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arXiv 2023年

作者： Yao, Shanle Ardabili, Babak Rahimi Pazho, Armin Danesh Noghre, Ghazal Alinezhad Neff, Christopher Bourque, Lauren Tabkhi, Hamed Department of Electrical and Computer Engineering University of North Carolina at Charlotte United States Public Policy Program University of North Carolina at Charlotte 9201 University City Blvd CharlotteNC28223 United States

This article adopts and evaluates an AI-enabled Smart Video Solution (SVS) designed to enhance safety in the real world. The system integrates with existing infrastructure camera networks, leveraging recent advancements in AI for easy adoption. Prioritizing privacy and ethical standards, pose based data is used for downstream AI tasks such as anomaly detection. Cloud-based infrastructure and mobile app are deployed, enabling real-time alerts within communities. The SVS employs innovative data representation and visualization techniques, such as the Occupancy Indicator, Statistical Anomaly Detection, Bird’s Eye View, and Heatmaps, to understand pedestrian behaviors and enhance public safety. Evaluation of the SVS demonstrates its capacity to convert complex computer vision outputs into actionable insights for stakeholders, community partners, law enforcement, urban planners, and social scientists. This article presents a comprehensive real-world deployment and evaluation of the SVS, implemented in a community college environment across 16 cameras. The system integrates AI-driven visual processing, supported by statistical analysis, database management, cloud communication, and user notifications. Additionally, the article evaluates the end-to-end latency from the moment an AI algorithm detects anomalous behavior in real-time at the camera level to the time stakeholders receive a notification. The results demonstrate the system’s robustness, effectively managing 16 CCTV cameras with a consistent throughput of 16.5 frames per second (FPS) over a 21-hour period and an average end-to-end latency of 26.76 seconds between anomaly detection and alert issuance. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Integrated proteomic profiling identifies amino acids selectively cytotoxic to pancreatic cancer cells

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The Innovation 2024年第3期5卷 138-145页

作者： Alfred Akinlalu Zachariah Flaten Komila Rasuleva Md Saimon Mia Aaron Bauer Santhalingam Elamurugan Nega Ejjigu Sudipa Maity Amara Arshad Min Wu Wenjie Xia Jia Fan Ang Guo Sijo Mathew Dali Sun Department of Electrical and Computer Engineering University of Denver2155 E Wesley AvenueDenverCO 80210USA Biomedical Engineering Program North Dakota State University1401 Centennial BoulevardEngineering AdministrationRoom 203FargoND 58102USA Department of Pharmaceutical Sciences School of PharmacyNorth Dakota State University1001 S.1401 Albrecht Boulevard Sudro HallFargoND 58102USA Materials and Nanotechnology Program North Dakota State University1410 North 14th AvenueCIE 201FargoND 58102USA Wenzhou Institute University of Chinese Academy of SciencesWenzhouZhejiang 325000China Department of Aerospace Engineering Iowa State UniversityAmesIA 50011USA Center for Cellular and Molecular Diagnostics Tulane University School of Medicine1430 Tulane AvenueNew OrleansLA 70112USA Department of Biochemistry and Molecular Biology Tulane University School of Medicine1430 Tulane AvenueNew OrleansLA 70112USA Knoebel Institute for Healthy Aging University of Denver2155 E Wesley AvenueDenverCO 80210USA

Pancreatic adenocarcinoma(PDAC)is one of the most deadly cancers,characterized by extremely limited therapeutic options and a poor prognosis,as it is often diagnosed during late disease *** and selective treatments are urgently needed,since current therapies have limited efficacy and significant side *** proteomics analysis of extracellular vesicles,we discovered an imbalanced distribution of amino acids secreted by PDAC tumor *** findings revealed that PDAC cells preferentially excrete proteins with certain preferential amino acids,including isoleucine and histidine,via extracellular *** amino acids are associated with disease progression and can be targeted to elicit selective toxicity to PDAC tumor *** in vitro and in vivo experiments demonstrated that supplementation with these specific amino acids effectively eradicated PDAC ***,we also identified XRN1 as a potential target for these amino *** high selectivity of this treatment method allows for specific targeting of tumor metabolism with very low toxicity to normal ***,we found this treatment approach is easy-to-administer and with sustained tumor-killing ***,our findings reveal that exocytosed amino acids may serve as therapeutic targets for designing treatments of intractable PDAC and potentially offer alternative treatments for other types of cancers.

