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Wyniki wyszukiwania dla: DATA CLUSTERING: SIGNAL SEGMENTATION
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Instance segmentation of stack composed of unknown objects
PublikacjaThe article reviews neural network architectures designed for the segmentation task. It focuses mainly on instance segmentation of stacked objects. The main assumption is that segmentation is based on a color image with an additional depth layer. The paper also introduces the Stacked Bricks Dataset based on three cameras: RealSense L515, ZED2, and a synthetic one. Selected architectures: DeepLab, Mask RCNN, DEtection TRansformer,...
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Image Segmentation of MRI image for Brain Tumor Detection
Publikacjathis research work presents a new technique for brain tumor detection by the combination of Watershed algorithm with Fuzzy K-means and Fuzzy C-means (KIFCM) clustering. The MATLAB based proposed simulation model is used to improve the computational simplicity, noise sensitivities, and accuracy rate of segmentation, detection and extraction from MR...
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Controlling computer by lip gestures employing neural network
PublikacjaResults of experiments regarding lip gesture recognition with an artificial neural network are discussed. The neural network module forms the core element of a multimodal human-computer interface called LipMouse. This solution allows a user to work on a computer using lip movements and gestures. A user face is detected in a video stream from a standard web camera using a cascade of boosted classifiers working with Haar-like features....
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Human-Computer Interface Based on Visual Lip Movement and Gesture Recognition
PublikacjaThe multimodal human-computer interface (HCI) called LipMouse is presented, allowing a user to work on a computer using movements and gestures made with his/her mouth only. Algorithms for lip movement tracking and lip gesture recognition are presented in details. User face images are captured with a standard webcam. Face detection is based on a cascade of boosted classifiers using Haar-like features. A mouth region is located in...
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Method for Clustering of Brain Activity Data Derived from EEG Signals
PublikacjaA method for assessing separability of EEG signals associated with three classes of brain activity is proposed. The EEG signals are acquired from 23 subjects, gathered from a headset consisting of 14 electrodes. Data are processed by applying Discrete Wavelet Transform (DWT) for the signal analysis and an autoencoder neural network for the brain activity separation. Processing involves 74 wavelets from 3 DWT families: Coiflets,...
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Automatic Clustering of EEG-Based Data Associated with Brain Activity
PublikacjaThe aim of this paper is to present a system for automatic assigning electroencephalographic (EEG) signals to appropriate classes associated with brain activity. The EEG signals are acquired from a headset consisting of 14 electrodes placed on skull. Data gathered are first processed by the Independent Component Analysis algorithm to obtain estimates of signals generated by primary sources reflecting the activity of the brain....
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Clustering Bathymetric Data for Electronic Navigational Charts
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K-means clustering for SAT-AIS data analysis
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Methods of data extraction from sub-bottom profiler's signal
PublikacjaData obtain during sounding Gdansk Bay with SES-2000 Standard parametric sub-bottom profiler has two types of information: envelope and pure signal. First is used to plot echograms in real time and contain envelope of echo. The second one is stored during sounding and can be processed after recording data. Comparison of results will be shown and discussed. First step in investigation was proper configuration of small measurement...
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Impact of Clustering on a Synthetic Instance Generation in Imbalanced Data Streams Classification
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MATCHED FILTER APPROACH FOR MICROSEISMIC SIGNAL PROCESSING OF REAL DATA FROM EAST POMERANIA SHALE GAS
PublikacjaThe microseismic monitoring is a method of monitoring of fracture propagation during hydraulic fracturing (HF)process. An array of several hundred geophones is placed on the surface to record little ground tremors induced by fracturing process. Filtration and summation of signals from geophones is essential to identify and locate fracturing events from underground. Authors propose a method of matched filtering, that is usually...
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Impact of the Time Window Length on the Ship Trajectory Reconstruction Based on AIS Data Clustering
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Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
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Comparison of selected clustering algorithms of raw data obtained by interferometric methods using artificial neural networks
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Data Compression in Ultrasonic Network Communication via Sparse Signal Processing
PublikacjaThis 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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Michał Lech dr inż.
OsobyMichał Lech was born in Gdynia in 1983. In 2007 he graduated from the faculty of Electronics, Telecommunications and Informatics of Gdansk University of Technology. In June 2013, he received his Ph.D. degree. The subject of the dissertation was: “A Method and Algorithms for Controlling the Sound Mixing Processes with Hand Gestures Recognized Using Computer Vision”. The main focus of the thesis was the bias of audio perception caused...
