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Asia-Pacific Conference on Microwave

作者： Cries Avian Jenq-Shiou Leu Erfansyah Ali Nur Achmad Sulistyo Putro Hang Song Jun-Ichi Takada Setya Widyawan Prakosa Ariana Tulus Purnomo Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan School of Electrical Engineering Telkom University Bandung Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia Department of Transdisciplinary Science and Engineering Tokyo Insitute of Technology Tokyo Japan Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia

This paper proposed non-contact monitoring by classifying breathing patterns using a frequency-modulated continuous wave (FMCW) radar sensor. First, the data were from the subject to conducting breathing pattern: normal, quick, hold, deep, deep quick. Second, we extract the breath wave patterns by employing several block preprocessing and extracting its temporal information that was not investigated by our previous work. Then, to distinguish those five breath wave conditions, the designated Transformer architecture is proposed. Finally, we compared our previous work and demonstrated that our model could enhance accuracy and attained 98.6% of F1-Scores.

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Electrocoagulation-based AZO DYE (P4R) Removal Rate Prediction Model using Deep Learning

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Procedia computer Science 2024年 236卷 51-58页

作者： Meryem Akoulih Smail Tigani Fouzia Byoud Meryem El Rharib Rachid Saadane Samuel Pierre Abdellah Chehri Sanae El Ghachtouli IME Lab Faculty of Science Hassan 2 University 20250 Casablanca Morocco Research and Development Unit Accsellium LLC Fez Morocco Electrical Engineering Department SIRC-LaGeS Hassania School of Public Labors 20250 Casablanca Morocco Department of Computer and Software Engineering Polytechnique Montréal Montréal Québec Canada Department of Mathematics and Computer Science Royal Military College of Canada Kingston ON 11 K7K 7B4 Canada

This article presents a study on the effectiveness of electrocoagulation (EC) for the removal of azo dyes from wastewater. The analysis was performed using a combination of statistical methods, including density estimation, correlation analysis, and deep learning for electrocoagulation performance prediction. The results showed that electrocoagulation was able to effectively remove azo dyes from the wastewater, considering the energy consumption and the mass of flocs being important factors in the process. Deep Learning (DL) is used to build our predictive model using the datasets collected during the experimentation stage. Overall, the findings suggest that electrocoagulation is a promising technique for the treatment of wastewater containing azo dyes, and that the use of statistical and machine learning methods can aid in the optimization of the process.

关键词： Electrocoagulation Wastewater Azo dye Deep learning

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Hybrid fiber-based single-pass cascaded Raman laser generation

Hybrid fiber-based single-pass cascaded Raman laser generati...

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2023 Optica Nonlinear Optics Topical Meeting, NLO 2023

作者： Kim, Hansol Kang, Hyungu Jung, Hongjae Jeong, Yoonchan SNU Laser Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Seoul08226 Korea Republic of Institute of Applied Physics Seoul National University Seoul08226 Korea Republic of BK 21 FOUR Seoul National University Seoul08226 Korea Republic of

ISBN: (纸本)9781957171234

We present a hybrid-fiber-based single-pass cascaded Raman laser consisting of a dispersion-shifted fiber combined with other commercial fibers, which outperforms a conventional single-fiber-based Raman laser in terms of stably generating spectral components over 1550 nm. Optica Nonlinear Optics Topical Meeting 2023 © Optica Publishing Group 2023 © 2023 The Author(s)

关键词： Fibers

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Metamaterial-induced-transparency engineering through quasi-bound states in the continuum by using dielectric cross-shaped trimers

arXiv

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arXiv 2024年

作者： Ghahremani, Maryam Zapata-Rodríguez, Carlos J. Photonics Research Laboratory Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Optics and Optometry and Vision Sciences Faculty of Physics University of Valencia Dr. Moliner 50 Burjassot46100 Spain

This study presents a novel approach to activate a narrowband transparency line within a reflecting broadband window in all-dielectric metasurfaces, in analogy to the electromagnetically-induced transparency effect, by means of a quasi-bound state in the continuum (qBIC). We demonstrate that the resonance overlapping of a bright mode and a qBIC-based nearly-dark mode with distinct Q-factor can be fully governed by a silicon trimer-based unit cell with broken-inversion-symmetry cross shape, thus providing the required response under normal incidence of a linearly-polarized light. Our analysis that is derived from the far-field multipolar decomposition and near-field electromagnetic distributions uncovers the main contributions of different multipoles on the qBIC resonance, with governing magnetic dipole and electric quadrupole terms supplied by distinct parts of the dielectric "molecule." The findings extracted from this research open up new avenues for the development of polarization-dependent technologies, with particular interest in its capabilities for sensing and biosensing. © 2024, CC BY-NC-ND.

