This letter introduces a pre-rejection technique for wireless channel distorted speech with application to automatic speech recognition (ASR). Based on analysis of distorted speech signals over a wireless communicatio...
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This letter introduces a pre-rejection technique for wireless channel distorted speech with application to automatic speech recognition (ASR). Based on analysis of distorted speech signals over a wireless communication channel, we propose a method to reject the channel distorted speech with a small computational load. From a number of simulation results, we can discover that the pre-rejection algorithm enhances the robustness of speech recognition operation.
We propose a novel orthogonal frequency division multiplexing (OFDM) transmission sch.me using variable-length groupwise (VLG) orthogonal code multiplexing (OCM). It reduces significantly the complexity for multiplexi...
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ISBN:
(纸本)0780379543
We propose a novel orthogonal frequency division multiplexing (OFDM) transmission sch.me using variable-length groupwise (VLG) orthogonal code multiplexing (OCM). It reduces significantly the complexity for multiplexing and demultiplexing with just a little sacrifice in the performance as compared with the ordinary OCM sch.me. In the proposed sch.me, all the subcarriers are divided into multiple subcarrier groups, in which each group is composed of only relatively less correlated subcarriers. The sch.me then transmits modulation symbols through two-step interleaving and groupwise OCM. As a proper set of VLG orthogonal codes, we use the set of discrete Fourier transform (DFT) codes. We consider the coded and uncoded VLG-OCM OFDM systems. Finally, we evaluate the bit error rate (BER) performance and the complexity according to the OCM code length.
Wireless Sensor Networks (WSN) are characterized by the dense deployment of sensor nodes that continuously observe physical phenomenon. Due to high density in the network topology, sensor observations are highly corre...
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This paper presents the SERF (System for elec.ronic Recommendation Filtering) which is a collaborative filtering system that recommends context-sensitive, high-quality information sources for document search. Collabor...
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Dynamic voltage restorer (DVR)s are known as effective and economic means to compensate for voltage sags and swells [1]-[11], This paper proposes a novel method to generate reference waves synchronized with the grid w...
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Dynamic voltage restorer (DVR)s are known as effective and economic means to compensate for voltage sags and swells [1]-[11], This paper proposes a novel method to generate reference waves synchronized with the grid whether or not the line voltages are distorted by a fault. The proposed reference wave generator (RWG) looks for the normal voltage waveforms in the grid from the corrupted line voltages and synthesizes the expected positive sequence reference waves for DVRs. There is no transient problem on the generated reference waves when the line voltages are distorted by faults. Experimental results verified the proposed theory very well.
The growth of use of graph-structured databases modeled on n-partite graphs has increased the ability to generate more flexible databases. However, the calculation of certain features in these databases may be highly ...
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ISBN:
(纸本)0780385152
The growth of use of graph-structured databases modeled on n-partite graphs has increased the ability to generate more flexible databases. However, the calculation of certain features in these databases may be highly resource-consuming. This paper proposes a method for approximating these features by sampling. A discussion of the difficulty of sampling in n-partite graphs is made and an evolutionary algorithm-based method is presented that uses the information from a smaller subset of the graph to infer the amount of sampling needed for the rest of the graph. Experimental results are shown on a publications database on Anthrax for finding the most important authors.
Multiple transmit and multiple receive antennas system, referred to Multiple-Input Multiple-Output (MIMO) system, is known to achieve large channel capacity without bandwidth expansion. In this paper, we propose a sim...
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Multiple transmit and multiple receive antennas system, referred to Multiple-Input Multiple-Output (MIMO) system, is known to achieve large channel capacity without bandwidth expansion. In this paper, we propose a simplified ML detection sch.me for MIMO system which has reduced computational complexity compared to Maximum Likelihood (ML) detection sch.me. According to simulation results, the bit error rate (BER) performance of the proposed sch.me shows similar with that of ML detection sch.me with significant reduction in the computational complexity.
