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Deep Learning Based Pepper Maturity Detection 24

Deep Learning Based Pepper Maturity Detection

引用

8th international conference on Electronic Information technology and computer engineering, EITCE 2024

作者： Ma, Yiqun Peng, Ziyang Ruan, Yujie College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

China is one of the world’s important pepper production area, the existing picking method still rely on manual picking method for picking, and pepper is a kind of batch maturity of the herb, manual picking is not only time-consuming but also very laborious, and the pepper target detection technology is the key to realize the automatic picking of pepper, the existing research for pepper target detection is relatively small, this paper intends to choose the image processing and machine learning methods for pepper to carry out target detection. First of all, the processed pepper image data are passed into Yolov5s, Yolov5m, Faster R-CNN, SSD to compare the four common deep learning methods, the comprehensive comparison in the pepper recognition stage Yolov5s is the most appropriate deep learning algorithm in the deep learning algorithms mentioned above, and in the second stage, using the binary classification method, the pepper data are divided into immature pepper and mature pepper, and directly sent to Yolov5s. The second stage uses a binary classification method to classify pepper data into immature and mature pepper, which is directly fed into the Yolov5s model with an accuracy of 97.4% for pepper maturity discrimination, providing diversified detection and corresponding technical support for the intelligent pepper picking summary. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Optimization and Deployment of Joint Source-Channel Coding for Enhance Future 6G Network Performance 1

Optimization and Deployment of Joint Source-Channel Coding f...

引用

1st international conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Acharjya, Kalyan Thimmappa, Arunkumar Devalapura Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Ramnagar District Karnataka India

ISBN: (纸本)9798331518578

Research on semantic communication has advanced because of deep learning in the construction for collaborative (joint) resource-channel coding (JSCC) project. Deep learning technology (DL)-based JSCC systems currently in practice are developed based upon a preset signal-to-noise ratio (SNR);though, such design is not intended in situations when SNR is changing. Thus, new semantic adaptive system with the semantic adaption modal selectioning (SAMS) modules is developed for semantic communication (SC). It is named collaborative (joint) resource-channel coding with adaptive models (AMJSCC). Both the real-time channel circumstances as well as the model's computing capabilities are taken into account by collaborative (joint) resource-channel encoding (JSCE) & collaborative (joint) resource-channel decoding (JSCD) models. In order to further increase the precision of information recovery, residual networks with various layers are also being studied. Simulation findings show that the model is flexible with the SNR as well as communication services, and it can achieve increased recovery similarity. Meanwhile, it uses less store space and communication resources than the most advanced deep JSCC techniques. © 2025 IEEE.

关键词： Signal to noise ratio

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Optimization design of nuclear power plant monitoring display interface based on SEEV model 24

Optimization design of nuclear power plant monitoring displa...

引用

5th international conference on computer Science and Management technology, ICCSMT 2024

作者： Xing, Rui Dou, Jinzhen Xue, Chengqi School of Mechanical Engineering Southeast University Jiangsu Nanjing China

ISBN: (纸本)9798400709999

Based on the SEEV model, the nuclear power plant monitoring and display interface is optimized and ***, we summarize the key contents and existing problems of the interface, then introduce the salience and effort factors in the SEEV model to optimize the design, and finally use eye tracking methods to study the old and new *** with the analysis of eye movement experimental data, the visual cognitive characteristics of subjects performing visual search tasks in different interfaces were *** results show that combining the SEEV model can better guide subjects' attention allocation strategies, thereby improving work efficiency. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Nuclear power plants

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MetaCon: Revitalizing Internet Congestion Control with Meta-Reinforcement Learning

MetaCon: Revitalizing Internet Congestion Control with Meta-...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bai, He Li, Hui Que, Jianming Zhang, Minglong Chong, Peter Han Joo Sudheera, Kalupahana Liyanage Kushan Pei, Xinyuan School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Fuyao University of Science and Technology Fuzhou China Department of Electrical and Computer Engineering Mississippi State University Starkville United States School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Electrical and Information Engineering Faculty of Engineering University of Ruhuna Sri Lanka

ISBN: (纸本)9798350368741

Effective congestion control algorithms (CCAs) are crucial for the smooth operation of Internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive Internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions. © 2025 IEEE.

