Search results for: INTELLIGENT SIGNAL PROCESSING, MACHINE LEARNING, DATASETS
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Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from Cracow and machine learning
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Determinants of anxiety levels among young males in a threat of experiencing military conflict–Applying a machine-learning algorithm in a psychosociological study
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IET Signal Processing
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Instrumentation optical fibres for wave transformation, signal processing, sensors, and photonic functional components, manufactured at Białystok University of Technology in Dorosz Fibre Optics Laboratory
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Data, Information, Knowledge, Wisdom Pyramid Concept Revisited in the Context of Deep Learning
PublicationIn this paper, the data, information, knowledge, and wisdom (DIKW) pyramid is revisited in the context of deep learning applied to machine learningbased audio signal processing. A discussion on the DIKW schema is carried out, resulting in a proposal that may supplement the original concept. Parallels between DIWK pertaining to audio processing are presented based on examples of the case studies performed by the author and her collaborators....
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Michał Mazur dr inż.
PeopleAktualne zainteresowania inżynieria mechaniczna, robotyka, drgania mechaniczne, analiza modalna, sterowanie, systemy czasu rzeczywistego Wybrane publikacje Kaliński K., Galewski M., Mazur M., Chodnicki M, 2017, Modelling and Simulation Of A New Variable Stiffness Holder for Milling Of Flexible Details, Polish Maritime Research, vol 24, ss. 115-124 Kaliński K. J., Mazur M.: Optimal control at energy performance index of the mobile...
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JOURNAL OF MACHINE LEARNING RESEARCH
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Signal Image and Video Processing
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Intra-subject class-incremental deep learning approach for EEG-based imagined speech recognition
PublicationBrain–computer interfaces (BCIs) aim to decode brain signals and transform them into commands for device operation. The present study aimed to decode the brain activity during imagined speech. The BCI must identify imagined words within a given vocabulary and thus perform the requested action. A possible scenario when using this approach is the gradual addition of new words to the vocabulary using incremental learning methods....
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Ewa Hermanowicz prof. dr hab. inż.
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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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Zaawansowane Przetwarzanie Sygnałów - angielski - Nowy
e-Learning CoursesLecture presents development of advanced signal processing techniques. Seminar, after the lectures, considers selected issues/subjects of signal processing techniques presented by individual students.
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Improving listeners' experience for movie playback through enhancing dialogue clarity in soundtracks
PublicationThis paper presents a method for improving users' quality of experience through processing of movie soundtracks. The dialogue clarity enhancement algorithms were introduced for detecting dialogue in movie soundtrack mixes and then for amplifying the dialogue components. The front channel signals (left, right, center) are analyzed in the frequency domain. The selected partials in the center channel signal, which yield high disparity...
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Marek Sylwester Tatara dr inż.
PeopleMarek Tatara achieved his master's degree in the field of Automatic Control and Robotics with specialization Intelligent Decision-making Systems in 2014 at Faculty of Electronics, Telecommunications and Informatics of Gdańsk University of Technology. Earlier this year achieved bachelor's degree in the field of Technical Physics with Nanotechnology specialization. In 2014 started job as lecturer in the Department of Robotics and...
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Active Control of Highly Autocorrelated Machinery Noise in Multivariate Nonminimum Phase Systems
PublicationIn this paper, a novel multivariate active noise control scheme, designed to attenuate disturbances with high autocorrelation characteristics and preserve background signals, is proposed. The algorithm belongs to the class of feedback controllers and, unlike the popular feedforward FX-LMS approach, does not require availability of a reference signal. The proposed approach draws its inspiration from the iterative learning control...
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Asian Conference on Machine Learning
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International Conference on Machine Learning
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Signal Processing: An International Journal (SPIJ)
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Source code - AI models (MLM1-5 - series I-III - QNM opt)
Open Research DataSource code - AI models (MLM1-5 - series I-III - QNM opt) for the paper "Computational Complexity and Its Influence on Concrete Compressive Strength Prediction Capabilities of Machine Learning Models for Concrete Mix Design Support" accepted for publication.
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European Signal Processing Conference
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Damage localisation in a stiffened plate structure using a propagating wave
PublicationThe paper presents an application of changes in propagating waves for damage detection in a stiffened aluminium plate. The experimental investigation was conducted on an aluminium plate with riveted two L-shape stiffeners. The wave has been excited with a piezoelectric transducer and measured with the Laser Scanning Doppler Vibrometer. Recorded signals were analysed using the special signal processing techniques developed for damage...