关键词： finding identif selectivity

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Fast Tracking of Maximum Power in a Shaded Photovoltaic System Using Ali Baba and the Forty Thieves (AFT) Algorithm

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Processes 2023年第10期11卷

作者： Rehman, Khalil Ur Sajid, Injila Lu, Shiue-Der Ahmad, Shafiq Liu, Hwa-Dong Bakhsh, Farhad Ilahi Tariq, Mohd Sarwar, Adil Lin, Chang-Hua Department of Electrical Engineering ZHCET Aligarh Muslim University Aligarh202002 India Department of Electrical Engineering National Chin-Yi University of Technology Taichung411 Taiwan Industrial Engineering Department College of Engineering King Saud University Riyadh11421 Saudi Arabia Undergraduate Program of Vehicle and Energy Engineering National Taiwan Normal University Taipei106 Taiwan Department of Electrical Engineering National Institute of Technology Srinagar190006 India Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States Department of Electrical Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Photovoltaic (PV) generation systems that are partially shaded have a non-linear operating curve that is highly dependent on temperature and irradiance conditions. Shading from surrounding objects like clouds, trees, and buildings creates partial shading conditions (PSC) that can cause hot spot formation on PV panels. To prevent this, bypass diodes are installed in parallel across each panel, resulting in a global maximum power point (GMPP) and multiple local maximum power points (LMPPs) on the power-voltage (P-V) curve. Traditional methods for maximum power point tracking (MPPT), such as perturb and observe (P&O) and incremental conductance (INC), converge for LMPPs on the P-V curve, but metaheuristic algorithms can track the GMPP effectively. This paper proposes a new, efficient, and robust GMPP tracking technique based on a nature-inspired algorithm called Ali Baba and the Forty Thieves (AFT). Utilizing the AFT algorithm for MPPT in PV systems has several novel features and advantages, including its adaptability, exploration-exploitation balance, simplicity, efficiency, and innovative approach. These characteristics make AFT a promising choice for enhancing the efficiency of PV systems under varied circumstances. The performance of the proposed method in tracking the GMPP is evaluated using a simulation model under MATLAB/Simulink environment, the achieved simulation results are compared to particle swarm optimization (PSO). The proposed method is also tested in real-time using the Hardware-in-the-loop (HIL) emulator to validate the achieved simulation results. The findings indicate that the proposed AFT-based GMPP tracking method performs better under complex partial irradiance conditions than PSO. © 2023 by the authors.

关键词： Efficiency

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Microstructuring of cobalt (Co) thin films by UV laser irradiation

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Applied Physics A: Materials Science and Processing 2023年第12期129卷 864-864页

作者： Itapu, Srikanth Borra, Vamsi Selvendran, S. Department of Electronics and Communication Engineering Alliance University Chandapura-Anekal Road Bengaluru562106 India Electrical and Computer Engineering Program Rayen College of Engineering Youngstown State University 1 University Plaza YoungstownOH44555 United States Vellore Institute of Technology Chennai Kelambakam-Vandaloor Road Chennai600127 India

This work reports the microstructuring of cobalt (Co) thin films by high-energy UV pulsed laser irradiation. The parameters responsible for the efficient formation of such microstructures include laser fluence and film parameters. Cracks and island formations were observed for Co thin films deposited by RF sputtering. Such rough films rendered to produce inefficient microstructuring. To overcome this limitation, Co thin films were deposited by vacuum evaporation resulting in a smooth thin film compatible for laser-induced microstructuring. Two stages of conical patterning are discussed in terms of localized melting and abrupt condensation of the melted Co film owing to laser irradiation. An efficiency of 90% for laser-based microstructuring suggests that it is an efficient and non-invasive technique for fabricating nanostructures. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

关键词： Vacuum evaporation

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PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising

arXiv

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arXiv 2023年

作者： Jang, Hyemi Park, Junsung Jung, Dahuin Lew, Jaihyun Bae, Ho Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Cyber Security Ewha Womans University Korea Republic of

