Search results for: deep learning - Bridge of Knowledge

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Search results for: deep learning

Search results for: deep learning

  • IFE: NN-aided Instantaneous Pitch Estimation

    Publication

    Pitch estimation is still an open issue in contemporary signal processing research. Nowadays, growing momentum of machine learning techniques application in the data-driven society allows for tackling this problem from a new perspective. This work leverages such an opportunity to propose a refined Instantaneous Frequency and power based pitch Estimator method called IFE. It incorporates deep neural network based pitch estimation...

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  • Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention

    Publication

    - Year 2021

    This 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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  • Międzynarodowa Szkoła Letnia na temat algorytmów

    Events

    06-07-2020 08:30 - 11-07-2020 17:00

    Katedra Algorytmów i Modelowania Systemów WETI PG organizuje 4. edycję Międzynarodowej Szkoły Letniej na temat algorytmów dla problemów optymalizacji dyskretnej i głębokiego uczenia

  • Jacek Rumiński prof. dr hab. inż.

    Wykształcenie i kariera zawodowa 2022 2016   2002   1995   1991-1995 Tytuł profesora Habilitacja   Doktor nauk technicznych   Magister inżynier     Prezydent RP, dziedzina nauk inżynieryjno-technicznych, dyscyplina: inzyniera biomedyczna Politechnika Gdańska, Biocybernetyka i inżyniera biomedyczna, tematyka: „Metody wyodrębniania sygnałów i parametrów z różnomodalnych sekwencji obrazów dla potrzeb diagnostyki i wspomagania...

  • Pedestrian detection in low-resolution thermal images

    Over one million people die in car accidents worldwide each year. A solution that will be able to reduce situations in which pedestrian safety is at risk has been sought for a long time. One of the techniques for detecting pedestrians on the road is the use of artificial intelligence in connection with thermal imaging. The purpose of this work was to design a system to assist the safety of people and car intelligence with the use...

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  • Evaluating Accuracy of Respiratory Rate Estimation from Super Resolved Thermal Imagery

    Non-contact estimation of Respiratory Rate (RR) has revolutionized the process of establishing the measurement by surpassing some issues related to attaching sensors to a body, e.g. epidermal stripping, skin disruption and pain. In this study, we perform further experiments with image processing-based RR estimation by using various image enhancement algorithms. Specifically, we employ Super Resolution (SR) Deep Learning (DL) network...

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  • Deep neural networks for data analysis

    e-Learning Courses
    • K. Draszawka

    The aim of the course is to familiarize students with the methods of deep learning for advanced data analysis. Typical areas of application of these types of methods include: image classification, speech recognition and natural language understanding. Celem przedmiotu jest zapoznanie studentów z metodami głębokiego uczenia maszynowego na potrzeby zaawansowanej analizy danych. Do typowych obszarów zastosowań tego typu metod należą:...

  • Artificial intelligence for software development — the present and the challenges for the future

    Since the time when first CASE (Computer-Aided Software Engineering) methods and tools were developed, little has been done in the area of automated creation of code. CASE tools support a software engineer in creation the system structure, in defining interfaces and relationships between software modules and, after the code has been written, in performing testing tasks on different levels of detail. Writing code is still the task...

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  • Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices

    Publication
    • A. G. Pereira
    • A. Ojo
    • C. Edward
    • L. Porwol

    - Year 2020

    There 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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  • Multiclass AdaBoost Classifier Parameter Adaptation for Pattern Recognition

    The article presents the problem of parameter value selection of the multiclass ``one against all'' approach of an AdaBoost algorithm in tasks of object recognition based on two-dimensional graphical images. AdaBoost classifier with Haar features is still used in mobile devices due to the processing speed in contrast to other methods like deep learning or SVM but its main drawback is the need to assembly the results of binary...

