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IEEE Signal Processing Society Editorial Board
Year: 2019 | Volume: 26 | Issue: 7
In this letter, we consider the problem of direction-of-arrival (DOA) estimation with one-bit quantized array measurements. With analysis, it is shown that, under mild conditions the one-bit covariance matrix can be approximated by the sum of a scaled unquantized covariance matrix and a scaled identity matrix. Although the scaling parameters are unknown because of the extreme quantization, they do...Show More
Optimal Sensor-Target Geometries for 3-D Static Target Localization Using Received-Signal-Strength Measurements
Publication Year: 2019,Page(s):966 - 970
Cited by: Papers (30)
This letter investigates how to place the received-signal-strength (RSS) sensors to improve the static target localization accuracy in the three-dimensional (3-D) space. By using the A-optimality criterion, i.e., minimizing the trace of the inverse Fisher information matrix (FIM), a new optimal RSS sensor placement strategy is developed when sensors can be placed freely in the 3-D space. The small...Show More
Effect of User Cooperation on Smart Meter Privacy With Rechargeable Batteries
Publication Year: 2019,Page(s):971 - 975
Cited by: Papers (6)
In smart metering systems, a rechargeable battery can be utilized to protect the privacy of a user from the utility provider by partially masking the load profile of the user. In this line of research on using rechargeable batteries for privacy protection, most existing works have studied only single-user systems using rechargeable batteries. In this letter, we consider a multi-user scenario where...Show More
Effect of User Cooperation on Smart Meter Privacy With Rechargeable Batteries
Year: 2019 | Volume: 26 | Issue: 7
Unsupervised Image Manipulation Localization With Non-Binary Label Attribution
Publication Year: 2019,Page(s):976 - 980
Cited by: Papers (8)
Existing forensic techniques for image manipulation localization crucially assume that probe pixels belong to one of exactly two classes, genuine or manipulated. This letter argues that this convention fuels mis-labeling particularly in unsupervised settings, where singular but genuine content or the presence of multiple distinct manipulations may easily induce non-optimal partitions of the featur...Show More
Dark Channel: The Devil is in the Details
Publication Year: 2019,Page(s):981 - 985
Cited by: Papers (12)
Dark channel prior is a highly valued theory mostly used in the area of haze removal. It reveals the wide existence of pixels with very small channel. However, the definition is in a decorated way that relies on the concept of dark channel. The dark channel works like an image domain wherein non-sky regions of clear outdoor images are close to zero. In this letter we show that, such a concept is u...Show More
Phase Retrieval From Quantized Measurements via Approximate Message Passing
Publication Year: 2019,Page(s):986 - 990
Cited by: Papers (11)
In this letter, the problem of sparse signal reconstruction from quantized noisy magnitudes y = Q (|z + w| + n) is studied, where z = Ax, Q(·) denotes a quantizer, and x is the sparse signal. According to expectation propagation, the abovementioned problem can be solved by exchanging extrinsic information between the standard linear model (SLM) module and the minimum mean square error (MMSE) modul...Show More
Phase Retrieval From Quantized Measurements via Approximate Message Passing
Year: 2019 | Volume: 26 | Issue: 7
Phase-Rotated Spectral Correlation Detection for Spectrum Sensing at Low SNR Regimes
Publication Year: 2019,Page(s):991 - 995
Cited by: Papers (6)
Spectrum sensing at low signal-to-noise ratio regimes is a challenging area that has recently attracted much interest from the research community. In this letter, two novel detectors for weak signals are presented, the phase-rotated spectral correlation detector and the complex-decomposition-based PSCD, by means of exploiting the linear relationship between samples from two sampling channels. Fals...Show More
Phase-Rotated Spectral Correlation Detection for Spectrum Sensing at Low SNR Regimes
Year: 2019 | Volume: 26 | Issue: 7
Invariance Theory for Adaptive Radar Detection in Heterogeneous Environment
Publication Year: 2019,Page(s):996 - 1000
Cited by: Papers (14)
