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Indium Phosphide and Related Materials Conference

作者： J.J. Dudley D.L. Crawford J.E. Bowers P. Silvestre G.Y. Robinson Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Jet Propulsion Laboratory Pasadena CA USA Department of Electrical Engineering Center for Optoelectronic Computing Systems Fort Collins CO USA

The authors have measured and modeled the temperature characteristics and material properties of quarter wavelength mirrors made using InP and InGaAsP. The center wavelength of the mirror moves at 0.110 nm/ degrees C, in good agreement with the theory. This is an important result for the thermal design of such devices as VCSELs. This result can be used to find the shift in the resonant mode of the cavity or the change in penetration depth of the mirror with temperature. The authors have also characterized the reflectivity and bandwidth of this mirror as a function of the number of layers. Using selective wet chemical etchants, they removed one layer at a time and then measured the spectrum of the mirror. From this structural analysis, it was found that the center wavelength is much more sensitive than the bandwidth or peak reflectivity in detecting drift in layer thicknesses. This technique can be used to find the actual reflectivity for different numbers of layers without resorting to several expensive, time-consuming mirror growths. The measured reflectivity and bandwidth are in good agreement with theory.< >

关键词： Temperature dependence Material properties Indium phosphide Mirrors Reflectivity Wavelength measurement Bandwidth Vertical cavity surface emitting lasers Resonance Chemicals

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On the complexity of neural networks with sigmoidal units

On the complexity of neural networks with sigmoidal units

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IEEE Workshop on Neural Networks for Signal Processing

作者： K.-Y. Siu V. Roychowdhury T. Kailath Department of Electrical & Computer Engineering University of California Irvine CA USA School of Electrical Engineering Purdue University West Lafayette IN USA Information Systems Laboratory University of Stanford Stanford CA USA

Novel techniques based on classical tools such as rational approximation and harmonic analysis are developed to study the computational properties of neural networks. Using such techniques, one can characterize the class of function whose complexity is almost the same among various models of neural networks with feedforward structures. As a consequence of this characterization, for example, it is proved that any depth-(d+1) network of sigmoidal units computing the parity function of n inputs must have Omega (dn/sup 1/d- in /) units, for any fixed in >0. This lower bound is almost tight since one can compute the parity function with O(dn/sup 1/d/) sigmoidal units in a depth-(d+1) network. The techniques also generalize to networks whose elements can be approximated by piecewise low degree rational functions.< >

关键词： Neural networks computer networks Feedforward neural networks Multi-layer neural network Ear Harmonic analysis Information systems Laboratories Intelligent networks Boolean functions

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Optimal Non-Parametric System Identification From Arbitrary Corrupt Finite Time Series: A Control-Oriented Approach

Optimal Non-Parametric System Identification From Arbitrary ...

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American Control Conference (ACC)

作者： Jie Chen Carl N. Nett Michael K. H. Fan Schools of Aerospace and Electrical Engineering Georgia Institute of Technology Atlanta GA USA Laboratory for Identification and Control of Complex Highly Uncertain Systems (LICCHUS) School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA Computer-Aided Control Systems Engineering Laboratory (CACSEL) School of Electrical Engineering Georgia Institute of Technology Atlanta GA USA

In this paper we formulate and solve a control-oriented system identification problem for single-input, single-output, linear, shift-invariant, distributed parameter plants. In this problem the available apriori information is minimal, consisting only of worst-case/deterministic, time dependent, upper and lower bounds on the plant impulse response and the additive output noise. The available aposteriori information consists of a corrupt finite output time series obtained in response to a known, non-zero but otherwise arbitrary, applied input. A novel system identification method is presented for this problem. This method maps the available apriori and aposteriori information into an "uncertain model" of the plant. The resulting uncertain plant model is comprised of a nominal plant model, a bounded additive output noise, and a bounded additive model uncertainty. The upper bound on the model uncertainty is explicit, worst-case/deterministic in nature, and expressed in terms of both the l 1 and H ∞ system norms. Under the assumption that the available apriori information is "correct" for the underlying physical plant, the resulting uncertain plant model has the property that it not only "explains" the available aposteriori information, but will also explain all aposteniori information observed in the future. Because this property hinges on the correctness of the available apriori information, a method is also presented for developing confidence that the available apriori information is in fact correct.

