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Optofluidic transport and assembly of nanoparticles using an all-dielectric quasi-BIC metasurface

Light(science & Applications) 2023年第9期12卷 1771-1781页

作者： Sen Yang Justus C.Ndukaife Department of Electrical and Computer Engineering Vanderbilt UniversityNashvilleTNUSA Interdisciplinary Materials Science Vanderbilt UniversityNashvilleTNUSA Full list of author information is available at the end of the article

Manipulating fluids by light at the micro/nanoscale has been a long-sought-after goal for lab-on-a-chip *** heating has been demonstrated to control microfluidic dynamics due to the enhanced and confined light absorption from the intrinsic losses of ***,the counterpart of metals,has been used to avoid undesired thermal effects due to its negligible light ***,we report an innovative optofluidic system that leverages a quasi-BIC-driven all-dielectric metasurface to achieve subwavelength scale control of temperature and fluid *** experiments show that suspended particles down to 200 nanometers can be rapidly aggregated to the center of the illuminated metasurface with a velocity of tens of micrometers per second,and up to millimeter-scale particle transport is *** strong electromagnetic field enhancement of the quasi-BIC resonance increases the flow velocity up to three times compared with the off-resonant situation by tuning the wavelength within several nanometers *** also experimentally investigate the dynamics of particle aggregation with respect to laser wavelength and power.A physical model is presented and simulated to elucidate the phenomena and surfactants are added to the nanoparticle colloid to validate the *** study demonstrates the application of the recently emerged all-dielectric thermonanophotonics in dealing with functional liquids and opens new frontiers in harnessing non-plasmonic nanophotonics to manipulate microfluidic ***,the synergistic effects of optofluidics and high-Q all-dielectric nanostructures hold enormous potential in high-sensitivity biosensing applications.

关键词： dielectric quasi negligible

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Intelligent Underwater Sound Surveillance for Intrusion Detection and Emergency Alerting 15

Intelligent Underwater Sound Surveillance for Intrusion Dete...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mohite, Vaishnavi Shivaji Patra, Krishna Gayatri Sankar, Ch Hari Srinivas, M. Ullas, S. Amrita School of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

With the demand of protective necessity of underwater environment and infrastructures, it is high time for the technological advancement towards intelligently monitoring and ensuring responsive security mechanism. A novel approach uses modern signal analysis and machine learning methods for the differentiation and subsequent analysis of underwater sounds based on the specific source, focusing on interfering objects like submarines, ships, or other noise caused by humans. The introduced system entails the use of the real-time monitoring system so as to allow constant assessment of the acoustic data collected beneath the water. In case of exposure to any abnormal sounds, an alarm system is activated meant to inform a monitoring and control centre. Its versatility for various underwater conditions is addressed by exercising of the system that considers the fluctuations of water temperature, pressure, and acoustic background. The state centralized monitoring of the system makes it easy for the system to respond to the potential threats, faster than using the graphical user interface. The following sections of this paper describe both the general system design and the architecture and feature extraction of the proposed machine learning algorithms, specific experimental test results and where appropriate demonstrations of the specific features and an assessment of the envisioned role of this system in underwater security and emergency alerting. © 2024 IEEE.

关键词： Emergency Alerting Machine Learning for Underwater Surveillance Underwater Acoustic Audio Processing

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Advancements in Heart Disease Classification: Leveraging Deep Learning Techniques for ECG Analysis 15

Advancements in Heart Disease Classification: Leveraging Dee...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Manimaran, V. Shanthi, N. Aravindhraj, N. Aatarsh, K.M. Adharshini, G. Gokul, P. Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350370249

Heart disease, a global health burden, demands early and accurate detection. While vast medical datasets exist, extracting crucial diagnostic patterns remains a challenge. This study investigates the potential of deep learning architectures to improve heart disease diagnosis accuracy. While traditional machine learning approaches have shown promise, this research delves deeper. It explores Convolutional Neural Networks (CNNs) and pre-trained models like VGG19, ResNet50, and DenseNet201 to identify patterns within medical data. The proposed framework leverages deep learning models to analyze diverse medical records, potentially including electrocardiograms (ECGs) and imaging data. These models automatically extract significant features, which subsequently build a classification model for heart disease detection. The study aims to compare the performance of deep learning models against traditional methods using metrics like accuracy, sensitivity, and specificity. By harnessing the power of deep learning for both feature extraction and classification, this research strives to contribute to the development of more robust and accurate diagnostic tools for heart disease. © 2024 IEEE.

