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检索条件"主题词=Signal processing algorithms"

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Autonomous Driving Decision Algorithm based on Hierarchical Dueling Double Deep Q-Network

Autonomous Driving Decision Algorithm based on Hierarchical ...

引用

signal processing and Intelligent Computing (SPIC), International Conference on

作者： Bo Qian Bowen Huang College of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

The sparse reward in traditional reinforcement learning will lead to poor training effect of autonomous vehicles. This paper proposes a hierarchical automatic driving decision method based on reinforcement learning. The multi-modal fusion of camera images and radar data ensures the state input of autonomous vehicles. The action space is divided into high-level options and low-level actions. The high-level option network and the underlying action network are built respectively. The vehicle interacts with the environment to learn the optimal driving strategy. Through simulation experiments, compared with DDDQN and DQN algorithms, the average speed of autonomous vehicles based on HDDDQN increases by about 4.48%, the average number of lane changes decreases by about 4.54%, and the average number of overtaking increases by about 18.6 %. Therefore, the safety and driving efficiency of automatic driving are significantly improved.

关键词： Training Decision making signal processing algorithms Reinforcement learning Radar signal processing Radar imaging Cameras Safety Autonomous vehicles

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Dynamic Filter Length Reduction in NLMS for System Identification

Dynamic Filter Length Reduction in NLMS for System Identific...

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Measurement and Simulation (ATOMS), Advanced Topics on

作者： Elena-Valentina Dumitrașcu Robert-Alexandru Dobre Computers Department National University of Science and Technology Politehnica Bucharest Bucharest Romania Electronic Technology and Reliability National University of Science and Technology Politehnica Bucharest Bucharest Romania

ISBN: (数字)9798350358377

ISBN: (纸本)9798350358384

Adaptive filtering techniques are fundamental in system identification, enabling the estimation and modeling of unknown systems from observed input-output data. Among these methods, the Normalized Least Mean Squares (NLMS) algorithm holds paramount importance due to its versatility and widespread application in real-time adaptive signal processing. In system identification, NLMS algorithms serve as robust tools for approximating unknown systems by continuously adjusting the impulse response of adaptive filters based on incoming data. Conventional implementations of the NLMS algorithm assume a fixed filter length, creating challenges in scenarios where the length of the filter to be identified is unknown, thus hindering accurate system modeling, often leading to "overmodeling" and ineffective use of computational resources. This paper introduces an adaptation in the NLMS algorithm to dynamically modify the length of the adaptive filter, enhancing its computational resource efficiency. The proposed methodology addresses the challenge of accurately approximating unknown filters while decreasing the computational complexity. Simulation results demonstrate the efficacy of the proposed adaptive length modification technique, showcasing reduced computational overhead compared to traditional fixed-length NLMS methods, while still obtaining good misalignment values.

关键词： Adaptive systems Accuracy Filtering Heuristic algorithms Adaptive filters signal processing algorithms Filtering algorithms Approximation algorithms System identification Yield estimation

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Practical Waveform Design for ISAC Systems: An Instrumental Variable Approach

Practical Waveform Design for ISAC Systems: An Instrumental ...

引用

Asilomar Conference on signals, Systems & Computers

作者： Byunghyun Lee Hwanjin Kim David J. Love James V. Krogmeier Elmore Family School of Electrical Engineering Purdue University USA School of Electronics Engineering Kyungpook National University Daegu Korea

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

With the advancement of hardware and signal processing technologies, next-generation wireless networks are expected to be multi-functional, offering various radio-based services such as communications and sensing. In this context, integrated sensing and communications (ISAC) is a promising solution for improving spectral efficiency, but its implementation is challenging because of practical constraints such as complexity. In this paper, we propose a novel waveform design principle for ISAC systems, which can decompose a complex ISAC waveform design problem into more tractable subproblems. The proposed method enables superimposing a sensing signal onto the communication signal without affecting the communication channel via the instrumental variable (IV) method. We apply the proposed waveform structure to a beampattern matching problem and develop the corresponding solution. We provide an in-depth analysis with respect to degrees of freedom (DoF), channel mismatch, and so forth. We evaluate the performance of the proposed waveform design algorithm through simulations.