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Breast MRI segmentation by deep learning: key gaps and challenges
PublicationBreast MRI segmentation plays a vital role in early diagnosis and treatment planning of breast anomalies. Convolutional neural networks with deep learning have indicated promise in automating this process, but significant gaps and challenges remain to address. This PubMed-based review provides a comprehensive literature overview of the latest deep learning models used for breast segmentation. The article categorizes the literature...
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Iterative learning approach to active noise control of highly autocorrelated signals with applications to machinery noise
PublicationThis paper discusses the design and application of iterative learning control (ILC) and repetitive control (RC) for high modal density systems. Typical examples of these systems are structural and acoustical systems considered in active structural acoustic control (ASAC) and active noise control (ANC) applications. The application of traditional ILC and RC design techniques, which are based on a parametric system model, on systems...
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International Conference on Machine Learning and Cybernetics
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International Conference on Machine Learning and Applications
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Grzegorz Szwoch dr hab. inż.
PeopleGrzegorz Szwoch was born in 1972 in Gdansk. In 1991-1996 he studied at the Technical University of Gdansk. In 1996 he graduated as a student from the Sound Engineering Department. His thesis was related to physical modeling of musical instruments. Since that time he has been a member of the research staff at the Multimedia Systems Department as a PhD student (1996-2001), Assistant (2001-2004), Assistant professor (2004-2020) and...
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The instantaneous frequency rate spectogram
PublicationAn accelerogram of the instantaneous phase of signal components referred to as an instantaneous frequency rate spectrogram (IFRS) is presented as a joint time-frequency distribution. The distribution is directly obtained by processing the short-time Fourier transform (STFT) locally. A novel approach to amplitude demodulation based upon the reassignment method is introduced as a useful by-product. Additionally, an estimator of energy...
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Mobilenet-V2 Enhanced Parkinson's Disease Prediction with Hybrid Data Integration
PublicationThis study investigates the role of deep learning models, particularly MobileNet-v2, in Parkinson's Disease (PD) detection through handwriting spiral analysis. Handwriting difficulties often signal early signs of PD, necessitating early detection tools due to potential impacts on patients' work capacities. The study utilizes a three-fold approach, including data augmentation, algorithm development for simulated PD image datasets,...
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Music information retrieval—The impact of technology, crowdsourcing, big data, and the cloud in art.
PublicationThe exponential growth of computer processing power, cloud data storage, and crowdsourcing model of gathering data bring new possibilities to music information retrieval (mir) field. Mir is no longer music content retrieval only; the area also comprises the discovery of expressing feelings and emotions contained in music, incorporating other than hearing modalities for helping this issue, users’ profiling, merging music with social...
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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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Towards classification of patients based on surface EMG data of temporomandibular joint muscles using self-organising maps
PublicationThe study considers the need for an effective method of classification of patients with a temporomandibular joint disorder (TMD). The self-organising map method (SOM) was applied to group patients and used together with the cross-correlation approach to interpret the processed (rectified and smoothed by using root mean square (RMS) algorithm) surface electromyography signal (sEMG) obtained from testing the muscles (two temporal...
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International Conference on Image and Signal Processing
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International Workshop on Multimedia Signal Processing
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IFE: NN-aided Instantaneous Pitch Estimation
PublicationPitch estimation is still an open issue in contemporary signal processing research. Nowadays, growing momentum of machine learning techniques application in the data-driven society allows for tackling this problem from a new perspective. This work leverages such an opportunity to propose a refined Instantaneous Frequency and power based pitch Estimator method called IFE. It incorporates deep neural network based pitch estimation...
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Online sound restoration system for digital library applications
PublicationAudio signal processing algorithms were introduced to the new online non-commercial service for audio restoration intended to enhance the content of digitized audio repositories. Missing or distorted audio samples are predicted using neural networks and a specific implementation of the Jannsen interpolation method based on the autoregressive model (AR) combined with the iterative restoring of missing signal samples. Since the distortion...
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Adrian Kastrau mgr inż.
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A nine-input 1.25 mW, 34 ns CMOS analog median filter for image processing in real time
PublicationIn this paper an analog voltage-mode median filter, which operates on a 3 × 3 kernel is presented. The filter is implemented in a 0.35 μm CMOS technology. The proposed solution is based on voltage comparators and a bubble sort configuration. As a result, a fast (34 ns) time response with low power consumption (1.25 mW for 3.3 V) is achieved. The key advantage of the configuration is relatively high accuracy of signal processing,...
