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Search results for: FEATURE-BASED OPTIMIZATION
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Low-Cost Quasi-Global Optimization of Expensive Electromagnetic Simulation Models by Inverse Surrogates and Response Features
PublicationConceptual design of contemporary high-frequency structures is typically followed by a careful tuning of their parameters, predominantly the geometry ones. The process aims at improving the relevant performance figures, and may be quite expensive. The reason is that conventional design methods, e.g., based on analytical or equivalent network models, often only yield rough initial designs. This is especially the case for miniaturized...
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Grzegorz Boczkaj dr hab. inż.
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Specification-Oriented Automatic Design of Topologically Agnostic Antenna Structure
PublicationDesign of antennas for modern applications is a challenging task that combines cognition-driven development of topology intertwined with tuning of its parameters using rigorous numerical optimization. However, the process can be streamlined by neglecting the engineering insight in favor of automatic de-termination of structure geometry. In this work, a specification-oriented design of topologically agnostic antenna is considered....
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Performance Assessment of Using Docker for Selected MPI Applications in a Parallel Environment Based on Commodity Hardware
PublicationIn the paper, we perform detailed performance analysis of three parallel MPI applications run in a parallel environment based on commodity hardware, using Docker and bare-metal configurations. The testbed applications are representative of the most typical parallel processing paradigms: master–slave, geometric Single Program Multiple Data (SPMD) as well as divide-and-conquer and feature characteristic computational and communication...
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On Decision-Making Strategies for Improved-Reliability Size Reduction of Microwave Passives: Intermittent Correction of Equality Constraints and Adaptive Handling of Inequality Constraints
PublicationDesign optimization of passive microwave components is an intricate process, especially if the primary objective is a reduction of the physical size of the structure. The latter has become an important design consideration for a growing number of modern applications (mobile communications, wearable/implantable devices, internet of things), where miniaturization is imperative due to a limited space allocated for the electronic circuitry....
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Machine-Learning-Powered EM-Based Framework for Efficient and Reliable Design of Low Scattering Metasurfaces
PublicationPopularity of metasurfaces has been continuously growing due to their attractive properties including the ability to effectively manipulate electromagnetic (EM) waves. Metasurfaces comprise optimized geometries of unit cells arranged as a periodic lattice to obtain a desired EM response. One of their emerging application areas is the stealth technology, in particular, realization of radar cross section (RCS) reduction. Despite...
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Cezary Orłowski prof. dr hab. inż.
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Adrian Bekasiewicz dr hab. inż.
PeopleAdrian Bekasiewicz received the MSc, PhD, and DSc degrees in electronic engineering from Gdansk University of Technology, Poland, in 2011, 2016, and 2020, respectively. In 2014, he joined Engineering Optimization & Modeling Center where he held a Research Associate and a Postdoctoral Fellow positions, respectively. Currently, he is an Associate Professor with Gdansk University of Technology, Poland. His research interests include...
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A comparative study of English viseme recognition methods and algorithms
PublicationAn elementary visual unit – the viseme is concerned in the paper in the context of preparing the feature vector as a main visual input component of Audio-Visual Speech Recognition systems. The aim of the presented research is a review of various approaches to the problem, the implementation of algorithms proposed in the literature and a comparative research on their effectiveness. In the course of the study an optimal feature vector construction...
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A comparative study of English viseme recognition methods and algorithm
PublicationAn elementary visual unit – the viseme is concerned in the paper in the context of preparing the feature vector as a main visual input component of Audio-Visual Speech Recognition systems. The aim of the presented research is a review of various approaches to the problem, the implementation of algorithms proposed in the literature and a comparative research on their effectiveness. In the course of the study an optimal feature vector...
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Planning optimised multi-tasking operations under the capability for parallel machining
PublicationThe advent of advanced multi-tasking machines (MTMs) in the metalworking industry has provided the opportunity for more efficient parallel machining as compared to traditional sequential processing. It entailed the need for developing appropriate reasoning schemes for efficient process planning to take advantage of machining capabilities inherent in these machines. This paper addresses an adequate methodical approach for a non-linear...
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Accelerated Parameter Tuning of Antenna Structures by Means of Response Features and Principal Directions
PublicationPopularity of numerical optimization has been steadily on the rise in the design of modern antenna systems. Resorting to mathematically rigorous parameter tuning methods is a matter of practical necessity as interactive techniques (e.g., parameter sweeping) are no longer adequate when handling several performance figures over multi-dimensional parameter spaces. The most common design scenarios involve local tuning since decent...
