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共 12982 条记录，以下是161-170 订阅

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Active Element Arrangement and Prediction Model for Channel Estimation in IRS-Assisted Systems

Active Element Arrangement and Prediction Model for Channel ...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Tsuchiya, Yoshihiko Suga, Norisato Uruma, Kazunori Fujisawa, Masaya Tokyo University of Science Dept. of Information and Computer Technology Tokyo Japan Shibaura Institute of Technology Dept. of Information and Communication Engineering Tokyo Japan Kogakuin University Dept. of Computer Science Tokyo Japan

ISBN: (纸本)9798350350821

Intelligent reflecting surfaces (IRS) that can dynamically control the phase of radio waves and reflect them are attracting attention to realize non line-of-sight communication in the high-frequency band. Channel state information (CSI) is estimated to properly control the phase, however the overhead of the estimation reduces communication efficiency. To reduce the overhead, IRS systems with active elements that perform channel estimation are considered. One approach treats the partial CSI obtained from the active elements as an image and utilizes a super-resolution technique to predict the entire CSI. However, because the active elements are arranged in grid, prediction is difficult when the active elements spacing and the CSI fluctuation period match. In this study, we propose a new arrangement that gathers the active elements in the center of the IRS. Furthermore, we regard prediction as an image-inpainting problem that recovers missing pixels from known pixel values, and we propose applying the U-Net to solve this problem. Numerical experiments show that the proposed method outperforms conventional methods in prediction accuracy and spectral efficiency. © 2024 IEEE.

关键词： Channel estimation

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AI-Based Home Automation Using Voice Recognition and Biometric Finger Print Authentication 4

AI-Based Home Automation Using Voice Recognition and Biometr...

引用

4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Pydala, Bhasha Bhargavi, M. Vyshnavi, Balaji Krishna, Machavaram Gopi Kumar, Aravadasari Lenin Bindu, K Hima dept. of Data Science Tirupati India Sree Vidyanikethan Engineering College dept. of Information Technology Tirupati India

ISBN: (纸本)9798350385793

The proposed home automation system introduces a cutting-edge solution that harnesses the power of Artificial Intelligence (AI) to upgrade residential spaces into smart, dynamic, and user-friendly environments. With a simple vocal command, the AI system swiftly and precisely executes tasks. The existing home automation system lacks in authentication so to ensure security and personalization, the proposed system utilizes fingerprint module integrated with a microcontroller like Arduino Uno and Bluetooth module. It interfaces seamlessly with a fingerprint sensor, providing reliable user authentication. This ensures that the smart home system responds only to authorized users, enhancing overall safety. The implementation includes essential components such as Arduino Uno, Bluetooth modules, regulated power supply and fingerprint module. This innovative system showcases the potential of integrating AI into homes, making them not only intelligent but also adaptable to the different requirements of each resident. With speech recognition and fingerprint authentication, this system offers a glimpse into a future where homes are not just structures but responsive companions that enhance the quality of life. © 2024 IEEE.

关键词： Bluetooth

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Reviewlogy: An Interface for Analyzing Sentiments 3

Reviewlogy: An Interface for Analyzing Sentiments

引用

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

作者： Shyam, Radhey Khanna, Shilpi Srivastava, Kavya Srivastava, Ajay Kumar Chaudhary, Ishika Bbd University Dept. of Computer Science & Engineering Lucknow India Srmcem Dept. of Information Technology Lucknow India

ISBN: (纸本)9798350384598

This paper investigates the impact of early reviews on product sales by analyzing the traits of initial reviewers on major e-commerce platforms. It segments product life-cycles into three phases: early, majority, and laggards, focusing on early reviewers who post reviews during the initial stages. The paper introduces an innovative interface for sentiment analysis to identify and predict early reviewers. Quantitative analysis reveals that early reviewers often provide more insightful comments and give higher overall ratings. Their ratings and helpfulness scores significantly affect product popularity. The methodology, tested on extensive datasets from Amazon and Yelp, extracts crucial attributes of early reviewers, including purchasing patterns and social impact. The interface employs a hybrid machine learning approach combining Naive Bayes, margin-based embedding ranking models (MERM), and support vector machines (SVMs) to predict potential early reviewers accurately. This approach balances probabilistic reasoning with robust classification, ensuring comprehensive forecasting. Evaluation metrics such as precision and accuracy demonstrate the effectiveness of the models over baseline methods. Furthermore, the study enhances the understanding of how early reviews influence consumer behavior and product success in e-commerce. The interface aids in refining product marketing strategies by enabling precise targeting of early reviewers, accelerating product adoption, amplifying sales, and optimizing marketing tactics in the competitive e-commerce landscape. © 2024 IEEE.

