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Wyniki wyszukiwania dla: MAGNETIC SIGNATURES, MEASUREMENT DEPTH, MODELING, NEURAL NETWORKS
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SegSperm - a dataset of sperm images for blurry and small object segmentation
Dane BadawczeMany deep learning applications require figure-ground segmentation. The performance of segmentation models varies across modalities and acquisition settings.
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Magnetic field maps of an astable multivibrator in frequency range from 30 MHz to 3 GHz – selective detection
Dane BadawczeThe data presents a result of near field measurements of electromagnetic emissions radiated from the PCB of a small electronic device. An efficient method of modelling the magnetic and electric field emissions is the measurements in the near field using electric and magnetic probes. The attached files contain magnetic field maps created on based measurements...
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Paweł Rościszewski dr inż.
OsobyPaweł Rościszewski received his PhD in Computer Science at Gdańsk University of Technology in 2018 based on PhD thesis entitled: "Optimization of hybrid parallel application execution in heterogeneous high performance computing systems considering execution time and power consumption". Currently, he is an Assistant Professor at the Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Poland....
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Problems of modelling toxic compounds emitted by a marine internal combustion engine in unsteady states
PublikacjaContemporary engine tests are performed based on the theory of experiment. The available versions of programmes used for analysing experimental data make frequent use of the multiple regression model, which enables examining effects and interactions between input model parameters and a single output variable. The use of multi-equation models provides more freedom in analysing the measured results, as those models enable simultaneous...
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Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks
PublikacjaIn this article, the usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) is presented. The developed approach, using channel impulse response, allows higher efficiency in identifying the direct visibility conditions between nodes in off-body communication with comparison to the methods described in the literature. The effectiveness of the proposed deep feedforward neural network was checked on...
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Stanisław Galla dr inż.
OsobyUrodził się w 1970 w Gdańsku. Jest absolwentem Technikum Mechaniczno Elektrycznego w Gdańsku (1990). Studiował na Wydziale Elektrotechniki Politechniki Gdańskiej (studia ukończył w 1996). Rozprawę doktorską pt. „Metodyka zwiększania dokładności pomiarów małoczęstotliwościowych wskaźników zaburzeń okresowych występujących w sieciach niskiego napięcia” obronił w 2009 r na Wydziale Elektrotechniki i Automatyki Politechniki Gdańskiej....
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Residual MobileNets
PublikacjaAs modern convolutional neural networks become increasingly deeper, they also become slower and require high computational resources beyond the capabilities of many mobile and embedded platforms. To address this challenge, much of the recent research has focused on reducing the model size and computational complexity. In this paper, we propose a novel residual depth-separable convolution block, which is an improvement of the basic...
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Chronology and geochemistry of varved sediments from Lake Gorzyńskie, western Poland: a new archive of climatic and environmental changes during the Late Glacial and the Holocene in central Europe
Dane BadawczeHere, we present a new dataset related to varved sediment record of Lake Gorzyńskie located in western Poland. The complete sediment profile from this lake is 10.45 meters long and covers the last ca. 13,250 years. The dataset includes results of varve counting, age depth modeling, and geochemical proxies. The Lake Gorzyńskie sediment record offers...
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Research of leakage magnetic field in deenergized transformer
PublikacjaThe article deals with the issue of the numerical analysis of the magnetic field occurring around the transformer after it has been powered down. The main goal of this analysis was to examine if it is possible to identify the residual fluxes in the transformer legs based on this fields’ measurements. It was also intended to determine the type and the location of magnetic sensors. Numerical analysis of the magnetic field was performed....
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Special techniques and future perspectives: Simultaneous macro- and micro-electrode recordings
PublikacjaThere are many approaches to studying the inner workings of the brain and its highly interconnected circuits. One can look at the global activity in different brain structures using non-invasive technologies like positron emission tomography (PET) or functional magnetic resonance imaging (fMRI), which measure physiological changes, e.g. in the glucose uptake or blood flow. These can be very effectively used to localize active patches...
