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Search results for: train neural networks
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Deep Features Class Activation Map for Thermal Face Detection and Tracking
PublicationRecently, capabilities of many computer vision tasks have significantly improved due to advances in Convolutional Neural Networks. In our research, we demonstrate that it can be also used for face detection from low resolution thermal images, acquired with a portable camera. The physical size of the camera used in our research allows for embedding it in a wearable device or indoor remote monitoring solution for elderly and disabled...
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Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices
PublicationThere are growing opportunities to leverage new technologies and data sources to address global problems related to sustainability, climate change, and biodiversity loss. The emerging discipline of GeoAI resulting from the convergence of AI and Geospatial science (Geo-AI) is enabling the possibility to harness the increasingly available open Earth Observation data collected from different constellations of satellites and sensors...
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Automatic Rhythm Retrieval from Musical Files
PublicationThis paper presents a comparison of the effectiveness of two computational intelligence approaches applied to the task of retrieving rhythmic structure from musical files. The method proposed by the authors of this paper generates rhythmic levels first, and then uses these levels to compose rhythmic hypotheses. Three phases: creating periods, creating simplified hypotheses and creating full hypotheses are examined within this study....
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Early warning models against bankruptcy risk for Central European and Latin American enterprises
PublicationThis article is devoted to the issue of forecasting the bankruptcy risk of enterprises in Latin America and Central Europe. The author has used statistical and soft computing methods to program the prediction models. It compares the effectiveness of twelve different early warningmodels for forecasting the bankruptcy risk of companies. In the research conducted, the author used data on 185 companies listed on the Warsaw Stock Exchange...
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Investigating Feature Spaces for Isolated Word Recognition
PublicationThe study addresses the issues related to the appropriateness of a two-dimensional representation of speech signal for speech recognition tasks based on deep learning techniques. The approach combines Convolutional Neural Networks (CNNs) and time-frequency signal representation converted to the investigated feature spaces. In particular, waveforms and fractal dimension features of the signal were chosen for the time domain, and...
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Michał Tomasz Kucewicz dr
PeopleMichal Kucewicz was born in 1986 in Gdansk. In 2005 he completed International Baccalaureate programme in Topolowka (III High School in Gdańsk). Thanks to the G. D. Fahrenheit scholarship, he moved to the United Kingdom to study neuroscience. He received his Bachelor’s and Master’s degree from the Cambridge University, and his doctoral degree from the University of Bristol specializing in electrophysiology of memory and cognitive...
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Optimized Deep Learning Model for Flood Detection Using Satellite Images
PublicationThe increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection...
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Underground Water Level Prediction in Remote Sensing Images Using Improved Hydro Index Value with Ensemble Classifier
PublicationThe economic sustainability of aquifers across the world relies on accurate and rapid estimates of groundwater storage changes, but this becomes difficult due to the absence of insitu groundwater surveys in most areas. By closing the water balance, hydrologic remote sensing measures offer a possible method for quantifying changes in groundwater storage. However, it is uncertain to what extent remote sensing data can provide an...
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High frequency oscillations in human memory and cognition: a neurophysiological substrate of engrams?
PublicationDespite advances in understanding the cellular and molecular processes underlying memory and cognition, and recent successful modulation of cognitive performance in brain disorders, the neurophysiological mechanisms remain underexplored. High frequency oscillations beyond the classic electroencephalogram spectrum have emerged as a potential neural correlate of fundamental cognitive processes. High frequency oscillations are detected...
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Circularly Polarized Antenna Array design with the Potential of Gain-Size Trade-off and Omnidirectional Radiation for Millimeter-Wave Small Base Station Applications
PublicationThis paper presents the design and validation of a slot-patch-hybrid circularly polarized antenna array for 28 GHz millimeter (mm) wave (mm-wave) applications. The proposed design has a simple geometry that facilitates the fabrication process, which is otherwise a challenging task due to the sub-mm dimensions of the circuit in the mm-wave band. In the proposed structure, aperture-coupled series slot-fed array is utilized to excite...
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Design of a Coplanar Waveguide-Fed Wideband Compact-Size Circularly Polarized Antenna and polarization-sense alteration
PublicationThis paper presents the design and validation of a geometrically simple circularly polarized(CP) structure featuring flat gain in the sub-6 GHz 5th generation spectrum. The proposed structure is based on coplanar-waveguide-fed, modified wide slot etched in the ground plane. For generating CP waves, the coplanar ground planes are designed with slight asymmetry in both the horizontal and vertical directions. Furthermore, the ground...
