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检索条件"主题词=Signal Processing Algorithms"

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A GNSS RTK/INS Tight Coupling Method Under Challenging Scenarios

A GNSS RTK/INS Tight Coupling Method Under Challenging Scena...

引用

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

作者： Zikang Ji Yanyan Zhao Hongwei Zhao Xiaoang Li School of Electronics and Information Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

The Global Navigation Satellite System (GNSS) Real-Time Kinematic (RTK) positioning technology is a robust algorithm capable of providing centimeter-level accuracy in environments with favorable satellite visibility. However, in GNSS-Challenging environments such as urban canyons, where buildings and foliage may weaken GNSS signals, impede signal tracking by GNSS receivers, and exacerbate multi-path effects, RTK positioning can suffer performance degradation. The tight integration of Inertial Navigation Systems (INS) with RTK overcomes the limitations inherent when each system operates independently, delivering enhanced performance. This study realizes a tightly coupled RTK/INS algorithm. Performance tests and analyses were conducted on public vehicular datasets and one empirically measured static data set, which presents challenges in RTK algorithms. The results indicate that the tight integration algorithm improved the positioning accuracy of the empirically measured static data and the open dynamic data set. Researchers may access our datasets via the provided link.

关键词： Couplings Global navigation satellite system Accuracy Satellites Heuristic algorithms Dynamics signal processing algorithms Receivers signal processing Vehicle dynamics

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Autonomous Vehicle Positioning with LiDAR-Only Sensing: An Application on the RACECAR Dataset

Autonomous Vehicle Positioning with LiDAR-Only Sensing: An A...

引用

IEEE Workshop on signal processing Systems (SIPS)

作者： Francesco Pigato Luca Barbieri Mattia Brambilla Monica Nicoli Politecnico di Milano Milan Italy

ISBN: (数字)9798350373752

ISBN: (纸本)9798350373769

This paper presents a LiDAR-based self-localization method for autonomous vehicles on racetracks, without relying on the global navigation satellite system (GNSS) technology. The methodology leverages a-priori knowledge of fixed geo-referenced landmarks (e.g., pylons) in the racetrack environment to achieve accurate vehicle positioning by processing LiDAR point clouds. We design a processing pipeline to extract a set of candidate detections, which are then associated to the known pylons along the racing circuit and integrated in a Bayesian tracking filter for vehicle self-localization. The proposed method is validated with the RACECAR dataset, the first open dataset for full-scale and high-speed autonomous racing of the Indy autonomous challenge. Performance results highlight an average positioning mismatch of 1.42 m with respect to GNSS real time kinematic.

关键词： Point cloud compression Surveys Global navigation satellite system Poles and towers Pipelines signal processing algorithms signal processing Trajectory Vehicle dynamics Autonomous vehicles

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IoT Data and Random Forest Algorithm for Optimized Crop Rotation Planning for Sustainable Agriculture

IoT Data and Random Forest Algorithm for Optimized Crop Rota...

引用

International Conference on Communications and signal processing

作者： Nukala Sujata Gupta C. Jehan M. Raja Suguna S. Priya G. Arunachalam Vijayakumar Kadumbadi Department of Computer Engineering and Technology Chaitanya Bharathi Institute of Technology Gandipet Financial District Hyderabad Telangana India Department of Computer Science and Engineering Vel Tech Multitech Dr.Rangarajan Dr.Sagunthala Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Academy of Maritime Education and Training (AMET) Deemed to be University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Gnanamani College of Technology (Autonomous) Namakkal Tamil Nadu India Department of Technology Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Modern farming has prioritized developing sustainable agricultural practices to combat food insecurity and environmental damage. A reliable strategy for maintaining healthy soil and increasing yields is crop rotation. This study proposes a novel method that integrates Internet of Things (IoT) data with the Random Forest algorithm to optimize crop rotation plans. IoT sensors are real-time monitoring of the soil’s moisture, temperature, and nutrient content using IoT, and a comprehensive overview is presented by combining this data with historical records and predictions. This dataset is analyzed using the Random Forest technique, which considers various characteristics, including crop compatibility, soil conditions, and climatic trends. The program uses machine learning to create optimal crop rotation plans for productivity, pest and disease control, and the promotion of sustainable agriculture. Our findings show that this strategy enhances crop rotation planning, increasing agricultural output while decreasing its ecological footprint. It helps in the development of sustainable farming methods and the expansion of precision agriculture via the use of $\mathrm{Io} T$ and machine learning.

关键词： Temperature sensors Productivity Crops signal processing algorithms Soil Real-time systems Planning

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Energy Efficient Multi-User Computation Offloading Based on Throughput Prediction

Energy Efficient Multi-User Computation Offloading Based on ...