关键词： communication network congestion control meta-learning reinforcement learning transport protocol

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Leveraging Machine Learning for Early Detection of Cardiovascular Diseases

Leveraging Machine Learning for Early Detection of Cardiovas...

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2025 IEEE international conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Shrivastava, Anurag Maindola, Meenakshi Kumar, Rakesh Thethi, H. Pal Sreeja, S.P. Sirisha, N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India GLA University Department of Computer Engineering & Applications Mathura India Lovely Professional University Phagwara India New Horizon College of Engineering Master of Computer Application Bangalore India MLR Institute of Technology Department of Computer Science & Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

In order to enhance patient outcomes, rapid and precise detection approaches are needed for cardiovascular diseases (CVDs), which are among the top causes of death globally. By examining a wide range of demographic and clinical data, this study investigates how machine learning approaches could improve the early diagnosis of CVDs. We explore how many different machine learning methods could identify different heart diseases. Decision trees, support vector machines and more, are included in these algorithms. By using feature selection and optimization methods we want to strengthen these models' prediction abilities and make them more resilient. The dataset used by this research includes all of the medical information of patients as they include all the pertinent biomarkers such as age, blood pressure, cholesterol levels and etc. The results suggest that machine learning models, especially ensemble methods like Random Forest and gradient boosting, are indeed able to predict the risk of CVD better than conventional diagnostic strategies. Results suggest that early risk assessment using machine learning embedded within healthcare processes is a reliable and non-invasive approach. This study has found machine learning to have promise for change and further research into the practical use of these models to revolutionize treatment of cardiovascular diseases. © 2025 IEEE.

关键词： Risk assessment

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Target center extraction method based on joint calibration of camera and radar 24

Target center extraction method based on joint calibration o...

引用

8th international conference on Electronic Information technology and computer engineering, EITCE 2024

作者： Wang, Feng Zhu, Zhifeng Yan, Hongjie Qian, Cheng Cai, Bohua Yao, Yong School of Electrical and Information Engineering Anhui University of Technology Anhui Ma’anshan China Anhui Fcar Electronic Technology Co. Ltd. Anhui Ma’anshan China

ISBN: (纸本)9798400710094

Compared with the traditional feature point extraction, an improved algorithm is proposed to accurately extract the center point coordinates of the target with deflection angle. On the one hand, the algorithm extracts the pixel center point coordinates through the improved fitting ellipse method. On the other hand, the joint calibration is carried out through the pose design of the camera and the radar to calculate the required parameters and improve the accuracy of the algorithm. The error comparison experiment is designed. The experimental results show that, first of all, for the average root mean square error, the error after joint calibration is reduced by about 17.01%∼ 20.54%, indicating that the deviation between the measured value and the real value is reduced, which is closer to the actual model. Secondly, for the average re-projection error, the error after joint calibration is reduced by about 17.22%∼17.89%, indicating that the results of three-dimensional reconstruction are more accurate than the original. Finally, the accuracy of the joint calibration results of the camera and radar is verified by the error histogram. © 2024 Copyright held by the owner/author(s).

关键词： Cameras

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Automated Disease Detection and Classification in Jackfruit Leaves: An Efficient Deep Learning-Based Approach 3rd

Automated Disease Detection and Classification in Jackfrui...

引用

3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Saha, Dip Kumar Mahmud Joy, Md. Ashif Reza, Md. Rokonuzzaman Shovon, Reduanul Bari Department of Computer Science and Engineering American International University-Bangladesh Kuratoli Bangladesh Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering The International University of Scholars Dhaka Bangladesh Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9789819739363

Jackfruit is the national fruit of Bangladesh, and one of the most consumed fruits in India, Sri Lanka, Philippines, Indonesia, Malaysia, Australia, and many more countries. The every year due to diseases jackfruit production stays lower than the expectation. The early identification of these diseases is crucial for agricultural productivity, and our research showcases the effectiveness of deep learning techniques in addressing this issue. This study introduces an innovative approach to detect and classify two common jackfruit leaf diseases, algal spot and black spot, combining the YOLOv8 object detection model and the EfficientNetB7 pre-trained deep convolutional neural network model. The models trained on 6,360 images of those two diseases and our methodology achieved an accuracy of 99.9% on training and 99.5% on testing. This work represents a valuable contribution to agriculture, emphasizing the synergy between cutting-edge technology and age-old challenges, with the potential to positively impact the agricultural industry and global food security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Citrus fruits

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Automatic Vehicle Number Plate Recognition: A Narrative review of End-to-end Workflow

Automatic Vehicle Number Plate Recognition: A Narrative revi...

