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Search results for: LOMBARD EFFECT, SPEECH DETECTION, NOISE SIGNAL, SELF-SIMILARITY MATRIX, CONVOLUTIONAL NEURAL NETWORK
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A new methof for identyfication of RTS noise
PublicationIn the paper a new method, called the Noise Scattering Pattern (NSP) method, for RTS noise identyfication in a noise signal is presented. Examples of patterns of the NSP method are included.
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Tensor Decomposition for Imagined Speech Discrimination in EEG
PublicationMost of the researches in Electroencephalogram(EEG)-based Brain-Computer Interfaces (BCI) are focused on the use of motor imagery. As an attempt to improve the control of these interfaces, the use of language instead of movement has been recently explored, in the form of imagined speech. This work aims for the discrimination of imagined words in electroencephalogram signals. For this purpose, the analysis of multiple variables...
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POSSIBILITY OF IDENTIFICATION OF TECHNICAL CONDITION OF BEARINGS FOR SELF-IGNITION ENGINES BY APPLICATION OF ACOUSTIC EMISSION AS A DIAGNOSTIC SIGNAL
PublicationThis paper presents the results of empirical studies where the acoustic emission (AE) method was applied to identify the technical condition of sliding surfaces of main and crank bearings for main diesel engines. The test results indicate that the measurements of the AE parameters allow the technical condition identification for bearings of this type. The results refer to the measurements of the parameters for AE generated in the...
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Enhancing Renal Tumor Detection: Leveraging Artificial Neural Networks in Computed Tomography Analysis
PublicationRenal cell carcinoma is one of the most common cancers in Europe, with a total incidence rate of 18.4 cases per 100 000 population. There is currently significant overdiagnosis (11% to 30.9%) at times of planned surgery based on radiological studies. The purpose of this study was to create an artificial neural network (ANN) solution based on computed tomography (CT) images as an additional tool to improve the differentiation of...
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Neural Architecture Search for Skin Lesion Classification
PublicationDeep neural networks have achieved great success in many domains. However, successful deployment of such systems is determined by proper manual selection of the neural architecture. This is a tedious and time-consuming process that requires expert knowledge. Different tasks need very different architectures to obtain satisfactory results. The group of methods called the neural architecture search (NAS) helps to find effective architecture...
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Three solvers for MIMO noise radar clutter cancellation - a performance comparison
PublicationThe problem of canceling strong clutter echos in a MIMO noise radar is considered. Execution times of three algorithms is compared. The first solution is a standard Least Squares approach employing Cholesky decomposition of the transmitted signal sample autocorrelation matrix. The second approach is based on careful waveform design which guarantees that the signal sample autocorrelation matrix has Toeplitz structure. This enables...
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Mutual Coupling Reduction in Antenna Arrays Using Artificial Intelligence Approach and Inverse Neural Network Surrogates
PublicationThis paper presents a novel approach to reduce undesirable coupling in antenna arrays using custom-designed resonators and inverse surrogate modeling. To illustrate the concept, two stand-ard patch antenna cells with 0.07λ edge-to-edge distance are designed and fabricated to operate at 2.45 GHz. A stepped-impedance resonator is applied between the antennas to suppress their mutual coupling. For the first time, the optimum values...
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An audio-visual corpus for multimodal automatic speech recognition
Publicationreview of available audio-visual speech corpora and a description of a new multimodal corpus of English speech recordings is provided. The new corpus containing 31 hours of recordings was created specifically to assist audio-visual speech recognition systems (AVSR) development. The database related to the corpus includes high-resolution, high-framerate stereoscopic video streams from RGB cameras, depth imaging stream utilizing Time-of-Flight...
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Dataset Related Experimental Investigation of Chess Position Evaluation Using a Deep Neural Network
PublicationThe idea of training Articial Neural Networks to evaluate chess positions has been widely explored in the last ten years. In this paper we investigated dataset impact on chess position evaluation. We created two datasets with over 1.6 million unique chess positions each. In one of those we also included randomly generated positions resulting from consideration of potentially unpredictable chess moves. Each position was evaluated...
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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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Metal heksacyanoferrate network synthesized inside polymer matrix for electrochemical capacitors
PublicationPrzedmiotem publikacji jest charakterystyka elektrochemiczna polimeru przewodzącego (poli(3,4-etylenodioksytiofenu)) modyfikowanego błękitem pruskim oraz jego pochodnymi, pochodną kobaltową oraz pochodną niklową. Materiały są stabilne elektrochemicznie, a uzyskane wartości pojemności elektrycznej pozwalają na wykorzystanie ich przy konstrukcji kondensatorów elektrochemicznych.
