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Wyniki wyszukiwania dla: DESIGN SPACE SEGMENTATION
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Reliable low-cost surrogate modeling and design optimisation of antennas using implicit space mapping with substrate segmentation
PublikacjaAbstract: In this work, a reliable methodology for fast simulation-driven design optimisation of antenna structures is proposed. The authors’ approach exploits implicit space mapping (ISM) technology. To adopt it for handling antenna structures, they introduce substrate segmentation with separate dielectric permittivity value assigned for each segment as ISM preassigned parameters. At the same time, the coarse model for space mapping...
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Multi-objective design of miniaturized impedance transformers by domain segmentation
PublikacjaFast multi-objective design optimization of compact microstrip impedance transformers is discussed. Our approach exploits approximation models constructed using sampled coarse- mesh EM simulation data in a partitioned design space and response correction techniques for design refinement. Demonstra
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Accelerated multi-objective design optimization of antennas by surrogate modeling and domain segmentation
PublikacjaMulti-objective optimization yields indispensable information about the best possible design trade-offs of an antenna structure, yet it is challenging if full-wave electromagnetic (EM) analysis is utilized for performance evaluation. The latter is a necessity for majority of contemporary antennas as it is the only way of achieving acceptable modeling accuracy. In this paper, a procedure for accelerated multi-objective design of...
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Analytical design method for widening the intertrack space
PublikacjaThe paper involved the issue of arching of the railway turnouts. This is an issue which is given relatively less attention to scientific and research activities. Reference has been made to the book by Wladyslaw Rzepka, under the title „Curved turnouts in plan and profile”, which has been used in Poland for more than 50 years as the main source of information relating to the turnouts on the curve. The book is a compilation of elaborations...
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Domain segmentation for low-cost surrogate-assisted multi-objective design optimisation of antennas
PublikacjaAbstract: Information regarding the best possible design trade-offs of an antenna structure can be obtained through multiobjective optimisation (MO). Unfortunately, MO is extremely challenging if full-wave electromagnetic (EM) simulation models are used for performance evaluation. Yet, for the majority of contemporary antennas, EM analysis is the only tool that ensures reliability. This study introduces a procedure for accelerated...
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The design of an intelligent medical space supporting automated patient interviewing
PublikacjaThe article presents the architecture and results of implementing an application for the intelligent medical space UbiDoDo (Ubiquitous Domestic Doctor's Office). The main purpose of the application is real-time monitoring of the biomedical parameters of a patient in his domestic environment. It allows an immediate reaction to appearing symptoms and provides means to automatically interview the patient and deliver his results to...
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MULTILEVEL INTEGRATION OF DESIGN AND THE QUALITY OF LIFE IN URBAN SPACE ISSUE
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Design Space Reduction for Expedited Multi-Objective Design Optimization of Antennas in Highly-Dimensional Spaces
PublikacjaA surrogate-based technique for efficient multi-objective antenna optimization is discussed. Our approach exploits response surface approximation (RSA) model constructed from low-fidelity antenna model data (here, obtained through coarse-discretization electromagnetic simulations). The RSA model enables fast determination of the best available trade-offs between conflicting design goals. The cost of RSA model construction for multi-parameter...
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High-Speed Binary-to-Residue Converter Design Using 2-Bit Segmentation of the Input Word
PublikacjaIn this paper a new approach to the design of the high-speed binary-to-residue converter is proposed that allows the attaining of high pipelining rates by eliminating memories used in modulo m generators. The converter algorithm uses segmentation of the input binary word into 2-bit segments. The use and effects of the input word segmentation for the synthesis of converters for five-bit moduli are presented. For the number represented...
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Fast multi-objective design optimization of microwave and antenna structures using data-driven surrogates and domain segmentation
PublikacjaPurpose Strategies and algorithms for expedited design optimization of microwave and antenna structures in multi-objective setup are investigated. Design/methodology/approach Formulation of the multi-objective design problem oriented towards execution of the population-based metaheuristic algorithm within the segmented search space is investigated. Described algorithmic framework exploit variable fidelity modeling, physics- and...
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Substrate-integrated waveguide (SIW) filter design using space mapping
PublikacjaIn this paper, we present a fast technique for an automated design of microwave filters in substrate integrated wave (SIW) technology. The proposed methodology combines the space mapping technique with a cost function defined using the location of complex zeros and poles of filter’s transfer and reflection function and uses a rectangular waveguide as a surrogate model. The effectiveness of the proposed technique is presented with...
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An automated microwave planar filter design based on space mapping optimization
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Multi-objective design optimization of antennas for reflection, size, and gain variability using kriging surrogates and generalized domain segmentation
PublikacjaCost-efficient multi-objective design optimization of antennas is presented. The framework exploits auxiliary data-driven surrogates, a multi-objective evolutionary algorithm for initial Pareto front identification, response correction techniques for design refinement, as well as generalized domain segmentation. The purpose of this last mechanism is to reduce the volume of the design space region that needs to be sampled in order...
