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2022 International Conference on Industry Sciences and Computer Science Innovation, iSCSi 2022

作者： Suttiponpisarn, Pintusorn Charnsripinyo, Chalermpol Usanavasin, Sasiporn Nakahara, Hiro TAIST Tokyo Tech ICTES Program Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathum Thani Thailand School of ICT Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand Dept. of Information and Communications Engineering Tokyo Institute of Technology Tokyo Japan

Driving in the wrong direction is one of the main reasons that cause road accidents in Thailand. To efficiently detect wrong direction driving vehicles, we proposed a system that can track those moving vehicles from CCTVs using deep learning and image processing techniques. Our proposed system is composed of two main algorithms: Road Lane Boundary detection from CCTV algorithm (RLB-CCTV) and Majority-Based Correct Direction Detection algorithm (MBCDD). In this paper, we introduced the RLB-CCTV algorithm and the enhanced version of MBCDD algorithms for detecting road boundaries and wrong-direction driving vehicles. Based on our experiments, our system can identify the road lane boundary and track vehicles driving in the wrong direction on each lane with an accuracy of 96.61%. © 2022 Elsevier B.V.. All rights reserved.

关键词： Hough transforms

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Improving embedded programming skills through physical computing activities in engineering education: A course experience

Improving embedded programming skills through physical compu...

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International Symposium on engineering Accreditation (ICACIT)

作者： Jonathan Álvarez Ariza Program of Technology in Electronics Engineering Faculty Corporación Universitaria Minuto de Dios-UNIMINUTO Bogotá Colombia

ISBN: (纸本)9781665492799

Embedded programming is one of the most requested abilities by companies in industry sectors such as automation, the Internet of Things (IoT), robotics, and power electronics, among others. While these abilities are required to be developed in the curricula of the engineering programs, the dropout rate of the courses of programming in which these abilities are developed continues to increase. Several factors such as socioeconomic, the lack of support by the academic staff, or the teaching methodology are claimed of the most common dropout factors. In this context, this article proposes a methodology to learn to program embedded devices employing Arduino and Raspberry Pi in a course known as open hardware. Some identified factors in the students who took the course such as the lack of motivation and confidence in programming abilities were the catalysts to create and deploy the educational methodology. In addition, physical computing was incorporated as a conceptual pillar in the methodology. N=20 engineering students of a technological program in electronics participated in the methodology for 16 weeks. The methodology had a special emphasis on problem-solving, construction of algorithms, their interaction with sensors, motors, displays, etc., and teamwork. The results of the methodology show that it served to improve the abilities in programming, the understanding of the algorithms in embedded systems, the motivation, and it promoted habits such as the comments in the codes and the creation of flow diagrams that helped the students to comprehend the algorithms and their interaction with the hardware devices.

关键词： Industries Service robots Hardware Power electronics Teamwork Sensors Internet of Things

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Email Spam Detection Using Logistic Regression and Explainable AI

Email Spam Detection Using Logistic Regression and Explainab...

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International Conference on Electrical, Computer and Communication engineering (ECCE)

作者： Sumit Kanti Sarker Ratul Bhattacharjee Md Abu Sufian Md Solaiman Ahamed Md Abu Talha Faiza Tasnim K.M. Nazmul Islam Sharia Tasnim Adrita Department of Computer Science & Engineering (CSE) Faculty of Science & Technology (FST) American International University Bangladesh Dhaka Bangladesh Bachelor of Science in Information Technology (BSIT) Cybersecurity Program Concentrations Washington University of Science and Technology (WUST) VA USA Department of Electrical and Electronics Engineering (EEE) Faculty of Engineering (FE) American International University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Faculty of Science & Engineering Northern University Bangladesh-(NUB) Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Email spam detection is crucial for ensuring a positive user experience and maintaining communication security. This study presents a novel spam detection approach leveraging Logistic Regression, optimized through hyperparameter tuning and enhanced with Explainable Artificial Intelligence. The proposed method is evaluated on a large-scale dataset of emails collected from a real-world spam detection system. Term Frequency-Inverse Document Frequency is utilized for feature extraction, converting email text into numerical representations. XAI introduces interpretability by highlighting critical features such as "pill," "PHP," and "Businessweek," which indicate spam classification patterns. Metrics such as precision, recall, and the confusion matrix are employed to assess the model’s performance, ensuring a balanced evaluation. Hyperparameter optimization using Grid Search CV achieves an optimal regularization parameter (C = 100) and maximum iterations (maxiter=100), resulting in an impressive F1 score of 98.76%, accuracy of 98.82%, precision of 98.62%, and recall of 98.90%.

