What is he best known for?
The fields of study he is best known for:
- Algorithm
- Electrical engineering
- Artificial intelligence
His scientific interests lie mostly in Electronic engineering, Chip, Decoding methods, Clock rate and CMOS.
His research integrates issues of Phase-locked loop, Fast Fourier transform, Clock generator and Integrated circuit in his study of Electronic engineering.
Chen-Yi Lee focuses mostly in the field of Fast Fourier transform, narrowing it down to topics relating to Orthogonal frequency-division multiplexing and, in certain cases, Computation.
The various areas that Chen-Yi Lee examines in his Chip study include Control theory, Asynchronous communication, Static random-access memory and Feature extraction.
His research in Decoding methods intersects with topics in Codec, Computer hardware and Error detection and correction.
He interconnects Reduction, Viterbi decoder and Electrical efficiency in the investigation of issues within CMOS.
His most cited work include:
- A 1-GS/s FFT/IFFT processor for UWB applications (206 citations)
- An all-digital phase-locked loop for high-speed clock generation (179 citations)
- Design of an FFT/IFFT Processor for MIMO OFDM Systems (122 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Electronic engineering, Decoding methods, Computer hardware, Chip and CMOS.
Chen-Yi Lee undertakes interdisciplinary study in the fields of Electronic engineering and Throughput through his works.
His Decoding methods study incorporates themes from Real-time computing and Parallel computing.
The concepts of his Computer hardware study are interwoven with issues in Data compression, Code word and Video decoder.
His Chip study frequently draws connections to other fields, such as Scheduling.
His biological study spans a wide range of topics, including Clock generator, Jitter and Digital control.
He most often published in these fields:
- Electronic engineering (39.60%)
- Decoding methods (24.50%)
- Computer hardware (22.82%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (7.05%)
- Biochip (5.03%)
- Microfluidics (5.03%)
In recent papers he was focusing on the following fields of study:
Chen-Yi Lee mostly deals with Artificial intelligence, Biochip, Microfluidics, MEDA and Convolutional neural network.
The study incorporates disciplines such as Machine learning and Pattern recognition in addition to Artificial intelligence.
Clock rate, Application-specific integrated circuit, Data transmission and Authenticated encryption is closely connected to Chip in his research, which is encompassed under the umbrella topic of Biochip.
His work carried out in the field of Microfluidics brings together such families of science as Embedded system and Electronic design automation.
His studies deal with areas such as Microcell, Electronic circuit, Electronic engineering and Computer hardware as well as Fluidics.
Chen-Yi Lee carries out multidisciplinary research, doing studies in Electronic engineering and Neuromodulation.
Between 2016 and 2021, his most popular works were:
- QuatNet: Quaternion-Based Head Pose Estimation With Multiregression Loss (37 citations)
- A Fully Integrated 16-Channel Closed-Loop Neural-Prosthetic CMOS SoC With Wireless Power and Bidirectional Data Telemetry for Real-Time Efficient Human Epileptic Seizure Control (23 citations)
- Structural and Functional Test Methods for Micro-Electrode-Dot-Array Digital Microfluidic Biochips (17 citations)
In his most recent research, the most cited papers focused on:
- Electrical engineering
- Artificial intelligence
- Telecommunications
His primary areas of investigation include Microfluidics, MEDA, Biochip, Electronic engineering and Artificial intelligence.
His Microfluidics research is multidisciplinary, incorporating elements of Embedded system and Integer programming.
His Embedded system research includes themes of Automation and Chip.
In the subject of general Electronic engineering, his work in CMOS is often linked to Neuromodulation, thereby combining diverse domains of study.
His work investigates the relationship between Artificial intelligence and topics such as Machine learning that intersect with problems in Pipeline.
He has researched Fluidics in several fields, including Microcell, Electronic circuit, Computer hardware and Control reconfiguration.
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