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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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OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems
PublicationCurrently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered...
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Zero-pole approach to computer aided design of in-line siw filters with transmission zeros
PublicationThis paper presents a design of a new type of in-line pseudo-elliptic filters implemented in substrate integrated waveguide(SIW) technology. To realize transmission zeros in in-line topology,frequency-dependent couplings were used. Such dispersive couplingswere implemented as shorted stubs. The design process startswith the generation of a suitable starting point. To this end, anapproximation of SIW as a rectangular waveguide is...
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Application of mechanistic and data-driven models for nitrogen removal in wastewater treatment systems
PublicationIn this dissertation, the application of mechanistic and data-driven models in nitrogen removal systems including nitrification and deammonification processes was evaluated. In particular, the influential parameters on the activity of the Nitrospira activity were assessed using response surface methodology (RSM). Various long-term biomass washout experiments were operated in two parallel sequencing batch reactor (SBR) with a different...
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Voice command recognition using hybrid genetic algorithm
PublicationAbstract: Speech recognition is a process of converting the acoustic signal into a set of words, whereas voice command recognition consists in the correct identification of voice commands, usually single words. Voice command recognition systems are widely used in the military, control systems, electronic devices, such as cellular phones, or by people with disabilities (e.g., for controlling a wheelchair or operating a computer...
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Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy
PublicationBiochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine-learning-based forecasts,...
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Patryk Ziółkowski dr inż.
PeopleAssistant Professor at Gdansk Tech. He participated in international projects, including projects for the Ministry of Transportation of the State of Alabama (2015), he is also the winner of a grant from the Kosciuszko Foundation for conducting research in the USA, which he completed in 2018. An expert in the field of artificial intelligence. His main area of research interest is the application of artificial intelligence in Civil...
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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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Investigating Feature Spaces for Isolated Word Recognition
PublicationMuch attention is given by researchers to the speech processing task in automatic speech recognition (ASR) over the past decades. The study addresses the issue related to the investigation of the appropriateness of a two-dimensional representation of speech feature spaces for speech recognition tasks based on deep learning techniques. The approach combines Convolutional Neural Networks (CNNs) and timefrequency signal representation...
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Gender approaches to evolutionary multi-objective optimization using pre-selection of criteria
PublicationA novel idea to perform evolutionary computations (ECs) for solving highly dimensional multi-objective optimization (MOO) problems is proposed. Following the general idea of evolution, it is proposed that information about gender is used to distinguish between various groups of objectives and identify the (aggregate) nature of optimality of individuals (solutions). This identification is drawn out of the fitness of individuals...
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Gesture Recognition With the Linear Optical Sensor and Recurrent Neural Networks
PublicationIn this paper, the optical linear sensor, a representative of low-resolution sensors, was investigated in the multiclass recognition of near-field hand gestures. The recurrent neural network (RNN) with a gated recurrent unit (GRU) memory cell was utilized as a gestures classifier. A set of 27 gestures was collected from a group of volunteers. The 27 000 sequences obtained were divided into training, validation, and test subsets....
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Rapid Design of 3D Reflectarray Antennas by Inverse Surrogate Modeling and Regularization
PublicationReflectarrays (RAs) exhibit important advantages over conventional antenna arrays, especially in terms of realizing pencil-beam patterns without the employment of the feeding networks. Unfortunately, microstrip RA implementations feature narrow bandwidths, and are severely affected by losses. A considerably improved performance can be achieved for RAs involving grounded dielectric layers, which are also easy to manufacture using...
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Expedited Acquisition of Database Designs for Reduced-Cost Performance-Driven Modeling and Rapid Dimension Scaling of Antenna Structures
PublicationFast replacement models have been playing an increasing role in high-frequency electronics, including the design of antenna structures. Their role is to improve computational efficiency of the procedures that normally entail large numbers of expensive full-wave electromagnetic (EM) simulations, e.g., parametric optimization or uncertainty quantification. Recently introduced performance-driven modeling methods, such as the nested...
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On Computationally-Efficient Reference Design Acquisition for Reduced-Cost Constrained Modeling and Re-Design of Compact Microwave Passives
PublicationFull-wave electromagnetic (EM) analysis has been playing a major role in the design of microwave components for the last few decades. In particular, EM tools allow for accurate evaluation of electrical performance of miniaturized structures where strong cross-coupling effects cannot be adequately quantified using equivalent network models. However, EM-based design procedures (parametric optimization, statistical analysis) generate...
