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# IEEE Signal Processing Letters

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• ### On the Performance of Non-Orthogonal Multiple Access in 5G Systems with Randomly Deployed Users

Publication Year: 2014, Page(s):1501 - 1505
Cited by:  Papers (152)
| | PDF (998 KB) | HTML

In this letter, the performance of non-orthogonal multiple access (NOMA) is investigated in a cellular downlink scenario with randomly deployed users. The developed analytical results show that NOMA can achieve superior performance in terms of ergodic sum rates; however, the outage performance of NOMA depends critically on the choices of the users' targeted data rates and allocated power. In parti... View full abstract»

• ### Deep Learning for Quality Assessment in Live Video Streaming

Publication Year: 2017, Page(s):736 - 740
| | PDF (611 KB) | HTML

Video content providers put stringent requirements on the quality assessment methods realized on their services. They need to be accurate, real-time, adaptable to new content, and scalable as the video set grows. In this letter, we introduce a novel automated and computationally efficient video assessment method. It enables accurate real-time (online) analysis of delivered quality in an adaptable ... View full abstract»

• ### Light-Field Image Super-Resolution Using Convolutional Neural Network

Publication Year: 2017, Page(s):848 - 852
| | PDF (415 KB) | HTML Media

Commercial light field cameras provide spatial and angular information, but their limited resolution becomes an important problem in practical use. In this letter, we present a novel method for light field image super-resolution (SR) to simultaneously up-sample both the spatial and angular resolutions of a light field image via a deep convolutional neural network. We first augment the spatial reso... View full abstract»

• ### Compressive Acquisition and Least-Squares Reconstruction of Correlated Signals

Publication Year: 2017, Page(s):933 - 937
| | PDF (187 KB) | HTML

This letter presents a framework for the compressive acquisition of correlated signals. We propose an implementable sampling architecture for the acquisition of ensembles of correlated (lying in an a priori unknown subspace) signals at a sub-Nyquist rate. The sampling architecture acquires structured compressive samples of the signals after preprocessing them wit... View full abstract»

• ### Combining Convolutional and Recurrent Neural Networks for Human Skin Detection

Publication Year: 2017, Page(s):289 - 293
| | PDF (502 KB) | HTML

Skin detection from images, typically used as a preprocessing step, has a wide range of applications such as dermatology diagnostics, human computer interaction designs, and etc. It is a challenging problem due to many factors such as variation in pigment melanin, uneven illumination, and differences in ethnicity geographics. Besides, age and gender introduce additional difficulties to the detecti... View full abstract»

• ### A Robust Iteratively Reweighted $\ell _2$ Approach for Spectral Compressed Sensing in Impulsive Noise

Publication Year: 2017, Page(s):938 - 942
| | PDF (383 KB) | HTML

This letter concentrates on the problem of spectral compressed sensing in impulsive noise, which aims to recover a spectrally sparse signal from its contaminated and undersampled measurements. We propose a robust formulation for joint sparse signal and frequency recovery, which includes the generalized $\ell _p$-norm ... View full abstract»

• ### Fairness for Non-Orthogonal Multiple Access in 5G Systems

Publication Year: 2015, Page(s):1647 - 1651
Cited by:  Papers (78)
| | PDF (797 KB) | HTML

In non-orthogonal multiple access (NOMA) downlink, multiple data flows are superimposed in the power domain and user decoding is based on successive interference cancellation. NOMA's performance highly depends on the power split among the data flows and the associated power allocation (PA) problem. In this letter, we study NOMA from a fairness standpoint and we investigate PA techniques that ensur... View full abstract»

• ### A universal image quality index

Publication Year: 2002, Page(s):81 - 84
Cited by:  Papers (1696)  |  Patents (39)
| | PDF (248 KB) | HTML

We propose a new universal objective image quality index, which is easy to calculate and applicable to various image processing applications. Instead of using traditional error summation methods, the proposed index is designed by modeling any image distortion as a combination of three factors: loss of correlation, luminance distortion, and contrast distortion. Although the new index is mathematica... View full abstract»

• ### Beyond Frame-level CNN: Saliency-Aware 3-D CNN With LSTM for Video Action Recognition

Publication Year: 2017, Page(s):510 - 514
Cited by:  Papers (1)
| | PDF (597 KB) | HTML