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From Linear Classifier to Convolutional Neural Network for Hand Pose Recognition
PublikacjaRecently gathered image datasets and the new capabilities of high-performance computing systems have allowed developing new artificial neural network models and training algorithms. Using the new machine learning models, computer vision tasks can be accomplished based on the raw values of image pixels instead of specific features. The principle of operation of deep neural networks resembles more and more what we believe to be happening...
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The electromagnetic field intensity in industrial buildings
Dane BadawczeThe dataset contains the results of measurements of electromagnetic fields, separately electric and magnetic, carried out at selected places in the building of an operating industrial enterprise.
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Decomposition of the induced magnetism degaussing problem for fast determination of currents in demagnetization coils wrapped outside an object under arbitrary external field conditions
PublikacjaSafe passage of ships in the presence of sea mines can be ensured by limiting or reducing the ship’s magnetic footprint. For vessels with plastic hulls, the main component that requires magnetic damping is the engine. Demagnetization of such an object can be achieved by wrapping it with coils and setting the direct current appropriately. For each specific geographic location, the currents in the coils can be determined iteratively...
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Model of ship's magnetic signature
PublikacjaShips made of ferromagnetic metals interfere with Earth's magnetic field in their surrounding. The disturbance of the magnetic field makes possible localization and even identification of the ship, which could determine a threat to the ship. The measurement of the magnetic field around the ship enables to determine its magnetic signature. The paper presents a multidipoles model of the ship magnetic field, which allows to determine...
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Multidipoles model of ship's magnetic field
PublikacjaShips made of ferromagnetic metals interfere with Earth's magnetic field in their surrounding. The disturbance of the magnetic field makes possible localization and even identification of the ship, which could determine a threat to the ship. The measurement of the magnetic field around the ship enables to determine its magnetic signature. The paper presents a multidipoles model of the ship magnetic field, which allows to determine...
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Multidipoles model of ship's magnetic field
PublikacjaShips made of ferromagnetic metals interfere with Earth's magnetic field in their surrounding. The disturbance of the magnetic field makes possible localization and even identification of the ship, which could determine a threat to the ship. The measurement of the magnetic field around the ship enables to determine its magnetic signature. The paper presents a multidipoles model of the ship magnetic field, which allows to determine...
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Evaluation of Facial Pulse Signals Using Deep Neural Net Models
PublikacjaThe reliable measurement of the pulse rate using remote photoplethysmography (PPG) is very important for many medical applications. In this paper we present how deep neural networks (DNNs) models can be used in the problem of PPG signal classification and pulse rate estimation. In particular, we show that the DNN-based classification results correspond to parameters describing the PPG signals (e.g. peak energy in the frequency...
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Identyfikacja instrumentu muzycznego z nagrania fonicznego za pomocą sztucznych sieci neuronowych
PublikacjaCelem rozprawy jest zbadanie algorytmów do identyfikacji instrumentów występujących w sygnale polifonicznym z wykorzystaniem sztucznych sieci neuronowych. W części teoretycznej przywołano podstawy przetwarzania sygnałów fonicznych w kontekście ekstrakcji parametrów sygnałów wykorzystywanych w treningu sieci neuronowych. Dodatkowo dokonano analizy rozwoju metod uczenia maszynowego z uwzględnieniem podziału na sieci neuronowe pierwszej,...
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Open-Set Speaker Identification Using Closed-Set Pretrained Embeddings
PublikacjaThe paper proposes an approach for extending deep neural networks-based solutions to closed-set speaker identification toward the open-set problem. The idea is built on the characteristics of deep neural networks trained for the classification tasks, where there is a layer consisting of a set of deep features extracted from the analyzed inputs. By extracting this vector and performing anomaly detection against the set of known...
