prof. dr hab. inż. Maciej Niedźwiecki
Employment
- Professor at Department of Marine Electronic Systems
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total: 110
Catalog Publications
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Interfejs urządzenia wykrywającego i odczytującego napisy dla osoby niewidomej
PublicationZadaniem projektowanego urządzenia wykrywającego i odczytującego napisy jest umożliwienie niewidomemu samodzielnego rozpoznawania treści napisów i w konsekwencji wyboru właściwego tramwaju, sklepu, ulicy czy pokoju w urzędzie. Urządzenia takiego nie można sobie oczywiście wyobrazić bez zastosowania nowoczesnych metod przetwarzania i rozpoznawania obrazów. Najlepsze jednak metody nie dadzą oczekiwanych rezultatów, o ile urządzenie...
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Zastosowanie filtracji cząsteczkowej w systemie nawigacji dla niewidomych
PublicationW pracy opisano system nawigacji dla niewidomych wyposażony w odbiornik GPS, mapę cyfrową i czujniki nawigacji zliczeniowej. Problem estymacji położenia pieszego w oparciu o informacje z różnych źródeł rozwiązano przy użyciu podejścia zwanego filtracją cząsteczkową. Zastosowano techniki grupowania cząsteczek i odwzorowania w obszar wypukły, aby zagwarantować, że oszacowania położenia w każdej chwili spełniają ograniczenia nakładane...
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New Approach to Noncasual Identification of Nonstationary Stochastic FIR Systems Subject to Both Smooth and Abrupt Parameter Changes
PublicationIn this technical note, we consider the problem of finite-interval parameter smoothing for a class of nonstationary linear stochastic systems subject to both smooth and abrupt parameter changes. The proposed parallel estimation scheme combines the estimates yielded by several exponentially weighted basis function algorithms. The resulting smoother automatically adjusts its smoothing bandwidth to the type and rate of nonstationarity...
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Gradient based basis function algorithms for identification of quasi periodically varying processes.
PublicationW pracy przedstawiono problem identyfikacji systemów, których parametry zmieniają się w sposób pseudookresowy. Pokazano sposób, w jaki można modelować takie systemy przy zastosowaniu metody harmonicznych funkcji bazowych.Przedstawiono dwa sposoby dekompozycji (struktura szeregowa i równoległa) takich układów na elementy związane z poszczególnymi funkcjami bazowymi. Zaprezentowany został sposób śledzenia częstotliwości funkcji...
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Fast algorithms for identyfication of periodiccaly varying systems.
PublicationPraca dotyczy identyfikacji obiektów o parametrach zmieniających się w sposób okresowy. Zaproponowane algorytmy śledzenia parametrów cechują się niską złożonością obliczeniową, typową dla podejścia gradientowego a zarazem wysoką jakością śledzenia typową dla złożonych algorytmów opartych na metodzie funkcji bazowych.
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New approach to localization of clicks in archive speech signals.
PublicationPrzedstawiono problem lokalizacji zniekształceń impulsowych w archiwalnych sygnałach mowy. Pokazano, że detekcja oparta na dwuzakresowym modelu autoregresyjnym i przetwarzanie dwukierunkowe pozwala uzyskać znaczącą poprawę działania w stosunku do istniejących metod lokalizacji zniekształceń.
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New approach to noncausal identification of nonstationary stochastic systems subject to both smooth and abrupt parameter changes
PublicationIn this paper we consider the problem of finiteintervalparameter smoothing for a class of nonstationary linearstochastic systems subject to both smooth and abrupt parameterchanges. The proposed parallel estimation scheme combines theestimates yielded by several exponentially weighted basis functionalgorithms. The resulting smoother automatically adjustsits smoothing bandwidth to the type and rate of nonstationarityof the identified...
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Multichannel self-optimizing active noise control scheme
PublicationThe problem of cancellation of a nonstationary sinusoidal interference, acting at the output of an unknown multivariable linear stable plant, is considered. The proposed cancellation scheme is a nontrivial extension of the SONIC (self-optimizing narrowband interference canceller) algorithm, developed earlier for single-input, single-output plants. In the important benchmark case - for disturbances with randomwalk-type amplitude...
