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Random Interpulse Frequency and PRT Joint Agile Waveform Design based on Whales Optimization Algorithm

IEEE Transactions on Aerospace and electronic Systems 2025年

作者： Lang, Ping Fu, Xiongjun Dong, Jian Yin, Junjun Yang, Jian Tsinghua University Department of Electronic Engineering Beijing100084 China Beijing Institute of Technology School of Integrated Circuits and Electronics Beijing100081 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Recent advancements in waveform generators have propelled the active agile waveform design as a prominent research direction for the anti-jamming solutions. Nevertheless, the efficient design of optimal agile waveforms with enhanced performance metrics, such as low integrated sidelobe level (ISL), remains a significant challenge. This deficiency may directly compromise radar capabilities in target detection, recognition, and anti-jamming. This paper proposes a Whale optimization algorithm (WOA) based random interpulse frequency and pulse repetition time (PRT) joint agile (RI-FPrtJA) waveform. Firstly, the unique ambiguity function (AF) of RI-FPrtJA waveform within a coherent processing interval (CPI) are mathematically derived;Then, we theoretically formulate the WOA-based RI-FPrtJA waveform optimization design framework, integrated with a multi-objective optimization function that incorporates a weighted combination of the mutual incoherence property (MIP) factor and ISL of the AF;Lastly, the simulations are carried out to verify the effectiveness of our proposed method. The qualitative and quantitative numerical simulation results demonstrate that the WOA-based optimized RI-FPrtJA waveform has better or competitive performances in terms of peak-to-sidelobe level ratio (PSLR), ISL, and so on, compared to some current existing methods. © 1965-2011 IEEE.

关键词： Multiobjective optimization

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Value Stream Mapping for Smart Pharmaceutical Management in a Thai Hospital 6

Value Stream Mapping for Smart Pharmaceutical Management in ...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Boonsothonsatit, Ganda Silapunt, Rardchawadee Vongbunyong, Supachai Kaemarungsi, Kamol Chanpuypetch, Wirachchaya Chonsawat, Nilubon Graduate School of Management and Innovation King Mongkut's University of Technology Thonburi Thailand Electronic and Telecommunication Engineering King Mongkut's University of Technology Thonburi Thailand Hospital Automation Research Center Institute of Field Robotics King Mongkut's University of Technology Thonburi Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand Faculty of Agro-Industry Chiang Mai University Thailand Department of Agricultural Economy and Developement Faculty of Agriculture Chiang Mai University Thailand

The evolution of healthcare systems worldwide necessitates continual improvement in hospital management practices, particularly pharmaceutical management. This paper explores transforming traditional pharmaceutical management systems in large and medium-sized hospitals in Thailand into smart systems using Value Stream Mapping (VSM) as a guiding tool. Traditional systems often face challenges such as inefficiencies and medication errors. VSM, rooted in lean management principles, offers a structured approach to visualize and optimize processes. Additionally, smart technologies like automated inventory systems and electronic health records enhance pharmaceutical management effectiveness. Through a comprehensive research methodology encompassing case studies, data collection, and VSM analysis, this study identifies inefficiencies and proposes evidence-based strategies for improvement. Results from a case study highlight the benefits of VSM implementation in enhancing pharmacy operations, emphasizing improved patient care and operational efficiency. The implementation of a smart medication dispensing system shows promising results in reducing workload and accommodating increased patient arrivals. Continued evaluation and refinement are crucial for maximizing benefits and addressing emerging challenges effectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： electronic health record

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A Hybrid Transmitting and Reflecting Beyond Diagonal Reconfigurable Intelligent Surface with Independent Beam Control and Power Splitting

arXiv

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arXiv 2025年

作者： Ming, Zhaoyang Shen, Shanpu Rao, Junhui Li, Zan Zhang, Jichen Chiu, Chi Yuk Murch, Ross Department of Electronic and Computer Engineering University of Hong Kong Science and Technology Hong Kong Department of Electrical Engineering and Electronics University of University of Liverpool Liverpool United Kingdom Hong Kong University of Science and Technology Hong Kong

