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Institution

Mitre Corporation

CompanyBedford, Massachusetts, United States
About: Mitre Corporation is a company organization based out in Bedford, Massachusetts, United States. It is known for research contribution in the topics: Air traffic control & National Airspace System. The organization has 4884 authors who have published 6053 publications receiving 124808 citations. The organization is also known as: Mitre & MITRE.


Papers
More filters
Journal ArticleDOI
H. Ma1, M. Poole
TL;DR: This paper provides performance analyses of a broad spectrum of error-correcting codes in an antijam communication system under worst-case partial-band noise jamming conditions and demonstrates the coding advantages available for systems operating with and without frequency diversity.
Abstract: This paper provides performance analyses of a broad spectrum of error-correcting codes in an antijam communication system under worst-case partial-band noise jamming conditions. These analyses demonstrate the coding advantages available for systems operating with and without frequency diversity. Utilizing both the exact approach (where possible) and upper-bounding approaches (Chernoff and union bounds), the decoded bit error rates for typical error-correcting codes (binary and M -ary, block and convolutional) have been obtained, and these codes have been compared according to the E_{b}/N_{0} required to achieve a bit error rate of 10-5. The best performance is achieved with the use of M -ary signaling and optimum diversity with M -ary codes, such as Reed-Solomon block codes, dual- k convolutional codes, convolutional orthogonal codes, or concatenated codes.

34 citations

Patent
07 Sep 1990
TL;DR: In this article, an associative memory that finds the location of at least one string of characters in the associative RAM that matches a string of words presented sequentially as an input to the RAM is defined.
Abstract: An associative memory that finds the location of at least one string of characters in the associative memory that matches a string of characters presented sequentially as an input to the associative memory. The string of characters in the associative memory, the input string of characters, or both may include a specially marked characters, or set of characters, that acts as a "variable indicator." The specially marked character, or set of characters, will "match" a portion of the other string. A flag is set in the associative memory at either the starting locations or the ending locations of the matching strings. Flags are provided only at locations of stored matching strings of characters found within a selected addressable area or areas. Each flag can be moved from a first byte to a second byte in the associative memory that has a predetermined location relative to the first byte. A selection circuit selects one of the matching stored strings of characters by enabling a test signal which selects one of the flags to propagate through the associative memory circuit in a daisy-chain manner. The daisy-chain path is segmented in order to decrease the propagation time of the test signal. A summation circuit, useful in neural network applications, adds a number presented as at least one input byte to the associative memory to a number stored as at least one byte in the associative memory at the location of a stored string of characters that matches the input string.

34 citations

Journal ArticleDOI
07 Apr 2009
TL;DR: This work develops a framework for applying cognitive radio technology to mobile WiMax networks to increase capacity and simplify network operations and evaluates the performance of ldquocognitive channel assignmentrdquo relative to conventional dynamic channel assignment.
Abstract: Cognitive radios have the ability to sense the radio spectrum environment and to switch dynamically to available frequency ranges. Mobile WiMax is an emerging wireless networking standard that could potentially benefit from cognitive radio technology. We develop a framework for applying cognitive radio technology to mobile WiMax networks to increase capacity and simplify network operations. In the proposed cognitive WiMax architecture, base stations are equipped with sensitive detectors and assign channels to subscriber stations dynamically based on spectrum availability. Power control is employed to increase frequency reuse in conjunction with spectrum sensing. Using computer simulation, we evaluate the performance of ldquocognitive channel assignmentrdquo relative to conventional dynamic channel assignment. Our numerical results show that cognitive radios can substantially increase the capacity of emerging WiMax networks by exploiting inherent spectrum hole opportunities. The key performance parameters determining the achievable capacity of cognitive WiMax networks are the detection and interference range, which depend in turn on characteristics of the radio propagation environment.

34 citations

Proceedings ArticleDOI
06 Dec 2015
TL;DR: Questions are found to be asking ourselves frequently, and this panel paper provided a good opportunity to stimulate a discussion along these lines and to open it up to the M&S community.
Abstract: Hybrid Simulation (HS) is not new. However there is contention in academic discourse as to what qualifies as HS? Is there a distinction between multi-method, multi-paradigm and HS? How do we integrate methods from disciplines like OR and computer science that contribute to the success of a M&S study? How do we validate a hybrid model when the whole (the combined model) is greater than the sum of its parts (the individual models)? Most dynamic simulations have a notion of time, how do we realize a unified representation of simulation time across methodologies, techniques and packages, and how do we prevent causality during inter-model message exchange? These are but some of the questions which we found to be asking ourselves frequently, and this panel paper provided a good opportunity to stimulate a discussion along these lines and to open it up to the M&S community.

34 citations


Authors

Showing all 4896 results

NameH-indexPapersCitations
Sushil Jajodia10166435556
Myles R. Allen8229532668
Barbara Liskov7620425026
Alfred D. Steinberg7429520974
Peter T. Cummings6952118942
Vincent H. Crespi6328720347
Michael J. Pazzani6218328036
David Goldhaber-Gordon5819215709
Yeshaiahu Fainman5764814661
Jonathan Anderson5719510349
Limsoon Wong5536713524
Chris Clifton5416011501
Paul Ward5240812400
Richard M. Fujimoto5229013584
Bhavani Thuraisingham5256310562
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20234
202210
202195
2020139
2019145
2018132