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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Xue, Junpei Liu, Jiang Shimamoto, Shigeru Department of Computer Science and Communications Engineering Waseda University Tokyo Japan

ISBN: (纸本)9798331521165

Diabetes is one of the most common diseases worldwide. Managing diabetes often involves frequent and painful finger-prick tests. This study introduces a non-invasive approach using NIR (NIR) spectroscopy and the DLinear model to accurately predict glucose levels. We developed a custom device with a NIR LED and photodiode to collect spectral data. The data processing steps included feature selection, data augmentation, and cross-validation to enhance performance. The DLinear model achieved an average RMSE of 5.04 ± 1.17 during cross-validation and 3.27 in testing. All predictions fell within the A-zone of Clarke's Error Grid, indicating promising performance for clinical applications. Our approach produced superior results compared to other time series models and non-invasive glucose measurement methods. This method is cost-effective, reliable, and easy to use, providing a promising new solution for diabetes management. © 2025 IEEE.

关键词： Data integration

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Optimizing Logistics with Regularization Techniques: A Comparative Study

Optimizing Logistics with Regularization Techniques: A Compa...

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Grace, Anne Lourdu Thenmozhi, M. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350310061

Optimising logistics operations is critical for modern businesses as it can significantly impact supply chain efficiency, customer satisfaction, and overall profitability. One way to enhance logistics performance is by employing machine learning models, such as logistic regression, to predict and optimise various logistics outcomes. However, these models often face the issue of overfitting, which can result in poor generalisation and inaccurate predictions. This paper presents a comparative study of various regularisation techniques, such as Ridge and Lasso, applied to logistic regression models for optimising logistics operations. Our results indicate that regularisation techniques can significantly improve the performance of logistic regression models in predicting and optimising logistics outcomes. The Lasso regularisation technique is particularly effective in feature selection, reducing the number of variables needed to achieve high accuracy. Our analysis furthers to the research on machine learning applications in logistics and provides practical insights for logistics managers to optimise their operations. © 2023 IEEE.

关键词： Forecasting

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Investigations and Characterization of a New FM Active Receiving Antenna for Modern Vehicles Applications

Investigations and Characterization of a New FM Active Recei...

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7th International Conference on Electronics Technology, ICET 2024

作者： Ashour, Ahmed F. Fouda, Mostafa M. Chrysler, Andrew Chiu, Steve C. Ramadan, Ashraf Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Higher Technological Institute 10th of Ramadan Department of Electronics and Communications Engineering Egypt

ISBN: (纸本)9798350363951

This paper describes a new approach for a broadband frequency modulation (FM) active antenna. The new approach makes use of the low-noise GaAs FET transistor. The frequency response and third-order intermodulation (IM) characteristics are presented. An approach to represent the measured antenna impedance is presented to facilitate circuit design and match the simulation with measurements. The use of the presented active antenna is described with examples. Finally, the new design applications are reported. © 2024 IEEE.

关键词： Receiving antennas

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Lung Disease Detection Using Scale-Invariant Weighted Ensemble Neural Architecture 1st

Lung Disease Detection Using Scale-Invariant Weighted Ensemb...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Abdelhamid, Abeer Akinniyi, Oluwatunmise Saleh, Gehad A. Khalifa, Fahmi Department of Electronics and Communications Engineering Mansoura University Mansoura35516 Egypt Department of Electrical and Computer Engineering Morgan State University BaltimoreMD21251 United States Department of Diagnostic and Interventional Radiology Faculty of Medicine Mansoura University Mansoura Egypt

ISBN: (纸本)9783031823763

Among several diseases, COVID-19 has highlighted the urgent need for AI-based tools o develop precise, accurate, and effective containment and treatment plans. This paper utilizes chest images and introduces a hybrid architecture tailored for COVID-19 detection. The proposed architecture integrates multiple modules to extract texture and shape cues, thereby facilitating more accurate and discriminative classification. The analysis pipeline starts with an auto-encoder-based input scaling module to filter out noise and to keep prominent image features. Subsequently, two sets of features are extracted from the preprocessed images representing rich concentration of local and global features. We integrated various texture descriptors (histogram of oriented gradients, gray-level co-occurrence matrix, and local binary patterns) with deeper features extracted from an optimized neural architecture. Thirdly, a feature fusion and weighing module is used to weigh the feature sets to produce an optimal feature vector for classification using a cascaded machine classification module. Evaluation of the proposed methods on publicly available datasets using various metrics and ablation studies documents the robustness of our weighted ensemble classification system. Cross-validation evaluation demonstrate higher accuracy of the proposed system with an average accuracy of 96.5% and 95.5% on two datasets and when compared against other recent methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Fine-grained Single-layer Tiling for Viewport-Adaptive 360-degree Video Streaming

Fine-grained Single-layer Tiling for Viewport-Adaptive 360-d...