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RENOVATION OF ARCHIVE AUDIO RECORDINGS USING SPARSE AUTOREGRESSIVE MODELING AND BIDIRECTIONAL PROCESSING
PublicationThe paper presents a new approach to elimination of broadband noise and impulsive disturbances from archive audio recordings. The proposed adaptive Kalman-like algorithm, based on a sparse autoregressive model of the audio signal, simultaneously detects noise pulses, interpolates the irrevocably distorted samples and performs signal smoothing. It is shown that bidirectional (forward-backward) processing of the archive signal improves...
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Localization of impulsive disturbances in archive audio signals using predictive matched filtering
PublicationThe problem of elimination of impulsive disturbances from archive audio signals is considered and its new solution, called predictive matched filtering, is proposed. The new approach is based on the observation that a large percentage of noise pulses corrupting archive audio recordings have highly repetitive shapes that match several typical “patterns”, called click templates. To localize noise pulses, click templates can be correlated...
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New results on estimation bandwidth adaptation
PublicationThe problem of identification of a nonstationary autoregressive signal using non-causal estimation schemes is considered. Noncausal estimators can be used in applications that are not time-critical, i.e., do not require real-time processing. A new adaptive estimation bandwidth selection rule based on evaluation of pseudoprediction errors is proposed, allowing one to adjust tracking characteristics of noncausal estimators to unknown...
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From the multiple frequency tracker to the multiple frequency smoother
PublicationThe problem of extraction/elimination of nonstationary sinusoidalsignals from noisy measurements is considered. This problem is usually solved using adaptive notch filtering (ANF)algorithms. It is shown that the accuracy of frequency estimates can be significantly increased if the results obtained from ANF are backward-time filtered by an appropriately designed lowpass filter. The resulting adaptive notch smoothing (ANS)algorithm...
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Active Suppression of Nonstationary Narrowband Acoustic Disturbances
PublicationIn this chapter, a new approach to active narrowband noise control is presented. Narrowband acoustic noise may be generated, among others, by rotating parts of electro-mechanical devices, such as motors, turbines, compressors, or fans. Active noise control involves the generation of “antinoise”, i.e., the generation of a sound that has the same amplitude, but the opposite phase, as the unwanted noise, which causes them to interfere...
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Identification of Fast Time-varying Communication Channels Using the Preestimation Technique
PublicationAccurate identification of stochastic systems with fast-varying parameters is a challenging task which cannot be accomplished using model-free estimation methods, such as weighted least squares, which assume only that system coefficients can be regarded as locally constant. The current state-of-the-art solutions are based on the assumption that system parameters can be locally approximated by a linear combination of appropriately...
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Active feedback noise control in the presence of impulsive disturbances
PublicationThe problem of active feedback control of a narrowband acoustic noise in the presence of impulsive disturbances is considered. It is shown that, when integrated with appropriately designed outlier detector, the proposed earlier feedback control algorithm called SONIC is capable of isolating and rejecting noise pulses. According to our tests this guarantees stable and reliable operation of the closed-loop noise cancelling...
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Adaptive Identification of Underwater Acoustic Channel with a Mix of Static and Time-Varying Parameters
PublicationWe consider the problem of identification of communication channels with a mix of static and time-varying parameters. Such scenarios are typical, among others, in underwater acoustics. In this paper, we further develop adaptive algorithms built on the local basis function (LBF) principle resulting in excellent performance when identifying time-varying systems. The main drawback of an LBF algorithm is its high complexity. The subsequently...
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Multiple-channel frequency-adaptive active vibration control using SONIC
PublicationSONIC (self-optimizing narrowband interference canceller) is an acronym of a new approach to rejection of sinusoidal disturbances acting at the output of a discretetime stable linear plant with unknown and possibly timevarying dynamics. The paper presents two frequency-adaptive extensions of the multivariate SONIC algorithm. The efficacy of the proposed solutions is tested using our laboratory-scale active vibration control plant.
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Fully Adaptive Savitzky-Golay Type Smoothers
PublicationThe problem of adaptive signal smoothing is consid-ered and solved using the weighted basis function approach. Inthe special case of polynomial basis and uniform weighting theproposed method reduces down to the celebrated Savitzky-Golaysmoother. Data adaptiveness is achieved via parallel estimation.It is shown that for the polynomial and harmonic bases andcosinusoidal weighting sequences, the competing signal estimatescan be computed...
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Finite-window RLS algorithms
PublicationTwo recursive least-squares (RLS) adaptive filtering algorithms are most often used in practice, the exponential and sliding (rectangular) window RLS algorithms. This popularity is mainly due to existence of low-complexity versions of these algorithms. However, these two windows are not always the best choice for identification of fast time-varying systems, when the identification performance is most important. In this paper, we...
