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A Pruning Based Continuous RkNN Query Algorithm for Large k

Chinese Journal of Electronics 2023年第3期21卷 523-527页

作者： Shengsheng WANG Sheng CHAI Qiannan LV College of Computer Science and Technology Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Reverse k-nearest neighbor (RkNN) query is a hot-spot in spatio-temporal database. With the developing of mobile devices, continuous RkNN query becomes more and more important. Most of the previous methods use the two-step (filter-refinement) processing. However, for large k, the amount of calculation becomes very heavy, especially in the filter step. This is not acceptable for most mobile devices. This paper presents a novel algorithm for continuous RkNN queries based on pruning heuristics. The experiments show that the processing time of our method is still acceptable for most mobile devices when k is large.

关键词： Location awareness Databases Heuristic algorithms filtering algorithms Mobile handsets Mobile applications

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A System Design for 3D Visualization of Underwater Multi-Target Tracking Based on Unity 3D and GMPHD Filter

A System Design for 3D Visualization of Underwater Multi-Tar...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Seungho Kim Scott Uk-Jin Lee Department of Computer Science and Engineering Major in Bio-Artificial Intelligence Hanyang University Ansan Republic of Korea Department of Computer Science and Engineering Hanyang University Ansan Republic of Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This paper proposes a design for a visualization system for underwater multi-target tracking based on the Gaussian Mixture Probability Hypothesis Density (GMPHD) filter using Unity 3D engine. Traditional analysis methods for underwater tracking algorithms rely primarily on two-dimensional graphs and numerical data, making it challenging to intuitively understand the complex behavior of tracking systems in three-dimensional underwater environments. Our proposed system design provides a framework for 3D visualization of target movements, sonar sensor detection areas, and filter estimation results, along with an interactive interface for parameter adjustment. The proposed architecture consists of four main components: simulation core, visualization module, environment simulation, and user interface, which would enable researchers to analyze algorithm performance under various scenarios effectively. Through real-time visualization and parameter adjustment capabilities, the system is designed to offer immediate feedback on how different parameters affect tracking performance. This architectural approach aims to provide researchers with a more intuitive understanding of the GMPHD filter's operation and facilitate the optimization of tracking algorithms for underwater environments. The proposed system design demonstrates the potential of 3D visualization tools in advancing underwater multi-target tracking research and algorithm development.

关键词： Three-dimensional displays Target tracking Data visualization Systems architecture filtering algorithms Information filters Real-time systems Underwater tracking System analysis and design Sonar detection

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L${}_{\text{2}}$min${}^{\text{2/2s}}$: Efficient Linear Reconstruction Filter for Incremental Delta-Sigma ADCs

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IEEE Transactions on Signal Processing 2023年 71卷 3229-3241页

作者： Bo Wang Man-Kay Law Jens Schneider College of Science and Engineering Hamad Bin Khalifa University Doha Qatar State Key Laboratory of Analog and Mixed-Signal VLSI Institute of Microelectronics and FST-ECE University of Macau Macau China

While it becomes more challenging to improve the energy efficiency of incremental delta-sigma data converters (IDCs) from the analog circuit design perspective, we propose two novel linear reconstruction filters for IDCs to enhance their performance in a digital way, including the L ${}_{\mathbf{2}}$ min ${}^{\mathbf{2}}$ filter and its symmetric version, the L ${}_{\mathbf{2}}$ min ${}^{\mathbf{2s}}$ filter. Compared to the classical linear reconstruction filters, such as the cascade-of-integrators (CoI) and cascaded integrator-comb (CIC) filter (an implementation of sinc filter), the proposed filters can achieve efficient quantization and thermal noise suppression, with the lowest thermal noise penalty factor of 1.2 among the high-order linear reconstruction filters. In this paper, we present analytical, numerical, and experimental results to demonstrate the superior performance of the filters for first-order and second-order IDC output reconstruction. The proposed filters are hardware-friendly and example digital implementations in a standard complementary metal-oxide-semiconductor (CMOS) and field-programmable gate array (FPGA) platforms are included in this paper.

关键词： Nonlinear filters Maximum likelihood detection Digital filters Quantization (signal) filtering algorithms Modulation Noise reduction

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Robust Attitude Estimation With Quaternion Left-Invariant EKF and Noise Covariance Tuning

Robust Attitude Estimation With Quaternion Left-Invariant EK...

