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Coherent optical-OFDM system’s contribution to the management of chromatic and polarization mode dispersion using DSP

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Journal of Optical Communications 2024年

作者： Kherici, Cheikh Kandouci, Malika Faculty of Mathematics and Computer Science Computer Science Department Laboratory of Electromagnetism Photonics and Optronics L.E.P.O. Sidi Bel Abbes Algeria Ibn Khaldoun University of Tiaret Tiaret Algeria Faculty of Electrical Engineering Electronics Department Laboratory of Electromagnetism Photonics and Optronics L.E.P.O. Sidi Bel Abbes Algeria Djillali Liabes University of Sidi Bel Abbes Sidi Bel Abbes Algeria

The CO-OFDM system plays a critical role in managing the effects of polarization mode dispersion (PMD) in optical communications, providing opportunities to optimize performance, durability and polarization state capability. DSP digital compensation is crucial to maintain signal quality, reducing error vector magnitude (EVM) and binary error rate (BER). DSP optimizes transmission, adapts to changing conditions, compensates for chromatic dispersion (CD) and PMD without physical intervention, reducing costs and extending network reach. Using modulations such as 16-QAM and 4-QPSK at 100 Gbps, it is advantageous for data centers and inter-satellite communications. A Python-based DSP model has been proposed to mitigate these effects, significantly improving signal integrity and system performance with a binary error rate (BER)∼10-4 © 2024 Walter de Gruyter GmbH. All rights reserved.

关键词： Chromatic dispersion

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Synthetic Biology Construct of Ebola Virus in Bacteria Surrogate Is Stable and Safe for Rapid Detection Studies in a BSL-2 laboratory Setting

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Advances in Microbiology 2022年第1期12卷 25-41页

作者： Nwadiuto Esiobu Douglas Holmes Chad Coarsey Waseem Asghar Bodhi Stone Microbial Biotech Laboratory Biological Sciences Department Florida Atlantic University Boca Raton Florida USA Department of Electrical Engineering and Computer Science Florida Atlantic University Boca Raton Florida USA

Rapid detection of virulent pathogens during an outbreak is critical for public health advisories and control of the disease in a population. While many molecular techniques for point of care and clinical diagnosis abound, the US experience with the COVID-19 testing in the early stages of the pandemic underscores the critical importance of determining the appropriate target gene(s) with in-built controls that reliably detect pathogens with high sensitivity and specificity. Assays and research for diagnostics and therapy could be slowed during an epidemic because access to the required BSL-3 and BSL-4 laboratories are limited. So, during the 2014 West Africa Ebola outbreak, we tested the hypothesis that using synthetic cDNA of Ebolavirus in a bacteria surrogate (fit for all lab settings), would remain unmutated and safe after several generations, serving as an effective positive control in research settings, self test and point-of-care detection platforms. Primers were designed for the detection and quantification of the nucleoprotein (NP) gene of the 2014 Makona Ebola strain (KR781608.1, 733 - 1332 bp). To test the stability of artificially inserted translation arrest in the Orf of the model gene, it was edited to include three STOP codons in the RNA transcript using SNAP GENE. The segment was then spliced into a high copy number plasmid, cloned into One Shot^TM TOP10 Escherichia coli (Invitrogen), and tested for stability and safety by periodic subculture, extraction and sequencing. Unlike COVID-19, rapid detection of blood-borne etiologies like Ebola requires optimized protocols for blood matrix. Using real-time PCR and newly designed primer pairs, the EBOV surrogate was detected and enumerated in human blood and regular broth and buffers. Based on aligned sequence analysis, the EBOV synthetic NP gene was stable (>99.9999% similarity coefficient) for at least 3 months. Detection sensitivity in broth and blood was at least 100 cells/ml or about 5.8

关键词： Ebola Virus Synthetic Biology Biosafety Point-of-Care Rapid Detection Plasmid Vector

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Interpretable Data-Driven Probabilistic Power System Load Margin Assessment with Uncertain Renewable Energy and Loads