Adaptive detection of a signal competing with Gaussian interference with spatially varying power is addressed in this letter. Invariance principle is used to focus the attention on a class of decision rules exhibiting some specific symmetries, which represent a sufficient condition to ensure the constant false alarm rate property. The class of invariant decision rules is characterized via the desi...Show More
Invariance Theory for Adaptive Radar Detection in Heterogeneous Environment
Year: 2019 | Volume: 26 | Issue: 7
Touch-Stroke Dynamics Authentication Using Temporal Regression Forest
Publication Year: 2019,Page(s):1001 - 1005
Cited by: Papers (13)
Touch-stroke dynamics is a relatively recent behavioral biometrics. It authenticates an individual by observing his behavior when swiping a “stroke” on a smartphone or tablet. Several studies have attempted to determine the optimum authentication accuracy of classifiers, but none of them has used time series or temporal machine learning techniques. We postulate that when a user performs a series o...Show More
Touch-Stroke Dynamics Authentication Using Temporal Regression Forest
Year: 2019 | Volume: 26 | Issue: 7
The Multi-Lane Capsule Network
Vanderson Martins do Rosario;Edson Borin;Mauricio Breternitz
Publication Year: 2019,Page(s):1006 - 1010
Cited by: Papers (48)
We introduce multi-lane capsule networks (MLCN), which are a separable and resource efficient organization of capsule networks (CapsNet) that allows parallel processing while achieving high accuracy at reduced cost. A MLCN is composed of a number of (distinct) parallel lanes, each contributing to a dimension of the result, trained using the routing-by-agreement organization of CapsNet. Our results...Show More
The Multi-Lane Capsule Network
Vanderson Martins do Rosario;Edson Borin;Mauricio Breternitz
Year: 2019 | Volume: 26 | Issue: 7
Multi-Modal Reflection Removal Using Convolutional Neural Networks
Publication Year: 2019,Page(s):1011 - 1015
Although color images are easily interfered by glass, depth images captured by infrared sensors are robust to reflection. In this letter, we propose multi-modal reflection removal using convolutional neural networks (CNNs). We build a multi-modal CNN for reflection removal to separate transmission from reflection using depth information. The proposed network consists of two sub-networks: image res...Show More
Multi-Modal Reflection Removal Using Convolutional Neural Networks
Year: 2019 | Volume: 26 | Issue: 7
Nonlinear MMSE Equalizer for Impulsive Noise Mitigation in OFDM-Based Communications
Cinna Soltanpur;Ramezan Paravi;Mohammad Ghamari;Bamidele Adebisi
Publication Year: 2019,Page(s):1016 - 1020
Cited by: Papers (9)
Destructive effects of impulsive noise has been broadly observed not only in wireless communication systems but also in power-line communications. Impulsive noise is a common impediment in orthogonal frequency division multiplexing (OFDM) based communication systems for industry applications. This non-Gaussian noise degrades the performance of conventional equalizers and, hence, elicit a modified ...Show More
Kullback–Leibler Divergence Between Multivariate Generalized Gaussian Distributions
Publication Year: 2019,Page(s):1021 - 1025
Cited by: Papers (30)
The Kullback-Leibler divergence (KLD) between two multivariate generalized Gaussian distributions (MGGDs) is a fundamental tool in many signal and image processing applications. Until now, the KLD of MGGDs has no known explicit form, and it is in practice either estimated using expensive Monte-Carlo stochastic integration or approximated. The main contribution of this letter is to present a closed...Show More
Kullback–Leibler Divergence Between Multivariate Generalized Gaussian Distributions
Year: 2019 | Volume: 26 | Issue: 7
Automatic Classification of Retinal Optical Coherence Tomography Images With Layer Guided Convolutional Neural Network
Laifeng Huang;Xingxin He;Leyuan Fang;Hossein Rabbani;Xiangdong Chen
Publication Year: 2019,Page(s):1026 - 1030
Cited by: Papers (95)
Optical coherence tomography (OCT) enables instant and direct imaging of morphological retinal tissue and has become an essential imaging modality for ophthalmology diagnosis. As one of the important morphological retinal characteristics, the structural information of retinal layers provides meaningful diagnostic information and is closely related to several retinal diseases. In this letter, we pr...Show More
Likelihood-Based Adaptive Learning in Stochastic State-Based Models
Peter Martin Vieting;Rodrigo C. de Lamare;Lukas Martin;Guido Dartmann;Anke Schmeink
Publication Year: 2019,Page(s):1031 - 1035
Cited by: Papers (1)
