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检索条件"任意字段=3rd International Conference on Signal and Data Processing, ICSDP 2023"

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Research On Lane Change Fusion Decision Model For Complex Traffic 24

Research On Lane Change Fusion Decision Model For Complex Tr...

引用

3rd international conference on signal processing, Computer Networks and Communications, SPCNC 2024

作者： Chen, Yuanjing Zhang, Yongjie GuangXi Vocational Normal University Guangxi Nanning China Key Laboratory of Application Technology of Intelligent Connected Vehicle Guangxi Vocational Normal University Education Department of Guangxi Zhuang Autonomous Region Guangxi Nanning China

ISBN: (纸本)9798400710834

Aiming at the problem that driverless cars are unable to accurately detect the behavior of surrounding vehicles when performing lane changing in complex traffic. This paper proposes a lane changing fusion decision model to solve the problem of low effectiveness of driverless cars under complex traffic. The model improves the multi-objective trajectory prediction problem of driverless cars under complex traffic by using multi-source information fusion technology. At the same time, the input data is processed to realise the automatic learning and analysis of a large amount of data in combination with the deep learning algorithm of the artificial neural network. Statistical analysis of the model’s simulation experiment outcomes was conducted, and the fusion outcomes were then contrasted to the single-sensor situation. Under the optimized lane-changing model, the driverless car is improved in both lane-changing prediction and multi-vehicle perception, which solves the problems of inaccurate multi-objective trajectory prediction and inefficient lane-changing decision-making of the driverless car during lane-changing. © 2024 Copyright held by the owner/author(s).

关键词： data fusion

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Classification of Currencies as Authentic or Counterfeit: A data-Driven Approach 3rd

Classification of Currencies as Authentic or Counterfeit: A ...

引用

3rd international conference on signal and data processing, icsdp 2023

作者： Koushik, K. Venkat Sai, G. Chaurasiya, Rahul K. Department of ECE Maulana Azad National Institute of Technology Bhopal India

ISBN: (纸本)9789819795772

The pervasive issue of counterfeit currency poses a significant threat to global economies, leading to substantial financial losses and eroding trust in financial institutions. While traditional methods of counterfeit detection rely on visual inspections and security feature verification, the advent of sophisticated counterfeiting techniques necessitates more advanced solutions. This study explores the application of machine learning (ML) algorithms to the problem of currency authentication, utilizing a dataset comprising images of banknotes. We employed a convolutional neural network (CNN) architecture, tailored for feature extraction from image data, which has demonstrated exceptional performance in pattern recognition tasks. Our proposed model introduces a novel preprocessing algorithm that enhances the distinctive characteristics of genuine and counterfeit notes, improving CNN's classification efficacy. We benchmarked the performance of our model against traditional ML classifiers, such as Support Vector Machines (SVM) and Random Forests. The analysis reveals the strengths of our model, including its robustness to variations in image quality and its ability to generalize from limited training data. However, limitations were observed in terms of computational intensity and the need for a substantial dataset to optimize model parameters. The findings underscore the potential of automated, ML-driven techniques in revolutionizing the detection of counterfeit currencies, presenting a scalable and effective solution to a complex challenge. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Feature Selection

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Enhanced One-Bit Passive Radar Detection with Dual-Use of Pilot and data signals 24

Enhanced One-Bit Passive Radar Detection with Dual-Use of Pi...

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3rd international conference on signal processing, Computer Networks and Communications, SPCNC 2024

作者： Wu, Peiwen Huang, Lei Xiao, Yuhang Gan, Yuxi Lu, Zhaohua Yan, Qiao Xie, Jian Shenzhen University Guangdong Shenzhen China Intelligent Co. Ltd. Guangdong Shenzhen China ZTE Corporation Guangdong Shenzhen China Shenzhen University Guangdong Shenzhen China Shenzhen Bamboocloud Co. Ltd. Guangdong Shenzhen China

ISBN: (纸本)9798400710834

Unmanned Aerial Vehicles are crucial carriers in the low-altitude economy, and with large-scale applications, ensuring airspace order is becoming increasingly important. Active radar, which occupies significant spectrum resources, is unsuitable for urban deployment, making passive radar the primary solution for low-altitude detection. However, existing radar systems that rely on high-precision quantization face high storage and computational demands in large-scale monitoring. Furthermore, traditional technologies often treat communication signals as completely unknown for target detection, neglecting the potential of prior-known communication information, such as the waveform of pilot signals. To address these issues, this paper proposes a one-bit passive radar target detection method with dual-use of pilot and data signals. The method enhances target detection accuracy by leveraging the information of these signals and reduces system complexity through one-bit quantization. Additionally, we analyze the asymptotic performance of the proposed detector under both null and alternative hypotheses in low SNR conditions. Finally, simulation results validate the accuracy of our theoretical analysis and demonstrate the superiority of the proposed detector. © 2024 Copyright held by the owner/author(s).

关键词： Unmanned aerial vehicles (UAV)

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Investigation of the Impact of EEG signal processing Techniques on Classification Performance 3rd

Investigation of the Impact of EEG Signal Processing Techniq...

