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Simulative study of vertical cavity with distributed feedback optical sources coupling to fiber systems evaluation

Journal of Optical Communications 2024年第0期45卷 s1483-s1494页

作者： Rashed, Ahmed Nabih Zaki Reddy, Mittamidi Madhusudhan Nagendram, Sanam Anwer, Twana Mohammed Kak Ahammad, Shaik Hasane Hossain, Md. Amzad Shetea, Eslam Fahmy Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of VLSI Microelectronics Institute of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai602105 India Department of Civil Engineering Institute of Aeronautical Engineering Hyderabad Dundigal500043 India Department of Artificial Intelligence KKR & KSR Institute of Technology and Sciences Guntur India Department of Physics College of Education Salahaddin University-Erbil Kurdistan Region Erbil44002 Iraq Department of ECE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Institute of Theoretical Electrical Engineering Faculty of Electrical Engineering and Information Technology Ruhr University Bochum Bochum44801 Germany Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore7408 Bangladesh

This paper has demonstrated the simulative study of vertical cavity with distributed feedback light sources coupling to the fiber systems. The simulative study of piezoresistive, capacitive and piezoelectric sensing for micro electro mechanical system has been clarified. The span against the diaphragm thickness for the square diaphragm, round diaphragm, a round bossed diaphragm and rectangular diaphragm is clarified. Acceleration against displacement for a capacitive accelerometer for step signal based on signal sided and double sided is reported. Besides, acceleration against max pulse time for a capacitive accelerometer is outlined. The frequency response plot for a piezoelectric accelerometer under longitudinal load and for thin film based piezoelectric accelerometer is demonstrated based on MEMSolver simulation software version 3.3. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Thin films

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A Novel Lightweight Block Cipher for Robust data Protection in IoT Devices

A Novel Lightweight Block Cipher for Robust Data Protection ...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Manish Nagpal Umakanth S Prutha P Kulkarni K.S. Bhuvaneshwari Rajat Saini Srisathirapathy S Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka ENTC Vishwakarma Institute of Technology Pune India Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Mech Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Due to the weak processing power and energy constraint associated with IoT devices, data security and privacy are major issues enormously important for the growing environment of IoT. Cryptographic method is incompatible with the Internet of Things (IoT) as this concept is characterized by too high resource demand. In this work, a new lightweight block cypher, designed for the IoT devices is introduced which is supposed to provide proper protection to the data. There are advantages of the suggested cypher that reduces computational time, energy requirements and memory usage while promoting efficiency as well as high security. In order to avoid usual cryptographic attacks such as differential and linear, the cipher employs advanced encryption techniques together with an enhanced key scheduling mechanism. It is stated from experiments that the cypher is suitable for the implementation in energy-constrained IoT networks based on the fact that it attains an equilibrium between efficiency, security, and energy conservation. This work enhances data protection in IoT applications significantly but affords devices with limited power to avoid operating slower.

关键词： Ciphers Technological innovation Processor scheduling Memory management data protection Signal processing algorithms Signal processing Encryption Computational efficiency Internet of Things

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Optical duobinary transmitter based OQPSK line coding phase offset for the prediction of OWC channel systems performance efficiency

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Journal of Optical Communications 2024年第0期45卷 s1593-s1605页

作者： Rashed, Ahmed Nabih Zaki Reddy, Mittamidi Madhusudhan Nagendram, Sanam Kak Anwer, Twana Mohammed Ahammad, Shaik Hasane Kumar, Munuswamy Siva Hossain, Md. Amzad Essam, Ahmed Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of VLSI Microelectronics Institute of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai602105 India Department of Civil Engineering Institute of Aeronautical Engineering Hyderabad Dundigal500043 India Department of Artificial Intelligence KKR & KSR Institute of Technology and Sciences Guntur India Department of Physics College of Education Salahaddin University-Erbil Kurdistan Region Erbil44002 Iraq Department of ECE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Institute of Theoretical Electrical Engineering Faculty of Electrical Engineering and Information Technology Ruhr University Bochum Bochum44801 Germany Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore7408 Bangladesh

