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# IEEE Transactions on Signal Processing

## Filter Results

Displaying Results 1 - 25 of 34
• ### [Front cover]

Publication Year: 2014, Page(s): C1
| PDF (392 KB)
• ### IEEE Transactions on Signal Processing publication information

Publication Year: 2014, Page(s): C2
| PDF (135 KB)

Publication Year: 2014, Page(s):4035 - 4036
| PDF (227 KB)

Publication Year: 2014, Page(s):4037 - 4038
| PDF (228 KB)
• ### Towards the Asymptotic Sum Capacity of the MIMO Cellular Two-Way Relay Channel

Publication Year: 2014, Page(s):4039 - 4051
Cited by:  Papers (12)
| | PDF (4486 KB) | HTML

In this paper, we consider the transceiver and relay design for the multiple-input multiple-output (MIMO) cellular two-way relay channel (cTWRC), where a multi-antenna base station (BS) exchanges information with multiple multiantenna mobile stations via a multi-antenna relay station (RS). We propose a novel two-way relaying scheme to approach the sum capacity of the MIMO cTWRC. A key contribution... View full abstract»

• ### Distributed Hybrid Power State Estimation Under PMU Sampling Phase Errors

Publication Year: 2014, Page(s):4052 - 4063
Cited by:  Papers (8)
| | PDF (4216 KB) | HTML

Phasor measurement units (PMUs) have the advantage of providing direct measurements of power states. However, as the number of PMUs in a power system is limited, the traditional supervisory control and data acquisition (SCADA) system cannot be replaced by the PMU-based system overnight. Therefore, hybrid power state estimation taking advantage of both systems is important. As experiments show that... View full abstract»

• ### Estimation of Amplitude, Phase and Unbalance Parameters in Three-phase Systems: Analytical Solutions, Efficient Implementation and Performance Analysis

Publication Year: 2014, Page(s):4064 - 4076
Cited by:  Papers (12)
| | PDF (3305 KB) | HTML

This paper focuses on the estimation of the instantaneous amplitude, phase, and unbalance parameters in three-phase power systems. Due to the particular structure of three-phase systems, we demonstrate that the maximum-likelihood estimates (MLEs) of the unknown parameters have simple closed-form expressions and can be easily implemented without matrix algebra libraries. We also derive and analyze ... View full abstract»

• ### Tomlinson–Harashima Precoding for Multiuser MIMO Systems With Quantized CSI Feedback and User Scheduling

Publication Year: 2014, Page(s):4077 - 4090
Cited by:  Papers (2)
| | PDF (3790 KB) | HTML

This paper studies the sum rate performance of a low complexity quantized CSI-based Tomlinson-Harashima (TH) precoding scheme for downlink multiuser MIMO transmission, employing greedy user selection. The asymptotic distribution of the output-signal-to-interference-plus-noise ratio of each selected user and the asymptotic sum rate as the number of users K grows large are derived by using extreme v... View full abstract»

• ### Relabeling and Summarizing Posterior Distributions in Signal Decomposition Problems When the Number of Components is Unknown

Publication Year: 2014, Page(s):4091 - 4104
| | PDF (2439 KB) | HTML

This paper addresses the problems of relabeling and summarizing posterior distributions that typically arise, in a Bayesian framework, when dealing with signal decomposition problems with an unknown number of components. Such posterior distributions are defined over union of subspaces of differing dimensionality and can be sampled from using modern Monte Carlo techniques, for instance, the increas... View full abstract»

• ### Compressive Shift Retrieval

Publication Year: 2014, Page(s):4105 - 4113
Cited by:  Papers (2)
| | PDF (2809 KB) | HTML

The classical shift retrieval problem considers two signals in vector form that are related by a shift. This problem is of great importance in many applications and is typically solved by maximizing the cross-correlation between the two signals. Inspired by compressive sensing, in this paper, we seek to estimate the shift directly from compressed signals. We show that under certain conditions, the... View full abstract»

