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11th EAI International Conference on Advancement of science and technology, ICAST 2023

作者： Ayalew, Hailu Dessalegn Talabattula, Srinivas Annamalai, Pushparaghavan Electrical and Computer Engineering Faculty Bahir Dar University-Institute of Technology Bahir Dar Ethiopia Department of Electrical Communication Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9783031641503

One of the key components of wavelength division multiplexing (WDM) networks is tunable optical filters (TOFs). This paper focused on the theoretical analysis and design of a new dual-channel micro-electromechanical system (MEMS) actuated tunable optical filter based on polarization insensitive compound ring resonators (PICRRs) for dense wavelength division multiplexing (DWDM) optical networking applications. It is basically structured on an optimally designed capacitive type microcantilever beam, which is fixed at both ends, with built-in PICRRs, which are placed at both fixed ends of the beam to gain linearly varying longitudinal strain and stress on the resonators. Also, the dimensions of the rings are properly chosen to achieve a full width at half maximum (FWHM) of 0.4078 nm with a flat pass band at 1 dB of 0.3626 nm and a good shape factor for both channels. It also shows a free spectral range of about 20.05 nm for the first channel and 20.046 nm for the second channel, with a high extinction ratio that allows filtering of adjacent channels of 50 GHz DWDM systems. Thus, when the electrostatic attraction force (EAF) is applied, the beam will bend, which induces strain and stress in the rings, which brings a change in effective refractive index of the rings and also affects the perimeter and coupling coefficients of the rings that leads to a change in the output spectrum shift, providing the tunability as high as 4.779 nm/μN, and it is capable of tuning up to 355 GHz with negligible variations of FWHM and channel spacing within the tunability range of the two channels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dense wavelength division multiplexing

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Securing MIMO Wiretap Channel With Learning-Based Friendly Jamming Under Imperfect CSI

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IEEE Internet of Things Journal 2025年第11期12卷 16009-16022页

作者： Minh Tuan, Bui Nguyen, Diep N. Linh Trung, Nguyen Nguyen, Van-Dinh Van Huynh, Nguyen Thai Hoang, Dinh Krunz, Marwan Dutkiewicz, Eryk University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia University of Engineering and Technology Vietnam National University Faculty of Electronics and Telecommunications Hanoi100000 Viet Nam College of Engineering and Computer Science VinUniversity Hanoi100000 Viet Nam University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom The University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States

Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g., for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g., cryptography-based solutions, which unfortunately may not be feasible in practice. This challenge is even more acute in low-end IoT devices. We thus introduce a novel deep learning-based FJ framework that can effectively defeat eavesdropping attacks with imperfect CSI and even without CSI of legitimate channels. In particular, we first develop an autoencoder-based communication architecture with FJ, namely, AEFJ, to jointly maximize the secrecy rate and minimize the block error rate (BLER) at the receiver without requiring perfect CSI of the legitimate channels. In addition, to deal with the case without CSI, we leverage the mutual information neural estimation (MINE) concept and design a MINE-based FJ scheme that can achieve comparable security performance to the conventional FJ methods that require perfect CSI. Extensive simulations in a multiple-input-multiple-output (MIMO) system demonstrate that our proposed solution can effectively deal with eavesdropping attacks in various settings. Moreover, the proposed framework can seamlessly integrate MIMO security and detection tasks into a unified end-to-end learning process. This integrated approach can significantly maximize the throughput and minimize the BLER, offering a good solution for enhancing communication security in wireless communication systems. © 2014 IEEE.

