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作者： Robert Cichorowski Olga Kocikowska Marek Bajtoš Roman Radil Ladislav Janoušek Daria Gendosz De Carrillo Sebastian Student Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology liwice Poland Department of Engineering and Systems Biology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Department of Physiology Faculty of Medical Sciences Silesian University of Medicine Katowice Poland Department of Electromagnetic and Biomedical Engineering Faculty of Electrical Engineering and Informational Technology Žilina Slovakia Department of Histology Faculty of Medical Sciences in Zabrze Silesian University of Medicine Zabrze Poland Biotechnology Center Silesian University of Technology Gliwice Poland

ISBN: (数字)9798350372359

ISBN: (纸本)9798350372366

This work presents the results of the examination of the HeLa cell line exposure on the ELF-EMF (extremely low-frequency electromagnetic field). In particular, the relationship between ELF-EMF exposition time and cell death. The examination of cell death hallmarks were examined by estimation levels of selected proteins - FACL4 (a protein that is a part of the ferroptosis pathway) and CK18 (Cytokeratin related to necrosis and apoptosis pathways) in the proposed model of workers exposed to ELF-EMF week.

关键词： Proteins Biological system modeling Estimation Electromagnetic fields

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DFD-Net:lung cancer detection from denoised CT scan image using deep learning

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Frontiers of computer Science 2021年第2期15卷 119-131页

作者： Worku J.SORI Jiang FENG Arero W.GODANA Shaohui LIU Demissie J.GELMECHA School of Electrical Engineering and Computing Department of Computer Science and EngineeringAdama Science and Technology UniversityAdama1888Ethiopia Faculty of Natural Computational Science Department of Mathematics Bule Hora UniversityBule HoraHagere Maryam144Ethiopia School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China Peng Cheng Laboratory Shenzhen518052China School of Electrical Engineering and Computing Department of Electronics and Communications EngineeringAdama Science and Technology UniversityAdama1888Ethiopia

The availability of pulmonary nodules in CT scan image of lung does not completely specify *** noise in an image and morphology of nodules,like shape and size has an implicit and complex association with cancer,and thus,a careful analysis should be mandatory on every suspected nodules and the combination of information of every *** this paper,we introduce a“denoising first”two-path convolutional neural network(DFD-Net)to address this *** introduced model is composed of denoising and detection part in an end to end ***,a residual learning denoising model(DR-Net)is employed to remove noise during the preprocessing ***,a two-path convolutional neural network which takes the denoised image by DR-Net as an input to detect lung cancer is *** two paths focus on the joint integration of local and global *** this end,each path employs different receptive field size which aids to model local and global *** further polish our model performance,in different way from the conventional feature concatenation approaches which directly concatenate two sets of features from different CNN layers,we introduce discriminant correlation analysis to concatenate more representative ***,we also propose a retraining technique that allows us to overcome difficulties associated to the image labels *** found that this type of model easily first reduce noise in an image,balances the receptive field size effect,affords more representative features,and easily adaptable to the inconsistency among nodule shape and *** intensive experimental results achieved competitive results.

关键词： medical image discriminant correlation analysis features fusion image detection denoising

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Design, Verification and Testing of a Readout Integrated Circuit for Hybrid Pixel X-ray Detectors with In-Pixel Time Measurement Functionality in 28 nm CMOS

Design, Verification and Testing of a Readout Integrated Cir...

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International Conference on Mixed Design of Integrated Circuits and Systems (MIXDES)

作者： Lukasz A. Kadlubowski Department of Measurement and Electronics Faculty of Electrical Engineering AGH University of Science and Technology Automatics Computer Science and Biomedical Engineering Krakow Poland

This paper summarizes the design of a prototype integrated circuit for hybrid pixel X-ray detectors fabricated in 28 nm CMOS technology. The chip consists of 32 pixels with dimensions 50 μ m: × 50 μm each. Each pixel is divided into three sections: a front-end, two-ring oscillators with their frequency control circuits, and a digital part. It offers singlephoton counting functionality as well as in-pixel time measurement. In previous papers, a detailed design of the chip and simulation results were reported, and an approach was described that utilized System Verilog language together with the AMS simulator for the verification of this asynchronous, mixed-mode circuitry. This paper focuses more on testing fabricated chips. It describes a test setup and PCB design and mentions how the prior System Verilog verification approach helped with the development of a testing software. Finally, preliminary measurement results are presented that illustrate the performance of front-end and oscillators.