• ### Deep Scattering Spectrum

Publication Year: 2014, Page(s):4114 - 4128
Cited by:  Papers (39)
| | PDF (3815 KB) | HTML

A scattering transform defines a locally translation invariant representation which is stable to time-warping deformation. It extends MFCC representations by computing modulation spectrum coefficients of multiple orders, through cascades of wavelet convolutions and modulus operators. Second-order scattering coefficients characterize transient phenomena such as attacks and amplitude modulation. A f... View full abstract»

Publication Year: 2014, Page(s):4129 - 4144
Cited by:  Papers (71)
| | PDF (4862 KB) | HTML

Adaptive networks are suitable for decentralized inference tasks. Recent works have intensively studied distributed optimization problems in the case where the nodes have to estimate a single optimum parameter vector collaboratively. However, there are many important applications that are multitask-oriented in the sense that there are multiple optimum parameter vectors to be inferred simultaneousl... View full abstract»

• ### Ramanujan Sums in the Context of Signal Processing—Part I: Fundamentals

Publication Year: 2014, Page(s):4145 - 4157
Cited by:  Papers (32)
| | PDF (3640 KB) | HTML

The famous mathematician S. Ramanujan introduced a summation in 1918, now known as the Ramanujan sum cq(n). For any fixed integer q, this is a sequence in n with periodicity q. Ramanujan showed that many standard arithmetic functions in the theory of numbers, such as Euler's totient function φ(n) and the Möbius function μ(n), can be expressed as linear combinations... View full abstract»

• ### Ramanujan Sums in the Context of Signal Processing—Part II: FIR Representations and Applications

Publication Year: 2014, Page(s):4158 - 4172
Cited by:  Papers (29)
| | PDF (3393 KB) | HTML

The mathematician Ramanujan introduced a summation in 1918, now known as the Ramanujan sum cq(n). In a companion paper (Part I), properties of Ramanujan sums were reviewed, and Ramanujan subspaces Sq introduced, of which the Ramanujan sum is a member. In this paper, the problem of representing finite duration (FIR) signals based on Ramanujan sums and spaces is considered. Fir... View full abstract»

• ### Efficient Hardware Architecture for Sparse Coding

Publication Year: 2014, Page(s):4173 - 4186
Cited by:  Papers (4)
| | PDF (3276 KB) | HTML

Sparse coding encodes natural stimuli using a small number of basis functions known as receptive fields. In this work, we design custom hardware architectures for efficient and high-performance implementations of a sparse coding algorithm called the sparse and independent local network (SAILnet). A study of the neuron spiking dynamics uncovers important design considerations involving the neural n... View full abstract»

• ### Sum-Rate Maximization for Active Channels With Unequal Subchannel Noise Powers

Publication Year: 2014, Page(s):4187 - 4198
Cited by:  Papers (8)
| | PDF (2588 KB) | HTML

In this paper, an active channel, between a source and a destination, refers to a parallel channel where the source transmits power over different subchannels as well as the powers of the subchannels can be adjusted. We herein study the sum-rate maximization for an active channel subject to two constraints, one on the source total transmit power and one on the total channel power. Although this ma... View full abstract»

• ### Bayesian Estimation of Clean Speech Spectral Coefficients Given a Priori Knowledge of the Phase

Publication Year: 2014, Page(s):4199 - 4208
Cited by:  Papers (19)
| | PDF (2197 KB) | HTML

While most short-time discrete Fourier transform-based single-channel speech enhancement algorithms only modify the noisy spectral amplitude, in recent years the interest in phase processing has increased in the field. The goal of this paper is twofold. First, we derive Bayesian probability density functions and estimators for the clean speech phase when different amounts of prior knowledge about ... View full abstract»

• ### Sparse Recovery of Streaming Signals Using $ell_1$-Homotopy

Publication Year: 2014, Page(s):4209 - 4223
Cited by:  Papers (32)
| | PDF (3139 KB) | HTML