关键词： Logistic regression Explainable AI Filtering Unsolicited e-mail Computational modeling Predictive models Transformers Frequency conversion Feature extraction Tuning

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SoC-Oriented Implementation of Machine Learning Based Breast Cancer Classification Algorithm

SoC-Oriented Implementation of Machine Learning Based Breast...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Abdelrahman Saeed Ayman Tawfik Hassan Mostafa Ahmed Hussein Khalil Electronics and Communications Engineering Department Cairo University Giza Egypt Department of Electrical and Computer Engineering Ajman University United Arab Emirates (UAE) University of Science and Technology Nanotechnology and Nanoelectronics Program Zewail City of Science and Technology Giza Egypt

Convolutional Neural Networks (CNN) have drawn the attention of researchers in the medical imaging field. Many researchers have exploited CNN for breast cancer detection. This study provides an Internet of Things (IoT) friendly implementation of CNN for breast cancer detection. To achieve faster time to Market, Deep-learning Processing Unit (DPU) on Field programmable Gate Array (FPGA) is adopted for the CNN hardware implementation. CNN inference on the proposed system achieves a 1.6x speed-up factor and 91.5% reduction in energy consumption compared to the conventional general-purpose multi-core Central Processing Unit (CPU).

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A Comparative Study of SnO2 Deposition Using Spray and Spin-Coating Technique for Electron Transport Layer of Inorganic Perovskite Solar Cells

A Comparative Study of SnO2 Deposition Using Spray and Spin-...

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International Conference on Radar, Antenna, Microwave, electronics, and Telecommunications (ICRAMET)

作者： Almas Shafwan Wilman Septina Natalita Maulani Nursam Xorell Ivanov Monov Prima Fitri Rusliani Shobih Adhi Kusumadjati Nunik Nurhayati Lia Yuliantini Physics Study Program Republic of Indonesia Defense University Bogor Indonesia Research Centre for Electronics National Research and Innovation Agency Republic of Indonesia Bandung Indonesia Master of Engineering Physics Faculty of Industrial Engineering Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9798350389920

ISBN: (纸本)9798350389937

Organic-inorganic halide perovskites have become notable for their efficiency in light harvesting. Nonetheless, the incorporation of organic cations like $\mathbf{M A}^{+}$and $\mathrm{FA}^{+}$presents stability challenges when exposed to environmental factors. This study explores the potential of replacing organic components with inorganic $\mathrm{Cs}^{+}$to enhance stability and efficiency in PSCs, focusing on CsPbBr 3 as the light harvester. We compare spin-coating and spray-coating techniques for depositing SnO 2 as the electron transport layer, revealing that spray-coating 7 cycles achieves higher PCE. Through detailed characterization, including UV-Vis, FTIR, SEM, I-V, and IPCE analyses, we optimized the manufacture of $\mathrm{SnO}_{2}, \mathrm{CsPbBr}_{3}$, and carbon deposition at room temperature, resulting in a PCE of $\mathbf{2. 2 1 \%}$ using the spray-coating process, demonstrating significant improvements over the spin-coating method.

