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Sanjeev N. Trika

dblp:78/6526 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2008
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 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
Memory systems · 77% Storage systems · 23%
Computer graphics and multimedia
2 papers
Geometric modeling and processing · 100%
Human-computer interaction and pervasive computing
1 paper
Immersive interaction · 100%

Topics — the 5 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Storage systems
storage hierarchy
0.012008
Intel Turbo Memory: Nonvolatile disk caches in the storage hierarchy of mainstream computer systems · ACM Trans. Storage 2008
Immersive interaction › 3d user interface
virtual reality interface
0.011997
Virtual reality interfaces for feature-based computer-aided design systems · Comput. Aided Des. 1997
Geometric modeling and processing
feature recognition
0.011994
Geometric Reasoning for Extraction of Manufacturing Features in Iso-Oriented Polyhedrons · IEEE Trans. Pattern Anal. Mach. Intell. 1994
Geometric modeling and processing › feature recognition
machining feature recognition
0.011994
Geometric Reasoning for Extraction of Manufacturing Features in Iso-Oriented Polyhedrons · IEEE Trans. Pattern Anal. Mach. Intell. 1994
Geometric modeling and processing › solid modeling
constructive solid geometry
0.011994
Geometric Reasoning for Extraction of Manufacturing Features in Iso-Oriented Polyhedrons · IEEE Trans. Pattern Anal. Mach. Intell. 1994

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

graph matching · 0.0geometric reasoning · 0.0constructive solid geometry · 0.0
YearPublicationVenuePosition
2008 Intel Turbo Memory: Nonvolatile disk caches in the storage hierarchy of mainstream computer systems
abstract
Hard-disk drives are a significant bottleneck to system performance and are also responsible for a significant fraction of total system power consumption. Intel Turbo Memory addresses these problems by adding a new layer to the storage hierarchy: a platform-based and nonvolatile, disk cache. In this article, we describe the hardware and software elements of the Intel Turbo Memory architecture. We show how it supports the new ReadyBoost and ReadyDrive features in Microsoft Vista and describe its key caching algorithms. We present performance, power savings, and wear-leveling results achieved by Intel Turbo Memory.
Jeanna Matthews, Sanjeev N. Trika, Debra Hensgen, Rick Coulson, Knut Grimsrud
ACM Trans. Storage2
1997 Virtual reality interfaces for feature-based computer-aided design systems
Sanjeev N. Trika, Pat P. Banerjee, Rangasami L. Kashyap
Comput. Aided Des.1
1994 Geometric Reasoning for Extraction of Manufacturing Features in Iso-Oriented Polyhedrons
abstract
This paper investigates the extraction of machining features from boundary descriptions of iso-oriented (having no inclined faces) polyhedrons. We prove that manufacturing the features proposed by our feature extractor results exactly in the desired part-in this respect, the approach is both sound and complete. Our method uses the adjacency information between faces to derive the features. This keeps the determination of isolated features in a part straightforward. However, interaction of features creates difficulties since the adjacency information between some faces is lost. We derive this lost information by considering faces that when extended intersect other faces to form concave edges. The derived face adjacencies are termed virtual links. Augmenting the virtual links to the cavity graph of the object leads to its feature graph, and subgraph matching of primitive graphs in this graph results in feature hypotheses. A feature hypothesis is considered valid if the volume corresponding to it is not shared with the part in question; therefore, we verify the feature hypotheses by checking the regularized intersection of the feature volume and the part. Thus, feature verification employs a constructive solid geometry approach. We have implemented a prototype of the system in the Smalltalk-80 environment.>
Sanjeev N. Trika, Rangasami L. Kashyap
IEEE Trans. Pattern Anal. Mach. Intell.1