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7th International Conference on Vocational Education and Electrical engineering, ICVEE 2024

作者： Pradhana, Anak Agung Surya Funabiki, Nobuo Kotama, I Nyoman Darma Desnanjaya, I Gusti Made Ngurah Radhitya, Made Leo Mahajaya, Nyoman Sarasuartha Okayama University Graduate School of Environmental Life Natural Science and Technology Okayama Japan Okayama University Department of Electrical and Communication Engineering Okayama Japan Institute of Business and Technology Indonesia Department of Computer System Engineering Denpasar Indonesia Institute of Business and Technology Indonesia Department of Informatic Technology Denpasar Indonesia Institut Teknologi Dan Bisnis Stikom Departement of Magister Information Systems Denpasar Indonesia

ISBN: (纸本)9798331505103

This study presents the development of an Internet of Things (IoT) system for a water heater model, focusing on enhancing reliability during sensor malfunctions that could disrupt operations. Using the SEMAR IoT platform, the system efficiently collects and analyzes data, while the integration of the Holt-Winter algorithm ensures accurate temperature estimates, even with unreliable or missing sensor data. The algorithm successfully auto-calibrates sensors, predicts temperature at specific time intervals based on real sensor behavior, and fills in missing data during sensor failures. Tests show strong correlations between estimated and actual temperatures, allowing uninterrupted operation. This solution offers a flexible, cost-effective alternative to traditional redundancy, ensuring system reliability during sensor failures. Future research will explore alternative predictive models and conduct real-world testing to validate the system's resilience under complex conditions. © 2024 IEEE.

关键词： Temperature measurement

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A Dual-Band Slotted Antenna at 2.4/5.8GHz for ISM Band Applications

A Dual-Band Slotted Antenna at 2.4/5.8GHz for ISM Band Appli...

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IEEE Symposium on Wireless Technology and Applications (ISWTA)

作者： Muhammad Syarul Nizwa Mohd Noor Sarah Yasmin Mohamad Norun Farihah Abdul Malek Farah Nadia Mohd Isa Aiman Hakimi Rahimi Huda Adibah Ramli Department of Electrical and Computer Engineering Microwave Communication and Information System Engineering Research Group (MCISE) Kulliyyah of Engineering International Islamic University Gombak Selangor Darul Ehsan Malaysia

ISBN: (数字)9798350351354

ISBN: (纸本)9798350351361

This paper presents a microstrip antenna with dual-band operating frequencies by etching multiple slots on the radiating patch of the antenna. In wireless communication systems, microstrip antenna operating at multiple frequencies are in demand and has a significant influence for ISM (Industrial Scientific Medical) applications. The proposed antenna is specifically designed to operate at both 2.4 and 5.8 GHz ISM frequency band. The dual-band antenna was designed and simulated using CST-MWS software with a patch dimension of $39.4\ \text{mm}\times 39.5\ \text{mm}\times 1.575\ \text{mm}$ , and mounted on a RT/duroid 5880 substrate with a dielectric constant $\varepsilon_{r}=2.2$ , loss tangent of $\delta=0.0009$ and thickness of 1.575 mm. Based on the results, the antenna exhibit an $S_{11}$ of −29.23 dB and VSWR of 1.07 at 2.4 GHz, and $S_{11}$ of −18.55 dB and VSWR of 1.26 at 5.8 GHz. The good consistency between $S_{11}$ predicted and measured results confirm the antenna's ability to be implemented for ISM band applications such as Wi-Fi, WLAN and wireless sensor network.

关键词： Antenna measurements Slot antennas Dual band Resonant frequency Microstrip antennas Loss measurement Microstrip

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A Large-Area LTPS-TFT-Based Bi-directional Biomedical Interface with Process-Invariant In-pixel Biopotential-to-Digital Converters

A Large-Area LTPS-TFT-Based Bi-directional Biomedical Interf...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Hanbo Zhang Yuqing Lou Zhihang Zhang Yongfu Li Fakhrul Rokhani Guoxing Wang Jian Zhao Department of Micro/Nano Electronics Shanghai Jiao Tong University Shanghai China Department of Computer and Communication System Engineering University Putra Serdang Selangor Malaysia