关键词： System identification Optimal control Control systems Additive noise Robust control Uncertainty Noise level Aerospace engineering Upper bound Fasteners

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Rational approximation, harmonic analysis and neural networks

Rational approximation, harmonic analysis and neural network...

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International Joint Conference on Neural Networks (IJCNN)

作者： K.-Y. Siu V. Roychowdhury T. Kailath Department of Electrical & Computer Engineering University of California Irvine Irvine CA USA School of Electrical Engineering Purdue University West Lafayette IN USA Information Systems Laboratory University of Stanford Stanford CA USA

Techniques based on classical tools such as rational approximation and harmonic analysis are developed to study the computational properties of neural networks. Using such techniques, one can characterize the class of functions whose complexity is almost the same among various models of neural networks with feedforward structures. As a consequence of this characterization, for example, it is proved that any depth-(d+1) network of sigmoidal units computing the parity function of n inputs must have Omega (dn/sup 1/d- epsilon /) units, for any fixed epsilon >0. This lower bound is almost tight as one can compute the parity function with O(dn/sup 1/d/) sigmoidal units in a depth-(d+1) network. The techniques also generalize to networks whose elements can be approximated by piecewise low degree rational functions. These almost tight bounds are the first known complexity results on the size of neural networks computing Boolean functions with continuous-output elements and with depth more than two.< >

关键词： Harmonic analysis Neural networks computer networks Feedforward neural networks Multi-layer neural network Intelligent networks Information systems Laboratories Boolean functions Nonlinear control systems

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Some novel capabilities of CNN: game of life and examples of multipath algorithms

Some novel capabilities of CNN: game of life and examples of...

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： L.O. Chua T. Roska P.L. Venetianer A. Zarandy Department of Electrical Engineering and Computer Sciences and the Electronics Research Laboratory University of California Berkeley Berkeley CA USA Dual and Neural Computing Systems Laboratory Computer & Automation Institute (MTA SzTAKI) Research Division Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

The authors show that the game of life algorithm, which is equivalent to a Turing machine, can be realized by cellular neural networks (CNNs). They also present a multipath CNN algorithm that demonstrates the capabilities of analog/logic (analogic) software. Complex image processing tasks can be realized by programmable dual computing CNNs. A specific example of blob-counting in a gray-scale image is presented.< >

关键词： Cellular neural networks Turing machines Laboratories Logic Analog computers Biological system modeling Automation Games Fading Neural networks

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Competitive learning in asynchronous pulse density integrated circuits

Competitive learning in asynchronous pulse density integrate...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： D. Watola D. Gembala J. Meador Digital Systems Research Group Jet Propulsion Laboratory Pasadena CA USA School of Electrical Engineering and Computer Science Washington State University Pullman WA USA

The authors introduce a MOS circuit for the integrated implementation of pulse-coded competitive learning. They describe an autoadaptive synapse circuit for a pulse-coded competitive learning rule. The specific focus is upon an adaptive synapse cell which combines a capacitive analog storage element with subthreshold adaptation circuitry. The adaptation circuitry is designed to compensate for nonlinear device transconductance in the subthreshold operating region. The simulation results presented verify circuit operation in a 2-input-3-output competitive network. Accurate clustering of random training data was demonstrated.< >

关键词： Pulse circuits Pulse measurements Circuit simulation Training data Neurons Convergence Prototypes Lifting equipment computer science Transconductance

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Sufficient Conditions for the Robust Stability of systems with Multiaffine Parameter Dependence

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IFAC Proceedings Volumes 1992年第21期25卷 168-171页

作者： B.D.O. Anderson F.J. Kraus M. Mansour S. Dasgupta Department of Systems Engineering Research School of Physical Sciences and Engineering Australian National University GPO Box 4 Canberra ACT 2601 Australia. Automatic Control Laboratory ETH Zurich 8092 Zürich Switzerland Department of Electrical & Computer Engineering University of Iowa Iowa City lA 52242 USA.

