# IEEE Journal of Selected Topics in Signal Processing

### Early Access Articles

Early Access articles are made available in advance of the final electronic or print versions. Early Access articles are peer reviewed but may not be fully edited. They are fully citable from the moment they appear in IEEE Xplore.

## Filter Results

Displaying Results 1 - 25 of 38
• ### Universal Joint Image Clustering and Registration using Multivariate Information Measures

Publication Year: 2018, Page(s): 1
| | PDF (2920 KB)

We consider the problem of universal joint clustering and registration of images. Image clustering focuses on grouping similar images, while image registration refers to the task of aligning copies of an image that have been subject to rigid-body transformations such as rotations and translations. We first study registering two images using maximum mutual information and prove its asymptotic optim... View full abstract»

• ### Zero-Delay Rate Distortion via Filtering for Vector-Valued Gaussian Sources

Publication Year: 2018, Page(s): 1
| | PDF (4127 KB)

We deal with zero-delay source coding of a vector-valued Gauss-Markov source subject to a mean-squared error (MSE) fidelity criterion FS{characterized by the operational zero-delay vector-valued Gaussian rate distortion function (RDF). We address this problem by considering the nonanticipative RDF (NRDF) which is a lower bound to the causal and zero-delay RDF. We recall the realization that corres... View full abstract»

• ### Improved Target Acquisition Rates with Feedback Codes

Publication Year: 2018, Page(s): 1
| | PDF (764 KB)

This paper considers the problem of acquiring an unknown target location (among a finite number of locations) via a sequence of measurements, where each measurement consists of simultaneously probing a group of locations. The resulting observation consists of a sum of an indicator of the target's presence in the probed region, and a zero mean Gaussian noise term whose variance is a function of the... View full abstract»

• ### Optimal detection and error exponents for hidden semi-Markov models

Publication Year: 2018, Page(s): 1
| | PDF (1745 KB)

We study detection of random signals corrupted by noise that over time switch their values (states) between a finite set of possible values, where the switchings occur at unknown points in time. We model such signals as hidden semi-Markov signals (HSMS), which generalize classical Markov chains by introducing explicit (possibly non-geometric) distribution for the time spent in each state. Assuming... View full abstract»

• ### Performance Analysis of Approximate Message Passing for Distributed Compressed Sensing

Publication Year: 2018, Page(s): 1
| | PDF (1761 KB)

Bayesian approximate message passing (BAMP) is an efficient method in compressed sensing that is nearly optimal in the minimum mean squared error (MMSE) sense. Multiple Measurement Vector (MMV)-BAMP performs joint recovery of multiple vectors with identical support and accounts for correlations in the signal of interest and in the noise. In this paper, we show how to reduce the complexity of vecto... View full abstract»

• ### Efficiently Computing Messages that Reveal Selected Inferences While Protecting Others

Publication Year: 2018, Page(s): 1
| | PDF (518 KB)

We study a fine-grained model in which a perturbed version of some data ($D$) is to be disclosed, with the aims of permitting the receiver to accurately infer some {useful} aspects ($X=f(D)$) of it, while preventing her from inferring other {private} aspects ($Y=g(D)$). Correlation between the bases for these... View full abstract»

• ### Adaptive Contextual Learning for Unit Commitment in Microgrids with Renewable Energy Sources

Publication Year: 2018, Page(s): 1
| | PDF (951 KB)

In this paper, we study a unit commitment (UC) problem where the goal is to minimize the operating costs of a microgrid that involves renewable energy sources. Since traditional UC algorithms use a priori information about the system uncertainties such as the load demand and the renewable power outputs, their performances highly depend on the accuracy of the a priori information, especially in mic... View full abstract»

• ### Scheduling and Pricing of Load Flexibility in Power Systems

Publication Year: 2018, Page(s): 1
| | PDF (573 KB)

This paper proposes a fundamental approach for scheduling and pricing of load flexibility in power systems operation. An optimal control model is proposed to co-optimize the continuous-time flexibility of loads with the operation of generating units to supply the flexibility requirements of the net-load, while satisfying delay-based and deadline-based service quality constraints of the flexible lo... View full abstract»

• ### Estimation of Deterioration Levels of Transmission Towers via Deep Learning Maximizing Canonical Correlation between Heterogeneous Features