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Lattice filter based autoregressive spectrum estimation with joint model order and estimation bandwidth adaptation
PublicationThe problem of parametric, autoregressive model based estimation of a time-varying spectral density function of a nonstationary process is considered. It is shown that estimation results can be considerably improved if identification of the autoregressive model is carried out using the two-sided doubly exponentially weighted lattice algorithm which combines results yielded by two one-sided lattice algorithms running forward in...
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Decoupled Kalman filter based identification of time-varying FIR systems
PublicationWhen system parameters vary at a fast rate, identification schemes based on model-free local estimation approaches do not yield satisfactory results. In cases like this, more sophisticated parameter tracking procedures must be used, based on explicit models of parameter variation (often referred to as hypermodels), either deterministic or stochastic. Kalman filter trackers, which belong to the second category, are seldom used in...
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Robust algorithm for active feedback control of narrowband noise
PublicationThe problem of active control of narrowband acoustic noise is considered. It is shown that the proposed earlier feedback control algorithm called SONIC (self-optimizing narrowband interference canceller), based on minimization of the L2-norm performance measure, can be re-derived using the L1 approach. The resulting robust SONIC algorithm is more robust to heavy-tailed measurement noise, such as the αlpha-stable noise, than the...
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Sparse vector autoregressive modeling of audio signals and its application to the elimination of impulsive disturbances
PublicationArchive audio files are often corrupted by impulsive disturbances, such as clicks, pops and record scratches. This paper presents a new method for elimination of impulsive disturbances from stereo audio signals. The proposed approach is based on a sparse vector autoregressive signal model, made up of two components: one taking care of short-term signal correlations, and the other one taking care of long-term correlations. The method...
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Generalized adaptive notch filters with frequency debiasing for tracking of polynomial phase systems
PublicationGeneralized adaptive notch filters are used for identification/tracking of quasi-periodically varying dynamic systems and can be considered an extension, to the system case, of classical adaptive notch filters. For general patterns of frequency variation the generalized adaptive notch filtering algorithms yield biased frequency estimates. We show that when system frequencies change slowly in a smooth way, the estimation bias can...
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New semi-causal and noncausal techniques for detection of impulsive disturbances in multivariate signals with audio applications
PublicationThis paper deals with the problem of localization of impulsive disturbances in nonstationary multivariate signals. Both unidirectional and bidirectional (noncausal) detection schemes are proposed. It is shown that the strengthened pulse detection rule, which combines analysis of one-step-ahead signal prediction errors with critical evaluation of leave-one-out signal interpolation errors, allows one to noticeably improve detection results...
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Regularized Local Basis Function Approach to Identification of Nonstationary Processes
PublicationThe problem of identification of nonstationary stochastic processes (systems or signals) is considered and a new class of identification algorithms, combining the basis functions approach with local estimation technique, is described. Unlike the classical basis function estimation schemes, the proposed regularized local basis function estimators are not used to obtain interval approximations of the parameter trajectory, but provide...
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Parallel frequency tracking with built-in performance evaluation
PublicationThe problem of estimation of instantaneous frequency of a nonstationary complex sinusoid (cisoid) buried in wideband noise is considered. The proposed approach employs a bank of adaptive notch filters, extended with a nontrivial performance assessment mechanism which automatically chooses the best performing filter in the bank. Additionally, a computationally attractive method of implementing the bank is proposed. The new structure...
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Multichannel self-optimizing narrowband interference canceller
PublicationThe problem of cancellation of a nonstationary sinusoidal interference, acting at the output of an unknown multivariable linear stable plant, is considered. No reference signal is assumed to be available. The proposed feedback controller is a nontrivial extension of the SONIC (self-optimizing narrowband interference canceller) algorithm, developed earlier for single-input, single-output plants. The algorithm consists of two loops:...
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Adaptive filtering approach to dynamic weighing: a checkweigher case study
PublicationDynamic weighing, i.e., weighing of objects in motion, with out stopping them on the weighing platform, allows one to increase the rate of operation of automatic weighing systems used in industrial production processes without compromising their accuracy. The paper extends and compares two approaches to dynamic weighing, based on system identification and variable-bandwidth filtering, respectively. Experiments, carried on a conveyor...