Interpretable Data-Driven Probabilistic Power System Load Ma...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： Bendong Tan Junbo Zhao Weijia Liu Nan Duan Department of Electrical and Computer Engineering University of Connecticut Storrs CT National Renewable Energy Laboratory Golden CO Lawrence Livermore National Laboratory Livermore CA

ISBN: (纸本)9798350399677

The increasing uncertainties caused by the high-penetration of stochastic renewable generation resources poses a significant threat to the power system voltage stability. To address this issue, this paper proposes a probabilistic deep kernel learning enabled surrogate model to extract the hidden relationship between uncertain sources, i.e., wind power and loads, and load margin for probabilistic load margin assessment (PLMA). Unlike other deep learning approaches, a kernel SHAP provides the sensitivity analysis as well as interpretability of the inputs to outputs influences. This allows identifying the critical factors that affect load margin so that corrective control can be initiated for stability enhancement. Numerical results carried out on the IEEE 118-bus power system demonstrate the accuracy and efficiency of the proposed data-driven PLMA scheme.

关键词： Deep learning Renewable energy sources Asia Power system stability Wind power generation Probabilistic logic Stability analysis

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1200 V GaN-on-Si Junction Barrier Schottky (JBS) Diodes by an E-Mode-Compatibe Process

1200 V GaN-on-Si Junction Barrier Schottky (JBS) Diodes by a...

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International Electron Devices Meeting (IEDM)

作者： Wensong Zou Jiawei Chen David Zhou Jinshi Zhang Ran Chen Xiong Liu Mengyuan Hua Jun Ma Department of Electronic and Electrical Engineering Southern University of Science and Technology (SUSTech) Shenzhen People's Republic of China Shenzhen Pinghu Laboratory Building C Chinese Sciences Vally Industrial Park(iBT) Shenzhen China JT Microelectronics Co. Ltd. Shenzhen China

ISBN: (数字)9798350365429

ISBN: (纸本)9798350365436

Here we report GaN-on-Si JBS diodes based on an e-mode-compatible process. Thanks to a judicious design of p-GaN islands in the anode, the JBS diode presents enhanced breakdown voltage $(V_{\text{BR}})$ of $-1267\pm 23\ \mathrm{V}$ with diminished reverse current $(I_{\mathrm{R}})$ of $0.40\pm 0.07\ \mu \mathrm{A}/\text{mm}$ at −600 V. The JBS structure also decreases the charge $(Q_{\mathrm{C}})$ to $76.6\pm 2.4\ \text{pC}/\text{mm}$ due to depletion from p-GaN, resulting in ultra-small reverse-recovery time $(t_{\text{rr}})$ of 2 ns that is independent of the current. The device also demonstrates excellent reliability, showing a small increase of 12.7% in dynamic ON-resistance $(R_{\text{ON}})$ , excellent robustness against electrostatic discharge (ESD) and repetitive surge currents, and an estimated life time of over 90 years at 600 V and 150°C. These results show the potential of GaN JBS diodes as promising and cost-effective rectifiers for ultra-compact all-GaN power solutions.

关键词： Schottky diodes Extrapolation Voltage measurement Current measurement Optical variables measurement Benchmark testing Electrostatic discharges Surges Voltage control Stress

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Learning on Forecasting HIV Epidemic Based on Individuals’ Contact Networks

Learning on Forecasting HIV Epidemic Based on Individuals’ ...

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17th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Sun, Chaoyue Liu, Yiyang Parisi, Christina Fisk-Hoffman, Rebecca Salemi, Marco Fang, Ruogu Danforth, Brandi Prosperi, Mattia Marini, Simone Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Department of Epidemiology College of Public Health and Health Professions and College of Medicine University of Florida GainesvilleFL United States Department of Pathology Immunology and Laboratory Medicine College of Medicine University of Florida GainesvilleFL United States J. Crayton Pruitt Family Department of Biomedical Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Center for Cognitive Aging and Memory McKnight Brain Institute University of Florida GainesvilleFL United States Florida Department of Health 4025 Esplanade Way TallahasseeFL United States Emerging Pathogens Institute University of Florida GainesvilleFL United States

