis in analog circuits is designed by proposing a new optimization algorithm, named, rider optimization algorithm (ROA). The development of ROA is based on a group of riders, racing toward a target location. Moreover, a classifier, termed RideNN, is developed by including the proposed algorithm as the training algorithm for the neural network (NN). RideNN, along with the orthogonal transformation and Bhattacharyya coefficient, is applied for the fault diagnosis of analog circuits. The proposed technique is experimented using three basic circuits, such as triangular wave generator (TWG), low noise bipolar transistor amplifier (BTA), and differentiator (DIF) and an application circuit, solar power converter (SPC). The performance is evaluated using two evaluation metrics, namely, accuracy (ACC) and false alarm ratio (FAR). The analysis results show that the proposed technique attains an ACC of 99.9% in TWG, 99.9% in BTA, 99% in DIF, and 95% in SPC without noise.

RideNN: A New Rider Optimization Algorithm-Based Neural Network for Fault Diagnosis in Analog Circuits

Road transport consumes significant quantities of fossil fuel and accounts for a significant proportion of CO2 and pollutant emissions worldwide. The driver is a major and often overlooked factor that determines vehicle performance. Eco-driving is a relatively low-cost and immediate measure to reduce fuel consumption and emissions significantly. This paper reviews the major factors, research methods and implementation of eco-driving technology. The major factors of eco-driving are acceleration/deceleration, driving speed, route choice and idling. Eco-driving training programs and in-vehicle feedback devices are commonly used to implement eco-driving skills. After training or using in-vehicle devices, immediate and significant reductions in fuel consumption and CO2 emissions have been observed with slightly increased travel time. However, the impacts of both methods attenuate over time due to the ingrained driving habits developed over the years. These findings imply the necessity of developing quantitative eco-driving patterns that could be integrated into vehicle hardware so as to generate more constant and uniform improvements, as well as developing more effective and lasting training programs and in-vehicle devices. Current eco-driving studies mainly focus on the fuel savings and CO2 reduction of individual vehicles, but ignore the pollutant emissions and the impacts at network levels. Finally, the challenges and future research directions of eco-driving technology are elaborated.

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Eco-driving technology for sustainable road transport: A review

It was shown recently that the $K$ L1-norm principal components (L1-PCs) of a real-valued data matrix $\mathbf X \in \mathbb {R}^{D \times N}$ ( $N$ data samples of $D$ dimensions) can be exactly calculated with cost $\mathcal {O}(2^{NK})$ or, when advantageous, $\mathcal {O}(N^{dK - K + 1})$ where $d=\mathrm{rank}(\mathbf X)$ , $K . In applications where $\mathbf X$ is large (e.g., “big” data of large $N$ and/or “heavy” data of large $d$ ), these costs are prohibitive. In this paper, we present a novel suboptimal algorithm for the calculation of the $K L1-PCs of $\mathbf X$ of cost $\mathcal O (ND \mathrm{min} \lbrace N,D\rbrace + N^2K^2(K^2 + d))$ , which is comparable to that of standard L2-norm PC analysis. Our theoretical and experimental studies show that the proposed algorithm calculates the exact optimal L1-PCs with high frequency and achieves higher value in the L1-PC optimization metric than any known alternative algorithm of comparable computational cost. The superiority of the calculated L1-PCs over standard L2-PCs (singular vectors) in characterizing potentially faulty data/measurements is demonstrated with experiments in data dimensionality reduction and disease diagnosis from genomic data.

Efficient L1-Norm Principal-Component Analysis via Bit Flipping

Recently, Markopoulos et al. [1], [2] presented an optimal algorithm that computes the L
 1
 maximum-projection principal component of any set of N real-valued data vectors of dimension D with complexity polynomial in N, O(N
 D
). Still, moderate to high values of the data dimension D and/or data record size N may render the optimal algorithm unsuitable for practical implementation due to its exponential in D complexity. In this paper, we present for the first time in the literature a fast greedy single-bit-flipping conditionally optimal iterative algorithm for the computation of the L
 1
 principal component with complexity O(N
 3
). Detailed numerical studies are carried out demonstrating the effectiveness of the developed algorithm with applications to the general field of data dimensionality reduction and direction-of-arrival estimation.

Fast computation of the L 1 -principal component of real-valued data

Wireless physical-layer security is an emerging field of research aiming at preventing eavesdropping in an open wireless medium. In this paper, we propose a novel waveform design approach to minimize the likelihood that a message transmitted between trusted single-antenna nodes is intercepted by an eavesdropper. In particular, with knowledge first of the eavesdropper's channel state information (CSI), we find the optimum waveform and transmit energy that minimize the signal-to-interference-plus-noise ratio (SINR) at the output of the eavesdropper's maximum-SINR linear filter, while at the same time provide the intended receiver with a required pre-specified SINR at the output of its own max-SINR filter. Next, if prior knowledge of the eavesdropper's CSI is unavailable, we design a waveform that maximizes the amount of energy available for generating disturbance to eavesdroppers, termed artificial noise (AN), while the SINR of the intended receiver is maintained at the pre-specified level. The extensions of the secure waveform design problem to multiple intended receivers are also investigated and semidefinite relaxation (SDR) -an approximation technique based on convex optimization- is utilized to solve the arising NP-hard design problems. Extensive simulation studies confirm our analytical performance predictions and illustrate the benefits of the designed waveforms on securing single-input single-output (SISO) transmissions and multicasting.

Waveform Design for Secure SISO Transmissions and Multicasting

We address the problem of recovering an unknown image of interest, when only few, severely corrupted copies are available. We employ, for the first time in the literature, corruption-resistant L 1 -Principal-Components (L 1 -PCs) of the image data-set at hand. Specifically, the calculated L 1 -PCs are used for reliability-based patch-by-patch fusion of the corrupted image copies into a single high-quality representation of the original image (L 1 -fusion). Our experimental studies illustrate that the proposed method offers remarkable recovery results for several common corruption types, even under high corruption rate, small number of copies, and varying corruption type among copies. An additional theoretical contribution of this work is that the L 1 -PC of a data matrix of non-negative entries (e.g., image data) is for the first time shown to be optimally calculable with complexity linear to the matrix dimensions — as of now, the fastest-known optimal algorithm is of polynomial complexity. In the light of this result, L 1 -fusion is carried out with linear cost comparable to that of the simple copy-averaging alternative. The linear-low cost of L 1 -fusion allows for the recovered image to be, optionally, further refined by means of sophisticated single-image restoration techniques.

L 1 -fusion: Robust linear-time image recovery from few severely corrupted copies

Given the rise in fuel prices and the harmful environmental consequences of excessive fuel consumption, we address a new problem in eco-driving, which examines how the upcoming V2V/V2I technology can be harnessed to improve fuel-efficiency. Unlike most of the existing studies in this area where the focus of control is on infrastructure side (i.e., signal timing plans), we present a new approach to eco-speed control at a microscopic level. We use a concept of platoon of vehicles to reduce fuel consumption in a journey covering multiple intersections in a multiple vehicle setting. Three heuristic algorithms are proposed and numerical results from simulations are also presented.

Sandipan Kundu

Papers

Efficient L1-Norm Principal-Component Analysis via Bit Flipping

Fast computation of the L 1 -principal component of real-valued data

Waveform Design for Secure SISO Transmissions and Multicasting

L 1 -fusion: Robust linear-time image recovery from few severely corrupted copies

Vehicle speed control algorithms for eco-driving