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Indoor positioning system using a WDM device based on a-SiC: H technology

JOURNAL OF LUMINESCENCE 2017年第PartB期191卷 135-138页

作者： Vieira, M. A. Vieira, M. Louro, P. Mateus, L. Vieira, R. ISEL Elect Telecommun & Comp Dept R Conselheiro Emidio Navarro P-1949014 Lisbon Portugal CTS UNINOVA Quinta Torre P-2829516 Monte De Caparica Caparica Portugal DEE FCT UNL Quinta Torre P-2829516 Monte De Caparica Caparica Portugal

In this paper, we present an indoor positioning system were trichromatic white LEDs are used as transmitters and an optical processor based on a-SiC:H technology as mobile receiver. The optical processor is realized using a double p-i-n photodetector with two UV light biased gates. The relationship between the optical inputs (transmitted data) and the corresponding digital output levels (received data) is established and decoded. The received signal is used in coded multiplexing techniques for supporting communications and navigation concomitantly on the same channel. The position of the device is estimated using the visible multilateration method through the strength of the signal received from several non-collinear transmitters. The location and motion information is found by mapping position and estimates the location areas. Since the indoor position and transmitted data of the different LED light sources is known from building floor plans and lighting plans, the corresponding transmitted data information, indoor position and travel direction of the mobile device can be determined. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Indoor positioning system a-SiC:H technology MUX/DEMUX device Visible multilateration metodh decode algorithm

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Functional identification of complex cells from spike times and the decoding of visual stimuli

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BMC Neuroscience 2015年第1期16卷 1-2页

作者： Aurel A Lazar Nikul H Ukani Yiyin Zhou Department of Electrical Engineering Columbia University New York NY 10027 USA

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Information Systems Opportunities in Brain-Machine Interface decoders

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PROCEEDINGS OF THE IEEE 2014年第5期102卷 666-682页

作者： Kao, Jonathan C. Stavisky, Sergey D. Sussillo, David Nuyujukian, Paul Shenoy, Krishna V. Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Sch Med Neurosci Program Stanford CA 94305 USA Stanford Univ Dept Bioengn Stanford CA 94305 USA Stanford Univ Sch Med Stanford CA 94305 USA Stanford Univ Dept Neurobiol Stanford CA 94305 USA Stanford Univ Neurosci Grad Program Stanford CA 94305 USA Stanford Univ BioX Program Stanford CA 94305 USA Stanford Univ Stanford Neurosci Inst Stanford CA 94305 USA

Brain-machine interface (BMI) systems convert neural signals from motor regions of the brain into control signals to guide prosthetic devices. The ultimate goal of BMIs is to improve the quality of life for people with paralysis by providing direct neural control of prosthetic arms or computer cursors. While considerable research over the past 15 years has led to compelling BMI demonstrations, there remain several challenges to achieving clinically viable BMI systems. In this review, we focus on the challenge of increasing BMI performance and robustness. We review and highlight key aspects of intracortical BMI decoder design, which is central to the conversion of neural signals into prosthetic control signals, and discuss emerging opportunities to improve intracortical BMI decoders. This is one of the primary research opportunities where information systems engineering can directly impact the future success of BMIs.

关键词： Brain-computer interface (BCI) brain-machine interface (BMI) control algorithm decode algorithm intracortical array neural network neural prosthesis

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Syntax processing properties of generic cortical circuits

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BMC Neuroscience 2013年第1期14卷 1-2页

作者： Renato Duarte Abigail Morrison Peggy Seriès Bernstein Center Freiburg Albert-Ludwig University of Freiburg Freiburg im Breisgau 79104 Germany Institute for Adaptive and Neural Computation School of Informatics University of Edinburgh Edinburgh EH8 9AB UK Institute of Neuroscience and Medicine (INM-6) Computational and Systems Neuroscience Jülich Research Center Jülich 52425 Germany Institute of Cognitive Neuroscience Faculty of Psychology Ruhr-University Bochum Bochum 44801 Germany

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Multiclass relevance units machine: benchmark evaluation and application to small ncRNA discovery