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Neural Network-Based Sequential Global Sensitivity Analysis Algorithm
PublikacjaPerforming global sensitivity analysis (GSA) can be challenging due to the combined effect of the high computational cost, but it is also essential for engineering decision making. To reduce this cost, surrogate modeling such as neural networks (NNs) are used to replace the expensive simulation model in the GSA process, which introduces the additional challenge of finding the minimum number of training data samples required to...
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CNN Architectures for Human Pose Estimation from a Very Low Resolution Depth Image
PublikacjaThe paper is dedicated to proposing and evaluating a number of convolutional neural network architectures for calculating a multiple regression on 3D coordinates of human body joints tracked in a single low resolution depth image. The main challenge was to obtain a high precision in case of a noisy and coarse scan of the body, as observed by a depth sensor from a large distance. The regression network was expected to reason about...
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Predictions of cervical cancer identification by photonic method combined with machine learning
PublikacjaCervical cancer is one of the most commonly appearing cancers, which early diagnosis is of greatest importance. Unfortunately, many diagnoses are based on subjective opinions of doctors—to date, there is no general measurement method with a calibrated standard. The problem can be solved with the measurement system being a fusion of an optoelectronic sensor and machine learning algorithm to provide reliable assistance for doctors...
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Minimization of a ship's magnetic signature under external field conditions using a multi-dipole model
PublikacjaThe paper addresses the innovative issue of minimizing the ship's magnetic signature under any external field conditions, i.e., for arbitrary values of ambient field modulus and magnetic inclination. Varying values of the external field, depending on the current geographical location, affect only the induced part of ship's magnetization. A practical problem in minimizing the ship signature is separating permanent magnetization...
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Abdominal Aortic Aneurysm segmentation from contrast-enhanced computed tomography angiography using deep convolutional networks
PublikacjaOne of the most common imaging methods for diagnosing an abdominal aortic aneurysm, and an endoleak detection is computed tomography angiography. In this paper, we address the problem of aorta and thrombus semantic segmentation, what is a mandatory step to estimate aortic aneurysm diameter. Three end-to-end convolutional neural networks were trained and evaluated. Finally, we proposed an ensemble of deep neural networks with underlying...
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The influence of the geographic positioning system error on the quality of ship magnetic signature reproduction based on measurements in sea conditions
PublikacjaIn previous studies, the authors performed the magnetic signature reconstruction of the marine ship Zodiak as part of the measurement campaign focused on recording magnetic data and the relative position of a ship during its passage over a magnetometer immersed on the testing ground. A high degree of representation of the magnetic signature was obtained. However, the recorded measurement data revealed new patterns of the multidipole model...
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The decay of quantum correlations between quantum dot spin qubits and the characteristics of its magnetic-field dependence
PublikacjaWe address the question of the role of quantum correlations beyond entanglement in the context of quantum magnetometry. We study the evolution of the rescaled variant of the geometric quantum discord of two electron-spin qubits interacting with an environment of nuclear spins via the hyperfine interaction. We have found that quantum correlations display a strong magnetic-field sensitivity which can be utilized for decoherence-driven...
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Magnetic disturbances caused by magnetic contamination of plastics
PublikacjaIn the study of low magnetic fields precision magnetometers working in a differential system are used. Two optically pumped magnetometers working in a differential system allow for precise measuring of disturbances in the magnetic field. In order to attain high accuracy of magnetic field measurement, it is necessary to use appropriate materials for the construction of a magnetometric system, particularly of those located closely...
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OBTAINING FLUID FLOW PATTERN FOR TURBINE STAGE WITH NEURAL MODEL.
PublikacjaIn the paper possibility of applying neural model to obtaining patterns of proper operation for fluid flow in turbine stage for fluid-flow diagnostics is discussed. Main differences between Computational Fluid Dynamics (CFD) solvers and neural model is given, also limitations and advantages of both are considered. Time of calculations of both methods was given, also possibilities of shortening that time with preserving the accuracy...
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Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning
PublikacjaThe aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was...
