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Search results for: sparse autoregressive models
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Sparse autoregressive modeling
PublicationIn the paper the comparison of the popular pitch determination (PD) algorithms for thepurpose of elimination of clicks from archive audio signals using sparse autoregressive (SAR)modeling is presented. The SAR signal representation has been widely used in code-excitedlinear prediction (CELP) systems. The appropriate construction of the SAR model is requiredto guarantee model stability. For this reason the signal representation...
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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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Elimination of clicks from archive speech signals using sparse autoregressive modeling
PublicationThis paper presents a new approach to elimination of impulsivedisturbances from archive speech signals. The proposedsparse autoregressive (SAR) signal representation is given ina factorized form - the model is a cascade of the so-called formantfilter and pitch filter. Such a technique has been widelyused in code-excited linear prediction (CELP) systems, as itguarantees model stability. After detection of noise pulses usinglinear...
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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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Low order autoregressive (AR) models for FDTD analysis of microwave filters.
PublicationArtykuł opisuje zastosowanie modeli AR w celu poprawy efektywności analizy struktur filtrujących metodą różnic skończonych w dziedzinie czasu (FD-TD). Opisanych jest szereg kryteriów pozwalających na automatyczne tworzenie modeli AR sygnałów czasowych, w tym wybór fragmentu odpowiedzi układu stanowiący podstawę ekstrakcji współczynników modelu, współczynnika decymacji oraz rzędu modelu. Skuteczność wprowadzonych kryteriów...
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A Cost-Effective Method for Reconstructing City-Building 3D Models from Sparse Lidar Point Clouds
PublicationThe recent popularization of airborne lidar scanners has provided a steady source of point cloud datasets containing the altitudes of bare earth surface and vegetation features as well as man-made structures. In contrast to terrestrial lidar, which produces dense point clouds of small areas, airborne laser sensors usually deliver sparse datasets that cover large municipalities. The latter are very useful in constructing digital...
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Variable-Fidelity Simulation Models and Sparse Gradient Updates for Cost-Efficient Optimization of Compact Antenna Input Characteristics
PublicationDesign of antennas for the Internet of Things (IoT) applications requires taking into account several performance figures, both electrical (e.g., impedance matching) and field (gain, radiation pattern), but also physical constraints, primarily concerning size limitation. Fulfillment of stringent specifications necessitates the development of topologically complex structures described by a large number of geometry parameters that...
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Prognozowanie ostrzegawcze w małej firmie
PublicationW artykule pokazano możliwości zastosowania prostych metod prognostycznych do ostrzegania w małej firmie przed niekorzystnymi zjawiskami. Przedmiotem badań jest sprzedaż, a ze względu na występowanie w szeregu czasowym sezonowości, w celu wyznaczenia sygnałów ostrzegawczych dokonano porównań trendów prognozowanych i rzeczywistych w okresach jednoimiennych za pomocą pierwszych różnic. Do prognozowania sprzedaży wykorzystano proste...
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Vident-synth: a synthetic intra-oral video dataset for optical flow estimation
Open Research DataWe introduce Vident-synth, a large dataset of synthetic dental videos with corresponding ground truth forward and backward optical flows and occlusion masks. It can be used for:
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On Adaptive Spectrum Estimation of Multivariate Autoregressive Locally Stationary Processes
PublicationAutoregressive modeling is a widespread parametricspectrum estimation method. It is well known that, in the caseof stationary processes with unknown order, its accuracy canbe improved by averaging models of different complexity usingsuitably chosen weights. The paper proposes an extension of thistechnique to the case of multivariate locally stationary processes.The proposed solution is based on local autoregressive...
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TIME SERIES MODELING (PG_00063724)
e-Learning CoursesEffectively uses in-depth knowledge of economic time series analysis methods, applying the results of analyzes to formulate forecasts. Subject contents: 1. Classical time series analysis (trend, cyclical fluctuations) 2. Exponential smoothing models 3. Holt and Winters model 4. Stochastic processes and time series 5. Characteristics of stochastic processes 6. Process spectrum autocorrelation functions 7. Study of the stationarity...
