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Fano Resonance Biosensor of Asymmetric Splitting Ring in The-Band 12

Fano Resonance Biosensor of Asymmetric Splitting Ring in The...

引用

12th IEEE International Conference on Information and Communication Networks, ICICN 2024

作者： He, Quanlin Li, Qiming Yang, Hongyan Yang, Yuhang Sun, Hongrui Li, Jianqing Chen, Yunyi School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering Macau University of Science and Technology 999078 China

ISBN: (纸本)9798350355802

Metasurface is a kind of artificial layered material whose thickness is less than the wavelength, which can flexibly and effectively control the polarization, amplitude, phase, polarization mode and propagation mode of electromagnetic waves. In this paper, a split ring metasurface structure with two openings is designed to excite Fano resonance by breaking the symmetry of the structure. Based on the finite element method (FEM), the asymmetric splitting ring of Thz metsurface is studied. It is shown that the peak value of Fano resonance and the quality factor (Q) can be modulated by changing the inner and outer diameters of the splitting ring. The Fano resonance is very sensitive to the asymmetry of the structure. A small asymmetry will excite the Fano resonance with high intensity, high quality factor and very narrow resonance linewidth. The refractive index sensitivity of the designed sensor reaches 7.23THz/RIU, and the Q value is up to 93.33. It can be used as a sensor to monitor the ambient refractive index, ambient temperature and liquid concentration. © 2024 IEEE.

关键词： Terahertz waves

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SWM-YOLO: A New PCB Surface Defect Detection Algorithm Based on YOLOv8 9

SWM-YOLO: A New PCB Surface Defect Detection Algorithm Based...

引用

9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Yang, Ruijun Zhao, Wen Shanghai Institute of Technology College of computer science and Information Engineering Shanghai China

ISBN: (纸本)9798350363043

Printed circuit boards (PCBs) present significant challenges for defect detection due to their complex structures, small size, and high false positive/negative rates associated with traditional methods. To address these issues, this paper proposes SWM-YOLO, a novel network model based on YOLOv8n. By incorporating Swin Transformer backbone, the model enhances feature extraction capabilities, effectively capturing global contextual information. Furthermore, Mixed Local Channel Attention is integrated into the C2f structure to accurately extract detailed features while preserving overall feature representation. Finally, the Wise-IoU loss function is adopted to improve the model's ability to detect subtle defects. Experimental results on a PCB surface defect dataset demonstrate a 3.2% precision improvement over the baseline YOLOv8n, significantly reducing false positives and negatives in PCB surface defect detection. © 2024 IEEE.

关键词： Image coding

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Instance-Level Neural Feature Selection Based on Disentanglement Enhancement 15

Instance-Level Neural Feature Selection Based on Disentangle...

引用

15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Liu, Zihao Pan, Jun Wang, Hao Yu, Kui Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798331508821

Feature selection aims to select the optimal subset from the original features to reduce data dimensionality, eliminate irrelevant features, and improve the interpretability and performance of the model. In recent years, feature selection methods using neural networks have received much attention, but they often lead to suboptimal results due to feature entanglement problems. To this end, we propose a novel instance-level neural feature selection method based on disentanglement enhancement, including a feature selection network, a label prediction network, and a disentanglement bootstrap network. The feature selection network divides the features into strongly and weakly relevant ones, the label prediction network uses the strongly relevant features for supervised learning, and the disentanglement bootstrap network promotes feature disentanglement by minimizing the mutual information between strong and weak features. The designed gradient-based solution algorithm effectively solves the joint optimization problem. Experimental results validate the effectiveness of the method on tabular and image datasets. © 2024 IEEE.