关键词： Spectral efficiency Instruments Wireless networks signal processing algorithms Radar Communication channels signal processing Integrated sensing and communication Hardware Next generation networking

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A Study of EEG signal Classification in Alzheimer's Disease Using the IMPSO for Channel Selection

A Study of EEG Signal Classification in Alzheimer's Disease ...

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International Congress on Image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Ruofan Wang Haojie Xu Deri Yi Changzhi Song Jitong Wang School of Information Technology Engineering Tianjin University of Technology and Education

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Alzheimer's disease (AD) is a neurodegenerative disease that is primarily characterised by insidious onset and subtle clinical symptoms, which can make it challenging to diagnose through unconventional means. EEG has been extensively employed as a primary diagnostic tool for Alzheimer's disease (AD), facilitating the assessment and diagnosis of various neurological disorders. An accurate diagnosis is crucial for the prevention of the transition from early cognitive impairment to Alzheimer's disease (AD) and the provision of early treatment for AD patients. The maj ority of classification methods concentrate solely on the time or frequency domain, thereby neglecting the interconnectivity between the two. Accordingly, this paper generates time- frequency spectrograms based on a wavelet transform for analysis. However, the time-frequency spectrograms contain a considerable amount of redundant information, which results in a sudden increase in computation. To address this issue, this paper proposes a bidirectional principal component analysis (BDPCA) method to reduce the dimensionality of the time- frequency spectrogram, thereby reducing the computational burden while effectively restoring the features of the image. Subsequently, the improved PSO algorithm was employed to select the 16 channels, and seven key channels (Fp1, F7, F8, F3, T3, T4 and 01) were ultimately identified. The features of these selected channels were then inputted into the XGBoost classifier to achieve the recognition of patients and normal controls, with an accuracy rate of 93.56%. This study is the inaugural application of the IMPSO optimisation algorithm and time- frequency spectrogram to EEG channel selection, thereby establishing an efficient framework for AD detection. The method reduces the computational complexity and improves the generalisation ability, which facilitates the rapid identification and reduction of misdiagnosis in a clinical setting while also advancing the understanding of t

关键词： Wavelet transforms Neurological diseases Time-frequency analysis Accuracy signal processing algorithms Electroencephalography Classification algorithms Alzheimer's disease Spectrogram Optimization

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Joint Channel Estimation and Classification for Task-Oriented Communications

Joint Channel Estimation and Classification for Task-Oriente...

引用

International Conference on Wireless Communications and signal processing (WCSP)

作者： Ziyuan Liu Hong Shen Wei Xu Feng Jiang Shen Wang Mengyu Li Chunming Zhao National Mobile Communications Research Laboratory Southeast University Nanjing China The Purple Mountain Laboratories Nanjing China China Mobile Group Design Institute Co. Ltd. Beijing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Task-oriented communications, which focus on the effective execution of the task of interest rather than the recovery of the transmitted symbols, have great potentials in future Internet of Things (IoT) systems. In particular, the fundamental change in the design philosophy brings new opportunities for the physical-layer technique. In this paper, we consider a new joint channel estimation and classification (JCEC) receiver design for a multi-device edge inference system. By fully exploiting both the feature and the channel distributions, we establish a maximum $a$ posteriori (MAP) based JCEC formulation, which turns out to be a challenging non-convex problem. A majorization-minimization (MM) algorithm is proposed to solve the difficult problem, where only a closed-form expression needs to be calculated in each iteration. Simulations validate that our scheme outperforms the benchmark with decoupled channel estimation and MAP classifier on both a synthetic feature dataset and the widely used CIFAR-10 dataset.