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Rotational Design Space Reduction for Cost-Efficient Multi-Objective Antenna Optimization
PublikacjaCost-efficient multi-objective design of antenna structures is presented. Our approach is based on design space reduction algorithm using auxiliary single-objective optimization runs and coordinate system rotation. The initial set of Pareto-optimal solutions is obtained by optimizing a response surface approximation model established in the reduced space using coarse-discretization EM simulation data. The optimization engine is...
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Implicit Space Mapping for Variable-Fidelity EM-Driven Design of Compact Circuits
PublikacjaSpace mapping (SM) belongs to the most successful surrogate-based optimization (SBO) methods in microwave engineering. Among available SM variations, implicit SM (ISM) is particularly attractive due to its simplicity and separation of extractable surrogate model parameters and design variables of the circuit/system at hand. Unlike other SM approaches, ISM exploits a set of preassigned parameters to align the surrogate with the...
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Design of Substrate Integrated Waveguide Filters Using Implicit Space Mapping Technique
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Research, theories and practical experience of therapeutic landscapes as a tool for urban open space design
PublikacjaThis paper reviews the research on therapeutic landscapes and discusses the design of urban parks and open green space. Firstly, the most relevant research on therapeutic landscapes is reviewed. Secondly, the practical experience of healing and therapeutic gardens is discussed. Finally, the conclusions leading to future use of therapeutic landscape concept in urban open space design are presented.
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Design space reduction and variable-fidelity EM simulations for feasible Pareto optimization of antennas
PublikacjaA computationally efficient procedure for multi-objective optimization of antenna structures is presented. In our approach, a response surface approximation (RSA) model created from sampled coarse-discretization EM antenna simulations is utilized to yield an initial set of Pareto-optimal designs using a multi-objective evolutionary algorithm. The final Pareto front representation for the high-fidelity model is obtained using surrogate-based...
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Nested Space Mapping Technique for Design and Optimization of Complex Microwave Structures with Enhanced Functionality
PublikacjaIn this work, we discuss a robust simulation-driven methodology for rapid and reliable design of complex microwave/RF circuits with enhanced functionality. Our approach exploits nested space mapping (NSM) technology, which is dedicated to expedite simulation-driven design optimization of computationally demanding microwave structures with complex topologies. The enhanced func-tionality of the developed circuits is achieved by means...
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Rapid EM-Driven Design of Compact RF Circuits By Means of Nested Space Mapping
PublikacjaA methodology for rapid design of RF circuits constituted by compact microstrip resonant-cells (CMRCs) is presented. Our approach exploits nested space mapping (NSM) technology, where the inner SM layer is used to correct the equivalent circuit model at the CMRC level, whereas the outer layer enhances the coarse model of the entire structure under design. We demonstrate that NSM dramatically improves performance of surrogate-based...
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Local-Global Space Mapping for Rapid EM-Driven Design of Compact RF Structures
PublikacjaIn this work, we introduce a robust and efficient technique for rapid design of compact RF circuits. Our approach exploits two-level space mapping (SM) correction of an equivalent circuit model of the structure under design. The first SM layer (local correction) is utilized to ensure good matching between the equivalent circuit and the electromagnetic model at the component level. On the other hand, the global correction allows...
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Space-mapping-based design flow of miniaturized broadband matching transformer with perforated metallization
PublikacjaA fast design procedure of unconventional passive components has been presented and experimentally validated on the basis of a wideband microstrip four-section matching transformer (50:6 OHm). A classic impedance matching circuit has been miniaturized by means of compact microstrip resonant cells (CMRCs) leading to a considerable length reduction (34%) together with minor degradation in its performance. The spacemapping-based design...
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An Automated Design of Substrate Integrated Waveguide Filters Based on Implicit Space Mapping Optimization
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Nested Space Mapping Technology for Expedite EM-driven Design of Compact RF/microwave Components
PublikacjaA robust simulation-driven methodology for rapid and reliable design of RF/microwave circuits comprising compact microstrip resonant cells (CMRCs) is presented. We introduce a nested space mapping (NSM) technology, in which the inner space mapping layer is utilized to improve the generalization capabilities of the equivalent circuit model corresponding to a constitutive element of the circuit under consideration. The outer layer...
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Accelerated simulation-driven design optimisation of compact couplers by means of two-level space mapping
PublikacjaIn this study, the authors discuss a robust and efficient technique for rapid design of compact couplers. The approach exploits two-level space mapping (SM) correction of an equivalent circuit model of the coupler structure under design. The first SM layer (local correction) is utilised to ensure good matching between the equivalent circuit and the electromagnetic model at the component level. Subsequent global correction allows...
