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Cavity-agnostic acoustofluidic manipulations enabled by guided flexural waves on a membrane acoustic waveguide actuator

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Microsystems & Nanoengineering 2024年第2期10卷 153-171页

作者： Philippe Vachon Srinivas Merugu Jaibir Sharma Amit Lal Eldwin J.Ng Yul Koh Joshua E.-Y.Lee Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Institute of Microelectronics Agency for ScienceTechnology and Research(A*STAR)SingaporeSingapore SonicMEMS Laboratory School of Electrical and Computer EngineeringCornell UniversityIthacaNYUSA School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnostic advanced particle manipulation functions are achieved,unlocking new avenues for microfluidic systems and lab-on-a-chip *** localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising *** traditional acoustofluidic technologies,the GFWs propagating on the MAWA’s membrane waveguide allow for cavity-agnostic particle manipulation,irrespective of the resonant properties of the fluidic ***,the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane *** demonstrations using two types of particles include in-sessile-droplet particle transport,mixing,and spatial separation based on particle diameter,along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS *** experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA’s compatibility with existing microfluidic systems.

关键词： waveguide fluid cavity

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LED-Based Adaptive Algorithm: A Novel Integration of Logit and Exponential Decay for Self-Organizing Sequential Search Problem 5

LED-Based Adaptive Algorithm: A Novel Integration of Logit a...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Baisakh Swain, Bishnu Prasad Patel, Nileshkumar Kalia, Sarthak Gmr Institute of Technology Department of Computer Science and Engineering Andhra Pradesh Rajam India Sambalpur University Institute of Information Technology Department of Computer Science and Engineering Odisha Sambalpur India Nirma University School Of Technology Computer Science and Engineering Department Gujarat Ahmedabad India

ISBN: (纸本)9798331522100

This research presents an innovative adaptive list reorganization strategy aimed at minimizing the total access cost for dynamic sequences. The proposed algorithm leverages a dual-parameter approach combining Logit and Exponential Decay (LED) model for the self-organizing linear list problem, also known as the list update problem. The proposed method presented in this article, is one of the few existing approaches that offers an alternative to deterministic strategies, specifically designed to handle random input sequences for this *** strategy significantly improves performance compared to deterministic algorithms like Move-to-Front (MTF) and Move-to-Middle (MTM), which fail to account for varying patterns of locality and frequency in request sequences. The comparative analysis is performed with the well-known deterministic list update algorithms such as MTF and MFM by considering the Calgary Corpus standard dataset. The experimental results show that the adaptive reorganization strategy outperformed both algorithms. © 2025 IEEE.

关键词： Adaptive algorithms

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AI-Enabled Robotic Surveillance System for Critical Infrastructure

AI-Enabled Robotic Surveillance System for Critical Infrastr...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Thankappan, Manesh Nataraj, Neetha K Divya, K.S. Jayaraj, Akshaya Joby, Alen Asokan, Abhilash Adi Shankara Institute of Engineering and Technology Department of Computer Science and Engineering Kerala Kalady India

ISBN: (纸本)9798331507541

Traditional monitoring approaches often experience blind spots and high resource utilization at such a crucial time of security. In our study, we design a small-scaled autonomous vehicle model with smart sensors that is to be equipped with an AI model in a Raspberry Pi for effective and reliable self-sustained monitoring of critical infrastructure to overcome the problems. The core AI model was specifically designed to detect in real-time anomalies such as a fire, a fight, vandalism, explosion, etc. The AI model is a modified version of an open source repository available on github named 'Real-world Anomaly Detection in Surveillance Videos (pytorch) [11]'. This technology ensures speedy response time by stopping the vehicle, starting video recording, and forwarding the real-time footage to security personnel upon detecting an anomaly. This innovative solution allows for mobilization and cost-effectiveness, improving surveillance coverage, and diminished dependence on constant human oversight. Exploiting advances in artificial intelligence and robotics, the project delivers a scalable real-time monitoring system deployable into a wide range of critical infrastructure environments. This initiative represents practical application of AI towards improving public safety and building a stronger security posture in facilities that house critical infrastructure. © 2025 IEEE.

关键词： Critical infrastructures

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DemoCraft: Using In-Context Learning to Improve Code Generation in Large Language Models 3

DemoCraft: Using In-Context Learning to Improve Code Generat...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Joshua, Kapu Nirmal Sreejith, Mihit Indian Institute of Technology Department of Electrical Engineering Kanpur India Indian Institute of Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331515911

Producing executable code from natural-language directives via Large Language Models (LLMs) involves obstacles like semantic uncertainty and the requirement for task-focused context interpretation. To resolve these difficulties, we introduce DemoCraft, a framework that refines code generation through in-context learning and demonstration selection, in tandem with latent concept learning. Latent concept learning appends extra concept tokens, which act as trainable embeddings encapsulating task-oriented insights. We evaluate our approach on two promi- nent datasets: MBPP and Humaneval. Our experimental findings reveal that this method attains roughly a 2x boost in the pass@k metric compared to baseline models. Moreover, we propose two new evaluation measurements: correctness@k and similarity@k. Empirical analyses indicate that our system achieves nearly a 3x advancement in these measures as well. © 2025 IEEE.