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MAC contention in a wireless LAN with noncooperative anonymous stations
PublicationRozpatruje się model sieci bezprzewodowej wykorzystywanej przez wzajemnie nieprzenikalne grupy stacji anonimowych. Przy ustalonej regule wyłaniania zwycięzcy rywalizacji o dostęp do medium, stacje posiadają swobodę wyboru strategii selekcji szczeliny rywalizacyjnej. Dla szerokiego zbioru możliwych strategii proponuje się metodologię ich oceny i testowania wydajności opartą na pojęciu zbliżonym do ewolucyjnej stabilności.
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System monitoringu odbieraków prądu pojazdów kolejowych w aspekcie bezpieczeństwa i ciągłości ruchu.
PublicationOmówiono metodę monitorowania stanu technicznego odbieraków prądu w oparciu o pomiar uniesienia przewodów jezdnych sieci trakcyjnej w czasie przejazdu pociągu. Opisano konstrukcję i zasadę działania wdrożonego na linii kolejowej pilotażowego stanowiska monitoringu. Przedstawiono metodę analizy i sposób wizualizacji wyników pomiarów. Zastosowany algorytm szacowania siły statycznej odbieraka uwzględnia prędkości pociągu i działanie...
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Weakly-Supervised Word-Level Pronunciation Error Detection in Non-Native English Speech
PublicationWe propose a weakly-supervised model for word-level mispronunciation detection in non-native (L2) English speech. To train this model, phonetically transcribed L2 speech is not required and we only need to mark mispronounced words. The lack of phonetic transcriptions for L2 speech means that the model has to learn only from a weak signal of word-level mispronunciations. Because of that and due to the limited amount of mispronounced...
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Jacek Andrzej Litka mgr inż.
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Mariusz Dzwonkowski dr inż.
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Autonomous pick-and-place system based on multiple 3Dsensors and deep learning
PublicationGrasping objects and manipulating them is the main way the robot interacts with its environment. However, for robots to operate in a dynamic environment, a system for determining the gripping position for objects in the scene is also required. For this purpose, neural networks segmenting the point cloud are usually applied. However, training such networks is very complex and their results are unsatisfactory. Therefore, we propose...
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Autonomous Perception and Grasp Generation Based on Multiple 3D Sensors and Deep Learning
PublicationGrasping objects and manipulating them is the main way the robot interacts with its environment. However, for robots to operate in a dynamic environment, a system for determining the gripping position for objects in the scene is also required. For this purpose, neural networks segmenting the point cloud are usually applied. However, training such networks is very complex and their results are unsatisfactory. Therefore, we propose...
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How to Sort Them? A Network for LEGO Bricks Classification
PublicationLEGO bricks are highly popular due to the ability to build almost any type of creation. This is possible thanks to availability of multiple shapes and colors of the bricks. For the smooth build process the bricks need to properly sorted and arranged. In our work we aim at creating an automated LEGO bricks sorter. With over 3700 different LEGO parts bricks classification has to be done with deep neural networks. The question arises...
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MobileNet family tailored for Raspberry Pi
PublicationWith the advances in systems-on-a-chip technologies, there is a growing demand to deploy intelligent vision systems on low-cost microcomputers. To address this challenge, much of the recent research has focused on reducing the model size and computational complexity of contemporary convolutional neural networks (CNNs). The state-of-the-art lightweight CNN is MobileNetV3. However, it was designed to achieve a good trade-off between...
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Playback detection using machine learning with spectrogram features approach
PublicationThis paper presents 2D image processing approach to playback detection in automatic speaker verification (ASV) systems using spectrograms as speech signal representation. Three feature extraction and classification methods: histograms of oriented gradients (HOG) with support vector machines (SVM), HAAR wavelets with AdaBoost classifier and deep convolutional neural networks (CNN) were compared on different data partitions in respect...
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Analiza wyników monitoringu odbieraków prądu w warunkach ruchowych na linii kolejowej
PublicationOmówiono metodę ciągłego monitorowania stanu odbieraków prądu na linii kolejowej w oparciu o pomiar uniesienia przewodów jezdnych sieci trakcyjnej w czasie przejazdu pociągu. Omówiono zasadę działania wdrożonego stanowiska monitoringu. Opisano algorytmy analizy danych pomiarowych - w szczególności oszacowania siły statycznej odbieraka prądu - i przyjęte kryteria oceny odbieraka. W analizie bierze się pod uwagę także prędkość pociągu...