This work demonstrates the optical duobinary transmitter based optical quadature phase shift keying line coding phase offset for the prediction of optical fiber wireless channel systems performance efficiency. The max signal light power variations with the spectral wavelength variations for different degree phase offsets based optical quadrature phase shift keying (OQPSK) transmitter are clarified. The max signal light power variations with the time variations for various degrees phase offset based OQPSK transmitter are demonstrated. The total power lighted after OWC channel for various degree phase offset based OQPSK transmitter are indicated. The max electrical power signal amplitude variations with the time variations for various degree phase offset based OQPSK transmitter are observed. Total electrical power signal after optical receiver for various degree phase offset based OQPSK transmitter is clarified. Total optical power variations after OWC channel for various degrees phase offset based OQPSK transmitter is studied. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Quadrature phase shift keying

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LHQ-SVC: Lightweight and High Quality Singing Voice Conversion Modeling

arXiv

引用

arXiv 2024年

作者： Huang, Yubo Lai, Xin Ye, Muyang Zhu, Anran Wang, Zixi Xu, Jingzehua Zhang, Shuai Zhou, Zhiyuan Niu, Weijie School of Civil Engineering Southwest Jiaotong University China SWJTU-Leeds Joint School Southwest Jiaotong University China School of Computing and Artificial Intelligence Southwest Jiaotong University China Shenzhen International Graduate School Tsinghua University China Department of Data Science New Jersey Institute of Technology United States School of Economics and Management Southwest Jiaotong University China Technology Co. Ltd China

Singing Voice Conversion (SVC) has emerged as a significant subfield of Voice Conversion (VC), enabling the transformation of one singer’s voice into another while preserving musical elements such as melody, rhythm, and timbre. Traditional SVC methods have limitations in terms of audio quality, data requirements, and computational complexity. In this paper, we propose LHQ-SVC, a lightweight, CPU-compatible model based on the SVC framework and diffusion model, designed to reduce model size and computational demand without sacrificing performance. We incorporate features to improve inference quality, and optimize for CPU execution by using performance tuning tools and parallel computing frameworks. Our experiments demonstrate that LHQ-SVC maintains competitive performance, with significant improvements in processing speed and efficiency across different devices. The results suggest that LHQ-SVC can meet real-time performance requirements, even in resource-constrained environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Static Var compensators

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Efficient Federated Learning with Adaptive Channel Pruning for Edge Devices

Efficient Federated Learning with Adaptive Channel Pruning f...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yongzhe Jia Xuyun Zhang Bowen Liu Wanchun Dou Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen China Department of Computing Macquarie University Sydney NSW Australia Faculty of Data Science Shiga University Hikone Japan

Federated learning (FL) is an emerging machine learning paradigm that allows distributed participants to train a global model collaboratively. In edge networks, FL involves numerous edge devices updating the local models with the assistance of a central server while keeping the local data on the devices. However, edge devices are commonly resource-limited while neural network models used in FL require significant resource consumption to reach a satisfactory accuracy. Moreover, different devices are various in terms of system capabilities, and employing a uniform model on all devices leads to the derogation of model accuracy. Several pioneer work integrate model pruning techniques into the FL process and focus on reducing the resource consumption on edge devices, whereas neglecting the impact of model generality on accuracy. In our work, we propose FedACP, an efficient FL approach with adaptive channel pruning for edge devices to achieve better resource-accuracy trade-offs. Specifically, we design a two-phase channel pruning approach to adaptively prune local models, in which both the resource constraints and model generalities are taken into consideration. In addition, we also design an aggregation algorithm for aggregating heterogeneous local models produced by the two-phase channel pruning approach. To evaluate the performance of the FedACP, we implement it with a real FL framework FedML and compare it with several state-of-the-art methods. The experimental results show that FedACP achieves better model accuracy while simultaneously reducing 22.41% to 27.34% model parameters.

