What is he best known for?
The fields of study he is best known for:
- Computer network
- Artificial intelligence
- The Internet
Faramarz Fekri spends much of his time researching Low-density parity-check code, Algorithm, Theoretical computer science, Turbo code and Forward error correction.
All of his Algorithm and Tornado code, Error detection and correction and Decoding methods investigations are sub-components of the entire Algorithm study.
He focuses mostly in the field of Decoding methods, narrowing it down to topics relating to Channel state information and, in certain cases, Molecular communication.
His research investigates the connection with Theoretical computer science and areas like Distributed source coding which intersect with concerns in Parity bit.
His work deals with themes such as Serial concatenated convolutional codes and Concatenated error correction code, which intersect with Turbo code.
Faramarz Fekri has included themes like Discrete mathematics and Block code in his Forward error correction study.
His most cited work include:
- Nonuniform error correction using low-density parity-check codes (252 citations)
- Rateless Codes With Unequal Error Protection Property (242 citations)
- On decoding of low-density parity-check codes over the binary erasure channel (148 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Computer network, Algorithm, Network packet, Distributed computing and Decoding methods.
His Computer network study integrates concerns from other disciplines, such as Wireless network and Wireless ad hoc network.
In Algorithm, he works on issues like Theoretical computer science, which are connected to Factor graph.
The concepts of his Distributed computing study are interwoven with issues in Network topology, Wireless Routing Protocol and Mobile computing.
His work focuses on many connections between Decoding methods and other disciplines, such as Upper and lower bounds, that overlap with his field of interest in Discrete mathematics and Minimax.
His study on Low-density parity-check code also encompasses disciplines like
- Turbo code which is related to area like Serial concatenated convolutional codes, Block code and Forward error correction,
- Concatenated error correction code and related Linear code.
He most often published in these fields:
- Computer network (29.01%)
- Algorithm (26.34%)
- Network packet (17.94%)
What were the highlights of his more recent work (between 2016-2021)?
- Algorithm (26.34%)
- Artificial intelligence (8.78%)
- Data compression (8.02%)
In recent papers he was focusing on the following fields of study:
Faramarz Fekri focuses on Algorithm, Artificial intelligence, Data compression, Artificial neural network and Compression.
His Algorithm study frequently intersects with other fields, such as Upper and lower bounds.
His Artificial intelligence research incorporates themes from Machine learning and Pattern recognition.
His work carried out in the field of Data compression brings together such families of science as Mixture model, Entropy, Dictionary learning and Geophone.
His studies deal with areas such as Principal component analysis, Simulation and Data mining as well as Compression.
In general Decoding methods study, his work on Distributed source coding often relates to the realm of Model parameters, thereby connecting several areas of interest.
Between 2016 and 2021, his most popular works were:
- ISI-Avoiding Modulation for Diffusion-Based Molecular Communication (39 citations)
- A Distributed Principal Component Analysis Compression for Smart Seismic Acquisition Networks (12 citations)
- Inductive Logic Programming via Differentiable Deep Neural Logic Networks. (9 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Artificial intelligence
- Algorithm
The scientist’s investigation covers issues in Algorithm, Data compression, Artificial intelligence, Communication channel and Encoder.
His work in the fields of Algorithm, such as Quantization, overlaps with other areas such as Quantization.
His Data compression research incorporates elements of Mixture model, Automation and Geophone.
The Communication channel study combines topics in areas such as Modulation, Access control, Decoding methods, Parity-check matrix and Molecular communication.
His study in Decoding methods focuses on Low-density parity-check code and Belief propagation.
His research integrates issues of Network simulation, Protocol stack and Computer architecture simulator, Simulation in his study of Molecular communication.
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