关键词： Wireless communication Channel estimation signal processing algorithms Symbols Receivers signal processing Inference algorithms Classification algorithms Internet of Things Servers

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Pose Estimation for Weakly Textured Aircraft with Improved CDPN

Pose Estimation for Weakly Textured Aircraft with Improved C...

引用

International Congress on Image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Mengting Tao Ling Wu Niannian Chen School of Computer Science and Technology Southwest University of Science and Technology Sichuan Mianyang China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Obtaining 6d attitude information of a target object from a single RGB image can provide accurate environment awareness and localization capabilities for an aircraft, thereby improving mission execution efficiency and success, while reducing risk and increasing safety. Aiming at the problem that the existing pose estimation algorithms have low accuracy in the small error range in the pose estimation task of weakly textured aircraft, a learning framework based on CDPN network and Convolutional Block Attention Module (CBAM) is proposed. The accuracy of the position estimation of weakly textured aircraft images is effectively improved by increasing the set of predefined keypoints of the wing and tail in order to strengthen the establishment of the correspondence. Experiments have been carried out under the weakly textured aircraft simulation dataset and Linemod dataset, and the results show that the method proposed in this paper has high accuracy within a small error range for weakly textured aircraft images.

关键词： Visualization Accuracy Working environment noise Pose estimation signal processing algorithms Tail signal processing Feature extraction Safety Aircraft

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Design and Implementation of a Lightweight Underwater Acoustic Modulation Classification

Design and Implementation of a Lightweight Underwater Acoust...

引用

IEEE International Conference on signal processing, Communications and Computing (ICSPCC)

作者： Chaojin Ding Xianbin Hu Wei Su Yifeng Zhao Dept. of Information and Communication Engineering Xiamen University Xiamen China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

To develop an efficient and accurate lightweight underwater acoustic (UWA) modulation recognition algorithm, this study proposes an algorithm called Channel Expansion-Lightweight Recognition Network (CE-LRN). The CE-LRN sig-nificantly reduces computational complexity by adopting small convolutional kernels and quantized convolutional layer techniques, achieving deployment standards friendly to embedded devices. Additionally, a channel expansion module is introduced in the CE-LRN algorithm, which effectively improves recognition accuracy with only a slight increase in computational complexity. Compared with two recently proposed recognition algorithms, the CE-LRN greatly reduced computational complexity, with its floating-point operations (FLOPs) being only 16.9M, a reduction of over 83.4%. Experimental results demonstrated that CE-LRN has good classification performance for Multi-Frequency Shift Keying (MFSK) and Multi-Phase Shift Keying (MPSK) signals.

关键词： Accuracy Convolution signal processing algorithms Modulation Robustness Classification algorithms Underwater acoustics Computational complexity Standards signal to noise ratio

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Deciphering Social Media Sentiment for Enhanced Analytical Accuracy: Leveraging Random Forest, KNN, and Naive Bayes

Deciphering Social Media Sentiment for Enhanced Analytical A...

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International Conference on Communications and signal processing

作者： B. Mridula A. Hency Juliet N. Legapriyadharshini Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The research aims to determine the most effective algorithm for social media sentiment analysis, evaluating Random Forest, K-Nearest Neighbor (KNN), and Naive Bayes models. This investigation holds importance as it seeks to unveil the key factors influencing sentiment analysis on social media using rigorous methodologies. Employing a dataset obtained from Kaggle, the study examined the predictive capabilities of Random Forest, KNN, and Naive Bayes in accurately predicting social media sentiment. Notably, Naive Bayes emerged as the most precise predictor of social media sentiment compared to Random Forest and KNN algorithms. The study's results highlight the potential of machine learning algorithms in providing accurate predictions for social media sentiment analysis. A statistically significant level of p= 0.04, where $\mathrm{p}\lt0.05$, further underscores the significance of the study, indicating notable variations among the three algorithms. With mean accuracies of 86.06% for Random Forest, 89.28% for KNN, and 92.01% for Naive Bayes, the findings affirm Naive Bayes' superior performance over the Random Forest-KNN combination, emphasizing its superior predictive accuracy.