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Innovative Urban Blue Space Design in a Changing Climate: Transition Models in the Baltic Sea Region
PublikacjaWaterfront areas in cities are subject to constant changes. The desire to integrate the transformed waterside areas with the urban fabric involves shaping high-quality public spaces related to water, which are often referred to as urban blue spaces (UBS). The aim of the research was to examine the transformation processes of urban waterfront areas in the Baltic Sea Region and identify emerging transition models and types of blue...
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Fast Multi-Objective Optimization of Narrow-Band Antennas Using RSA Models and Design Space Reduction
PublikacjaComputationally efficient technique for multi-objective design optimization of narrow-band antennas is presented. In our approach, the corrected low-fidelity antenna model (obtained through coarse-discretization EM simulations) is enhanced using frequency scaling and response correction, sampled, and utilized to obtain a fast response surface approximation (RSA) antenna surrogate. The RSA model is constructed in the reduced design space....
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Low-Cost Yield-Driven Design of Antenna Structures Using Response-Variability Essential Directions and Parameter Space Reduction
PublikacjaQuantifying the effects of fabrication tolerances and uncertainties of other types is fundamental to improve antenna design immunity to limited accuracy of manufacturing procedures and technological spread of material parameters. This is of paramount importance especially for antenna design in the industrial context. Degradation of electrical and field properties due to geometry parameter deviations often manifests itself as, e.g.,...
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Three-objective antenna optimization by means of kriging surrogates and domain segmentation
PublikacjaIn this paper, an optimization framework for multi-objective design of antenna structures is discussed which exploits data-driven surrogates, a multi-objective evolutionary algorithm, response correction techniques for design refinement, as well as generalized domain segmentation. The last mechanism is introduced to constrain the design space region subjected to sampling, which permits reduction of the number of training data samples...
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Expedited Multi-Objective Design Optimization of Miniaturized Microwave Structures Using Physics-Based Surrogates
PublikacjaIn this paper, a methodology for fast multi-objective design optimization of compact microwave circuits is presented. Our approach exploits an equivalent circuit model of the structure under consideration, corrected through implicit and frequency space mapping, then optimized by a multi-objective evolutionary algorithm. The correction/optimization of the surrogate is iterated by design space confinement and segmentation based on...
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Rapid multi-objective design optimisation of compact microwave couplers by means of physics-based surrogates
PublikacjaThe authors introduce a methodology for fast multi-objective design optimisation of miniaturised microwave couplers. The approach exploits the surrogate-based optimisation paradigm with an underlying low-fidelity model constructed from an equivalent circuit of the structure under consideration, corrected through implicit and frequency space mapping. A fast prediction tool obtained this way is subsequently optimised by a multi-objective...
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Leveraging spatio-temporal features for joint deblurring and segmentation of instruments in dental video microscopy
PublikacjaIn dentistry, microscopes have become indispensable optical devices for high-quality treatment and micro-invasive surgery, especially in the field of endodontics. Recent machine vision advances enable more advanced, real-time applications including but not limited to dental video deblurring and workflow analysis through relevant metadata obtained by instrument motion trajectories. To this end, the proposed work addresses dental...
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MEAN SHIFT BASED SEGMENTATION FOR BLEEDING REGIONS IN ENDOSCOPIC VIDEOS
PublikacjaWith a set of 38 manually marked bleeding regions form endoscopic videos, the authors attempted to find an optimal image segmentation method for reproducing doctor’s markup. Mean shift segmentation combined with HSV histogram segmentation were used as a segmentation method, which was then optimized by tuning the parameters of the method using global optimization algorithm. A target function for measuring the quality of segmentation was...
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Instance segmentation of stack composed of unknown objects
PublikacjaThe article reviews neural network architectures designed for the segmentation task. It focuses mainly on instance segmentation of stacked objects. The main assumption is that segmentation is based on a color image with an additional depth layer. The paper also introduces the Stacked Bricks Dataset based on three cameras: RealSense L515, ZED2, and a synthetic one. Selected architectures: DeepLab, Mask RCNN, DEtection TRansformer,...
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Ensembling noisy segmentation masks of blurred sperm images
PublikacjaBackground: Sperm tail morphology and motility have been demonstrated to be important factors in determining sperm quality for in vitro fertilization. However, many existing computer-aided sperm analysis systems leave the sperm tail out of the analysis, as detecting a few tail pixels is challenging. Moreover, some publicly available datasets for classifying morphological defects contain images limited only to the sperm head. This...