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Przegląd metod szybkiego prototypowania algorytmów uczenia maszynowego w FPGA
PublicationW artykule opisano możliwe do wykorzystania otwarte narzędzia wspomagające szybkie prototypowanie algorytmów uczenia maszynowego (ML) i sztucznej inteligencji (AI) przy użyciu współczesnych platform FPGA. Przedstawiono przykład szybkiej ścieżki przy realizacji toru wideo wraz z implementacją przykładowego algorytmu prze-twarzania w trybie na żywo.
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Dataset Characteristics and Their Impact on Offline Policy Learning of Contextual Multi-Armed Bandits
PublicationThe Contextual Multi-Armed Bandits (CMAB) framework is pivotal for learning to make decisions. However, due to challenges in deploying online algorithms, there is a shift towards offline policy learning, which relies on pre-existing datasets. This study examines the relationship between the quality of these datasets and the performance of offline policy learning algorithms, specifically, Neural Greedy and NeuraLCB. Our results...
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Online sound restoration system for digital library applications.
PublicationAudio signal processing algorithms were introduced to the new online non-commercial service for audio restoration intended to enhance the content of digitized audio repositories. Missing or distorted audio samples are predicted using neural networks and a specific implementation of the Jannsen interpolation method based on the autoregressive model (AR) combined with the iterative restoring of missing signal samples. Since the distortion...
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International Conference on Information, Communications and Signal Processing
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Podstawy uczenia maszynowego AI
e-Learning CoursesPodstawy uczenia maszynowego. Machine Learning fundamentals.
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Deep learning in the fog
PublicationIn the era of a ubiquitous Internet of Things and fast artificial intelligence advance, especially thanks to deep learning networks and hardware acceleration, we face rapid growth of highly decentralized and intelligent solutions that offer functionality of data processing closer to the end user. Internet of Things usually produces a huge amount of data that to be effectively analyzed, especially with neural networks, demands high...
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Marcin Sikorski prof. dr hab. inż.
PeopleMarcin Sikorski is a professor at the Department of Informatics in Management at the Faculty of Management and Economics of the Gdańsk University of Technology. Earlier he had numerous fellowships in academic institutions, among others in Germany (Universities in Bonn and in Heidelberg), Switzerland (ETH Zurich), the Netherlands (TU Eindhoven) and the USA (Harvard University). Professor Sikorski is a representative of Poland in...
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International Machine Vision and Image Processing Conference
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Efkleidis Katsaros
PeopleEfklidis Katsaros received the B.Sc. degree in mathematics from the Aristotle University of Thessaloniki, Greece, in 2016, and the M.Sc. degree (cum laude) in data science: statistical science from Leiden University, The Netherlands, in 2019. He is currently pursuing the Ph.D. degree in deep video multi-task learning with the Department of Biomedical Engineering, Gdańsk University of Technology, Poland. Since 2020, he has been...
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Grzegorz Lentka dr hab. inż.
PeopleGrzegorz Lentka obtained his MSc title in electronics, specialization Measurement Systems at Gdańsk University of Technology, Faculty of Electronics, Telecommunications and Informatics in 1996. He obtained the PhD title in 2003 and habilitation in 2014, respectively. Currently he is an professor in Department of Metrology and Optoelectronics. His main scientific interests are focused on digital signal processing for metrology,...
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Graph Representation Integrating Signals for Emotion Recognition and Analysis
PublicationData reusability is an important feature of current research, just in every field of science. Modern research in Affective Computing, often rely on datasets containing experiments-originated data such as biosignals, video clips, or images. Moreover, conducting experiments with a vast number of participants to build datasets for Affective Computing research is time-consuming and expensive. Therefore, it is extremely important to...
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Human Feedback and Knowledge Discovery: Towards Cognitive Systems Optimization
PublicationCurrent computer vision systems, especially those using machine learning techniques are data-hungry and frequently only perform well when dealing with patterns they have seen before. As an alternative, cognitive systems have become a focus of attention for applications that involve complex visual scenes, and in which conditions may vary. In theory, cognitive applications uses current machine learning algorithms, such as deep learning,...
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Analysis of the Capability of Deep Learning Algorithms for EEG-based Brain-Computer Interface Implementation
PublicationMachine learning models have received significant attention for their exceptional performance in classifying electroencephalography (EEG) data. They have proven to be highly effective in extracting intricate patterns and features from the raw signal data, thereby contributing to their success in EEG classification tasks. In this study, we explore the possibilities of utilizing contemporary machine learning algorithms in decoding...