关键词： Microwave antennas Vibrations Photovoltaic cells Radar Radar antennas Perovskites Stability analysis Telecommunications Thermal stability Electrons

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A Reduced Spiking Neural Network Architecture for Energy Efficient Context-Dependent Reinforcement Learning Tasks

A Reduced Spiking Neural Network Architecture for Energy Eff...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Hira Rasheed Peyman Mirtaheri Ali Muhtaroglu Dept. of Machines Electronics & Chemistry Faculty of Technology Art and Design (TKD) Oslo Metropolitan University Oslo Norway Biomedical Engineering MS Program Advanced Health Intelligence and Brain-Inspired Technologies (ADEPT)

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Neuromorphic circuits and systems involving spiking neural networks (SNN) have resulted in disruptive advances in performance/joule for relevant applications. A novel reinforcement learning (RL) digital hardware architecture is presented in this work that achieves energy consumption improvements through three fundamental techniques: The first two techniques comprise reduction in the complexity of the arithmetic unit for the optimization of recurring synapse and neuron cores in the network array, which is inspired by "crude" nature of the building blocks in the biological neurons that are tolerant to inaccuracy and noise. As the third technique, the RL SNN middle (hippocampus) layer is equipped with a simple scratchpad to facilitate temporal hysteresis in synaptic plasticity during the RL processes of long-term potentiation/depression (LTP / LTD). This feature is inspired by the non-temperamental behavior of biological synapses. The intelligent allocation significantly reduces learning time in a given task. Implementation on Intel Cyclone IV FPGA demonstrated significant advantages in cost, power dissipation and execution time, resulting in more than two orders of magnitude benefit in energy consumption for a context-dependent learning task on a 16-node 3-layer RL network presented in the literature.

关键词： Energy consumption Circuits and systems Neurons Reinforcement learning Biology Power dissipation Resource management

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Characterization of Double HfZrO2based FeFET toward Low-Voltage Multi-Level Operation for High Density Nonvolatile Memory

Characterization of Double HfZrO2based FeFET toward Low-Volt...

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2022 International Symposium on VLSI technology, Systems and Applications, VLSI-TSA 2022

作者： Lou, Z.-F. Liao, C.-Y. Hsiang, K.-Y. Lin, C.-Y. Lin, Y.-D. Yeh, P.-C. Wang, C.-Y. Yang, H.-Y. Tzeng, P.-J. Hou, T.-H. Tang, Y.-T. Lee, M.H. National Taiwan Normal University Institute and Undergraduate Program of Electro-Optical Engineering Taipei Taiwan National Yang Ming Chiao Tung University Department of Electronics Engineering Hsinchu Taiwan National Central University Department of Electrical Engineering Taoyuan Taiwan Industrial Technology Research Institute Electronic and Optoelectronic System Research Laboratories Hsinchu Taiwan

ISBN: (纸本)9781665409230

A double HfZrO2 (HZO) ferroelectric field-effect transistor (FeFET) is demonstrated with ultra-low operating voltage as |VP/E| = 3 V to achieve multilevel cell (MLC) nonvolatile memory (NVM). Compared to a single FE-HZO FeFET, the metal/ferroelectric/metal/ferroelectric/Si (MFMFS) structure results not only in a reduction of VP/E, but also provides a feasible memory window (MW) of 1.9 V for MLC operation. In addition, the metal/ferroelectric/insulator/ ferroelectric/Si (MFIFS) FeFET exhibits a MW as high as >2.5 V. The double HZO FeFET has potential to improve power consumption and enhance memory density for MLC-NVM applications. © 2022 IEEE.

关键词： Nonvolatile storage

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Performance Evaluation of Multi-ONU Customers Access Network using Spectrum Slicing and Duplication Techniques 5

Performance Evaluation of Multi-ONU Customers Access Network...

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5th International Conference on Emerging Electrical Energy, electronics and Computing Technologies, ICE4CT 2024

作者： Rahman, Juwairiyyah Abdul Mugunthan, Sivaguru A.L. Hassan, Hasliza Abu Muslim, Norliana Ab-Rahman, Mohammad Syuhaimi Department of Electrical Engineering Faculty of Engineering and Life Sciences Universiti Selangor Selangor Bestari Jaya45600 Malaysia Department of Computer and Communication Technology Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Perak Kampar31900 Malaysia Department of Electrical Electronics and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Malaysia Electrical and Computer Engineering Program Faculty of Engineering Sohar University PC:311 Sohar Oman

