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Time-Stretched Imaging Flow Cytometry and Photonic Neuromorphic Processing for Particle Classification

Time-Stretched Imaging Flow Cytometry and Photonic Neuromorp...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Ioannis Tsilikas Aris Tsirigotis Stavros Deligiannidis Georgios N. Tsigaridas Adonis Bogris Charis Mesaritakis Dept. Physics National Technical University of Athens Athens Zografou Greece Dept. Information and Communication Systems Engineering University of the Aegean Karlovasi Samos Greece Dept. Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (纸本)9798350345995

In this work, we investigate the compression capabilities of a photonic neuromorphic accelerator relying on an optical spectrum slicing technique [1], following a high-flow 1D imaging cytometry setup, able to image 62000 particles/sec. The utilized image scheme relies on a technique, where spatial information is transfered to the spectrum and subsequently through dispersion to the temporal domain. Therefore, it can replace high speed spectrometers with a single photodetector [2]. On the other hand, the elevated image acquisition rate, trigger the generation of a staggering amount of data, which can cause a processing backlog at the digital backend. In this context, we explore, for the first time, the use of a photonic neuromorphic pre-processing unit, following the cytometer that can reduce the number of trainable parameters at a lightweight digital backend by a factor of 2.5, while at the same time, it can project incoming data to a high dimensional space boosting accuracy.

关键词： Cytometry Diffraction gratings Mode-locked fiber lasers Neural networks Photodetectors Spectrometers

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Design of a Wideband Reflectarray Element for Dual Band Operation in 5G Communications 16

Design of a Wideband Reflectarray Element for Dual Band Oper...

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16th International Conference on Emerging Technologies, ICET 2021

作者： Bashir, Shanza Hashim Dahri, M. Shah, Syed Nazim Nasir, Jamal Comsats University Islamabad Abbottabad Campus Dept. of Electrical and Computer Engineering Pakistan Dawood University of Engineering and Technology Duet Dept. of Electronic Engineering Karachi Pakistan

ISBN: (纸本)9781665494373

The performance analysis of reflectarray element for dual band application in 5G communication systems is presented in this research work. A dual frequency design at 22 GHz and 33 GHz based on dual circular rings is proposed with an additional feature of bandwidth enhancement. Linearity in phase range, bandwidth and progressive phase distribution of the proposed element has been investigated. It has been shown that dual frequency design attains 0.6 GHz and 0.9 GHz bandwidth at the two selected frequencies. To obtain the progressive phase distribution a slot is used in ground for the lower frequency and variable size technique is used for higher frequency. It has also been observed that the progressive phase distribution of 275° and 305° degrees is achieved at 23 GHz and 31.5 GHz frequencies, respectively. The proposed unit cell element of reflectarray antenna has been characterized for enhancement in bandwidth, which is requirement for 5G communication systems. © 2021 IEEE.

关键词： Bandwidth

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Secrecy Analysis of RIS-Assisted Uplink NOMA Systems Under Nakagami-$m$ Fading

Secrecy Analysis of RIS-Assisted Uplink NOMA Systems Under N...

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IEEE International Conference on Communications Workshops, ICC

作者： Khawaja Muhammad Hamza Sarah Basharat Haejoon Jung Mikael Gidlund Syed Ali Hassan School of Electrical Engineering and Computer Science (SEECS) National University of Sciences & Technology (NUST) Islamabad Pakistan Dept. of Electronic Engineering Kyung Hee University Yongin South Korea Department of Computer and Electrical Engineering Mid Sweden University Sundsvall Sweden

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In this paper, we consider a reconfigurable intelligent surface (RIS)-enhanced uplink non-orthogonal multiple access (NOMA) communication system, where the users transmit confidential information to the base station (BS) in the presence of an eavesdropper. We evaluate the impact of transmit power, distance of users and eavesdropper from RIS, and split factor of the RIS on the secrecy performance of the considered system under Nakagami-m fading channels. The simulation results reveal that the secrecy rate increases with an increase in both the transmit power of the users and the distance of the eavesdropper from RIS, while the secrecy rate degrades with an increase in the distance of users from the RIS. Moreover, there is an increase in the secrecy rate of weak user of the NOMA pair and a decrease in the secrecy rate of NOMA strong user with an increase in the split ratio as the number of RIS elements for the weak user increases.