Human activity recognition in videos with convolutional neural network (CNN) features has received increasing attention in multimedia understanding. Taking videos as a sequence of frames, a new record was recently set on several benchmark datasets by feeding frame-level CNN sequence features to long short-term memory (LSTM) model for video activity recognition. This recurrent model-based visual re... View full abstract»

• ### Efficient Compressed Sensing for Wireless Neural Recording: A Deep Learning Approach

Publication Year: 2017, Page(s):863 - 867
| | PDF (393 KB) | HTML

Data compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and compressed sensing (CS) theory has successfully demonstrated its potential in neural recording applications. Based on deep learning theory, this paper presents a binarized autoencoder scheme for CS, in which a binary sensing matrix and a noniterative recovery solver are join... View full abstract»

• ### Image Fusion With Cosparse Analysis Operator

Publication Year: 2017, Page(s):943 - 947
| | PDF (413 KB) | HTML

The letter addresses the image fusion problem, where multiple images captured with different focus distances are to be combined into a higher quality all-in-focus image. Most current approaches for image fusion strongly rely on the unrealistic noise-free assumption used during the image acquisition, and then yield limited fusion robustness. In our approach, we formulate the multifocus image fusion... View full abstract»

• ### Overlapped Subarray Based Hybrid Beamforming for Millimeter Wave Multiuser Massive MIMO

Publication Year: 2017, Page(s):550 - 554
| | PDF (429 KB) | HTML

For massive multiple input multiple output systems at millimeter wave (mmWave) bands, we consider an efficient hybrid array architecture, namely, overlapped subarray (OSA), and develop a Unified Low Rank Sparse (ULoRaS) recovery algorithm for hybrid beamforming in downlink multiuser scenarios. The ULoRaS scheme takes advantage of the transmit-receive coordinated beamforming procedure to achieve la... View full abstract»

• ### Robust Edge-Stop Functions for Edge-Based Active Contour Models in Medical Image Segmentation

Publication Year: 2016, Page(s):222 - 226
Cited by:  Papers (5)
| | PDF (866 KB) | HTML

Edge-based active contour models are effective in segmenting images with intensity inhomogeneity but often fail when applied to images containing poorly defined boundaries, such as in medical images. Traditional edge-stop functions (ESFs) utilize only gradient information, which fails to stop contour evolution at such boundaries because of the small gradient magnitudes. To address this problem, we... View full abstract»

• ### sEMG Signal-Based Lower Limb Human Motion Detection Using a Top and Slope Feature Extraction Algorithm

Publication Year: 2017, Page(s):929 - 932
| | PDF (362 KB) | HTML

This letter presents lower limb human motion detection using a surface electromyogram (sEMG) with a top and slope (TAS) feature extraction algorithm. Lower limb human motion detection using sEMG signal is generally divided into gait subphase detection, locomotion mode recognition, and mode change detection. Existing feature extraction algorithms using sEMG signal have several innate problems in re... View full abstract»

• ### Joint Sparse Recovery With Semisupervised MUSIC

Publication Year: 2017, Page(s):629 - 633
| | PDF (393 KB) | HTML

Discrete multiple signal classification (MUSIC) with its low computational cost and mild condition requirement becomes a significant noniterative algorithm for joint sparse recovery (JSR). However, it fails in rank defective problem caused by coherent or limited amount of multiple measurement vectors (MMVs). In this letter, we provide a novel sight to address this problem by interpreting JSR as a ... View full abstract»

• ### Nonnegative Matrix Factorization Using Nonnegative Polynomial Approximations

Publication Year: 2017, Page(s):948 - 952
| | PDF (995 KB) | HTML

Nonnegative matrix factorization is a key tool in many data analysis applications such as feature extraction, compression, and noise filtering. Many existing algorithms impose additional constraints to take into account prior knowledge and to improve the physical interpretation. This letter proposes a novel algorithm for nonnegative matrix factorization, in which the factors are modeled by nonnega... View full abstract»

• ### SkeletonNet: Mining Deep Part Features for 3-D Action Recognition

Publication Year: 2017, Page(s):731 - 735
| | PDF (323 KB) | HTML

This letter presents SkeletonNet, a deep learning framework for skeleton-based 3-D action recognition. Given a skeleton sequence, the spatial structure of the skeleton joints in each frame and the temporal information between multiple frames are two important factors for action recognition. We first extract body-part-based features from each frame of the skeleton sequence. Compared to the original... View full abstract»