A hybrid transmitting and reflecting beyond diagonal reconfigurable intelligent surface (BD-RIS) design is proposed. Operating in the same aperture, frequency band and polarization, the proposed BD-RIS features independent beam steering control of its reflected and transmitted waves. In addition it provides a hybrid mode with both reflected and transmitted waves using tunable power splitting between beams. The BD-RIS comprises two phase reconfigurable antenna arrays interconnected by an array of tunable two-port power splitters. The two-port power splitter in each BD-RIS cell is built upon a varactor in parallel with a bias inductor to exert tunable impedance variations on transmission lines. Provided with variable reverse DC voltages, the two-port power splitter can control the power ratio of S11 over S21 from −20 dB to 20 dB, thus allowing tunable power splitting. Each antenna is 2-bit phase reconfigurable with 200 MHz bandwidth at 2.4 GHz so that each cell of BD-RIS can also achieve independent reflection and transmission phase control. To characterize and optimize the electromagnetic response of the proposed BD-RIS design, a Thévenin equivalent model and corresponding analytical method is provided. A BD-RIS with 4×4 cells was also prototyped and tested. Experiments show that in reflection and transmission mode, the fabricated BD-RIS can realize beam steering in reflection and transmission space, respectively. It is also verified that when operating in hybrid mode, the BD-RIS enables independent beam steering of the reflected and transmitted waves. This work helps fill the gap between realizing practical hardware design and establishing an accurate physical model for the hybrid transmitting and reflecting BD-RIS, enabling hybrid transmitting and reflecting BD-RIS assisted wireless communications. © 2025, CC BY-NC-ND.

关键词： Beam forming networks

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Development of Cutaneous Feedback Haptic Glove for VR Industrial Training

Development of Cutaneous Feedback Haptic Glove for VR Indust...

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Consumer technology (ICCT-Pacific), International Conference on

作者： Vencec Jumar A. Sasing Aaron Raymond See Thad Jacob T. Tiong Aaron Benjmin R. Alcuitas Hang-Hong Kuo Ralf Seepold Department of Electronic Engineering National Chin-Yi Universtiy of Technology Taichung Taiwan Department of Electronics Engineering National Chin-Yi Universtiy of Technology Taichung Taiwan Department of Computer Science HTWG Konstanz - University of Applied Science Konstanz Germany

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Traditional industrial training methods often require extensive time, cost, and resources for effective preparation and execution. This paper presents a virtual reality (VR)-based solution for industrial training, enhanced by a cutaneous feedback haptic glove that improves the immersive learning experience. The developed haptic glove integrates a high-resolution tactile pin array that provides realistic feedback through the fingertips, allowing users to sense interactions with virtual objects. The VR training environment, created using Unity, simulates a crane sorting system that replicates real-world industrial operations. This setup enables users to manipulate a virtual crane through hand motions, with the haptic glove delivering tactile feedback that enhances the sense of realism. This approach not only advances skill acquisition but also opens opportunities for safe and cost-effective training in complex industrial tasks. By leveraging VR and cutaneous feedback, this study contributes to the modernization of training methods, reducing risks associated with real-world training scenarios and offering a pathway toward more immersive, effective industrial training.

关键词： Industrial training Hands Cranes Costs Tactile sensors Haptic interfaces Pins Sorting Immersive learning

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Hybrid Convtranslstm for Spatio-Temporal Classification: Identifying Early Parkinson's Disease from Gait Patterns

SSRN

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SSRN 2025年

作者： Mahali, Muhammad Izzuddin Avian, Cries Putro, Nur Achmad Sulistyo Prakosa, Setya Widyawan Leu, Jenq-Shiou Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan Department of Electronics and Informatics Engineering Faculty of Engineering Universitas Negeri Yogyakarta Indonesia Department Electrical Engineering Faculty of Engineering Universitas Brawijaya Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Indonesia

Parkinson's disease (PD) is a prevalent neurodegenerative disorder that affects motor function and impacts millions worldwide. Early diagnosis and precise staging of PD are critical for effective management and timely intervention. Traditional gait analysis focuses on lower limb movement to differentiate between moderate and advanced PD stages;however, integrating upper and lower limb dynamics offers a more comprehensive approach to distinguishing early-stage PD from *** study introduces a novel spatio-temporal model that integrates CNN, Transformer, and BiLSTM layers to classify PD based on the Hoehn and Yahr (H&Y) score. The architecture captures both spatial and temporal dependencies in gait patterns, with a CNN serving as the primary spatial feature extractor, utilizing a temporal-focused kernel, and augmented by a Transformer to enhance temporal feature learning. Additionally, a BiLSTM module is incorporated to effectively capture complex spatio-temporal gait dynamics across all modules. For data augmentation, sliding windows of 500, 800, and 1200 samples were employed, and the model's performance was rigorously assessed through 3-fold and 5-fold cross-subject *** included accuracy, F1-score, recall, precision, and a voting-based confidence score, while t-SNE visualization provided insights into spatio-temporal feature differentiation. The model demonstrated high performance, achieving optimal accuracy of 0.90 and an average confidence score of 0.97 with a 1200-sample window in 5-fold validation. These findings highlight the potential of a spatio-temporal approach incorporating upper and lower limb characteristics in gait analysis as a robust method for PD multi-class classification, supporting early and effective PD diagnosis from raw IMU signals. © 2025, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

