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Optimized Parameter Design of Binocular Structured Light System for Precision Reflective Surface CPU in Industrial Assembly Processes

Optimized Parameter Design of Binocular Structured Light Sys...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Yiu, Cheuk Tung Shadow Woo, Kam Tim The Hong Kong University of Science and Technology Dept. of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350350821

This paper constructs a simulated CPU assembly line robot system. The system incorporates binocular structured light vision technology for CPU recognition and localization. The reflective surface of the CPU poses a significant challenge for 3D reconstruction, mainly when dealing with tilted angle situations. This inherent characteristic of the CPU adds complexity to the vision system to capture and reconstruct the object's 3D information accurately. This research aims to optimize the vision system's key parameters, balancing cost-effectiveness and the required precision. By achieving the necessary level of accuracy in picking the CPUs to the desired location, our work aims to enhance robotic assembly operations, offering an optimized parameter configuration approach and supporting the needs of modern industrial processes. © 2024 IEEE.

关键词： Robotic assembly

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Tracking Dynamic Conditional Neural Correlation during Task Learning 46

Tracking Dynamic Conditional Neural Correlation during Task ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Zixu Chen, Shuhang Li, Mingdong Wang, Yiwen The Hong Kong University of Science and Technology Dept. Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350371499

Single neuron modulates the external stimuli, and neural population coordinates to encode information. An alternate method for examining the coordinated populational activity in neural encoding is conditional neural correlation (CNC). However, such correlations are not static during a new task learning process as neurons adapt their tunings over time for better performance. To investigate how neurons adjust their firing patterns during learning, it's essential to track the time-variant correlation. In this paper, we propose to mathematically model the dynamical CNC by implementing the integrated point process filter which incorporates neural correlation and single neural tuning into decoding. Specifically, we generate synthetic M1 neurons' firing data to simulate the dynamic change of the conditional neural correlation over days, while a rat learns a two-lever discrimination task. By comparing the tracked CNC with the designed CNC, our results show that the CNC can be better tracked over time by CIPPF than that of decoder assuming conditional independence among neurons, which indicates the possibility to better understand the brain dynamics during task learning. © 2024 IEEE.

关键词： Decoding

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Beyond the ROC Curve: Classification Trees Using Cost-Optimal Curves, with Application to Imbalanced Datasets 41

Beyond the ROC Curve: Classification Trees Using Cost-Optima...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Gabidolla, Magzhan Zharmagambetov, Arman Carreira-Perpiñán, Miguel Á. Dept. of Computer Science and Engineering University of California Merced United States United States

Important applications such as fraud or spam detection or churn prediction involve binary classification problems where the datasets are imbalanced and the cost of false positives greatly differs from the cost of false negatives. We focus on classification trees, in particular oblique trees, which subsume both the traditional axis-aligned trees and logistic regression, but are more accurate than both while providing interpretable models. Rather than using ROC curves, we advocate a loss based on minimizing the false negatives subject to a maximum false positive rate, which we prove to be equivalent to minimizing a weighted 0/1 loss. This yields a curve of classifiers that provably dominates the ROC curve, but is hard to optimize due to the 0/1 loss. We give the first algorithm that can iteratively update the tree parameters globally so that the weighted 0/1 loss decreases monotonically. Experiments on various datasets with class imbalance or class costs show this indeed dominates ROC-based classifiers and significantly improves over previous approaches to learn trees based on weighted purity criteria or over- or undersampling. Copyright 2024 by the author(s)

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Accessing Visual Information for Divyangjan 4

Accessing Visual Information for Divyangjan

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Rani, M. S. Divya Ghodke, Hrishikesh Vivekanand Manikandan, M. Manaswini, R. Shivani, K. Dhara, Sri Sai Venkata Gayathri Shreya Presidency University Dept. of Electronics and Communication Engineering Bengaluru560064 India Presidency University Dept. of Computer Science Engineering Bengaluru560064 India

ISBN: (纸本)9798331518981

Approximately 1.3 billion people worldwide suffer from a visual impairment. Typically, they must use Braille to read printed materials. However, when the content is not printed in Braille, these individuals have difficulties. Even though there are a lot of electronic reading aids available, the costs remain prohibitive. Therefore, our work suggests a low-cost document reader that uses a webcam to take pictures of printed materials. The Optical Character Recognition (OCR) framework's image-to-text conversion is then used to turn the acquired image into text. Finally, the Text to Speech (TTS) framework's text-to-speech conversion will be used to read the text aloud in speech format. Consequently, a person with a visual impairment can use hearing rather than touch to comprehend printed materials that are not written in Braille. Users may view documents printed in five different languages. The choice of different document sizes is this work's greatest novelty. The system is equipped with a Raspberry Pi, Arduino Uno, NEMA 17 stepper motors, a web camera and speaker. The Raspberry Pi, Arduino Uno, NEMA 17 stepper motors, webcam, and speaker are all part of the system. © 2024 IEEE.