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Towards neural knowledge DNA
PublikacjaIn this paper, we propose the Neural Knowledge DNA, a framework that tailors the ideas underlying the success of neural networks to the scope of knowledge representation. Knowledge representation is a fundamental field that dedicates to representing information about the world in a form that computer systems can utilize to solve complex tasks. The proposed Neural Knowledge DNA is designed to support discovering, storing, reusing,...
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Method of determining the residual fluxes in transformer core
PublikacjaThe article presents the method of calculating the residual induction in transformer columns. The method is based on measurement of the magnetic induction in selected points around the transformer core. The values of residual induction are calculated as linear combination of the results of measurement.
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Prognostic and diagnostic capabilities of OOBN in assessing investment risk of complex construction projects
PublikacjaModelling decision problems using Bayesian networks is extremely valuable especially in case of issues related to uncertainty; it is also very helpful in constructing and understanding visual representation of the elements and their relations. This approach facilitates subsequent application of Bayesian networks, however there can be situations where using simple Bayesian networks is impractical or even ineffective. The aim of...
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Developing Methodology for Model Tests of Floating Platforms in Low -Depth Towing Tank
PublikacjaThe paper presents two different methods of physical modeling of semi-submersible platform mooring system for research in low depth towing tank. The tested model was made in the scale of 1:100 resembling the "Thunder Horse" platform moored in the Gulf of Mexico at a depth of 1,920 m. Its mooring system consisted of 16 semi-taut mooring lines (chain-wire-chain) spaced starshape and attached at the bottom to suction piles. The tests...
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Machine Learning Applied to Aspirated and Non-Aspirated Allophone Classification—An Approach Based on Audio "Fingerprinting"
PublikacjaThe purpose of this study is to involve both Convolutional Neural Networks and a typical learning algorithm in the allophone classification process. A list of words including aspirated and non-aspirated allophones pronounced by native and non-native English speakers is recorded and then edited and analyzed. Allophones extracted from English speakers’ recordings are presented in the form of two-dimensional spectrogram images and...
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TOXIC GASES IDENTIFICATION USING SINGLE ELECTROCATALYTIC SENSOR RESPONSES AND ARTIFICIAL NEURAL NETWORK
PublikacjaThe need for precise detection of toxic gases drives development of new gas sensors structures and methods of processing the output signals from the sensors. In literature, artificial neural networks are considered as one of the most effective tool for the analysis of gas sensors or sensors arrays responses. In this paper a method of toxic gas components identification using a electrocatalytic gas sensor as a detector and an artificial...
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Super-resolved Thermal Imagery for High-accuracy Facial Areas Detection and Analysis
PublikacjaIn this study, we evaluate various Convolutional Neural Networks based Super-Resolution (SR) models to improve facial areas detection in thermal images. In particular, we analyze the influence of selected spatiotemporal properties of thermal image sequences on detection accuracy. For this purpose, a thermal face database was acquired for 40 volunteers. Contrary to most of existing thermal databases of faces, we publish our dataset...
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Neural network training with limited precision and asymmetric exponent
PublikacjaAlong with an extremely increasing number of mobile devices, sensors and other smart utilities, an unprecedented growth of data can be observed in today’s world. In order to address multiple challenges facing the big data domain, machine learning techniques are often leveraged for data analysis, filtering and classification. Wide usage of artificial intelligence with large amounts of data creates growing demand not only for storage...
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Charge density wave, enhanced mobility, and large nonsaturating magnetoresistance across the magnetic states of HoNiC2 and ErNiC2
PublikacjaWe report on magnetotransport and thermoelectric properties of two ternary carbides HoNiC2 and ErNiC2 hosting both charge density wave and long-range magnetic order. In the charge density wave state, both compounds show relatively large magnetoresistance MR ≈ 150% in HoNiC2 and ≈ 70%in ErNiC2 at a magnetic field of 9 T and temperature as low as 2 K. This positive field-linear magnetoresistance shows no signatures of saturation....
