Results 1  10
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114
Dynamic Power Management in Wireless Sensor Networks
 IEEE Design & Test of Computers
, 2001
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Decoupling dynamical systems for pathway identification from metabolic profiles
 Bioinformatics
, 2004
"... Rationale: Modern molecular biology is generating data of unprecedented quantity and quality. Particularly exciting for biochemical pathway modeling and proteomics are comprehensive, timedense profiles of metabolites or proteins that are measurable, for instance, with mass spectrometry, nuclear mag ..."
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Cited by 30 (3 self)
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Rationale: Modern molecular biology is generating data of unprecedented quantity and quality. Particularly exciting for biochemical pathway modeling and proteomics are comprehensive, timedense profiles of metabolites or proteins that are measurable, for instance, with mass spectrometry, nuclear magnetic resonance, or protein kinase phosphorylation. These profiles contain a wealth of information about the structure and dynamics of the pathway or network from which the data were obtained. The retrieval of this information requires a combination of computational methods and mathematical models, which are typically represented as systems of ordinary differential equations. Results: We show that, for the purpose of structure identification, the substitution of differentials with estimated slopes in nonlinear network models reduces the coupled system of differential equations to several sets of decoupled algebraic equations, which can be processed efficiently in parallel or sequentially. The estimation of slopes for each time series of the metabolic or proteomic profile is accomplished with a “universal function ” that is computed directly from the data by crossvalidated training of an artificial neural network (ANN).
A Unified Approach to the SteadyState and Tracking Analyses of Adaptive Filters
 IEEE Trans. Signal Processing
, 2001
"... Most adaptive filters are inherently nonlinear and timevariant systems. The nonlinearities in the update equations tend to lead to difficulties in the study of their steadystate performance as a limiting case of their transient performance. This paper develops a unified approach to the steadystat ..."
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Cited by 29 (5 self)
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Most adaptive filters are inherently nonlinear and timevariant systems. The nonlinearities in the update equations tend to lead to difficulties in the study of their steadystate performance as a limiting case of their transient performance. This paper develops a unified approach to the steadystate and tracking analyses of adaptive algorithms that bypasses many of these difficulties. The approach is based on studying the energy flow through each iteration of an adaptive filter, and it relies on a fundamental error variance relation. Index Terms  adaptive filter, meansquare error, feedback analysis, tracking analysis, steadystate analysis, transient analysis. I.
Downlink Channel Decorrelation in CDMA Systems with Long Codes
 in Proc. IEEE Veh. Technol. Conf
, 1999
"... In this paper we develop linear detectors suitable for a Code Division Multiple Access (CDMA) mobile receiver using long codes. The special signal structure in the downlink transmission is exploited to obtain a simple detection rule. A leastsquares (LS) detector, a best linear unbiased estimator (B ..."
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In this paper we develop linear detectors suitable for a Code Division Multiple Access (CDMA) mobile receiver using long codes. The special signal structure in the downlink transmission is exploited to obtain a simple detection rule. A leastsquares (LS) detector, a best linear unbiased estimator (BLUE) detector, and a linear minimum meansquare error (LMMSE) detector are derived. For the LMMSE detector we consider an adaptive implementation. The results show that improvement can be achieved using the proposed detectors compared with that of the conventional RAKE receiver. I. INTRODUCTION In CDMA reception at the mobile end there are several special requirements. The resources for processing are severely constrained by the physical size of the receiver and the strict limitations for power consumption. Furthermore, the other users' codes are not necessarily known at the mobile receiver and the estimation of other users' channel parameters especially may involve too complex processing ...
A design methodology for highlyintegrated lowpower receivers for wireless communications
, 2001
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Characterization and Comparison of Google Cloud Load versus Grids. http: //hal.archivesouvertes.fr/hal00705858
, 2012
"... Abstract—A new era of Cloud Computing has emerged, but the characteristics of Cloud load in data centers is not perfectly clear. Yet this characterization is critical for the design of novel Cloud job and resource management systems. In this paper, we comprehensively characterize the job/task load a ..."
