Optimal Feature Extraction and Classification of Tensors via Matrix Product State Decomposition

© 2015 IEEE. Big data consists of large multidimensional datasets that would often be difficult to analyze if working with the original tensor. There is a rising interest in the use of tensor decompositions for feature extraction due to the ability to extract necessary features from a large dimensional feature space. In this paper the matrix product state (MPS) decomposition is used for feature extraction of large tensors. The novelty of the paper is the introduction of a single core tensor obtained from the MPS that not only contains a significantly reduced feature space, but can perform classification with high accuracy without the need of feature selection methods

Near-infrared parameters extraction: A potential method to detect skin cancer

The wavelength-dependent absorption coefficients can be used to analyse optical properties of human skin. Existing absorption models for narrow ranges in the visible and near infrared are insufficient to simultaneously incorporate the spectral contrast produced by differences in chromophores, water and lipid content of skin tissue into skin cancer detection. In the broad range up to 1600 nm, recent analysis approaches for absorption spectra do not consistently provide significant differences between healthy and cancerous skins. We propose an absorption model to fit the absorption coefficient spectra of skin samples over the range from 400 nm to 1600 nm and an advanced algorithm to find the optimal estimation. The extracted parameters of this model are analysed by a statistical t-test. The test results demonstrate the significant differences between all pairs of tumour-normal skin. Therefore, our approach has strong potential for early skin cancer detection using near infrared spectroscopy (NIRS). © 2013 IEEE

Three-phase optimal power flow for smart grids by iterative nonsmooth optimization

© 2017 by SCITEPRESS Science and Technology Publications, Lda. All Rights Reserved. Optimal power flow is important for operation and planning of smart grids. The paper considers the so called unbalanced thee-phase optimal power flow problem (TOPF) for smart grids, which involves multiple quadratic equality and indefinite quadratic inequality constraints to model the bus interconnections, hardware capacity and balance between power demand and supply. The existing Newton search based or interior point algorithms are often trapped by a local optimum while semidefinite programming relaxation (SDR) even fails to locate a feasible point. Following our previously developed nonsmooth optimization approach, computational solution for TOPF is provided. Namely, an iterative procedure for generating a sequence of improved points that converges to an optimal solution, is developed. Simulations for TOPF in unbalanced distributed networks are provided to demonstrate the practicability and efficiency of our approach

Joint power allocation for MIMO-OFDM full-duplex relaying communications

© 2017, The Author(s). In this paper, we address the problem of joint power allocation in a two-hop MIMO-OFDM network, where two full-duplex users communicate with each other via an amplify-and-forward relay. We consider a general model in which the full-duplex relay can forward the received message in either one-way or two-way mode. Our aim is to maximize the instantaneous end-to-end total throughput, subject to (i) the separate sum-power constraints at individual nodes or (ii) the joint sum-power constraint of the whole network. The formulated problems are large-scale nonconvex optimization problems, for which efficient and optimal solutions are currently not available. Using the successive convex approximation approach, we develop novel iterative algorithms of extremely low complexity which are especially suitable for large-scale computation. In each iteration, a simple closed-form solution is derived for the approximated convex program. The proposed algorithms guarantee to converge to at least a local optimum of the nonconvex problems. Numerical results verify that the devised solutions converge quickly, and that our optimal power allocation schemes significantly improve the throughput of MIMO-OFDM full-duplex one-way/two-way relaying over the conventional half-duplex relaying strategy

Energy-Efficient Signalling in QoS Constrained Heterogeneous Networks

© 2013 IEEE. This paper considers a heterogeneous network, which consists of one macro base station and numerous small cell base stations (SBSs) cooperatively serving multiple user terminals. The first objective is to design cooperative transmit beamformers at the base stations to maximize the network energy efficiency (EE) in terms of bits per joule subject to the users' quality of service (QoS) constraints, which poses a computationally difficult optimization problem. The commonly used Dinkelbach-type algorithms for optimizing a ratio of concave and convex functions are not applicable. This paper develops a path-following algorithm to address the computational solution to this problem, which invokes only a simple convex quadratic program of moderate dimension at each iteration and quickly converges at least to a locally optimal solution. Furthermore, the problem of joint beamformer design and SBS service assignment in the three-objective (EE, QoS, and service loading) optimization is also addressed. Numerical results demonstrate the performance advantage of the proposed solutions