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Complementary Information Mutual Learning for Multimodality Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Shen, Chuyun Li, Wenhao Chen, Haoqing Wang, Xiaoling Zhu, Fengping Li, Yuxin Wang, Xiangfeng Jin, Bo School of Computer Science and Technology East China Normal University Shanghai200062 China School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China Huashan Hospital Fudan University Shanghai200040 China School of Computer Science and Technology East China Normal University Shanghai AI Laboratory Shanghai200062 China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200092 China

Radiologists must utilize medical images of multiple modalities for tumor segmentation and diagnosis due to the limitations of medical imaging technology and the diversity of tumor signals. This has led to the development of multimodal learning in medical image segmentation. However, the redundancy among modalities creates challenges for existing subtraction-based joint learning methods, such as misjudging the importance of modalities, ignoring specific modal information, and increasing cognitive load. These thorny issues ultimately decrease segmentation accuracy and increase the risk of overfitting. This paper presents the complementary information mutual learning (CIML) framework, which can mathematically model and address the negative impact of inter-modal redundant information. CIML adopts the idea of addition and removes inter-modal redundant information through inductive bias-driven task decomposition and message passing-based redundancy filtering. CIML first decomposes the multimodal segmentation task into multiple subtasks based on expert prior knowledge, minimizing the information dependence between modalities. Furthermore, CIML introduces a scheme in which each modality can extract information from other modalities additively through message passing. To achieve non-redundancy of extracted information, the redundant filtering is transformed into complementary information learning inspired by the variational information bottleneck. The complementary information learning procedure can be efficiently solved by variational inference and cross-modal spatial attention. Numerical results from the verification task and standard benchmarks indicate that CIML efficiently removes redundant information between modalities, outperforming SOTA methods regarding validation accuracy and segmentation effect. To emphasize, message-passing-based redundancy filtering allows neural network visualization techniques to visualize the knowledge relationship among different modalitie

关键词： Redundancy

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Efficient employment of optical DPSK transmitter with linear multimode fibers and FSO channel under various duty cycle coding effects

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Journal of Optical Communications 2024年第0期45卷 s1633-s1648页

作者： Rashed, Ahmed Nabih Zaki Reddy, Mittamidi Madhusudhan Nagendram, Sanam Kak Anwer, Twana Mohammed Ahammad, Shaik Hasane Inthiyaz, Syed Hossain, Md. Amzad Farouk, Sara Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of VLSI Microelectronics Institute of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai602105 India Department of Civil Engineering Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Department of Artificial Intelligence KKR & KSR Institute of Technology and Sciences Guntur India Department of Physics College of Education Salahaddin University-Erbil Kurdistan Region Erbil44002 Iraq Department of ECE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Institute of Theoretical Electrical Engineering Faculty of Electrical Engineering and Information Technology Ruhr University Bochum Bochum44801 Germany Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore7408 Bangladesh

This study has demonstrated the efficient employment of optical DPSK transmitter with linear multimode fibers and FSO channel under various duty cycle coding effects. The light signal power form variations after linear multimode fiber versus spectral wavelength based various RZ-percentage duty cycle coding is clarified. The light signal power form variations after linear multimode fiber versus spectral wavelength based various NRZ duty cycle coding is also clarified. The light power signal form variations after FSO channel versus spectral wavelength based different RZ percentage duty cycle coding is analyzed. Moreover the light power signal form variations after FSO channel versus spectral wavelength based NRZ percentage duty cycle coding is investigated. The light signal power form variations after linear multimode fiber versus spectral wavelength based various RZ percentage duty cycle coding and NRZ modulation code are indicated. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Multimode fibers

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Transition in collective motion decision making

引用

Physical Review E 2022年第1期106卷 014611-014611页

作者： Biao Wang Yue Wu Guangwei Wang Lan Liu Guanrong Chen Hai-Tao Zhang School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan 430074 China National Engineering Laboratory for Educational Big Data Central China Normal University Wuhan 430079 China Department of Electrical Engineering City University of Hong Kong Hong Kong SAR China

Collective decision making in a biological motion group requires fast and robust transmission of information. Typically, directional switching information propagation across the whole group obeys a linear dispersion law. However, conventional dynamic collective motion models, such as the Vicsek model and the Couzin model did not take into account ultrafast directional synchronous motions. In the present paper, a multiparticle model is proposed based on inertial spin self-propel action, which can provide adequate description of such group motion. By considering both spin mechanism and collision avoidance, the proposed self-propelled particle spin model can nicely describe collective motion with fast directional switching. By analyzing the order parameter of the group-velocity synchronization, a mechanism of group decision making is revealed, which is based on the difference between two clusters of divergent leaders, showing a transition from the compromising phase (i.e., following the group average) to the preferred phase (i.e., aligning to a leader cluster). The finding provides new insight to the decision-making process of followers when they face with divergent leaders in group motion.