Although supervised image denoising networks have shown remarkable performance on synthesized noisy images, they often fail in practice due to the difference between real and synthesized noise. Since clean-noisy image pairs from the real world are extremely costly to gather, self-supervised learning, which utilizes noisy input itself as a target, has been studied. To prevent a self-supervised denoising model from learning identical mapping, each output pixel should not be influenced by its corresponding input pixel;This requirement is known as J -invariance. Blind-spot networks (BSNs) have been a prevalent choice to ensure J -invariance in self-supervised image denoising. However, constructing variations of BSNs by injecting additional operations such as downsampling can expose blinded information, thereby violating J -invariance. Consequently, convolutions designed specifically for BSNs have been allowed only, limiting architectural flexibility. To overcome this limitation, we propose PUCA, a novel J -invariant U-Net architecture, for self-supervised denoising. PUCA leverages patch-unshuffle/shuffle to dramatically expand receptive fields while maintaining J -invariance and dilated attention blocks (DABs) for global context incorporation. Experimental results demonstrate that PUCA achieves state-of-the-art performance, outperforming existing methods in self-supervised image denoising. Copyright © 2023, The Authors. All rights reserved.

关键词： Image denoising

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A Comparative Study of Fishing Activity Detection Approaches in Maritime Surveillance

A Comparative Study of Fishing Activity Detection Approaches...

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computer Science, computer engineering, & Applied Computing (CSCE), Congress in

作者： Vinicius D. Do Nascimento Tiago A. O. Alves Diego L. C. Dutra Sandip Kundu Systems Engineering and Computer Science Program (PESC) Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Mathematics and Statistics Institute State University of Rio de Janeiro (UERJ) Rio de Janeiro Brazil Electrical and Computer Engineering (ECE) Department University of Massachusetts Amherst USA

Maritime authorities (MA) must track fishing vessels to ensure that fishing activities are limited to permitted areas. If illegal fishing is suspected, resources must be allocated to intercept and inspect the vessels. Thus, a false flag by the MA is costly, so it is important to use accurate detection methods. We compare the accuracy and computational time of the main approaches for detecting fishing activities described in the literature, using the Global Fishing Watch (GFW) dataset. We find that Long Short-Term Memory (LSTM) neural networks achieves an optimal accuracy of 1.00, while the random forest approach comes second with an accuracy of 0.87. Given the high cost of mistakes for MA, we conclude that the LSTM's high computational cost is worthwhile.

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Fq-Uwf: Unpaired Generative Image Enhancement for Fundus Quality Ultra-Widefield Retinal Images

SSRN

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SSRN 2024年

作者： Lee, Kang Geon Song, Su Jeong Lee, Soochahn Kim, Bo Hee Kong, Mingui Lee, Kyoung Mu Department of Electrical and Computer Engineering ASRI Seoul National University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of Department of Ophthalmology Kangbuk Samsung Hospital Sungkyunkwan University School of Medicine Seoul Korea Republic of Sungkyunkwan University Suwon Korea Republic of School of Electrical Engineering Kookmin University Seoul Korea Republic of

Background and objectives :Ultra-widefield (UWF) retinal imaging stands as a pivotal modality for detecting major eye diseases such as diabetic retinopathy or retinal detachment. However, UWF exhibits a well-documented limitation in terms of low resolution and artifacts in the macular area, thereby constraining its clinical diagnostic accuracy, particularly for macular diseases like age-related macular *** supervised super-resolution techniques aim to address this limitation by enhancing the resolution of the macular region through the utilization of meticulously paired and aligned fundus image ground truth. However, obtaining such refined paired ground truth is a formidable *** :To tackle this issue, we propose an unpaired degradation-aware super-resolution technique for enhancing UWF retinal images. Our approach leverages recent advancements in deep learning, specifically employing generative adversarial networks and attention mechanisms. Results :Notably, our method excels in enhancing and super-resolving UWF images without relying on paired clean ground *** extensive experimentation and evaluation, we demonstrate that our approach not only produces visually pleasing results, but also establishes state-of-the-art performance in enhancing and super-resolving UWF retinal *** :We anticipate that our method will contribute to improving the accuracy of clinical assessments and treatments, ultimately leading to better patient outcomes. © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Synthetic Batik Pattern Generator Using Wasserstein Generative Adversarial Network with Gradient Penalty