This study aims to increase the number of access users by limiting the sample size to 30 users while ensuring that every Optical Network Unit (ONU) receives data from the Optical Line Terminal (OLT). The proposed solution allows for the determination of power requirements at each ONU based on bus-star topologies, facilitating an increase in user capacity. The power received by each ONU is a critical parameter that system designers must consider when planning the implementation of Fiber to the Home (FTTH), as it impacts the output data in terms of Bit Error Rate (BER) and Signal Quality Factor (QF) for each user. Various parameters were evaluated to ensure that the enhanced network improves scalability while adhering to standard specifications. These parameters included the Q-factor, BER, eye diagram, user count, and received power. The study employed slicing and duplication techniques, utilizing Arrayed Waveguide Grating (AWG) for signal duplication and Wavelength Division Multiplexing for signal slicing. The simulations were conducted using a transmitter operating at a frequency of 1491 nm with a power output of 0 dBm, utilizing the latest version of OptiSystem 20 software. The results highlighted the system's capability to support a total of 73,728 users, meeting stringent performance criteria. Specifically, the system achieved a Q-factor greater than 6, indicating high signal quality, and a bit error rate (BER) lower than 1 × 10-9, ensuring reliable data transmission. For the sampled 30 users, the average Q factor was 8.1527, and the average BER was 5.3648 × 10^-11, with an output power level of -54.278 dBm. Despite an increase in users, the quality of the data supplied to clients remained unchanged. These outcomes underscore the robustness and efficiency of the design in delivering high-capacity, error-resilient optical communication. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Q factor measurement

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Deep learning-based denoising for unbiased analysis of morphology and stiffness in amyloid fibrils

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Computers in Biology and Medicine 2025年 184卷 109410-109410页

作者： Park, Jaehee Cheong, Da Yeon Lee, Gyudo Han, Cheol E. Department of Electronics and Information Engineering Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of Department of Biotechnology and Bioinformatics Korea University Sejong30019 Korea Republic of

Understanding the morphology of amyloid fibrils is crucial for comprehending the aggregation and degradation mechanisms of abnormal proteins implicated in various diseases, such as Alzheimer's disease, Parkinson's disease, type II diabetes, and various forms of amyloidosis. Atomic force microscopy (AFM) stands as the most representative method for studying amyloid fibril morphology. However, obstacles in AFM images, including noise, salt, and amorphous aggregates, often impede accurate sample quantification. In this study, we developed denoising software employing a U-Net deep learning architecture to address this issue. The software efficiently eliminated various impediments that interfere with fibril analysis in noisy AFM images, thereby facilitating precise quantification of amyloid fibrils. We also developed automated fibril analysis technologies using the denoised AFM images, leading to quicker, more precise, and more objective assessments of fibril morphology. Furthermore, we presented a method for fibril stiffness extraction from a modulus image through mask creation based on a denoised height image. Our approach secures time efficiency and precision in analyzing amyloid morphology, and we believe it will significantly advance the currently stagnant research on amyloid-related diseases. © 2024 The Authors

关键词： Neurodegenerative diseases

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Machine Learning-based Modulation Format Identification and Optical Performance Monitoring Techniques Implementation

Machine Learning-based Modulation Format Identification and ...

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2021 Advanced Communication Technologies and Signal Processing, ACTS 2021

作者： Devi, Pukhrambam Puspa Vincent Simatupang, Joni Welman National Institute of Technology Silchar Department of Electronics and Communication Engineering Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung40132 Indonesia President University Study Program of Electrical Engineering Bekasi17530 Indonesia

ISBN: (纸本)9781665423373

In this paper, machine learning-based techniques are used to solve and analyze the modulation format recognition problem. The combination of intelligent software and high-performance hardware provides a large scope for innovation in optical networking. Machine learning algorithms can use a large amount of data available from the network monitors to learn and make the network more robust. This is a problem in optical communication that consists of defining the type of digital modulation process in which an electrical signal should be sent. A dataset to represent realistic transmission behaviors was generated using a simulator based on a Gaussian noise model. A multi-layer perceptron was used and tested with different architectures to show that a high level of accuracy is achievable with machine learning. An analysis of the input features was made by using the select K best features method. Finally, an attempt to visualize the data in 2-dimension was made using the Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) methods to reduce the dimensionality of the input features and see their relationships. © 2021 IEEE.

关键词： Principal component analysis

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