Abstract In this paper we consider the problem of noncausal identification of nonstationary, linear stochastic systems, i.e., identification based on prerecorded input/output data. We show how several competing weight...
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Abstract In this paper we consider the problem of noncausal identification of nonstationary, linear stochastic systems, i.e., identification based on prerecorded input/output data. We show how several competing weighted least squares parameter smoothers, differing in memory settings, can be combined together to yield a better and more reliable smoothing algorithm. The resulting parallel estimation scheme automatically adjusts its smoothing bandwidth to the unknown, and possibly time-varying, rate of nonstationarity of the identified system. It also allows one to account for the distribution of measurement noise, and in particular – to cope with heavy-tailed disturbances, such as Laplacian noise, or light-tailed disturbances, such as uniform noise.
Abstract The problem of identification of linear quasi-periodically varying systems is considered. This problem can be solved using generalized adaptive notch filtering (GANF) algorithms. It is shown that accuracy of ...
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Abstract The problem of identification of linear quasi-periodically varying systems is considered. This problem can be solved using generalized adaptive notch filtering (GANF) algorithms. It is shown that accuracy of system parameter estimation can be increased if the results obtained from GANF are further processed using a cascade of appropriately designed filters. The resulting generalized adaptive notch smoothing (GANS) algorithms can be employed in offline applications where causality constraints do not apply. When the instantaneous frequency of parameter changes varies in a sufficiently smooth manner, the proposed GANS algorithm, based on a new, quasi-linear model of frequency drift, outperforms the existing solutions.
In this paper we suggest how several competing image denoising algorithms, differing in design parameters, or even in design principles, can be combined together to yield a better and more reliable denoising algorithm...
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In this paper we suggest how several competing image denoising algorithms, differing in design parameters, or even in design principles, can be combined together to yield a better and more reliable denoising algorithm. The proposed fusion mechanism allows one to combine practically all kinds of noise reduction tools. It also allows one to account for the distribution of measurement noise, and in particular - to cope with heavy-tailed disturbances, such as Laplacian noise, or light-tailed disturbances, such as uniform noise.
Modern detectors used in high energy physics experiments are complex instruments designed to register collisions of particles at a rate in the MHz range. Data that correspond to a single collision of particles, referr...
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Modern detectors used in high energy physics experiments are complex instruments designed to register collisions of particles at a rate in the MHz range. Data that correspond to a single collision of particles, referred to as an event, are acquired from millions of readout channels, and filtered, first by dedicated hardware, and then by computing farms running sophisticated filtering algorithms. In case of data acquisition systems with single-stage software filtration, due to the high input rate (the order of 100 kHz), the data are usually distributed in a static way between filtering nodes. However, the static distribution determines strongly the system, and results in decreased fault tolerance. The main objective of the presented studies is to increase the system's overall fault tolerance through dynamic load balancing. The proposed method aims to balance the workload inside heterogeneous systems, as well as, homogeneous systems, where the imbalance could be caused by faults. Moreover, our research includes developing a scalable load balancing protocol along with a distributed asynchronous load assignment policy. As a case study we consider the Data Acquisition system of the Compact Muon Solenoid experiment at CERN's new Large Hadron Collider.
The problem of estimation of the slowly-varying instantaneous frequency of a nonstationary complex sinusoidal signal buried in noise is considered. This problem is usually solved using frequency tracking algorithms. I...
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The problem of estimation of the slowly-varying instantaneous frequency of a nonstationary complex sinusoidal signal buried in noise is considered. This problem is usually solved using frequency tracking algorithms. It is shown that the accuracy of frequency estimates can be considerably increased if the results yielded by the frequency tracker are further processed using the appropriately designed filters. The resulting frequency smoother can be employed in many off-line applications. Whenever signal frequency varies in a sufficiently smooth manner, the proposed algorithm, based on a new, quasi-linear model of frequency changes, outperforms the existing solutions.
The paper deals with the LQG controller design optimizing the amount of power produced by two bladed horizontal variable speed wind turbines. The proposed controller ensures not only an optimal operation of turbines b...
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This study is an attempt to take advantage of a cerebellar model to control a biomimetic arm. The cerebellar controller is a modified MOSAIC model which adaptively controls the arm. We call this model ORF-MOSAIC (Orga...
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Parkinson's disease is a neurodegenerative disorder and is associated with motor symptoms, including tremor. The DBS (Deep Brain Stimulation) involves electrode implantation into subcortical structures for long-te...
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Parkinson's disease is a neurodegenerative disorder and is associated with motor symptoms, including tremor. The DBS (Deep Brain Stimulation) involves electrode implantation into subcortical structures for long-term stimulation at frequencies greater than 100Hz. The mechanism by which chronic, electrical Deep Brain Stimulation with high frequency, suppresses tremor in Parkinson's disease is unknown, but might involve a gradual change in network properties controlling the generation of tremor. First, we performed linear and nonlinear analysis of the tremor signals to determine a set of parameters and rules for recognizing the behavior of the investigated patient and to characterize the typical responses for several forms of DBS. Second, we found patterns for homogeneous group for data reduction. We used Data Mining and Knowledge discovery techniques to reduce the number of data. Then, we found "clusters" the most well-known used and commonly partitioning methods used: K-means and K-medoids. To support such predictions, we develop a model of the tremor, to perform tests determining the DBS reducing the tremor or inducing tolerance and lesion if the stimulation is chronic.
This study is an attempt to take advantage of a cerebellar model to control a biomimetic arm. The cerebellar controller is a modified MOSAIC model which adaptively controls the arm. We call this model ORF-MOSAIC (Orga...
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ISBN:
(纸本)9781457721366
This study is an attempt to take advantage of a cerebellar model to control a biomimetic arm. The cerebellar controller is a modified MOSAIC model which adaptively controls the arm. We call this model ORF-MOSAIC (Organized by Receptive Fields MOdular Selection And Identification for control). The arm features a musculoskeletal model which is controlled through muscle activations by means of optimization techniques. With as few as 16 modules, we were able to control the arm in a workspace of 30×30 cm. The system was able to adapt to an external field as well as handling new objects despite delays. The discussion section suggests that there are similarities between the microzones in the cerebellum and the modules of this new model.
The development of electronic technology today has allowed the implementation of complex architectures, which led to the emergence of multicore processors technology. Multicore architectures are built from superscalar...
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The development of electronic technology today has allowed the implementation of complex architectures, which led to the emergence of multicore processors technology. Multicore architectures are built from superscalar and multithreaded processors. Integrating new technologies in embedded applications requires the development of multicore processors that can be integrated into a smaller area like a classic microcontroller. These processors must manage fewer resources and be able to manage multiple tasks simultaneously. In this paper we present a method of modeling, simulation and evaluation of two multithreaded architectures with limited resources, which could be integrated into embedded systems: Interleaved multithreading (IMT) and Blocked multithreading (BMT). Both techniques permit the processing of multiple independent threads, concurrently. In this paper we propose a SimpleScalar Interleaved Multithreading architecture (SS-IMT) and a SimpleScalar Blocked Multithreading architecture (SS-BMT) that are derived from SimpleScalar simulator. We will evaluate the performances of these architectures compared to the performance of standard SimpleScalar architecture.
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