关键词： Transparency

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Power Quality Enhancement of Solar Based Microgrids Using ML and AI

Power Quality Enhancement of Solar Based Microgrids Using ML...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： K. Paul Joshua C. Vennila Kurikyala Prasad Yadav D. Kirubakaran Vartika Kulshrestha B.R. Praveen Department of Electronics and Communication Engineering St. Peter's College of Engineering and Technology Tamil Nadu India Department of Electrical and Electronics Engineering Alagappa Chettiar Government College of Engineering and Technology Tamil Nadu India Engineering Department University of Technology and Applied Sciences-Ibri (UTAS-Ibri) Dept. of Electrical and Electronics Eng. St. Joseph's Institute of Technology Tamil Nadu India Computer Science & Engineering ACED Alliance University-Central Campus Karnataka India Dept. of Electrical and Electronics Engineering Sai Vidya Institute of Technology Karnataka India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Solar-powered micro grids are gaining popularity as a cost-effective and environmentally friendly way to provide localized electricity. Microgrids struggle with solar power's fluctuation and intermittent nature. Sun-based microgrids' power quality is improved via machine learning (ML) and synthetic intelligence (AI). The recommended solution employs modern ML algorithms to predict solar power generation and micro grid energy output variations. ML models can accurately forecast changes in solar irradiance, temperature, and other crucial aspects by evaluating ancient and real-time data, enabling proactive micro grid power float management. The proposed ML and AI-based technology offers superior power best, proactive power control, and better predictions than standard management methods. Simulation and experimental results show that the technique reduces load interruptions, improves balance, and reduces solar energy oscillations on the micro grid. Its capabilities are excellent. to provide reliable and excellent power supply, large-scale renewable energy adoption, and sustainable power ecosystems with ML and AI in solar-based micrgarids.

关键词： Solar irradiance Renewable energy sources Temperature distribution Statistical analysis Power quality Microgrids Real-time systems Solar power generation Standards Systematic literature review

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High-frequency sound wave detection with ease-to-use fiber-ball lens

High-frequency sound wave detection with ease-to-use fiber-b...

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Optica Sensing Congress 2023, AIS, FTS, HISE, Sensors, ES 2023 - Part of Optical Sensors and Sensing Congress 2023

作者： Kim, Hansol Jung, Hongjae Kim, Yeeun Jeong, Yoonchan SNU Laser Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Seoul08226 Korea Republic of Institute of Applied Physics Seoul National University Seoul08226 Korea Republic of BK 21 FOUR Seoul National University Seoul08226 Korea Republic of

We propose a fiber-ball lens-based high-frequency sound detection technique. Owing to the excellent coupling efficiency with external signals, we verify that its detection range can go up as high as 16 kHz. © 202...

ISBN: (纸本)9781957171241

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Frozen Mode in Three-Way Waveguide Slow-Wave Structure for Three-Mode Synchronization

Frozen Mode in Three-Way Waveguide Slow-Wave Structure for T...

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International Conference on Vacuum Electronics

作者： Robert Marosi Tarek Mealy Alexander Figotin Filippo Capolino Department of Electrical Engineering and Computer Science University of California Irvine Irvine CA USA Department of Mathematics University of California Irvine Irvine CA USA

We introduce a new geometry for serpentine waveguide slow-wave structures (SWS) for traveling-wave tubes (TWTs) with enhanced interaction impedance, or Pierce impedance, due to the stationary inflection point (SIP), i.e., a frozen mode. The three-way waveguide geometry, the serpentine ladder waveguide (SLWG), can be easily designed to exhibit such SIPs within the dominant TE10 mode of the waveguide cross section, below the first upper band-edge of the serpentine waveguide (SWG), where beam-wave synchronization typically occurs in SWG TWTs. These structures exhibit an enhanced interaction impedance due to the three-modes that are synchronized with an electron beam, and it may be useful for improving the power gain and basic extraction efficiency of millimeter- wave TWTs. Additionally, the introduced structure exhibits directional coupler-like behavior, which makes distributed power extraction (DPE) possible at frequencies at or near the introduced SIPs.

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Experiments with SDN-based Adaptable Non-IP Protocol Stacks in Smart-City Environments

Experiments with SDN-based Adaptable Non-IP Protocol Stacks ...