关键词： Deep learning Densely Connected Convolutional Networks Heart disease classification Residual Neural Networks Visual Geometry Group

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Intelligent Guitar Chord Recognition Using Spectrogram-Based Feature Extraction and AlexNet Architecture for Categorization

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International Journal of Advanced computer science and Applications 2025年第4期16卷 758-767页

作者： Korade, Nilesh B. Salunke, Mahendra B. Bhosle, Amol A. Sangve, Sunil M. Joshi, Dhanashri M. Asalkar, Gayatri G. Kadu, Sujata R. Sarwade, Jayesh M. Department of Computer Engineering JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India Department of Computer Engineering PCET’s Pimpri Chinchwad College of Engineering and Research Ravet Maharashtra Pune412101 India Department of Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Bibwewadi Pune411037 India Department of Computer Engineering Terna Engineering College Maharashtra Mumbai India Department of Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Chord prediction plays a key role in the advancement of musical technological innovations, such as automatic music transcription, real-time music tutoring, and intelligent composition tools. Accurate chord prediction can assist musicians, educators, and developers in constructing tools that help in learning, playing, and composing music. Background noise and audio distortions may have an impact on chord prediction accuracy, particularly in real-world situations. Chords can have distinct voicings or finger positions on the guitar, resulting in slight variations in audio representation. This study focuses on the classification of guitar chords using techniques of deep learning. There are eight major and minor guitar chords in the dataset. They have been turned into spectrograms, chromagrams, and Mel Frequency Cepstral Coefficients (MFCC) so that features can be extracted. Various deep learning architectures, including CNN, ResNet50, AlexNet, and VGG, were employed to classify the chords. Experimental results demonstrated that the spectrogram-based AlexNet model outperforms others, achieving good accuracy and robustness in chord classification. The proposed study demonstrates the efficiency of spectrograms and advanced deep learning models for audio signal processing in music applications. By automating chord detection, this study provides beneficial resources for music learners as well as educators, enabling more efficient learning and real-time feedback during practice sessions. © (2025), (science and Information Organization). All Rights Reserved.

关键词： Deep learning

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On Improving Solar Forecasts with Enhanced IR Imaging and CNNs 26

On Improving Solar Forecasts with Enhanced IR Imaging and CN...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Nijhum, Ifran Rahman Hossain, Md Yearat Rakib, Md Mahbub Hasan Dev, Soumyabrata The ADAPT SFI Research Centre Ireland School of Computer Science University College Dublin Ireland Department of Electrical and Computer Engineering North South University Bangladesh

ISBN: (纸本)9781837242672

Renewable energy such as solar power has been widely adopted lately due to its sustainability and cost-effectiveness. However, due to the intermittent nature of photovoltaic systems, accurate solar forecasting is vital for efficient integration into the grid. In this paper, we propose a novel framework for effective solar irradiance estimation from infrared sky images. The proposed method utilizes contrast-limited adaptive Histogram Equalization (CLAHE) and OpenCV colormaps for pre-processing low-contrast and low-resolution infrared sky images. Additionally, it introduces an Inception module-based CNN-Regression model to get rich feature information and avoid feature loss. We perform a detailed comprehensive study to evaluate the effectiveness of the proposed method. While the baseline CNN regression model (without enhancement and Inception module) achieves an RMSE of 81.30 W/m2, employing the proposed technique reduces the loss to an impressive 47.85 W/m2. With a novel approach to address the existing issues of infrared sky images, the research advances the field of solar power estimation and forecasting for seamless integration into grids and residential settings. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Solar irradiance

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Probabilistic Feamework for Modeling and Analyzing Complex Networks 15

Probabilistic Feamework for Modeling and Analyzing Complex N...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nair, Lekshmi S Cheriyan, Jo Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India SAINTGITS College of Engineering Department of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350370249

Complex networks, characterised by intricate structures arising from the relationships and interactions among their constituent elements, play a pivotal role in various domains such as social networks, biological systems, and technological infrastructures. The evolution and dynamics of complex networks are analysed for various applications such as link prediction, information diffusion, and recommendation systems. Recent research has focused on the study of structural motifs and the probabilistic analysis of motifs to capture the dynamics of the network. The motifs, considered building blocks of the larger network structure, are recurrent patterns in the network. This study focuses on the different probabilistic models built on triads, which are sets of three interconnected nodes. We investigate six different probability distributions, including Poisson, Gamma, Exponential, Power Law, Gaussian, Lorentzian, and Gaussian Mixture Models (GMMs) that can be applied in the complex network to capture the insight of the connectivity pattern. The applications of this research extend to refining network models, improving simulations, and enhancing our understanding of the likelihood of observing distinct patterns within complex social networks. © 2024 IEEE.

关键词： Poisson distribution

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Enhancing LLM Performance on Legal Textual Entailment with Few-Shot CoT-based RAG

Enhancing LLM Performance on Legal Textual Entailment with F...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Reji, Sneha Ann Sheik, Reshma Sharon, A. Avisha Rai, M. Jaya Nirmala, S. National Institute of Technology Department of Computer Science and Engineering Tamil Nadu Tiruchirappalli India TKM College of Engineering Department of Computer Science and Engineering Kerala Kollam India

ISBN: (纸本)9798350376135

Large Language Models (LLMs), leveraging extensive data, have become successful in text generation applications. However, their integration into the legal domain presents distinct challenges, prompting deviations in thought processes and potentially misleading outcomes. Such discrepancies can significantly impact the efficacy of legal text applications. In our study, we propose a novel approach that combines retrieval-based methods with generation-based techniques to provide relevant explanations and labels for the given query for legal textual entailment task, leveraging the concept of Chain of Thought (CoT) for reasoning and few-shot prompting. Later, we enhance LLM outputs through the application of various text generation strategies. Our findings demonstrate an enhancement, with a 5% increase in F1 score and a 3% improvement in accuracy when compared with zero-shot baselines. This highlights the effectiveness of utilizing these strategies in specialized fields such as law, where precision and reliability are of utmost importance. © 2024 IEEE.