Synthetic Batik Pattern Generator Using Wasserstein Generati...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Kus Andriadi Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Batik is an Indonesian world cultural heritage. Batik consists of many kinds of patterns depending on where the batik comes from, Batik-making techniques continue to develop along with technology development. Among the batik making techniques that are widely used are hand-written, stamping, and printing. Batik motifs have been widely used as research material, especially in the field of artificial intelligence. The diverse appearance of batik motifs has attracted many researchers to carry out research on making synthetic batik patterns, one of which uses a Generative Adversarial Network. This paper presents a synthetic batik pattern model based on the Wasserstein Generative Adversarial Network with Gradient Penalty. This model has been proven to create new synthetic batik patterns quite well and almost identical with images provided in the dataset, with the notes if the dataset provided is large.

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Leveraging population information in brain connectivity via Bayesian ICA with a novel informative prior for correlation matrices

arXiv

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arXiv 2023年

作者： Mejia, Amanda F. Bolin, David Spencer, Daniel A. Eloyan, Ani Department of Statistics Indiana University BloomingtonIN United States Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division KAUST Saudi Arabia Department of Biostatistics Brown University ProvidenceRI United States

Brain functional connectivity (FC), the temporal synchrony between brain networks, is essential to understand the functional organization in the brain and to identify changes due to neurological disorders, development, treatment, and other phenomena. Independent component analysis (ICA) is a matrix decomposition method used extensively for simultaneous estimation of functional brain topography and connectivity. However, estimation of FC via ICA is often sub-optimal due to the use of ad-hoc estimation methods or temporal dimension reduction prior to ICA. Bayesian ICA methods can avoid dimension reduction, produce more accurate estimates of latent variables and model parameters, and facilitate inference via posterior distributions. In this paper, we develop a novel, computationally feasible Bayesian ICA method with population-derived priors on both the spatial ICs and their temporal correlation. For the latter we consider two priors: the inverse-Wishart, which is designed for covariance matrices and has limitations for modeling correlation matrices;and a novel informative prior for correlation matrices. For both choices of prior, we derive a variational Bayes algorithm to estimate the model variables and obtain posterior variances or distributions of quantities of interest. Through extensive realistic simulation studies, we evaluate the performance of the proposed methods and compare them with existing approaches. Finally, we analyze fMRI data from over 400 healthy adults in the Human Connectome Project. We find that our Bayesian ICA algorithms produce highly accurate measures of functional connectivity and spatial brain features. Our informative prior for correlation matrices outperforms the inverse-Wishart, but comes with a higher computational burden. The proposed framework is applicable to single-subject analysis, making it potentially clinically viable. Copyright © 2023, The Authors. All rights reserved.

关键词： Independent component analysis

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Raspberry Pi-Based Monitoring System for Grid-Connected PV Systems using Deep Learning Technique*

Raspberry Pi-Based Monitoring System for Grid-Connected PV S...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Zahra Yahyaoui Wajdi Saadaoui Mansour Hajji Majdi Mansouri Mohamed Nounou Kais Bouzrara Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The evolution of embedded systems has demonstrated their reliability as a solution for monitoring and controlling industrial systems, particularly in renewable energy conversion systems like photovoltaic (PV) energy. The increasing adoption of PV systems highlights the critical need for effective fault diagnosis to ensure their reliable operation. In this paper, we present a novel fault diagnosis approach utilizing Long Short-Term Memory (LSTM) networks optimized through Bayesian optimization techniques. Our methodology is implemented on a Raspberry Pi platform, demonstrating the feasibility of deploying sophisticated fault diagnosis algorithms in resource-constrained environments. Through extensive experiments, we demonstrate the effectiveness of our approach to accurately diagnose faults in grid-connected photovoltaic systems, thereby improving the reliability and efficiency of integrated environmental monitoring *** obtained results highlight the potential of combining advanced deep learning techniques with embedded systems to address complex diagnostic challenges, as demonstrated by achieving a 100% accuracy rate.

关键词： Fault diagnosis Deep learning Photovoltaic systems Embedded systems Accuracy Economic indicators Bayes methods Environmental monitoring Long short term memory Optimization

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