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2023 IEEE International Conference on Visual communications and Image Processing, VCIP 2023

作者： Jeong, Jong-Beom Park, Jun-Hyeong Lee, Soonbin Ryu, Eun-Seok Department of Computer Science Education Seoul Korea Republic of Department of Immersive Media Engineering Seoul Korea Republic of Multimedia Communications Group Berlin Germany

ISBN: (纸本)9798350359855

Tile-based streaming is widely adopted for viewport-Adaptive 360-degree video streaming due to its potential for bitrate reduction. However, for legacy devices equipped with a single decoder, managing multiple tile bitstreams poses a challenge, particularly with regards to synchronizing multiple decoders. This paper introduces Butterfly360, a fine-grained single-layer tiling method that advances two main ideas: (i) an optimal tiling scheme decision method for single-layer merging of tiles using various tile sizes, and (ii) a simple rate adaptation method for viewport tiles. Experimental results demonstrate that the proposed method offers advantages in terms of bitrate savings and reduction in decoding runtime. Furthermore, the proposed method facilitates the use of either multiple tile bitstreams or a single-layer merged bitstream. © 2023 IEEE.

关键词： Video streaming

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Survey on Weeding Tools, Equipment, AI-IoT Robots with Recent Advancements

Survey on Weeding Tools, Equipment, AI-IoT Robots with Recen...

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2023 International Conference on Integration of Computational Intelligent System, ICICIS 2023

作者： Michael, J Justina Thenmozhi, M. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350318784

Agriculture is the backbone of many countries across the globe. Nevertheless, the challenges faced by farmers with an aim to provide quality food are enormous. From among these difficulties, weeding is the most troublesome. Right from the neolithic era till this modern era, there exist numerous solutions for eradicating weeds. Modern technologies support agriculturalists to handle these weeds in overcoming the issues. However, there still exists a gap in satisfying the real needs of a farmer. This paper presents a survey of weeds and their problems, categories of weeds, consequences of herbicide, the importance of weed removal, weeding tools, and equipment, modern AI-weeding robots, algorithms used for weed identification, and mobile applications used for weed detection. This paper also focuses on state-of-the-art techniques for weed identification and removal around the world. Future works in the area of weeds were also discussed before concluding. © 2023 IEEE.

关键词： Agricultural robots

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Misalignment-Robust Modulation Scheme for UCA-Based OAM Multiplexing Systems

Misalignment-Robust Modulation Scheme for UCA-Based OAM Mult...

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IEEE Conference on Vehicular Technology (VTC)

作者： Yusho Nakawaki Kazuma Uesugi Kazutoshi Yoshii Shigeru Shimamoto Department of Communications and Computer Engineering Waseda University Tokyo Japan

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Orbital Angular Momentum (OAM) has attracted significant attention in the field of wireless communications due to its potential for high-capacity data transmission through mode multiplexing. However, OAM experiences severe performance degradation due to inter-mode interference (IMI) caused by antenna misalignment. In this paper, we propose a novel method to suppress IMI caused by misalignment for uniform circular array (UCA)-based OAM multiplexing. We discovered that IMI can be minimized when the IQ data of each OAM mode are close to each other in the constellation, enhancing robustness to misalignment. Therefore, we propose a method, where the IQ data of one OAM mode (reference OAM mode) is selected from the entire constellation range, and the IQ data of the other OAM modes are selected from a range close to this reference OAM mode. The proposed method is shown to prevent system capacity degradation compared to traditional method when the antenna is tilted. Moreover, in the proposed method, the reference OAM mode stream shows minimal performance degradation when the antenna is tilted. Furthermore, our proposed method is well-suited for adaptive modulation, effectively adjusting to varying communication conditions to maintain optimal performance.