关键词： Modeling languages

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Identification of Real or Fake Currency Using Semi-supervised Generative Adversarial Networks (SSGANs) 6th

Identification of Real or Fake Currency Using Semi-supervise...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Mukherjee, Anupam Ghosh, Anupam Department of Computer Science & Engineering Siliguri Institute of Technology West Bengal Siliguri India Department of Computer Science & Engineering Netaji Subhash Engineering College West Bengal Kolkata India

ISBN: (纸本)9789819780891

Traditional paper money and contemporary electronic money are two significant forms of exchange. However, due to the new and improved methods that counterfeiters are using, it is now becoming an increasingly important issue. This work suggests a machine-assisted method that has been created to distinguish between fake and real currency. Generative adversarial networks (GANs), a cutting-edge machine learning model, are used for this purpose. To develop a model that can be used for supervised prediction, GANs utilize semi-supervised learning. Semi-supervised learning is an exceptional approach in building a model that is fit for supervised prediction. It offers a multitude of benefits, allowing the training of unlabeled data while achieving highly accurate predictions. In this work, detecting counterfeit money using GANs involves a multi-step methodology that leverages the power of deep learning for accurate identification. Preprocess the data by resizing, normalizing, and augmenting the images to ensure uniformity and improve model generalization. The discriminator minimizes the loss when making correct classifications, while the generator minimizes the loss when the discriminator classifies its generated images as genuine. Indian banknotes were subjected to this procedure. Modern feature identification and image processing techniques were employed to develop the overall strategy of a valid input. The trials demonstrate that a high-precision system may be created to identify real paper money. During the training process, the generator network and the discriminator networks update their loss functions. Once training is complete, the model can accurately identify real money notes from fake ones with an 80% accuracy rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Speech Recording Analysis for Parkinson’s Detection Using Machine Learning Approach 8th

Speech Recording Analysis for Parkinson’s Detection Using M...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Deepika, G. Vinothiyalakshmi, P. Department of Computer Science and Engineering Sri Venkateswara College of Engineering Chennai India

ISBN: (纸本)9783031736162

Parkinson’s Disease (PD) is a neuro-degenerative disorder that affects the motor skills of a person when the production of dopamine is reduced and eventually motor dysfunction. Dopamine is a chemical produced by brain cells, which is responsible for controlling movements. Thus, if Parkinson’s disease is detected earlier, then the progression and the effects of its symptoms can be controlled. The proposed Parkinson’s Disease Detection (PDD) model used for the detection of Parkinson’s Disease using various Machine Learning (ML) algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Voting Classifier, and Random Forest (RF). The algorithms are applied on balanced data scales and on features selected using LDA and PCA from the speech recording dataset. On experimental analysis, the SVM algorithm is found to be more efficient on the balanced dataset and PCA extracted features, giving an accuracy of 96.73% and 95.42% respectively as compared to the other classification algorithms. While the Random Forest algorithm gives the best results on LDA applied dataset giving an accuracy of 95.85%. © IFIP International Federation for Information Processing 2025.

关键词： Random forests

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VisionReli6G: Enhancing 6G Wireless Reliability with CNN-LSTM for LOS Blockage Prediction 2024

VisionReli6G: Enhancing 6G Wireless Reliability with CNN-LST...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Bajaj, Sagar Patel, Dhaval K. Raval, Mehul S. Zaveri, Mukesh Merchant, Shabbir N. School of Engineering and Applied Science Ahmedabad University Ahmedabad India Department of Computer Science and Engineering SVNIT Surat India Department of Electrical Engineering IIT Bombay Mumbai India

ISBN: (纸本)9798400709722

The mmWave and sub-THz signals rely on Line-of-Sight (LOS) links for higher throughput. Blocking these links can lead to a sudden drop in the received SNR and increase the latency of the communication network. We propose a CNN-LSTM pipeline designed to enhance the reliability of 6G wireless networks by predicting LOS blockages in advance using a sequential stream of RGB images. The proposed solution's efficiency is demonstrated by comparing key performance indicators between the baseline (YOLO-GRU) and the developed pipelines (ResNet-LSTM, ResNeXt-LSTM). The results are validated on the first real-world 6G communication dataset, DeepSense 6G, which contains diverse data modalities captured at different locations and times of the day. The Top-1 accuracy for future 1, 5, and 10 instances of scenario 22 yields an average increase of 10.63% and 12.60% with the Resnet-LSTM and ResNeXt-LSTM pipelines respectively compared to the baseline YOLO-GRU model. The F1 score of scenarios 17-22 for future-1 time window prediction increased by an average of 7.58% and 8.13% for both pipelines respectively. © 2024 Copyright held by the owner/author(s).

关键词： Phase locked loops

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