• ### Wavelet-Based Total Variation and Nonlocal Similarity Model for Image Denoising

Publication Year: 2017, Page(s):877 - 881
| | PDF (487 KB) | HTML Media

To suppress the heavy noise and keep the distinct edges of the images in the low light condition, we propose a denoising model based on the combination of total variation (TV) and nonlocal similarity in the wavelet domain. The TV regularization in the wavelet domain effectively suppresses the heavy noise with the biorthogonal wavelet function; the nonlocal similarity regularization improves the fi... View full abstract»

• ### Locally Normalized Filter Banks Applied to Deep Neural-Network-Based Robust Speech Recognition

Publication Year: 2017, Page(s):377 - 381
| | PDF (404 KB) | HTML

This letter describes modifications to locally normalized filter banks (LNFB), which substantially improve their performance on the Aurora-4 robust speech recognition task using a Deep Neural Network-Hidden Markov Model (DNN-HMM)-based speech recognition system. The modified coefficients, referred to as LNFB features, are a filter-bank version of locally normalized cepstral coefficients (LNCC), wh... View full abstract»

• ### Analysis of the FFT-FBMC Equalization in Selective Channels

Publication Year: 2017, Page(s):897 - 901
| | PDF (382 KB) | HTML

Recently, fast Fourier transform filter-bank multicarrier (FFT-FBMC) modulation was proposed to enable diverse multiple-input multiple-output (MIMO) techniques with FBMC by eliminating the intrinsic interference. A per-subcarrier (PSC) channel equalizer was derived under the assumption of low frequency selectivity. However, this equalization scheme cannot be directly applied to highly frequency se... View full abstract»

• ### An Experimental Study on Speech Enhancement Based on Deep Neural Networks

Publication Year: 2014, Page(s):65 - 68
Cited by:  Papers (91)
| | PDF (607 KB) | HTML

This letter presents a regression-based speech enhancement framework using deep neural networks (DNNs) with a multiple-layer deep architecture. In the DNN learning process, a large training set ensures a powerful modeling capability to estimate the complicated nonlinear mapping from observed noisy speech to desired clean signals. Acoustic context was found to improve the continuity of speech to be... View full abstract»

• ### Empirical mode decomposition as a filter bank

Publication Year: 2004, Page(s):112 - 114
Cited by:  Papers (926)  |  Patents (1)
| | PDF (160 KB) | HTML

Empirical mode decomposition (EMD) has recently been pioneered by Huang et al. for adaptively representing nonstationary signals as sums of zero-mean amplitude modulation frequency modulation components. In order to better understand the way EMD behaves in stochastic situations involving broadband noise, we report here on numerical experiments based on fractional Gaussian noise. In such a case, it... View full abstract»

• ### Multiscale Decomposition in Low-Rank Approximation

Publication Year: 2017, Page(s):1015 - 1019
| | PDF (304 KB) | HTML Media

In low-rank approximation methods, it is often assumed that the data matrix is composed of two globally low-rank and sparse matrices. Moreover, real data matrices often consist of local patterns in multiple scales. The conventional low-rank approximation techniques do not reveal the local patterns from the data matrices. This letter presents an approach based on decomposition of matrices into low-... View full abstract»

• ### Period Estimation of an Almost Periodic Signal Using Persistent Homology With Application to Respiratory Rate Measurement

Publication Year: 2017, Page(s):958 - 962
| | PDF (434 KB) | HTML

Time-frequency techniques have difficulties in yielding efficient online algorithms for almost periodic signals. We describe a new topological method to find the period of signals that have an almost periodic waveform. Proposed method is applied to signals received from a pyro-electric infrared sensor array for the online estimation of the respiratory rate (RR) of a person. Time-varying analog sig... View full abstract»

• ### A Novel Construction of Z-Complementary Pairs Based on Generalized Boolean Functions

Publication Year: 2017, Page(s):987 - 990
| | PDF (126 KB) | HTML

Binary Golay complementary pairs exist for quite limited lengths whereas the binary Z-complementary pairs (ZCPs) are available for more lengths. Therefore, the ZCPs can potentially find more engineering applications. In this letter, we propose a novel construction of binary and nonbinary (q-ary for even q) ZCPs based on generalized Boolean functions. Both even- and odd-length ZCPs can be obtained ... View full abstract»