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Performance improvement of NN based RTLS by customization of NN structure - heuristic approach
PublicationThe purpose of this research is to improve performance of the Hybrid Scene Analysis – Neural Network indoor localization algorithm applied in Real-time Locating System, RTLS. A properly customized structure of Neural Network and training algorithms for specific operating environment will enhance the system’s performance in terms of localization accuracy and precision. Due to nonlinearity and model complexity, a heuristic analysis...
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BETWEEN IDEA AND INTERPRETATION - DESIGN PROCESS AUGMENTATION
PublicationThe following paper investigates the idea of reducing the human digital intervention to a minimum during the advanced design process. Augmenting the outcome attributes beyond the designer's capabilities by computational design methods, data collection, data computing and digital fabrication, altogether imitating the human design process. The primary technical goal of the research was verification of restrictions and abilities used...
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Prediction of maximum tensile stress in plain-weave composite laminates with interacting holes via stacked machine learning algorithms: A comparative study
PublicationPlain weave composite is a long-lasting type of fabric composite that is stable enough when being handled. Open-hole composites have been widely used in industry, though they have weak structural performance and complex design processes. An extensive number of material/geometry parameters have been utilized for designing these composites, thereby an efficient computational tool is essential for that purpose. Different Machine Learning...
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Olga Błaszkiewicz mgr inż.
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Predictions of cervical cancer identification by photonic method combined with machine learning
PublicationCervical 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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Performance Analysis of the OpenCL Environment on Mobile Platforms
PublicationToday’s smartphones have more and more features that so far were only assigned to personal computers. Every year these devices are composed of better and more efficient components. Everything indicates that modern smartphones are replacing ordinary computers in various activities. High computing power is required for tasks such as image processing, speech recognition and object detection. This paper analyses the performance of...
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Automatic Emotion Recognition in Children with Autism: A Systematic Literature Review
PublicationThe automatic emotion recognition domain brings new methods and technologies that might be used to enhance therapy of children with autism. The paper aims at the exploration of methods and tools used to recognize emotions in children. It presents a literature review study that was performed using a systematic approach and PRISMA methodology for reporting quantitative and qualitative results. Diverse observation channels and modalities...
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A comparative analysis of the effectiveness of corporate bankruptcy prediction models based on financial ratios: Evidence from Colombia, 2008 to 2015
PublicationLogit and discriminant analyses have been used for corporate bankruptcy prediction in several studies since the last century. In recent years there have been dozens of studies comparing the several models available, including the ones mentioned above and also probit, artificial neural networks, support vector machines, among others. For the first time for Colombia, this paper presents a comparative analysis of the effectiveness...
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Soft Sensor Application in Identification of the Activated Sludge Bulking Considering the Technological and Economical Aspects of Smart Systems Functioning
PublicationThe paper presented the methodology for the construction of a soft sensor used for activated sludge bulking identification. Devising such solutions fits within the current trends and development of a smart system and infrastructure within smart cities. In order to optimize the selection of the data-mining method depending on the data collected within a wastewater treatment plant (WWTP), a number of methods were considered, including:...
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Rapid Surrogate-Aided Multi-Criterial Optimization of Compact Microwave Passives Employing Machine Learning and ANNs
PublicationThis article introduces an innovative method for achieving low-cost and reliable multi-objective optimization (MO) of microwave passive circuits. The technique capitalizes on the attributes of surrogate models, specifically artificial neural networks (ANNs), and multi-resolution electromagnetic (EM) analysis. We integrate the search process into a machine learning (ML) framework, where each iteration produces multiple infill points...
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Runge-Kutta bicharacteristic methods for first order partial functional di- fferential equations
PublicationW pracy prezentujemy nową klasę metod numerycznych dla równań różniczkowo-funkcyjnych. Są to metody bicharakterystyk Rungego-Kutty. Ponadto porównuje-my wprowadzone metody z metodami klasycznymi.
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Conference on Security and Cryptography for Networks
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IEEE International Conference on Networks
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Embedded Sensor Networks Workshop
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Information Processing in Sensor Networks
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Badanie i analiza efektywności alokacji strumieni danych w heterogenicznej sieci WBAN
PublicationW niniejszej dysertacji doktorskiej poddano dyskusji efektywność alokacji strumieni danych w heterogenicznej radiowej sieci WBAN (Wireless Body Area Networks). Biorąc pod uwagę dynamiczny rozwój nowoczesnych sieci radiokomunikacyjnych piątej generacji (5G), którego część stanowią radiowe sieci działające w obrębie ciała człowieka, bardzo ważnym aspektem są metody maksymalizujące wykorzystanie dostępnych zasobów czasowo –częstotliwościowych...