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International Conference on Computer and Automation Engineering, ICCAE

作者： Yash Pandey Rahul Bhattacharyya Yatindra Nath Singh Department of Electronics Engineering Harcourt Butler Technical University Kanpur India Department of Electrical Engineering Indian Institute of Technology Kanpur Kanpur India

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Accurate estimation of noise parameters is critical for optimal filter performance, especially in systems where true noise parameter values are unknown or time-varying. This article presents a quaternion left-invariant extended Kalman filter (LI-EKF) for attitude estimation, integrated with an adaptive noise covariance estimation algorithm. By employing an iterative expectation-maximization (EM) approach, the filter can effectively estimate both process and measurement noise covariances. Extensive simulations demonstrate the superiority of the proposed method in terms of attitude estimation accuracy and robustness to initial parameter misspecification. The adaptive LI-EKF's ability to adapt to time-varying noise characteristics makes it a promising solution for various applications requiring reliable attitude estimation, such as aerospace, robotics, and autonomous systems.

关键词： Accuracy filtering Quaternions Noise Estimation filtering algorithms filtering theory Kalman filters Time-varying systems Tuning

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An Adaptive Low-Complexity Compression Algorithm for Vehicle Systems

An Adaptive Low-Complexity Compression Algorithm for Vehicle...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Chun-Hsiang Huang Shih-Lun Chen He-Sheng Chou Ya-Yun Huang Department of Electronic Engineering Chung Yuan Christian University Taoyuan Taiwan Department of Program on Semiconductor Manufacturing Technology Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Tainan Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Advanced Driver Assistance Systems (ADAS) are increasingly integrated into modern vehicles, relying on high-speed image transmission from on-board cameras to control systems. This study presents an adaptive lossless image compression algorithm designed to reduce transmission data while maintaining high image quality. By dynamically switching between Color Filter Array (CFA) compression and predictive compression, the proposed method achieves an average compression rate of 3.835 and a Peak Signal-to-Noise Ratio (PSNR) of 30.57 dB, significant improvements in efficiency, making it suitable for automotive applications.

关键词： Image quality Image coding PSNR Image color analysis Heuristic algorithms Automotive applications filtering algorithms Prediction algorithms Real-time systems Compression algorithms

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System Identification for Yaw Dynamics Prediction Model of an Autonomous Underwater Glider: Comparison of N4SID and ARX Models Using Preprocessed Data

System Identification for Yaw Dynamics Prediction Model of a...

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Advancement in Data Science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Yansha Ramadhan Pratama Muhammad Hanif Hibatullah Bambang Riyanto Trilaksono Egi Hidayat Simon Siregar Yansen Dwiputra School of Electrical Engineering Bandung Institute of Technology Bandung Indonesia Faculty of Applied Science Telkom University Bandung Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

This paper aims to compare two algorithms of system identification between Auto-Regressive with eXogenous inputs (ARX) and Numerical Subspace State Space System Identification (N4SID). This comparison is based on the development of a Model Predictive Control (MPC) for heading control of an Autonomous Underwater Glider (AUG). The comparison was done by compared best-fit values of those models, made from data through a noise filtering process. The filtering process is using a Maximum Likelihood-based Kalman Filter (ML-based KF). ML is used to get the optimal parameters of Q and R value for K $F$ , thus that the optimal value for both parameters can be achieved. Both models have good accuracy performance on best-fit percentage and have low errors. Nevertheless, the ARX model demonstrates a bit advantage over N4SID, achieving the highest best-fit percentage than N4SID model. The ARX achieved the best fit percentage of 71.76 % when the na and nb tuned at 6 and 4, respectively with the errors achieved 3.127 for MSE and 1.768 for RMSE. Then the N4SID achieved the best fit percentage of 71.75% at the seventh-order tuning with MSE and RMSE are 3.1586 and 1.7725, respectively. This algorithm will be used as a prediction model for MPC, in future.