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On autoregressive spectrum estimation using the model averaging technique
PublicationThe problem of estimating spectral density of a nonstationary process satisfying local stationarity conditions is considered. The proposed solution is a two step procedure based on local autoregressive (AR) modeling. In the first step Bayesian-like averaging of AR models, differing in order, is performed. The main contribution of the paper is development of a new final-prediction-error-like statistic, which can be used to select...
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On Noncausal Identification of Nonstationary Multivariate Autoregressive Processes
PublicationThe problem of identification of nonstationary multivariate autoregressive processes using noncausal local estimation schemes is considered and a new approach to joint selection of the model order and the estimation bandwidth is proposed. The new selection rule, based on evaluation of pseudoprediction errors, is compared with the previously proposed one, based on the modified Akaike’s final prediction error criterion.
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On joint order and bandwidth selection for identification of nonstationary autoregressive processes
PublicationWhen identifying a nonstationary autoregressive process, e.g. for the purpose of signal prediction or parametric spectrum estimation, two important decisions must be taken. First, one should choose the appropriate order of the autoregressive model, i.e., the number of autoregressive coefficients that will be estimated. Second, if identification is carried out using the local estimation technique, such as the localized version of...
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A memory efficient and fast sparse matrix vector product on a Gpu
PublicationThis paper proposes a new sparse matrix storage format which allows an efficient implementation of a sparse matrix vector product on a Fermi Graphics Processing Unit (GPU). Unlike previous formats it has both low memory footprint and good throughput. The new format, which we call Sliced ELLR-T has been designed specifically for accelerating the iterative solution of a large sparse and complex-valued system of linear equations arising...
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A Task-Scheduling Approach for Efficient Sparse Symmetric Matrix-Vector Multiplication on a GPU
PublicationIn this paper, a task-scheduling approach to efficiently calculating sparse symmetric matrix-vector products and designed to run on Graphics Processing Units (GPUs) is presented. The main premise is that, for many sparse symmetric matrices occurring in common applications, it is possible to obtain significant reductions in memory usage and improvements in performance when the matrix is prepared in certain ways prior to computation....
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Data fusion of sparse, heterogeneous, and mobile sensor devices using adaptive distance attention
PublicationIn environmental science, where information from sensor devices are sparse, data fusion for mapping purposes is often based on geostatistical approaches. We propose a methodology called adaptive distance attention that enables us to fuse sparse, heterogeneous, and mobile sensor devices and predict values at locations with no previous measurement. The approach allows for automatically weighting the measurements according to a priori...
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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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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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Estimation of the amplitude of the signal for the active optical gesture sensor with sparse detectors
PublicationIn this paper we deal with the problem of precise gesture recognition for the active optical proximity sensor with sparse 8 photodiodes. We particularly focus on developing the method of estimating the real, usually not observable, maximum signal value representing maximum intensity of light reflected from an obstacle present in the front of the sensor. Different configurations of the fingers were used as an obstacle. The Monte Carlo...
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Models in Spatial Development
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Two-Stage Identification of Locally Stationary Autoregressive Processes and its Application to the Parametric Spectrum Estimation
PublicationThe problem of identification of a nonstationary autoregressive process with unknown, and possibly time-varying, rate of parameter changes, is considered and solved using the parallel estimation approach. The proposed two-stage estimation scheme, which combines the local estimation approach with the basis function one, offers both quantitative and qualitative improvements compared with the currently used single-stage methods.
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Expedited Gradient-Based Design Closure of Antennas Using Variable-Resolution Simulations and Sparse Sensitivity Updates
PublicationNumerical optimization has been playing an increasingly important role in the design of contemporary antenna systems. Due to the shortage of design-ready theoretical models, optimization is mainly based on electromagnetic (EM) analysis, which tends to be costly. Numerous techniques have evolved to abate this cost, including surrogate-assisted frameworks for global optimization, or sparse sensitivity updates for speeding up local...
