Results 1 - 10 of 13,198

TOSSIM: Accurate and Scalable Simulation of Entire TinyOS Applications

by Philip Levis, Nelson Lee, Matt Welsh, David Culler , 2003
"... Accurate and scalable simulation has historically been a key enabling factor for systems research. We present TOSSIM, a simulator for TinyOS wireless sensor networks. By exploiting the sensor network domain and TinyOS’s design, TOSSIM can capture network behavior at a high fidelity while scaling to ..."
Abstract - Cited by 784 (19 self) - Add to MetaCart
Accurate and scalable simulation has historically been a key enabling factor for systems research. We present TOSSIM, a simulator for TinyOS wireless sensor networks. By exploiting the sensor network domain and TinyOS’s design, TOSSIM can capture network behavior at a high fidelity while scaling

The University of Florida sparse matrix collection

by Timothy A. Davis - NA DIGEST , 1997
"... The University of Florida Sparse Matrix Collection is a large, widely available, and actively growing set of sparse matrices that arise in real applications. Its matrices cover a wide spectrum of problem domains, both those arising from problems with underlying 2D or 3D geometry (structural enginee ..."
Abstract - Cited by 536 (17 self) - Add to MetaCart
engineering, computational fluid dynamics, model reduction, electromagnetics, semiconductor devices, thermodynamics, materials, acoustics, computer graphics/vision, robotics/kinematics, and other discretizations) and those that typically do not have such geometry (optimization, circuit simulation, networks

Model-Driven Data Acquisition in Sensor Networks

by Amol Deshpande , Carlos Guestrin, Samuel R. Madden, et al. - IN VLDB , 2004
"... Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that "the sensornet is a database" is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor ..."
Abstract - Cited by 449 (36 self) - Add to MetaCart
on several real-world sensor-network data sets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition

Bayesian network modeling of acoustic sensor measurements

by Chenghui Cai, Silvia Ferrari, Ming Qian - in Proc. IEEE , 2007
"... Abstract — Control and optimization of acoustic sensors can significantly impact the effectiveness of sonar deployment in variable and uncertain underwater environments. On the other hand, the design of optimal control systems requires tractable models of system dynamics, which in this case include ..."
Abstract - Cited by 8 (7 self) - Add to MetaCart
acoustic-wave propagation phenomena. High-fidelity acoustic models that capture the influence of environmental conditions on wave propagation involve partial differential equations (PDEs), and are computationally intensive. Also, by relying on the numerical solution of PDEs for given boundary and initial

Tempo and beat analysis of acoustical musical signals.

by Eric D Scheirer - Journal of the Acoustical Society of America, , 1998
"... A method is presented for using a small number of bandpass filters and banks of parallel comb filters to analyze the tempo of, and extract the beat from, musical signals of arbitrary polyphonic complexity and containing arbitrary timbres. This analysis is performed causally, and can be used predict ..."
Abstract - Cited by 386 (4 self) - Add to MetaCart
predictively to guess when beats will occur in the future. Results in a short validation experiment demonstrate that the performance of the algorithm is similar to the performance of human listeners in a variety of musical situations. Aspects of the algorithm are discussed in relation to previous high

A Post-Processing System To Yield Reduced Word Error Rates: Recognizer Output Voting Error Reduction (ROVER)

by Jonathan G. Fiscus , 1997
"... This paper describes a system developed at NIST to produce a composite Automatic Speech Recognition (ASR) system output when the outputs of multiple ASR systems are available, and for which, in many cases, the composite ASR output has lower error rate than any of the individual systems. The system i ..."
Abstract - Cited by 422 (2 self) - Add to MetaCart
implements a "voting" or rescoring process to reconcile differences in ASR system outputs. We refer to this system as the NIST Recognizer Output Voting Error Reduction (ROVER) system. As additional knowledge sources are added to an ASR system, (e.g., acoustic and language models), error rates

HIGH-FIDELITY MODELS

by M. E. Kuhl, N. M. Steiger, F. B. Armstrong, J. A. Joines, Gengxun Huang
"... Product design is a complex decision-making process requiring intense interaction between designers and the designed product. Consequently, the design process is significantly different from a pure mathematical optimization. This paper presents a decision support platform for interactive design that ..."
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that integrates mathematical optimization with human interaction based on VE-Suite. Current efforts are geared toward seamlessly linking high fidelity models, numerical optimization and human interaction to improve efficiency and quality in system performance. The designer’s interaction causes the optimization

Speech Analysis

by Tony Robinson , 1998
"... Contents 1 Introduction 4 1.1 What is Speech Analysis? . . . . . . . . . . . . . . . . . . . . 4 1.1.1 So what is an acoustic vector? . . . . . . . . . . . . . . 4 1.2 Why Speech Analysis? . . . . . . . . . . . . . . . . . . . . . . 4 1.3 The problems of speech analysis . . . . . . . . . . . . . . ..."
Abstract - Cited by 359 (0 self) - Add to MetaCart
Contents 1 Introduction 4 1.1 What is Speech Analysis? . . . . . . . . . . . . . . . . . . . . 4 1.1.1 So what is an acoustic vector? . . . . . . . . . . . . . . 4 1.2 Why Speech Analysis? . . . . . . . . . . . . . . . . . . . . . . 4 1.3 The problems of speech analysis

High-fidelity blind separation of acoustic signals using SIMO-model-based independent component analysis

by Tomoya Takatani, Tsuyoki Nishikawa, Hiroshi Saruwatari, Kiyohiro Shikano - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences , 2004
"... We propose a new Single-Input Multiple-Output (SIMO)-modelbased ICA with information-geometric learning algorithm for highfidelity blind source separation. The SIMO-ICA consists of multiple ICAs and a fidelity controller, and each ICA runs in parallel under the fidelity control of the entire separat ..."
Abstract - Cited by 13 (2 self) - Add to MetaCart
We propose a new Single-Input Multiple-Output (SIMO)-modelbased ICA with information-geometric learning algorithm for highfidelity blind source separation. The SIMO-ICA consists of multiple ICAs and a fidelity controller, and each ICA runs in parallel under the fidelity control of the entire

Kansei:A High-Fidelity

by Anish Arora, Emre Ertin, Rajiv Ramnath, William Leal
"... Hardware and software testbeds are becoming the preferred basis for experimenting with embedded wireless sensor network applications. The Kansei testbed at the Ohio State University features a heterogeneous hardware infrastructure,with dedicated node resources for local computation,storage,data retr ..."
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,storage,data retrieval,and back-channel communication.Kansei includes a time-accurate hybrid simulation engine that uses testbed hardware resources to simulate large arrays. It supports high-fidelity sensor data generation as well as real-time data and event injection. The testbed also includes software components
Results 1 - 10 of 13,198