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共 2955 条记录，以下是81-90 订阅

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Hybrid Beamforming for Wireless Communication System

Hybrid Beamforming for Wireless Communication System

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Smart Systems for applications in Electrical Sciences (ICSSES), international conference on

作者： Kumuda D K M Prerana N Himabindu Chandana A S Dept. of Electronics and Telecommunication Siddaganga Institute of Technology Tumkur Karnataka

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Advancements in phased array antenna designs have led to the development of the beamforming technique, in which the antenna beams are focused in the desired direction. This focus reduces interference, which is a critical requirement for wireless applications. Beamforming can be implemented using three schemes: Analog, Digital, and Hybrid beamforming. This paper compares these three beamforming techniques for a 5G system serving three users. Narrow beams were initially generated by using linear array of 8 elements at 4 GHz for all three users, and the analysis was extended to three different frequencies: 3.5 GHz, 4 GHz, and 4.5 GHz, assigned to each of the user respectively. The simulation results demonstrate that digital beamforming provides consistent gain across different frequencies and achieves the highest capacity among the three. Analog beamforming, while producing a narrow beam that minimizes interference, delivers a comparatively lower data rate. Hybrid beamforming combines the advantages of both analog and digital approaches, offering a narrow beam with improved capacity. Given its ability to minimize interference while enhancing capacity, thereby making hybrid beamforming a suitable choice for 5G communication systems.

关键词： Phased arrays Array signal processing 5G mobile communication OFDM Wireless networks Simulation Symbols Interference Directive antennas Smart systems

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Improved Support Vector Machine for ECG signal Classification in Implantable Biomedical Devices

Improved Support Vector Machine for ECG Signal Classificatio...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Pavan Srikanth SubbaRaju Patchamatla Zayd Ajsan Balsem KG Parthiban Ramachandra A C Latha Bhuvaneswari S RKInfo-Tech LLC Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Al Diwaniyah Iraq Department of Biomedical Engineering Dhaanish Ahmed Institute of Technology Coimbatore India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Dhanalakshmi Srinivasan College Of Engineering Technology Mamallapuram India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Electrocardiogram (ECG) signal classification in implantable biomedical devices plays a significant role for continuous heart monitoring and detecting cardiac abnormalities such as arrhythmias. These devices analyse ECG patterns to enable accurate diagnostics and provide necessary interventions. However, traditional ECG monitoring tools faces certain challenges like low power transfer efficiency, failure in capturing most paroxysmal Atrial Fibrillation (AFib), and difficulty in automatic determination of AFib. To solve this issue, the implantable ECG sensor with improved Support Vector Machine (SVM) is proposed to monitor the patient continuously and detect AFib effectively. The QRS complex is observed from ECG signals of 29 AFib patients is extracted by utilizing signal processing techniques such as wavelet transform and statistical analysis. The Minimum Description Length (MDL) method is applied to extract and select 12 features of variability-based heart rate parameters. Then, the improved SVM classifier with a Radial Basis Function (RBF) is used to classify the AFib in ECG data accurately. Experimental results demonstrate that the proposed implantable ECG sensor with optimized SVM achieves a better accuracy of 99.2%, sensitivity of 98.8%, specificity of 99.5%, and an F1-score of 98.9% which outperforms the existing methods like traditional SVM and CNN-based models.

关键词： Support vector machines Accuracy Pattern classification Implants Electrocardiography Feature extraction Integrated circuit modeling Biomedical monitoring Monitoring Context modeling

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Vehicle-Mounted Millimeter-Wave Radar Vital Signs Detection and Early Warning Control System

Vehicle-Mounted Millimeter-Wave Radar Vital Signs Detection ...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Wenshan Li Hunan Institute of Traffic Engineering Hengyang Hunan China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Traditional methods of monitoring vital signs in vehicles often fall short when it comes to response time, especially in fast-moving situations. To tackle this, our paper introduces a groundbreaking solution: leveraging millimeter-wave radar technology to monitor vital signs in real time within the vehicle, providing timely alerts when necessary. This approach not only enhances the accuracy of readings but also ensures a swift response that is crucial during vehicle operation. By tapping into the unique abilities of millimeter-wave radar to detect subtle human signs, we propose a novel method that processes radar signals to monitor vital signs like heart rate and respiratory rate. This technique is highly effective in meeting the stringent demands of real-time accuracy and speed, especially while driving. Our system includes three main components: radar sensors, a signal processing unit, and an early-warning control module, working together seamlessly to ensure optimal monitoring. The radar sensor continuously transmits and receives millimeter-wave signals, allowing it to track vital signs without interruption. A specially designed feature extraction algorithm filters out noise and extracts critical information, including heart and respiratory rates. Then, advanced algorithms analyze this data in real time, spotting any abnormalities and triggering alerts through the control module. What sets this system apart is its rigorous testing under various vehicle speeds and environmental conditions, confirming its stability, reliability, and fast response times. In real-world applications, the system responds to a high heart rate in 3.7 to 4.0 seconds and to a rapid breathing rate in 4.1 to 4.3 seconds. This millimeter-wave radar-based system offers a fresh approach to vehicle safety, combining practicality with promising future potential.

