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Institution

Motorola

CompanySchaumburg, Illinois, United States
About: Motorola is a company organization based out in Schaumburg, Illinois, United States. It is known for research contribution in the topics: Signal & Communications system. The organization has 27298 authors who have published 38274 publications receiving 968710 citations. The organization is also known as: Motorola, Inc. & Galvin Manufacturing Corporation.


Papers
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Proceedings ArticleDOI
30 Apr 1995
TL;DR: This work proposes a simple extension to the scan chain to diagnose faults in scan chains, which is likely to occupy nearly 30% of a chip area and hence warrants consideration during fault diagnosis.
Abstract: Scan based diagnostic schemes are used to diagnose faults in faulty circuits. Such techniques assume that the scan path itself is fault-free. However, the logic circuitry associated with the scan chain may occupy nearly 30% of a chip area and hence warrants consideration during fault diagnosis. In this work we propose a simple extension to the scan chain to diagnose faults in scan chains.

129 citations

Proceedings ArticleDOI
Kari Torkkola1
07 May 1996
TL;DR: A network architecture capable of coping with blind source separation where the sources may have been delayed with respect to each other is presented, and the adaptation equations for the delays and the weights in the network are derived by maximizing the information transferred through the network.
Abstract: Bell and Sejnowski (see Neural Computation, vol.7, no.6, p.1004-34, 1995) have presented an approach to blind source separation based on the information maximization principle. We extend this approach into more general cases where the sources may have been delayed with respect to each other. We present a network architecture capable of coping with such sources, and we derive the adaptation equations for the delays and the weights in the network by maximizing the information transferred through the network. Examples using wideband sources such as speech are presented to illustrate the algorithm.

129 citations

Journal ArticleDOI
TL;DR: In this paper, a circuit-level model that accounts for the thermal and spatial dependence of a VCSEL's behavior is presented, based on multimode rate equations and empirical expressions for thermal dependence of the active-layer gain and carrier leakage.
Abstract: The increasing interest in vertical-cavity surface-emitting lasers (VCSEL's) requires the corresponding development of circuit-level VCSEL models for use in the design and simulation of optoelectronic applications. Unfortunately, existing models lack either the computational efficiency or the comprehensiveness warranted by circuit-level simulation. Thus, in this paper we present a comprehensive circuit-level model that accounts for the thermal and spatial dependence of a VCSEL's behavior. The model is based on multimode rate equations and empirical expressions for the thermal dependence of the active-layer gain and carrier leakage, thereby facilitating the simulation of VCSEL's in the context of an optoelectronic system. To confirm that our model is valid, we present sample simulations that demonstrate its ability to replicate typical dc, small-signal, and transient operation, including temperature-dependent light-current (LI) curves and modulation responses, multimode behavior, and diffusive turn-off transients. Furthermore, we verify our model against experimental data from four devices reported in the literature. As the results will show, we obtained excellent agreement between simulation and experiment.

129 citations

Journal ArticleDOI
TL;DR: A systematic investigation of several linear (Wiener filter, LMS adaptive filters, gamma filter, subspace Wiener filters) and nonlinear models applied to datasets from two experiments in monkeys performing motor tasks (reaching for food and target hitting).
Abstract: The field of brain–machine interfaces requires the estimation of a mapping from spike trains collected in motor cortex areas to the hand kinematics of the behaving animal. This paper presents a systematic investigation of several linear (Wiener filter, LMS adaptive filters, gamma filter, subspace Wiener filters) and nonlinear models (time-delay neural network and local linear switching models) applied to datasets from two experiments in monkeys performing motor tasks (reaching for food and target hitting). Ensembles of 100–200 cortical neurons were simultaneously recorded in these experiments, and even larger neuronal samples are anticipated in the future. Due to the large size of the models (thousands of parameters), the major issue studied was the generalization performance. Every parameter of the models (not only the weights) was selected optimally using signal processing and machine learning techniques. The models were also compared statistically with respect to the Wiener filter as the baseline. Each of the optimization procedures produced improvements over that baseline for either one of the two datasets or both.

129 citations


Authors

Showing all 27298 results

NameH-indexPapersCitations
Georgios B. Giannakis137132173517
Yonggang Huang13679769290
Chenming Hu119129657264
Theodore S. Rappaport11249068853
Chang Ming Li9789642888
John Kim9040641986
James W. Hicks8940651636
David Blaauw8775029855
Mark Harman8350629118
Philippe Renaud7777326868
Aggelos K. Katsaggelos7694626196
Min Zhao7154724549
Weidong Shi7052816368
David Pearce7034225680
Douglas L. Jones7051221596
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20232
20229
202129
2020131
2019134
2018144