scispace - formally typeset
Search or ask a question
Institution

IBM

CompanyArmonk, New York, United States
About: IBM is a company organization based out in Armonk, New York, United States. It is known for research contribution in the topics: Layer (electronics) & Signal. The organization has 134567 authors who have published 253905 publications receiving 7458795 citations. The organization is also known as: International Business Machines Corporation & Big Blue.


Papers
More filters
Journal ArticleDOI
TL;DR: The adaptive noise smoothing filter is a systematic derivation of Lee's algorithm with some extensions that allow different estimators for the local image variance and its easy extension to deal with various types of signal-dependent noise.
Abstract: In this paper, we consider the restoration of images with signal-dependent noise. The filter is noise smoothing and adapts to local changes in image statistics based on a nonstationary mean, nonstationary variance (NMNV) image model. For images degraded by a class of uncorrelated, signal-dependent noise without blur, the adaptive noise smoothing filter becomes a point processor and is similar to Lee's local statistics algorithm [16]. The filter is able to adapt itself to the nonstationary local image statistics in the presence of different types of signal-dependent noise. For multiplicative noise, the adaptive noise smoothing filter is a systematic derivation of Lee's algorithm with some extensions that allow different estimators for the local image variance. The advantage of the derivation is its easy extension to deal with various types of signal-dependent noise. Film-grain and Poisson signal-dependent restoration problems are also considered as examples. All the nonstationary image statistical parameters needed for the filter can be estimated from the noisy image and no a priori information about the original image is required.

1,475 citations

Proceedings ArticleDOI
01 Nov 1996
TL;DR: This document specifies Mobile IPv6, a protocol which allows nodes to remain reachable while moving around in the IPv6 Internet, and defines a new IPv6 protocol and a new destination option.
Abstract: This document specifies a protocol which allows nodes to remain reachable while moving around in the IPv6 Internet. Each mobile node is always identified by its home address, regardless of its current point of attachment to the Internet. While situated away from its home, a mobile node is also associated with a care-of address, which provides information about the mobile node's current location. IPv6 packets addressed to a mobile node's home address are transparently routed to its care-of address. The protocol enables IPv6 nodes to cache the binding of a mobile node's home address with its care-of address, and to then send any packets destined for the mobile node directly to it at this care-of address. To support this operation, Mobile IPv6 defines a new IPv6 protocol and a new destination option. All IPv6 nodes, whether mobile or stationary can communicate with mobile nodes.

1,470 citations

Journal ArticleDOI
TL;DR: This tutorial explores the most salient and stable specifications in each of the three major areas of the emerging Web services framework, which are the simple object access protocol, the Web Services Description Language and the Universal Description, Discovery, and Integration directory.
Abstract: This tutorial explores the most salient and stable specifications in each of the three major areas of the emerging Web services framework. They are the simple object access protocol, the Web Services Description Language and the Universal Description, Discovery, and Integration directory, which is a registry of Web services descriptions.

1,470 citations

Proceedings ArticleDOI
12 Oct 2005
TL;DR: A modern object-oriented programming language, X10, is designed for high performance, high productivity programming of NUCC systems and an overview of the X10 programming model and language, experience with the reference implementation, and results from some initial productivity comparisons between the X 10 and Java™ languages are presented.
Abstract: It is now well established that the device scaling predicted by Moore's Law is no longer a viable option for increasing the clock frequency of future uniprocessor systems at the rate that had been sustained during the last two decades. As a result, future systems are rapidly moving from uniprocessor to multiprocessor configurations, so as to use parallelism instead of frequency scaling as the foundation for increased compute capacity. The dominant emerging multiprocessor structure for the future is a Non-Uniform Cluster Computing (NUCC) system with nodes that are built out of multi-core SMP chips with non-uniform memory hierarchies, and interconnected in horizontally scalable cluster configurations such as blade servers. Unlike previous generations of hardware evolution, this shift will have a major impact on existing software. Current OO language facilities for concurrent and distributed programming are inadequate for addressing the needs of NUCC systems because they do not support the notions of non-uniform data access within a node, or of tight coupling of distributed nodes.We have designed a modern object-oriented programming language, X10, for high performance, high productivity programming of NUCC systems. A member of the partitioned global address space family of languages, X10 highlights the explicit reification of locality in the form of places}; lightweight activities embodied in async, future, foreach, and ateach constructs; a construct for termination detection (finish); the use of lock-free synchronization (atomic blocks); and the manipulation of cluster-wide global data structures. We present an overview of the X10 programming model and language, experience with our reference implementation, and results from some initial productivity comparisons between the X10 and Java™ languages.

1,469 citations

Journal ArticleDOI
Ralph Linsker1
TL;DR: It is shown that even a single developing cell of a layered network exhibits a remarkable set of optimization properties that are closely related to issues in statistics, theoretical physics, adaptive signal processing, the formation of knowledge representation in artificial intelligence, and information theory.
Abstract: The emergence of a feature-analyzing function from the development rules of simple, multilayered networks is explored. It is shown that even a single developing cell of a layered network exhibits a remarkable set of optimization properties that are closely related to issues in statistics, theoretical physics, adaptive signal processing, the formation of knowledge representation in artificial intelligence, and information theory. The network studied is based on the visual system. These results are used to infer an information-theoretic principle that can be applied to the network as a whole, rather than a single cell. The organizing principle proposed is that the network connections develop in such a way as to maximize the amount of information that is preserved when signals are transformed at each processing stage, subject to certain constraints. The operation of this principle is illustrated for some simple cases. >

1,469 citations


Authors

Showing all 134658 results

NameH-indexPapersCitations
Zhong Lin Wang2452529259003
Anil K. Jain1831016192151
Hyun-Chul Kim1764076183227
Rodney S. Ruoff164666194902
Tobin J. Marks1591621111604
Jean M. J. Fréchet15472690295
Albert-László Barabási152438200119
György Buzsáki15044696433
Stanislas Dehaene14945686539
Philip S. Yu1481914107374
James M. Tour14385991364
Thomas P. Russell141101280055
Naomi J. Halas14043582040
Steven G. Louie13777788794
Daphne Koller13536771073
Network Information
Related Institutions (5)
Carnegie Mellon University
104.3K papers, 5.9M citations

93% related

Georgia Institute of Technology
119K papers, 4.6M citations

92% related

Bell Labs
59.8K papers, 3.1M citations

90% related

Microsoft
86.9K papers, 4.1M citations

89% related

Massachusetts Institute of Technology
268K papers, 18.2M citations

88% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202330
2022137
20213,163
20206,336
20196,427
20186,278