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  • Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models

    Publication
    • R. Yurt
    • H. Torpi
    • P. Mahouti
    • A. Kizilay
    • S. Kozieł

    - IEEE Access - Year 2023

    This work addresses artificial-intelligence-based buried object characterization using 3-D full-wave electromagnetic simulations of a ground penetrating radar (GPR). The task is to characterize cylindrical shape, perfectly electric conductor (PEC) object buried in various dispersive soil media, and in different positions. The main contributions of this work are (i) development of a fast and accurate data driven surrogate modeling...

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  • Fully Automated AI-powered Contactless Cough Detection based on Pixel Value Dynamics Occurring within Facial Regions

    Publication

    - Year 2021

    Increased interest in non-contact evaluation of the health state has led to higher expectations for delivering automated and reliable solutions that can be conveniently used during daily activities. Although some solutions for cough detection exist, they suffer from a series of limitations. Some of them rely on gesture or body pose recognition, which might not be possible in cases of occlusions, closer camera distances or impediments...

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  • Equal Baseline Camera Array—Calibration, Testbed and Applications

    Publication

    - Applied Sciences-Basel - Year 2021

    This paper presents research on 3D scanning by taking advantage of a camera array consisting of up to five adjacent cameras. Such an array makes it possible to make a disparity map with a higher precision than a stereo camera, however it preserves the advantages of a stereo camera such as a possibility to operate in wide range of distances and in highly illuminated areas. In an outdoor environment, the array is a competitive alternative...

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  • Toward Robust Pedestrian Detection With Data Augmentation

    Publication

    In this article, the problem of creating a safe pedestrian detection model that can operate in the real world is tackled. While recent advances have led to significantly improved detection accuracy on various benchmarks, existing deep learning models are vulnerable to invisible to the human eye changes in the input image which raises concerns about its safety. A popular and simple technique for improving robustness is using data...

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  • Mask Detection and Classification in Thermal Face Images

    Publication

    Face 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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  • Medical Image Segmentation Using Deep Semantic-based Methods: A Review of Techniques, Applications and Emerging Trends

    Publication

    - Information Fusion - Year 2022

    Semantic-based segmentation (Semseg) methods play an essential part in medical imaging analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is classified into an instance, where each class is corresponded by an instance. In particular, the semantic segmentation can be used by many medical experts in the domain of radiology, ophthalmologists, dermatologist, and image-guided radiotherapy. The authors...

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  • High-quality academic teachers in business school. The case of The University of Gdańsk, Poland

    Publication

    The Bologna process, the increasing number of higher education institutions, the mass education and the demographic problems make the quality of education and quality of the academic teachers a subject of wide public debate and concern. The aim of the paper is to identify the most preferred characteristics of a teacher working at a business school. The research problem was: What should a high-quality business school academic teacher...

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  • Sensors and System for Vehicle Navigation

    Publication

    - SENSORS - Year 2022

    In recent years, vehicle navigation, in particular autonomous navigation, has been at the center of several major developments, both in civilian and defense applications. New technologies, such as multisensory data fusion, big data processing, or deep learning, are changing the quality of areas of applications, improving the sensors and systems used. Recently, the influence of artificial intelligence on sensor data processing and...

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  • Segmentation Quality Refinement in Large-Scale Medical Image Dataset with Crowd-Sourced Annotations

    Publication

    Deployment of different techniques of deep learning including Convolutional Neural Networks (CNN) in image classification systems has accomplished outstanding results. However, the advantages and potential impact of such a system can be completely negated if it does not reach a target accuracy. To achieve high classification accuracy with low variance in medical image classification system, there is needed the large size of the...

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  • Speaker Recognition Using Convolutional Neural Network with Minimal Training Data for Smart Home Solutions

    Publication

    - Year 2018

    With the technology advancements in smart home sector, voice control and automation are key components that can make a real difference in people's lives. The voice recognition technology market continues to involve rapidly as almost all smart home devices are providing speaker recognition capability today. However, most of them provide cloud-based solutions or use very deep Neural Networks for speaker recognition task, which are...