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

In this paper, we demonstrate a bi-directional biomedical process-invariant pixel interface based on the LTPS-TFT that integrates a front-end amplifier, an analog-to-digital converter and a stimulator. The pixel interface can convert biopotential into digital signals. This near-sensor signal processing can avoid the interference of motion artifacts, and the all-digital transfer has a high noise tolerance. Under the process variation of 1×-1.4× threshold voltage and ±10% mobility fluctuations, the DC gain of the operational amplifier changes from 58.83 dB to 57.21 dB, and 39.09 dB to 37.88 dB for the front-end amplifier. Compared with the single-stage amplifier, the stability has been improved by 13.2×. The proposed pseudo differential VCO-based ADC can effectively eliminate second-order non-linearity which achieves the best performance in state-of-the-art TFT-ADCs. When the threshold voltage and mobility fluctuate, ENOB changes from 7.36 bit to 7.30 bit (OSR=64) and 11.52 bit to 11.14 bit (OSR=256). The change rate does not exceed 4%.

关键词： Fluctuations Voltage-controlled oscillators Simulation Stacking Bidirectional control Threshold voltage Stability analysis

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Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13847-13867页

作者： Balasubramanian, E. Elangovan, E. Tamilarasan, P. Kanagachidambaresan, G.R. Chutia, Dibyajyoti Department of Mechanical Engineering Head-Centre for Autonomous System Research Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India Department of Electronics and Communication Engineering Centre for Autonomous System Research Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India Department of Aeronautical Engineering Centre for Autonomous System Research Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India Department of Computer Science and Engineering and Associate Editor Wireless Networks Springer Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India Department of Space North Eastern Space Applications Centre ISRO Meghalaya Umiam793103 India

Autonomous mission capabilities with optimal path are stringent requirements for Unmanned Aerial Vehicle (UAV) navigation in diverse applications. The proposed research framework is to identify an energy-efficient optimal path to achieve the designated missions for the navigation of UAVs in various constrained and denser obstacle prone regions. Hence, the present work is aimed to develop an optimal energy-efficient path planning algorithm through combining well known modified ant colony optimization algorithm (MACO) and a variant of A*, namely the memory-efficient A* algorithm (MEA*) for avoiding the obstacles in three dimensional (3D) environment and arrive at an optimal path with minimal energy consumption. The novelty of the proposed method relies on integrating the above two efficient algorithms to optimize the UAV path planning task. The basic design of this study is, that by utilizing an improved version of the pheromone strategy in MACO, the local trap and premature convergence are minimized, and also an optimal path is found by means of reward and penalty mechanism. The sole notion of integrating the MEA* algorithm arises from the fact that it is essential to overcome the stringent memory requirement of conventional A* algorithm and to resolve the issue of tracking only the edges of the grids. Combining the competencies of MACO and MEA*, a hybrid algorithm is proposed to avoid obstacles and find an efficient path. Simulation studies are performed by varying the number of obstacles in a 3D domain. The real-time flight trials are conducted experimentally using a UAV by implementing the attained optimal path. A comparison of the total energy consumption of UAV with theoretical analysis is accomplished. The significant finding of this study is that, the MACO-MEA* algorithm achieved 21% less energy consumption and 55% shorter execution time than the MACO-A*. moreover, the path traversed in both simulation and experimental methods is 99% coherent with each other.

关键词： Energy utilization

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A Novel and Accurate Method of Diagnosis via AI and ML for Smart HC system 4

A Novel and Accurate Method of Diagnosis via AI and ML for S...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Elizabeth, J. Ruby Emmanuel, Mathews Aswini, E. Pathak, Shriram Bhuvaneswari, M. Choubey, Abhishek Nehru Institute of Engineering and Technology Department of Computer Science and Business System Tamil Nadu T.M.Palayam India Sathyabhama University of Science and Technology chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering India Ies Insititute of Technology and Management Ies University Department of Mechanical Engineering M.P. Bhopal India Karpagam Academy of Higher Education Department of Biomedical Engineering Coimbatore India Sreenidhi Institute of Science and Technology ghatkesar Hyderabad Yamnampet India