A number of robust stability problems take the following form: A polynomial has real coefficients wvhich are multiaffine in real parameters that are confined to a box in parameter space. An efficient method is required for checking the stability of this set of polynomials. We present two sufficient conditions in this paper. They involve: checking certain properties at the corners and edges of the parameter space box.

关键词： Robust Stability Multiaffine Parameter Dependence

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A HIGHLY PARALLEL ARCHITECTURE FOR REAL-TIME COLLISION DETECTION IN FLIGHT SIMULATION

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computerS & GRAPHICS 1991年第3期15卷 355-363页

作者： BICKERSTAFF, MA HELLESTRAND, GR The VLSI and Systems Technology (VaST) Laboratory The School of Electrical Engineering and Computer Science The University of New South Wales P.O. Box 1 Kensington NSW 2033 Australia

This article describes the first implementation of a hardware architecture that solves the problem of collision detection for an arbitrarily complex collection of arbitrarily complex objects in a visual simulation system in real time. Resolving all collisions between N moving objects in real-time requires O(N2) calculations are performed every frame time. One possibility is to have one central processor performing all the calculations. This quickly reaches a performance limit. Alternatively, N processors can each do O(N) calculations, since moving objects only require calculations to be performed relative to themselves. Moreover, only active objects need to do calculations. The hardware architecture described in this article uses this calculation partitioning to solve the collision detection problem. There are a number of collision detection chips per active object, which compare that active object's polygons against all other polygons within the pilot's 360-degrees viewing range. The number of collision detection chips per active object is proportional to the polygon complexity of an object-not the number of objects in the scenario. Further, we describe the model governing the collision detection hardware design and how the hardware is to be incorporated in the visual simulation system.

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An Integrated Approach to Controls and Diagnostics Using the Four Parameter Controller

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IEEE Control systems 1991年第6期11卷 22-29页

作者： Jacobson, C.A. Nett, C.N. Department of Electrical and Computer Engineering Northeastern University Boston MA 02115 United States School of Aerospace Engineering Laboratory for Identification Control of Complex Highly Uncertain Systems (LICCHUS) Georgia Institute of Technology Atlanta GA 30332-0150 United States

This paper presents a foundation for an integrated approach to the design of controls and diagnostics in reliable control systems. In this approach the control module and diagnostic module of the control system are designed together, instead of independently, thereby accounting for the interactions which occur between these two modules in a functioning control system. This approach makes use of a generalization of the familiar two parameter controller known as the four parameter controller. This controller has two vector inputs (rather than one) and two vector outputs;correspondingly, this controller is comprised of four matrix parameters (rather than two). The additional controller output may be viewed as a diagnostic output which is monitored to detect and isolate sensor and actuator faults. The presentation develops a parameterization of all four parameter controllers, delineates the fundamental limitations and inherent tradeoffs governing the design of controls and diagnostics, and gives special attention to the issues of control/diagnostic interaction and uncertainty. Several means for effectively resolving these design tradeoffs are proposed and illustrated by way of an example. © 1991 IEEE

关键词： Control systems Fault detection Jacobian matrices Monitoring Actuators Neutron spin echo Research and development Aerospace engineering Laboratories Uncertain systems

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REFINED BOUNDS ON SIGNATURE ANALYSIS ALIASING FOR RANDOM TESTING

REFINED BOUNDS ON SIGNATURE ANALYSIS ALIASING FOR RANDOM TES...

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IEEE International Test Conference

作者： N.R. Saxena P. Franco E.J. McCluskey Center for Reliable Computing Computer Systems Laboratory Departments of Electrical Engineering and Computer Science University of Stanford Stanford CA USA

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