关键词： Stepping motors

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A Machine Learning Model for Predicting Internet Addiction in Teenage and Young Students 3

A Machine Learning Model for Predicting Internet Addiction i...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Johar, Geetika Patel, Ravindra University Institute of Technology Rgpv Dept. of Computer Science & Engineering Bhopal India University Institute of Technology Rgpv Dept. of Computer Applications Bhopal India

ISBN: (纸本)9798350384598

Internet addiction is becoming one of the critical issues among teenagers and young university students. This habit not only negatively impacts the student's learning performance, but also affects the student's physical and mental health. This paper is focusing on designing a Machine Learning (ML) model for accurately identifying internet addiction. The model incorporates data preprocessing and feature selection techniques for reducing the computational complexity. Next, five different ML algorithms have been implemented and compared to identify appropriate ML algorithms. The identified ML model has been trained using the behavioral features of the students for predicting internet addiction. The experiments have been carried out and the performance of the models has been measured. The results demonstrate that a random forest classifier provides 83% accurate detection for university students and 96% accurate results are provided by an SVM-based classifier for teenager internet addiction detection. Therefore, by using the entire observation of experiments it is found that the proposed model can successfully identify internet addiction in students by analyzing the student's behavioral features. © 2024 IEEE.

关键词： Contrastive Learning

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Transformer based Compressed Data Recovery for Efficient Data Collection in Wireless Sensor Networks

Transformer based Compressed Data Recovery for Efficient Dat...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Balaji, V.S. Sekar, K. Devendar Rao, B. Dheepa, T. Rajalakshmi Engineering College Dept. of Artificial Intelligence and Machine Learning Chennai India Kongunadu College of Engineering and Technology Dept. of Computer Science and Engineering Tiruchirappalli India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374711

Data collection is challenging in wireless sensor networks (WSNs) since energy consumption remains a significant constraint. Although energy consumption has increased, most data collection methods incur excessive computation overhead. Compressive sensing is used to minimize the number of sensing samples with a suitable sparse basis. A novel Transformer neural network-based compressed data recovery (TCDR) method has been proposed for extracting temporal correlation structures from compressed signals and reconstructing them with high accuracy. The compressed signal is forwarded to the base station through multi-hop communication. The base station uses an attentionbased Transformer Neural Network (TNN) to recover the compressed signal. This model provides greater energy saving performance than the other baseline models considered for evaluation, which are 5.23 percent, 6.82 percent, 10.26 percent, and 11.47 percent, respectively. © 2024 IEEE.

关键词： Spatio-temporal data

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Real World Deployment of Dual Branch MRC Enhanced by Adaptive Algorithms LMS and RLS in 5G Networks to Improve SNR and QoS 4

Real World Deployment of Dual Branch MRC Enhanced by Adaptiv...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Gupta, Mridul Sharma, Kunj Bihari Maindola, Meenakshi Garg, Shobhit Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Management Studies Dehradun India

ISBN: (纸本)9798350349900

This research is aimed at understanding the performance benefits of the use of adaptive algorithms in dual-branch MRC systems as a solution for developing 5G networks. We developed controlled simulations to determine the performance influences of the algorithms in SNR, QoS, BER, and throughput under different network conditions. Our results indicate that the algorithms significantly reduce BER safe power divergence that improves the reliability and performance of the communication platform. The adaptive systems in addition reveal a significantly impressive ability to maintain a higher rate of throughput that is necessary in the current networks due to the increasing volume of data consumed by different users. In conclusion, therefore, this research has showed that adaptive MRC techniques might be a highly-viable option toward enabling the development of 5G technology, provided a suitable method for boosting performance during uncertain communications. © 2024 IEEE.