• ### Dynamic Hand Gesture Recognition With Leap Motion Controller

Publication Year: 2016, Page(s):1188 - 1192
Cited by:  Papers (2)
| | PDF (492 KB) | HTML

Dynamic hand gesture recognition is a crucial but challenging task in the pattern recognition and computer vision communities. In this paper, we propose a novel feature vector which is suitable for representing dynamic hand gestures, and presents a satisfactory solution to recognizing dynamic hand gestures with a Leap Motion controller (LMC) only. These have not been reported in other papers. The ... View full abstract»

• ### Making a “Completely Blind” Image Quality Analyzer

Publication Year: 2013, Page(s):209 - 212
Cited by:  Papers (248)
| | PDF (563 KB) | HTML

An important aim of research on the blind image quality assessment (IQA) problem is to devise perceptual models that can predict the quality of distorted images with as little prior knowledge of the images or their distortions as possible. Current state-of-the-art “general purpose” no reference (NR) IQA algorithms require knowledge about anticipated distortions in the form of trainin... View full abstract»

• ### On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems

Publication Year: 2017, Page(s):972 - 976
| | PDF (175 KB) | HTML

In problems involving the optimization of atomic norms, an upper bound on the dual atomic norm often arises as a constraint. For the special case of line spectral estimation, this upper bound on the dual atomic norm reduces to upper-bounding the magnitude response of a finite impulse response filter by a constant. It is well known that this can be rewritten as a semidefinite constraint, leading to... View full abstract»

• ### Maximum Likelihood TDOA Estimation From Compressed Sensing Samples Without Reconstruction

Publication Year: 2017, Page(s):564 - 568
| | PDF (175 KB) | HTML

One application for time-difference-of-arrival (TDOA) estimation is in emitter localization. A signal from an emitter reaching a group of sensors, each in a separate location, will have different arrival times. Finding the TDOAs between the output of pairs of sensors will provide the necessary measurements for the hyperbolic localization of the emitter. When the sensors acquire the signal by compr... View full abstract»

• ### Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks

Publication Year: 2016, Page(s):1499 - 1503
Cited by:  Papers (1)
| | PDF (757 KB) | HTML

Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations, and occlusions. Recent studies show that deep learning approaches can achieve impressive performance on these two tasks. In this letter, we propose a deep cascaded multitask framework that exploits the inherent correlation between detection and alignment to boost up their performance. In ... View full abstract»

• ### Optimal Power Splitting for Simultaneous Information Detection and Energy Harvesting

Publication Year: 2017, Page(s):963 - 967
| | PDF (334 KB) | HTML

This letter deals with the joint information and energy processing at the receiver of a point-to-point communication channel. In particular, the tradeoff between the achievable information rate and harvested energy for a multiple-antenna power splitting receiver is investigated. Here, the rate-energy region characterization is of particular interest, which is intrinsically a nonconvex problem. In ... View full abstract»

• ### Joint Distance Maps Based Action Recognition With Convolutional Neural Networks

Publication Year: 2017, Page(s):624 - 628
| | PDF (689 KB) | HTML

Motivated by the promising performance achieved by deep learning, an effective yet simple method is proposed to encode the spatio-temporal information of skeleton sequences into color texture images, referred to as joint distance maps (JDMs), and convolutional neural networks are employed to exploit the discriminative features from the JDMs for human action and interaction recognition. The pair-wi... View full abstract»

• ### Static Optimal Sensor Selection via Linear Integer Programming: The Orthogonal Case

Publication Year: 2017, Page(s):953 - 957
| | PDF (178 KB) | HTML

We consider the static optimal sensor selection problem, where we optimally select $d$ sensors among $s$ possible sensors with $d < s$ . Under the assumption that the ... View full abstract»

• ### Toward High-Quality Real-Time Signal Reconstruction From STFT Magnitude

Publication Year: 2017, Page(s):892 - 896
| | PDF (398 KB) | HTML

An efficient algorithm for real-time signal reconstruction from the magnitude of the short-time Fourier transform (STFT) is introduced. The proposed approach combines the strengths of two previously published algorithms: the real-time phase gradient heap integration and the Gnann and Spiertz's real-time iterative spectrogram inversion with look-ahead. An extensive comparison with the state-of-the-... View full abstract»