In this work, we have developed an approach to determination of optical constants of Si nanocrystals embedded in SiO2 matrix synthesized with Si ion implantation. The approach is based on the effective medium approxim...
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In this work, we have developed an approach to determination of optical constants of Si nanocrystals embedded in SiO2 matrix synthesized with Si ion implantation. The approach is based on the effective medium approximation, and appropriate models are developed to simulate the secondary ion mass spectroscopy and spectroscopic ellipsometry measurements on the material system. The energy gap expansion of the Si nanocrystals due to the nanocrystal size effect has been obtained by modeling the optical properties with the single-oscillator model. From the energy gap expansion the nanocrystal size can be also obtained with the phenomenological model based on quantum confinement and the bond contraction model, In addition, a novel approach to quantitative determination of depth profiles of optical constants of Si nanocrystals embedded in SiO2 thin films, which is useful to the applications of light emitting and waveguiding of the nanocrystals, is also developed in this work.
Machine Learning has traditionally been a topic of research and instruction in computerscience and computerengineering programs. Yet, due to its wide applicability in a variety of fields, its research use has expand...
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Machine Learning has traditionally been a topic of research and instruction in computerscience and computerengineering programs. Yet, due to its wide applicability in a variety of fields, its research use has expanded in other disciplines, such as elec.rical engineering, industrial engineering, civil engineering, and mechanical engineering. Currently, many undergraduate and first-year graduate students in the aforementioned fields do not have exposure to recent research trends in Machine Learning. This paper reports on a project in progress, funded by the National science Foundation under the program Combined Research and Curriculum Development (CRCD), whose goal is to remedy this shortcoming. The project involves the development of a model for the integration of Machine Learning into the undergraduate curriculum of those engineering and science disciplines mentioned above. The goal is increased exposure to Machine Learning technology for a wider range of students in science and engineering than is currently available. Our approach of integrating Machine Learning research into the curriculum involves two components. The first component is the incorporation of Machine Learning modules into the first two years of the curriculum with the goal of sparking student interest in the field. The second is the development of new upper level Machine Learning courses for advanced undergraduate students. The paper will focus on the details of the integration of a machine learning module (related to neural networks) applied to a Numerical Analysis class, taught to sophomores and juniors in the engineering Departments at the University of Central Florida. Furthermore, it will report results on the assessment and evaluation of the effectiveness of the module by the students taking the class. Finally, based on the assessment results some conclusions will be drawn regarding the potential of the modules in attracting undergraduate students into research, specifically machine-learning
Tracing garbage collectors traverse references from live program variables, transitively tracing out the closure of live objects. Memory accesses incurred during tracing are essentially random: a given object may cont...
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ISBN:
(纸本)1581138040
Tracing garbage collectors traverse references from live program variables, transitively tracing out the closure of live objects. Memory accesses incurred during tracing are essentially random: a given object may contain references to any other object. Since application heaps are typically much larger than hardware caches, tracing results in many cache misses. Technology trends will make cache misses more important, so tracing is a prime target for prefetching. Simulation of Java benchmarks running with the Boehm-Demers-Weiser mark-sweep garbage collector for a projected hardware platform reveal high tracing overhead (up to 65% of elapsed time), and that cache misses are a problem. Applying Boehm's default prefetching strategy yields improvements in execution time (16% on average with incremental/generational collection for GC-intensive benchmarks), but analysis shows that his strategy suffers from significant timing problems: prefetches that occur too early or too late relative to their matching loads. This analysis drives development of a new prefetching strategy that yields up to three times the performance improvement of Boehm's strategy for GC-intensive benchmarks (27% average speedup), and achieves performance close to that of perfect timing (ie, few misses for tracing accesses) on some benchmarks. Validating these simulation results with live runs on current hardware produces average speedup of 6% for the new strategy on GC-intensive benchmarks with a GC configuration that tightly controls heap growth. In contrast, Boehm's default prefetching strategy is ineffective on this platform.
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