What is he best known for?
The fields of study he is best known for:
- Computer network
- Statistics
- Artificial intelligence
His primary scientific interests are in Algorithm, Nonlinear system, Communication channel, Telecommunications and Computer network.
Stephen McLaughlin has included themes like Radial basis function network, Statistics, Cluster analysis, Frequency domain and Signal in his Algorithm study.
The study incorporates disciplines such as Reflectance spectroscopy, Speech synthesis, Speech recognition, Speech processing and Least squares in addition to Nonlinear system.
His Communication channel study incorporates themes from Duplex and Equalizer.
His Computer network study combines topics from a wide range of disciplines, such as Relay channel, Relay, Cooperative diversity and Channel allocation schemes.
His Relay research is multidisciplinary, relying on both Interference and Routing protocol.
His most cited work include:
- Relay selection for secure cooperative networks with jamming (428 citations)
- Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding (416 citations)
- Comparative study of textural analysis techniques to characterise tissue from intravascular ultrasound (412 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Algorithm, Computer network, Artificial intelligence, Electronic engineering and Communication channel.
His Algorithm study combines topics in areas such as Statistics, Bayesian inference and Signal processing.
The various areas that he examines in his Computer network study include Wireless, Wireless ad hoc network and Wireless network.
The Artificial intelligence study combines topics in areas such as Lidar, Machine learning, Computer vision and Pattern recognition.
His Communication channel study frequently links to related topics such as Control theory.
Stephen McLaughlin studied Low-density parity-check code and Concatenated error correction code that intersect with Linear code.
He most often published in these fields:
- Algorithm (24.52%)
- Computer network (15.05%)
- Artificial intelligence (14.62%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (14.62%)
- Lidar (7.10%)
- Pixel (7.31%)
In recent papers he was focusing on the following fields of study:
Stephen McLaughlin focuses on Artificial intelligence, Lidar, Pixel, Computer vision and 3D reconstruction.
His Artificial intelligence research includes elements of Signal and Pattern recognition.
Stephen McLaughlin works mostly in the field of Pattern recognition, limiting it down to concerns involving Gaussian noise and, occasionally, Outlier.
Stephen McLaughlin combines subjects such as Multispectral image, Detector, Ranging, Waveform and Algorithm with his study of Lidar.
His study looks at the intersection of Algorithm and topics like Bayesian inference with Markov chain Monte Carlo and Noise.
His study in Pixel is interdisciplinary in nature, drawing from both Image processing, Convergence, Scattering and Photon.
Between 2017 and 2021, his most popular works were:
- Quantum-inspired computational imaging (77 citations)
- High-resolution depth profiling using a range-gated CMOS SPAD quanta image sensor (48 citations)
- Bayesian 3D Reconstruction of Complex Scenes from Single-Photon Lidar Data (37 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Artificial intelligence
- Statistics
Stephen McLaughlin spends much of his time researching Lidar, Pixel, Artificial intelligence, 3D reconstruction and Computer vision.
His Lidar study also includes fields such as
- Ranging that connect with fields like Waveform, Adaptive sampling, Sampling, Poisson distribution and Photonics,
- Detector, which have a strong connection to Optical fiber, Linear system, Gaussian noise, Signal processing and Algorithm design.
His studies examine the connections between Pixel and genetics, as well as such issues in Multispectral image, with regards to Optics and Image processing.
His Artificial intelligence research includes themes of Signal and Pattern recognition.
He interconnects Algorithm, Spectral bands and Reversible-jump Markov chain Monte Carlo in the investigation of issues within Spatial correlation.
His biological study spans a wide range of topics, including Posterior probability, Bayesian probability, Monte Carlo method and Image restoration.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