This letter presents an adaptive learning framework for estimating structural parameters in stochastic state-based models (SSMs). SSMs are a useful modeling tool in systems biology and medicine. While models in these disciplines are traditionally hand-crafted, an automated generation based on experimental data becomes a topic of research interest. In particular, our goal is to classify measured pr...Show More
Filtering of Nonuniformly Sampled Bandlimited Functions
Publication Year: 2019,Page(s):1036 - 1040
Cited by: Papers (3)
The filtering of irregularly sampled bandlimited functions is discussed in this letter. An algorithm is given that enables the filtering of bandlimited functions with bandlimited filters to a desired precision, provided some requirements on the sampling density are met. All operations are carried out on the irregular samples themselves. The resulting algorithm is iterative in nature and converges ...Show More
Combined Belief Propagation-Mean Field Message Passing Algorithm for Dirichlet Process Mixtures
Publication Year: 2019,Page(s):1041 - 1045
Cited by: Papers (2)
This letter deals with variational inference for Dirichlet process mixtures (DPM) models. We propose a combined message-passing algorithm introducing belief propagation (BP) into the original mean field (MF) rules, which leads to a more precise approximate posterior in DPM. To compute the BP message, we change an exponential distribution to a non-exponential utilizing a flexible expression of Dira...Show More
Differentially Modulated Spectrally Efficient Frequency-Division Multiplexing
Publication Year: 2019,Page(s):1046 - 1050
Cited by: Papers (8)
This letter proposes a differentially modulated non-orthogonal spectrally efficient frequency-division multiplexing (D-SEFDM) architecture, which allows us to dispense with any pilot overhead needed for channel estimation at the receiver, while increasing the bandwidth efficiency in comparison to the orthogonal frequency-division multiplexing counterpart. While it is a challenging task to carry ou...Show More
We consider the problem of recovering a signal from the norms of its linear transforms. We formulate the recovery as a minimization problem, and propose algorithms for solving the formulation. We discuss the convergence of the algorithms and compare their behaviors. We demonstrate the usefulness of the formulation/algorithm on the problem of recovering positions of points given their pairwise dist...Show More
Constructing Grassmannian Frames by an Iterative Collision-Based Packing
Publication Year: 2019,Page(s):1056 - 1060
Cited by: Papers (19)
Grassmannian frames consist of unit-norm vectors with a maximum cross correlation between each other that is minimal. A property like that is desired in many applications, such as in wireless communications, sparse recovery, quantum information theory, and more. In this letter, we present an iterative algorithm targeting the construction of Grassmannian frames, based on a collision-based packing o...Show More
Constructing Grassmannian Frames by an Iterative Collision-Based Packing
Year: 2019 | Volume: 26 | Issue: 7
Blind Separation of Frequency-Hopped Signals Using a Space-Polarization Receiver
Publication Year: 2019,Page(s):1061 - 1064
Cited by: Papers (3)
An approach for associating signals from frequency-hopped (FH) emitters has recently been proposed that leverages polarization-frequency characterizations obtained from dual-polarized antenna architectures. An effective extension of these methods is considered here that incorporates multiple spatially separated dual-polarized antennas. Correlations in polarization-frequency responses, space-freque...Show More
Blind Separation of Frequency-Hopped Signals Using a Space-Polarization Receiver
Year: 2019 | Volume: 26 | Issue: 7
Selective Ensemble Classification of Image Steganalysis Via Deep Q Network
Publication Year: 2019,Page(s):1065 - 1069
Cited by: Papers (16)
Currently many important advances in digital media field have been achieved through the ensemble method. In image steganalysis, the application of classifiers has evolved from the early single classifier to the ensemble classifiers. The performance of the ensemble classifier is better than that of a single classifier, but the classifiers may have a certain degree of redundancy. Therefore, it is of...Show More
Selective Ensemble Classification of Image Steganalysis Via Deep Q Network
Year: 2019 | Volume: 26 | Issue: 7
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