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Network economy and innovation policy st 2024/2025
e-Learning CoursesGlobal Studies / Economics Analytics, II stopień (magisterskie), 3 semestr The aim of the course is to provide framework for exploring innovation theory in the context of the network economy.
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Mask Detection and Classification in Thermal Face Images
PublicationFace masks are recommended to reduce the transmission of many viruses, especially SARS-CoV-2. Therefore, the automatic detection of whether there is a mask on the face, what type of mask is worn, and how it is worn is an important research topic. In this work, the use of thermal imaging was considered to analyze the possibility of detecting (localizing) a mask on the face, as well as to check whether it is possible to classify...
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Neural Manoeuvre Detection of the Tracked Target in ARPA Systems
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A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multi-Head Convolutional Attention
PublicationTogether with fast advancement of the Internet of Things (IoT), smart healthcare applications and systems are equipped with increasingly more wearable sensors and mobile devices. These sensors are used not only to collect data, but also, and more importantly, to assist in daily activity tracking and analyzing of their users. Various human activity recognition (HAR) approaches are used to enhance such tracking. Most of the existing...
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Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention
PublicationThis paper describes two novel complementary techniques that improve the detection of lexical stress errors in non-native (L2) English speech: attention-based feature extraction and data augmentation based on Neural Text-To-Speech (TTS). In a classical approach, audio features are usually extracted from fixed regions of speech such as the syllable nucleus. We propose an attention-based deep learning model that automatically de...
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NIRCa: An artificial neural network-based insulin resistance calculator
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Artificial neural network based sensorless control ofinduction motor.
PublicationW artykule przedstawiono bezczujnikowy układ sterowania silnikiem indukcyjnym wykorzystujący sztuczne sieci neuronowe (ANN). Sieć neuronową wykorzystano w regulatorze prędkości silnika. Zaprezentowano wyniki badań symulacyjnych.
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The fuzzy neural network: application for trends in river pollution prediction
PublicationPraca przedstawia zastosowanie rozmytych sieci neuronowych do przygotowywania prognoz zmian w stężeniu zanieczyszczeń w rzekach. Opisane są pokrótce inne narzędzia stosowane w tym celu.
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Energy-Efficient Neural Network Inference with Microcavity Exciton Polaritons
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Neural network approach to 2D Kalman filtering in image processing
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Neural network modelling of the influence of channelopathies on reflex visual attention
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Application of a fuzzy neural network for river water quality prediction
PublicationMonitoring i modelowanie zmian w jakości wód powierzchniowych stanowią jeden z kluczowych elementów monitoringu i zarządzania ochroną środowiska na skalę globalną. Kontrolowanie tak złożonych i nieliniowych w swojej charakterystyce obiektów, jakimi są rzeki, jest trudnym zadaniem. Zazwyczaj do tego celu wykorzystuje się modele matematyczne, jednak czasem wymagają one bardzo dużej ilości danych, lub czas oczekiwania na odpowiedź...
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Feature Reduction Using Similarity Measure in Object Detector Learning with Haar-like Features
PublicationThis paper presents two methods of training complexity reduction by additional selection of features to check in object detector training task by AdaBoost training algorithm. In the first method, the features with weak performance at first weak classifier building process are reduced based on a list of features sorted by minimum weighted error. In the second method the feature similarity measures are used to throw away that features...
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MECHANICAL SYSTEMS AND SIGNAL PROCESSING
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Noise reduction in audio employing spectral unpredictability measure and neural net.
Publicationmodelu psychoakustycznym zostały przedyskutowane. Uczący się algorytm decyzjny, działający w opraciu o sztuczną sieć neuronową wykorzystany został w klasyfikacji składowych na pasożytnicze i użyteczne. Przedstawiona została również nowa iteracyjna procedura obliczania progu maskowania. W pracy zawarte zostały wyniki eksperymentów, oraz konkluzje odnoszące się do przedstawionych algorytmów.
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Application of autoencoder to traffic noise analysis
PublicationThe aim of an autoencoder neural network is to transform the input data into a lower-dimensional code and then to reconstruct the output from this code representation. Applications of autoencoders to classifying sound events in the road traffic have not been found in the literature. The presented research aims to determine whether such an unsupervised learning method may be used for deploying classification algorithms applied to...
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Main complications connected with detection, identification and determination of trace organic constituents in complex matrix samples
PublicationIt is well known that some problems with the determination of organic analytes at trace level can occur. This issue is connected with contamination during each stage of the analytical procedure from sampling to sample preparation up to chromatographic analysis, which often leads to false-positive or overestimated results. Another problem associated with determination of analytes occurs at trace- and ultra-trace level is a background...