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Localization of impulsive disturbances in audio signals using template matching
PublicationIn this paper, a new solution to the problem of elimination of impulsive disturbances from audio signals, based on the matched filtering technique, is proposed. The new approach stems from the observation that a large proportion of noise pulses corrupting audio recordings have highly repetitive shapes that match several typical “patterns”. In many cases a representative set of exemplary pulse waveforms can be extracted from the...
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On tracking properties of real-valued generalized adaptive notch filters
PublicationGeneralized adaptive notch filters (GANFs) are used for identification/tracking of quasi-periodically varying dynamic systems and can be considered an extension, to the system case, of classical adaptive notch filters. The paper presents results of local performance analysis of a real-valued GANF algorithm, i.e., algorithm designed to track parameters of a real-valued system. This is an extension of the previous work which focused...
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Adaptive identification of sparse underwater acoustic channels with a mix of static and time-varying parameters
PublicationWe consider identification of sparse linear systems with a mix of static and time-varying parameters. Such systems are typical in underwater acoustics (UWA), for instance, in applications requiring identi- fication of the acoustic channel, such as UWA communications, navigation and continuous-wave sonar. The recently proposed fast local basis function (fLBF) algorithm provides high performance when identi- fying time-varying systems....
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ESTIMATION OF NONSTATIONARY HARMONIC SIGNALS AND ITS APPLICATION TO ACTIVE CONTROL OF MRI NOISE
PublicationA new adaptive comb filtering algorithm, capable of tracking the fundamental frequency and amplitudes of different frequency components of a nonstationary harmonic signal embedded in white measurement noise, is proposed. Frequency tracking characteristics of the new scheme are studied analytically, proving (under Gaussian assumptions and optimal tuning) its statistical efficiency for quasi-linear frequency changes. Laboratory tests...
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Lattice filter based multivariate autoregressive spectral estimation with joint model order and estimation bandwidth adaptation
PublicationThe problem of parametric, autoregressive model based estimation of a time-varying spectral density function of a multivariate nonstationary process is considered. It is shown that estimation results can be considerably improved if identification of the autoregressive model is carried out using the two-sided doubly exponentially weighted lattice algorithm which combines results yielded by two one-sided lattice algorithms running...
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Generalized adaptive comb filters/smoothers and their application to the identification of quasi-periodically varying systems and signals
PublicationThe problem of both causal and noncausal identification of linear stochastic systems with quasiharmonically varying parameters is considered. The quasi-harmonic description allows one to model nonsinusoidal quasi-periodic parameter changes. The proposed identification algorithms are called generalized adaptive comb filters/smoothers because in the special signal case they reduce down to adaptive comb algorithms used to enhance...
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Detection of impulsive disturbances in archive audio signals
PublicationIn this paper the problem of detection of impulsive disturbances in archive audio signals is considered. It is shown that semi-causal/noncausal solutions based on joint evaluation of signal prediction errors and leave-one-out signal interpolation errors, allow one to noticeably improve detection results compared to the prediction-only based solutions. The proposed approaches are evaluated on a set of clean audio signals contaminated...
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Statistically efficient smoothing algorithm for time-varying frequency estimation
PublicationThe problem of extraction/elimination of a nonstationary sinusoidal signal from noisy measurements is considered. This problem is usually solved using adaptive notch filtering (ANF) algorithms. It is shown that the accuracy of frequency estimates can be significantly increased if the results obtained from ANF are backward-time filtered by an appropriately designed lowpass filter. The resulting adaptive notch smoothing (ANS) algorithm...
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Generalized adaptive notch smoothers for real-valued signals and systems
PublicationSystems with quasi-periodically varying coefficients can be tracked using the algorithms known as generalized adaptive notch filters (GANFs). GANF algorithms can be considered an extension, to the system case, of classical adaptive notch filters (ANFs). We show that estimation accuracy of the existing algorithms, as well as their robustness to the choice of design parameters, can be considerably improved by means of compensating...
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On the lower smoothing bound in identification of time-varying systems
PublicationIn certain applications of nonstationary system identification the model-based decisions can be postponed, i.e. executed with a delay. This allows one to incorporate in the identification process not only the currently available information, but also a number of ''future'' data points. The resulting estimation schemes, which involve smoothing, are not causal. Assuming that the infinite observation history is available, the paper...
