What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Algorithm
His scientific interests lie mostly in Algorithm, Theoretical computer science, Compressed sensing, Multiple description coding and Pixel.
His research integrates issues of Estimator, Mathematical optimization and Signal reconstruction in his study of Algorithm.
His Theoretical computer science research includes themes of Information theory, Network packet, Tunstall coding and Robustness.
The study incorporates disciplines such as Sparse approximation, Pattern recognition, Inverse problem and Lasso in addition to Compressed sensing.
His Multiple description coding research includes elements of Transform coding and Coding tree unit.
In his research on the topic of Pixel, Shot noise, Detector and Signal is strongly related with Photon.
His most cited work include:
- Multiple description coding: compression meets the network (1387 citations)
- Quantized Frame Expansions with Erasures (468 citations)
- Quantized overcomplete expansions in IR/sup N/: analysis, synthesis, and algorithms (427 citations)
What are the main themes of his work throughout his whole career to date?
Vivek K Goyal mainly investigates Algorithm, Artificial intelligence, Optics, Mathematical optimization and Quantization.
His work on Compressed sensing as part of general Algorithm study is frequently connected to Source code, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
As a member of one scientific family, Vivek K Goyal mostly works in the field of Artificial intelligence, focusing on Pattern recognition and, on occasion, Signal-to-noise ratio.
His work in the fields of Photon, Pixel and Image resolution overlaps with other areas such as Secondary electrons.
His studies deal with areas such as Vector quantization, Entropy encoding and Transform coding as well as Quantization.
His Theoretical computer science research incorporates elements of Multiple description coding, Communication channel and Variable-length code, Tunstall coding, Context-adaptive binary arithmetic coding.
He most often published in these fields:
- Algorithm (42.96%)
- Artificial intelligence (22.54%)
- Optics (15.14%)
What were the highlights of his more recent work (between 2017-2021)?
- Optics (15.14%)
- Artificial intelligence (22.54%)
- Computer vision (12.32%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Optics, Artificial intelligence, Computer vision, Lidar and Detector.
As a part of the same scientific family, he mostly works in the field of Optics, focusing on Shot noise and, on occasion, Microscopy.
His work carried out in the field of Artificial intelligence brings together such families of science as Weighting and Sequence.
His Lidar study integrates concerns from other disciplines, such as Photon counting, Photon, Dither and Light intensity.
Vivek K Goyal combines subjects such as Algorithm design, Distortion, Linear system and Root-mean-square deviation with his study of Detector.
Vivek K Goyal interconnects Algorithm, Computational physics and Photon detection in the investigation of issues within Markov chain.
Between 2017 and 2021, his most popular works were:
- Quantum-inspired computational imaging (77 citations)
- Computational periscopy with an ordinary digital camera (69 citations)
- Dead Time Compensation for High-Flux Ranging (22 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Optics
Artificial intelligence, Computer vision, Optics, Lidar and Detector are his primary areas of study.
Vivek K Goyal studies Artificial intelligence, namely Motion.
His studies in Computer vision integrate themes in fields like Ultrashort pulse, Opacity and Photon.
His study in Optics is interdisciplinary in nature, drawing from both Dither, Shot noise and Randomness.
His research in Lidar intersects with topics in Algorithm design, Modality, Real-time computing, Signal processing and Gaussian noise.
The concepts of his Detector study are interwoven with issues in Linear system and Quantization.
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