Publication Year: 2018, Page(s): 1
| | PDF (2887 KB)

This paper presents estimation of deterioration levels of transmission towers via deep learning maximizing the canonical correlation between heterogeneous features. In the proposed method, we newly construct a correlation-maximizing deep extreme learning machine based on a local receptive field (CMDELM-LRF). For accurate deterioration level estimation, it is necessary to obtain semantic informatio... View full abstract»

• ### Decentralized PMU-assisted Power System State Estimation with Reduced Inter-Area Communication

Publication Year: 2018, Page(s): 1
| | PDF (364 KB)

In this paper, we present a decentralized approach to hybrid, multi-area power system state estimation, i.e., state estimation using both conventional RTU measurements and newer PMU measurements. We employ a reduced-order approach to hybrid state estimation, wherein the PMU observable and unobservable components of the state vector are estimated separately in each control area. The estimation prob... View full abstract»

• ### Communication Complexity of Dual Decomposition Methods for Distributed Resource Allocation Optimization

Publication Year: 2018, Page(s): 1
| | PDF (3178 KB)

Dual decomposition methods are among the most prominent approaches for finding primal/dual saddle point solutions of resource allocation optimization problems. To deploy these methods in the emerging Internet of Things (IoT) networks, which will often have limited data-rates, it is important to understand the level of communication they require. Motivated by this, we introduce and explore two meas... View full abstract»

• ### Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms

Publication Year: 2018, Page(s): 1
| | PDF (1246 KB)

We investigate the problem of estimating the causal effect of a treatment on individual subjects from observational data; this is a central problem in various application domains, including healthcare, social sciences, and online advertising. Within the Neyman-Rubin potential outcomes model, we use the Kullback-Leibler (KL) divergence between the estimated and true distributions as a measure of ac... View full abstract»

• ### Intelligent Soft Computing-based Security Control for Energy Management Architecture of Hybrid Emergency Power System for More-Electric Aircrafts

Publication Year: 2018, Page(s): 1
| | PDF (1861 KB)

This paper proposes an attack-resilient intelligent soft computing based security control for energy management architecture for a hybrid emergency power system of More-Electric Aircrafts (MEAs). Our proposed architecture develops an Adaptive Neuro-Fuzzy Inference System (ANFIS) -based method in conjunction with a specific Recurrent Neural Network architecture called Long Short-Term Memory (LSTM) ... View full abstract»

• ### Reactance Perturbation for Detecting and Identifying FDI Attacks in Power System State Estimation

Publication Year: 2018, Page(s): 1
| | PDF (2623 KB)

False data injection (FDI) attacks have recently been introduced as an important class of cyberattacks in modern power systems. By coordinating the injection of false data in selected meters readings, FDI attacks can bypass bad data detection methods in power system state estimation. In this paper, we propose a strategy to enhance detection and identification of FDI that leverages reactance pertur... View full abstract»

• ### Compression-Based Regularization with an Application to Multi-Task Learning

Publication Year: 2018, Page(s): 1
| | PDF (3246 KB)

This paper investigates, from information theoretic grounds, a learning problem based on the principle that any regularity in a given dataset can be exploited to extract compact features from data, i.e., using fewer bits than needed to fully describe the data itself, in order to build meaningful representations of a relevant content (multiple labels). We begin studying a {multi-task learning} (MTL... View full abstract»

• ### Joint Energy Procurement and Demand Response towards Optimal Deployment of Renewables

Publication Year: 2018, Page(s): 1
| | PDF (1150 KB)

In this paper, joint energy procurement and demand response is studied from the perspective of the operator of a power system. The operator procures energy from both renewable energy sources (RESs) and the spot market. We observe the fact that the RESs may incur considerable infrastructure cost. This cost is taken into account and the optimal planning of renewables is examined by controlling the i... View full abstract»

• ### Averting Cascading Failures in Networked Infrastructures: Poset-constrained Graph Algorithms

Publication Year: 2018, Page(s): 1
| | PDF (752 KB)

Cascading failures in critical networked infrastructures that result even from a single source of failure often lead to rapidly widespread outages as witnessed in the 2013 Northeast blackout in northern America. The ensuing problem of containing future cascading failures by placement of protection or monitoring nodes in the network is complicated by the uncertainty of the failure source and the mi... View full abstract»

