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Securing Data in Multimode Fibers by Exploiting Mode-Dependent Light Propagation Effects

Research 2023年第3期2023卷 669-682页

作者： Stefan Rothe Karl-Ludwig Besser David Krause Robert Kuschmierz Nektarios Koukourakis Eduard Jorswieck Jürgen W.Czarske TU Dresden Faculty of Electrical and Computer EngineeringLaboratory of Measurement and Sensor System Technique01062 DresdenGermany TU Braunschweig Faculty of Electrical EngineeringInformation TechnologyPhysicsInstitute for Communications Technology38106 BraunschweigGermany TU Dresden Faculty of Physics01062 DresdenGermany

Multimode fibers hold great promise to advance data rates in optical communications but come with the challenge to compensate for modal crosstalk and mode-dependent losses,resulting in strong *** holographic measurement of the transmission matrix enables not only correcting distortions but also harnessing these effects for creating a confidential data connection between legitimate communication parties,Alice and *** feasibility of this physical-layer-security-based approach is demonstrated experimentally for the first time on a multimode fiber link to which the eavesdropper Eve is physically *** the proper structured light field is launched at Alice’s side,the message can be delivered to Bob,and,simultaneously,the decipherment for an illegitimate wiretapper Eve is *** a real communication scenario,we implement wiretap codes and demonstrate confidentiality by quantifying the level of *** to an uncoded data transmission,the amount of securely exchanged data is enhanced by a factor of *** complex light transportation phenomena that have long been considered limiting and have restricted the widespread use of multimode fiber are exploited for opening new perspectives on information security in spatial multiplexing communication systems.

关键词： distortion destroyed holographic

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Strategies for Effective EEG Preprocessing 22

Strategies for Effective EEG Preprocessing

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22nd International Learning and Technology Conference, L and T 2025

作者： Salem, Nema Alamoudi, Nora Mustafa, Lama Bagais, Fatima Effat University Electrical Research Laboratory College of Engineering Jeddah Saudi Arabia Effat University College of Engineering Department of Electrical and Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

The administration of anesthesia is critical for ensuring the safety of surgical procedures, yet it comes with inherent risks associated with medication mistakes. Monitoring EEG signals during anesthesia is crucial for understanding patient physiology;however, the accuracy of this information heavily relies on effectively eliminating noise. Decreasing noise in EEG signals is vital for accurately interpreting data, ultimately enhancing patient safety and refining anesthesia management. This research concentrates on an advanced preprocessing technique for EEG signals, employing segmentation, detrending, variational mode decomposition (VMD), and noise reduction through Gaussian filtering, moving averages, Independent Component Analysis (ICA), and Discrete Wavelet Transform (DWT). The study utilized EEG data collected during surgeries and applied these preprocessing approaches to reduce noise and artifacts. Our results indicate that employing the Discrete Wavelet Transform (DWT) for denoising is robust and produces superior outcomes compared to other techniques investigated, achieving a high Signal-To-Noise Ratio (SNR) of 43.23 dB. © 2025 IEEE.

关键词： Anesthesia Discrete Wavelet Transform EEG preprocessing Gaussian Independent Component Analysis Moving average Noise removal SDG 17

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Correntropy-Based Data Selective Adaptive Filtering

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IEEE Transactions on Circuits and systems I: Regular Papers 2024年第2期71卷 754-766页

作者： Chien, Ying-Ren Wu, Sheng-Teng Tsao, Hen-Wai Diniz, Paulo S.R. Adaptive and Autonomous Communication Laboratory Department of Electrical Engineering National Ilan University Yilan City260007 Taiwan College of Electrical Engineering and Computer Science Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Program of Electrical Engineering Department of Electronics and Computer Engineering COPPE/Poli Universidade Federal do Rio de Janeiro Rio de Janeiro68504 Brazil

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Adaptive filters

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Diversified and compatible web APIs recommendation based on game theory in IoT

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Digital Communications and Networks 2024年第4期10卷 1198-1209页