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Application of data segmentation and segregation in alarm dedicated glass breaks detection method,based on Wavelet Transformatio
PublikacjaAutor opracowuje nowoczesną, dedykowaną dla systemów alarmowych, metodę bezkontaktowej detekcji zbicia szyby, bazującą na analizie sygnałów akustycznych i transformacji falkowej. Struktura badanego sygnału oraz pierwotne metody mające zastosowanie w fazie badawczej projektu przedstawione zostały we wcześniejszych publikacjach autora [1] i [2]. Ze względu na ich dużą złożoność obliczeniowe i przetwarzanie off-line nie mogły być...
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Classifying type of vehicles on the basis of data extracted from audio signal characteristics
PublikacjaThe aim of this study is to find and optimize a feature vector for an automatic recognition of the type of vehicles, extracted form an audio signal. First, the influence of weather-based conditions of road surface on spectral characteristic of the audio signal recorded from a passing vehicle in close proximity to the road is discussed. Next, parameterization of the recorded audio signal is performed. For that purpose, the MIRtoolbox,...
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Sampling Theory, Signal Processing, and Data Analysis
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Fast multi-objective design optimization of microwave and antenna structures using data-driven surrogates and domain segmentation
PublikacjaPurpose Strategies and algorithms for expedited design optimization of microwave and antenna structures in multi-objective setup are investigated. Design/methodology/approach Formulation of the multi-objective design problem oriented towards execution of the population-based metaheuristic algorithm within the segmented search space is investigated. Described algorithmic framework exploit variable fidelity modeling, physics- and...
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Detection of molecular signatures of oral squamous cell carcinoma and normal epithelium - application of a novel methodology for unsupervised segmentation of imaging mass spectrometry data
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Fiber optic interface channels for united data and power supply transmission for neutral interaction application in signal transmission networks
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Karol Flisikowski dr inż.
OsobyKarol Flisikowski jest profesorem uczelni w Katedrze Statystyki i Ekonometrii, Wydziału Zarządzania i Ekonomii Politechniki Gdańskiej. Jest odpowiedzialny jest za prowadzenie zajęć ze statystyki opisowej i matematycznej (w języku polskim i angielskim), a także badań naukowych w zakresie statystyki społecznej. Był uczestnikiem wielu konferencji o zasięgu krajowym, jak i międzynarodowym, gdzie prezentował wyniki prowadzonych przez...
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General concept of reduction process for big data obtained by interferometric methods
PublikacjaInterferometric sonar systems apply the phase content of the sonar signal to measure the angle of a wave front returned from the seafloor or from a target. It collect a big data – datasets that are so large or complex that traditional data processing application software is inadequate to deal with them. The recording a large number of data is associated with the difficulty of their efficient use. So data have to be reduced. The main...
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Grzegorz Szwoch dr hab. inż.
OsobyGrzegorz Szwoch urodził się w 1972 roku w Gdańsku. W latach 1991-1996 studiował na wydziale Elektroniki Politechniki Gdańskiej. W roku 1996 ukończył studia w Zakładzie Inżynierii Dźwięku (obecnie Katedra Systemów Multimedialnych), broniąc pracę dyplomową pt. Modelowanie fizyczne wybranych instrumentów muzycznych. W tym samym roku dołączył do zespołu badawczego Katedry jako uczestnik Studium Doktoranckiego. Od stycznia 2001 roku...
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Zdzisław Kowalczuk prof. dr hab. inż.
OsobyW 1978 ukończył studia w zakresie automatyki i informatyki na Wydziale Elektroniki Politechniki Gdańskiej, następnie rozpoczął pracę na macierzystej uczelni. W 1986 obronił pracę doktorską, w 1993 habilitował się na Politechnice Śląskiej na podstawie pracy Dyskretne modele w projektowaniu układów sterowania. W 1996 mianowany profesorem nadzwyczajnym, w 2003 otrzymał tytuł profesora nauk technicznych. W 2006 założył i od tego czasu...
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Deep learning based thermal image segmentation for laboratory animals tracking
PublikacjaAutomated systems for behaviour classification of laboratory animals are an attractive alternative to manual scoring. However, the proper animals separation and tracking, especially when they are in close contact, is the bottleneck of the behaviour analysis systems. In this paper, we propose a method for the segmentation of thermal images of laboratory rats that are in close contact during social behaviour tests. For this, we are...
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MSIS sonar image segmentation method based on underwater viewshed analysis and high-density seabed model
PublikacjaHigh resolution images of Mechanically Scanned Imaging Sonars can bring detailed representation of underwater area if favorable conditions for acoustic signal to propagate are provided. However to properly asses underwater situation based solely on such data can be challenging for less than proficient interpreter. In this paper we propose a method to enhance interpretative potential of MSIS image by dividing it in to subareas depending...