关键词： Multiplexing Degradation Wireless communication Orbital calculations Vehicular and wireless technologies Modulation Interference Robustness Data communication Antennas

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Infrared-Based Non-Invasive Blood Glucose Measurement Utilizing the DLinear Model

Infrared-Based Non-Invasive Blood Glucose Measurement Utiliz...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Junpei Xue Jiang Liu Shigeru Shimamoto Department of Computer Science and Communications Engineering Waseda University Tokyo Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Diabetes is one of the most common diseases world-wide. Managing diabetes often involves frequent and painful finger-prick tests. This study introduces a non-invasive approach using NIR (NIR) spectroscopy and the DLinear model to accurately predict glucose levels. We developed a custom device with a NIR LED and photodiode to collect spectral data. The data processing steps included feature selection, data augmentation, and cross-validation to enhance performance. The DLinear model achieved an average RMSE of 5.04 ± 1.17 during cross-validation and 3.27 in testing. All predictions fell within the A-zone of Clarke's Error Grid, indicating promising performance for clinical applications. Our approach produced superior results compared to other time series models and non-invasive glucose measurement methods. This method is cost-effective, reliable, and easy to use, providing a promising new solution for diabetes management.

关键词： Performance evaluation Spectroscopy Predictive models Light emitting diodes Data models Glucose Diabetes Reliability Photodiodes Blood

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SuRaksha: AI-Powered Blockchain Framework for HVAC Tamper Detection and Authentication in Smart Classrooms

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IEEE Internet of Things Journal 2025年

作者： Singh, Sushil Kumar Vaghela, Krunal Khanna, Ashish Virdee, Bal Kumar, Manish Department of Computer Science and Engineering India London Metropolitan University Centre for Communications Technology Research Group Director United Kingdom Marwadi University Department of Computer Engineering Rajkot India London Metropolitan University The Center for Communications Technology United Kingdom Marwadi University Department of Computer Engineering Gujarat Rajkot India

Smart Classrooms (SCR) are reshaping the learning experience with their interactive technology and personalized knowledge, leading to improved student engagement by seamlessly integrating digital gadgets. In this rapidly evolving landscape, the integrity and efficiency of Heating, Ventilation, and Air Conditioning (HVAC) systems are essential to the futuristic student's life. This paper introduces SuRaksha: AI-Powered Blockchain Framework for HVAC Tamper Detection and Authentication in Smart Classrooms. Leveraging the power of ensemble learning (EL) at the intelligent and connection layer of the proposed framework, our approach employs IoT sensors to collect comprehensive data from HVAC appliances. The EL algorithms then analyze the collected data to detect any instances of tampering in real-time. At the security layer, robust authentication mechanisms are implemented to verify the integrity of the HVAC data before securely storing it on the cloud. This multi-layered framework enhances the detection and authentication processes and ensures the reliability and security of HVAC operations in intelligent classroom environments. Extensive experiments and validations demonstrate the efficacy of the proposed framework in identifying tampering incidents and providing a secure, authenticated, and reliable system for modern educational facilities. The validation outcomes of the proposed framework demonstrate excellent performance, with an average processing time of 3.725 Secs and 99.84% accuracy for Smart Classrooms compared to existing works. © 2025 IEEE.

关键词： HVAC

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Enhanced Super-Resolution Using Cross Attention: Refining HMA for Local Image Restoration

Enhanced Super-Resolution Using Cross Attention: Refining HM...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Yuya Masuda Hiroshi Ishikawa Department of Computer Science and Communications Engineering Waseda University Tokyo Japan

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

In this paper, we propose a novel method that integrates Cross Attention into the existing Hybrid Multi-Axis Aggregation Network for Image Super-Resolution(HMANet) to improve local super-resolution accuracy. While previous HMANet methods primarily focused on enhancing the resolution of the entire image, our approach emphasizes local image regions for more detailed restoration. By leveraging Cross Attention for context interaction, we achieve localized super-resolution with a focus on specific parts of the image. Our experiments demonstrate that the proposed method outperforms existing approaches in terms of accuracy, and shows promising results when evaluated with different loss functions. Our source code is available at https://***/msdsmlhmaca-for-Iocal-image-restoration.

关键词： Adaptation models Accuracy Source coding Superresolution Refining Image restoration Artificial intelligence Tuning

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