作者： Wenwen Gong Huiping Wu Xiaokang Wang Xuyun Zhang Yawei Wang Yifei Chen Mohammad R.Khosravi College of Information and Electrical Engineering China Agricultural UniversityBeijing100000China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang262700China Department of Computer Science St.Francis Xavier UniversityAntigonishNSCanada Department of Computing Macquarie UniversitySydneyNSW2122Australia Department of Computer Engineering Persian Gulf UniversityBushehr***Iran Department of Electrical and Electronic Engineering Shiraz University of TechnologyShiraz71557-13876Iran

With the ever-increasing popularity of Internet of Things(IoT),massive enterprises are attempting to encapsulate their developed outcomes into various lightweight Web Application Programming Interfaces(APIs)that can be accessible *** this context,finding and writing a list of existing Web APIs that can collectively meet the functional needs of software developers has become a promising approach to economically and easily develop successful mobile ***,the number and diversity of candidate IoT Web APIs places an additional burden on application developers’Web API selection decisions,as it is often a challenging task to simultaneously ensure the diversity and compatibility of the final set of Web APIs *** this challenge and latest successful applications of game theory in IoT,a Diversified and Compatible Web APIs Recommendation approach,namely DivCAR,is put forward in this *** of all,to achieve API diversity,DivCAR employs random walk sampling technique on a pre-built“API-API”correlation graph to generate diverse“API-API”correlation ***,with the diverse“API-API”correlation subgraphs,the compatible Web APIs recommendation problem is modeled as a minimum group Steiner tree search problem.A sorted set of multiple compatible and diverse Web APIs are returned to the application developer by solving the minimum group Steiner tree search *** last,a set of experiments are designed and implemented on a real dataset crawled from *** results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the Web APIs recommendation diversity and compatibility.

关键词： Internet of things Web APIs recommendation Game theory Diversity and compatibility

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Network Embedding Methods: Study and Comparison

EEA - Electrotehnica, Electronica, Automatica

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EEA - Electrotehnica, Electronica, Automatica 2024年第4期72卷 72-78页

作者： Benbatata, Sabrina Saoud, Bilal LIMPAF laboratory Department of Computer Sciences University of Bouira Bouira10000 Algeria Electrical Engineering Department Sciences and Applied Sciences Faculty LISEA laboratory University of Bouira Bouira10000 Algeria

Graph is a powerful language that can model many systems in different fields such as information sciences, social sciences, Biology, mathematics, physics, etc. Graphs can capture very well the relationships between nodes and their structure. Representing data through graphs has some limitations and is challenging to use them like input in machine learning and deep learning models. This challenge can be overcome by using network embedding. Embedding represents a network into low-dimensional vector space. Several methods have been proposed to embed networks. Methods like DeepWalk, Node2Vec and GraphSAGE have gained traction. DeepWalk employs random walks to capture local neighborhood information, generating embeddings using skip-gram models. Node2Vec extends this by incorporating biased random walks, balancing exploration and exploitation of the network. GraphSAGE adopts a neighborhood aggregation strategy, aggregating features from a node's local graph structure through different convolutional layers. These methods facilitate downstream tasks like node classification, link prediction and clustering. Challenges include scalability to large networks, handling heterogeneous information and preserving structural characteristics. Recent advancements integrate techniques like attention mechanisms and reinforcement learning for improved embeddings. Network embedding methods continue to evolve, catering to diverse applications in social networks, biology and recommendation systems, offering insights into complex network structures. In this paper we provide networks embedding concepts and some methods. A comparison between embedding methods is presented. Embedding network methods on node classification tasks on different benchmark datasets have been conducted in order to evaluate them. Through rigorous evaluation, we assess their effectiveness in capturing latent features and improving classification accuracy across various domains. © Editura ELECTRA 2024. All rights reserved

关键词： Graph embeddings

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Salp Swarm Algorithm Based Optimal Network Reconfiguration with Distributed Generation for Power Loss Minimization and Voltage Profile Improvement

Salp Swarm Algorithm Based Optimal Network Reconfiguration w...

