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Arif Kareem

dblp:141/8416 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 1987
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Processor architecture and microarchitecture · 87% Performance modeling and evaluation · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Processor architecture and microarchitecture › special-purpose processor
digital signal processor		 0.011987
Digital signal processor for test and measurement environment · Proc. IEEE 1987
Processor architecture and microarchitecture › instruction-level parallelism
parallel instruction execution		 0.011987
Digital signal processor for test and measurement environment · Proc. IEEE 1987

Methods — techniques the papers use, named apart from their topics

parallel architecture · 0.0instruction fetch unit · 0.0address computation unit · 0.0
YearPublicationVenuePosition
1987 Digital signal processor for test and measurement environment
abstract
This paper describes a high-performance digital signal processor, TriStar, developed for signal processing applications in test and measuring instruments. It executes typical signal processing tasks with very high throughput because of the concurrent operation of three units: instruction fetch unit, arithmetic unit, and address computation unit. A parallel architecture in addition to single-cycle operations allows the TriStar to run signal processing algorithms with an efficiency never before available to an instrument designer.
Arif Kareem, Charles L. Saxe, Eric Etheridge, David McKinney
 Proc. IEEE1
1982 Spectral window in a MEM based spectrum analyzer
abstract
In this paper one aspect of a new high resolution spectrum analyzer is discussed. The analyzer is based on computing the sampled auto-correlation function of the signal and employs the Maximum Entropy Method (MEM) of spectrum analysis. The Maximum Entropy Method as applied to this instrument has some drawbacks when analyzing sinusoidal signals. This paper identifies these problems and suggests windowing of the data as a possible solution. Three different types of spectral windows are discussed for this purpose.
Tran-Thong, Arif Kareem
 ICASSP2