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

作者： Zhengtao Liao Jianxian Lu Xuewei Huang Xiaocong Zou Zhiwei He Meng Zhang Fangjiong Chen School of Electronic and Information Engineering South China University of Technology China Telecom Co. Ltd Guangdong Branch

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

Mobile Edge Computing(MEC) pushes computation and data processing capabilities toward the network edge to meet the demands of fast and low-latency applications. With the increasing interest in mobile applications such as vehicular networks, channel prediction-based edge computing become a crucial task due to the time-varying channels. This paper proposes a multi-user, multi-task computation offloading model with uplink throughput prediction. First, the proposed scheme uses an exponential smoothing model to predict future Reference signal Received Power (RSRP) accurately. Based on the collected RSRP and uplink throughput datasets in real network environments, machine learning is employed to train prediction models for RSRP and uplink throughput. Second, based on the predicted uplink throughput, we then formulate the offloading slot selection as an integer linear programming problem based on minimizing energy consumption. Since this problem is considered NP-hard, the paper proposes a polynomial time complexity computation offloading algorithm. Simulation results show that the algorithm can approximately achieve the minimum energy consumption and has a more efficient solution rate than branch and bound (B&B) algorithm.

关键词： Smoothing methods Computational modeling Heuristic algorithms signal processing algorithms Predictive models Throughput Prediction algorithms Approximation algorithms Uplink Time complexity

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Voice Recognition Acquisition and Storage System using VHDL and the DE1-SoC FPGA

Voice Recognition Acquisition and Storage System using VHDL ...

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IEEE International Congress on Electronics, Electrical Engineering and Computing (INTERCON)

作者： Elmer Arellanos Luis Dominguez Remigio Jose Luis Ostos Marquez Moises Nunez Electrical Engineering Department Universidad de Ingenieria y Tecnologia - UTEC Lima Peru Electrical Engineering Department Universidad Tecnológica del Perú Lima Peru

ISBN: (数字)9798350378344

ISBN: (纸本)9798350378351

This paper presents the design of a voice acqui-sition and recognition system in VHDL, mainly optimized for FPGA DE1-SoC. The system implements advanced digital signal processing techniques combined with classification through the K-Nearest Neighbors (KNN) algorithm. The system operates at a 320 kHz sampling rate, with a latency of 10 ns and power consumption of 1.2 W. The design enhances precision and noise rejection through the use of Hamming windows and integer-based processing. Future improvements will focus on the implementation of neural networks to replace the KNN algorithm and further increase system accuracy.

关键词： VHDL Data acquisition signal processing algorithms Speech recognition Nearest neighbor methods Solids Registers Synchronization Field programmable gate arrays Clocks

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Research on Imaging algorithms under Two Three-Dimensional Turntable Measurement Modes

Research on Imaging Algorithms under Two Three-Dimensional T...

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IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Guo Feixiang Lan Yu Tian Jiahao Zhou Kaixin Li Hui Zhou Jianxiong National University of Defense Technology College of Electronic Science and Technology Changsha China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

With the improvement in radar performance and the continuous enhancement of demands for target detection and identification, research on three-dimensional imaging of radar targets has increasingly attracted the attention of researchers. This paper discusses two measurement modes resulting from the inconsistency in the sequence of azimuth and elevation rotations when the turntable target simulates the radar observation perspective. It analyzes the imaging geometry of the target under different modes and proposes a spherical format algorithm (SFA) in three-dimensional space. The algorithm is a three-dimensional expansion of the polar coordinate format algorithm. The wave number domain support set of the target in spherical coordinate system is transformed to cartesian coordinate support set by interpolation, and then fast Fourier transform is used for imaging. This paper analyzes the differences in the manifestation of imaging errors of the RD algorithm under two modes through point target simulations. The experimental results verify the effectiveness of this method.

关键词： Solid modeling Accuracy signal processing algorithms Imaging Radar detection Radar Radar imaging signal processing Approximation algorithms Rotation measurement

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Multi-Map Fusion for Vision-and-Language Navigation in Continuous Environment

Multi-Map Fusion for Vision-and-Language Navigation in Conti...

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IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Jidong Wang Mingchen Sun Yan Zhou School of Automation and Electronic Information Xiangtan University Xiangtan China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Visual Language Navigation (VLN) is a task that challenges agents to navigate through unseen environments solely based on language and visual supervision. In the recently proposed continuous setting (Continuous VLN), agents must navigate within a free 3D space. To achieve better performance in Continuous VLN, this paper introduces a new navigation framework. In this framework, the agent observes the environment in a continuous setting and constructs grid maps and topological maps based on acquired information, which are respectively used for short-term reasoning and long-term reasoning. Finally, through hybrid action fusion, the navigation scores from both maps are combined using an algorithm to determine the agent's final action space. This paper elaborates on how to construct grid maps and topological maps and demonstrates through ablation experiments the improvement in the agent's navigation success rate with the introduction of map representations. Furthermore, to enhance the model's understanding of multimodal information and utilization of map information, three pretraining tasks are employed during the pretraining phase. Ultimately, experimental results on the Habitat simulator validate the effectiveness of the proposed model, as demonstrated by its excellent performance on the VLN-CE dataset.