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BMC GENOMICS 2013年第2-Sup期14卷 1-15页

作者： Menor, Mark Baek, Kyungim Poisson, Guylaine Univ Hawaii Manoa Dept Informat & Comp Sci Honolulu HI 96822 USA

Background: Classification is the problem of assigning each input object to one of a finite number of classes. This problem has been extensively studied in machine learning and statistics, and there are numerous applications to bioinformatics as well as many other fields. Building a multiclass classifier has been a challenge, where the direct approach of altering the binary classification algorithm to accommodate more than two classes can be computationally too expensive. Hence the indirect approach of using binary decomposition has been commonly used, in which retrieving the class posterior probabilities from the set of binary posterior probabilities given by the individual binary classifiers has been a major issue. Methods: In this work, we present an extension of a recently introduced probabilistic kernel-based learning algorithm called the Classification Relevance Units Machine (CRUM) to the multiclass setting to increase its applicability. The extension is achieved under the error correcting output codes framework. The probabilistic outputs of the binary CRUMs are preserved using a proposed linear-time decoding algorithm, an alternative to the generalized Bradley-Terry (GBT) algorithm whose application to large-scale prediction settings is prohibited by its computational complexity. The resulting classifier is called the Multiclass Relevance Units Machine (McRUM). Results: The evaluation of McRUM on a variety of real small-scale benchmark datasets shows that our proposed naive decoding algorithm is computationally more efficient than the GBT algorithm while maintaining a similar level of predictive accuracy. Then a set of experiments on a larger scale dataset for small ncRNA classification have been conducted with naive McRUM and compared with the Gaussian and linear SVM. Although McRUM's predictive performance is slightly lower than the Gaussian SVM, the results show that the similar level of true positive rate can be achieved by sacrificing false positive rat

关键词： Support Vector Machine Receiver Operating Characteristic Curve Binary Classifier decode algorithm Negative Class

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Complexity analysis of Reed-Solomon decoding over GF(2^m) without using syndromes

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2008年第unknown期2008卷 843634-843634页

作者： Chen, Ning Yan, Zhiyuan Lehigh Univ Dept Elect & Comp Engn Bethlehem PA 18015 USA

There has been renewed interest in decoding Reed-Solomon (RS) codes without using syndromes recently. In this paper, we investigate the complexity of syndromeless decoding, and compare it to that of syndrome-based decoding. Aiming to provide guidelines to practical applications, our complexity analysis focuses on RS codes over characteristic-2 fields, for which some multiplicative FFT techniques are not applicable. Due to moderate block lengths of RS codes in practice, our analysis is complete, without big O notation. In addition to fast implementation using additive FFT techniques, we also consider direct implementation, which is still relevant for RS codes with moderate lengths. For high-rate RS codes, when compared to syndrome-based decoding algorithms, not only syndromeless decoding algorithms require more field operations regardless of implementation, but also decoder architectures based on their direct implementations have higher hardware costs and lower throughput. We also derive tighter bounds on the complexities of fast polynomial multiplications based on Cantor's approach and the fast extended Euclidean algorithm.

关键词： Complexity Analysis Block Length decode algorithm Polynomial Multiplication Field Operation

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Efficient non-unique probes selection algorithms for DNA microarray

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BMC GENOMICS 2008年第Sup1期9卷 S22-S22页

作者： Deng, Ping Thai, My T. Ma, Qingkai Wu, Weili Univ Illinois Springfield Dept Comp Sci Springfield IL 62703 USA Univ Florida Comp & Informat Sci & Engn Dept Gainesville FL 32611 USA SUNY Coll Technol Utica Rome Dept Econ Crime & Justice Studies Utica NY 13502 USA Univ Texas Dallas Dept Comp Sci Richardson TX 75081 USA