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Three-objective antenna optimization by means of kriging surrogates and domain segmentation
PublikacjaIn this paper, an optimization framework for multi-objective design of antenna structures is discussed which exploits data-driven surrogates, a multi-objective evolutionary algorithm, response correction techniques for design refinement, as well as generalized domain segmentation. The last mechanism is introduced to constrain the design space region subjected to sampling, which permits reduction of the number of training data samples...
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Spectral Clustering Wikipedia Keyword-Based search Results
PublikacjaThe paper summarizes our research in the area of unsupervised categorization of Wikipedia articles. As a practical result of our research, we present an application of spectral clustering algorithm used for grouping Wikipedia search results. The main contribution of the paper is a representation method for Wikipedia articles that has been based on combination of words and links and used for categoriation of search results in this...
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Towards Effective Processing of Large Text Collections
PublikacjaIn the article we describe the approach to parallelimplementation of elementary operations for textual data categorization.In the experiments we evaluate parallel computations ofsimilarity matrices and k-means algorithm. The test datasets havebeen prepared as graphs created from Wikipedia articles relatedwith links. When we create the clustering data packages, wecompute pairs of eigenvectors and eigenvalues for visualizationsof...
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Multi-objective design optimization of antennas for reflection, size, and gain variability using kriging surrogates and generalized domain segmentation
PublikacjaCost-efficient multi-objective design optimization of antennas is presented. The framework exploits auxiliary data-driven surrogates, a multi-objective evolutionary algorithm for initial Pareto front identification, response correction techniques for design refinement, as well as generalized domain segmentation. The purpose of this last mechanism is to reduce the volume of the design space region that needs to be sampled in order...
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Comparison of selected electroencephalographic signal classification methods
PublikacjaA variety of methods exists for electroencephalographic (EEG) signals classification. In this paper, we briefly review selected methods developed for such a purpose. First, a short description of the EEG signal characteristics is shown. Then, a comparison between the selected EEG signal classification methods, based on the overview of research studies on this topic, is presented. Examples of methods included in the study are: Artificial...
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Semantic segmentation training using imperfect annotations and loss masking
PublikacjaOne of the most significant factors affecting supervised neural network training is the precision of the annotations. Also, in a case of expert group, the problem of inconsistent data annotations is an integral part of real-world supervised learning processes, well-known to researchers. One practical example is a weak ground truth delineation for medical image segmentation. In this paper, we have developed a new method of accurate...
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Ensembling noisy segmentation masks of blurred sperm images
PublikacjaBackground: Sperm tail morphology and motility have been demonstrated to be important factors in determining sperm quality for in vitro fertilization. However, many existing computer-aided sperm analysis systems leave the sperm tail out of the analysis, as detecting a few tail pixels is challenging. Moreover, some publicly available datasets for classifying morphological defects contain images limited only to the sperm head. This...
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Smart Approach for Glioma Segmentation in Magnetic Resonance Imaging using Modified Convolutional Network Architecture (U-NET)
PublikacjaSegmentation of a brain tumor from magnetic resonance multimodal images is a challenging task in the field of medical imaging. The vast diversity in potential target regions, appearance and multifarious intensity threshold levels of various tumor types are few of the major factors that affect segmentation results. An accurate diagnosis and its treatment demand strict delineation of the tumor affected tissues. Herein, we focus on...
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Marta Kuc-Czarnecka dr
OsobyMarta Kuc-Czarnecka jest zastępczynią kierownika Katedry Statystyki i Ekonomii na Wydziale Zarządzania i Ekonomii Politechniki Gdańskiej. Pełni również funkcję pełnomocniczki Dziekana ds. akredytacji AMBA. Jest współzałożycielką Rethinking Economics Gdańsk oraz członkinią Fundacji im. Edwarda Lipińskiego na rzecz promocji pluralizmu w naukach ekonomicznych. W latach 2018-2022 była ekspertką Europejskiej Fundacji na Rzecz Poprawy...
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Patryk Ziółkowski dr inż.
OsobyAbsolwent Wydziału Inżynierii Lądowej i Środowiska Politechniki Gdańskiej, w specjalności Konstrukcje Budowlane i Inżynierskie. Pracuje na stanowisku adiunkta w Katedrze Konstrukcji Inżynierskich. Brał udział w projektach międzynarodowych, w tym projektach dla Ministerstwa Transportu stanu Alabama (2015), jest także laureatem grantu Fundacji Kościuszkowskiej na prowadzanie badań w USA, który zrealizował w 2018 roku. Współautor...
