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A Resource Optimization Approach Based on Deep Integration of Vision-Motion System for Sustainable Agriculture

IEEE Transactions on Consumer Electronics 2025年

作者： Cheng, Minghao Zhou, Nan Bhatti, Mughair Aslam Li, Di Wang, Shiyong Alasiry, Areej Marzougui, Mehrez South China University of Technology School of Mechanical and Automotive Engineering Guangzhou510640 China Peng Cheng Laboratory Department of Strategic and Advanced Interdisciplinary Shenzhen518000 China Department of Robotics and AI Pakistan King Khalid University College of ComputerScience Abha61413 Saudi Arabia

The integration of vision and motion systems represents a critical phase in the intelligent transformation of consumer electronics aimed at enhancing productivity. However, owing to the operational modes and distributional characteristics inherent in existing systems, achieving large-scale, stable, and consistent agricultural applications on consumer electronics remains a significant challenge. To address this problem, this paper proposes a unified resource optimization approach for different configurations of agricultural consumer electronics to achieve deep integration of vision and motion systems. The optimization is mainly at two levels: on the one hand, we design a velocity observer for the vision-motion integrated system represented by the visual servoing system, which makes native changes to the characteristics of the visual servoing’s non-real-time instruction issuance. By converting long-time commands with uncertain period to short-time commands with certain period, the design difficulty of real-time trajectory planning of the real underlying motion controller is simplified. On the other hand, in image-based visual servoing (IBVS) system, the mixture parameter of the image Jacobian matrix also affect the control performance of the visual servoing system. For most IBVS-based agricultural applications, there is a lack of a systematic approach to ensure that the mixture parameter is adaptively and continuously varied. To solve this problem, this paper proposes a fuzzy logic-based method to adaptively adjust this parameter and ensure its continuity by introducing a suitable membership function. The experimental results of visual servoing based on the consumer electronics show that our proposed method can significantly improve the integrated vision-motion controllability, and can trade-off the convergence efficiency and feature retention constraints to effectively improve the overall efficiency of the system operation. © 1975-2011 IEEE.

关键词： Visual servoing

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A flexible graphical user interface for embedding heterogeneous neural network simulators

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IEEE TRANSACTIONS ON EDUCATION 1996年第3期39卷 367-374页

作者： Drossu, R Obradovic, Z Fletcher, J School of Electrical Engineering and ComputerScience Washington State University Pullman WA USA

The graphical user interface (GUI) for heterogeneous neural network simulators proposed in this article is intended to be of use both for the novice and for the experienced neural network user, For the novice, it provides an easy-to-use neural network simulation package that insulates the user from the requirements of knowing the simulator implementation details or the configuration file syntax, For the experienced neural network professional it provides an interface that is easily extensible to include any additional neural network simulator in its binary form. To satisfy both academic and personal computer environments, the GUI has been developed by using the free TCL/TK software package, available on workstations running UNIX and on PC's running the free Linux operating system, Although the GUI and the embedded simulators have been successfully tested both in neural network research and training programs, a more extensive testing in undergraduate and graduate level classes is in progress.

关键词： .operating system Graphical User synaptic network technology configuration file TCL/TK

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THE PHASE PERTURBATION APPROXIMATION FOR ROUGH-SURFACE SCATTERING FROM A PIERSON-MOSKOWITZ SEA-SURFACE

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1993年第1期31卷 278-283页

作者： BROSCHAT, SL The School of Electrical Engineering and ComputerScience Washington State University Pullman WV USA

In this work the accuracy of the phase perturbation approximation for scattering from a multiscale Pierson-Moskowitz sea surface is examined for large angles of incidence and wind speeds of 10 and 20 m/s. Numerical results for the incoherent bistatic radar cross section and the coherent reflection loss are compared with exact integral equation results. The study is limited to scalar-wave scattering from one-dimensional surfaces with Gaussian surface statistics for the Dirichlet problem. It is found that, for the examples studied, the phase perturbation bistatic radar cross section is accurate away from low scattered grazing angles. In addition, it contains approximations to higher-order classical perturbation cross section terms that partially account for the incoherent intensity structure in the specular region. Finally, the phase perturbation approximation accurately predicts the coherent reflection loss.