关键词： Heart rate Accuracy signal processing algorithms Millimeter wave radar Real-time systems Stability analysis Sensor systems Sensors Time factors Monitoring

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Structured and Sparse Principle Component Analysis for Multi-modal data Fusion Approach 3rd

Structured and Sparse Principle Component Analysis for Multi...

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3rd international conference on Machine Intelligence and Smart Systems, MISS 2023

作者： Khalaf, Hameed Hassan Jawad, Israa Abed Almulla, Ausama A. Hussein, Mustafa Asaad Nair, Preeti Sharma Department of Medical Laboratories Technology Al-Manara College for Medical Sciences Maysan Iraq Department of Medical Laboratories Technology AL-Nisour University College Baghdad Iraq Department of Medical Laboratories Technology Al-Hadi University College Baghdad10011 Iraq College of Nursing National University of Science and Technology Dhi Qar Iraq Bansal College of Engineering Bhopal India

ISBN: (纸本)9783031691140

The field of image processing has paid a lot more attention to pixel-level picture fusion over the past two decades. Taking an average of each pixel in the source photos is the simplest method for fusing images at the pixel level. In, we see an example of this type of method where two medical images are averaged together to get a single coherent image. While the averaging approach is straightforward to put into practise, it has a number of downsides, including decreased contrast that can result in a significant defeat of data. The authors of this manuscript report deriving a novel fusion model in terms of an unsupervised classification model called principle component analysis, which takes into account the structure and sparse restrictions present in multi-modality images. Eigen value and Eigen vector correlation and covariance analyses make image fusion more adaptable and precise. CT images are ideal for studying rigid objects like bones and implants because of the minimal distortion they introduce, but CT scans are incapable of picking up physiological shifts. Soft tissue can be more clearly seen in an MR image. To measure the effectiveness of the proposed workbench, its performance is simulated in MATLAB for state-of-the-art methods. Key metrics include PSNR (peak signal-to-noise ratio), MSE (Mean square error), association, and precision. All metrics point to the suggested workbench's superior performance, and it also reduces computational load. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mean square error

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Analysis on Stock Market Stream data Using Kafka, AWS and PowerBI 3rd

Analysis on Stock Market Stream Data Using Kafka, AWS and Po...

引用

3rd international conference on Machine Intelligence and Smart Systems, MISS 2023

作者： Baseer, K.K. Siva Siddartha Reddy, B. Vishnuvardhan, D. Chandravathi, K. Abhishek, L. Karthikaa, M. Tirupati India Department of Information Technology Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9783031691140

Real-time data analysis is becoming increasingly important, as it allows businesses to act quickly and with greater decision accuracy. Traditionally, data analysis occurs after data has been collected and saved, but real-time data analysis provides an opportunity for businesses to extract valuable insights from data as it’s generated. A streaming platform like Kafka is necessary to handle the constant intake of data in a systematic and efficient way, and to build data pipelines that reliably integrate and transfer information between platforms. Cloud computing services like AWS S3, AWS Crawler, AWS Glue data Catalog, and AWS Athena can be used in conjunction with Kafka to process and analyze the data. This framework is useful for processing real-time data in various sectors, such as the Stock market, e-commerce applications, gaming, and more, and extracting useful features and hidden patterns. Machine learning models can also be trained on the data for better analysis and *** enhance this framework, it’s important to integrate various cloud computing services with Kafka more seamlessly to better handle large-scale data. Additionally, real-time data visualization tools could help generate insights and provide interactive feedback on data streams. The incorporation of predictive analytics capabilities and intelligent, data-driven decision-making processes could help businesses make more informed decisions. By integrating real-time data analysis with intelligent automation tools, human error can be reduced, and overall efficiency and effectiveness in the decision-making process can be improved. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Marketplaces

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IoT-Integrated Convolutional Neural Network for Accurate Vehicle Acoustic signal Analysis

IoT-Integrated Convolutional Neural Network for Accurate Veh...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： P. Sasikala K Subha Sharmini Venkatesan Sorakka Ponnappan K. R. Kavitha K. Gunasekaran C. Srinivasan Department of Mathematics Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed to be University) Salem India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Mechanical Engineering Sathyabama Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Sona College of Technology Salem India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

This paper examines the use of IoT-integrated Convolutional Neural Networks (CNNs) for precise analysis of vehicle audio signals. The context highlights the growing intricacy of vehicle systems and the need for advanced monitoring methods. Acoustic signals generated by different vehicle components may provide significant insights about vehicle health; nevertheless, traditional analysis techniques are often ineffective. The Internet of Things (IoT) technology allows real-time data collection from sensors installed on vehicles, which is then analyzed using CNNs to identify certain patterns and abnormalities in auditory signals. The approaches used include using IoT sensors to record various vehicle noises and utilizing CNNs for feature extraction and classification, providing great defect detection precision. The results indicate the system's capacity to detect engine faults, braking deficiencies, and other mechanical concerns early, enabling quick treatments. It presents a novel methodology for vehicle monitoring, enhancing predictive maintenance, safety, and performance, with prospects for future developments in autonomous diagnostics. The proposed system improved vehicle acoustic signal processing using IoT and CNN models, achieving high accuracy of 99% in anomaly detection, preventive maintenance, and real-time traffic monitoring.

