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Neuromorphic Camera Assisted High-Flow Imaging Cytometry for Particle Classification

Neuromorphic Camera Assisted High-Flow Imaging Cytometry for...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Tsilikas, Ioannis Deligiannidis, Stavros Tsirigotis, Aris Tsigaridas, Georgios N. Bogris, Adonis Mesaritakis, Charis National Technical University of Athens Dept. Physics Iroon Polytechniou 9 Zografou Athens15772 Greece University of West Attica Dept. Informatics and Computer Engineering Ag. Spiridonos Egaleo12243 Greece University of the Aegean Dept. Information and Communication Systems Engineering Palama 2 str. Karlovasi Samos83200 Greece

ISBN: (纸本)9798350345995

In this work we combine a high flow cytometry experimental setup and a 10Kframe/sec capable neuromorphic event-based camera, followed by lightweight machine learning schemes, thus allowing the simultaneous imaging and real-time classification of test particles, moving at a speed of 0.8m/sec with an accuracy of 97.6%. The key advantage of the utilized microscopy system, is the use of an event-based camera, generating spiking events, triggered by pixel's contrast changes. This bio-inspired operation, contrary to conventional CMOS cameras [1], alleviates bandwidth constraints and can significantly boost frame-rate capabilities, thus capturing high speed events. Following this paradigm, medical imaging modalities, where the detection and analysis of fast-moving particles is a necessity, such as high-flow cytometry, can greatly proliferate from the proposed approach. © 2023 IEEE.

关键词： Cameras

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Decomposition Algorithm for Security-Constrained Unit Commitment with AC Optimal Power Flows

Decomposition Algorithm for Security-Constrained Unit Commit...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Weihang Ren Mingze Li Lei Fan Yongpei Guan Dept. of Industrial and Systems Engineering University of Florida FL USA Dept. of Electrical and Computer Engineering University of Houston TX USA Engineering Technology University of Houston TX USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

This paper presents a comprehensive approach to solving the security-constrained unit commitment with alternating current power flows (SCUC-ACPF) problem in contemporary power systems. We introduce algorithms that decompose the problem into subproblems suitable for specialized solvers, so that the large-scale mixed-integer nonlinear program-ming SCUC-ACPF problem can be solved. In case studies, we validate the efficiency and effectiveness of our algorithms on synthetic and industry-scale power system networks.

关键词： Runtime Scalability Power systems Planning Security Optimization AC power transmission Load flow

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An Effective Detection of Brain Tumor Detection Using ResNet-50 Model

An Effective Detection of Brain Tumor Detection Using ResNet...

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Advancement in Renewable Energy and Intelligent systems (AREIS), International Conference on

作者： V. Raji S. Maheswari S. Sree Dharinya P. Chinniah Kavin K. S. Kiruba Thangam Raja Dept. of Information Technology S.K.P. Engineering College Tiruvannamalai India Dept. of CSE(IoT) Nandha Engineering College Erode Tamilnadu India Dept. of Software and Systems Engineering School of Computer Science Engineering and Information Systems VIT University Vellore India Dept. of ECE ST. Joseph College of Engineering Sriperumbudur Tamilnadu India Dept. of EEE Government College of Engineering Tirunelveli Tamil Nadu India Dept. of Software and Systems Engineering School of Computer Science Engineering and Information Systems(SCORE) VIT University Vellore India

ISBN: (数字)9798350387230

ISBN: (纸本)9798350387247

Rapid proliferation of aberrant neurons in brain tumors significantly damages organ function or causes death, endangering adult health. These tumors vary widely in terms of their locations, sizes, and textures. Finding malignant tumors requires the use of Magnetic Resonance Imaging (MRI). However, physically diagnosing brain tumors is a timeconsuming and potentially unpredictable technique. Cropping and resizing to fixed dimensions ensures consistency before submitting brain MRI images for analysis using deep learning models such as modified ResNet-50. This study uses the modified ResNet-50 model, a deep Convolutional Neural Network (CNN) renowned for its accuracy and resilience in image recognition tasks, to provide an efficient method for brain tumor diagnosis. Initially trained on Image Net, the modified ResNet-50 model further improved using transfer learning on many annotated brain MRI scan datasets. By adding variation to the dataset, data augmentation makes the model more robust and helps it to generalize. Deeper networks are trained without sacrificing performance to the model's architecture, which addresses the vanishing gradient problem. This work classification model implemented using Python software, which facilitated the development, training, and evaluation processes, allowing for an analysis of their performance. The optimized model outperformed conventional Machine Learning (ML) techniques, demonstrating over 94% accuracy in tumor identification and classification tests. With the potential to enhance patient outcomes and diagnostic efficiency, the integration of modified ResNet-50 in brain tumor identification offers a significant development towards accurate and dependable automated diagnostic systems. The accuracy of the modified Resnet-50 is 94%, and the specificity comparison of the modified ResNet-50 is 93.5%, respectively.