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2023 International Conference on Sustainable Technology and engineering, i-COSTE 2023

作者： Alende, Azmerawu Argawu School of Electrical and Computer Engineering Hawassa University Department of Power System and Energy Engineering Hawassa Ethiopia

ISBN: (纸本)9798350329711

In a distribution system, power loss and voltage deviation are the main concern for the customers and utility. The distribution system has a lower voltage level and higher amount of flowing current than the transmission system, so that, the percentage real power loss in the distribution system is higher. This study addresses power loss and voltage deviation concerns in distribution systems. It optimizes the Yirgalem-Ethiopia radial distribution network using the Salp Swarm Algorithm (SSA) and solar Distributed Generation (DG). SSA outperforms other algorithms in reducing real and reactive power losses and improving the voltage profile. The approach minimizes network costs and maintains voltage within acceptable limits. It proves cost-effective, demonstrating significant power loss reduction and improved voltage profiles. The resource feasibility of solar and wind power in Yirgalem city was analyzed and the outcomes showed that solar power generation is more desirable. For this reason, the solar-type Distributed Generation (DG) is used. The proposed SSA algorithm was compared with Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) in three various scenarios (only optimal network reconfiguration, only optimal DG size and site, and simultaneous optimal network reconfiguration and DG allocation) for the Aposto feeder. As stated, the SSA method performs better in terms of reducing both real and reactive power losses and improvement of voltage profile. The model has been formulated to minimize the total cost of the network by determining the optima of the substation locations and power, the load transfers between the demand centers, the feeder routes and the load flow in the network subject to a set of constraints. From the point of view of economic evaluations, the proposed approach is cost-effective. Generally, the simulation results show that the proposed technique is effective to maintain all buses voltage magnitude within the IEEE acceptable lim

关键词： Distributed power generation

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A passive,reusable,and resonating wearable sensing system for ondemand,non-invasive,and wireless molecular stress biomarker detection

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Nano Research 2024年第8期17卷 7542-7556页

作者： Shingirirai Chakoma Xiaochang Pei Huiting Qin Anita Ghandehari Sahar NajafiKhoshnoo Jerome Rajendran Rahim Esfandyarpour Department of Electrical Engineering and Computer Science University of CaliforniaIrvineCA 92697USA Laboratory for Integrated Nano BioElectronics Innovation The Henry Samueli School of EngineeringUniversity of CaliforniaIrvineCA 92697USA Henry Samueli School of Engineering University of CaliforniaIrvineCA 92697USA

The significant impact of stress on health necessitates accurate assessment methods,where traditional questionnaires lack reliability and *** advancements like wearables with electrocardiogram(ECG)and galvanic skin response(GSR)sensors face accuracy and artifact *** biosensors detecting cortisol,a critical stress hormone,present a promising ***,existing cortisol assays,requiring saliva,urine,or blood,are complex,expensive,and unsuitable for continuous *** study introduces a passive,molecularly imprinted polymer-radio-frequency(MIP-RF)wearable sensing system for real-time,non-invasive sweat cortisol *** system is wireless,flexible,battery-free,reusable,environmentally stable,and designed for long-term monitoring,using an inductance-capacitance *** transducer translates cortisol concentrations into resonant frequency shifts with high sensitivity(~160 kHz/(log(μM)))across a physiological range of 0.025–1μ*** with near-field communication(NFC)for wireless and battery-free operation,and threedimensional(3D)-printed microfluidic channel for in-situ sweat collection,it enables daily activity cortisol level *** of cortisol circadian rhythm through morning and evening measurements demonstrates its effectiveness in tracking and monitoring sweat cortisol levels.A 28-day stability test and the use of cost-effective 3D nanomaterials printing enhance its economic viability and *** innovation paves the way for a new era in realistic,on-demand health monitoring outside the laboratory,leveraging wearable technology for molecular stress biomarker detection.