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  • Long Distance Vital Signs Monitoring with Person Identification for Smart Home Solutions

    Publication

    - Year 2018

    Abstract— Imaging photoplethysmography has already been proved to be successful in short distance (below 1m). However, most of the real-life use cases of measuring vital signs require the system to work at longer distances, to be both more reliable and convenient for the user. The possible scenarios that system designers must have in mind include monitoring of the vital signs of residents in nursing homes, disabled people, who...

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  • Enabling Deeper Linguistic-based Text Analytics – Construct Development for the Criticality of Negative Service Experience

    Publication

    - IEEE Access - Year 2019

    Significant progress has been made in linguistic-based text analytics particularly with the increasing availability of data and deep learning computational models for more accurate opinion analysis and domain-specific entity recognition. In understanding customer service experience from texts, analysis of sentiments associated with different stages of the service lifecycle is a useful starting point. However, when richer insights...

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  • Autoencoder application for anomaly detection in power consumption of lighting systems

    Publication

    - IEEE Access - Year 2023

    Detecting energy consumption anomalies is a popular topic of industrial research, but there is a noticeable lack of research reported in the literature on energy consumption anomalies for road lighting systems. However, there is a need for such research because the lighting system, a key element of the Smart City concept, creates new monitoring opportunities and challenges. This paper examines algorithms based on the deep learning...

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  • Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

    Age prediction from X-rays is an interesting research topic important for clinical applications such as biological maturity assessment. It is also useful in many other practical applications, including sports or forensic investigations for age verification purposes. Research on these issues is usually carried out using high-resolution X-ray scans of parts of the body, such as images of the hands or images of the chest. In this...

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  • Multiplicative Long Short-Term Memory with Improved Mayfly Optimization for LULC Classification

    Publication
    • A. Stateczny
    • S. M. Bolugallu
    • P. B. Divakarachari
    • K. Ganesan
    • J. R. Muthu

    - Remote Sensing - Year 2022

    Land Use and Land Cover (LULC) monitoring is crucial for global transformation, sustainable land control, urban planning, urban growth prediction, and the establishment of climate regulations for long-term development. Remote sensing images have become increasingly important in many environmental planning and land use surveys in recent times. LULC is evaluated in this research using the Sat 4, Sat 6, and Eurosat datasets. Various...

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  • Playback detection using machine learning with spectrogram features approach

    Publication

    - Year 2017

    This 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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  • Minimizing Distribution and Data Loading Overheads in Parallel Training of DNN Acoustic Models with Frequent Parameter Averaging

    Publication

    In the paper we investigate the performance of parallel deep neural network training with parameter averaging for acoustic modeling in Kaldi, a popular automatic speech recognition toolkit. We describe experiments based on training a recurrent neural network with 4 layers of 800 LSTM hidden states on a 100-hour corpora of annotated Polish speech data. We propose a MPI-based modification of the training program which minimizes the...

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  • Smartphones as tools for equitable food quality assessment

    Background: The ubiquity of smartphones equipped with an array of sophisticated sensors, ample processing power, network connectivity and a convenient interface makes them a promising tool for non-invasive, portable food quality assessment. Combined with the recent developments in the areas of IoT, deep learning algorithms and cloud computing, they present an opportunity for advancing wide-spread, equitable and sustainable food...

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  • Graph Representation Integrating Signals for Emotion Recognition and Analysis

    Data reusability is an important feature of current research, just in every field of science. Modern research in Affective Computing, often rely on datasets containing experiments-originated data such as biosignals, video clips, or images. Moreover, conducting experiments with a vast number of participants to build datasets for Affective Computing research is time-consuming and expensive. Therefore, it is extremely important to...

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  • Comparative study on the effectiveness of various types of road traffic intensity detectors

    Publication

    - Year 2019

    Vehicle detection and speed measurements are crucial tasks in traffic monitoring systems. In this work, we focus on several types of electronic sensors, operating on different physical principles in order to compare their effectiveness in real traffic conditions. Commercial solutions are based on road tubes, microwave sensors, LiDARs, and video cameras. Distributed traffic monitoring systems require a high number of monitoring...