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Abstract—A new era of Cloud Computing has emerged, but the characteristics of Cloud load in data centers is not perfectly clear. Yet this characterization is critical for the design of novel Cloud job and resource management systems. In this paper, we comprehensively characterize the job/task load and host load in a realworld production data center at Google Inc. We use a detailed trace of over 25 million tasks across over 12,500 hosts. We study the differences between a Google data center and other Grid/HPC systems, from the perspective of both work load (w.r.t. jobs and tasks) and host load (w.r.t. machines). In particular, we study the job length, job submission frequency, and the resource utilization of jobs in the different systems, and also investigate valuable statistics of machine’s maximum load, queue state and relative usage levels, with different job priorities and resource attributes. We find that the Google data center exhibits finer resource allocation with respect to CPU and memory than that of Grid/HPC systems. Google jobs are always submitted with much higher frequency and they are much shorter than Grid jobs. As such, Google host load exhibits higher variance and noise. I.
Noncoherent MMSE multiuser receivers and their blind adaptive implementations
 IEEE Trans. Communications
, 2002
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Adaptive Multiuser Receivers for DSCDMA Using Minimum BER GradientNewton Algorithms
"... In this paper we investigate the use of adaptive minimum bit error rate (MBER) GradientNewton algorithms in the design of linear multiuser receivers (MUD) for DSCDMA systems. The proposed algorithms approximate the bit error rate (BER) from training data using linear multiuser detection structures ..."
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Cited by 4 (0 self)
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In this paper we investigate the use of adaptive minimum bit error rate (MBER) GradientNewton algorithms in the design of linear multiuser receivers (MUD) for DSCDMA systems. The proposed algorithms approximate the bit error rate (BER) from training data using linear multiuser detection structures. A comparative analysis of linear MUDs, employing minimum mean squared error (MMSE), previously reported MBER and the proposed MBER algorithms is carried out. Computer simulation experiments show that the MBER GradientNewton approaches outperform other analysed algorithms and can operate with shorter training sequences. I.
SetMembership Adaptive Algorithms based on TimeVarying Error Bounds and their Application to Interference Suppression
"... Abstract — This work presents setmembership adaptive algorithms based on timevarying error bounds. A bounding ellipsoidal adaptive constrained (BEACON) recursive leastsquares algorithm is described for parameter estimation subject to timevarying error bounds. The important issue of error bound sp ..."
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Abstract — This work presents setmembership adaptive algorithms based on timevarying error bounds. A bounding ellipsoidal adaptive constrained (BEACON) recursive leastsquares algorithm is described for parameter estimation subject to timevarying error bounds. The important issue of error bound specification is addressed in a new framework that takes into account parameter estimation dependency, multiaccess (MAI) and intersymbol interference (ISI) for DSCDMA communications. An algorithm for tracking and estimating the interference power is presented and incorporated into the timevarying error bound and the BEACON technique. Computer simulations show that the new algorithms are capable of outperforming previously reported techniques with a smaller number of parameter updates and a reduced risk of overbounding or underbounding. I.
Fault Diagnosis for a Rolling Bearing Used in a Reciprocating Machine by Adaptive Filtering Technique and Fuzzy Neural Network
"... Abstract: This paper presents a method of fault diagnosis for a rolling bearing used in a reciprocating machine by the adaptive filtering technique and a fuzzy neural network. The adaptive filtering is used for noise cancelling and feature extraction from vibration signal measured for the diagnosis ..."
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Abstract: This paper presents a method of fault diagnosis for a rolling bearing used in a reciprocating machine by the adaptive filtering technique and a fuzzy neural network. The adaptive filtering is used for noise cancelling and feature extraction from vibration signal measured for the diagnosis. A fuzzy neural network is used to automatically distinguish the fault types of a bearing by time domain features. Using the signals processed by adaptive filtering, the neural network can quickly converge when learning, and can quickly distinguish fault types when diagnosing. The spectrum analysis of an enveloped time signal is also used for the fault diagnosis. Practical examples of diagnosis for a rice husking machine are shown in order to verify the efficiency of the method. All diagnosis results of the spectrum analysis and the fuzzy neural network show that the method proposed in this paper is very effective even for cancelling highly correlated noise, and for automatically discriminating the fault types with a high accuracy. KeyWords: fault diagnosis, adaptive filtering, fuzzy neural network, rolling bearing, Reciprocating Machine,