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The adaptive spatio-temporal clustering method in classifying direct labor costs for the manufacturing industry
PublikacjaEmployee productivity is critical to the profitability of not only the manufacturing industry. By capturing employee locations using recent advanced tracking devices, one can analyze and evaluate the time spent during a workday of each individual. However, over time, the quantity of the collected data becomes a burden, and decreases the capabilities of efficient classification of direct labor costs. However, the results obtained...
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Fuzzy Divisive Hierarchical Clustering of Solvents According to Their Experimentally and Theoretically Predicted Descriptors
PublikacjaThe present study describes a simple procedure to separate into patterns of similarity a large group of solvents, 259 in total, presented by 15 specific descriptors (experimentally found and theoretically predicted physicochemical parameters). Solvent data is usually characterized by its high variability, dierent molecular symmetry, and spatial orientation. Methods of chemometrics can usefully be used to extract and explore accurately...
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Breast MRI segmentation by deep learning: key gaps and challenges
PublikacjaBreast 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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Evaluation of Path Based Methods for Conceptual Representation of the Text
PublikacjaTypical text clustering methods use the bag of words (BoW) representation to describe content of documents. However, this method is known to have several limitations. Employing Wikipedia as the lexical knowledge base has shown an improvement of the text representation for data-mining purposes. Promising extensions of that trend employ hierarchical organization of Wikipedia category system. In this paper we propose three path-based...
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ECG measurement in the bathtub - electrodes on the sides of the bathtub at the knees, signal amplification x2 - women
Dane BadawczeThe measurement data shows the measurement of the ECG signal in water in the bathtub. The data includes the measurement time, the reference ECG signal from the chest, and the ECG signal measured by electrodes placed in the bathtub without contact with the human body. Using the presented data, it is possible to estimate the optimal arrangement of measuring...
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Training of Deep Learning Models Using Synthetic Datasets
PublikacjaIn order to solve increasingly complex problems, the complexity of Deep Neural Networks also needs to be constantly increased, and therefore training such networks requires more and more data. Unfortunately, obtaining such massive real world training data to optimize neural networks parameters is a challenging and time-consuming task. To solve this problem, we propose an easy-touse and general approach to training deep learning...
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Self-Organizing Map representation for clustering Wikipedia search results
PublikacjaThe article presents an approach to automated organization of textual data. The experiments have been performed on selected sub-set of Wikipedia. The Vector Space Model representation based on terms has been used to build groups of similar articles extracted from Kohonen Self-Organizing Maps with DBSCAN clustering. To warrant efficiency of the data processing, we performed linear dimensionality reduction of raw data using Principal...
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Self–Organizing Map representation for clustering Wikipedia search results
PublikacjaThe article presents an approach to automated organization of textual data. The experiments have been performed on selected sub-set of Wikipedia. The Vector Space Model representation based on terms has been used to build groups of similar articles extracted from Kohonen Self-Organizing Maps with DBSCAN clustering. To warrant efficiency of the data processing, we performed linear dimensionality reduction of raw data using Principal...
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Shape-Based Pose Estimation of Robotic Surgical Instruments
PublikacjaWe describe a detector of robotic instrument parts in image-guided surgery. The detector consists of a huge ensemble of scale-variant and pose-dedicated, rigid appearance templates. The templates, which are equipped with pose-related keypoints and segmentation masks, allow for explicit pose estimation and segmentation of multiple end-effectors as well as fine-grained non-maximum suppression. We train the templates by grouping examples...
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APPLICATION OF STATISTICAL FEATURES AND MULTILAYER NEURAL NETWORK TO AUTOMATIC DIAGNOSIS OF ARRHYTHMIA BY ECG SIGNALS
PublikacjaAbnormal electrical activity of heart can produce a cardiac arrhythmia. The electrocardiogram (ECG) is a non-invasive technique which is used as a diagnostic tool for cardiac diseases. Non-stationarity and irregu- larity of heartbeat signal imposes many difficulties to clinicians (e.g., in the case of myocardial infarction arrhythmia). Fortunately, signal processing algorithms can expose hidden information within ECG signal contaminated...
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ECG measurement from the hand with standing - hands resting on the device - Woman 27 age
Dane BadawczeThe data set shows the measurements of the ECG signal from the hand made with the use of the proprietary measuring system, based on the AD8232 and STM32 chips. Thanks to such a constructed device, it is possible to measure the ECG signal in a non-standard way - from the hand. The results show the ECG signal for a specific hand and forearm position relative...
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ECG measurement from the hand with elbows in the air - Woman 27 age
Dane BadawczeThe data set shows the measurements of the ECG signal from the hand made with the use of the proprietary measuring system, based on the AD8232 and STM32 chips. Thanks to such a constructed device, it is possible to measure the ECG signal in a non-standard way - from the hand. The results show the ECG signal for a specific hand and forearm position relative...