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Electric field maps of an astable multivibrator in frequency range from 30 MHz to 3 GHz
Dane BadawczeThe data presents a result of near field measurements of electromagnetic emissions radiated from the PCB of a small electronic device. An efficient method of modelling the magnetic and electric field emissions is the measurements in the near field using electric and magnetic probes. The attached files contain electric field maps created on based measurements...
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Influence of User Mobility on System Loss and Depolarization in a BAN Indoor Scenario
PublikacjaIn this article, an analysis of system loss and depolarization in body area networks (BANs) for body-toinfrastructure (B2I) communications based on a measurement campaign in the 5.8 GHz band in an indoor environment is performed. Measurements were performed with an off-body antenna transmitting linearly polarized signals and dual-polarized receiving antennas carried by the user on the body. A normal distribution with a mean of...
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Neural network simulator's application to reference performance determination of turbine blading in the heat-flow diagnostics.
PublikacjaIn the paper, the possibility of application of artificial neural networks to perform the fluid flow calculations through both damaged and undamaged turbine blading was investigated. Preliminary results are presented and show the potentiality of further development of the method for the purpose of heat-flow diagnostics.
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Deep Learning: A Case Study for Image Recognition Using Transfer Learning
PublikacjaDeep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities,...
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Deep Learning
PublikacjaDeep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities,...
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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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Deep neural network architecture search using network morphism
PublikacjaThe paper presents the results of the research on neural architecture search (NAS) algorithm. We utilized the hill climbing algorithm to search for well-performing structures of deep convolutional neural network. Moreover, we used the function preserving transformations which enabled the effective operation of the algorithm in a short period of time. The network obtained with the advantage of NAS was validated on skin lesion classification...
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The conducted immunity test of a power supply unit in accordance with EMC standards
Dane BadawczeThe dataset presents a result of measurements that are a part of immunity tests to conducted disturbances, induced by radio-frequency fields. The immunity tests were carried out on the mains cable of the DF1723003TC NDN power supply. Tests of immunity of electronic systems to conducted disturbances in the frequency range from 150 kHz to 230 MHz are...
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Analysis of magnetic disturbances of air platform caused by induced magnetisation
PublikacjaMagnetometric systems installed on air platforms like airplanes or helicopters are equipped in compensators of magnetic disturbances. Time changes of the platform's position caused generation of magnetic disturbances. The permanent and induced magnetization of ferromagnetic elements and eddy currents induced in conducting parts of the platform are sources of these disturbances. The linear model of magnetic disturbances of platforms...
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Aleksandra Parteka dr hab. inż.
OsobyAbout me: I am an associate professor and head of doctoral studies at the Faculty of Management and Economics, Gdansk University of Technology (GdanskTech, Poland). I got my MSc degree in Economics from Gdansk University of Technology (2003) and Universita’ Politecnica delle Marche (2005), as well as MA degree in Contemporary European Studies from Sussex University (2006, with distinction). I received my PhD in Economics...
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Enhanced Mobility and Large Linear Nonsaturating Magnetoresistance in the Magnetically Ordered States of TmNiC2
PublikacjaWe have studied the magnetic, magnetotransport, and galvanomagnetic properties of TmNiC2. We find that the antiferromagnetic and field induced metamagnetic and ferromagnetic orderings do not suppress the charge density wave. The persistence of Fermi surface pockets, open as a result of imperfect nesting accompanying the Peierls transition, results in an electronic carriers mobility of the order of 4 × 103 cm2 V−1 s−1 in ferromagnetic...
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Influence of plastic deformation on stray magnetic field distribution of soft magnetic steel sample
PublikacjaThe effect of various combinations of conditions, i.e., presence of the Earth’s magnetic field during and after deformation on the distribution of stray magnetic field of S355 steel sample, which is locally deformed, was investigated. Some of the stages of the experiment were carried out in zero magnetic field. Compensation of the Earth’s magnetic field was obtained by the application of a pair of Helmholtz coils. These coils are...