关键词： Jamming

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Design of Transformer Theft Prevention System Utilizing an Integrated Sensor Network and Real-Time Alerting 8

Design of Transformer Theft Prevention System Utilizing an I...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Hoshen, Md. Rifat Choity, Afsana Mimi Wahid, Md. Ferdous Hajee Mohammad Danesh Science and Technology University Department of Electronics and Communication Engineering Dinajpur Bangladesh Hajee Mohammad Danesh Science and Technology University Department of Computer Science and Engineering Dinajpur Bangladesh Hajee Mohammad Danesh Science and Technology University Department of Electrical and Electronic Engineering Dinajpur Bangladesh

ISBN: (纸本)9798350376425

Transformers are essential components of power systems that provide a steady and reliable supply of electricity. Due to the design of these power systems, transformers are often situated in remote areas, which makes regular physical inspection and oversight challenging and increases the potential risk of theft. Therefore, in this paper an intelligent monitoring system has been proposed in order to prevent transformer theft, thereby reducing power disruptions and minimizing economic losses. The proposed system consists of sensors such as wire-cut feedback, an accelerometer, a pressure sensor, and a proximity sensor. These sensors are strategically located in the control box, together with a cellular communication module. When anomalies are detected by the system, it promptly notifies relevant authorities through SMS and phone calls to predefined contact numbers. It operates on mains electricity, switches to battery power during outages, and provides periodic status updates on system functionalities. Moreover, the system includes a maintenance mode allowing operators to disable and reactivate the alarm via predefined SMS commands. © 2024 IEEE.

关键词： Proximity sensors

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A Multi-Modal Smart Switching Based Image Transmission Using Semantic Communication 17

A Multi-Modal Smart Switching Based Image Transmission Using...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Tiwari, Sanjana Tripathi, Sharda Joshi, Sandeep Bhatt, Priyanshu Natarajan, Balasubramaniam Birla Institute of Technology & Science Pilani Department of Electrical & Electronics Engineering 333031 India Kansas State University Department of Electrical & Computer Engineering 66506 United States

ISBN: (纸本)9798331531195

The conventional paradigm of communication primarily concentrates on the transmission of raw data, often disregarding its contextual meaning. However, to tackle the exponential growth in data demands along with the limited availability of transmission bandwidth, there is an increasing need to transition from Shannon's classical information-theoretic communication to a more advanced framework centered on semantics. This work presents a multi-modal semantic-based communication method for the transmission of high-definition images aimed at optimizing the transmitted data volume while maintaining a high throughput and mean intersection over union score. To this end, two architectural models are explored: a denser ResNet-based and a lightweight U-Net-based. Depending on the required QoS and resource availability, the raw image is either semantically segmented to obtain a fine-grained, pixel-level classification of the image or represented as label semantics, which provides only a higher-level, object-based, or region-based classification prior to its transmission. The experimental results show that such an adaptive semantic image processing approach leads to around 63% reduction in the transmitted data volume without compromising on the quality of image reconstruction. © 2025 IEEE.

关键词： Semantic Segmentation

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Spacecraft potential effects on low-energy ion measurements to be made by probe B1 of Comet Interceptor

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RAS Techniques and Instruments 2024年第1期3卷 333-346页

作者： Bergman, S. Kasahara, S. Stenberg Wieser, G. Division of Space and Plasma Physics School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Department of Earth and Planetary Science Graduate School of Science The University of Tokyo Swedish Institute of Space Physics

Spacecraft charging causes notorious issues for low-energy plasma measurements. The charged particles are accelerated towards or repelled from the spacecraft surface, affecting both their energy and travel direction. The latter results in a distortion of the effective field of view (FOV) of the instrument making the measurements. The Comet Interceptor mission, planned to be launched in 2029, will make a flyby of a long-period or interstellar comet that ideally is dynamically new. The mission comprises one main spacecraft A, developed by the European Space Agency (ESA), and two sub-probes B1 and B2, developed by the Japan Aerospace Exploration Agency and ESA, respectively. The low-energy plasma measurements made by Comet Interceptor will likely be affected by the spacecraft potential in the case of low relative flyby velocities. On probe B1, the Cometary Ion Mass Spectrometer (CIMS) of the Plasma Suite is an ion mass spectrometer, capable of measuring ions with energies down to 10 eV/q. In this work, we use the Spacecraft Plasma Interaction Software to study the influence of the spacecraft potential on the low-energy ion measurements to be made by CIMS in the inner cometary magnetosphere. The results show that the effective FOV of CIMS is distorted at low energies when the flyby velocity is low. The distortion level is highly geometry dependent, and the largest distortions are caused by the magnetometer boom. Furthermore, the results show that cold ions with bulk velocities in the range 1-10 km s-1, flowing both radially away from and inward towards the nucleus, are detectable by the instrument considering the nominal observation geometry. © 2024 The Author(s).