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Communication-Efficient Stochastic Distributed Learning

arXiv

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arXiv 2025年

作者： Ren, Xiaoxing Bastianello, Nicola Johansson, Karl H. Parisini, Thomas Department of Electrical and Electronic Engineering Imperial College London London United Kingdom School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Trieste Italy

We address distributed learning problems, both nonconvex and convex, over undirected networks. In particular, we design a novel algorithm based on the distributed Alternating Direction Method of Multipliers (ADMM) to address the challenges of high communication costs, and large datasets. Our design tackles these challenges i) by enabling the agents to perform multiple local training steps between each round of communications;and ii) by allowing the agents to employ stochastic gradients while carrying out local computations. We show that the proposed algorithm converges to a neighborhood of a stationary point, for nonconvex problems, and of an optimal point, for convex problems. We also propose a variant of the algorithm to incorporate variance reduction thus achieving exact convergence. We show that the resulting algorithm indeed converges to a stationary (or optimal) point, and moreover that local training accelerates convergence. We thoroughly compare the proposed algorithms with the state of the art, both theoretically and through numerical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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An Open-Source Framework for Developing Human Behavior in Humanoid Robots

An Open-Source Framework for Developing Human Behavior in Hu...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Mohammad Tarek Aziz Nayeem Uddin Ahmed Khan Md. Maskat Sharif Md. Shahriar Abdullah Md. Riduan Tanjim Mahmud Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Department of Electrical and Electronic Engineering University of Chittagong Chittagong Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Department of Civil and Environmental Engineering Lamar University Texas United States Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

We need someone to rise from childhood to adulthood and then to help us work the graveyard. Humans are mortal, and we engage in many activities that carry a higher risk of death, such as those performed by the military, firefighters, astronauts, and divers, among other professions. To rescue people, this includes ourselves if we replace people from those kinds of riskier sectors. So, in the interest of mankind, we aim to create a robot that looks like a man from head to toe and mimics our movements. The goal of this research is to develop a robot that can act like a human, be fully controlled by an electronic device or voice, converse with humans, walk but not run, connect to the internet and gather data from there, and express emotion using holographic eyes and faces so that humans can believe the robot is alive even though it is incapable of making decisions on its own. This paper proposes an open-source and Arduino-based scientific and technological machine to develop a humanoid robot. This robot is designed internally and externally with a circuit designed in the back-end system. The behavior of the proposed robot is most similar to human behavior. To find out the performance of the proposed robotics system, some software libraries, such as VoiceHexData for voice command, and some hardware instruments, such as circuit diagrams, OLED displays, and Arduino IDEs for software modules, were used.

关键词： Legged locomotion Software libraries Instruments MIMICs Humanoid robots Organic light emitting diodes Software Magnetic heads Hardware Behavioral sciences

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Wi-Fi Sensing Based on Deep Supervised Dictionary Learning for Robust Device-Free Localization

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IEEE Transactions on Vehicular technology 2025年

作者： Huang, Huakun Wang, Chenyang Zhao, Lingjun Wang, Weizheng Ding, Shuxue Vasilakos, Athanasios The School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China The School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou China The Department of Computer Science City University of Hong Kong Hong Kong The School of Artificial Intelligence Guilin University of Electronic Technology China The College of Computer Science and Information Technology IAU Saudi Arabia Grimstad Norway