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3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Babu, Shyam Wadhwani, Arun Kumar Department of Electrical Engineering Madhav Institute of Technology and Science Gwalior India

ISBN: (纸本)9789819743582

This research paper is aimed at a practical analysis of the impact of the electroencephalogram (EEG) signal processing techniques on novel classification algorithms. The study examines how newly developed classification algorithms may perform significantly depending on the methods used to interpret EEG signals (brain activity data). In this context, first, we have conducted a review on recently contributed research articles based on EEG signal classification. Using this review, we are trying to recover the essential datasets, features, feature selection techniques, classification techniques, and applications. Next, the work is extended to study the nature of EEG data and its influence on data scaling. Further, we have also investigated the performance influence of feature selection technique on scaled data. Additionally, the convolutional neural network has been trained to learn from scaled and non-scaled data. Using four different experimental scenarios we have evaluated the performance of implemented data scaling and feature selection techniques over the EEG signal classification. Based on experimental analysis we have found that standardization is much beneficial than normalization. Additionally, it also helps to improve training performance of a classification model. However, with the merging of principal component analysis and both the scaling technique, we found the PCA and standardization are providing better training yield, but at the time of validation the standardized data is superior then other implemented techniques. Finally, conclusion and future extension plan have been discussed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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SSVEP EEG signal classification model based on EEMD-FastICA-TRCA 24

SSVEP EEG signal classification model based on EEMD-FastICA-...

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3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Liu, Yunfeng Zhang, Xueru Wan, Sijia Zhan, Weijian Liu, Yongming Zhao, Zhuanzhe School of Mechanical Engineering Anhui Polytechnic University Anhui Wuhu China School of Artificial Intelligence Anhui Polytechnic University Anhui Wuhu China

ISBN: (纸本)9798400707308

In order to retain as many effective signals in EEG as possible and improve the accuracy of a signal classification and the performance of the brain-computer interface system, a steady-state visual evoked potential EEG classification model combining ensemble empirical mode decomposition, fast, independent component analysis and task-related component analysis was proposed to solve the problem that noise would reduce the accuracy of signal classification and recognition. Firstly, it is proved that the proposed EEMD-FastICA noise reduction model has certain advantages over the standard noise reduction methods, and the performance of the model is verified by using the Benchmark dataset of the brain-computer interface group of Tsinghua University. On this basis, the EEMD-FastICA noise reduction model is combined with the TRCA algorithm to classify EEG signals, and the classification results are compared with other classification models. The comparison results show that under different time Windows, the classification accuracy and information transmission rate of the proposed model are higher than those of other models. Within 1.5 S, the highest classification recognition accuracy was 85.83%, and the highest ITR was 238.34 bits/min. © 2024 Copyright held by the owner/author(s).

关键词： Empirical mode decomposition

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Integrating Attention Mechanisms with Bidirectional Long Short-term Memory Recurrent Neural Networks for Improved Speech Recognition 24

Integrating Attention Mechanisms with Bidirectional Long Sho...

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3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Kimani, Mercy Wairimu Nderu, Lawrence Ndirangu, Dalton Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya United States International University Nairobi Kenya

ISBN: (纸本)9798400718120

Speech-to-text technology is indispensable for converting spoken words into text, facilitating easy storage and retrieval. This process involves several critical stages, progressing from a basic model through signal pre-processing, feature extraction, feature selection, and modeling. There has been extensive literature on improving speech recognition, however, challenges persist, particularly in addressing word error rates, accuracy, latency, and computational efficiency in continuous input streams in a noisy environment. This research systematically evaluates the effectiveness of recurrent neural networks (RNNs), long short-term memory neural networks (LSTMs), gated recurrent units (GRUs), bi-directional long short-term memory (Bi-LSTM), and bi-directional long short-term memory (Bi-LSTM) with attention mechanism networks. It further proposes integrating attention mechanisms with a Bidirectional Long Short Memory Recurrent neural network to enable the model to focus on the crucial parts of the input sequence thus increasing computational efficiency. Experimental results, based on the Libri speech and TIMIT training dataset, demonstrate promising outcomes. The proposed model exhibits superior performance, achieving a word error rate of 1.6%, and an accuracy of 98.4% on the training dataset. As a future avenue of exploration, the study proposes evaluating the use of transformer models to reduce character error rates further and enhance accuracy in continuous input streams. © 2024 Copyright held by the owner/author(s).

关键词： Long short-term memory

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Radar Characteristics Analysis of the Hailstorm Event in Hohhot on July 27, 2020 24

Radar Characteristics Analysis of the Hailstorm Event in Hoh...

