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检索条件"主题词=Signal processing algorithms"

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RepFYOLOv5: A Lightweight Re-parameterization YOLOv5 Network with Four Detection Heads

RepFYOLOv5: A Lightweight Re-parameterization YOLOv5 Network...

引用

International Conference on signal processing Proceedings (ICSP)

作者： Yansen Huang Fei Gan Linna Zhang Fugui Zhang College of Civil Engineering College of Mechanical Engineering Guizhou University Guiyang China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Deep neural networks have received wide applications in many areas in recent years, but the high requirements of computational cost and storage space limit its applications in embedded devices. To deal with this problem, we proposed a lightweight re-parameterization YOLOv5 network with four detection heads, called RepFYOLOv5. First, a structure re-parameterization module RepBlock is proposed to reconstruct the network backbone, which has multiple topology branches during the training process, but in the inference and deployment, it uses the equivalent convolutional module fusion structure to speed up the network inference. In addition, in order to improve the object detection accuracy (especially for the small objects), except for the original three detection heads of YOLOv5, an extra detection head based on larger feature maps is added to the network to improve the detection accuracy for the small objects. Experimental results on the COCO dataset and our own traffic surveillance dataset showed that our proposed lightweight RepFYOLOv5 obtained a better balance among detection accuracy, inference time and parameter amount etc.

关键词： YOLO Training Accuracy Network topology Surveillance signal processing algorithms Traffic control Feature extraction Inference algorithms Topology

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Recognition of Bengali Vowels from Auditory Evoked Potentials Using CNN

Recognition of Bengali Vowels from Auditory Evoked Potential...

引用

IEEE International Conference on signal processing, Information, Communication & Systems (SPICSCON)

作者： Tithi Das Md Raihan Khan Md. Mahbub Hasan Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

Audio evoked potentials, or AEPs, have recently gained popularity. It can be described as the EEG signal generated when a person is exposed to auditory stimuli. The human brain behaves differently when exposed to diverse sounds. It also produces a distinct hemodynamic activity pattern for speech sounds compared to non-speech sounds. This paper presents a system for capturing AEP. The proposed approach covers seven different Bengali vowels. A total of 208 sets of data were obtained from five different subjects. The various AEP signals were then filtered and pre-processed. Different features Power Spectral Density (PSD), Entropy, and Variance have been extracted from the preprocessed signal. Convolutional Neural Network (CNN) has the highest accuracy (90.96 %) for PSD features. The dataset has been made public for future research purposes.

关键词： Technological innovation Accuracy signal processing algorithms Transforms signal processing Feature extraction Prediction algorithms Electroencephalography Convolutional neural networks Speech processing

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A Smoking Behavior Detection Algorithm Based on YOLOv5 and Key Points of Human Skeleton

A Smoking Behavior Detection Algorithm Based on YOLOv5 and K...

引用

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

作者： Yan Qi Chaohui Yan School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

As people realize the harm of smoking in public places, the problem of how to improve the detection efficiency of smoking behavior in surveillance video remains to be solved. In recent years, the algorithm of pose estimation based on the key points of human bones has become more and more mature. In this paper, an algorithm for smoking behavior detection based on the relationship between the position information of the key points of human bones and smoking behavior is proposed. The algorithm first uses AlphaPose to detect the key point information of human bones, and determines whether the behavior is smoking by calculating the distance ratio (SBR) between the wrist and the nose and the wrist and the eye on the same side. YOLOv5 was used to detect whether there were cigarette butts in the video. Weight fusion of cigarette probability detected by SBR and YOLOv5 was performed to determine whether there was smoking behavior. The experimental results show that the proposed method can effectively detect smoking behavior with an accuracy of 93.3%. Finally, the abnormal behavior recognition system in the library environment is developed through Python3.8 and PyQt development languages, and the two algorithms proposed in this paper are integrated into the system to realize the visualization of the two abnormal behavior recognition results, which proves the effectiveness of the abnormal behavior recognition algorithm proposed in this paper, and also shows that the system has certain practical application value.

关键词： YOLO Wrist Visualization Accuracy Target recognition Surveillance signal processing algorithms signal processing Bones Behavioral sciences

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NILM Load Identification Based on CNN and V-I Trajectory

NILM Load Identification Based on CNN and V-I Trajectory

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Power Science and Technology (ICPST), IEEE International Conference on

作者： Rui Ma Wenni Kang Jiangbo Sha Jia Liu Dongge Zhu Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd Yinchuan China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

In the process of grid intelligence and digitization, non-intrusive load monitoring (NILM) is a new technology that enables energy consumption monitoring and rational power supply. It identifies the energy consumption of various electrical appliances by monitoring the overall power consumption in a household or building. Traditional NILM methods typically use signal processing and statistical techniques to analyze power consumption signals. In recent years, convolutional neural networks (CNN) have achieved great success in the fields of image and signal processing. This paper aims to explore the use of CNN for event-based NILM load recognition. Firstly, we will discuss event detection methods and data preprocessing techniques for NILM. Then, we will introduce the basic principles and structures of CNN, as well as its application in recognizing V-I trajectory of power data. Afterwards, we will select appropriate loss functions and optimization algorithms to train the CNN and evaluate its performance in load recognition tasks. The experimental results demonstrate that the proposed method achieves excellent performance in the identification of six typical residential loads, with an overall load identification F1-score of 96.2%. Specifically, the F1-score for identifying purely resistive loads reaches 99.75%. This significantly improves the level of NILM load recognition, enabling more precise and intelligent device identification and energy management.

关键词： Energy consumption Power demand Accuracy Event detection signal processing algorithms Voltage signal processing

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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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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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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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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...

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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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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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