关键词： Sentiment analysis Machine learning algorithms Social networking (online) signal processing algorithms Forestry signal processing Prediction algorithms

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Diagnosis Through Adventitious Sounds Detection and Respiratory Phases Identification Using XGBoost

Diagnosis Through Adventitious Sounds Detection and Respirat...

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International Conference on Frontiers of signal processing (ICFSP)

作者： Takuma Mitsuke Hiromitsu Shimakawa Fumiko Harada Graduate School of Information Science and Technology Ritsumeikan University Kusatsu Japan Graduate School of Information Science and Technology Ritsumeikan University Connect Dot Ltd Japan

ISBN: (数字)9798350353235

ISBN: (纸本)9798350353242

The study proposes a method to assist physicians who are not respiratory specialists to diagnose specific diseases from lung sounds without advanced medical equipment. The method uses frequency feature images to estimate the presence and progression of disease by performing in parallel the process of comprehensively detecting fine crackles suggestive of disease and the process of identifying the respiratory phases in which the abnormal sounds occur. The validation results of this study showed a detection of fine crackles with an accuracy of 0.90 and an identification accuracy of 0.85 for the respiratory phases. These results are integrated to estimate the progression of the disease. The method provides a visual indication of the location of abnormal sounds, which helps physicians and patients understand and explain their diagnosis.

关键词： Visualization Accuracy Pneumonia Lungs signal processing algorithms Medical services Stethoscope signal processing Feature extraction Diseases

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5G Channel Estimation Kernels on RISC-V Vector Digital signal Processors

5G Channel Estimation Kernels on RISC-V Vector Digital Signa...

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International Conference on Microelectronics, ICM

作者： Javier Acevedo Frank H. P. Fitzek Patrick Seeling Deutsche Telekom Chair of Communication Networks Dresden TU Centre for Tactile Internet with Human-in-the-Loop (CeTI) Department of Computer Science Central Michigan University

ISBN: (数字)9798350379396

ISBN: (纸本)9798350379402

Each generation of the wireless communication networks has contiguous demands for higher computing capabilities and lower the power consumption in the integrated circuitry. Thus, the optimization of the base-band signal processing kernels and the hardware architectures plays a pivotal role in the performance, latency, and energy efficiency of the Physical Layer (PHY). Channel Estimation (CE), a computationally intensive task with hard real-time requirements, involves significant amounts of matrix multiplications and inversions, when data flows between transmitter (TX) and receiver (RX). In this work, we present a software implementation of CE for the 5th Generation Cellular Networks (5G) New Radio (NR) in the Reduced Instruction Set Computer (RISC)-V architecture, showcasing how its vector processing capabilities can satisfy the throughput and delay requirements of 5G use cases. Specifically, we ported two CE kernels, the Least-Squares (LSE) and Minimum Mean Square Error (MMSE), onto a state-of-the-art single core vector processor. By exploiting data vectorization, the ARA core computes matrix arithmetic operations on multiple data sets simultaneously. Our single vector core serial optimization demonstrates that with a $16\times 16$ matrix window size and 16 vector lanes, the application's performance achieves a speedup of 78.27 when computing the LSE algorithm. When the number of operations per cycle are compared, the vector processor outperforms its scalar counterpart by 181.91 for the MMSE kernel. Our implementation orders the nested loops in a latency-reducing fashion to compute matrix arithmetic operations, resulting in LSE and MMSE estimator computing times of $8.44 \mu\iota \mathrm{s}$ and $12.31\ \mu\mathrm{s}$ , respectively.

关键词： Channel estimation signal processing algorithms Computer architecture Vectors Hardware Vector processors Matrix decomposition Kernel Optimization Clocks

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