IEEE Journal of Selected Topics in Signal Processing (IEEE J-STSP )

Publisher: Institute of Electrical and Electronics Engineers; IEEE Signal Processing Society, Institute of Electrical and Electronics Engineers

Journal description

Current impact factor: 3.63

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013/2014 Impact Factor 3.629
2012 Impact Factor 3.297
2011 Impact Factor 2.88
2010 Impact Factor 2.571
2009 Impact Factor 1.2

Impact factor over time

Impact factor
Year

Additional details

5-year impact 3.82
Cited half-life 3.50
Immediacy index 0.32
Eigenfactor 0.02
Article influence 1.99
Other titles IEEE journal of selected topics in signal processing, Selected topics in signal processing
ISSN 1932-4553
OCLC 158906070
Material type Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Institute of Electrical and Electronics Engineers

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    • Publisher's version/PDF cannot be used
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  • Classification
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: Supporting a wide set of linked non-verbal resources remains an evergreen challenge for communication technology, limiting effectiveness in many applications. Interpersonal distance, gaze, posture and facial expression, are interpreted together to manage and add meaning to most conversations. Yet today’s technologies favor some above others. This induces confusion in conversations, and is believed to limit both feelings of togetherness and trust, and growth of empathy and rapport. Solving this problem will allow technologies to support most rather than a few interactional scenarios. It is likely to benefit teamwork and team cohesion, distributed decision-making and health and wellbeing applications such as tele-therapy, tele-consultation, and isolation. We introduce withyou, our telepresence research platform. This article describes the end-to-end system including the psychology of human interaction and how this drives requirements throughout the design and implementation. Our technology approach is to combine the winning characteristics of video conferencing and immersive collaborative virtual environments. This is to allow, for example, people walking past each other to exchange a glance and smile. A systematic explanation of the theory brings together the linked nature of non-verbal communication and how it is influenced by technology. This leads to functional requirements for telepresence, in terms of the balance of visual, spatial and temporal qualities. The first end-to-end description of withyou describes all major processes and the display and capture environment. An unprecedented characterization of our approach is given in terms of the above qualities and what influences them. This leads to non-functional requirements in terms of number and place of cameras and the avoidance of resultant bottlenecks. Proposals are given for improved distribution of processes across networks, computers, and multi-core CPU and GPU. Simple conservative estimation shows that both approaches should meet our requirements. One is implemented and shown to meet minimum and come close to desirable requirements.
    IEEE Journal of Selected Topics in Signal Processing 03/2015;
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    ABSTRACT: Wideband ranging is essential for numerous emerging applications that rely on accurate location awareness. The quality of range information, which depends on network intrinsic properties and signal processing techniques, affects the localization accuracy. A popular class of ranging techniques is based on energy detection owing to its low-complexity implementation. This paper establishes a tractable model for the range information as a function of wireless environment, signal features, and energy detection techniques. Such a model serves as a cornerstone for the design and analysis of wideband ranging systems. Based on the proposed model, we develop practical soft-decision and hard-decision algorithms. A case study for ranging and localization systems operating in a wireless environment is presented. Sample-level simulations validate our theoretical results.
    IEEE Journal of Selected Topics in Signal Processing 02/2015; 9(2):216-228.
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    ABSTRACT: Head-Related Transfer Function (HRTF) measurements underlie the signal processing used in binaural auditory displays, but measurement techniques, equipment, and postprocessing vary substantially between laboratories. This variation can result in significant differences in measured spectral and timing data taken from the same subject for the same sound source locations. An ongoing project for comparing databases from laboratories across the world (colloquially titled “Club Fritz”) employs a single dummy head microphone for measurements (Neumann KU-100) at various sites. The current study examines magnitude and timing differences between left and right ear data from 12 different HRTF sets taken from 10 different laboratories. Results revealed spectral magnitude variations up to 12.5 dB for frequency bands below 6 kHz and up to 23 dB above that, as well as large spectral left/right asymmetries (dcorr 0.4) for high frequency content. Further subjective studies are necessary to determine the perceptual relevance of these findings. Nevertheless, the observed ITD variations of up to 235 sec are alarming as they often exceeded reported JND values. Such findings highlight the potential impact of physical spaces, measurement routines, and equipment types on the collected HRTF data.