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Identification of nonstationary multivariate autoregressive processes– Comparison of competitive and collaborative strategies for joint selection of estimation bandwidth and model order
PublicationThe problem of identification of multivariate autoregressive processes (systems or signals) with unknown and possibly time-varying model order and time-varying rate of parameter variation is considered and solved using parallel estimation approach. Under this approach, several local estimation algorithms, with different order and bandwidth settings, are run simultaneously and compared based on their predictive performance. First,...
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On noncausal weighted least squares identification of nonstationary stochastic systems
PublicationIn 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 (windowed) 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...
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Easy recipes for cooperative smoothing
PublicationIn this paper we suggest how several competing signal smoothers, differing in design parameters, or even in design principles, can be combined together to yield a better and more reliable smoothing algorithm. The proposed heuristic, but statistically well motivated, fusion mechanism allows one to combine practically all kinds of smoothers, from simple local averaging or order statistic filters, to parametric smoothers designed...
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Self-Optimizing Adaptive Vibration Controller
PublicationThis paper presents a new approach to rejection of sinusoidal disturbances acting at the output of a discrete-time linear stable plant with unknown dynamics. It is assumed that the frequency of the sinusoidal disturbance is known, and that the output signal is contaminated with wideband measurement noise. The proposed controller, called SONIC (self-optimizing narrowband interference canceller), combines the coefficient fixing technique,...
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Elimination of Impulsive Disturbances From Stereo Audio Recordings Using Vector Autoregressive Modeling and Variable-order Kalman Filtering
PublicationThis paper presents a new approach to elimination of impulsive disturbances from stereo audio recordings. The proposed solution is based on vector autoregressive modeling of audio signals. Online tracking of signal model parameters is performed using the exponential ly weighted least squares algo- rithm. Detection of noise pulses an d model-based interpolation of the irrevocably distorted sampl es is realized using an adaptive, variable-order...
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Akaike's final prediction error criterion revisited
PublicationWhen local identification of a nonstationary ARX system is carried out, two important decisions must be taken. First, one should decide upon the number of estimated parameters, i.e., on the model order. Second, one should choose the appropriate estimation bandwidth, related to the (effective) number of input-output data samples that will be used for identification/ tracking purposes. Failure to make the right decisions results...
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Generalized adaptive notch smoothing revisited
PublicationThe problem of identification of quasi-periodically varying dynamic systems is considered. This problem can be solved using generalized adaptive notch filtering (GANF) algorithms. It is shown that the accuracy of parameter estimates can be significantly increased if the results obtained from GANF are further processed using a cascade of appropriately designed filters. The resulting generalized adaptive notch smoothing (GANS) algorithm...
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Systemidentificationbasedapproachtodynamicweighing revisited
PublicationDynamicweighing,i.e.,weighingofobjectsinmotion,withoutstoppingthemonthe weighing platform,allowsonetoincreasetherateofoperationofautomaticweighing systems, usedinindustrialproductionprocesses,withoutcompromisingtheiraccuracy. Sincetheclassicalidentification-basedapproachtodynamicweighing,basedonthe second-ordermass–spring–dampermodeloftheweighingsystem,doesnotyieldsa- tisfactoryresultswhenappliedtoconveyorbelttypecheckweighers,severalextensionsof thistechniqueareexamined.Experimentsconfirmthatwhenappropriatelymodifiedthe identification-basedapproachbecomesareliabletoolfordynamicmassmeasurementin checkweighers.
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Generalized adaptive notch filter with a self-optimization capability
PublicationW pracy przedstawiono samonastrajalny wariant tzw. uogólnionego adaptacyjnego filtru wycinającego. Automatycznym strojeniem objęte są dwa współczynniki wzmocnienia adaptacji, odpowiedzialne za śledzenie amplitud i częstotliwości parametrów identyfikowanego obiektu.
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On adaptive covariance and spectrum estimation of locally stationary multivariate processes
PublicationWhen estimating the correlation/spectral structure of a locally stationary process, one has to make two important decisions. First, one should choose the so-called estimation bandwidth, inversely proportional to the effective width of the local analysis window, in the way that complies with the degree of signal nonstationarity. Too small bandwidth may result in an excessive estimation bias, while too large bandwidth may cause excessive...
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High-Precision FIR-Model-Based Dynamic Weighing System
PublicationConveyor belt-type checkweighers are increasingly popular components of modern production lines. They are used to assess the weight of the produced items in motion, i.e., without stopping them on the weighing platform. The main challenge one faces when designing a dynamic weighing system is providing high measurement accuracy, especially at high conveyor belt speeds. The approach proposed in this paper can be characterized as a...
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