ISBN: (纸本)9798350360165

Recent breakthroughs in the internet of Issues (IoT), computing through the cloud, and the use of AI have changed the standard healthcare system, making a case for smart health care. By combining key technologies including IoT and AI, there is a major improvement in medical services. The merging of IoT and AI offers multiple possibilities within the healthcare industry. This study piece presents a new artificial intelligence- and internet of things convergence-based framework for disease detection in smart health care systems, emphasizing especially on cardiovascular diseases and diabetes. The suggested model includes separate steps, including data collection, preparation, classification, and parameter modification. Utilizing Internet of Things devices like smartwatches along with sensors enables smooth collection of information, while AI methods harness this data for the detection of illnesses. The technique presents a new approach, applying to the Crowd Search Optimizer algorithm-based Flowed Long and Short-Term Memory (CSO-CLSTM) approach to disease detection. CSO is applied to improve both 'weights' while 'bias' factors of the CLSTM framework, while the isolation Fg (iForest) method is implemented to remove outliers. Through testing with health care information, the CSO-LSTM model got excellent accuracy ratings of 96.16% when 97.26% in identifying heart disease while diabetes, respectively. Consequently, the suggested CSO-LSTM model appears as a solid and reliable method of disease evaluation in digital healthcare systems, showing its potential for broad application. © 2024 IEEE.

关键词： Electronic health record

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Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

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computers, Materials & Continua 2022年第9期72卷 4509-4523页

作者： Elias Hossain Md.Shazzad Hossain Md.Selim Hossain Sabila Al Jannat Moontahina Huda Sameer Alsharif Osama S.Faragallah Mahmoud M.A.Eid Ahmed Nabih Zaki Rashed Department of Software Engineering Daffodil International UniversityDhaka 1207Bangladesh Department of Computing and Information System Daffodil International UniversityDhaka 1207Bangladesh Department of Computer Science&Engineering BRAC UniversityDhaka 1212Bangladesh Department of Information and Communication Engineering Bangladesh University of Professionals(BUP)Dhaka 1216Bangladesh Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Electrical Engineering College of EngineeringTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic EngineeringMenouf 32951Egypt

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional MagneticResonance Image (MRI) and Computed Tomography (CT) scans utilizing3D U-Net Design and ResNet50, taken after by conventional classificationstrategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, andthe 3D U-Net scored 97.99% among the different methods of deep *** is to be mentioned that traditional Convolutional Neural Network (CNN)gives 97.90% accuracy on top of the 3D MRI. In expansion, the imagefusion approach combines the multimodal images and makes a fused image toextricate more highlights from the medical images. Other than that, we haveidentified the loss function by utilizing several dice measurements approachand received Dice Result on top of a specific test case. The average mean scoreof dice coefficient and soft dice loss for three test cases was 0.0980. At thesame time, for two test cases, the sensitivity and specification were recordedto be 0.0211 and 0.5867 using patch level predictions. On the other hand,a software integration pipeline was integrated to deploy the concentratedmodel into the webserver for accessing it from the software system using theRepresentational state transfer (REST) API. Eventually, the suggested modelswere validated through the Area Under the Curve–Receiver CharacteristicOperator (AUC–ROC) curve and Confusion Matrix and compared with theexisting research articles to understand the underlying problem. ThroughComparative Analysis, we have extracted meaningful insights regarding braintumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imagingmodalities.

关键词： Brain cancer segmentation 3D U-Net ResNet50 Dice measurement ROC-AUC

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In-House Developed Graphene-Based Leaf Wetness Sensor with Enhanced Stability

IEEE Sensors Letters

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IEEE Sensors Letters 2025年第6期9卷

作者： Patle, Kamlesh Yogi, Pooja Maru, Devkaran Agrawal, Yash Palaparthy, Vinay S. Moez, Kambiz DAIICT System Design Lab Department of Information and Communication Technology Gandhinagar382007 India University of Alberta iCAS Department of Electrical and Computer Engineering Faculty of Engineering EdmontonABT6G 2V4 Canada