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Signal Reconstruction from Sparse Measurements Using Compressive Sensing Technique
PublicationThe paper presents the possibility of applying a new class ofmathematical methods, known as Compressive Sensing (CS) for recovering thesignal from a small set of measured samples. CS allows the faithful recon-struction of the original signal back from fewer random measurements bymaking use of some non-linear reconstruction techniques. Since of all thesefeatures, CSfinds its applications especially in the areas where, sensing is...
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Reduced-cost electromagnetic-driven optimisation of antenna structures by means of trust-region gradient-search with sparse Jacobian updates
PublicationNumerical optimisation plays more and more important role in the antenna design. Because of lack of design-ready theoretical models, electromagnetic (EM)-simulation-driven adjustment of geometry parameters is a necessary step of the design process. At the same time, traditional parameter sweeping cannot handle complex topologies and large number of design variables. On the other hand, high computational cost of the conventional...
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STOCHASTIC MODELS
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GRAPHICAL MODELS
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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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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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A GPU Solver for Sparse Generalized Eigenvalue Problems with Symmetric Complex-Valued Matrices Obtained Using Higher-Order FEM
PublicationThe paper discusses a fast implementation of the stabilized locally optimal block preconditioned conjugate gradient (sLOBPCG) method, using a hierarchical multilevel preconditioner to solve nonHermitian sparse generalized eigenvalue problems with large symmetric complex-valued matrices obtained using the higher-order finite-element method (FEM), applied to the analysis of a microwave resonator. The resonant frequencies of the low-order...
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Models in spatial development 2024/25
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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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Forecasting energy consumption and carbon dioxide emission of Vietnam by prognostic models based on explainable machine learning and time series
PublicationThis study assessed the usefulness of algorithms in estimating energy consumption and carbon dioxide emissions in Viet- nam, in which the training dataset was used to train the models linear regression, random forest, XGBoost, and AdaBoost, allowing them to comprehend the patterns and relationships between population, GDP, and carbon dioxide emissions, energy consumption. The results revealed that random forest, XGBoost, and AdaBoost...
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Disease Models & Mechanisms
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Kinetic and Related Models
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[AEiE] Selected topics of electrical engineering - models of electrical machines
e-Learning Courses{mlang pl} Dyscyplina: automatyka, elektronika i elektrotechnika Zajęcia fakultatywne dla doktorantów II roku Prowadzący: dr hab. inż. Andrzej Wilk, prof. PG, prof. dr hab. inż. Zbigniew Krzemiński Liczba godzin: 15 Forma zajęć: wykład {mlang} {mlang en} Discipline: control, electronic and electrical engineering Facultative course for 2nd-year PhD students Academic teachers: dr hab. inż. Andrzej Wilk, prof. PG, prof....
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Language Models in Speech Recognition
PublicationThis chapter describes language models used in speech recognition, It starts by indicating the role and the place of language models in speech recognition. Mesures used to compare language models follow. An overview of n-gram, syntactic, semantic, and neural models is given. It is accompanied by a list of popular software.
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Models of Structures in Didactics
PublicationThe final aim of teaching students subjects, such as structural mechanics, reinforced concrete, and steel structures is to teach them how structures work in a given building as well as to provide them with skills enabling them to calculate and design structures. The behavioral model of the structure, contrary to the architectural model, which focuses mainly on the external form of the building, shows workings from both the static...
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Bridging the gap between business process models and use-case models
PublicationToday's software development methodologies are equipped with a plethora of methods and techniques for business process engineering and Requirements Engineering. However, heavy investments in IT have not brought forth expected results. What seems to be lacking is a systematic approach that consolidates both disciplines to gain a synergistic effect. To address this challenge we extend Use-Case Driven Approach (UCDA) by binding use...
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Comparison of technology adoption models
PublicationThere are several technology adoption models, that try to explain, how and why the technologies are adopted and used. Among those, that are widely used to explain, how the older adults accept technologies, there are some general models and models specific to the group of older users. Among the general ones I would recommend paying attention to the following models: Technology Acceptance Model (TAM) proposed by Davis...