关键词： Semantics

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BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

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computers, Materials & Continua 2024年第6期79卷 4621-4639页

作者： Xuejie Wang Jianxun Zhang Ye Tao Xiaoli Yuan Yifan Guo Department of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China Liangjiang Institute of Artificial Intelligence Chongqing University of TechnologyChongqing400054China

While single-modal visible light images or infrared images provide limited information,infrared light captures significant thermal radiation data,whereas visible light excels in presenting detailed texture ***-bining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations,resulting in high-quality images with enhanced contrast and rich texture *** capabilities hold promising applications in advanced visual tasks including target detection,instance segmentation,military surveillance,pedestrian detection,among *** paper introduces a novel approach,a dual-branch decomposition fusion network based on AutoEncoder(AE),which decomposes multi-modal features into intensity and texture information for enhanced *** contrast enhancement module(CEM)and texture detail enhancement module(DEM)are devised to process the decomposed images,followed by image fusion through the *** proposed loss function ensures effective retention of key information from the source images of both *** comparisons and generalization experiments demonstrate the superior performance of our network in preserving pixel intensity distribution and retaining texture *** the qualitative results,we can see the advantages of fusion details and local *** the quantitative experiments,entropy(EN),mutual information(MI),structural similarity(SSIM)and other results have improved and exceeded the SOTA(State of the Art)model as a whole.

关键词： Deep learning feature enhancement computer vision

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Concurrent and Learned Data Structures 25

Concurrent and Learned Data Structures

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26th International Conference on Distributed Computing and Networking, ICDCN 2025

作者： Chatterjee, Bapi Peri, Sathya Department of Computer Science and Engineering IIIT Delhi Delhi India Department of Computer Science and Engineering Indian Institute of Technology Hyderabad Hyderabad India

ISBN: (纸本)9798400710629

We explore the landscape of modern data structures that employ two powerful scaling mechanisms simultaneously: (a) concurrency to harness the power of today's multicore processors, and (b) learned queries - the operations employing machine learning inference by models trained on the underlying data. We focus on advancements, challenges, and potential future directions in this field. We delve into the motivation for incorporating learned queries in concurrent data structures. We discuss the key techniques, applications, and open research questions. On the theoretical side, we discuss the mathematical foundations for building the learned data structures in addition to the correctness of queries in a concurrent setting. On the practical side, we present an overview of techniques used for managing concurrency with learned queries that need training the models with concurrent real-time updates. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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KGFakeNet: A Knowledge Graph-Enhanced Model for Fake News Detection 31

KGFakeNet: A Knowledge Graph-Enhanced Model for Fake News De...

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2025 Workshop on Generative AI and Knowledge Graphs, GenAIK 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Kumar, Anuj Kumar, Pardeep Yadav, Abhishek Ahlawat, Satyadev Prasad, Yamuna Department of Computer Science & Engineering Indian Institute of Technology Jammu India Department of Electrical Engineering Indian Institute of Technology Jammu India

ISBN: (纸本)9798891762121

The proliferation of fake news on social media has intensified the spread of misinformation, promoting societal biases, hate, and violence. While recent advancements in Generative AI (GenAI), particularly large language models (LLMs), have shown promise, these models often need more structured representation for accurate verification, as they rely on pre-trained data patterns without access to real-time or validated information. This study presents a framework that utilizes Open Information Extractor 6 (OpenIE6) to extract triplet relationships (subject-predicate-object) from statements and justifications to compute the cosine similarity between the Knowledge Graphs (KGs) of the statements and their supporting justification to precisely measure the relevance and alignment between them. This similarity feature is integrated with an attention mechanism over GenAI-generated embeddings to enhance the model’s ability to capture semantic features accurately. In addition, a Multi-Layer Perceptron (MLP) classifier is employed to integrate all features, resulting in a 4% improvement in accuracy and a 5% increase in F1-score over state-of-the-art LLM-based approaches. ©2025 International Committee on Computational Linguistics (ICCL)

关键词： Knowledge graph

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Stage-Wise Categorization and Prediction of Diabetic Retinopathy Using Ensemble Learning and 2D-CNN

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Intelligent Automation & Soft Computing 2023年第4期36卷 499-514页

