Institution
Motorola
Company•Schaumburg, 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 published on a yearly basis
Papers
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129 citations
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30 Apr 1995TL;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
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07 May 1996TL;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
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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
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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
Name | H-index | Papers | Citations |
---|---|---|---|
Georgios B. Giannakis | 137 | 1321 | 73517 |
Yonggang Huang | 136 | 797 | 69290 |
Chenming Hu | 119 | 1296 | 57264 |
Theodore S. Rappaport | 112 | 490 | 68853 |
Chang Ming Li | 97 | 896 | 42888 |
John Kim | 90 | 406 | 41986 |
James W. Hicks | 89 | 406 | 51636 |
David Blaauw | 87 | 750 | 29855 |
Mark Harman | 83 | 506 | 29118 |
Philippe Renaud | 77 | 773 | 26868 |
Aggelos K. Katsaggelos | 76 | 946 | 26196 |
Min Zhao | 71 | 547 | 24549 |
Weidong Shi | 70 | 528 | 16368 |
David Pearce | 70 | 342 | 25680 |
Douglas L. Jones | 70 | 512 | 21596 |