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Kriging Models for Microwave Filters
PublicationSurrogate modeling of microwave filters’ response is discussed. In particular, kriging is used to model either the scattering parameters of the filter or the rational representation of the filter’s characteristics. Surrogate models for these two variants of kriging are validated in solving a microwave filter optimization problem. A clear advantage of surrogate models based on the rational representation over the models based on scattering...
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Efficient List Cost Coloring of Vertices and∕or Edges of Some Sparse Graphs
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Efficient list cost coloring of vertices and/or edges of some sparse graphs
PublicationRozważane jest kolorowanie wierzchołków i krawędzi grafów w modelach klasycznym, totalnym i pseudototalnym z uwzględnieniem dodatkowego ograniczenia w postaci list dostępnych kolorów. Proponujemy wielomianowy algorytm oparty na paradygmacie programowania dynamicznego dla grafów o strukturze drzewa. Wynik ten można uogólnić na grafy o liczbie cyklomatycznej ograniczonej z góry przez dowolnie wybraną stała.
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Data Compression in Ultrasonic Network Communication via Sparse Signal Processing
PublicationThis document presents the approach of using compressed sensing in signal encoding and information transferring within a guided wave sensor network, comprised of specially designed frequency steerable acoustic transducers (FSATs). Wave propagation in a damaged plate was simulated using commercial FEM-based software COMSOL. Guided waves were excited by means of FSATs, characterized by the special shape of its electrodes, and modeled...
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Multibody models for gait analysis
PublicationThe aim of this study was to create multibody biomechanical models to analyze a normal gait of the human. Proposed models can be used to identify joint moments of the lower limbs during normal gait in the single and double support phases. Applying Newton-Euler formulation, following planar models were developed: 1) a mathematical 6DOF model describing a gait in the sagittal plane of the body for single support phase and double...
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Physical models in the education of architectural history
PublicationIn this article, the authors present the long tradition and common use of physical models in the process of teaching the history of architecture in higher education institutions. The research described in the article is focused on the use of physical models and mock-ups as stimulants during architectural history classes to support lectures and increase the learning capabilities of students. The authors also cover the general use...
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Sophistication assessment of existing FEM models of orbital blowout trauma: Is models valuation justified?
PublicationAfter a thorough study of the work entitled “Development and validation of an optimized finite element model of the human orbit”, some doubts aroused concerning the sophistication assessment of the existing finite element method (FEM) models of orbital blow-out. Although the work was unquestionably innovative, and the results were not only fascinating but also invaluable, the authors stated that their model was the most sophisticated...
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Rapid Variable-Resolution Parameter Tuning of Antenna Structures Using Frequency-Based Regularization and Sparse Sensitivity Updates
PublicationGeometry parameter tuning is an inherent part of antenna design process. While most often performed in a local sense, it still entails considerable computational expenses when carried out at the level of full-wave electromagnetic (EM) simulation models. Moreover, the optimization outcome may be impaired if good initial design is not available. This paper proposes a novel approach to fast and improved-reliability gradient-based...
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Single and Dual-GPU Generalized Sparse Eigenvalue Solvers for Finding a Few Low-Order Resonances of a Microwave Cavity Using the Finite-Element Method
PublicationThis paper presents two fast generalized eigenvalue solvers for sparse symmetric matrices that arise when electromagnetic cavity resonances are investigated using the higher-order finite element method (FEM). To find a few loworder resonances, the locally optimal block preconditioned conjugate gradient (LOBPCG) algorithm with null-space deflation is applied. The computations are expedited by using one or two graphical processing...
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Fast EM-Driven Parameter Tuning of Microwave Circuits with Sparse Sensitivity Updates via Principal Directions
PublicationNumerical optimization has become more important than ever in the design of microwave components and systems, primarily as a consequence of increasing performance demands and growing complexity of the circuits. As the parameter tuning is more and more often executed using full-wave electromagnetic (EM) models, the CPU cost of the overall process tends to be excessive even for local optimization. Some ways of alleviating these issues...