What is he best known for?
The fields of study he is best known for:
- Electrical engineering
- Artificial intelligence
- Operating system
Peng Li mainly focuses on Electronic engineering, Electroacupuncture, Rostral ventrolateral medulla, Condensed matter physics and Internal medicine.
Peng Li combines subjects such as Neuromorphic engineering and Electronic circuit with his study of Electronic engineering.
Peng Li interconnects Volterra series and Nonlinear system in the investigation of issues within Electronic circuit.
His study in Rostral ventrolateral medulla is interdisciplinary in nature, drawing from both Neuroscience and Splanchnic nerves.
The study incorporates disciplines such as Electrical resistivity and conductivity, Magnetization and Magnetoresistance in addition to Condensed matter physics.
His Internal medicine study often links to related topics such as Endocrinology.
His most cited work include:
- Highly Stable Aqueous Zinc-Ion Storage Using a Layered Calcium Vanadium Oxide Bronze Cathode. (323 citations)
- Dynamical Properties and Design Analysis for Nonvolatile Memristor Memories (241 citations)
- Nonvolatile memristor memory: device characteristics and design implications (230 citations)
What are the main themes of his work throughout his whole career to date?
Peng Li mostly deals with Condensed matter physics, Electronic engineering, Magnetoresistance, Magnetization and Artificial intelligence.
His studies in Condensed matter physics integrate themes in fields like Exchange bias and Electrical resistivity and conductivity.
His research in Electronic engineering intersects with topics in Electronic circuit, Integrated circuit and Nonlinear system.
The various areas that Peng Li examines in his Magnetoresistance study include Semimetal and Sputter deposition.
His Artificial intelligence research incorporates themes from Machine learning and Pattern recognition.
His Spiking neural network research includes themes of Field-programmable gate array, Neuromorphic engineering and Backpropagation.
He most often published in these fields:
- Condensed matter physics (24.09%)
- Electronic engineering (17.63%)
- Magnetoresistance (11.40%)
What were the highlights of his more recent work (between 2018-2021)?
- Condensed matter physics (24.09%)
- Spiking neural network (7.53%)
- Artificial intelligence (7.74%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Condensed matter physics, Spiking neural network, Artificial intelligence, Spintronics and Artificial neural network.
His Condensed matter physics study combines topics in areas such as Magnetization and Magnetoresistance.
His Magnetization research is multidisciplinary, incorporating elements of Thin film and Spin-½.
Peng Li has included themes like Weyl semimetal and Semiconductor in his Magnetoresistance study.
Peng Li has researched Spiking neural network in several fields, including Neuromorphic engineering, Backpropagation, Field-programmable gate array and Boosting.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Pattern recognition.
Between 2018 and 2021, his most popular works were:
- Surface-Embedding of Functional Micro-/Nanoparticles for Achieving Versatile Superhydrophobic Interfaces (39 citations)
- Rethinking the performance comparison between SNNS and ANNS. (27 citations)
- Sliding mode control of neural networks via continuous or periodic sampling event-triggering algorithm (26 citations)
In his most recent research, the most cited papers focused on:
- Electrical engineering
- Artificial intelligence
- Operating system
His main research concerns Artificial neural network, Magnetization, Spiking neural network, Artificial intelligence and Condensed matter physics.
His biological study deals with issues like Algorithm, which deal with fields such as Control theory, Quantization and Synchronization.
His Magnetization research is multidisciplinary, incorporating perspectives in Thin film and Spintronics, Ferromagnetism.
His Ferromagnetism research incorporates elements of Topological insulator and Magnetoresistance.
As a member of one scientific family, he mostly works in the field of Spiking neural network, focusing on Backpropagation and, on occasion, Neuromorphic engineering and Pattern recognition.
His study in the field of Spin-½ also crosses realms of Competition.
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