关键词： Rough surfaces Surface roughness Sea surface Optical surface waves Optical scattering Radar scattering Acoustic scattering Integral equations Statistics Bistatic radar

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An entropy-coded lattice vector quantizer for transform and subband image coding

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IEEE TRANSACTIONS ON IMAGE PROCESSING 1996年第2期5卷 289-298页

作者： MohdYusof, Z Fischer, TR School of Electrical Engineering and ComputerScience Washington State University Pullman WA USA

A lattice-based vector quantizer (VQ) and noiseless code are proposed for transform and subband image coding. The quantization Is simple to implement, and no vector codebooks need to be stored. The noiseless code enumerates lattice codevectors based on their (weighted) l(1) norm, A software implementation is able to handle lattice codebooks of size 2(256). The image coding performance is shown to be comparable or superior to the best encoding methods reported in the literature.

关键词： Lattices Image coding Quantization Transform coding Entropy coding Senior members Books Redundancy Frequency measurement Humans

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A Two Tier Iterative Ensemble Method to Tackle Imbalance in Multiclass Classification

A Two Tier Iterative Ensemble Method to Tackle Imbalance in ...

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2020 International Conference on Decision Aid Sciences and Application, DASA 2020

作者： Sridhar, S. Kalaivani, A. Srm Institute of Science and Technology Vadapalani Campus Department of ComputerScience and Engineering Chennai TN India Saveetha School of Engineering Simats Department of ComputerScience and Engineering Chennai TN India

ISBN: (纸本)9781728196770

In real world applications, it is very common that the data skewness occurs among multiple classes. Several studies and various attempts were made in the past to overcome this imbalance problem which is a serious issue to the standard machine learning techniques especially classification and regression but, still there exists a need to handle the imbalance problem effectively. Datasets which are imbalanced generally include safe and unsafe minority samples. Our proposed approach is a classifier independent two tier iterative ensemble approach which focuses the rare minority sample's influence on learning from imbalanced datasets. Most of the informed oversampling techniques like SMOTE and its variants cannot be applied directly on rare class samples especially when the count of rare samples is too low. To alleviate this problem, in our proposed approach to learn from rare and outlying samples we proposed a hybrid oversampling technique used at different levels and make them balanced. The goal is to tone down the data imbalance at the data preprocessing stage itself by correcting or balancing the training data sets before moving to the learning part which makes the classifier to focus on its primary role and thereby it improves the learning process. The proposed two tier iterative ensemble approach shows a much significant improvement in the learning process among the multiclass imbalanced data which is clearly evident with the experimental results. © 2020 IEEE.

关键词： Classification (of information)

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Monetary Loan Eligibility Prediction using Logistic Regression Algorithm 2

Monetary Loan Eligibility Prediction using Logistic Regressi...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Deepa, M. Pal, Siddhartha Ghusey, Vaishnavi Prashant School of ComputerScience Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

In the banking sector, predicting customer loan eligibility is a crucial task. This paper presents a study of using machine learning algorithms to predict loan eligibility of customers based on credit history, income, and others. The algorithms used in the study are Logistic Regression. Using machine learning algorithms the results are demonstrated the potential of in the banking sector to improve loan decision-making processes and increase efficiency. The study utilized a dataset containing information on various loan applications and their respective approvals. The dataset was pre-processed and various machine learning algorithms were applied to the data to determine the best model for predicting loan approval. The findings of this study can be useful for financial institutions in making informed decisions about loan approvals, reducing the risk of default and improving the overall loan approval process. The model is trained using a dataset containing various demographic and financial parameters of loan applicants. The Logistic regression algorithm is utilized to build a predictive model that determines the eligibility of a loan applicant based on the input features. The system aims to modernize the loan approval process by using predictive algorithms to analyze and evaluate loan applications. The study compares different machine learning algorithms, including logistic regression and support vector machines, and evaluates their performance in terms of accuracy and efficiency. The findings also suggest that the use of machine learning can help banks streamline their loan approval processes. © 2024 IEEE.

关键词： Efficiency

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GITAAR-GIT based Abnormal Activity Recognition on UCF Crime Dataset 5

GITAAR-GIT based Abnormal Activity Recognition on UCF Crime ...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Sirisha, Uddagiri Chandana, Bolem Sai VIT-AP University School of ComputerScience and Engineering Andhra Pradesh Amaravathi India

ISBN: (纸本)9781665474672

Data and computer science advancements in recent years have greatly benefited people's day-to-day lives. On the other hand, criminals are embracing new technologies to bolster and broaden their operations. There is a great deal of promise in applying the Deep Learning (DL) paradigm to the analysis of highly structured data. However, the availability of public datasets in the crime detection area is low and task-specific, making it difficult to study and develop DL-assisted solutions that are both accurate and robust. This study aims to adapt the popular UCF-crime dataset for use with video subtitling and propose a hybrid model GITAAR (Generative Image-totext Transformer for abnormal activity recognition), a new transformer-architecture for video-caption-generation. In this paper, UCF crime dataset that compares a recently suggested video captioning system against a large number of state-of-theart methods, describing both the qualitative and quantitative aspects. © 2023 IEEE.