Prolonged leaf wetness is a critical factor influencing the development and spread of plant diseases, particularly fungal pathogens, which thrive in moist environments. These pathogens negatively impact crop health, photosynthesis, nutrient absorption, and agricultural productivity. Accurately measuring leaf wetness duration (LWD) is essential for early disease detection and mitigation strategies. Leaf wetness sensors (LWS) are designed to detect wetness on leaf surfaces. Traditional LWS, fabricated using printed circuit boards (PCBs), have been extensively studied and are commercially available, such as the PHYTOS-31. However, flexible LWS are preferred due to their ability to conform to the natural shape of leaves, improving accuracy, better contact resistance, and durability under field conditions. However, these sensors exhibited limitations such as electrode oxidation and peeling, reducing stability and wetness sensitivity over time. To overcome these challenges, this study investigates replacing conventional metal-based interdigitated electrodes (IDEs) with graphene-based IDEs, leveraging graphene's superior electrical, mechanical, and thermal properties. The fabricated flexible graphene LWS has been evaluated for its sensitivity, response time, hysteresis, temperature response, and stability, which are about ≈26,000%, ≈ 35 seconds, ≈ 5%, ≈ 6.79%, and 5 months, respectively. Benchmarking against the commercially available PHYTOS-31 LWS demonstrated an absolute error of ±3%, confirming the reliability and accuracy. These results validate the potential of graphenebased flexible LWS for accurate and long-term monitoring of leaf wetness duration in agricultural and ecological applications. © 2017 IEEE.

关键词： Microbial electrodes

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D2D Multi-hop Energy Efficiency Toward EMS in B5G 2

D2D Multi-hop Energy Efficiency Toward EMS in B5G

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2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022

作者： Al-Mansor, Asma Noordin, Nor Kamariah Saif, Abdu Hamood Alsamhi, Saeed Universiti Putra Malaysia Department of Computer and Communication System Engineering Serdang43400UPM Malaysia University Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia IBB University NUIG Galway Ir Eland Faculty of Engineering Ibb Yemen

ISBN: (数字)9781665461818

ISBN: (纸本)9781665461818

Beyond fifth generation (B5G), communication has attracted much attention from academia, industry, and mobile network operators due to network densification, ultra-low latency communication, and enhanced energy and spectrum efficiency. However, a post-disaster emergency management system (EMS), which increasingly relies heavily on wireless communication infrastructure, is falling far behind in innovation and funding. Because the B5G concept represents a telecommunications industry revolution, EMS provisioning is intended to be dispersed, autonomous, and robust to network weaknesses caused by human and natural calamities. When the network is congested, partially functioning, or entirely isolated, we provide multi- device-to-device (D2D) communication to extend the communication coverage area with improved energy efficiency. Furthermore, we examine D2D multi-hop energy efficiency performance in the proposed network. The results demonstrate that the improved D2D multi-hop energy efficiency can improve the EMS effectively and efficiently in extending the coverage area and enhancing energy efficiency. Moreover, the proposed approach has been proven to increase energy efficiency, which acts as a suitable network design to recover from natural disasters and potentially save many lives. © 2022 IEEE.

关键词： Energy efficiency

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Enhanced Condition Monitoring of Low Voltage Motors in Harsh Environments with Ladybug Beetle Optimization and Context-Aware Graph Convolutional Networks 4

Enhanced Condition Monitoring of Low Voltage Motors in Harsh...

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4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Rajasekar, P. Tiwari, Praveen Prakash Sharmila, L. Garg, Akriti Srinivas, Rayudu Tallapragada, V. V. Satyanarayana Srm Institute of Science and Technology Department of Data Science and Business System Tamil Nadu Kattankulathur603203 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India Mahatma Jyotiba Phule Rohilkhand University Department of Electrical Engineering Uttar Pradesh Bareilly243006 India Aditya College of Engineering & Technology Department of Electrical and Electronics Engineering Andhra Pradesh Surampalem533437 India School of Engineering Mohan Babu University Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India