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  • Feature Weighted Attention-Bidirectional Long Short Term Memory Model for Change Detection in Remote Sensing Images

    Publication

    - Remote Sensing - Year 2022

    In remote sensing images, change detection (CD) is required in many applications, such as: resource management, urban expansion research, land management, and disaster assessment. Various deep learning-based methods were applied to satellite image analysis for change detection, yet many of them have limitations, including the overfitting problem. This research proposes the Feature Weighted Attention (FWA) in Bidirectional Long...

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  • The High Quality Business School Academic Teacher of the 21st Century – Polish Students’ Perspective

    Publication

    The literature shows that the success and competence of future managers depend on the quality of their academic teachers. Moreover high quality study requires high quality lecturing/teaching that creates an environment in which deep learning outcomes are made possible for students. The aim was to identify the characteristics of the academic teacher working at business schools, according to the expectations of Polish students...

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  • Impact of AlphaFold on structure prediction of protein complexes: The CASP15‐CAPRI experiment

    Publication
    • M. F. Lensink
    • G. Brysbaert
    • N. Raouraoua
    • P. A. Bates
    • M. Giulini
    • R. V. Honorato
    • C. van Noort
    • J. S. Teixeira
    • A. M. J. J. Bonvin
    • R. Kong... and 103 others

    - PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS - Year 2023

    We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody–antigen complexes, and 7 large assemblies. On average 70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups...

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  • ARIMA vs LSTM on NASDAQ stock exchange data

    Publication

    - Procedia Computer Science - Year 2022

    This study compares the results of two completely different models: statistical one (ARIMA) and deep learning one (LSTM) based on a chosen set of NASDAQ data. Both models are used to predict daily or monthly average prices of chosen companies listed on the NASDAQ stock exchange. Research shows which model performs better in terms of the chosen input data, parameters and number of features. The chosen models were compared using...

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  • Computationally Efficient Surrogate-Assisted Design of Pyramidal-Shaped 3D Reflectarray Antennas

    Publication

    - IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION - Year 2022

    Reflectarrays (RAs) have been attracting considerable interest in the recent years due to their appealing features, in particular, a possibility of realizing pencil-beam radiation patterns, as in the phased arrays, but without the necessity of incorporating the feeding networks. These characteristics make them attractive solutions, among others, for satellite communications or mobile radar antennas. Notwithstanding, available microstrip...

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  • Sathwik Prathapagiri

    People

    Sathwik was born in 2000. In 2022, he completed his Master’s of Science in  Biological Sciences and Bachelor’s of Engineering in Chemical Engineering in an integrated dual degree program from Birla Institute Of Technology And Science, Pilani, India. During his final year, he worked as a research intern under Dr Giri P Krishnan at Bazhenov lab, University of California San Diego school of medicine to pursue his Master’s Thesis on...

  • Narzędzia i metody obliczeń z użyciem MATLABa

    Events

    19-03-2020 10:00 - 19-03-2020 13:15

    Dn. 19.03.2020 w godz. 10.00–13.15 na Politechnice Gdańskiej odbędzie się seminarium w języku angielskim poświęcone wykorzystaniu MATLABa w badaniach naukowych i dydaktyce.

  • Mohsan Ali Master of Science in Computer Science

    People

    Mohsan Ali is a researcher at the University of the Aegean. He won the Marie-Curie Scholarship in 2021 in the field of open data ecosystem (ODECO) to pursue his PhD degree at the University of the Aegean. Currently, he is working on the technical interoperability of open data in the information systems laboratory; this position is funded by ODECO. His areas of expertise are open data, open data interoperability, data science, natural...

  • Buried Object Characterization by Data-Driven Surrogates and Regression-Enabled Hyperbolic Signature Extraction

    Publication

    - Scientific Reports - Year 2023

    This work addresses artificial-intelligence-based buried object characterization using FDTD-based electromagnetic simulation toolbox of a Ground Penetrating Radar (GPR) to generate B-scan data. In data collection, FDTD-based simulation tool, gprMax is used. The task is to estimate geophysical parameters of a cylindrical shape object of various radii, buried at different positions in the dry soil medium simultaneously and independently...