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Feature Reduction Using Similarity Measure in Object Detector Learning with Haar-like Features
PublicationThis paper presents two methods of training complexity reduction by additional selection of features to check in object detector training task by AdaBoost training algorithm. In the first method, the features with weak performance at first weak classifier building process are reduced based on a list of features sorted by minimum weighted error. In the second method the feature similarity measures are used to throw away that features...
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Decisional DNA and Optimization Problem
PublicationMany researchers have proved that Decisional DNA (DDNA) and Set of Experience Knowledge Structure (SOEKS or SOE) is a technology capable of gathering information and converting it into knowledge to help decision-makers to make precise decisions in many ways. These techniques have a feature to combine with different tools, such as data mining techniques and web crawlers, helping organization collect information from different sources...
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Fast tolerance-aware design optimization of miniaturized microstrip couplers using variable-fidelity EM simulations and re-sponse features
PublicationManufacturing tolerances and other types of uncertainties may considerably affect operation and performance of microwave components and systems. Quantification of these effects is therefore an important part of the design process. It is even more important to obtain designs whose sensitivity to parameter deviations is reduced as much as possible. All of these require statistical analysis carried out at the level of electromagnetic...
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Tool Wear Monitoring Using Improved Dragonfly Optimization Algorithm and Deep Belief Network
PublicationIn recent decades, tool wear monitoring has played a crucial role in the improvement of industrial production quality and efficiency. In the machining process, it is important to predict both tool cost and life, and to reduce the equipment downtime. The conventional methods need enormous quantities of human resources and expert skills to achieve precise tool wear information. To automatically identify the tool wear types, deep...
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Paweł Rościszewski dr inż.
PeoplePaweł 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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Comparative analysis of spectral and cepstral feature extraction techniques for phoneme modelling
PublicationPhoneme parameter extraction framework based on spectral and cepstral parameters is proposed. Using this framework, the phoneme signal is divided into frames and Hamming window is used. The performances are evaluated for recognition of Lithuanian vowel and semivowel phonemes. Different feature sets without noise as well as at different level of noise are considered. Two classical machine learning methods (Naive Bayes and Support...
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Bi-GRU-APSO: Bi-Directional Gated Recurrent Unit with Adaptive Particle Swarm Optimization Algorithm for Sales Forecasting in Multi-Channel Retail
PublicationIn the present scenario, retail sales forecasting has a great significance in E-commerce companies. The precise retail sales forecasting enhances the business decision making, storage management, and product sales. Inaccurate retail sales forecasting can decrease customer satisfaction, inventory shortages, product backlog, and unsatisfied customer demands. In order to obtain a better retail sales forecasting, deep learning models...
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Using similar classification tasks in feature extractor learning
PublicationThe article presents and experimentally verify the idea of automatic construction of feature extractors in classification problems. The extractors are created by genetic programming techniques using classification examples taken from other problems then the problem under consideration.
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Facial Feature extraction from a monochromatic picture
PublicationFace pose determination represents an important area of research in Human Machine Interaction. In this paper, I describe a new method of extracting facial feature locations from a single monochromatic monocular camera for the purpose of estimating and tracking the three dimensional pose of human face and eye-gaze direction.
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Facial feature extraction from a monochromatic picture.
PublicationFace pose determination represents an important area of research in Human Machine Interaction. In this paper, I describe a new method of extracting facial feature locations from a single monochromatic monocular camera for the purpose of estimating and tracking the three dimensional pose of human face and eye-gaze direction.
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Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning
PublicationThe 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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Spiral Search Grasshopper Features Selection with VGG19-ResNet50 for Remote Sensing Object Detection
PublicationRemote sensing object detection plays a major role in satellite imaging and is required in various scenarios such as transportation, forestry, and the ocean. Deep learning techniques provide efficient performance in remote sensing object detection. The existing techniques have the limitations of data imbalance, overfitting, and lower efficiency in detecting small objects. This research proposes the spiral search grasshopper (SSG)...
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Evaluating the risk of endometriosis based on patients’ self-assessment questionnaires
PublicationBackground Endometriosis is a condition that significantly affects the quality of life of about 10 % of reproductive-aged women. It is characterized by the presence of tissue similar to the uterine lining (endometrium) outside the uterus, which can lead lead scarring, adhesions, pain, and fertility issues. While numerous factors associated with endometriosis are documented, a wide range of symptoms may still be undiscovered. Methods In...