关键词： Heuristic algorithms Noise Predictive models filtering algorithms Prediction algorithms Data models System identification Kalman filters Tuning Overfitting

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Time-varying Bias Estimation for Asynchronous Multi-sensor Multi-target Tracking Systems Using STF

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Chinese Journal of Electronics 2023年第3期22卷 525-529页

作者： HU Yanyan ZHOU Donghua Department of Automation Tsinghua University Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China

Time-varying bias estimation problem was studied for multi-target tracking systems with asynchronous sensors without knowing bias dynamics. We considered general situations, where the number of sensors was arbitrary as well as their sampling rates and initial sampling instants. A two-layer fusion structure was adopted. For each target, a pseudo-measurement of sensor bias was generated by fusing sensor measurements of this target. To make the pseudo-measurement decoupled from the target state, the fusion coefficient matrix was determined to be a basis for the left null space of an augmented observation matrix. The bias estimation algorithm was proposed based on the Strong tracking filter (STF) by fusing pseudomeasurements. The proposed algorithm makes use of all available sensor information, has strong tracking ability to abrupt changes, and avoids the matrix inversion for large dimensional matrices. Finally, the performance of the proposed algorithm is illustrated by the numerical simulation.

关键词： Target tracking Simulation Estimation Null space filtering algorithms Numerical simulation Sensor systems

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Details-preserved Skin Re-tendering Using Improved NLEMD*

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Chinese Journal of Electronics 2023年第2期19卷 288-290页

作者： Jing Zhao Hongfei Lin Department of Computer Dalian University of Technology Dalian China

The Neighborhood limited empirical mode decomposition (NLEMD) is improved in the local mean computation using refined symmetrical extrema distribution. The images are decomposed into the IMFs using the improved NLEMD. Then, the IMFs are filtered using one adaptive filter to remove the spots making the skin look aging. The adaptive filter is based on the IMFs' local statistics, which is estimated using the fast approach. The skin re-tendering algorithm in this paper makes the skin more tender than other approaches without losing the necessary details of the skin.

关键词： Empirical mode decomposition Adaptive filters filtering algorithms Aging Skin

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Particle Flow with a Continuous Formulation of the Nonlinear Measurement Update

Particle Flow with a Continuous Formulation of the Nonlinear...

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International Conference on Information Fusion

作者： Kristen Michaelson Andrey A. Popov Renato Zanetti Kyle J. DeMars Department of Aerospace Engineering and Engineering Mechanics The University of Texas at Austin Austin Texas Oden Institute for Computational Engineering and Sciences The University of Texas at Austin Austin Texas Department of Aerospace Engineering Texas A&M University College Station TX

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

The incorporation of nonlinear measurement information plays an important role in Bayesian state estimation for real-word systems. While many methods exist for propagating states through continuous-time nonlinear dynamics, a complementary continuous solution for discrete-time nonlinear measurements has so far remained elusive. Building on intuition from our previous work, the Bayesian Recursive Update Filter, we formulate the nonlinear measurement update as an ordinary differential equation (ODE). This formulation naturally extends to particle flow. We define two particle flows: the first is a deterministic flow based on the ODE solution, and the second is stochastic; the numerical integration contains a diffusion term. The proposed particle flows demonstrate excellent performance on a system with deterministic dynamics and a highly accurate nonlinear measurement, a setting known to be challenging for particle filters.

关键词： Power measurement Atmospheric measurements Density measurement filtering algorithms Particle measurements Information filters Bayes methods Noise measurement Covariance matrices Computational complexity

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Hybrid Consensus-Based Cubature Information Filter and Covariance Intersection Strategy for Hierarchical Distributed State Estimation

Hybrid Consensus-Based Cubature Information Filter and Covar...

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Chinese Automation Congress (CAC)

作者： Chentai Gao Yingce Zhao Xingyu Bao Jianxun Li Department of Automation Shanghai Jiao Tong University Shanghai China AVIC Shenyang Aircraft Design Institute Shenyang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In this paper, a novel hybrid consensus-based distributed cubature information filter and sequential covariance intersection estimator (CDCIF-SCI) is proposed for hierarchical distributed state estimation. A comprehensive hierarchical fusion structure is presented, utilizing the CDCIF filter at the local sensor layer and the SCI fuser at the cluster head layer. At the local sensor layer, the hybrid consensus strategy is integrated into the cubature information filter to achieve distributed state estimation. At the cluster head layer, the global fusion center employs a sequentially implemented multi-sensor covariance intersection fusion among the estimates from each cluster to enhance accuracy. Furthermore, an overall performance analysis of the CDCIF-SCI is provided, offering theoretical insights into the estimator's accuracy. Numerical simulation is conducted to validate the effectiveness of the estimator.

关键词： Accuracy Noise Clustering algorithms filtering algorithms Information filters Numerical simulation filtering theory Performance analysis Kalman filters State estimation

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