ISBN: (纸本)9798331540364

Low voltage motors are essential in many applications in contemporary industrial settings, as they power machines and guarantee efficient operations. However, these motors often operate in harsh environments where they are exposed to factors such as extreme temperatures, moisture, dust, and vibrations, which can lead to premature wear and failure. This paper proposes a hybrid method for low voltage motor condition monitoring in challenging industrial environments. The proposed method is the combined execution of Ladybug Beetle Optimization algorithm (LBOA) and Heterogeneous Context-Aware Graph Convolutional Network (HCAGCN). Hence it is named as LBOA-HCAGCN technique. Initially, the input data is collected from Triboelectric vibration sensors. Then, the collected data is fed into preprocessing utilizing Generalized Multi-kernel Maximum Correntropy Kalman Filter (GMKMCKF). This filter normalizes and cleans the data by effectively removing noise. Recursive Hilbert Transform (RHT) is used to extract time domain features such as mean, Root Mean Square (RMS), Peak-to-Peak Value, Skewness, and Kurtosis. Then the extracted features are given to HCAGCN for classify the motor health conditions such as normal, broken-bar, bowed-bar, bowed-rotor, faulty bearing, and voltage imbalance. In general, HCAGCN does not express adapting optimization strategies to determine optimal parameters to classify motor conditions. Hence, the LBOA is used to optimize the weight parameter of the HCAGCN which accurately classify motor conditions. The proposed LBOA-HCAGCN is implemented in MATLAB. Performance indicators such as Mean Absolute Error (MAE), Precision, and Accuracy were used to analyze the effectiveness of the proposed approach. Comparing the proposed LBOA-HCAGCN methodology to other existing techniques like Deep Residual Neural Network (DRNN), Convolutional Neural Network (CNN), and Recursive Neural Network (RNN), it achieves 25.8%, 26.4%, and 24.7% greater accuracy, 26.7%, 29.4%, and

关键词： Mean square error

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Cloud-Based IoT Solutions for Smart Grids: Advancing Smart Technologies in Energy Management 3

Cloud-Based IoT Solutions for Smart Grids: Advancing Smart T...

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3rd IEEE International Conference on Electrical Power and Energy systems, ICEPES 2024

作者： Antonius Alijoyo, Franciskus Vidhate, Deepak A. Kaur, Chamandeep Nivedan, V. Rani, V. Kanpur Balakumar, A. School of Business and Information Technology STMIK LIKMI Bandung Indonesia Dr. Vithalrao Vikhe Patil College of Engineering Department of Information Technology Maharashtra Ahmednagar India Jazan University Department of Computer Science Jazan Saudi Arabia Sri Ramakrishna College of Arts & Science Department of Electronics and Communication System Coimbatore India Hindustan Institute of Technology and Science Chennai India K.Ramakrishnan College of Engineering Department of ECE Trichy India

ISBN: (纸本)9798350390728

The incorporation of Internet of Things (IoT) cloud computing solutions with smart grids signifies a significant advancement in energy management. Traditional methods, though beneficial, often lack scalability and real-time capabilities, limiting their effectiveness in managing the complexity of modern energy systems. In this paper, a novel approach cloud-based IoT technologies to address these shortcomings and enhance smart grid functionalities. Existing energy management methods heavily rely on localized monitoring and control systems, constraining scalability and real-time responsiveness. Additionally, these methods struggle to handle the immense data generated by IoT devices dispersed throughout the grid. The proposed approach tackles these challenges by utilizing cloud computing to store, process, and analyze IoT sensor data in real-time. The novelty of this method lies in seamlessly integrating cloud-based platforms with IoT devices across the smart grid. Through utilising cloud resources, the limitations of traditional approaches are overcome, enabling scalable, real-time oversight and management of grid assets. The methodology involves deploying IoT sensors across the grid to compile information about energy usage, generation, and distribution, which is then transmitted to cloud servers for analysis and decision-making. Through simulations and real-world experiments, the effectiveness of this approach in optimizing grid performance, improving reliability, and facilitating demand-response programs is demonstrated. The results exhibit significant enhancements in grid efficiency and resilience, showcasing the potential of cloud-based IoT solutions to transform energy management in smart grids. Overall, this proposed method presents a promising pathway towards establishing more efficient, sustainable, and resilient energy infrastructure for the future. © 2024 IEEE.

关键词： Cloud platforms

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