Improving the diagnosis of HIV is a fundamental objective of the Ending the HIV Epidemic initiative, as it represents the initial step toward treatment and achieving undetectable status, thereby reducing transmission. To attain these objectives effectively, it is crucial to identify the groups most susceptible to HIV, allowing interventions to be tailored to their specific needs. In this study, we developed a predictive model designed to assess individual HIV risk within a high-risk contact network – predicting treatment or at-risk – leveraging surveillance data collected through routine HIV case interviews in Florida. Unique to our analysis, we explored the incorporation of behavioral network information with Graph Neural Networks to enhance the predictive capacity for identifying individuals within the treatment or intervention categories, when compared to models that mainly consider conventional HIV risk factors. Our deployed Graph Isomorphism Network achieved 77.3% and 73.2% balanced accuracy in inductive and trans ductive learning scenarios respectively, outperforming the traditional prediction algorithms that do not leverage the network structure. We then usedour model to further investigate the importance of demographic and behavioral factors in the HIV risk prediction process. Our findings provide valuable insights for healthcare practitioners and policymakers in their efforts to combat HIV infection. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Contrastive Learning

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Communication Network Layer State Estimation Measurement Model for a Cyber-Secure Smart Grid

Communication Network Layer State Estimation Measurement Mod...

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Innovative Smart Grid Technologies (ISGT)

作者： Reynold Mathieu Sharon Boamah Austin Cooper Dennis Agnew Janise McNair Arturo Bretas Department of Electrical and Computer Engineering University of Florida Gainesville FL Distributed Systems Group Pacific Northwest National Laboratory Richland WA G2Elab Grenoble INP CNRS Université Grenoble Alpes Grenoble France

Network communication has been proven to be a very important tool and a key factor in the recent development and progress of the power grid operation. It is also considered as the foundation for the smart grid because information and communication are integrated into electricity distribution to achieve reliable and accurate knowledge of the power grid. In previous years, absorbing energy from substations and delivering it to customers was the only type of interaction we knew between utility companies and customers. Presently, the growing connections of small distributed generation units caused by the cost reduction of most of the technologies used in the generation and storage of electrical energy, along with the potential benefits of renewable energy have pushed many researchers to look into the improvement of information and communication technologies (ICT) in order to ensure a bidirectional flow of power and data. Moreover, the evolution of information and communication technologies and its applications to smart grid have converted the smart grid into a cyber-physical system where vulnerabilities and additional security challenges such as cyber-threats and cyber-attacks have emerged. Previously, we have demonstrated that using machine learning-based processing on data gathered from communication networks and the power grid was a promising solution for detecting cyber threats by implementing a co-simulation of cyber-security for cross-layer strategy. Since the majority of the challenges observed can only be solved in the network communication layer, we present in this work a physics-based state estimation model of the communication network system towards enhanced cyber-physical security of the smart grid. Information integration with the previously developed machine learning model is developed, providing an enhanced cyber-physical security application for the smart grid. Easy-to-implement model, without hard-to-derive parameters, highlights potential aspects of th

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Comprehensive evaluation and detection of partial discharge in WBG motor drive using DBSCAN based feature extraction

Comprehensive evaluation and detection of partial discharge ...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Kushan Choksi Abdul Basit Mirza Sama Salehi Vala Fang Luo Spellman High Voltage Power Electronics Laboratory Stony Brook University Stony Brook NY USA Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY USA

The emergence of Wide Band Gap (WBG) technology has significantly enhanced the efficiency and power density of motor drives. As a result, WBG motor drives have widely been adopted in aircraft and transportation applications, particularly in More Electric Aircraft (MEA) systems. However, the reliability of the WBG high-power drive system is still a major concern. The reliability of medium and high-voltage motor drives sees an increased insulation degradation due to a more intense partial discharge (PD) exposure. Hence, PD condition monitoring, detection, and analysis are valuable for reliable operation and timely maintenance. This paper deals with PD detection at various voltage levels, dV/dt, and waveform shapes. This paper provides robust detection and analysis of PD events deriving the key relationship between PD probability and factors impacting PD events. This paper provides novel PD detection based on pattern recognition which is adaptable to varying voltage and frequency of supply. This algorithm can help the computation of charge per PD event as well as PD probability.