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  • Piotr Szczuko dr hab. inż.

    Piotr Szczuko received his M.Sc. degree in 2002. His thesis was dedicated to examination of correlation phenomena between perception of sound and vision for surround sound and digital image. He finished Ph.D. studies in 2007 and one year later completed a dissertation "Application of Fuzzy Rules in Computer Character Animation" that received award of Prime Minister of Poland. His interests include: processing of audio and video, computer...

  • Paweł Nadachowski mgr inż.

    People

  • AITP - AI Thermal Pedestrians Dataset

    Open Research Data
    open access
    • A. Górska
    • P. Guzal
    • I. Namiotko
    • A. Wędołowska
    • M. Włoszczyńska
    • J. Rumiński

    AITP is a pedestrian detection dataset consisting of 9178 annotated thermal images. The training set contains 7801 images on which15448 pedestrians were labeled.  The test set has 1377 images on which 2731 objects were marked. All images are in PNG file format (120x160) captured with FLIR Lepton Thermal Camera on the streets of Gdańsk, Poland. All pedestrians...

  • Adam Brzeski dr inż.

  • Sylwia Majchrowska

    People

  • Klasyfikacja sygnału EKG przy użyciu konwolucyjnych sieci neuronowych

    Automation and improvement of diagnostic process is a vital element of medicine development and patient’s condition self-control. For a long time different ECG signal classification methods exist and are successfully applied, nevertheless their accuracy is not always satisfying enough. The lack of identification of an existing abnormality, which is very similar to a normal heartbeat is the biggest issue - for example premature...

  • Klasyfikacja sygnału EKG przy użyciu konwolucyjnych sieci neuronowych

    Automation and improvement of diagnostic process is a vital element of medicine development and patient’s condition self-control. For a long time different ECG signal classification methods exist and are successfully applied, nevertheless their accuracy is not always satisfying enough. The lack of identification of an existing abnormality, which is very similar to a normal heartbeat is the biggest issue - for example premature...

  • Finding the Right Solvent: A Novel Screening Protocol for Identifying Environmentally Friendly and Cost-Effective Options for Benzenesulfonamide

    Publication

    - MOLECULES - Year 2023

    This study investigated the solubility of benzenesulfonamide (BSA) as a model compound using experimental and computational methods. New experimental solubility data were collected in the solvents DMSO, DMF, 4FM, and their binary mixtures with water. The predictive model was constructed based on the best-performing regression models trained on available experimental data, and their hyperparameters were optimized using a newly...

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  • Exploring the Solubility Limits of Edaravone in Neat Solvents and Binary Mixtures: Experimental and Machine Learning Study

    Publication

    - MOLECULES - Year 2023

    This study explores the edaravone solubility space encompassing both neat and binary dissolution media. Efforts were made to reveal the inherent concentration limits of common pure and mixed solvents. For this purpose, the published solubility data of the title drug were scrupulously inspected and cured, which made the dataset consistent and coherent. However, the lack of some important types of solvents in the collection called...

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  • Deep neural networks for human pose estimation from a very low resolution depth image

    Publication

    The work presented in the paper is dedicated to determining and evaluating the most efficient neural network architecture applied as a multiple regression network localizing human body joints in 3D space based on a single low resolution depth image. The main challenge was to deal with a noisy and coarse representation of the human body, as observed by a depth sensor from a large distance, and to achieve high localization precision....

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  • Toward Intelligent Vehicle Intrusion Detection Using the Neural Knowledge DNA

    Publication

    - CYBERNETICS AND SYSTEMS - Year 2018

    In this paper, we propose a novel intrusion detection approach using past driving experience and the neural knowledge DNA for in-vehicle information system security. The neural knowledge DNA is a novel knowledge representation method designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing systems. We examine our approach for classifying malicious vehicle control commands...

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