关键词： cortisol wearable flexible sensors three-dimensional(3D)nanomaterials printing reusable environmentally stable

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Area-Oriented Resubstitution For Networks of Look-Up Tables

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年第7期44卷 2571-2584页

作者： Costamagna, Andrea Calvino, Alessandro Tempia Mishchenko, Alan Micheli, Giovanni De Swiss Federal Institute of Technology Lausanne Integrated Systems Laboratory Lausanne1015 Switzerland University of California Department of Electrical Engineering and Computer Sciences Berkeley United States

This paper addresses the challenge of reducing the number of nodes in Look-Up Table (LUT) networks with two significant applications. First, Field-Programmable Gate Arrays (FPGAs) can be modelled as networks of LUTs, and minimizing the node count is imperative to meet resource constraints. Second, in area-oriented design space exploration for standard-cell designs, collapsing a circuit into a LUT network, restructuring it, and later remapping to the original representation helps escape local minima. Thus, the development of algorithms for optimizing and restructuring LUT networks holds considerable promise for area-oriented optimization. Substitution (also called resubstitution) is a powerful logic minimization method that can identify non-local logic dependencies and exploit them for logic minimization. State-of-the-art substitution algorithms for LUT networks rely heavily on SAT solving, limiting the number of optimization attempts and the size of the substitution sub-networks to one node mishchenko2011scalable. Conversely, our method relies on circuit simulation to increase the number of substitution candidates and enables substitutions with more than one node. The experimental results show that the proposed method identifies optimization opportunities overlooked by other methods, improving 11 out of 23 best-known results in the EPFL synthesis competition and yielding a 3.46% area reduction compared to the state-of-the-art. © 1982-2012 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Low-temperature metal–oxide thin-film transistor technologies for implementing flexible electronic circuits and systems

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Journal of Semiconductors 2023年第9期44卷 3-10页

作者： Runxiao Shi Tengteng Lei Zhihe Xia Man Wong State Key Laboratory of Advanced Displays and Optoelectronics and Technologies Department of Electronic and Computer EngineeringThe Hong Kong University of Science and TechnologyHong KongChina

Here we review two 300℃metal–oxide(MO)thin-film transistor(TFT)technologies for the implementation of flexible electronic circuits and ***-enhanced TFTs for suppressing the variation and shift of turn-on voltage(VON),and dual-gate TFTs for acquiring sensor signals and modulating VON have been deployed to improve the robustness and performance of the systems in which they are *** circuit building blocks based on fluorinated TFTs have been designed,fabricated,and characterized,which demonstrate the utility of the proposed low-temperature TFT technologies for implementing flexible electronic *** construction and characterization of an analog front-end system for the acquisition of bio-potential signals and an active-matrix sensor array for the acquisition of tactile images have been reported recently.

关键词： flexible electronics metal-oxide semiconductor thin-film transistor dual gate fluorination analog front-end system sensors

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A Scalable Open-Set ECG Identification system Based on Compressed CNNs

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IEEE Transactions on Neural Networks and Learning systems 2023年第8期34卷 4966-4980页

作者： Wu, Shun-Chi Wei, Shih-Ying Chang, Chun-Shun Swindlehurst, A. Lee Chiu, Jui-Kun National Tsing Hua University Department of Engineering and System Science Hsinchu30013 Taiwan University of California at Irvine Department of Electrical Engineering and Computer Science IrvineCA92697 United States

Deep learning (DL) is known for its excellence in feature learning and its ability to deliver high-accuracy results. Its application to ECG biometric recognition has received increasing interest but is also accompanied by several deficiencies. In this study, we focus on applying DL, especially convolutional neural networks (CNNs), to ECG biometric identification to address these deficiencies. Using prestored user-specific feature vectors, the proposed scheme can exclude unregistered subjects to realize 'open-set' identification. With the help of its scalable structure and 'transfer learning,' new subjects can be enrolled in an existing system without the need for storing the ECGs of those previously enrolled. Finally, schemes based on the quantum evolutionary algorithm (QEA) are presented to prune unnecessary filters in the proposed CNN model. The performance of the proposed scheme was evaluated using the ECGs of 285 subjects from the PTB dataset. The experimental results demonstrate an identification rate of more than 99% in closed-set identification. Although incorporating the proposed method for unregistered subject exclusion degraded the identification performance slightly, the ability of the approach to resist a dictionary attack was evident. Finally, using the QEA-based filter pruning method and its two-stage extension reduced the number of floating-point operations required to complete one identity recognition to 1.20% and 0.22% of the original value without significantly impacting the identification accuracy. © 2012 IEEE.

关键词： Biometrics

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