What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Computer vision
Chang Wen Chen mainly focuses on Artificial intelligence, Computer vision, Real-time computing, Computer network and Wireless sensor network.
His Artificial intelligence study incorporates themes from Identification and Pattern recognition.
His Computer vision study frequently draws connections to other fields, such as Visualization.
His Real-time computing research includes elements of Wireless, Forward error correction, Scalable Video Coding and Quality of experience.
His Computer network research is multidisciplinary, incorporating elements of Key distribution in wireless sensor networks, Communication channel, Fading and Compressed sensing.
His research in Wireless sensor network intersects with topics in Data transmission, Visual sensor network, Energy consumption, Wireless network and Robustness.
His most cited work include:
- Compressive data gathering for large-scale wireless sensor networks (501 citations)
- Joint source channel rate-distortion analysis for adaptive mode selection and rate control in wireless video coding (340 citations)
- Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications (221 citations)
What are the main themes of his work throughout his whole career to date?
Chang Wen Chen mostly deals with Artificial intelligence, Computer network, Computer vision, Algorithm and Real-time computing.
His study connects Pattern recognition and Artificial intelligence.
Chang Wen Chen combines subjects such as Wireless, Wireless network, Throughput and Communication channel with his study of Computer network.
His Communication channel research includes themes of Transmission and Error detection and correction.
His Algorithm research incorporates elements of Speech recognition and Theoretical computer science.
Chang Wen Chen works mostly in the field of Real-time computing, limiting it down to topics relating to Scalable Video Coding and, in certain cases, Multiview Video Coding.
He most often published in these fields:
- Artificial intelligence (30.92%)
- Computer network (22.14%)
- Computer vision (21.56%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (30.92%)
- Pattern recognition (10.69%)
- Computer network (22.14%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer network, Algorithm and Computer vision.
His Artificial intelligence study typically links adjacent topics like Key.
As part of the same scientific family, he usually focuses on Pattern recognition, concentrating on Structure and intersecting with Variation and Kronecker product.
The study incorporates disciplines such as Wireless and Throughput in addition to Computer network.
His research investigates the connection between Throughput and topics such as Real-time computing that intersect with issues in Mobile device and User experience design.
His studies in Computer vision integrate themes in fields like Visualization and Distortion.
Between 2016 and 2021, his most popular works were:
- No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization (139 citations)
- Blind Quality Assessment Based on Pseudo-Reference Image (93 citations)
- DA-GAN: Instance-Level Image Translation by Deep Attention Generative Adversarial Networks (85 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- The Internet
His primary scientific interests are in Artificial intelligence, Computer network, Computer vision, Pattern recognition and Quality of service.
His work on Image as part of general Artificial intelligence study is frequently linked to Set, therefore connecting diverse disciplines of science.
He has researched Computer network in several fields, including Wireless, Throughput and Adaptation.
Many of his studies involve connections with topics such as Visualization and Computer vision.
His Pattern recognition study combines topics in areas such as Image quality, Robustness and Cluster analysis.
As a part of the same scientific family, he mostly works in the field of Quality of service, focusing on Wireless sensor network and, on occasion, Key distribution in wireless sensor networks, Efficient energy use and Resource allocation.
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