关键词： Crime

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Device Fingerprinting in Wireless Networks using Deep Learning 20

Device Fingerprinting in Wireless Networks using Deep Learni...

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20th OITS International Conference on Information Technology, OCIT 2022

作者： Dalai, Asish Kumar Sahoo, Bibhudatta School of Computer Science and Engineering VIT-AP University India Nit Department of ComputerScience and Engineering Rourkela India

ISBN: (纸本)9781665493482

Device fingerprinting involves identifying devices based on attributes provided by their configuration and usage. In this work a Deep Neural Network (DNN) architecture is designed for device fingerprinting. DNN is fed Inter Arrival Time (IAT) and Transmission Time (TT) of preprocessed wireless network traffic. The DNN consists of multiple Convolution Neural Networks (CNN), Rectified Linear Units (ReLU), and maximum pooling layers. As a final step, two fully connected layers, a softmax layer and a classification layer, are applied to classify devices. To evaluate the proposed model, two benchmark datasets, SIGCOMM-2004 and SIGCOMM-2008, were used. Using only 200 frames, it can accurately fingerprint 74 devices in SIGCOMM-2004 and 48 devices in SIGCOMM-2008 with accuracy of 97.04% and 97.70% respectively. The experimental results indicate that the proposed method is more efficient, since it requires fewer frames and produces a higher level of accuracy. © 2022 IEEE.

关键词： Deep neural networks

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A Novel Oversampling Technique to Solve Class Imbalance Problem: A Case Study of Students' Grades Evaluation 2

A Novel Oversampling Technique to Solve Class Imbalance Prob...

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2nd International Conference on Computing, Networking, Telecommunications and engineering Sciences Applications, CoNTESA 2021

作者： Jahin, Dilshad Emu, Israt Jahan Akter, Subrina Patwary, Muhammed J.A. Bhuiyan, Mohammad Arif Sobhan Miraz, Mahdi H. International Islamic University ComputerScience and Engineering Chittagong Bangladesh Xiamen University School of Electrical and Computer Engineering Malaysia

ISBN: (纸本)9781665436106

The academic performance of the students is one of the critical aspects in ranking educational institutions, particularly at the secondary level. If the student's performance is not appropriately defined, then the institution's reputation is at risk. Therefore, data mining could be used for this purpose, to attain high accuracy. However, the data being incomplete, inaccurate and/or noisy, or with an imbalance class label in the dataset, is highly likely to affect the accuracy of the data mining model. This paper proposes a semi-supervised oversampling method to first prepare a balanced dataset and then to classify the students' grades into a binary class with overall performance in any given course. The student performance dataset from the UCI machine learning repository is used, which contains student performance related data of two different courses. A detailed validation result shows that the decision tree algorithm performs better with the balanced dataset compared to the imbalanced one. © 2021 IEEE.

关键词： Data mining

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Partnership between venture companies and universities through students' extra-curriculum activities

Partnership between venture companies and universities throu...

引用

2005 IEEE International Conference on Microelectronic Systems Education, MSE '05

作者： Yi, Kang Jung, Kyeong-Hoon School of ComputerScience and Electronic Engineering Handong Global University Pohang Korea Republic of School of Electrical Engineering Kookmin University Seoul Korea Republic of

ISBN: (纸本)0769523749

The objective of this paper is to introduce a new university-industry collaboration model for small-sized venture companies This niodel recognizes significant aspect:, of extra-curriculum activities in effectively meeting particular demands of various small-sized companies We propose to build a collaborative relationship between student clubs at a university and venture companies Upon the formation of the relationship, each company supports the student clubs by providing financial and technical sids for projects conceived in accordance with specific interests of the company, which will also help the students acquire knowledge and skills required for that specific area the company is targeting The university plays a role as a mediator and facilitator in enhancing the partnership, supplying equipment and operating the internship program We expect this niodel will not only reduce the re -education cost of the companies but also help them to find new workers who are skilled and experienced in the particularfiled of their own We also presented the case of Handong Global University, Korea, us a successful application d this model © 2005 IEEE.

关键词： Virtual corporation

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