Irving S. Reed

What is he best known for?

The fields of study he is best known for:

• Algorithm
• Algebra
• Artificial intelligence

His primary areas of study are Algorithm, Artificial intelligence, Detection theory, Clutter and Image processing. His Algorithm study frequently intersects with other fields, such as Very-large-scale integration. Irving S. Reed has researched Artificial intelligence in several fields, including Signal-to-noise ratio and Computer vision.

Irving S. Reed interconnects Signal-to-noise ratio, Constant false alarm rate and Matched filter in the investigation of issues within Detection theory. His biological study spans a wide range of topics, including Field, Bit-length, Binary number and Erasure code. His Binary number research focuses on subjects like Finite field, which are linked to Code.

His most cited work include:

• Polynomial Codes Over Certain Finite Fields (2527 citations)
• Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution (1070 citations)
• A Detection Algorithm for Optical Targets in Clutter (187 citations)

What are the main themes of his work throughout his whole career to date?

Irving S. Reed mainly investigates Algorithm, Discrete mathematics, Decoding methods, Very-large-scale integration and Arithmetic. His Algorithm research integrates issues from Fourier transform, Filter and Signal processing. As a part of the same scientific family, Irving S. Reed mostly works in the field of Filter, focusing on Matched filter and, on occasion, Artificial intelligence and Computer vision.

He studies Discrete mathematics, focusing on Finite field in particular. His Finite field research is multidisciplinary, relying on both Polynomial and Galois theory. His Arithmetic research is multidisciplinary, incorporating perspectives in Modulo and Finite field arithmetic.

He most often published in these fields:

• Algorithm (36.92%)
• Discrete mathematics (23.08%)
• Decoding methods (18.97%)

What were the highlights of his more recent work (between 1995-2009)?

• Algorithm (36.92%)
• Convolutional code (11.28%)
• Discrete mathematics (23.08%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Algorithm, Convolutional code, Discrete mathematics, Decoding methods and Quadratic residue code. His studies deal with areas such as Asynchronous communication and Signal processing as well as Algorithm. Irving S. Reed usually deals with Signal processing and limits it to topics linked to Constant false alarm rate and Normalization.

His work carried out in the field of Discrete mathematics brings together such families of science as Concatenated error correction code, Cyclic code, Serial concatenated convolutional codes, Polynomial code and Linear code. His research in Decoding methods intersects with topics in Computer architecture, Error detection and correction and Encoding. His Affine transformation research is multidisciplinary, incorporating elements of Polynomial and Finite field.

Between 1995 and 2009, his most popular works were:

• Subspace selection for partially adaptive sensor array processing (120 citations)
• Automatic target detection and recognition in multiband imagery: a unified ML detection and estimation approach (109 citations)
• Decoding the (47,24,11) quadratic residue code (83 citations)

In his most recent research, the most cited papers focused on:

• Algorithm
• Algebra
• Artificial intelligence

Algorithm, Artificial intelligence, Data compression, Image processing and Computer vision are his primary areas of study. His study in the field of Decoding methods and Fast Fourier transform is also linked to topics like Berlekamp's algorithm. His study in Decoding methods is interdisciplinary in nature, drawing from both Code, Polynomial, Galois theory and Finite field.

Irving S. Reed combines subjects such as Covariance matrix, Sample matrix inversion, Adaptive filter and Pattern recognition with his study of Artificial intelligence. His multidisciplinary approach integrates Image processing and Clutter in his work. His Computer vision study combines topics from a wide range of disciplines, such as Radiation, Black-body radiation and Hyperspectral imaging.