关键词： Comets Data Methods Instrumentation Numerical Methods Plasma SPIS

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An In-Context Learning Approach for LLM-Enabled Multi-Robot Task Allocation

An In-Context Learning Approach for LLM-Enabled Multi-Robot ...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Zhang, Huibo Chen, Shengkang Yin, Huan Zhang, Fumin School of Electrical and Computer Engineering Hong Kong University of Science and Technology SAR Hong Kong Hong Kong School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9781665481090

In-context learning for large language models (LLMs) is employed to design a novel hybrid optimization framework for multi-robot task allocation. Results show that this approach can improve existing approaches, such as the market based scheme or the speedup and slow-down (SUSD) scheme by leveraging the contextual understanding ability of a LLM. Incorporating LLM removes the need for detailed information regarding the utility function for multi-robot task allocation. LLM helps the algorithm to escape local optima without relying on any human feedback. However, in-context learning is necessary to allow the LLM to agree with the underlying optimization algorithms. This paper proposes an method to instigate in-context learning for LLMs and demonstrate convergence to consensus between an LLM and the underlying optimization algorithms. The proposed method reduces the need for expert intervention and enhances the applicability and scalability for complex task allocation problems. Experimental results verify that the hybrid approach can improve the effectiveness of task allocations compared with recent SUSD-only methods. © 2024 IEEE.

关键词： Multipurpose robots

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ReRobo Walker: Robotic Walker with Fall Detection and Active Safety 18

ReRobo Walker: Robotic Walker with Fall Detection and Active...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Ding, Damin Wang, Yagang Zhang, Houji Meng, Qiaoling Yu, Hongliu Lin, Yanzhao Chen, Qiu University of Shanghai for Science and Technology Rehabilitation Engineering and Technology Institute School of Optical-Electrical and Computer Engineering Shanghai China Rehabilitation Engineering and Technology Institute School of Optical-Electrical School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Guangzhou Foreign Language School Guangzhou China University of Shanghai for Science and Technology Rehabilitation Engineering and Technology Institute Shanghai China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai China Kogakuin University Major of Electrical Engineering and Electronics Graduate School of Engineering Tokyo Japan

ISBN: (纸本)9798350312201

In recent years, Chinese society has become increasingly ageing while the fertility rate continues to decline. This situation has led to a growing public demand for rehabilitation devices such as walking aids. A novel intelligent robotic walker named ReRobo Walker is proposed which can assist groups of elderly people with dysfunctional legs with rehabilitation training and indoor and outdoor walking. We design robust mechanical structures for robotic walker, install special 3D force sensor, 2D LIDAR and other sensors, and design new algorithms to enable intelligent functionality while guaranteeing the safety of robotic walker.(1) Real-time monitoring of the user's physical status, such as falls, through laser range sensors and 3D force sensor;(2) Modelling of the scene through 2D LIDAR for path planning, obstacle avoidance and navigation functions;(3) Precise control through LADRC-based algorithm for uphill assistance, downhill control and prevention of sharp shifts of the robotic walker. Experiment results demonstrate the solid mechanical structure, stable reliability and the effectiveness of intelligent control algorithms of the intelligent robotic walker. © 2023 IEEE.

关键词： Walking aids

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Utilizing Customized 3D U-Net Framework for the Classification and Segmentation of Multi-regional Brain Tumors in Volumetric MRI Images 3

Utilizing Customized 3D U-Net Framework for the Classificati...