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3rd international conference on signal processing, Computer Networks and Communications, SPCNC 2024

作者： Liu, Tianqi Kong, Wenjia Zhang, Lu Li, Yongli Inner Mongolia Autonomous Region Meteorological Data Center Inner Mongolia Hohhot China Inner Mongolia Autonomous Region Meteorological Observatory Inner Mongolia Hohhot China

ISBN: (纸本)9798400710834

This paper employs mathematical and statistical methodologies to quantitatively analyze the Doppler weather radar data of a hailstorm that occurred in Toketuo County, Hohhot City, on the night of July 27, 2020. T The analysis revealed that convective cells initially underwent rapid vertical development, followed by lateral expansion in area and enlargement in volume. Therefore, in hail forecasting, careful attention should be devoted to monitoring the top height of radar reflectivity during the initial phase. In terms of the volume changes of convective cell, the trends of 20 dBZ and 35 dBZ exhibit consistency, demonstrating rapid development during the initial phase, stable alterations in the mature phase, and gradual diminishment in the dissipation phase. However, notable fluctuations are observed during the mature phase of 45 dBZ;furthermore, strong echo at 55 dBZ exhibits a prolonged incubation period followed by sudden emergence. The initial rapid increase in volume was observed at 20:37, coinciding with the onset of the hailstorm in Toketuo County. At the same time, three-body scatter spike (TBSS) is evident on the intensity map, suggesting an imminent occurrence of hail, which holds significant implications for hail forecasting. A subsequent rapid increase in volume occurred around 21:35, followed by a gradual decline;notably, the echo intensity level corresponding to 55 dBZ exhibited the most rapid decay among large hail echoes, indicating that hail has indeed occurred. © 2024 Copyright held by the owner/author(s).

关键词： Radar reflection

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An FPGA Implementation of the Levinson–Durbin Algorithm for Speech Coding 3rd

An FPGA Implementation of the Levinson–Durbin Algorithm for...

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Singh, Dilip Chandel, Rajeevan Department of Electronics and Communication Engineering National Institute of Technology Hamirpur Himachal Pradesh Hamirpur177005 India

ISBN: (纸本)9789819719426

Speech coding is a widely used technique used for digital telephony and secure communication. The estimation of linear prediction coefficients (LPCs) for the development of synthetic speech is crucial and involves the use of the Levinson–Durbin (LD) algorithm. The computational complexity introduced by this algorithm affects the performance of speech codecs utilizing LPCs. In this paper, an FPGA implementation of the Levinson–Durbin algorithm is proposed for efficient autoregressive model parameter estimation. By harnessing FPGA’s parallel processing capabilities, the algorithm’s performance is accelerated, allowing real-time processing of signals. The work focuses on translating the algorithmic steps into hardware modules, optimizing memory access, and evaluating resource utilization, and power efficiency of the implemented hardware. This research contributes to the field of hardware-accelerated algorithms, showcasing the potential of FPGA platforms in enhancing signal processing tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Audio signal processing

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3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

3rd International Conference on Machine Vision and Augmented...

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3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on Machine Vision and Augmented Intelligence. The topics include: Survey on Robustness of Deep Learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral Image Classification: A Comparative Analysis;deep Learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI Images Using Deep Learning;software Maintenance Prediction Using Stack Ensemble Deep Learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture Recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic Applications;shaft Rotation Monitoring Using Radar signal processing and Wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus Images Using Pyramid Real Image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare data;multimodal Face Recognition System Using Hybrid Deep Learning Feature;Classification of Copy and Move Image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial Intelligence Approaches in Cybersecurity.

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Generalized Innovative Approach to Detect Pest signal based on Frequency using STM32 3

Generalized Innovative Approach to Detect Pest Signal based ...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Martin, Betty Sugumaran, G.S. Subhadra, M. Marshiana, D. Sastra Deemed University Thanjavur India Symbiosis Institute of Technology Pune Campus Pune India

ISBN: (纸本)9798331509675

Modern society requires innovative and creative advancements to arrive at practical solutions for real-time problems using microcontrollers. Hence, it is the responsibility of every researcher as well as an analyst to understand the basic working, processing flow, and programming of embedded systems. This paper aims to present a strategy for detecting the frequency of real-time signals followed by the manipulation of glowing patterns of various coloured LEDs (Light Emitting Diode) using a simple STM32 microcontroller. The detection of frequency of input signal is carried out using the timer module present in the STM32 microcontroller. Timer module is used to generate a ramp signal or triangular waveform of a desired frequency. The microcontroller uses embedded C programming (code loaded on the microcontroller) to identify the signal frequency and thereby controls the glowing pattern of coloured LEDs based on the detection of signal frequency. This includes designing, tuning, configuring the timer modules present in STM32 for generating signals of desired frequency and incorporating necessary conditions to control and manipulate the glowing pattern of LEDs according to the detected signal frequency. Moreover, experimental results also prove the efficiency and accuracy in detecting target frequency and in glowing the coloured LEDs with perfect synchronisation which shows the effectiveness of this approach. This paper aims to highlight the practical applications of microcontroller-based techniques and strategies for frequency detection and to provide solutions to the real time problems associated with embedded systems. This paper showcases a cost-effective method using STM32 microcontroller on chip timer module to solving the problem mentioned above. © 2025 IEEE.

关键词： Microcontrollers

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