关键词： Support vector machines

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Human Abnormal Activity Prediction of Long Short-Term Memory using CNNs and RNNs in Deep Learning 9

Human Abnormal Activity Prediction of Long Short-Term Memory...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Prathima, Ch. Suhel, Shaik Muhammed Rajeti, Roje Spandana Naik, Mude Sai Kumar Chaithanya, Singamsetty Krishna Reddy, Nallabathini Prabhakar Dept. of Ds A.Rangampet Tirupati India Sree Vidyanikethan Engineering College Dept. of Information Technology A.Rangampet Tirupati India JNTUGV-College of Engineering Department of Information Technology Vizianagaram India

ISBN: (纸本)9798350365092

This research presents a unique method of predicting aberrant activity in humans by integrating Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN). By using the advantages of CNNs for frame-by-frame spatial analysis and LSTM networks for temporal dependency modeling inside the frame sequence, our hybrid model seeks to efficiently capture both spatial and temporal characteristics in video sequences. Combining these two designs, we get a strong model that can identify complex patterns suggestive of anomalous human behavior. The CNN LSTM hybrid model that has been suggested performs better in terms of accuracy and resilience when predicting aberrant behaviors than conventional approaches and standalone models, as shown by experimental validation on a variety of benchmark datasets. The integration of both spatial and temporal information advances our knowledge of the dynamics at play in the identification of aberrant behavior. Furthermore, our approach is flexible enough to be used to real-world surveillance situations, demonstrating encouraging outcomes in scenarios with different camera viewpoints and environmental circumstances. The use of CNN and LSTM not only improves prediction accuracy but also makes the model more interpretable by shedding light on the spatiotemporal characteristics of aberrant behaviors. To sum up, this study advances the field of intelligent video analysis by providing a useful tool for predicting anomalous human behavior. This tool has potential uses in security monitoring, public safety, and anomaly detection systems. © 2024 IEEE.

关键词： Long short-term memory

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Skyline Algorithm based on QoS to improve Web Services: A Review 6

Skyline Algorithm based on QoS to improve Web Services: A Re...

引用

6th Parul International Conference on engineering and technology, PICET 2024

作者： Barge, Yamini Patel, Tanvi Purohit, Lalit Dept. of Computer Science Engineering Parul University Vadodara India Dept. of Information Technology SGSITS Indore India

ISBN: (纸本)9798350369748

The popularity of Web Service-based systems is due to the advantage of flexibility, reusability and support for rapid system development. Web Service selection is a very important step for these web service-based systems. The skyline technique is widely used in the past for the selection of web services. In this work, an exhaustive study of Skyline-based web service selection techniques is presented. A review of the existing state-of-the-art is presented based on the approach followed for selection of web services, the advantages of using the proposed approach, the limitations of the approach and the problem addressed. © 2024 IEEE.

关键词： Web services

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IRS controlling Method With Machine Learning Based on Position information 13

IRS Controlling Method With Machine Learning Based on Positi...

引用

13th IEEE Global Conference on Consumer Electronic, GCCE 2024

ISBN: (纸本)9798350355079

Intelligent Reflecting Surfaces (IRS) is attracting attention for wireless communications at high-frequency band. IRS can control radio propagation by reflecting radio waves and shifting their phase. As one of the methods to control IRS, location-based methods have been proposed. However, to achieve highly accurate IRS control by the methods, the assumed propagation model must match the actual propagation environment. This study proposes a method to control the IRS from location information adapted to the propagation environment by using machine learning. Numerical experiments confirm that the proposed method can appropriately control IRS regardless of the propagation environment. © 2024 IEEE.

关键词： Adversarial machine learning

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Quantitative Analysis and Forecasting in Finance: Leveraging Wavelet Packet Transform for Enhanced Market Insights

Quantitative Analysis and Forecasting in Finance: Leveraging...

引用

2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Libina, M. Kavitha, C. Kabilan, A. Ifet College Of Engineering Dept. Of Information Technology Villupuram India Manakula Vinayagar Institute Of Technology Dept. Of Computer Science Puducherry India

ISBN: (纸本)9798350352689

Finance forecasting is always known for its ever-changing and complex nature in geopolitical markets, requiring advanced analytical tools to provide valuable deep insights for financial advisors, investors, and vital decision-makers. In this study, we delve into the realm of both quantitative analysis and forecasting in finance-related markets, utilizing sophisticated techniques to enhance our understanding of the markets. Our study focuses on the innovative utilization of the Optimized Wavelet Packet Transform (WPT), an advanced tool for processing signals, to uncover complex patterns within financial time series data through quantitative analysis using Plotly's interactive line charts. It bridges the gap between quantitative analysis and forecasting. It also reaches an accuracy rate of 99.5%, which is considered the best among the other prediction models. These insights help stakeholders and advisors navigate the ever-evolving landscape of financial markets and make decisions wisely. © 2024 IEEE.