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Optimal Information Provision for Strategic Hybrid Workers

arXiv

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arXiv 2022年

作者： Shah, Sohil Amin, Saurabh Jaillet, Patrick The Laboratory for Information and Decision Systems The Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Operations Research Center CambridgeMA United States

We study the problem of information provision by a strategic central planner who can publicly signal about an uncertain infectious risk parameter. Signalling leads to an updated public belief over the parameter, and agents then make equilibrium choices on whether to work remotely or in-person. The planner maintains a set of desirable outcomes for each realization of the uncertain parameter and seeks to maximize the probability that agents choose an acceptable outcome for the true parameter. We distinguish between stateless and stateful objectives. In the former, the set of desirable outcomes does not change as a function of the risk parameter, whereas in the latter it does. For stateless objectives, we reduce the problem to maximizing the probability of inducing mean beliefs that lie in intervals computable from the set of desirable outcomes. We derive the optimal signalling mechanism and show that it partitions the parameter domain into at most two intervals with the signals generated according to an interval-specific distribution. For the stateful case, we consider a practically relevant situation in which the planner can enforce in-person work capacity limits that progressively get more stringent as the risk parameter increases. We show that the optimal signalling mechanism for this case can be obtained by solving a linear program. We numerically verify the improvement in achieving desirable outcomes using our information design relative to no information and full information benchmarks. Copyright © 2022, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Microgrid Frequency Stability: A Proactive Scheme Based on Dynamic Predictions

Microgrid Frequency Stability: A Proactive Scheme Based on D...

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Innovative Smart Grid Technologies (ISGT)

作者： Gian Paramo Arturo Bretas Sean Meyn Department of Electrical and Computer Engineering University of Florida Gainesville FL G2Elab Grenoble INP CNRS Université Grenoble Alpes Grenoble France Distributed Systems Group Pacific Northwest National Laboratory Richland USA

The dynamic nature of microgrids introduces challenges in the context of frequency stability. This work presents a framework where the future state of microgrid frequency is predicted and corrective actions are optimized. Predictions are generated through Bayesian filters leveraging synchronized data acquired via PMUs. Taking a proactive approach makes it possible to optimize corrective actions considering dynamic system conditions. Testing is conducted via Matlab simulations. The performance of the solution presented in this work is compared to traditional load-shedding schemes and to predictive solutions found in literature. The results indicate that the proposed framework outperforms both. Some of the advantages of this framework include a reduction in amount of load dropped during compensation, the use of adaptive parameters which eliminates the need to simulate contingency conditions, and dynamic uncertainty quantification provided the particle filter.

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Quantum Circuit for Non-Unitary Linear Transformation of Basis Sets

arXiv

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arXiv 2025年

作者： Zhu, Guorui Bierman, Joel Lu, Jianfeng Li, Yingzhou School of Mathematical Sciences Fudan University China Electrical and Computer Engineering North Carolina State University United States Department of Mathematics Department of Physics Department of Chemistry Duke University United States School of Mathematical Sciences Shanghai Key Laboratory for Contemporary Applied Mathematics Fudan University Key Laboratory of Computational Physical Sciences Ministry of Education China

This paper introduces a novel approach to implementing non-unitary linear transformations of basis on quantum computational platforms, a significant leap beyond the conventional unitary methods. By integrating Singular Value Decomposition (SVD) into the process, the method achieves an operational depth of O(n) with about n ancilla qubits, enhancing the computational capabilities for analyzing fermionic systems. The non-unitarity of the transformation allows us to transform a wave function from one basis to another, which can span different spaces. By this trick, we can calculate the overlap of two wavefunctions that live in different (but non-distinct Hilbert subspaces) with different basis representations. This provides the opportunity to use state specific ansatzes to calculate different energy eigenstates under orbital-optimized settings and may improve the accuracy when computing the energies of multiple eigenstates simultaneously in VQE or other framework. It allows for a deeper exploration of complex quantum states and phenomena, expanding the practical applications of quantum computing in physics and chemistry. © 2025, CC BY.

关键词： Wave functions

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