D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 36 Citations 4,639 195 World Ranking 5647 National Ranking 338

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Algorithm
  • Computer network

Algorithm, Decoding methods, Communication channel, Error detection and correction and Channel code are his primary areas of study. Algorithm is closely attributed to Theoretical computer science in his study. Vladimir Stankovic combines subjects such as Data encoding and Low-density parity-check code with his study of Theoretical computer science.

The concepts of his Decoding methods study are interwoven with issues in Upper and lower bounds and Data compression. His work deals with themes such as JPEG 2000, Set partitioning in hierarchical trees and Linear network coding, which intersect with Communication channel. His Error detection and correction research is multidisciplinary, incorporating perspectives in Forward error correction, Convolutional code and Bitstream.

His most cited work include:

  • Scalable Video Multicast Using Expanding Window Fountain Codes (140 citations)
  • On code design for the Slepian-Wolf problem and lossless multiterminal networks (130 citations)
  • Non-Intrusive Load Disaggregation Using Graph Signal Processing (115 citations)

What are the main themes of his work throughout his whole career to date?

Vladimir Stankovic mostly deals with Artificial intelligence, Decoding methods, Algorithm, Computer network and Computer vision. The study incorporates disciplines such as Signal processing and Pattern recognition in addition to Artificial intelligence. Vladimir Stankovic interconnects Data compression and Communication channel in the investigation of issues within Decoding methods.

His study on Forward error correction and Fading is often connected to Erasure as part of broader study in Communication channel. His Algorithm study frequently draws connections between related disciplines such as Theoretical computer science. His study looks at the intersection of Computer network and topics like Scalable Video Coding with Coding tree unit, Real-time computing and Digital Video Broadcasting.

He most often published in these fields:

  • Artificial intelligence (23.48%)
  • Decoding methods (22.27%)
  • Algorithm (21.05%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (23.48%)
  • Pattern recognition (7.29%)
  • Graph (6.07%)

In recent papers he was focusing on the following fields of study:

Vladimir Stankovic focuses on Artificial intelligence, Pattern recognition, Graph, Real-time computing and Algorithm. His work in Artificial intelligence addresses subjects such as Machine learning, which are connected to disciplines such as Spectrometer, Silicon photonics, Robustness and Nonintrusive load monitoring. His Pattern recognition study deals with Signal processing intersecting with Noise reduction, Filter and Image processing.

His Real-time computing research is multidisciplinary, incorporating elements of Scalability, Anomaly detection, Smart meter, AC power and Fault. Decoding methods is the focus of his Algorithm research. His Decoding methods research incorporates elements of Codebook, Hamming distance and Short Code.

Between 2018 and 2021, his most popular works were:

  • Transferability of Neural Network Approaches for Low-rate Energy Disaggregation (41 citations)
  • Can non-intrusive load monitoring be used for identifying an appliance's anomalous behaviour? (30 citations)
  • A Generic Optimisation-Based Approach for Improving Non-Intrusive Load Monitoring (16 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Algorithm
  • Computer network

His primary areas of study are Real-time computing, Artificial intelligence, Convolutional neural network, Smart meter and Metering mode. His Real-time computing study integrates concerns from other disciplines, such as Scalability, Energy consumption, Anomaly detection, AC power and Fault. His Scalability research focuses on Information extraction and how it connects with Hidden Markov model.

Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Graph. His study in Smart meter is interdisciplinary in nature, drawing from both Analytics, Demand response and Cluster analysis. His Metering mode study combines topics in areas such as Electrical load, Feature extraction, Quadratic problem and Optimization problem, Mathematical optimization.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Scalable Video Multicast Using Expanding Window Fountain Codes

D. Vukobratovic;V. Stankovic;D. Sejdinovic;L. Stankovic.
IEEE Transactions on Multimedia (2009)

168 Citations

On code design for the Slepian-Wolf problem and lossless multiterminal networks

V. Stankovic;A.D. Liveris;Zixiang Xiong;C.N. Georghiades.
IEEE Transactions on Information Theory (2006)

160 Citations

Compressive Sampling of Binary Images

Vladimir Stankovic;Lina Stankovic;Samuel Cheng.
european signal processing conference (2008)

153 Citations

On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing

Bochao Zhao;Lina Stankovic;Vladimir Stankovic.
IEEE Access (2016)

142 Citations

An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study.

David Murray;Lina Stankovic;Vladimir Stankovic.
Scientific Data (2017)

142 Citations

Non-Intrusive Load Disaggregation Using Graph Signal Processing

Kanghang He;Lina Stankovic;Jing Liao;Vladimir Stankovic.
IEEE Transactions on Smart Grid (2018)

139 Citations

Cooperative diversity for wireless ad hoc networks

V. Stankovic;A. Host-Madsen;Zixiang Xiong.
IEEE Signal Processing Magazine (2006)

127 Citations

Distributed joint source-channel coding of video using raptor codes

Qian Xu;V. Stankovic;Zixiang Xiong.
IEEE Journal on Selected Areas in Communications (2007)

124 Citations

Design of Slepian-Wolf codes by channel code partitioning

V. Stankovic;A.D. Liveris;Zixiang Xiong;C.N. Georghiades.
data compression conference (2004)

122 Citations

Optimized error protection of scalable image bit streams [advances in joint source-channel coding for images]

R. Hamzaoui;V. Stankovic;Zixiang Xiong.
IEEE Signal Processing Magazine (2005)

116 Citations

Best Scientists Citing Vladimir Stankovic

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Publications: 44

Zixiang Xiong

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Texas A&M University

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Feng Wu

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University of Science and Technology of China

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Pamela C. Cosman

Pamela C. Cosman

University of California, San Diego

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Lajos Hanzo

Lajos Hanzo

University of Southampton

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Eamonn Keogh

Eamonn Keogh

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Hamid Jafarkhani

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University of California, Irvine

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Beatrice Pesquet-Popescu

Beatrice Pesquet-Popescu

University of Paris-Saclay

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Enrico Magli

Enrico Magli

Polytechnic University of Turin

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Hamid Sharif

Hamid Sharif

University of Nebraska–Lincoln

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Mohammed Ghanbari

Mohammed Ghanbari

University of Essex

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Michael W. Marcellin

Michael W. Marcellin

University of Arizona

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Giuseppe Caire

Giuseppe Caire

Technical University of Berlin

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Petar Popovski

Petar Popovski

Aalborg University

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Xiaolin Wu

Xiaolin Wu

McMaster University

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Martin J. Wainwright

Martin J. Wainwright

University of California, Berkeley

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Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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