关键词： Forecasting

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A Novel Approach to Predict the Satellite Image Using CNN-ResNet Model Through Flask Application 9

A Novel Approach to Predict the Satellite Image Using CNN-Re...

引用

9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Madhuri, Ch. Bindu Prathima, Ch. Jyoshna, R. Varsha, Varikunta Sri Khan, Phatan Ayub Babu, Sammeta Mahesh JNTUGV-College of Engineering Department of Information Technology Vizianagaram India Dept. of Ds A.Rangampet Tirupati India Sree Vidyanikethan Engineering College Dept. of Information Technology A.Rangampet Tirupati India

ISBN: (纸本)9798350365092

In this work, we provide a hybrid Convolutional Neural Network (CNN) architecture coupled with Residual Network (ResNet) components, offering a robust method for satellite image prediction. By addressing issues with feature extraction and information degradation in deep networks, this novel approach seeks to improve the precision and effectiveness of predictions made from satellite images. By fusing residual connections with the advantages of standard CNNs, the hybrid architecture enhances gradient flow during training and facilitates efficient feature learning. Metrics like accuracy, precision, recall, and F1-score are used to evaluate the model's performance once it has undergone thorough testing on a wide range of datasets that cover different geographic regions. The hybrid CNN-ResNet architecture is positioned as a potent tool for precise land cover categorization, environmental monitoring, and geospatial analytics in a variety of remote sensing applications, thanks to comparative studies conducted against baseline CNN models. © 2024 IEEE.

关键词： Remote sensing

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Real-time Detection of Diverse Bangladeshi Traffic Signs Using YOLOv8

Real-time Detection of Diverse Bangladeshi Traffic Signs Usi...

引用

2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024

作者： Ehsanu Hoque, Md. Habib Mahbub, Mrittika Bin Md Tomari, Mohd Razali Bashar, Rezaul Rashed, Md. Golam Das, Dipankar University of Rajshahi Dept. of Information and Communication Engineering Bangladesh Pundra University of Science and Technology Dept. of Computer Science and Engineering Bangladesh Dept. of Electronic Engineering Malaysia Science Technology and Management Crescent Sydney Australia

ISBN: (纸本)9798350391213

The need for intelligent traffic management solutions is crucial in the rapidly evolving fields of autonomous traffic control and navigation systems, particularly for minimizing traffic accidents and ensuring the efficient operation of traffic signals. Improving traffic safety is of paramount importance, as road accidents claim over a million lives annually worldwide, with a significant proportion of these fatalities occurring in developing countries like Bangladesh. This research aims to develop a smart assistant capable of identifying and categorizing traffic signs to enhance road safety, especially in areas where drivers may not be fully aware of traffic regulations. To achieve this, we generated a comprehensive dataset containing 5,540 distinct images of traffic signs collected from various regions across Bangladesh, including highways, rural and urban roads, and challenging locations such as steep curves. The dataset comprises 41 categories of traffic signs. The dataset includes challenges such as color variations, blurriness, occlusion, small sign sizes, and low-light conditions. To further enhance the dataset's diversity, data augmentation techniques such as random rotations, shearing, and zooming were applied. We trained the YOLOv8 deep learning model, renowned for its real-time object detection capabilities, on this dataset. The model achieved impressive results, with a mean Average Precision (mAP) of 0.80, a recall of 0.87, a precision of 0.92, and an F1 score of 0.89, demonstrating its effectiveness in real-world traffic sign detection and classification. © 2024 IEEE.

关键词： Traffic signs

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DFT-based Channel Estimation with Multiple Windows for ATSC 3.0 Receiver 13

DFT-based Channel Estimation with Multiple Windows for ATSC ...

引用

13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Liu, Yu-Sun Li, Yin-Shan You, Shingchern D. National Taipei University of Technology Dept. of Electronic Engineering Taipei Taiwan National Taipei University of Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350355079

This study introduces a novel method for Discrete Fourie Transform (DFT)-based interpolation for channel estimation through the application of multiple windows in the channel impulse response. Utilizing a genetic algorithm, we determine the optimal positions and lengths of these windows. Our simulation results demonstrate that this approach has lower channel's Mean Squared Error (MSE) than other high-performance estimation approaches. © 2024 IEEE.

关键词： Mean square error

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