关键词： 5G mobile communication systems

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Implementation of Dynamic Spectrum Access Techniques in Cognitive Radio Networks under Generalized Fading Conditions 2

Implementation of Dynamic Spectrum Access Techniques in Cogn...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Garg, Shobhit Sharma, Amulya Devgan, Divyansh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to be University Dept. of Management Studies Dehradun India

ISBN: (纸本)9798331519056

This paper is organized as follows: Section II provides a background on our research work based on DSA in CRNs using CR under different fading conditions. c) Performance: We also study how effective adaptive algorithms along with the machine learning can prove in achieving higher spectrum sensing accuracy and decision making on run time. By conducting simulations and extensive performance evaluation, we show that the traditional solutions fall short when compared to our AI-based methodology in terms of accuracy especially under harsh fading conditions. Finally, sensitivity analysis highlights the control parameters that need to be submitted to further optimization for better system performance. With all above analysis, our results indicate that DSA can alleviate spectrum underutilization problem and enhance communication reliability in CRNs. Thus, with proper adaptation strategies and machine learning algorithms, the DSA systems provide an appealing opportunity to meet the continuously increasing requirements of bandwidth-consuming applications in current days wireless networks. The results of this research are helpful in providing effective value input to Advances in the understanding and design of robust, efficient spectrum management approaches for dynamic and diverse wireless settings. © 2024 IEEE.

关键词： Sensitivity analysis

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Machine Learning Approaches for Predictive Channel State Information in MIMO Systems Using LSTM 2

Machine Learning Approaches for Predictive Channel State Inf...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Upadhyay, Deepak Sharma, Amulya Gupta, Mridul Devgan, Divyansh Garg, Shobhit Upadhyay, Abhay Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to be University Dept. of Management Studies Dehradun India

ISBN: (纸本)9798331519056

In this paper, we analyse machine learning models, including Long Short-Term Memory networks, to predict the CSI and evaluate their impact on adaptive transmission in MIMO systems. To achieve this objective, we use simulation to generate CSI data in a MIMO environment and compare the performance of ML models to traditional methods. Our simulation reveals using our dataset that LSTM models improve the SNR values significantly, which means that their predictions are more accurate. At the same time, these models require greater computational and other resources. In contrast, despite its relative inefficiency in comparison with LSTM, Decision Trees improve the SNR moderately while consuming fewer resources but needing a substantial amount of time. Analysis of the source tries to examine the balance between performance and resource usage may validate the use of ML-based methods, and especially LSTM, to predict the CSI advancedly, but the actual application must take constraints into account. © 2024 IEEE.

关键词： Contrastive Learning

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Fool 'Em All - Fool-X: A Powerful & Fast Method for Generating Effective Adversarial Images

Fool 'Em All - Fool-X: A Powerful & Fast Method for Generati...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Khamaiseh, Samer Y. Mancino, Mathew Jost, Deirdre Al-Alaj, Abdullah Bagagem, Derek Serra, Edoardo Miami University Dept. of Computer Science & Software Eng. OH United States Caci International NJ United States Miami University Dept. of Computer Science & Software Eng. Oxford United States Virginia Wesleyan University Department of Computer Science VA United States Boise State University Deptartment of Computer Science ID United States Boise State University Department of Computer Science Boise United States

ISBN: (纸本)9798350362480

The well-trained image classification neural networks are vulnerable to adversarial examples. An adversarial example is a malicious input carefully crafted by adding small perturbations to the original input, leading to misclassification. Despite advancements in generating adversarial examples, to the best of our knowledge, none of the well-known adversarial attacks can generate effective adversarial examples that work efficiently on large-scale datasets and very deep neural network architectures. In contrast to ordinary adversarial examples, effective adversarial examples have all the following four characteristics: (1) the ability to maximize the loss of DNNs, (2) the ability to cause a high misclassification rate for both undefended and defended DNN models using various defense methods, (3) minimal perturbations with low computational overhead on large-scale datasets, (4) the ability to be transferable across different DNN *** fill this void, we propose Fool-X, an algorithm to generate effective adversarial examples with the least perturbations that can fool state-of-the-art image classification neural networks. To evaluate the performance of Fool-X, we have conducted extensive experiments using 12 baseline adversarial training defense methods and six state-of-the-art adversarial attacks. The results reported on ImageNet-ILSVRC, CIFAR-100, and CIFAR-10 demonstrate that the proposed Fool-X algorithm can generate effective adversarial examples on large-scale datasets that can successfully fool the well-trained, defended image classification neural networks and significantly outperform the state-of-the-art adversarial attacks. The code is available: https://***/LAiSR-SK/fool-X-Attack © 2024 IEEE.

关键词： Deep neural networks

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