    IEEE Journal of Selected Topics in Signal Processing 01/2015;
  • IEEE Journal of Selected Topics in Signal Processing 01/2015;
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    ABSTRACT: The recent increased interest in large-scale multiple- input multiple-output systems, combined with the cost of analog radio-frequency (RF) chains, necessitates the use of efficient antenna selection (AS) schemes. Capacity or signal-to-noise ratio (SNR) optimal AS has been considered to require an exhaustive search among all possible antenna subsets. In this work, we prove that, under a total power constraint on the beamformer, the maximum-SNR joint beamforming transmit AS problem with two receive antennas and an arbitrary number of transmit antennas $N$ is polynomially solvable and develop an algorithm that solves it with quartic complexity, independently of the number of selected antennas. The algorithm identifies with complexity ${cal O}(N^{4})$ a cubic-size collection of antenna subsets that contains the one that maximizes the post-processing receiver SNR. From a different perspective, for any given two-row complex matrix, our algorithm computes with quartic complexity its two-row submatrix with the maximum principal singular value, for any number of selected columns. In addition, our method also applies to receive AS with two transmit antennas. Finally, if we enforce a per-antenna-element power constraint on the beamformer (i.e., constant-envelope transmission), then the set of transmit AS subsets that contains the optimal one is the same as in the total power constraint case. Therefore, our algorithm offers a practical solution to the maximum-SNR antenna selection problem when either the transmitter or the receiver consists of a large number of antennas.
    IEEE Journal of Selected Topics in Signal Processing 10/2014; 8(5):891-901.
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    ABSTRACT: Joint Spatial Division and Multiplexing (JSDM) is a downlink multiuser MIMO scheme recently proposed by the authors in order to enable “massive MIMO” gains and simplified system operations for Frequency Division Duplexing (FDD) systems. The key idea lies in partitioning the users into groups with approximately similar channel covariance eigenvectors and serving these groups by using two-stage downlink precoding scheme obtained as the concatenation of a pre-beamforming matrix, that depends only on the channel second-order statistics, with a multiuser MIMO linear precoding matrix, which is a function of the effective channels including pre-beamforming. The role of pre-beamforming is to reduce the dimensionality of the effective channel by exploiting the near-orthogonality of the eigenspaces of the channel covariances of the different user groups. This paper is an extension of our initial work on JSDM, and addresses some important practical issues. First, we focus on the regime of finite number of antennas and large number of users and show that JSDM with simple opportunistic user selection is able to achieve the same scaling law of the system capacity with full channel state information. Next, we consider the large-system regime (both antennas and users growing large) and propose a simple scheme for user grouping in a realistic setting where users have different angles of arrival and angular spreads. Finally, we propose a low-overhead probabilistic scheduling algorithm that selects the users at random with probabilities derived from large-system random matrix analysis. Since only the pre-selected users are required to feedback their channel state information, the proposed scheme realizes important savings in the CSIT feedback.
    IEEE Journal of Selected Topics in Signal Processing 10/2014;
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    ABSTRACT: Constant envelope (CE) precoding is a very recently developed transmission approach for large antenna array systems, where each antenna is restricted to transmit CE signals and only phases are used to shape desired information signals at the receiver. CE precoding is proposed as a solution for circumventing the high peak-to-average power ratio (PAPR) problem arising in non-CE transmission approaches, which becomes a difficult hardware implementation issue in large antenna array systems. While CE precoding is a nonlinear precoding approach and introduces challenges not seen in the widely-used non-CE linear precoding approach, the former has been shown to hold great potential in large-scale single-user MISO channels from an information rate analysis viewpoint. The present paper considers single-user MISO CE precoding from a transceiver realization viewpoint. We first solve the noise-free receive signal region characterization problem. From the characterization proof, we derive a simple and efficient CE precoder algorithm whose complexity is linear in the number of antennas. Then, we consider optimal CE precoding designs when the system can perform either antenna-subset selection or unequal per-antenna power allocation—both aiming at maximizing the system performance under transmission power constraints. Polynomial-time exact algorithms for the proposed design, via simple search or convex optimization, are developed. Simulation results demonstrate that for large antenna array systems, the proposed CE precoding schemes can yield symbol error probability performance comparable to that of the non-CE maximum ratio transmission scheme.
