What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Computer network
His scientific interests lie mostly in Communication channel, Algorithm, Electronic engineering, Optics and Optical communication.
His Communication channel study incorporates themes from Wireless, Transmission, Markov process and Interference.
The concepts of his Algorithm study are interwoven with issues in Higher-order statistics, Noise and Statistics.
His Electronic engineering research includes themes of Telecommunications, Frequency domain, Quantization and Compressed sensing.
His work on Cognitive radio and Signal processing as part of general Telecommunications study is frequently linked to Terminology, therefore connecting diverse disciplines of science.
His work carried out in the field of Cognitive radio brings together such families of science as Time division multiple access, Spectrum, Data science and Taxonomy.
His most cited work include:
- A Survey of Dynamic Spectrum Access (2424 citations)
- Hierarchical digital modulation classification using cumulants (733 citations)
- COGNITIVE RADIOS FOR DYNAMIC SPECTRUM ACCESS - Dynamic Spectrum Access in the Time Domain: Modeling and Exploiting White Space (445 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Algorithm, Electronic engineering, Communication channel, Signal processing and Mathematical optimization.
He interconnects Statistics, Estimator and Additive white Gaussian noise in the investigation of issues within Algorithm.
His studies deal with areas such as Telecommunications and Compressed sensing as well as Electronic engineering.
Communication channel connects with themes related to Interference in his study.
The study incorporates disciplines such as Scattering, Optical communication, Monte Carlo method and Impulse response in addition to Path loss.
His Non-line-of-sight propagation study combines topics from a wide range of disciplines, such as Transmitter, Ultraviolet and Free-space optical communication.
He most often published in these fields:
- Algorithm (24.32%)
- Electronic engineering (23.10%)
- Communication channel (14.74%)
What were the highlights of his more recent work (between 2015-2021)?
- Electronic engineering (23.10%)
- Algorithm (24.32%)
- Artificial intelligence (9.83%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Electronic engineering, Algorithm, Artificial intelligence, Beamforming and Transmission.
Brian M. Sadler studies Electronic engineering, namely Bandwidth.
His Algorithm research incorporates themes from Change detection, Random variable, Test statistic, Graphical model and Upper and lower bounds.
Brian M. Sadler combines subjects such as Computer network, Wireless network, Mathematical optimization and Transmitter, Artificial noise with his study of Beamforming.
His Computer network study combines topics in areas such as Key, Communication channel and Secrecy.
Electrical engineering is closely connected to Wireless in his research, which is encompassed under the umbrella topic of Transmission.
Between 2015 and 2021, his most popular works were:
- On Generating Characteristic-rich Question Sets for QA Evaluation (49 citations)
- Proximity Without Consensus in Online Multiagent Optimization (44 citations)
- TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering (40 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Computer network
Brian M. Sadler focuses on Artificial intelligence, Algorithm, Computer network, Natural language processing and Electronic engineering.
His biological study spans a wide range of topics, including Undirected graph, Direction of arrival, Filter design, Uncertainty quantification and Estimator.
His study looks at the relationship between Computer network and topics such as Key, which overlap with Quality of service, Interference, Wireless network, Beamforming and Null.
The various areas that Brian M. Sadler examines in his Natural language processing study include Hierarchical clustering, Cluster analysis, Discriminative model and Taxonomy.
Brian M. Sadler merges Taxonomy with Term in his study.
Within one scientific family, Brian M. Sadler focuses on topics pertaining to Multipath propagation under Electronic engineering, and may sometimes address concerns connected to Vehicular ad hoc network, Fading and Code division multiple access.
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