引用

2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Raj, Alok Kumar Thakkar, Hiren Singh, Priyanka Pandit Deendayal Energy University School of Technology Department of Computer Science and Engineering Gujarat 382007 India Srm University Department of Computer Science and Engineering Andhra Pradesh Mangalagiri522240 India

ISBN: (纸本)9798331508685

In the current era, technology helps a lot to enhance human experiences leading to smart city development, which includes optimized energy usage, better healthcare availability, minimal governance, and intelligent mobility. However, considering the increase in day-to-day traffic, intelligent mobility is crucial, and its effective implementation is ensures flawless vehicle movements. The primary goal of is to design and develop the Intelligent Transportation Systems (ITS). The effective ITS ensures automatic vehicle movement management, and automatic traffic rule violation detection, which heavily relies on Automatic Vehicle Number Plate Recognition (AVNPR). In this paper, we described end-to-end workflow of AVNPR including the technologies used in each stage, the benefits and drawbacks of each technology followed by the limitations. The proposed paper exclusively reviews the modern, state-of-the-art models and architectures, and pioneered works carried out within the research area of AVNPR and the recent advancements. © 2025 IEEE.

关键词： Automatic vehicle identification

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A Method for Radio Frequency Model Parameter Estimation based on Improved Intelligent Particle Filter 24

A Method for Radio Frequency Model Parameter Estimation base...

引用

8th international conference on Electronic Information technology and computer engineering, EITCE 2024

作者： Pan, Juncheng Lei, Yingke Teng, Fe Jin, Hu Yu, Hongbing College of Electronic Countermeasures National University of Defense Technology Anhui Hefei China

ISBN: (纸本)9798400710094

According to the current application scenario in the field of specific emitter identification, a universal and extensible mathematical model is proposed. This model is utilized to characterize the electronic components that carry radio frequency fingerprints in communication transmitters. An improved intelligent particle filter (IIPF) algorithm is applied to estimate the hardware parameters of the model. Initially, the physical processes of IQ quadrature modulator and power amplifier nonlinear distortion in communication transmitter are modeled in this paper. Subsequently, the transmitted signals are treated as observations, and the particle filter algorithm is used to estimate the state values of the model. To achieve precision in estimation outcomes, an improved intelligent strategy is integrated into the resampling process of particle filtering. The simulation results show that compared with the intelligent particle filter and the traditional particle filter, the mean square error of the improved intelligent particle filter algorithm is significantly reduced in estimating hardware parameters. © 2024 Copyright held by the owner/author(s).

关键词： Transmitters

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Wideband Circularly Polarized Sequentially-Rotated Cubic-Shaped Dielectric Resonator Array Antenna 10

Wideband Circularly Polarized Sequentially-Rotated Cubic-Sha...

引用

10th international conference on Digital Arts, Media and technology and 8th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Supreeyatitikul, Nathapat Lertwiriyaprapa, Titipong Phongcharoenpanich, Chuwong Konpang, Jessada Rajamangala University of Technology Krungthep Faculty of Engineering Department of Electrical and Telecommunication Engineering Bangkok10120 Thailand Faculty of Technical Eduation Bangkok10800 Thailand King Mongkut's Institute of Technology Ladkrabang School of Engineering Bangkok10520 Thailand

ISBN: (纸本)9798331543273

This study proposes a cubic-shaped dielectric resonator (DR) broadband circularly polarized (CP) array antenna for S-band networks. The design includes 2 × 2 cubic-shaped DR units and a substrate. The upper and lower substrates feature a split-annular aperture ground plane and a sequential-phase feed. Employing the sequential-phase feed, the proposed array antenna aims to enhance performance metrics, including RLBW, ARBW, and antenna gain. Simulation results indicate a RLBW of 56.25% (2.25 - 3.6 GHz) and an ARBW of 43.33% (2.38 - 3.42 GHz). The optimal gain achieved is 9.14 dBic at 3.3 GHz. Furthermore, the array antenna demonstrates right-hand circular polarization (RHCP) characteristics, rendering it suitable for S-band technology. © 2025 IEEE.

关键词： Circular polarization

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