J. Raviv

5.8k citations
8 papers · 4.2k indexed · 1 hit paper · h-index 6

Impact in

Papers in

J. Raviv

8 papers receiving 4.0k citations

Hit Papers

Optimal decoding of linear codes for minimizing symbol error rate (Corresp.) 1974 · 3.8k citations
3.8k197420261991200810002.0k3.0k

Peers

J. Raviv
Comparison fields: 5 of 87
  • Computer Networks and Communications 3.1k
  • Electrical and Electronic Engineering 3.4k
  • Artificial Intelligence 1.3k
  • Signal Processing 361
  • Computational Theory and Mathematics 388
Replace I. M. Jacobs with:
I. M. Jacobs United States
D. L. Schilling United States
M.B. Pursley United States
Erdal Arıkan Türkiye
Sergio Benedetto Italy
V.K. Bhargava Canada
Mohammad Amin Shokrollahi United States
S. Pasupathy Canada
L. R. Welch United States
G. Caire Italy
J. Raviv relative to I. M. Jacobs United States I. M. Jacobs's profile →
Citations per field
00.5×11×
I. M. Jacobs · 1×
Citations per year

Countries citing papers authored by J. Raviv

Since Specialization
Citations

This map shows the geographic impact of J. Raviv's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by J. Raviv with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. Raviv more than expected).

Fields of papers citing papers by J. Raviv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J. Raviv. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by J. Raviv. The network helps show where J. Raviv may publish in the future.

Co-authorship network

The 8 scholars most cited alongside J. Raviv, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with J. Raviv Line = papers co-authored together J. Raviv links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1 19802
2 197868
3
Optimal decoding of linear codes for minimizing symbol error rate (Corresp.)
Hit paper breakdown →
19743761
4 1970214
5 196872
6 196783
7 19666
8 19653

About J. Raviv

J. Raviv is a scholar working on Computational Theory and Mathematics, Computer Networks and Communications, Control and Systems Engineering, Statistical and Nonlinear Physics and Cellular and Molecular Neuroscience, having authored 8 papers that have together received 4.2k indexed citations. Recurring topics across this work include Fault Detection and Control Systems (2 papers), Sparse and Compressive Sensing Techniques (1 paper), Photoreceptor and optogenetics research (1 paper), Plant and Biological Electrophysiology Studies (1 paper), Distributed Sensor Networks and Detection Algorithms (1 paper), Handwritten Text Recognition Techniques (1 paper), Neural Networks and Applications (1 paper) and graph theory and CDMA systems (1 paper). The work is most often cited by research in Computer Networks and Communications (3.1k citations), Electrical and Electronic Engineering (3.4k citations), Artificial Intelligence (1.3k citations), Signal Processing (361 citations) and Computational Theory and Mathematics (388 citations). J. Raviv has collaborated with scholars based in United States and Israel. Frequent co-authors include John Cocke, L.R. Bahl, F. Jelinek, Martin E. Hellman, J. A. McLaughlin, Moshe Ben-Bassat, Dan Atsmon and Dan Porath. Their work appears in journals such as IEEE Transactions on Information Theory, International Journal of Engineering Science, Information and Control, Plant Science Letters and PubMed.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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