What is he best known for?
The fields of study he is best known for:
- Algorithm
- Computer network
- Artificial intelligence
Nariman Farvardin spends much of his time researching Algorithm, Quantization, Data compression, Artificial intelligence and Vector quantization.
His research integrates issues of Speech recognition and Theoretical computer science in his study of Algorithm.
As part of the same scientific family, he usually focuses on Quantization, concentrating on Entropy and intersecting with Error detection and correction, Bit error rate, Channel code, Convolutional code and Discrete cosine transform.
His research in Artificial intelligence intersects with topics in Computer vision and Pattern recognition.
His Vector quantization research includes themes of Euclidean Distance Measurement, Codebook, Truncated binary encoding and Adaptive coding.
His studies examine the connections between Decoding methods and genetics, as well as such issues in Binary code, with regards to Robustness, Simulated annealing and Linde–Buzo–Gray algorithm.
His most cited work include:
- A study of vector quantization for noisy channels (452 citations)
- Optimum quantizer performance for a class of non-Gaussian memoryless sources (327 citations)
- Optimal quantizer design for noisy channels: An approach to combined source - channel coding (320 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Algorithm, Vector quantization, Quantization, Artificial intelligence and Data compression.
In the field of Algorithm, his study on Decoding methods overlaps with subjects such as Encoder.
His Vector quantization study combines topics from a wide range of disciplines, such as Finite state, Codebook, Harmonic Vector Excitation Coding and Discrete cosine transform.
Nariman Farvardin interconnects Discrete mathematics and Lapped transform in the investigation of issues within Quantization.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Computer vision and Pattern recognition.
His work carried out in the field of Theoretical computer science brings together such families of science as Convolutional code, Robustness and Rate–distortion theory.
He most often published in these fields:
- Algorithm (52.07%)
- Vector quantization (33.06%)
- Quantization (24.79%)
What were the highlights of his more recent work (between 1999-2006)?
- Real-time computing (8.26%)
- Network packet (6.61%)
- Encoder (16.53%)
In recent papers he was focusing on the following fields of study:
Nariman Farvardin mainly investigates Real-time computing, Network packet, Encoder, Algorithm and Wireless network.
His studies in Real-time computing integrate themes in fields like Code division multiple access, Error detection and correction and Adaptive algorithm.
His work in the fields of Packet switching overlaps with other areas such as Erasure.
As part of his studies on Algorithm, he often connects relevant areas like Theoretical computer science.
The Wireless network study combines topics in areas such as Quality of service, Time division multiple access, Statistical time division multiplexing and Channel access method.
As a part of the same scientific family, Nariman Farvardin mostly works in the field of Robustness, focusing on Speech coding and, on occasion, Heterogeneous network, Transcoding, Quantization and Vector quantization.
Between 1999 and 2006, his most popular works were:
- Progressive transmission of images over memoryless noisy channels (205 citations)
- Multiple description coding in networks with congestion problem (88 citations)
- Design of channel-optimized vector quantizers in the presence of channel mismatch (24 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Computer network
- Artificial intelligence
Nariman Farvardin focuses on Encoder, Algorithm, Wireless network, Network packet and Theoretical computer science.
Nariman Farvardin combines subjects such as Average distortion, Vector quantisation and Minimax with his study of Algorithm.
His Wireless network research incorporates themes from Convolutional code and Adaptive algorithm.
His Network packet research includes elements of Quality of service, Linear predictive coding, Speech coding and Statistical time division multiplexing.
His Theoretical computer science research incorporates elements of Network congestion, Communications system, Rate–distortion theory and Intelligent Network.
His Rate–distortion theory research is multidisciplinary, relying on both Decoding methods, Variable-length code, Multiple description coding, Telecommunications network and Binary erasure channel.
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