关键词： NOMA Base stations Simulation Conferences Rayleigh channels Reconfigurable intelligent surfaces Probability

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InterACT: Enhancing Human-AI Interaction

InterACT: Enhancing Human-AI Interaction

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Jithendra Katta Nikhil Reddy Kodumuru Swathi Katta School of Engineering and Applied Sciences The George Washington University Washington D.C USA Computer Science & Engineering dept. Vignana Bharathi Institute of Technology Hyderabad India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The rapid advancement of large language models and computer vision systems has opened new frontiers in artificial intelligence. This paper introduces InterACT, a novel cross-modal system that integrates leading language and vision models to enable more intuitive and context-aware human AI interactions. By leveraging the strengths of both modalities, InterACT processes natural language queries to guide object detection in visual environments, bridging the gap between linguistic and visual understanding. We evaluate the performance of three language models of varying sizes in conjunction with an advanced object detection model, exploring the balance between computational efficiency and interaction quality. Our experiments demonstrate InterACT's potential across diverse real-world scenarios, revealing promising results in maintaining contextual relevance and adaptability. This research contributes to the growing field of cross-modal AI systems and lays the groundwork for more sophisticated human AI interactions, with implications for applications ranging from assistive technologies to educational tools.

关键词： Visualization computer vision Adaptation models Computational modeling Large language models Object detection Machine learning Computational efficiency Artificial intelligence Context modeling

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Roadmap of Renewable Energy Generation

Roadmap of Renewable Energy Generation

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Advances in Science and technology (ICAST), International Conference on

作者： Manish Potey Manish Gudhe Sunayana Jadhav Dept. of Computer Engineering K.J.Somaiya College of Engineering Mumbai India Dept. of Information Technology K.J.Somaiya College of Engineering Mumbai India

Renewable energy is created from non-depleting, naturally replenishing sources like the sun and wind. In this research we have done the materialistic and mathematical study of solar panel, sun position, sun radiation, panel module temperature, cell temperature, wind speed, with respect to region such as latitude and longitude which leads us to propose new architecture and its simulated implementation based on the fundamental concept provided by NREL and its datasets for new energy *** proposed architecture will work on principles of Lotus flower, Sunflower, and bifacial panels. The new architecture is self-operable based on sunlight, as light falls on panel petals, it will open its petals. To receive the most sunlight, it rotates the solar panel petals in accordance with the position of the sun. Bifacial solar panels generate power using both sides of panel petals. It absorbs sun radiation from the front side as well as back sides. With this architecture we can generate 5-20 % of efficient energy. Photovoltaic (PV) performance index is used to validate the simulation findings in the end.

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Detection of Potato Leaves Using Convolutional Neural Networks (CNNs)

Detection of Potato Leaves Using Convolutional Neural Networ...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Priyanka Kaushik Manav Kakkar Meenakshi Yadav M. Sathish Beenu Mago Manas Manglik Kaushik Dept. of CSE (AIML) Chandigarh University Chandigarh India Dept. of Civil Engineering Dhanalakshmi Srinivasan College Coimbatore India Dept. of Computer Science Skyline University College Sharjah UAE Dept. of Electrical and Electronics Manipal Institute of Technology Karnataka India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Agricultural productivity plays a vital role in India’s economy, contributing approximately 17–18% to the nation’s GDP and serving as a primary livelihood for a significant portion of the population. With India possessing the largest net cropped area globally, effective pest and disease management is crucial to ensuring stable agricultural yields. This study utilizes advanced technologies, including machine learning, computer vision, and deep learning, to detect and identify common diseases such as early blight and late blight in potato plant leaves. Using a Kaggle-sourced dataset comprising roughly 2,000 images, rigorous data augmentation techniques were applied to enhance the robustness of the model. A customized convolutional neural network (CNN) was deployed, achieving an impressive accuracy of 97.12% after just 32 epochs. This performance surpasses many existing Kaggle models, which typically reach around 95% accuracy.

关键词： Training Productivity Deep learning Technological innovation Accuracy Crops Data augmentation Robustness Convolutional neural networks Diseases

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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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Optimizing Convolutional Neural Network Hyperparameters for MNIST Image Classification: Insights and Implications

Optimizing Convolutional Neural Network Hyperparameters for ...

