Enhancing Physical Layer Security in AF Relay Assisted Multi-Carrier Wireless Transmission

In this paper, we study the physical layer security (PLS) problem in the dual hop orthogonal frequency division multiplexing (OFDM) based wireless communication system. First, we consider a single user single relay system and study a joint power optimization problem at the source and relay subject to individual power constraint at the two nodes. The aim is to maximize the end to end secrecy rate with optimal power allocation over different sub-carriers. Later, we consider a more general multi-user multi-relay scenario. Under high SNR approximation for end to end secrecy rate, an optimization problem is formulated to jointly optimize power allocation at the BS, the relay selection, sub-carrier assignment to users and the power loading at each of the relaying node. The target is to maximize the overall security of the system subject to independent power budget limits at each transmitting node and the OFDMA based exclusive sub-carrier allocation constraints. A joint optimization solution is obtained through duality theory. Dual decomposition allows to exploit convex optimization techniques to find the power loading at the source and relay nodes. Further, an optimization for power loading at relaying nodes along with relay selection and sub carrier assignment for the fixed power allocation at the BS is also studied. Lastly, a sub-optimal scheme that explores joint power allocation at all transmitting nodes for the fixed subcarrier allocation and relay assignment is investigated. Finally, simulation results are presented to validate the performance of the proposed schemes.Comment: 10 pages, 7 figures, accepted in Transactions on Emerging Telecommunications Technologies (ETT), formerly known as European Transactions on Telecommunications (ETT

Digital Filter Design Using Improved Artificial Bee Colony Algorithms

Digital filters are often used in digital signal processing applications. The design objective of a digital filter is to find the optimal set of filter coefficients, which satisfies the desired specifications of magnitude and group delay responses. Evolutionary algorithms are population-based meta-heuristic algorithms inspired by the biological behaviors of species. Compared to gradient-based optimization algorithms such as steepest descent and Newton’s like methods, these bio-inspired algorithms have the advantages of not getting stuck at local optima and being independent of the starting point in the solution space. The limitations of evolutionary algorithms include the presence of control parameters, problem specific tuning procedure, premature convergence and slower convergence rate. The artificial bee colony (ABC) algorithm is a swarm-based search meta-heuristic algorithm inspired by the foraging behaviors of honey bee colonies, with the benefit of a relatively fewer control parameters. In its original form, the ABC algorithm has certain limitations such as low convergence rate, and insufficient balance between exploration and exploitation in the search equations. In this dissertation, an ABC-AMR algorithm is proposed by incorporating an adaptive modification rate (AMR) into the original ABC algorithm to increase convergence rate by adjusting the balance between exploration and exploitation in the search equations through an adaptive determination of the number of parameters to be updated in every iteration. A constrained ABC-AMR algorithm is also developed for solving constrained optimization problems.There are many real-world problems requiring simultaneous optimizations of more than one conflicting objectives. Multiobjective (MO) optimization produces a set of feasible solutions called the Pareto front instead of a single optimum solution. For multiobjective optimization, if a decision maker’s preferences can be incorporated during the optimization process, the search process can be confined to the region of interest instead of searching the entire region. In this dissertation, two algorithms are developed for such incorporation. The first one is a reference-point-based MOABC algorithm in which a decision maker’s preferences are included in the optimization process as the reference point. The second one is a physical-programming-based MOABC algorithm in which physical programming is used for setting the region of interest of a decision maker. In this dissertation, the four developed algorithms are applied to solve digital filter design problems. The ABC-AMR algorithm is used to design Types 3 and 4 linear phase FIR differentiators, and the results are compared to those obtained by the original ABC algorithm, three improved ABC algorithms, and the Parks-McClellan algorithm. The constrained ABC-AMR algorithm is applied to the design of sparse Type 1 linear phase FIR filters of filter orders 60, 70 and 80, and the results are compared to three state-of-the-art design methods. The reference-point-based multiobjective ABC algorithm is used to design of asymmetric lowpass, highpass, bandpass and bandstop FIR filters, and the results are compared to those obtained by the preference-based multiobjective differential evolution algorithm. The physical-programming-based multiobjective ABC algorithm is used to design IIR lowpass, highpass and bandpass filters, and the results are compared to three state-of-the-art design methods. Based on the obtained design results, the four design algorithms are shown to be competitive as compared to the state-of-the-art design methods

Representations and representation learning for image aesthetics prediction and image enhancement

With the continual improvement in cell phone cameras and improvements in the connectivity of mobile devices, we have seen an exponential increase in the images that are captured, stored and shared on social media. For example, as of July 1st 2017 Instagram had over 715 million registered users which had posted just shy of 35 billion images. This represented approximately seven and nine-fold increase in the number of users and photos present on Instagram since 2012. Whether the images are stored on personal computers or reside on social networks (e.g. Instagram, Flickr), the sheer number of images calls for methods to determine various image properties, such as object presence or appeal, for the purpose of automatic image management and curation. One of the central problems in consumer photography centers around determining the aesthetic appeal of an image and motivates us to explore questions related to understanding aesthetic preferences, image enhancement and the possibility of using such models on devices with constrained resources. In this dissertation, we present our work on exploring representations and representation learning approaches for aesthetic inference, composition ranking and its application to image enhancement. Firstly, we discuss early representations that mainly consisted of expert features, and their possibility to enhance Convolutional Neural Networks (CNN). Secondly, we discuss the ability of resource-constrained CNNs, and the different architecture choices (inputs size and layer depth) in solving various aesthetic inference tasks: binary classification, regression, and image cropping. We show that if trained for solving fine-grained aesthetics inference, such models can rival the cropping performance of other aesthetics-based croppers, however they fall short in comparison to models trained for composition ranking. Lastly, we discuss our work on exploring and identifying the design choices in training composition ranking functions, with the goal of using them for image composition enhancement

Compressive sensing based visual tracking using multi-task sparse learning method

8th International Conference on Wireless Communications and Signal Processing, WCSP 2016, Yangzhou, China, 13-15 October 2016In this paper, we propose a compressive sensing based framework for robust visual tracking. As a key part of the tracking framework, a new multi-task sparse learning method is designed to estimate the observation likelihood in order to determine the best target. Compared with the traditional multi-task sparse learning method, our method uses compressed appearance features to achieve multi-task sparse representation. Experimental results show that the proposed visual tracking framework can achieve a better tracking performance than state-of-the-art tracking methods with a significantly reduced computational complexity.Department of Electronic and Information Engineerin