    IEEE Journal of Selected Topics in Signal Processing 10/2014; 8(5):982-995.
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    IEEE Journal of Selected Topics in Signal Processing 10/2014; 8(5):739-741.
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    ABSTRACT: This paper presents an Immersive Telepresence system for Entertainment and Meetings (ITEM). The system aims to provide a radically new video communication experience by seamlessly merging participants into the same virtual space to allow a natural interaction among them and shared collaborative contents. With the goal to make a scalable, flexible system for various business solutions as well as easily accessible by massive consumers, we address the challenges in the whole pipeline of media processing, communication, and displaying in our design and realization of such a system. Particularly, in this paper we focus on the system aspects that maximize the end-user experience, optimize the system and network resources, and enable various teleimmersive application scenarios. In addition, we also present a few key technologies, i.e. fast object-based video coding for real world data and spatialized audio capture and 3D sound localization for group teleconferencing. Our effort is to investigate and optimize the key system components and provide an efficient end-to-end optimization and integration by considering user needs and preferences. Extensive experiments show the developed system runs reliably and comfortably in real time with a minimal setup requirement (e.g. a webcam and/or a depth camera, an optional microphone array, a laptop/desktop connected to the public Internet) for teleimmersive communication. With such a really minimal deployment requirement, we present a variety of interesting applications and user experiences created by ITEM.
    IEEE Journal of Selected Topics in Signal Processing 08/2014;
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    ABSTRACT: Signal feature extraction and classification are two common tasks in the signal processing literature. This paper investigates the use of source identities as a common mechanism for enhancing the classification accuracy of social signals. We define social signals as outputs, such as microblog entries, geotags, or uploaded images, contributed by users in a social network. Many classification tasks can be defined on such outputs. For example, one may want to identify the dialect of a microblog contributed by an author, or classify information referred to in a user's tweet as true or false. While the design of such classifiers is application-specific, social signals share in common one key property: they are augmented by the explicit identity of the source. This motivates investigating whether or not knowing the source of each signal (in addition to exploiting signal features) allows the classification accuracy to be improved. We call it provenance-assisted classification. This paper answers the above question affirmatively, demonstrating how source identities can improve classification accuracy, and derives confidence bounds to quantify the accuracy of results. Evaluation is performed in two real-world contexts: (i) fact-finding that classifies microblog entries into true and false, and (ii) language classification of tweets issued by a set of possibly multi-lingual speakers. We also carry out extensive simulation experiments to further evaluate the performance of the proposed classification scheme over different problem dimensions. The results show that provenance features significantly improve classification accuracy of social signals, even when no information is known about the sources (besides their ID). This observation offers a general mechanism for enhancing classification results in social networks.
    IEEE Journal of Selected Topics in Signal Processing 08/2014; 8(4):624-637.
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    ABSTRACT: The objective of this special issue is to contribute to the progress and success of signal processing methods in social networks. We received a large number of submitted manuscripts, out of which seventeen papers were accepted for publication in this issue. These papers can be divided roughly into three categories: modeling of social network dynamics, inference in social networks, and applications. These categories are not disjoint, however, as papers often treat multiple aspects of social network analysis.
    IEEE Journal of Selected Topics in Signal Processing 08/2014; 8(4):511-513.