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A Generative Adversarial Network-Based Approach for Reflective-Metasurface Unit-Cell Synthesis in mmWave Bands

A Generative Adversarial Network-Based Approach for Reflecti...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Panithan La-aiddee Paramin Sangwongngam Lunchakorn Wuttisittikulkij Pisit Vanichchanunt Department of Electrical Engineering Wireless Communication Ecosystem Research Unit Faculty of Engineering Chulalongkorn University Bangkok Thailand National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

In recent years, data-driven design approaches based on generative machine learning (ML) have been applied in the field of metasurface design. Even though generative machine learning has demonstrated remarkable capabilities in metasurface synthesis, training procedures to obtain an appropriate model remain a challenge. In this work, we present the deployment of conditional generative adversarial networks (cGAN) to design reflective standard unit cells for the use of 5G millimeter wave (mm Wave) passive metasurface reflectors. A total of 48 different configurations of cGAN were trained over a range of hyperparameters selected under proper guidelines used in the literature. The design performance of each cGAN configuration is measured by a mean absolute error (MAE) between a simulated reflection phase of a generated unit cell and a target reflection phase. The test results show that cGAN s trained by updating the discriminator one step for each single generator update tend to offer lower MAEs compared to cGANs trained by updating the discriminator three iterations for one generator update. The optimal cGAN among 48 hyperparameter configurations can generate unit cells with reflection phase resembling the desired target one.

关键词： Training Structural rings Phase measurement 5G mobile communication Machine learning Metasurfaces Reflection

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A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

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Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

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Intelligent System for Vehicles License Plate Recognition Using a Hybrid Model of GAN, CNN and ELM 2

Intelligent System for Vehicles License Plate Recognition Us...

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2nd International Conference for Innovation in technology, INOCON 2023

作者： Nirmala, B. Nithya, S. Vidhiya, R. Sunalini, K.K. Kumar, Buddha Hari Varadharajan, Bhoopathy CSE Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Porur Tamilnadu Chennai India SRMIST Electrical and Electronics Department RAMAPURAM Tamilnadu Chennai India KPR Institute of Engineering and Technology Department of English Tamilnadu India Koneru Lakshmaiah Education Foundation Department of English Andhra Pradesh Guntur India Vignan's Institute of Engineering for Women Department of Electronics and Communication Engineering Andhra Pradesh Visakhapatnam India Malla Reddy College of Engineering Department of Computer Science and Engineering Telangana Secunderabad India

ISBN: (纸本)9798350320923

The scientific community has given license plate recognition systems a lot of consideration. The current methods for vehicle identification need to be improved due to the swift increase in vehicle numbers. In order to lessen reliance on labor, a fully automated system is needed. With the growth of Intelligent Transportation Systems, demand for license plate recognition has increased significantly. License Plate Recognition (LPR) is susceptible to environmental factors such as a complex image background, angle view, and shift in illumination, it is still difficult to correctly recognize the digit letters on license plates. When reading license plates automatically, license plate recognition uses character recognition and image processing to identify the vehicles. The license plate detection and identification subsystems are typically combined into the vehicle license recognition system in order to locate the vehicle and identify the license plate. The Extreme Learning Machine (ELM) is used for categorization, identification, and training. This research suggests a GANCNN-ELM-based technique for detecting vehicle license plates. This method produces an accuracy of about 98.94% which outperforms the GAN-ELM, GAN-SVM, and GAN-CNN models. © 2023 IEEE.

关键词： Support vector machines

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Fast-OMRA: Fast Online Motion Resolution Adaptation for Neural B-Frame Coding

Fast-OMRA: Fast Online Motion Resolution Adaptation for Neur...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Sang NguyenQuang Zong-Lin Gao Kuan-Wei Ho Xiem HoangVan Wen-Hsiao Peng Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Faculty of Electronics and Telecommunications VNU University of Engineering and Technology Hanoi Vietnam

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

Most learned B-frame codecs with hierarchical temporal prediction suffer from the domain shift issue caused by the discrepancy in the Group-of-Pictures (GOP) size used for training and test. As such, the motion estimation network may fail to predict large motion properly. One effective strategy to mitigate this domain shift issue is to downsample video frames for motion estimation. However, finding the optimal downsampling factor involves a time-consuming rate-distortion optimization process. This work introduces lightweight classifiers to determine the downsampling factor. To strike a good rate-distortion-complexity trade-off, our classifiers observe simple state signals, including only the coding and reference frames, to predict the best downsampling factor. We present two variants that adopt binary and multi-class classifiers, respectively. The binary classifier adopts the Focal Loss for training, classifying between motion estimation at high and low resolutions. Our multi-class classifier is trained with novel soft labels incorporating the knowledge of the rate-distortion costs of different downsampling factors. Both variants operate as add-on modules without the need to re-train the B-frame codec. Experimental results confirm that they achieve comparable coding performance to the brute-force search methods while greatly reducing computational complexity.