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Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Network
PublicationDesigning microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on computationally expensive population-based methods, especially when exe-cuted with full-wave electromagnetic (EM) analysis to guarantee reliability. This paper introduces a novel and cost-effective...
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Generalized regression neural network and fitness dependent optimization: Application to energy harvesting of centralized TEG systems
PublicationThe thermoelectric generator (TEG) system has attracted extensive attention because of its applications in centralized solar heat utilization and recoverable heat energy. The operating efficiency of the TEG system is highly affected by operating conditions. In a series-parallel structure, due to diverse temperature differences, the TEG modules show non-linear performance. Due to the non-uniform temperature distribution (NUTD) condition,...
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Tire camber angle influence on tire-pavement noise
PublicationTaking into account tire-pavement noise and tires classification with respect to noise emission special measurement methods are usually used. When two of them are applied (the Laboratory Drum Method (DR) and the Close-Proximity Method (CPX)) the investigator has to be sure that the position of the tire is correct. The authors of this paper thought about tire position as tire (wheel) alignment in particular tire camber angle. They...
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Advanced acoustic signal analysis used for wheel-flat detection
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Theoretical and Measurement Investigations of DS CDMA Signal Detection Problem
PublicationPrzedstawiono opis teoretyczny wybranej motody wykrywania sygnałów z widmem rozproszonym DS CDMA. Przedstawiono prototypowe stanowisko laboratoryjne do wykrywania sygnałów DS CDMA i zaprezentowano wyniki pomiarowe.
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Detection and Direction-of-Arrival Estimation of Weak Spread Spectrum Signals Received with Antenna Array
PublicationThis paper presents a method for the joint detection and direction of arrival (DOA) estimation of low probability of detection (LPD) signals. The proposed approach is based on using the antenna array to receive spread-spectrum signals hidden below the noise floor. Array processing exploits the spatial correlation between phase-delayed copies of the signal and allows us to evaluate the parameter used to make the decision about the...
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SIGNAL PROCESSING
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Decoding of the FSK signal with noise and distortion with the use of coefficients of the time-frequency transform.
PublicationStreszczenie: Przeanalizowano sygnał z modulacją FSK wykorzystując metodę transformacji czasowo-częstotliwościowej różniące się sposobem podziału płaszczyzny TF na atomy: krótko-okresowej transformacji Fouriera, transformacji falkowej i transformacji pakietami falkowymi. Transformacja falkowa zapewnia dobrą lokalizację czasową zakłóceń o wielkiej częstotliwości, podczas gdy transformacja pakietami falkowymi zapewnia dobrą lokalizację...
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Dynamically positioned ship steering making use of backstepping method and artificial neural networks
PublicationThe article discusses the issue of designing a dynamic ship positioning system making use of the adaptive vectorial backstepping method and RBF type arti cial neural networks. In the article, the backstepping controller is used to determine control laws and neural network weight adaptation laws. e arti cial neural network is applied at each time instant to approximate nonlinear functions containing parametric uncertainties....
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Evaluation of excessive noise effects on hearing employing psychoacoustic dosimetry
PublicationResearch results regarding the noise impact on hearing applying the concept of the Psychoacoustic Noise Dosimetry (PND) are presented. The general characteristics of the PND algorithm are discussed. Additionally, the results of hearing examinations conducted in the laboratory conditions are shown. The main objective of the research was to determine the time needed for the Temporary Threshold Shift to reverse. The results were used...
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Artificial Neural Network based fatigue life assessment of riveted joints in AA2024 aluminum alloy plates and optimization of riveted joints parameters
PublicationThe objective of this paper is to provide the fatigue life of riveted joints in AA2024 aluminum alloy plates and optimization of riveted joints parameters. At first, the fatigue life of the riveted joints in AA2024 aluminum alloy plates is obtained by experimental tests. Then, an artificial neural network is applied to estimate the fatigue life of riveted lap joints based on the number of lateral and longitudinal holes, punch pressure,...
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Performance and Energy Aware Training of a Deep Neural Network in a Multi-GPU Environment with Power Capping
PublicationIn this paper we demonstrate that it is possible to obtain considerable improvement of performance and energy aware metrics for training of deep neural networks using a modern parallel multi-GPU system, by enforcing selected, non-default power caps on the GPUs. We measure the power and energy consumption of the whole node using a professional, certified hardware power meter. For a high performance workstation with 8 GPUs, we were...