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Oddziaływania taboru na mosty kolejowe przy zmiennych parametrach ruchu
PublicationW pracy poruszono zagadnienia związane z identyfikacją oraz modelowaniem układu dynamicznego most-pojazd ruchomy. Za element szczególnie istotny uznano czynnik obciążający. Opracowano i wdrożono metodykę identyfikacji cech dynamicznych rzeczywistego pojazdu szynowego. Eksperyment badawczy przeprowadzono dla jednostki trakcyjnej EN57, jednak nie ma żadnych ograniczeń w stosowaniu metody dla innego, konwencjonalnego taboru szynowego....
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Impact of R/X ratio of distribution network on selection and control of energy storage units
PublicationThe interest in energy storage is still increasing. Energy storage units are installed in high voltage networks, medium voltage networks and low voltage distribution networks as well. These units are often used to improve power quality. One of the criteria for improving power quality is reducing voltage deviations. Depending on the type of network and specifying its R/X ratio, this criterion can be fulfilled by control of active...
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Radio Communication Antennas and MIMO Techniques (2023/24)
e-Learning CoursesCourse assigned to the following subject: - Radio Communication Antennas and MIMO Techniques (profile Radio Communication Systems and Networks [EN])
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Workshop on Applications of Graph Theory in Wireless Ad hoc Networks and Sensor Networks
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Wiktoria Wojnicz dr hab. inż.
PeopleDSc in Mechanics (in the field of Biomechanics) - Lodz Univeristy of Technology, 2019 PhD in Mechanics (in the field of Biomechanics) - Lodz Univeristy of Technology, 2009 (with distinction) List of papers (2009 - ) Wojnicz W., Wittbrodt E., Analysis of muscles' behaviour. Part I. The computational model of muscle. Acta of Bioengineering and Biomechanics, Vol. 11, No.4, 2009, p. 15-21 Wojnicz W., Wittbrodt E., Analysis of...
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Michał Kowalewski dr inż.
PeopleResearch career: Doctoral dissertation "Tolerance robust, dictionary methods of fault diagnosis of electronic circuits with specialized neural classifier". Participation as a performer in four KBN research teams MNiSW and NCBiR concerning the development of diagnostic methods for analog electronic circuits and diagnostics of technical objects using impedance spectroscopy methods. 39 publications, including 10 in magazines,...
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Multi-Option Model of Railway Traffic Organization Including the Energy Recuperation
PublicationThe article presents the issue of organization of railway traffic within the station, taking into account energy recuperation. We identified a number of factors which affect the energy efficiency of recuperation, including the issue of transfer of energy technology between a few vehicles. We presented some aspects of the decision problem of graphic train timetable construction with regard to recuperation. As an indicator of quality...
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Alicja Olejniczak mgr inż.
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Rust QA: question answering dataset for "The Rust Programming Language" in SQuAD 2.0 format
Open Research DataRust QA is a dataset for training and evaluating QA systems. The dataset consists of 1068 questions to "The Rust Programming Language" book (https://doc.rust-lang.org/stable/book/) with the answers provided as text spans from the book. The dataset is released in SQuAD 2.0 format.
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A new multi-process collaborative architecture for time series classification
PublicationTime series classification (TSC) is the problem of categorizing time series data by using machine learning techniques. Its applications vary from cybersecurity and health care to remote sensing and human activity recognition. In this paper, we propose a novel multi-process collaborative architecture for TSC. The propositioned method amalgamates multi-head convolutional neural networks and capsule mechanism. In addition to the discovery...
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The Influence of Selecting Regions from Endoscopic Video Frames on The Efficiency of Large Bowel Disease Recognition Algorithms
PublicationThe article presents our research in the field of the automatic diagnosis of large intestine diseases on endoscopic video. It focuses on the methods of selecting regions of interest from endoscopic video frames for further analysis by specialized disease recognition algorithms. Four methods of selecting regions of interest have been discussed: a. trivial, b. with the deletion of characteristic, endoscope specific additions to the...
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Data governance: Organizing data for trustworthy Artificial Intelligence
PublicationThe rise of Big, Open and Linked Data (BOLD) enables Big Data Algorithmic Systems (BDAS) which are often based on machine learning, neural networks and other forms of Artificial Intelligence (AI). As such systems are increasingly requested to make decisions that are consequential to individuals, communities and society at large, their failures cannot be tolerated, and they are subject to stringent regulatory and ethical requirements....