• ### Near-Optimal Noisy Group Testing via Separate Decoding of Items

Publication Year: 2018, Page(s): 1
| | PDF (1173 KB)

The group testing problem consists of determining a small set of defective items from a larger set of items based on a number of tests, and is relevant in applications such as medical testing, communication protocols, pattern matching, and more. In this paper, we revisit an efficient algorithm for noisy group testing in which each item is decoded separately (Malyutov and Mateev, 1980), and develop... View full abstract»

• ### On the Fundamental Limit of Multipath Matching Pursuit

Publication Year: 2018, Page(s): 1
| | PDF (2810 KB)

Multipath matching pursuit (MMP) is a recent extension of the orthogonal matching pursuit (OMP) algorithm that recovers sparse signals with a tree-searching strategy[1]. In this paper, we present a new analysis for the MMP algorithm using the restricted isometry property (RIP). Our result shows that if the sampling matrix $mathbf{A} in mathbb{R}^{m times n}$ satisfies... View full abstract»

• ### Maximum entropy low-rank matrix recovery

Publication Year: 2018, Page(s): 1
| | PDF (8765 KB)

We propose in this paper a novel, information-theoretic method, called MaxEnt, for efficient data acquisition for low-rank matrix recovery. This proposed method has important applications to a wide range of problems, including image processing and text document indexing. Fundamental to our design approach is the so-called maximum entropy principle, which states that the measurement masks which max... View full abstract»

• ### Non-Circular Attacks on Phasor Measurement Units for State Estimation in Smart Grid

Publication Year: 2018, Page(s): 1
| | PDF (293 KB)

With the evolution of phasor measurement units (PMUs) and the proposition to incorporate a large number of PMUs in future smart grids, it is critical to identify and prevent potential (zero-day) cyber attacks on phasor signals. The PMUs are the forefront of sensor technologies used in the smart grid and produce phasor voltage and current readings, which are complex-valued in nature. In this regard... View full abstract»

• ### Classification and Representation via Separable Subspaces: Performance Limits and Algorithms

Publication Year: 2018, Page(s): 1
| | PDF (2978 KB)

We study the classification performance of Kronecker-structured models in two asymptotic regimes and developed an algorithm for separable, fast and compact K-S dictionary learning for better classification and representation of multidimensional signals by exploiting the structure in the signal. First, we study the classification performance in terms of diversity order and pairwise geometry of the ... View full abstract»

• ### Identifiability of Kronecker-structured Dictionaries for Tensor Data

Publication Year: 2018, Page(s): 1
| | PDF (530 KB)

This paper derives sufficient conditions for local recovery of coordinate dictionaries comprising a Kronecker-structured dictionary that is used for representing Kth-order tensor data. Tensor observations are assumed to be generated from a Kronecker-structured dictionary multiplied by sparse coefficient tensors that follow the separable sparsity model. This work provides sufficient conditions on t... View full abstract»

• ### Hypergraph Spectral Clustering in the Weighted Stochastic Block Model

Publication Year: 2018, Page(s): 1
| | PDF (1228 KB)

Spectral clustering is a celebrated algorithm that partitions objects based on pairwise similarity information. While this approach has been successfully applied to a variety of domains, it comes with limitations. The reason is that there are many other applications in which only multi-way similarity measures are available. This motivates us to explore the multi-way measurement setting. In this wo... View full abstract»

• ### Dynamic Power Distribution System Management With a Locally Connected Communication Network

Publication Year: 2018, Page(s): 1
| | PDF (4427 KB)

Coordinated optimization and control of distribution-level assets can enable a reliable and optimal integration of massive amount of distributed energy resources (DERs) and facilitate distribution system management (DSM). Accordingly, the objective is to coordinate the power injection at the DERs to maintain certain quantities across the network, e.g., voltage magnitude, line flows, or line losses... View full abstract»

## Aims & Scope

The Journal of Selected Topics in Signal Processing (J-STSP) solicits special issues on topics that cover the entire scope of the IEEE Signal Processing Society including the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals by digital or analog devices or techniques.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief

Shrikanth (Shri) S. Narayanan
Viterbi School of Engineering
University of Southern California
Los Angeles, CA 90089 USA
shri@sipi.usc.edu