关键词： Training Codecs Costs Motion estimation Search methods Rate-distortion Predictive models Encoding Signal resolution Optimization

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Prototype of Turbine Propulsion System with Controllable Thrust Vector for Long-Range Unmanned VTOL Aircraft 21st

Prototype of Turbine Propulsion System with Controllable Thr...

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21st Polish Control Conference, PCC 2023

作者： Czyba, Roman Lemanowicz, Marcin Domin, Jaroslaw Grzybowski, Jacek Stępień, Izabela Ungier, Lukasz Rosiek, Radoslaw Bialas, Piotr Niemiec, Michal Department of Automatic Control and Robotics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology ul. Akademicka 16 Gliwice44-100 Poland Department of Chemical Engineering and Process Design Faculty of Chemistry Silesian University of Technology ul. Ks. M. Strzody 7 Gliwice44-100 Poland Department of Mechatronics Faculty of Electrical Engineering Silesian University of Technology ul. Akademicka 10a Gliwice44-100 Poland

ISBN: (纸本)9783031351723

The aim of the project was to build a prototype of a turbine drive with a controllable thrust vector for a long-range vertical take-off and landing (VTOL) aircraft. The construction of the prototype required the development of a vectoring system for the tail propulsion system and a dedicated VTOL aircraft stabilization and control system. The prototype of the turbine drive system was based on energy-efficient electric motors. Research also involved modeling the turbine propulsion aerodynamics with usage of computational fluid dynamics (CFD) analysis. As a result of the design stage, a prototype of the propulsion system was created, on which flight tests were carried out. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computational fluid dynamics

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Application of LabVIEW to Determine Characteristics of Two-Terminal Passive Components 14

Application of LabVIEW to Determine Characteristics of Two-T...

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14th International Conference on Measurement, MEASUREMENT 2023

作者： Otomanski, Przemyslaw Pawlowski, Eligiusz Szlachta, Anna Poznan University of Technology Institute of Electrical Engineering and Electronics Piotrowo street 3A Poznań60-965 Poland Lublin University of Technology Faculty of Electrical Engineering and Computer Science Nadbystrzycka street 38A Lublin20-618 Poland Rzeszow University of Technology Department of Metrology and Measurement Systems W. Pola street 2 Rzeszow35-959 Poland

ISBN: (纸本)9788097262976

The paper discusses the topic of determining the current-voltage characteristics of two-terminal passive components. A virtual measurement instrument created in the LabVIEW environment using the NI USB-6211 data acquisition device is presented, as well as the results of tests performed for example elements. The described approach can also be applied to other measurement situations, such as for studying the self-heating effect of RTD. © 2023 Institute of Measurement Science, Slovak Academy o.

关键词： Current voltage characteristics

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Intelligent Headlights for Adapting Beam Patterns with Raspberry Pi and Convolutional Neural Networks 2

Intelligent Headlights for Adapting Beam Patterns with Raspb...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Maheswari, P. Gowriswari, S. Balasubramani, S. Ramesh Babu, A. Nk, Jijith Murugan, S. Srm Institute of Science and Technology Faculty of Management Studies Tamil Nadu Chennai India Sri Sairam Engineering College Department of Instrumentation and Communication Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India School of Computer Applications Vidya Vikas Institute of Engineering and Technology Karnataka Mysore India Dxn Holdings Bhd Maharashtra Pune India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350372847

This study uses intelligent headlights to improve car safety and economy. The suggested system uses Raspberry Pi and Convolutional Neural Networks (CNNs) to dynamically modify beam patterns to real-time ambient circumstances and context. The Raspberry Pi is a flexible computer platform, and CNNs automatically analyze sensor and video data to modify headlight beam patterns. A trained CNN model recognizes impending vehicles, pedestrians, and road conditions. By analyzing this information, the headlights dynamically optimize the beam pattern to maximize visibility and minimize road user glare. This flexibility improves road safety by optimizing lighting when required, minimizing driver fatigue, and enhancing response times. Raspberry Pi allows for a cost-effective and easy retrofitting of current vehicles with intelligent headlights. The system's scalability and adaptability make it suitable for many automotive applications, helping to construct smart and linked transportation systems. This discovery is a major step towards intelligent and adaptable lighting systems that improve vehicle safety and economy. © 2024 IEEE.

关键词： Vehicle safety

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