• ### Underdetermined DOA Estimation Method for Wideband Signals Using Joint Nonnegative Sparse Bayesian Learning

Publication Year: 2017, Page(s):535 - 539
| | PDF (515 KB) | HTML

Underdetermined direction-of-arrival (DOA) estimation for wideband signals by sparse arrays is discussed in the framework of sparse Bayesian learning (SBL). The problem is transformed to recovering multiple nonnegative sparse vectors, which share the same sparse support but correspond to distinct overcomplete basis matrices, from their noise contaminated linear combination vectors. A two-layer Bay... View full abstract»

• ### Unsupervised Speech Activity Detection Using Voicing Measures and Perceptual Spectral Flux

Publication Year: 2013, Page(s):197 - 200
Cited by:  Papers (35)
| | PDF (697 KB) | HTML

Effective speech activity detection (SAD) is a necessary first step for robust speech applications. In this letter, we propose a robust and unsupervised SAD solution that leverages four different speech voicing measures combined with a perceptual spectral flux feature, for audio-based surveillance and monitoring applications. Effectiveness of the proposed technique is evaluated and compared agains... View full abstract»

• ### Enhanced Low-Rank Matrix Approximation

Publication Year: 2016, Page(s):493 - 497
Cited by:  Papers (7)
| | PDF (460 KB) | HTML Media

This letter proposes to estimate low-rank matrices by formulating a convex optimization problem with nonconvex regularization. We employ parameterized nonconvex penalty functions to estimate the nonzero singular values more accurately than the nuclear norm. A closed-form solution for the global optimum of the proposed objective function (sum of data fidelity and the nonconvex regularizer) is also ... View full abstract»

• ### Image Projection Ridge Regression for Subspace Clustering

Publication Year: 2017, Page(s):991 - 995
| | PDF (262 KB) | HTML

Subspace clustering methods have been widely studied recently. When the inputs are two-dimensional (2-D) data, existing subspace clustering methods usually convert them into vectors, which severely damages inherent structures and relationships from original data. In this letter, we propose a novel subspace clustering method for 2-D data. It directly uses 2-D data as inputs such that the learning o... View full abstract»

• ### First Steps Toward Camera Model Identification With Convolutional Neural Networks

Publication Year: 2017, Page(s):259 - 263
| | PDF (497 KB) | HTML

Detecting the camera model used to shoot a picture enables to solve a wide series of forensic problems, from copyright infringement to ownership attribution. For this reason, the forensic community has developed a set of camera model identification algorithms that exploit characteristic traces left on acquired images by the processing pipelines specific of each camera model. In this letter, we inv... View full abstract»

• ### When Slepian Meets Fiedler: Putting a Focus on the Graph Spectrum

Publication Year: 2017, Page(s):1001 - 1004
| | PDF (444 KB) | HTML

The study of complex systems greatly benefits from graph models and their analysis. In particular, the eigendecomposition of the graph Laplacian lets emerge properties of global organization from local interactions; e.g., the Fiedler vector has the smallest nonzero eigenvalue and plays a key role for graph clustering. Graph signal processing focuses on the analysis of signals that are attributed t... View full abstract»

• ### Answer Selection in Community Question Answering via Attentive Neural Networks

Publication Year: 2017, Page(s):505 - 509
| | PDF (389 KB) | HTML

Answer selection in community question answering (cQA) is a challenging task in natural language processing. The difficulty lies in that it not only needs the consideration of semantic matching between question answer pairs but also requires a serious modeling of contextual factors. In this letter, we propose an attentive deep neural network architecture so as to learn the deterministic informatio... View full abstract»

• ### Universum Autoencoder-Based Domain Adaptation for Speech Emotion Recognition

Publication Year: 2017, Page(s):500 - 504
| | PDF (283 KB) | HTML

One of the serious obstacles to the applications of speech emotion recognition systems in real-life settings is the lack of generalization of the emotion classifiers. Many recognition systems often present a dramatic drop in performance when tested on speech data obtained from different speakers, acoustic environments, linguistic content, and domain conditions. In this letter, we propose a novel u... View full abstract»

• ### Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

Publication Year: 2017, Page(s):279 - 283
| | PDF (353 KB)

The ability of deep convolutional neural networks (CNNs) to learn discriminative spectro-temporal patterns makes them well suited to environmental sound classification. However, the relative scarcity of labeled data has impeded the exploitation of this family of high-capacity models. This study has two primary contributions: first, we propose a deep CNN architecture for environmental sound classif... View full abstract»

• ### On the Convergence of Constrained Particle Filters

Publication Year: 2017, Page(s):858 - 862
| | PDF (180 KB) | HTML

The power of particle filters in tracking the state of nonlinear and non-Gaussian systems stems not only from their simple numerical implementation but also from their optimality and convergence properties. In particle filtering, the posterior distribution of the state is approximated by a discrete mass of samples, called particles, that stochastically evolve in time according to the dynamics of t... View full abstract»

• ### RGB-D Salient Object Detection via Minimum Barrier Distance Transform and Saliency Fusion

Publication Year: 2017, Page(s):663 - 667
| | PDF (333 KB) | HTML Media

Automatic detection of salient objects in images has gained its popularity in computer vision field for its usage in numerous vision tasks in recent years. Depth information plays an important role in the human vision system while it is underutilized in most existing two-dimensional (2-D) saliency detection methods. In this letter, a multistage salient object detection framework via minimum barrie... View full abstract»

• ### No-Reference JPEG Image Quality Assessment Based on Blockiness and Luminance Change

Publication Year: 2017, Page(s):760 - 764
| | PDF (305 KB) | HTML

When scoring the quality of JPEG images, the two main considerations for viewers are blocking artifacts and improper luminance changes, such as blur. In this letter, we first propose two measures to estimate the blockiness and the luminance change within individual blocks. Then, a no-reference image quality assessment (NR-IQA) method for JPEG images is proposed. Our method obtains the quality scor... View full abstract»

• ### Energy Efficient Bidirectional Massive MIMO Relay Beamforming

Publication Year: 2017, Page(s):1010 - 1014
| | PDF (271 KB) | HTML

In this paper, we investigate the global energy efficiency of a bidirectional amplify-and-forward relay MIMO system. It is assumed that the relay serves two end-users, each requiring a minimum target rate. Two algorithms are proposed, a suboptimal one with lower complexity, and an optimal one with slightly higher complexity. We present numerical results that compare the two algorithms and exhibit ... View full abstract»

• ### Local Adaptive Binary Patterns Using Diamond Sampling Structure for Texture Classification

Publication Year: 2017, Page(s):828 - 832
| | PDF (345 KB) | HTML

Local binary pattern (LBP) is sensitive to the noise and suffers from limited discriminative capability, and many LBP variants are reported in the recent literatures. Although a lot of significant progresses have been made, most LBP variants still have limitations of noise sensitivity, high dimensionality, and computational inefficiency. In view of this, we propose a new noise-robust local image d... View full abstract»

• ### Contrast Enhancement Using Combined 1-D and 2-D Histogram-Based Techniques

Publication Year: 2017, Page(s):804 - 808
| | PDF (383 KB) | HTML

This letter presents an adaptive contrast enhancement algorithm considering both preservation of the shape of a one-dimensional (1-D) histogram and statistical information on the gray-level differences between neighboring pixels obtained by a 2-D histogram. The proposed system consists of two modules. One is to enhance the entire contrast by stretching the 1-D histogram while preserving the shape ... View full abstract»

• ### Cooperative Precoding for Wireless Energy Transfer and Secure Cognitive Radio Coexistence Systems

Publication Year: 2017, Page(s):540 - 544
| | PDF (301 KB) | HTML

This letter studies the cooperative precoding design for a coexisting wireless energy transfer (WET) and cognitive radio (CR) system, where the WET system share the same spectrum with the CR system. Different from the traditional wireless networks, interference here is regarded as a useful rather than harmful resource. Specifically, we address the transmit covariance design to minimize the total t... View full abstract»

## Aims & Scope

The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
James E. Fowler
Dept Electrical & Computer Engineering
Associate Director
Distributed Analytics and Security Institute
Mississippi State University
Mississippi State, MS 39762 USA
fowler@ece.msstate.edu