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Variable-fidelity response feature surrogates for accelerated statistical analysis and yield estimation of compact microwave components
PublicationAccounting for manufacturing tolerances is an essential part of a reliable microwave design process. Yet, quantification of geometry and/or material parameter uncertainties is challenging at the level of full-wave electromagnetic (EM) simulation models. This is due to inherently high cost of EM analysis and massive simulations necessary to conduct the statistical analysis. Here, a low-cost and accurate yield estimation procedure...
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Real-time facial feature tracking in poor quality thermal imagery
PublicationRecently, facial feature tracking systems have become more and more popular because of many possible use cases. Especially in medical applications location of the face and facial features are very useful. Many researches have presented methods to detect and track facial features in visible light. However, facial feature analysis in thermography may also be very advantageous. Some examples of using infrared imagery in medicine include...
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Digital fingerprinting for color images based on the quaternion encryption scheme
PublicationIn this paper we present a new quaternion-based encryption technique for color images. In the proposed encryption method, images are written as quaternions and are rotated in a three-dimensional space around another quaternion, which is an encryption key. The encryption process uses the cipher block chaining (CBC) mode. Further, this paper shows that our encryption algorithm enables digital fingerprinting as an additional feature....
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Rapid Yield Optimization of Miniaturized Microwave Passives by Response Features and Variable-Fidelity EM Simulations
PublicationThe operation of high-frequency devices, including microwave passive components, can be impaired by fabrication tolerances but also incomplete knowledge concerning operating conditions (temperature, input power levels) and material parameters (e.g., substrate permittivity). Although the accuracy of manufacturing processes is always limited, the effects of parameter deviations can be accounted for in advance at the design phase...
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Rapid Design Centering of Multi-Band Antennas Using Knowledge-Based Inverse Models and Response Features
PublicationAccounting for manufacturing tolerances as well as uncertainties concerning operating conditions and material parameters is one of the important yet often neglected aspects of antenna development. Appropriate quantification of uncertainties allows for estimating the fabrication yield but also to carry out robust design (e.g., yield maximization). For reliability reasons, statistical analysis should be executed at the accuracy level...
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Hazard Control in Industrial Environments: A Knowledge-Vision-Based Approach
PublicationThis paper proposes the integration of image processing techniques (such as image segmentation, feature extraction and selection) and a knowledge representation approach in a framework for the development of an automatic system able to identify, in real time, unsafe activities in industrial environments. In this framework, the visual information (feature extraction) acquired from video-camera images and other context based gathered...
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Content-Based Approach to Automatic Recommendation of Music
PublicationThis paper presents a content-based approach to music recommendation. For this purpose, a database which contains more than 50000 music excerpts acquired from public repositories was built. Datasets contain tracks of distinct performers within several music genres. All music pieces were converted to mp3 format and then parameterized based on MPEG-7, mel-cepstral and time-related dedicated parameters. All feature vectors are stored...
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IMPEDANCE MEASUREMENT MODULE FOR GAS SENSORS WITH TEMPERATURE CONDITIONING FEATURE
PublicationThe paper presents an impedance measurement module which is aimed for gas sensor. Sensor’s heater temperature conditioning and modulation feature is available in order to allow implementation of different techniques improving selectivity and sensitivity of gas sensors. The measurement module uses three-parameter sine-fitting technique for impedance measurement. To monitor sensor temperature, the resistance of the sensor’s heater...
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Viewpoint independent shape-based object classification for video surveillance
PublicationA method for shape based object classification is presented.Unlike object dimension based methods it does not require any system calibration techniques. A number of 3D object models are utilized as a source of training dataset for a specified camera orientation. Usage of the 3D models allows to perform the dataset creation process semiautomatically. The background subtraction method is used for the purpose of detecting moving objects...
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Speech Analytics Based on Machine Learning
PublicationIn this chapter, the process of speech data preparation for machine learning is discussed in detail. Examples of speech analytics methods applied to phonemes and allophones are shown. Further, an approach to automatic phoneme recognition involving optimized parametrization and a classifier belonging to machine learning algorithms is discussed. Feature vectors are built on the basis of descriptors coming from the music information...
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Reliable Microwave Modeling By Means of Variable-Fidelity Response Features
PublicationIn this work, methodologies for low-cost and reliable microwave modeling are presented using variable-fidelity response features. The two key components of our approach are: (i) a realization of the modeling process at the level of suitably selected feature points of the responses (e.g., S-parameters vs. frequency) of the structure at hand, and (ii) the exploitation of variable-fidelity EM simulation data, also for the response...