关键词： Feature Selection

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Transmission of Electromagnetic Waves Through a Clay Material 1

引用

32nd International Conference on Organization and technology of Maintenance, OTO 2023

作者： Mandrić, Vanja Rupčić, Slavko Vinko, Davor Bilandžija, Domagoj Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9783031514944

ISBN: (纸本)9783031514937

This paper deals with the simulation calculation of transmission (S21) and reflection (S22) parameters in a material parametrically based on clay (brick). The electromagnetic parameters of the clay that are the subject of the study are: relative permittivity Εr, loss tangent tan δ and electrical conductivity σ, while the only structural parameter is the thickness d of the sample. The values of all considered parameters are within the range of data from available literature and values that could occur due to external causes (e. g. an increase in clay moisture leads to an increase in electrical conductivity). The simulation model was carried out with the software tool ANSYS HFSS and validated with a laboratory measurement model. The laboratory model consists of a circular waveguide with an inner diameter of 150 mm and a total length of 700 mm, which is excited by 52 mm long probes placed 50 mm from the last wall of the waveguide. The lower cut-off frequency of this measurement system is 1.173 GHz, so all results (simulation and measurement) are limited by this lower cut-off frequency of the waveguide. All simulation calculations were performed for three clay sample thicknesses: 18 mm, 36 mm and 54 mm. These calculations were made with the aim of determining brick composites that have high attenuation of electromagnetic EM waves (low transmission parameter and high reflection parameter) in order to reduce the strength of EM fields inside the object made of such bricks. The measured results were used to verify the simulation model. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Circular waveguides

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An Analysis of the Proof-of-Driving (PoD) Consensus for VANET 2

An Analysis of the Proof-of-Driving (PoD) Consensus for VANE...

引用

2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Daimary, Surjya Kanta Kalita, Hemanta Kumar Central Institute of Technology Kokrajhar Department of Computer Science & Engineering Assam India

ISBN: (纸本)9798350366570

Vehicular Ad Hoc Networks (VANETs) are the backbone of Intelligent Transportation Systems (ITS), offering an effective way of communication between vehicles and roadside hardware. In such a highly dynamic and decentralized environment, ensuring trust and security has serious challenges. This paper investigates the consensus mechanism Proof-of-Driving (PoD) for VANET. Using real-time driving data, PoD establishes trust among the participating nodes in the network affected, thus providing a robust framework for secure communication. For consensus evaluation, efficiency, scalability, security, and energy consumption are considered key metrics. Further, this research offers a deep comparison concerning how well PoD stands alongside other consensus mechanisms and its design principles. Finally, the possible measures are suggested to enhance consensus mechanisms for VANET. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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2nd International Conference on Computing, Communication, and Learning, CoCoLe 2023

作者： Naveena, Prathi Dash, Sandeep Kumar Department of Computer Science and Engineering National Institute of Technology Mizoram Aizawl India

ISBN: (纸本)9783031569975

With the profligate progress of graph-based data in numerous areas essential to observe the similarities among the graph pairs is indispensable to speed up the search outcomes. The existence of near-duplicate text documents plays an imperative role in performance degradation while integrating information from diverse sources. The main issue is determining the similarity among the graphs from massive datasets. The principal goal of this paper is to enhance search optimization with improved indexing and condensed pairwise comparisons with the help of the proposed Graph Similarity Self Join of Documents (GSSJD) technique is formed by uniting the prefix filtering strategy to the traditional inverted index method. Experiments were carried out on the dataset to authenticate that our proposed approach illustrates the optimized results in speed and time. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Graphic methods

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Analyzing Farmers' Protest Tweets using Topic Modelling Techniques

Analyzing Farmers' Protest Tweets using Topic Modelling Tech...

引用

2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Sharaff, Aakanksha Papatla, Sai Rohith Rajput, Nandini National Institute of Technology Raipur Department of Computer Science and Engineering Raipur India

ISBN: (纸本)9798350373783

In natural language processing, topic modeling is an effective method for identifying underlying themes in large volumes of textual data. Topic modeling is a useful technique in many fields since it allows for a deeper understanding of the underlying patterns found in textual information through the examination of word distributions and co-occurrences. Topic modeling is a useful concept for social media data analysis. The Indian farmers' protest gained a lot of attention and became popular on Twitter. By creating a new dataset, this article investigates and examines the tweets against farmers' protest. A rudimentary exploratory data analysis is performed on this newly created dataset. Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), Hierarchical Dirichlet Process (HDP), and Non-negative Matrix factorization (NMF) are the four topic modeling techniques that are employed, and these approaches are compared by calculating each method's Coherence score. With a coherence score of 0.5002, the NMF model performed best when seven topics were chosen. © 2024 IEEE.