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Scale effect in the self-propulsion prediction for Ultra Large Container Ship with contra-rotating propellers
PublicationThis article addresses the problem of the scale effect for an Ultra Large Container Ship (ULCS) with a novel twin-crp-pod propulsion system. Twin-crp-pod steering-propulsion arrangement is an innovative solution that gains from three well-known systems: twin-propeller, contra-rotating propellers and pod propulsors. It is expected that applying the twin-crp-pod system to the analysed Ultra Large Container Ship will increase propulsion...
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Applying artificial neural networks for modelling ship speed and fuel consumption
PublicationThis paper deals with modelling ship speed and fuel consumption using artificial neural network (ANN) techniques. These tools allowed us to develop ANN models that can be used for predicting both the fuel consumption and the travel time to the destination for commanded outputs (the ship driveline shaft speed and the propeller pitch) selected by the ship operator. In these cases, due to variable environmental conditions, making...
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Detection of Alzheimer's disease using Otsu thresholding with tunicate swarm algorithm and deep belief network
PublicationIntroduction: Alzheimer’s Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is necessary to reduce the mortality rate through slowing down its progression. The prevention and detection of AD is the emerging research topic for many researchers. The structural Magnetic Resonance Imaging (sMRI) is an extensively used imaging technique in detection of AD, because...
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Sonar Pulse Detection Using Chirp Rate Estimation and CFAR Algorithms
PublicationThis paper presents a new approach to sonar pulse detection. The method uses chirp rate estimators and algorithms for the adaptive threshold, commonly used in radiolocation. The proposed approach allows detection of pulses of unknown parameters, which may be used in passive hydrolocation or jamming detection in underwater communication. Such an analysis is possible thanks to a new kind of imaging, which presents signal energy in...
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Speed Sensorless AC Drive with Inverter LC Filter and Fault Detection Using Load Torque Signal
PublicationThe industrial development in recent years has seen a major increase in the use of induction motors, whereby the cost has to be as low as possible and the lifetime as long as possible. To follow up this desire, investigations in this area have become very intense. For that reason, this paper presents a solution for driving an induction motor and simultaneous fault detection with no need for additional sensors. In order to achieve...
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Artificial-Neural-Network-Based Sensorless Nonlinear Control of Induction Motors
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Artificial neural network controller for underwater ship hull operation robot.
PublicationZaproponowano model matematyczny pojazdu podwodnego, który w uproszczonej wersji spełnia warunki dynamiki odpowiadające głowicy roboczej podwodnego robota. Uwzględniono niektóre czynniki oddziałujące na ruch podwodnej głowicy roboczej, jak np. gęstość wody oraz siły odśrodkowe i wypornościowe. Przedstawiono układ sterowania, w którym zastosowano regulator oparty na bazie sieci neuronowych, za pomocą którego można sterować...
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On thermal and Flow Expert Systems Based on Artificial Neural Network (ANN)
PublicationZaprezentowano możliwość realizacji jednego z zadań systemów eksperckich, polegającego na określaniu rozmiaru eksploatacyjnej degradacji parametrów geometrycznych układów łopatkowych turbin. Dyskusję przeprowadzono w oparciu o zastosowanie wybranego typu sztucznej sieci neuronowej (SSN). Badano jakość i dokładność polegającą na dobrej identyfikacji rozmiaru degradacji przez tę wybraną SSN wykrywającą rozmiar degradacji geometrycznej....
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Neural Network Application for Recognition of Geometry Degradation of Power Cycle Components
PublicationPrzedyskutowano problem rozpoznawania degradacji geometrycznej. Skuteczne zastosowanie wybranego typu sieci neuronowej (SSN) jest prezentowane w referacie. SSN wykrywająca typy degradacji geometrycznej wykazała wysoką jakość. Pokazano pewną możliwość ekstrapolacji takich SSN. Pokazano możliwość wykrywania typów degradacji geometrycznej nawet w przypadku pozyskiwania niepełnych danych pomiarowych.
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Artificial Neural Network-Based Sensorless Nonlinear Control Of Induction Motors
PublicationW niniejszym artykule przedstawiono strukturę sztucznej sieci neuronowej służącej do korygowania działania układu estymacji prędkości kątowej wirnika. Odtworzona prędkość kątowa wirnika zostały wykorzystane w bezczujnikowym układzie sterowania silnikiem indukcyjnym pracującym w zamkniętej pętli sprzężenia prędkościowego.Przedstawiono wyniki badań eksperymentalnych z silnikiem o mocy 1,1kW.