关键词： Deep learning Analytical models Accuracy Magnetic resonance imaging Transfer learning computer architecture Brain modeling Data augmentation Data models Convolutional neural networks

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Auction based Resource Allocation in Cloud Computing using Blockchain Techniques 14

Auction based Resource Allocation in Cloud Computing using B...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Kamaleshwar, T. Jayaraj, R. Vijayaraj, N. Sundaranarayana, D. Dr.Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept. of Computer Science & Engineering Chennai India SRM Institute of Science and technology Dept. of Data Science and Business systems Kattankulathur Chennai India

As the spine innovation of decentralized cryptocurrencies, blockchain has additionally proclaimed numerous applications in different fields, for example, resource allocation in cloud computing, Internet of Things (IoT) and resource offloading. The database state is decentrally kept up, and any part hub in the overlay blockchain organize is allowed to partake in the state support without character confirmation. The exchanges among part hubs are recorded in cryptographic hash-connected information structures known as squares. A progression of affirmed blocksis orchestrated in ordered request to shape a consecutive chain, henceforth named blockchain. All part hubs in the system are required to pursue consensus protocol on the value-based information, cryptographic hashes and computerized marks put away in the block to ensure trustworthiness of the blockchain. © Grenze Scientific Society, 2023.

关键词： Blockchain

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EEG-Based Fatigue Detection Using Binary Pattern Analysis and KNN Algorithm 10

EEG-Based Fatigue Detection Using Binary Pattern Analysis an...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Rahman, Faridah Abd Roslizan, Iszan Uwais Amer Gunawan, Teddy Surya Fitriawan, Helmy Kartiwi, Mira Habaebi, Mohamed Hadi International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia Universitas Lampung Department of Electrical Engineering Bandar Lampung35145 Indonesia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia

ISBN: (纸本)9798350365283

Fatigue is a prevalent issue that disrupts the overall well-being of individuals, leading to impaired cognitive functions such as learning, thinking, reasoning, remembering, and problem-solving. Chronic fatigue significantly increases the risk of accidents due to reduced focus, vigilance, and delayed reaction times. Traditional self-assessment methods for detecting fatigue are subjective and often unreliable. Recent advancements in neuroimaging have demonstrated that EEG signal analysis can objectively classify an individual's mental state. This research aims to develop a reliable and accurate EEG signal fatigue detection system. The EEG signals are decomposed into four levels using a one-dimensional discrete wavelet transform (1D-DWT). Textural features are extracted using binary pattern (BP) analysis and combined with seven statistical features. Then, these features are fed into a k-nearest neighbors (KNN) classifier to distinguish between the rest and fatigue states. Utilizing a dataset from the Mendeley Data website, the proposed system achieved an accuracy of 93.75%, precision between 93% and 95%, recall ranging from 92% to 95%, and an F1-score of 93% to 94%. This study highlights the potential of EEG-based systems to provide objective and accurate assessments of fatigue levels, thereby reducing the risks associated with chronic fatigue in daily life. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning 2

Machine Learning for Climate Change Impact Assessment and Ad...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Mohammed Nizarudeen, M. Kamal, N. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350384277

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural Networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level. © 2024 IEEE.

关键词： Adversarial machine learning

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Development of Skin Cancer Detection Using Deep Spiking Neural Networks 10

Development of Skin Cancer Detection Using Deep Spiking Neur...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Habaebi, Mohamed Hadi Zulkarnain, Muhammad Amin Gunawan, Teddy Surya Fitriawan, Helmy Kartiwi, Mira Abd.rahman, Faridah International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia Universitas Lampung Department of Electrical Engineering Bandar Lampung35145 Indonesia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia

ISBN: (纸本)9798350365283

The limitations of conventional techniques necessitate the development of new diagnostic strategies for early detection of skin cancer, the most prevalent form of cancer. This investigation investigates the application of Spiking Neural Networks (SNNs) in creating a dependable and automated skin cancer detection system. The primary goal is to evaluate the feasibility and efficacy of SNNs for this purpose. The methodology entails using the SpikingJelly framework to create and assess an SNN-based model, specifically a modified Spiking VGG-11. Focusing on images classified as melanoma or non-melanoma, the investigation employs the ISIC 2019 dataset. The Spiking VGG-11 model, which features approximately 15 million parameters, achieved a test accuracy of 81.3%, specificity of 81.7%, and precision of 81.5%. This performance is noteworthy in light of its reduced complexity compared to models such as the Spiking VGG-13, which has approximately 134 million parameters. The model's high specificity is especially advantageous in clinical environments, as it minimizes false positives and unnecessary testing. These results emphasize the potential of SNNs to offer diagnostic tools that are both efficient and effective in the detection of skin cancer. These findings underscore the potential of SNNs to enhance the early detection and diagnosis of skin cancer, offering a promising alternative to conventional diagnostic methods. © 2024 IEEE.