Background: Temperature and salt concentration are very helpful experimental conditions for a probe to hybridize uniquely to its intended target. In large families of closely related target sequences, the high degree of similarity makes it impossible to find a unique probe for every target. We studied how to select a minimum set of non-unique probes to identify the presence of at most d targets in a sample where each non-unique probe can hybridize to a set of targets. Results: We proposed efficient algorithms based on Integer Linear Programming to select a minimum number of non-unique probes using d-disjunct matrices. Our non-unique probes selection can also identify up to d targets in a sample with at most k experimental errors. The decoding complexity of our algorithms is as simple as O(n). The experimental results show that the decoding time is much faster than that of the methods using d-separable matrices while running time and solution size are comparable. Conclusions: Since finding unique probes is often not easy, we make use of non-unique probes. Minimizing the number of non-unique probes will result in a smaller DNA microarry design which leads to a smaller chip and considerable reduction of cost. While minimizing the probe set, the decoding ability should not be diminished. Our non-unique probes selection algorithms can identify up to d targets with error tolerance and the decoding complexity is O(n).

关键词： Integer Linear Program decode algorithm Greedy Heuristic Unique Probe Probe Candidate

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A Retargetable Framework for Instruction-Set Architecture Simulation

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ACM Transactions on Embedded Computing Systems 2006年第2期5卷 431-452页

作者： Reshadi, Mehrdad Dutt, Nikil Mishra, Prabhat University of California Center for Embedded Computer Systems Irvine CA 92697 United States University of Florida Department Computer and Information Science and Engineering FL 32611 United States

Instruction-set architecture (ISA) simulators are an integral part of today's processor and software design process. While increasing complexity of the architectures demands high-performance simulation, the increasing variety of available architectures makes retargetability a critical feature of an instruction-set simulator. Retargetability requires generic models while high-performance demands target specific customizations. To address these contradictory requirements, we have developed a generic instruction model and a generic decode algorithm that facilitates easy and efficient retargetability of the ISA-simulator for a wide range of processor architectures, such as RISC, CISC, VLIW, and variable length instruction-set processors. The instruction model is used to generate compact and easy to debug instruction descriptions that are very similar to that of architecture manual. These descriptions are used to generate high-performance simulators. Our retargetable framework combines the flexibility of interpretive simulation with the speed of compiled simulation. The generation of the simulator is completely separate from the simulation engine. Hence, we can incorporate any fast simulation technique in our retargetable framework without introducing any performance penalty. To demonstrate this, we have incorporated fast IS-CS simulation engine in our retargetable framework which has generated 70% performance improvement over the best known simulators in this category. We illustrate the retargetability of our approach using two popular, yet different, realistic architectures: the SPARC and the ARM. © Instruction-set architecture (ISA) simulators are an integral part of today's processor and software design process. While increasing complexity of the architectures demands high-performance simulation, the increasing variety of available architectures makes retargetability a critical feature of an instruction-set simulator. Retargetability requires generic models while hig

关键词： architecture description language decode algorithm Design generic instruction model instruction binary encoding Language Performance Retargetable instruction-set simulation

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A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins

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BMC BIOINFORMATICS 2005年第4-Sup期6卷 1-7页

作者： Fariselli, P Martelli, PL Casadio, R Univ Bologna Dept Biol I-40126 Bologna Italy

Background: Structure prediction of membrane proteins is still a challenging computational problem. Hidden Markov models (HMM) have been successfully applied to the problem of predicting membrane protein topology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the labels, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the HMM grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. Results: In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm. Conclusion: We show that PV decoding performs better than other algorithms when tested on the problem of the prediction of the topology of beta-barrel membrane proteins.

关键词： Posterior Probability Hide Markov Model decode algorithm Viterbi algorithm Hide Markov Model Model

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An efficient retargetable framework for instruction-set simulation 03

An efficient retargetable framework for instruction-set simu...

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1st IEEE/ACM/IFIP International Conference on Hardward/Software Codesign and System Synthesis

作者： Reshadi, M Bansal, N Mishra, P Dutt, N Univ Calif Irvine Ctr Embedded Comp Syst Irvine CA 92717 USA

ISBN: (纸本)1581137427

关键词： retargetable instruction-set simulation generic instruction model instruction binary encoding decode algorithm architecture description language

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