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IEEE Symposium on computers and Communications (ISCC)

作者： Sarantis Kalafatidis Vassilis Demiroglou Sotiris Skaperas Georgios Tsoulouhas Polychronis Valsamas Lefteris Mamatas Vassilis Tsaoussidis Athena Research and Innovation Center Greece Department of Applied Informatics University of Macedonia Greece Department of Electrical and Computer Engineering Democritus University of Thrace Greece

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

We propose an SDN-based Non-IP multi-protocol platform (REWIRE) that mixes and matches, on-demand, multi-ple Non-IP protocol strategies with real rapidly-detected network conditions and loT data communication patterns. The REWIRE solution supports centralized monitoring of Wireless Mesh Net-works (WMN) and manages alternative Non-IP protocols stacks (i.e., NDN, DTN & NoD) in an adaptable manner based on change-point analysis & clustering mechanisms. To this end, our proposal grafts flexibility and adaptability capabilities to the WMN providing the communication backbone in real Smart-City environments. Our platform was implemented in real WMNs environments over Fed4FIRE+ test-beds considering an loT sce-nario using traffic patterns based on real sensor measurements.

关键词： Wireless communication Wireless sensor networks Protocols Network topology Traffic control Real-time systems Topology

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Rethinking Skip Connections in Spiking Neural Networks with Time-To-First-Spike Coding

arXiv

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arXiv 2023年

作者： Kim, Youngeun Kahana, Adar Yin, Ruokai Li, Yuhang Stinis, Panos Karniadakis, George Em Panda, Priyadarshini Department of Electrical Engineering Yale University New HavenCT United States Division of Applied Mathematics Brown University ProvidenceRI United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant advantages in terms of energy efficiency, closely mimicking the behavior of biological neurons. In this work, we delve into the role of skip connections, a widely used concept in Artificial Neural Networks (ANNs), within the domain of SNNs with TTFS coding. Our focus is on two distinct types of skip connection architectures: (1) addition-based skip connections, and (2) concatenation-based skip connections. We find that addition-based skip connections introduce an additional delay in terms of spike timing. On the other hand, concatenation-based skip connections circumvent this delay but produce time gaps between after-convolution and skip connection paths, thereby restricting the effective mixing of information from these two paths. To mitigate these issues, we propose a novel approach involving a learnable delay for skip connections in the concatenation-based skip connection architecture. This approach successfully bridges the time gap between the convolutional and skip branches, facilitating improved information mixing. We conduct experiments on public datasets including MNIST and Fashion-MNIST, illustrating the advantage of the skip connection in TTFS coding architectures. Additionally, we demonstrate the applicability of TTFS coding on beyond image recognition tasks and extend it to scientific machine-learning tasks, broadening the potential uses of SNNs. © 2023, CC BY.

关键词： Image recognition

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An innovation analysis of Machine Learning model to Automate Network Anomaly Detection through Time Series Analysis

An innovation analysis of Machine Learning model to Automate...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Aruna Sri Rongali Anjum Nazir Qureshi Jyoti Upadhyay P. Lavanya Nanditha S Matad Sanjiv Kumar Jain Department of Information Technology Emphasis/Information Technology University of The Cumberlands US Department of Electronics and Communication Rajiv Gandhi College of Engineering Research and Technology Chandrapur Department of Computer Science GD Rungta College of Science and Technology Bhilai Chhattisgarh India Institute of Aeronautical Engineering Hyderabad Department of Mathematics New Horizon college Bangalore Department of Electrical Engineering Med-Caps University Indore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper explores the potential of machine-mastering models to automate network Anomaly Detection (NAD) through Time series analysis. We employ a two-level method wherein the primary degree entails function selection thru foremost component analysis (PCA), accompanied by gadget mastering (ML) model choice from more than a few supervised studying algorithms. The second stage evaluates the overall performance of the numerous selected ML models and optimizes theirhyperparameters when necessary. Our experiments demonstrate that ML-driven computerized network Anomaly Detection can provide accurate and well-timed detection of network anomalies with little supervision and parameter tuning attempts. The outcomes of our experiments display that Random Forests and Support Vector Machines (SVMs) carry out first-rate some of the model’s grid searches, demonstrating aggressive accuracy and precision ratings from an anomaly detection perspective. We also intensely evaluate the consequences and provide insightful discussion on the possibilities and challenges surrounding using ML for automatic community Anomaly Detection.

关键词： Training Support vector machines Analytical models Technological innovation Accuracy Computational modeling Time series analysis Anomaly detection Tuning Testing

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