关键词： Collective behavior in networks Network phase transitions Pattern formation Self-organized systems

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EEG-Based Discrimination Between Patients with MCI and Alzheimer

EEG-Based Discrimination Between Patients with MCI and Alzhe...

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Electrical, Energy, and artificial intelligence (EICEEAI), International engineering Conference on

作者： Borhan Kazimipour Reza Boostani Afshin Borhani-Haghighi Sattam Almatarneh Mohammad Aljaidi CSE Dept. Monash University Melbourne Australia CSE and IT Dept. Electrical and Computer Engineering Faculty Shiraz University Shiraz Iran Neurology Department Namazi hospital Shiraz University of Medical Sciences Shiraz Iran Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Department of Computer Science Zarqa University Zarqa Jordan

There is a high similarity between the signs and symptoms of patients with Alzheimer and those with mild cognitive impairment (MCI). Although several attempts have been made to differentiate these two groups of patients by decoding the fluctuation of their electroencephalogram (EEG), the achieved results are not yet promising. To increase the differentiation rate, in this study, 14 patients with Alzheimer from 13 patients with MCI have been voluntarily enrolled while their EEG signals are recorded in presence of visual stimuli. To suppress the disrupting artifacts and noises (e.g., eye-blink and movement artefact) from the recorded EEGs, independent component analysis is applied. Next, the visual evoke potential (VEP) patterns are extracted by synchronous averaging and then multi-linear principal component analysis (MPCA) is applied to elicit discriminative features from VEPs of the patients. After feature extraction by MPCA, the reduced feature vectors of both groups are applied to a nearest neighbor classifier, leading to 77.35% differentiation accuracy.

关键词： Visualization Patient monitoring Fluctuations Independent component analysis Feature extraction Electroencephalography Real-time systems

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HCA-NET: HIERARCHICAL CONTEXT ATTENTION NETWORK FOR INTERVERTEBRAL DISC SEMANTIC LABELING

arXiv

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arXiv 2023年

作者： Bozorgpour, Afshin Azad, Bobby Azad, Reza Velichko, Yury Bagci, Ulas Merhof, Dorit Faculty of Informatics and Data Science University of Regensburg Germany South Dakota State University Brookings United States Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Machine and Hybrid Intelligence Lab Northwestern University ChicagoIL United States Fraunhofer Institute for Digital Medicine MEVIS Germany

Accurate and automated segmentation of intervertebral discs (IVDs) in medical images is crucial for assessing spine-related disorders, such as osteoporosis, vertebral fractures, or IVD herniation. We present HCA-Net, a novel contextual attention network architecture for semantic labeling of IVDs, with a special focus on exploiting prior geometric information. Our approach excels at processing features across different scales and effectively consolidating them to capture the intricate spatial relationships within the spinal cord. To achieve this, HCA-Net models IVD labeling as a pose estimation problem, aiming to minimize the discrepancy between each predicted IVD location and its corresponding actual joint location. In addition, we introduce a skeletal loss term to reinforce the model’s geometric dependence on the spine. This loss function is designed to constrain the model’s predictions to a range that matches the general structure of the human vertebral skeleton. As a result, the network learns to reduce the occurrence of false predictions and adaptively improves the accuracy of IVD location estimation. Through extensive experimental evaluation on multi-center spine datasets, our approach consistently outperforms previous state-of-the-art methods on both MRI T1w and T2w modalities. The code-base is accessible to the public on GitHub. © 2023, CC BY.

关键词： Network architecture

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