Device-free localization (DFL) represents an emerging technology in autonomous driving assistance systems, sensing targets around a vehicle without requiring them carrying any devices. Current machine learning methodologies for DFL struggle with adaptively extracting discriminative features, resulting in weak robustness. Although dictionary learning techniques have shown potentiality in robust feature extraction for images and signals, their direct application to improving the robustness of DFL presents significant challenges. Specifically, the unsupervised mechanism of dictionary learning complicates the calibration of target location bases. The complex optimization processes also lead to inefficient parameter updating. To address these issues, we propose a deep-supervised dictionary learning (DSDL) approach for enhancing the robustness of DFL. The proposed DSDL method synergizes the advantages of sparse representation and deep learning, incorporating the robustness and interpretability of dictionary learning with the efficient parameter updating characteristic of deep learning. The supervised mechanism enables collaborative labeling of the learned signal bases. Experimental results, derived from an established DFL system, demonstrate that our DSDL outperforms existing techniques in both robustness and accuracy. DSDL maintains strong robustness and achieves high localization accuracy, surpassing 99% on clean data and sustaining over 97% accuracy under 30 dB signal-to-noise ratio (SNR) conditions. This work highlights the potential of existing WiFi infrastructure to provide cost-efficiency solutions for target localization, paving the way for future applications of wireless sensing. © 1967-2012 IEEE. All rights reserved.

关键词： Self-supervised learning

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IoT-Enabled Predictive Maintenance for Renewable Energy Systems

IoT-Enabled Predictive Maintenance for Renewable Energy Syst...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Selvan, G. Krishna, V. Anantha Thiyagesan, M. Venkatasubramanian, R. Vanitha, R. Sarojwal, Atul Sivaramkumar, Mathiyalagan Thanthai Hans Roever College Department of Physics Tamil Nadu Perambalur621220 India Sridevi Women's Engineering College Department of Computer Science and Engineering Telangana Hyderabad500075 India R.M.K. Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai601206 India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronic Engineering Tamil Nadu Chennai600073 India Aarupadai Veedu Institute of Technology Vinayaka Mission's Research Foundation Department of Electrical and Electronic Engineering Tamil Nadu Chennai603104 India Fet Mjp Rohilkhand University Bareilly Department of Electrical Engineering Utttar Pradesh 243006 India Saveetha University Department of Biosciences Chennai602105 India

ISBN: (纸本)9798331509828

The integration of Internet of Things (IoT) technology into renewable energy systems has revolutionized predictive maintenance, resulting in improved operational efficiency and reduced downtime. This project's objective is to use advanced feature selection, classification, and data preparation techniques to build a robust IoT-enabled predictive maintenance platform. Cleaning and normalizing sensor data is the first step in the proposed procedure to ensure data consistency and integrity. Principal Component Analysis (PCA) reduces dimensionality while maintaining crucial information when choosing features from high-dimensional IoT data streams. For categorization, the Random Forest approach is employed, which provides improved precision and interpretability in determining maintenance requirements. The approach demonstrated outstanding prediction performance and the ability to proactively identify maintenance requirements when validated using real-world statistics from renewable energy installations. The results demonstrate how combining IoT and machine learning may improve system reliability and optimize energy production, leading to smarter, greener energy solutions. © 2025 IEEE.

关键词： Predictive maintenance

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A New IoT-Based Smart Greenhouse for Cultivation of Water Convolvulus

A New IoT-Based Smart Greenhouse for Cultivation of Water Co...

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International Electrical Engineering Congress (iEECON)

作者： Prayoot Akkaraekthalin Tanee Somwong Nonchanutt Chudpooti Nuttawut Hirun Suwaluck. Meesomklin Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronic and Telecommunications Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronics Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper presents a new IOT system for monitoring and controlling plant cultivation in greenhouse system. The system aims to regulate and monitor the environmental conditions suitable for plant cultivation in real-time. The system utilizes sensors to measure environmental conditions, such as soil moisture sensor, light intensity sensor, temperature sensor, and air humidity sensor. The Node MCU ESP32 is used for processing and controlling these operations. The system is designed to send notifications through Line application and allows farmers to monitor the system's performance and plant growth in real-time via a smartphone application. It can utilize the Blynk IoT application on a smartphone for remotely monitoring and controlling the environmental conditions and provides historical data. This experiment demonstrates that the designed system can successfully cultivate plants and promote better growth compared to traditional cultivation methods with a 13.38% improvement. Additionally, it provides convenience and ease of management for plant cultivation.

关键词： Temperature sensors Temperature measurement Smart agriculture System performance Green products Sensor systems Real-time systems Sensors Internet of Things Monitoring

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