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Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： Eman Dar Khadija Rana Hoor Fatima Yousaf Talha Asif Muhammad Zunnurain Hussain Hussain Ijaz Muhammad Zulkifl Hasan Dept. of Computer Engineering Information Technology University Lahore Pakistan Bahria University Lahore Campus NCBA&E DHA Campus Dept. of Computer Science Bahria University Lahore Campus Cloud Engineer Information Technology University Researcher Cyber Security Center Universitie Putra Malaysia

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

The recognition of handwritten digits has been among the most enduring fundamental problems explored in the field of machine learning and computer vision. The objective of this work is to design a state-of-art Convolutional Neural Network (CNN) for the image classification on the MNIST dataset to resolve issues related to data pre-processing and parameter tuning effectively. The work starts with standardizing the image data and then using one-hot encoding for the models that will improve the generalization and representation. The CNN structure followed in this work includes the convolutional layer, batch normalization, pooling layers, and fully connected layers with an efficient integration of features. To measure such performance indicators as accuracy, the given dataset is divided for training and validation. The results are also preprocessed to control for overfitting and convergence using back propagation with early stop to stop training when significant accuracy improvement is not observed and learning rate decay to encourage the model to search for better weights in the final stages of training. The model’s performance is tested and evaluated by using different epoch intervals and learning rates, and it is shown how critical it is to select the correct default hyperparameters for achieving efficiency. This paper also presents the approach for the practical application of the preprocessing of images and the systematic assessment in enhancing CNN-based handwritten digit recognition.

关键词： Training Adaptation models Accuracy Image coding Computational modeling Machine learning Convolutional neural networks Batch normalization Testing Image classification

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Eggplant Disease Diagnosis Using a Robust Ensemble of Transfer Learning Architectures

Eggplant Disease Diagnosis Using a Robust Ensemble of Transf...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md Ismail Hossain Siddiqui Zishad Hossain Limon Shakil Khan Mahbub Alam Khan Hamdadur Rahman Shimanto Krishna Chakroborty Shamim Forhad Abdullah Al-Sakib S M Arafat Rahman Shafiur Rahman Dept. of Engineering/Industrial Management Westcliff University Irvine CA USA Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Business Analytics International American University CA USA Dept. of Management Information System Pacific State University USA Dept. of Management of Information System International American University USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Information Technology Westcliff University USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Eggplant diseases present major challenges to agricultural productivity, leading to yield losses and threatening farmers livelihoods. Traditional diagnostic methods tend to be resource-intensive, subjective, and often inaccurate, making them unsuitable for large-scale use. This study addresses these shortcomings by proposing an Ensemble Transfer Learning (ETL) approach for precise classification of eggplant diseases. The model combines the strengths of ConvNeXt, EfficientNetV2, NFNet, and RegNet architectures, enhancing generalization and minimizing overfitting. Two datasets containing over 19,800 images were utilized for training and validation, with advanced preprocessing and data augmentation techniques applied to ensure high input data quality and model robustness. To assess performance, evaluation metrics such as accuracy, F1-score, MCC, and PR-AUC were used. The model achieved an impressive classification accuracy of 99.76%, surpassing state-of-the-art methods and demonstrating strong generalization across various conditions. A web-based application was also developed, enabling real-time disease diagnostics through a user-friendly interface. This application provides farmers with actionable insights, promoting sustainable farming practices by reducing crop losses and minimizing the need for chemical interventions. However, its reliance on high-quality labeled images limits its applicability in uncontrolled environments. Future research will aim to integrate more diverse datasets and develop mobile-based solutions to enhance scalability and usability. This study emphasizes the potential of ensemble transfer learning to transform agricultural disease diagnosis and improve crop productivity.

关键词： Productivity Accuracy Transfer learning Crops computer architecture Real-time systems Medical diagnosis Chemicals Diseases Farming

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Creation of Juices with Interactive Genetic Algorithm via Taste Communication between Users in Remote Locations

Creation of Juices with Interactive Genetic Algorithm via Ta...

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International Conference on Business and Industrial Research (ICBIR)

作者： Makoto Fukumoto Yoshiko Hanada Dept. of Computer Science and Engineering Fukuoka Institute of Technology Fukuoka Japan Faculty of Engineering Science Kansai University Osaka Japan

Creating media content suited to many users is crucial from the viewpoint of business and a situation with multiple users' participation. Interactive Evolutionary Computation (IEC) is known as a search method that finds good solutions; however, it is originally for each user. This study proposes an IEC that gathers evaluations of multiple users in remote locations and utilizes them in the search, and the search target is good blended juices. Each user's good solution is exchanged between the users as a taste-communication. We had the experiment to investigate the proposed IEC's effectiveness with ten participants in five pairs: two participants of the pair located in different locations. The experimental results showed the effectiveness with a significant increase in fitness values and a significant convergence in the search space. Furthermore, details of the exchange of the GA individuals were investigated by observing the pair's exchange and comparing the fitness values of the exchanged GA individuals.

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