Most of the existing sparse-recovery methods assume a static system: the signal is a finite-length vector for which a fixed set of measurements and sparse representation are available and an l1 problem is solved for the reconstruction. However, the same representation and reconstruction framework is not readily applicable in a streaming system: the signal changes over time, and it is measured and ... View full abstract»

• ### Optimal Index Policies for Anomaly Localization in Resource-Constrained Cyber Systems

Publication Year: 2014, Page(s):4224 - 4236
Cited by:  Papers (10)
| | PDF (3470 KB) | HTML

The problem of anomaly localization in a resource-constrained cyber system is considered. Each anomalous component of the system incurs a cost per unit time until its anomaly is identified and fixed. Different anomalous components may incur different costs depending on their criticality to the system. Due to resource constraints, only one component can be probed at each given time. The observation... View full abstract»

• ### Likelihood Estimators for Dependent Samples and Their Application to Order Detection

Publication Year: 2014, Page(s):4237 - 4244
Cited by:  Papers (3)
| | PDF (1873 KB) | HTML

Estimation of the dimension of the signal subspace, or order detection, is one of the key issues in many signal processing problems. Information theoretic criteria are widely used to estimate the order under the independently and identically distributed (i.i.d.) sampling assumption. However, in many applications, the i.i.d. sampling assumption does not hold. Previous approaches address the depende... View full abstract»

• ### Blind Source Separation by Entropy Rate Minimization

Publication Year: 2014, Page(s):4245 - 4255
Cited by:  Papers (13)
| | PDF (2801 KB) | HTML

By assuming latent sources are statistically independent, independent component analysis separates underlying sources from a given linear mixture. Since in many applications, latent sources are both non-Gaussian and have sample dependence, it is desirable to exploit both properties jointly. In this paper, we use mutual information rate to construct a general framework for analysis and derivation o... View full abstract»

• ### A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal

Publication Year: 2014, Page(s):4256 - 4269
Cited by:  Papers (12)
| | PDF (5415 KB) | HTML

Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured “noises”. As their amplitude may be greater than signals of interest (primaries), additional prior information is especially important in performing efficient signal separation. We address here the problem of multiple reflections, caused by wave-field bouncing between layer... View full abstract»

• ### An MGF-Based Unified Framework to Determine the Joint Statistics of Partial Sums of Ordered i.n.d. Random Variables

Publication Year: 2014, Page(s):4270 - 4283
Cited by:  Papers (2)
| | PDF (4134 KB) | HTML

The joint statistics of partial sums of ordered random variables (RVs) are often needed for the accurate performance characterization of a wide variety of wireless communication systems. A unified analytical framework to determine the joint statistics of partial sums of ordered independent and identically distributed (i.i.d.) random variables was recently presented. However, the identical distribu... View full abstract»

• ### An Online Algorithm for Separating Sparse and Low-Dimensional Signal Sequences From Their Sum

Publication Year: 2014, Page(s):4284 - 4297
Cited by:  Papers (30)
| | PDF (4242 KB) | HTML

This paper designs and extensively evaluates an online algorithm, called practical recursive projected compressive sensing (Prac-ReProCS), for recovering a time sequence of sparse vectors St and a time sequence of dense vectors Lt from their sum, Mt: = St + Lt, when the Lt's lie in a slowly changing low-dimensional subspace of the full space. ... View full abstract»

• ### Kernel Additive Models for Source Separation

Publication Year: 2014, Page(s):4298 - 4310
Cited by:  Papers (21)
| | PDF (2590 KB) | HTML

Source separation consists of separating a signal into additive components. It is a topic of considerable interest with many applications that has gathered much attention recently. Here, we introduce a new framework for source separation called Kernel Additive Modelling, which is based on local regression and permits efficient separation of multidimensional and/or nonnegative and/or non-regularly ... View full abstract»

## Aims & Scope

IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Pier Luigi Dragotti
Imperial College London