作者： N.M.Balamurugan K.Maithili T.K.S.Rathish Babu M.Adimoolam Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudurChennaiIndia Department of Computer Science and Engineering KG Reddy College of Engineering and Technology(Autonomous)HyderabadTelanganaIndia Department of Computer Science and Engineering Sridevi Women’s Engineering CollegeHyderabadTelanganaIndia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalamChennaiIndia

Diabetic Eye Disease(DED)is a fundamental cause of blindness in human beings in the medical *** techniques are proposed to forecast and examine the stages in Prognostication of Diabetic Retinopathy(DR).The Machine Learning(ML)and the Deep Learning(DL)algorithms are the predomi-nant techniques to project and explore the images of *** though some solu-tions were adapted to challenge the cause of DR disease,still there should be an efﬁcient and accurate DR prediction to be adapted to reﬁne its *** this work,a hybrid technique was proposed for classiﬁcation and prediction of *** proposed hybrid technique consists of Ensemble Learning(EL),2 Dimensional-Conventional Neural Network(2D-CNN),Transfer Learning(TL)and Correlation ***,the Stochastic Gradient Boosting(SGB)EL method was used to predict the ***,the boosting based EL method was used to predict the DR of *** 2D-CNN was applied to categorize the various stages of DR ***,the TL was adopted to transfer the clas-siﬁcation prediction to training *** this TL was applied,a new predic-tion feature was *** the experiment,the proposed technique has achieved 97.8%of accuracy in prophecies of DR images and 98%accuracy in grading of *** experiment was also extended to measure the sensitivity(99.6%)and speciﬁcity(97.3%)*** predicted accuracy rate was com-pared with existing methods.

关键词： Diabetic retinopathy prediction and classiﬁcation ensemble learning conventional neural network diabetic eye disease

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A Review on Amalgamation of Textile-based Components for Standardization of Smart Fabrics 5

A Review on Amalgamation of Textile-based Components for Sta...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, Sandeep Haripriya Taneja, Neeti Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Computer Science Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

This article provides an overview of the fundamental ideas and methodologies utilized in the construction of fabric sensors, along with the frequently employed materials and procedures in the field of electronic textiles. It illustrates that depending on the level of integration within the fabric platform, sensing capabilities may be accomplished by either intrinsic or external modifications to textile substrates. The present study showcases the adaptability of fabric sensors in quantifying force, pressure, chemicals, humidity, and temperature fluctuations. The materials, connections, fabric circuits, interconnect, encapsulation, and manufacturing processes used in fabric technologies are very adaptable and diverse. However, they are not as durable as the components used in traditional electronics. The survey results indicate that it is feasible to create a comprehensive smart fabric system by combining various types of functional elements based on textiles. This effort aims to initiate the standardization of smart fabric sensing techniques and e-textile production procedures. © 2024 IEEE.

关键词： e-textiles Fabric sensors textile sensors

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Scale-Invariant Feature Transform based Single-Sample Face and Ear Recognition 2

Scale-Invariant Feature Transform based Single-Sample Face a...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Tomar, Vivek Kumar, Nitin Deshmukh, Maroti Singh, Maheep University National Institute of Technology Computer Science & Engineering Department Dehradun India Computer Science & Engineering Department Chandigarh India National Institute of Technology Computer Science & Engineering Department Srinagar Uttarakhand Garhwal India Uttarakhand Doon University National Institute of Technology Computer Science Department Dehradun India

ISBN: (纸本)9798350368109

Recognising a person with just one face or ear image is a challenging sub-problem in the field of biometric recognition. The difficulty arises as parameter estimation becomes challenging from a single image. This article proposes a methodology for recognition from a single face or ear image using scale-invariant feature transform (SIFT) for feature extraction. The proposed method is tested on five public databases, namely ORL, Yale, and AR (for facial images) and AMI and IITD (for ear images). Extensive image processing attacks like scaling (0.1 to 3.0), rotation (30 degrees to 330 degrees), and blurring (0.5 to 4.0) are applied to the test image to evaluate the performance of the method in real-time scenarios. The proposed method achieves excellent classification accuracy and shows robustness towards scaling, rotation, and blurring. The classification of the test image is done using the K-nearest neighbour (KNN) by measuring the Euclidean distance. The classification accuracy results are in the range of 88.86%-96.78% and 73.81%-90.50% for face and ear databases, respectively. The classification accuracy is in the range of 82.38%-96.78% and 81.30%-91.56% against scaling and blurring. Further, the proposed method shows acceptable classification accuracy in the range of 73.81%-95.56% on rotation as well. This proves that feature extraction methods like SIFT can be effective even in difficult scenarios where scarcity or only a few samples are available. © 2024 IEEE.

关键词： Face recognition

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