What is he best known for?
The fields of study he is best known for:
- Computer network
- Artificial intelligence
- Algorithm
His primary scientific interests are in Discrete wavelet transform, Wavelet, Motion compensation, Algorithm and Artificial intelligence.
Yiannis Andreopoulos brings together Discrete wavelet transform and Throughput to produce work in his papers.
His Wavelet study combines topics from a wide range of disciplines, such as Scalable Video Coding and Scalability.
His biological study spans a wide range of topics, including Decoding methods and Data compression.
In his research on the topic of Algorithm, Image processing and Signal processing is strongly related with Wavelet transform.
Artificial intelligence is closely attributed to Computer vision in his research.
His most cited work include:
- The performance of a self-excited fluidic energy harvester (174 citations)
- In-band motion compensated temporal filtering (166 citations)
- Optimized scalable video streaming over IEEE 802.11 a/e HCCA wireless networks under delay constraints (163 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Algorithm, Computer vision, Wavelet transform and Wavelet.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Codec and Pattern recognition.
His Algorithm research includes elements of Discrete wavelet transform, Theoretical computer science and Signal processing.
His work carried out in the field of Discrete wavelet transform brings together such families of science as Image processing and Wavelet packet decomposition.
The concepts of his Wavelet transform study are interwoven with issues in Transform coding and Image resolution.
Within one scientific family, Yiannis Andreopoulos focuses on topics pertaining to Motion compensation under Wavelet, and may sometimes address concerns connected to Motion estimation and Decoding methods.
He most often published in these fields:
- Artificial intelligence (24.54%)
- Algorithm (19.91%)
- Computer vision (13.43%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (24.54%)
- Pattern recognition (8.80%)
- Artificial neural network (5.09%)
In recent papers he was focusing on the following fields of study:
Yiannis Andreopoulos spends much of his time researching Artificial intelligence, Pattern recognition, Artificial neural network, Neuromorphic engineering and Convolutional neural network.
His Artificial intelligence study incorporates themes from Codec and Computer vision.
His Pattern recognition research is multidisciplinary, incorporating elements of Contextual image classification and Visual Word, Image retrieval.
His Artificial neural network research includes themes of Robustness and Precoding.
His Robustness study integrates concerns from other disciplines, such as Algorithm, Computation and Canonical normal form.
A large part of his Algorithm studies is devoted to Quantization.
Between 2016 and 2021, his most popular works were:
- Graph-Based Object Classification for Neuromorphic Vision Sensing (23 citations)
- PIX2NVS: Parameterized conversion of pixel-domain video frames to neuromorphic vision streams (19 citations)
- Voronoi-Based Compact Image Descriptors: Efficient Region-of-Interest Retrieval With VLAD and Deep-Learning-Based Descriptors (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Algorithm
Artificial intelligence, Pattern recognition, Convolutional neural network, Neuromorphic engineering and Pixel are his primary areas of study.
His Domain research extends to Artificial intelligence, which is thematically connected.
His work deals with themes such as Image retrieval, Visual Word, Space partitioning and Region of interest, which intersect with Convolutional neural network.
His Neuromorphic engineering research focuses on subjects like Discriminative model, which are linked to Training set and Regularization.
His Pixel research entails a greater understanding of Computer vision.
His work is dedicated to discovering how Motion vector, Codec are connected with Real-time computing and other disciplines.
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