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Medical Image Segmentation Using Deep Semantic-based Methods: A Review of Techniques, Applications and Emerging Trends
PublikacjaSemantic-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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Review of Segmentation Methods for Coastline Detection in SAR Images
PublikacjaSynthetic aperture radar (SAR) images acquired by airborne sensors or remote sensing satellites contain the necessary information that can be used to investigate various objects of interest on the surface of the Earth, including coastlines. The coastal zone is of great economic importance and is also very densely populated. The intensive and increasing use of coasts and changes of coastlines motivate researchers to try to assess...
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Deep learning based thermal image segmentation for laboratory animals tracking
PublikacjaAutomated systems for behaviour classification of laboratory animals are an attractive alternative to manual scoring. However, the proper animals separation and tracking, especially when they are in close contact, is the bottleneck of the behaviour analysis systems. In this paper, we propose a method for the segmentation of thermal images of laboratory rats that are in close contact during social behaviour tests. For this, we are...
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Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review
PublikacjaThe segmentation of liver blood vessels is of major importance as it is essential for formulating diagnoses, planning and delivering treatments, as well as evaluating the results of clinical procedures. Different imaging techniques are available for application in clinical practice, so the segmentation methods should take into account the characteristics of the imaging technique. Based on the literature, this review paper presents...
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Deep Instance Segmentation of Laboratory Animals in Thermal Images
PublikacjaIn this paper we focus on the role of deep instance segmentation of laboratory rodents in thermal images. Thermal imaging is very suitable to observe the behaviour of laboratory animals, especially in low light conditions. It is an non-intrusive method allowing to monitor the activity of animals and potentially observe some physiological changes expressed in dynamic thermal patterns. The analysis of the recorded sequence of thermal...
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Comparison of Selected Neural Network Models Used for Automatic Liver Tumor Segmentation
PublikacjaAutomatic and accurate segmentation of liver tumors is crucial for the diagnosis and treatment of hepatocellular carcinoma or metastases. However, the task remains challenging due to imprecise boundaries and significant variations in the shape, size, and location of tumors. The present study focuses on tumor segmentation as a more critical aspect from a medical perspective, compared to liver parenchyma segmentation, which is the...
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Accelerating Video Frames Classification With Metric Based Scene Segmentation
PublikacjaThis paper addresses the problem of the efficient classification of images in a video stream in cases, where all of the video has to be labeled. Realizing the similarity of consecutive frames, we introduce a set of simple metrics to measure that similarity. To use these observations for decreasing the number of necessary classifications, we propose a scene segmentation algorithm. Performed experiments have evaluated the acquired...
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Sperm segmentation and abnormalities detection during the ICSI procedure using machine learning algorithms
Publikacja(1) About 15-20% of couples struggle with the problem of infertility. 30 to 40% of these cases are caused by abnormalities in the structure and motility of sperm. Sometimes the only possibility for such people is to use the procedure of artificial insemination. CASA systems are used to increase the efficiency of this procedure by selecting the appropriate sperm cell. (2) This paper presents an approach to the sperm classification...
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Semantic segmentation training using imperfect annotations and loss masking
PublikacjaOne of the most significant factors affecting supervised neural network training is the precision of the annotations. Also, in a case of expert group, the problem of inconsistent data annotations is an integral part of real-world supervised learning processes, well-known to researchers. One practical example is a weak ground truth delineation for medical image segmentation. In this paper, we have developed a new method of accurate...
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Autonomous pick-and-place system based on multiple 3Dsensors and deep learning
PublikacjaGrasping 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
PublikacjaGrasping 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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Examining Quality of Hand Segmentation Based on Gaussian Mixture Models
PublikacjaResults of examination of various implementations of Gaussian mix-ture models are presented in the paper. Two of the implementations belonged to the Intel’s OpenCV 2.4.3 library and utilized Background Subtractor MOG and Background Subtractor MOG2 classes. The third implementation presented in the paper was created by the authors and extended Background Subtractor MOG2 with the possibility of operating on the scaled version of...
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Scene Segmentation Basing on Color and Depth Images for Kinect Sensor
PublikacjaIn this paper we propose a method for segmenting single images from Kinect sensor by considering both color and depth information. The algorithm is based on a series of edge detection procedures designed for particular features of the scene objects. RGB and HSV color planes are separately analyzed in the first step with Canny edge detector, resulting in overall color edges mask. In depth images both clear boundaries and smooth...
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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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Smart Approach for Glioma Segmentation in Magnetic Resonance Imaging using Modified Convolutional Network Architecture (U-NET)
PublikacjaSegmentation of a brain tumor from magnetic resonance multimodal images is a challenging task in the field of medical imaging. The vast diversity in potential target regions, appearance and multifarious intensity threshold levels of various tumor types are few of the major factors that affect segmentation results. An accurate diagnosis and its treatment demand strict delineation of the tumor affected tissues. Herein, we focus on...