关键词： Lung cancer

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Kinship Recognition through Stacked Self-Calibrated Attention Network

Kinship Recognition through Stacked Self-Calibrated Attentio...

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International Conference on computer Theory and Applications (ICCTA)

作者： Ahmed A. Dabas Nagia M. Ghanem Mohamed A. Ismail Dept. of Computer and Systems Engineering Faculty of Engineering Alexandria University Alexandria Egypt

ISBN: (数字)9798350309584

ISBN: (纸本)9798350309591

Kinship recognition is becoming an important path in the evolution of biometric recognition research. Over the past decade, notable progress has been achieved in this emerging field attracting more researchers to exploit new methods for its branches. The most common use case is facial kinship recognition with its two variants: verification and identification. In this paper, we propose a novel kinship verification network based on a self-calibrated attention model and residual blocks to expand the fields-of-view through the attention stack for producing more discriminative features. In addition, we extend our work to build a kinship identification network based on joint learning of self-calibrated verification ensembles. Experimental results on benchmark datasets are demonstrated to show the effectiveness of our proposed scheme when compared to state-of-the-art solutions on the kinship identification track.

关键词： Metalearning Deep learning Face recognition Benchmark testing

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A Comparative Analysis of Two Dielectric Nanostructures to Enhance Efficiency of Perovskite Solar Cells

A Comparative Analysis of Two Dielectric Nanostructures to E...

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2022 International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2022

作者： Hasen, Nayem Rahman, Aminur Singha, Showmik Chowdhury, Tahmid Aziz Shahjalal University of Science and Technology Dept. of Electrical and Electronic Engineering Sylhet3114 Bangladesh Rensselaer Polytechnic Institute Dept. of Electrical Computer and Systems Engineering TroyNY12180 United States

ISBN: (数字)9781665469449

ISBN: (纸本)9781665469449

Perovskite (CH3NH3PbI3) solar cells (PSCs) have recently been invented due to their desirable characteristics such as high absorption, low cost, ease of fabrication, and rapidly improving efficiencies. In recent years, the effectiveness of organometallic halide perovskite as an absorber layer has already surpassed 20%. Dielectric nanostructures are being considered as a possible option for third generation photovoltaics, such as PSCs, due to lower losses and simpler fabrication. Dielectric nanostructured antireflection coatings, on the other hand, have recently emerged as a method for reducing reflection and improving the power conversion efficiency (PCE) of a thin-film absorber that was previously inefficient. In this research, two different types of nanostructures are used on the perovskite absorber layer that reduced light reflection and enhanced the power absorption, short-circuit current, and efficiency of the solar cells. Furthermore, as demonstrated how changing the nanostructure size has an effect on the absorption enhancement. Utilizing the finite difference time domain (FDTD) and coupled optical-electrical models, the proposed optimum nanostructures exhibit greater than 90% power absorption, with the maximum enhanced photocurrent. Total photocurrent increased by 17-20% compared to conventional planar PSCs, with a maximum PCE of 16.78%, representing 37% enhancement throughout the entire wavelength range. © 2022 IEEE.

关键词： Perovskite

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Key Exchange in the Quantum Era: Evaluating a Hybrid System of Public-Key Cryptography and Physical-Layer Security 59

Key Exchange in the Quantum Era: Evaluating a Hybrid System ...

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59th Annual Conference on Information Sciences and systems, CISS 2025

作者： Staat, Paul Dorpinghaus, Meik Sheikholeslami, Azadeh Paar, Christof Fcllwcis, Gerhard Goeckel, Dennis Max Planck Institute for Security and Privacy Bochum Germany Technische Universität Dresden Vodafone Chair Mobile Communications Systems Dresden Germany Suffolk University Math and Computer Science Department Boston United States University of Massachusetts Dept. Electrical and Computer Engineering Amherst United States

ISBN: (纸本)9798331513269

Today's information society relies on cryptography to achieve security goals such as confidentiality, integrity, authentication, and non-repudiation for digital communications. Here, public-key cryptosystems play a pivotal role to share encryption keys and create digital signatures. However, quantum computers threaten the security of traditional public-key cryptosystems as they can tame computational problems underlying the schemes, i.e., discrete logarithm and integer factorization. The prospective arrival of capable-enough quantum computers already threatens today's secret communication in terms of their long-term secrecy when stored to be later decrypted. Therefore, researchers strive to develop and deploy alternative *** this work, we evaluate a key exchange protocol based on combining public-key schemes with physical-layer security, anticipating the prospect of quantum attacks: If a powerful quantum attacker cannot immediately obtain a private key, legitimate parties have a window of short-term secrecy to perform a physical-layer jamming key exchange (JKE) to establish a long-term shared secret. Thereby, the protocol constraints the computation time available to the attacker to break the employed public-key cryptography. In this paper, we outline the protocol, discuss its security, and point out challenges to be resolved. © 2025 IEEE.

关键词： Quantum computers

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