关键词： Tweets

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Enhancing Distribution System Resilience: A Comparative Study of Batteries and Hydrogen Energy Storage Systems 9

Enhancing Distribution System Resilience: A Comparative Stud...

引用

9th International Conference on technology and Energy Management, ICTEM 2024

作者： Kordrajabi, Moein Zakariazadeh, Alireza University of Science and Technology of Mazandaran Department of Electrical and Computer Engineering Behshahr Iran

ISBN: (纸本)9798350329797

Power distribution systems need to be more resilient to handle natural disasters and maintain a steady supply of loads. To enhance power distribution systems' resilience, microgrids based on energy storage systems (ESSs) are the most desirable option. Photovoltaic resources (PVs) are the most common sources used in microgrids, but due to their intermittent production, an energystorage system is essential alongside them. The study proposes the use of hydrogen energy storage systems (HESS) over batteries and analyzes the system's performance with HESS. This study examines the impact of PV and HESS on resilience metrics. To evaluate network resilience, three scenarios in two cases, moderate and heavy damage, are explored. Simulations are conducted on an IEEE 33-bus test system that includes three microgrids. The results evidence that HESS, owing to its long-term nature, has the potential toenhance the resilience of distribution networks more effectively than conventional batteries. © 2024 IEEE.

关键词： DC distribution systems

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Precision Agriculture: Using Deep Learning to Detect Tomato Crop Diseases 6th

Precision Agriculture: Using Deep Learning to Detect Tomato ...

引用

6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Dwivedi, Apoorv Goel, Ankit Raju, Mahak Bhardwaj, Disha Sharma, Ashish Kidwai, Farzil Gupta, Namita Sharma, Yogesh Tayal, Sandeep Computer Science and Engineering Maharaja Agrasen Institute of Technology New Delhi India

ISBN: (纸本)9789819940707

Modern farming practices such as precision agriculture make crop production more efficient. The earlier detection of plant diseases is one of the major challenges in the agricultural domain. There is currently a substantial time and accuracy gap between manual plant disease classification and counting. In order to prevent the damage that may be caused to plants, farmers, and the agricultural ecosystem at large, it is essential to detect different diseases of plants. The purpose of this project was to classify and detect plant diseases, especially those that affect tomato plants. We propose a deep convolutional neural network-based architecture for detecting and classifying leaf disease. Images from Unmanned Aerial Vehicles (UAVs) are used in the experiment. There is also a dataset of plant village images, a dataset of UAV images, real-time UAV images, and an image from the internet used as well. Hence, detection of diseases is done with more accuracy as multiple datasets are used. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Precision agriculture

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Apple Leaf Disease Prediction Using modified YOLOv8 Algorithm

Apple Leaf Disease Prediction Using modified YOLOv8 Algorith...

引用

2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Tamana Sharma, Sangeeta National Institute of Technology Department of Computer Science and Engineering HP Hamirpur India

ISBN: (数字)9798350364088

ISBN: (纸本)9798350364088

Apple orchards face formidable challenges in disease management, with black rot, apple scab, and cedar apple rust being prominent fungal adversaries. Dealing with such an apple orchard disease are no small feat. This study introduces a sophisticated methodology to accurately spot and classify these diseases using the YOLOv8 model. The YOLOv8 model is fine-tuned, tweaking specific parts to make it more responsive to the unique traits of black rot, apple scab, and cedar apple rust leveraging in the PlantVillage dataset. The YOLOv8 model represents a better evolution within the YOLO series, developed to increase the detection speed while maintaining a great level of accuracy. The architecture consists of numerous convolutional layers, which enable feature extraction from input images, resulting in pinpoint accuracy in disease identification, the model undergoes end-to-end training, and it acquires the ability to learn from input data, improving its predictive performance for leaf disease instances, boosting an impressive accuracy rate of 99.5 %, demonstrating its ability to pinpoint and categorize apple leaf diseases with exceptional precision. Additionally, the model showcases a strong recall rate of 97.43%, confirming its effectiveness in accurately identifying instances of the targeted diseases. The outstanding accuracy and recall rates highlight the potential of our improved YOLOv8 model as a crucial asset in monitoring and controlling fungal infections impacting apple crops using artificial intelligence. © 2024 IEEE.

关键词： Image enhancement

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