关键词： Degradation Visualization Three-dimensional displays Navigation Computational modeling Habitats Buildings signal processing algorithms signal processing Cognition

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Multi-Feature Fusion TOA Algorithm Based on Deep Learning and Compressed Sensing

Multi-Feature Fusion TOA Algorithm Based on Deep Learning an...

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IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Haoran Ji Lei Wang Shuhao Zhang Cong Peng Wenjie Zhou Hunan University college of electrical information engineering Changsha China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

In the field of signal processing, traditional time of array(TOA) algorithms, as well as those based on compressed sensing, excel in determining time estimates for data. However, these algorithms typically operate within linear models and may not fully extract data information, thereby affecting estimation accuracy and resolution. Capitalizing on their robust nonlinear processing capabilities, deep learning algorithms demonstrate proficiency in extracting features from data. Consequently, we propose a time delay estimation algorithm that initially preprocesses data through compressed sensing. Subsequently, the data undergo processing through a constructed multimodal model, where various features are fused, leading to the final time delay estimation result. By comprehensively processing various features, the algorithm extracts information more thoroughly, resulting in heightened resolution. Experimental assessments confirm the commendable performance of the algorithm.

关键词： Deep learning Delay effects signal processing algorithms Estimation Feature extraction Data models Sensors Data mining Compressed sensing signal resolution

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Channel Estimation in Low-Resolution Near-Field Massive MIMO Systems

Channel Estimation in Low-Resolution Near-Field Massive MIMO...

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Sensor Array and Multichannel signal processing Workshop

作者： Ly V. Nguyen Duy H. N. Nguyen Italo Atzeni Antti Tölli A. Lee Swindlehurst Center for Pervasive Communications and Computing University of California Irvine CA USA Department of Electrical and Computer Engineering San Diego State University CA USA Centre for Wireless Communications University of Oulu Finland

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Massive multiple-input-multiple-output (MIMO) is a core technology of current and future wireless networks. However, the very large dimension of a massive antenna array can lead to radical changes in the electromagnetic fields near the array, and the classical far-field channel model is no longer accurate. Instead, the channel should be modeled under the assumption of near-field spherical wavefronts. Furthermore, the very large dimension of the arrays can also result in high power consumption and hardware complexity. A practical solution for this problem is to use low-resolution analog-to-digital converters (ADCs). It is therefore of significance to study the near-field channel estimation problem for MIMO systems implemented with low-resolution ADCs. We propose an efficient on-grid polar-domain channel estimation method which relies on the polar-domain sparsity of the near-field channels. We first reformulate the sparse low-resolution near-field maximum-likelihood channel estimation problem by exploiting an approximation of the cu-mulative distribution function of a normal random variable as a logistic activation function. We then develop an on-grid polar-domain channel estimation method based on the gradient descent approach and the polar-domain sparsity of the near-field channel. Finally, we apply the deep unfolding technique to optimize the performance of the proposed method and illustrate its efficiency via several simulation studies.

关键词： Maximum likelihood estimation Power demand Wireless networks Channel estimation signal processing algorithms Massive MIMO Random variables

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Subjective and Objective VQA of Video Codecs for UHD Video Streaming

Subjective and Objective VQA of Video Codecs for UHD Video S...

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International Conference on Frontiers of signal processing (ICFSP)

作者： Ioanna Valiandi Moatasim Mahmoud Stamatia Rizou Andreas S. Panayides VIDEOMICS Group CYENS Centre of Excellence Cyprus Nicosia R&D Department Singular Logic Aristotle University of Thessaloniki R&D Department Singular Logic Athens Greece

ISBN: (数字)9798350353235

ISBN: (纸本)9798350353242

In this work, we conduct a subjective video quality assessment (VQA) experiment to evaluate the performance of modern video codecs when displayed on a large (30sqm) ultra-high-definition (UHD) video wall. Our comparative study involves four encoding standards: Advanced Video Coding (AVC) / H.264, High-Efficiency Video Coding (HEVC) / H.265, Versatile Video Coding (VVC) / H.266, and AOMedia Video 1 (AV1). Moreover, the study includes the evaluation in terms of correlation performance of a set of widely used full-reference objective VQA metrics against the subjective scores. Our results showcase the perceptual superiority of the VVC over rival encoding standards, aligning with the objective quality assessment and relevant literature.

关键词： Correlation Video sequences signal processing algorithms Streaming media Encoding Quality assessment Video codecs Video recording Standards High efficiency video coding

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