Concatenated image completion via tensor augmentation and completion

© 2016 IEEE. This paper proposes a novel framework called concatenated image completion via tensor augmentation and completion (ICTAC), which recovers missing entries of color images with high accuracy. Typical images are second-or third-order tensors (2D/3D) depending if they are grayscale or color, hence tensor completion algorithms are ideal for their recovery. The proposed framework performs image completion by concatenating copies of a single image that has missing entries into a third-order tensor, applying a dimensionality augmentation technique to the tensor, utilizing a tensor completion algorithm for recovering its missing entries, and finally extracting the recovered image from the tensor. The solution relies on two key components that have been recently proposed to take advantage of the tensor train (TT) rank: A tensor augmentation tool called ket augmentation (KA) that represents a low-order tensor by a higher-order tensor, and the algorithm tensor completion by parallel matrix factorization via tensor train (TMac-TT), which has been demonstrated to outperform state-of-the-art tensor completion algorithms. Simulation results for color image recovery show the clear advantage of our framework against current state-of-the-art tensor completion algorithms

2-D two-fold symmetric circular shaped filter design with homomorphic processing application

A design method of a linear-phased, two-dimensional (2-D), two-fold symmetric circular shaped filter is presented in this paper. Although the proposed method designs a non-separable filter, its implementation has linear complexity. The shape of the passband and the stopband is expressed in terms of level sets of second order trigonometric polynomials. This enables the transformation of the filter specifications to a Semi-Definite Program (SDP) of moderate dimension. The proposed filter outperforms currently available filter design methods. We present a performance comparison, as well as a homomorphic processing image enhancement example to illustrate the effectiveness of this method. ©2010 IEEE

Matrix Product State for Higher-Order Tensor Compression and Classification

© 2017 IEEE. This paper introduces matrix product state (MPS) decomposition as a new and systematic method to compress multidimensional data represented by higher order tensors. It solves two major bottlenecks in tensor compression: computation and compression quality. Regardless of tensor order, MPS compresses tensors to matrices of moderate dimension, which can be used for classification. Mainly based on a successive sequence of singular value decompositions, MPS is quite simple to implement and arrives at the global optimal matrix, bypassing local alternating optimization, which is not only computationally expensive but cannot yield the global solution. Benchmark results show that MPS can achieve better classification performance with favorable computation cost compared to other tensor compression methods

Two-hop power-relaying for linear wireless sensor networks

© 2016 IEEE. This paper presents two-hop relay gain-scheduling control in a Wireless Sensor Network to estimate a static target prior characterized by Gaussian probability distribution. The target is observed by a network of linear sensors, whose observations are transmitted to a fusion center for carrying out final estimation via a amplify-And-forward relay node. We are concerned with the joint transmission power allocation for sensors and relay to optimize the minimum mean square error (MMSE) estimator, which is deployed at the fusion center. Particularly, such highly nonlinear optimization problems are solved by an iterative procedure of very low computational complexity. Simulations are provided to support the efficiency of our proposed power allocation

Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train

© 1992-2012 IEEE. This paper proposes a novel approach to tensor completion, which recovers missing entries of data represented by tensors. The approach is based on the tensor train (TT) rank, which is able to capture hidden information from tensors thanks to its definition from a well-balanced matricization scheme. Accordingly, new optimization formulations for tensor completion are proposed as well as two new algorithms for their solution. The first one called simple low-rank tensor completion via TT (SiLRTC-TT) is intimately related to minimizing a nuclear norm based on TT rank. The second one is from a multilinear matrix factorization model to approximate the TT rank of a tensor, and is called tensor completion by parallel matrix factorization via TT (TMac-TT). A tensor augmentation scheme of transforming a low-order tensor to higher orders is also proposed to enhance the effectiveness of SiLRTC-TT and TMac-TT. Simulation results for color image and video recovery show the clear advantage of our method over all other methods