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3rd International Conference on Advancement in electrical and Electronic engineering, ICAEEE 2024

作者： Titu, Md. Minhaj Taher Mary, Mekhala Mariam Ahamed, Md. Faysal Oishee, Tasfia Mashud Hasan, Md Mahadi International Standard University Department of Computer Science & Engineering Dhaka1212 Bangladesh Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh International Standard University Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350388282

Precise diagnosis and treatment planning depend heavily on the segmentation and classification of brain tumors in medical pictures. The diagnostic and therapeutic challenges posed by brain tumors necessitate innovative solutions within the domain of medical imaging. This research endeavors to address this pressing need through the introduction of a pioneering approach to multi-regional tumor classification and segmentation. Utilizing volumetric MRI data, our study introduces a bespoke 3D U-Net framework meticulously tailored to the intricacies of brain tumor analysis. Our framework offers holistic solutions to two foundational objectives: classification and segmentation. In the classification facet, we strive to discriminate between various brain tumor types. Concurrently, our segmentation component aspires to precisely delineate tumor boundaries and partition distinct tumor regions. We used the BraTS 2020 dataset, and dice and Jaccard coefficient functions were employed in the model optimization process. The custom-tailored architecture of 3D U-Net, purpose-built for the nuances of brain tumor imaging, ensures resilient performance while preserving vital spatial information essential for accurate tumor delineation. Leveraging deep learning methodologies and a comprehensive dataset of volumetric MRI scans, our model showcases outstanding accuracy in classifying diverse tumor types and in the precise segmentation of tumor regions with a dice score are 88.1%, 79.4%, and 76.0% for WT dice, TC dice and ET dice. And Jaccard Scores are 79.4% for WT Jaccard, 68.2% for TC Jaccard and 64.3% for ET Jaccard. The proposed framework holds the potential to alleviate subjectivity in diagnostic processes, enhance the precision of treatment planning, and ultimately ameliorate patient outcomes. © 2024 IEEE.

关键词： Tumors

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TOWARDS THE FUNDAMENTAL LIMITS OF KNOWLEDGE TRANSFER OVER FINITE DOMAINS 12

TOWARDS THE FUNDAMENTAL LIMITS OF KNOWLEDGE TRANSFER OVER FI...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhao, Qingyue Zhu, Banghua Department of Computer Science and Technology Tsinghua University China Department of Electrical Engineering and Computer Sciences University of California Berkeley United States

We characterize the statistical efficiency of knowledge transfer through n samples from a teacher to a probabilistic student classifier with input space S over labels A. We show that privileged information at three progressive levels accelerates the transfer. At the first level, only samples with hard labels are known, via which the maximum likelihood estimator attains the minimax rate √|S||A|/n. The second level has the teacher probabilities of sampled labels available in addition, which turns out to boost the convergence rate lower bound to |S||A|/n. However, under this second data acquisition protocol, minimizing a naive adaptation of the cross-entropy loss results in an asymptotically biased student. We overcome this limitation and achieve the fundamental limit by using a novel empirical variant of the squared error logit loss. The third level further equips the student with the soft labels (complete logits) on A given every sampled input, thereby provably enables the student to enjoy a rate |S|/n free of |A|. We find any Kullback-Leibler divergence minimizer to be optimal in the last case. Numerical simulations distinguish the four learners and corroborate our theory. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Students

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High-Performance Flow Classification of Big Data Using Hybrid CPU-GPU Clusters of Cloud Environments

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Tsinghua science and technology 2024年第4期29卷 1118-1137页

作者： Azam Fazel-Najafabadi Mahdi Abbasi Hani H.Attar Ayman Amer Amir Taherkordi Azad Shokrollahi Mohammad R.Khosravi Ahmed A.Solyman Department of Computer Engineering Faculty of EngineeringBu-Ali Sina UniversityHamedan ***Iran Department of Energy Engineering Zarqa UniversityZarqa 13132Jordan Department of Informatics University of OsloOslo 0316Norway Department of Computer Science MalmöUniversityMalmö20506Sweden Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261100China Department of Electrical and Electronics Engineering NişantaşıUniversityIstanbul 34481742Türkiye

The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.

关键词： OpenMP Compute Unified Device Architecture(CUDA) Message Passing Interface(MPI) packet classification medical data tuple space algorithm Graphics Processing Unit(GPU)cluster

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