关键词： Training Accuracy Noise Real-time systems Acoustics Sensor systems Sensors Convolutional neural networks Internet of Things Intelligent sensors

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Parkinson’s Disease Detection Using Advanced Wave signal processing on Spiral Drawing Tests 3rd

Parkinson’s Disease Detection Using Advanced Wave Signal Pr...

引用

3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： SasiRekha, S. Shankar, R. Duraisamy, S. Department of Computer Science Chikkanna Government Arts College Tirupur India

ISBN: (纸本)9783031862892

The main motor skills are impacted by Parkinson’s disease (PD), a neurodegenerative ailment that is both chronic and progressive. The signs of this condition include tremors, stiffness, bradykinesia (slow movement), and postural instability. The degeneration of brain cells that produce dopamine is the root cause of these symptoms. This study introduces an innovative algorithmic framework for predicting Parkinson’s disease (PD) using spiral drawing tests analyzed through advanced wave signal processing techniques. By collecting spiral drawings from patients via a digital tablet and converting these into wave signals, the framework employs three key algorithms: Wavelet Transform-Based Feature Extraction (WTFE), which decomposes signals into frequency bands to extract subtle features indicative of PD;a Gated Recurrent Unit with Long Short-Term Memory (GRU-LSTM) hybrid neural network that captures both short-term and long-term dependencies in the signal data to detect tremor patterns;and Hybrid Ensemble Classification (HEC), which integrates multiple classifiers—Support Vector Machines, Random Forests, and Gradient Boosting Machines—aggregated through a weighted voting scheme. Evaluated on a dataset of drawings from PD patients and healthy controls, the framework demonstrated high accuracy in distinguishing PD, suggesting it as a promising tool for early and objective diagnosis, thereby facilitating timely and effective intervention. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Research on Financial and Accounting Risk Monitoring System Based on Computer Big data Technology 3

Research on Financial and Accounting Risk Monitoring System ...

引用

3rd international signal processing, Communications and Engineering Management conference, ISPCEM 2023

作者： Yang, Yuan School of Accountancy Xijing University Xi'an China

ISBN: (纸本)9798350307023

This paper proposes a financial risk control system based on cloud computing. Firstly, this paper assembles the high-precision chip to the computer host. This lays the foundation for the design of the device. The calculation method of financial risk management system based on SVM is proposed. Establish a system connection for system performance testing. Under the same data capacity and experimental platform, the computing power of this system is better than the original one. © 2023 IEEE.

关键词： Support vector machines

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DOA Estimation of LFM signals Based on Compressed Sening 3

DOA Estimation of LFM Signals Based on Compressed Sening

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IEEE 3rd international conference on signal and Image processing (ICSIP)

作者： Wang, Shuhao Ruan, Huailin Natl Univ Def Technol Hefei Anhui Peoples R China

ISBN: (纸本)9781538663967

when we estimate the DOA of LFM signal based on the traditional algorithm, it occurs many problems such as large sampled data and low estimation accuracy under low SNR. This paper proposes a new DOA estimation method based on compressed sensing theory, we use simulation experiments to verify the validity of its estimation of direction of arrival of LFM signals.

关键词： compressed sensing signal mode DOA estimation

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The Application of Machine Learning Techniques to the Diagnosis of Breast Cancer 3

The Application of Machine Learning Techniques to the Diagno...

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3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Prasad, Pratyush Manipal University Data Science and Engineering Jaipur India

ISBN: (纸本)9798350320749

Due to its high mortality rate and limited survival duration, early identification of breast cancer is vital. The primary application of machine learning models has been the prediction and early diagnosis of breast cancer. This article presents machine learning models and algorithms for automated breast cancer diagnosis using an exploratory approach. The technique makes use of eight distinct machine learning algorithms, including Gaussian Naive Bayes, Logistic Regression, Decision Tree, Random Forest, Support Vector Classifier, XG-Boost, and Deep Learning Model. Their precision is used to evaluate the performance of algorithms and models. Additionally, probabilistic predictions have been made using activation functions like Relu. In terms of test accuracy on the Kaggle data set, Deep Learning Model 2 and the Support Vector Classifier fared the best. © 2023 IEEE.

关键词： Decision trees

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