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Autocalibration for MEMS triaxial accelerometer based on a PID-based search algorithm

Autocalibration for MEMS triaxial accelerometer based on a P...

引用

Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Fei Li Deming Wang Nan Zhang Ruixi Zheng Yuxin Zhu School of Electronic Engineering Xi’an Shiyou University Xi’an China College of Petroleum Engineering Xi’an Shiyou University Xi’an China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

The errors can accumulate over time when microelectromechanical system (MEMS) accelerometers are in use, it is necessary to perform autocalibration to remove the systematic error effect. However, little research has been done on simultaneously considering the non-orthogonality and misalignment effect in MEMS triaxial accelerometer calibration. To enhance the calibration accuracy and extend the autocalibration methods, this paper proposed a new autocalibration method for MEMS triaxial accelerometers base on a PID-based search algorithm. Non-orthogonality and misalignment error factors were integrated into the error model and a PID-based search algorithm was developed to identify the optimal error factors, minimizing the difference between the actual accelerometer output and the local gravity. The proposed calibration method was tested through simulations, with results showing that the errors in scale factor, bias, non-orthogonality, and misalignment are within 1.5865×10 -4 , 2.0060×10 -4 , 5.0621×10 -4 , and 6.8104×10 -4 , respectively. The proposed method was also compared with the existing calibration methods, with the maximum errors of the proposed method within 0.41% and those of the others exceeding 0.80%. Simulation and comparison results verified the effectiveness and improvement of the proposed method. The average time of the autocalibration is 13.22 s, offering practical value for MEMS triaxial accelerometers.

关键词： Accelerometers Micromechanical devices Microelectromechanical systems Accuracy Systematics signal processing algorithms signal processing Calibration Intelligent control Gravity

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Integration Design of Motion Capture and Sensor Technology in Home Fitness

Integration Design of Motion Capture and Sensor Technology i...

引用

Sensors, Electronics and Computer Engineering (ICSECE), IEEE International Conference on

作者： He Chen Xu Hong Kaiwen Xiao Sujie Mao Sangmyung University Seoul South Korea Jiangsu Second Normal University Nanjing China Nanjing Institute of Physical Education Nanjing China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

Background and Purpose: This study aims to design an integrated home fitness system combining motion capture (Mocap) and sensor technology (Sensing Tech). With remote work and home-based lifestyles becoming the norm, traditional home fitness methods face challenges in personalized guidance and real-time feedback. This system aims to provide personalized fitness advice and instant corrections by monitoring users' movement quality and physiological state in real time, thereby enhancing the safety, effectiveness, and overall health level of home fitness. Technical Overview: Mocap captures human motion data through devices such as cameras and sensors, generating precise three-dimensional data models, primarily divided into optical, inertial, and hybrid systems. Sensing Tech utilizes accelerometers, gyroscopes, heart rate sensors, etc., to detect and respond to physical, chemical, or biological changes in the environment, providing real-time data for monitoring health conditions. System Design: The system comprises four parts: architecture, data acquisition, data processing, and user interface. It adopts a hierarchical architecture including the data acquisition layer, data processing layer, and user interface layer. User actions and physiological data are collected using IMU sensors, heart rate monitors, and camera systems. Integrated signal processing, feature extraction, behavior recognition algorithms run on microprocessors or cloud servers. Real-time feedback and personalized training advice are provided through a graphical user interface (GUI). Technical Implementation and Simulation: Software such as MATLAB/Simulink is used for system modeling and simulation to validate feasibility. Machine learning algorithms are developed for action recognition and health monitoring, optimizing model parameters and conducting validation. Performance Evaluation: Model performance is evaluated using metrics like confusion matrices, accuracy, recall rate, and F1 scores. User feedba

关键词： Heart rate Accuracy System performance signal processing algorithms Computer architecture Cameras Real-time systems Motion capture Biomedical monitoring Monitoring

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Investigation on End-to-End Congestion Control Algorithm in Multi-Hop Ad-Hoc Network

Investigation on End-to-End Congestion Control Algorithm in ...

引用

International Conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： M. Joseph Auxilius Jude V.C Diniesh A. Sneha S. Subhika S. Vimal Sanjeevi M. Sri Yaalini ECE Kongu Engineering College Perundurai India

ISBN: (数字)9798350350845

ISBN: (纸本)9798350350852

A multi-hop wireless network forms a formidable solution to overcome spectrum deficit and coverage issues in wireless conditions. As wireless internet remains crucial in connecting people from remote locations, a vast chunk of web traffic relies solely on Transmission Control Protocol (TCP) to deliver packets between end systems. In the paper, an initiative is made to analyze the traffic stability under a multi-hop wireless link. TCP manages congestion by adjusting the transmission window size. In this regard, two diverse end-to-end traffic or congestion control algorithms, i.e., linear and concave-based window growth patterns, are subjected to evolution in mobility, traffic, and error rate conditions. The traffic performance of the wireless network is measured with these algorithms using Network Simulator-3 (NS-3).

关键词： TCP Protocols Wireless networks signal processing algorithms Packet loss Spread spectrum communication signal processing

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Intrinsic Image Decomposition-based Multi-resolution Image Fusion

Intrinsic Image Decomposition-based Multi-resolution Image F...

引用

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

作者： Phonekham Hansana Puhong Duan Kaihua Xiao Xin Guo Zhuojun Xie College of Electrical and Information Engineering Hunan University Changsha China School of Robotics Hunan University Changsha China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Using intrinsic feature fusion, image decomposition offers a sophisticated method for amalgamating multiple images into a unified composite, enhancing visual appeal and informational depth. This process utilizes an intrinsic image decomposition algorithm to break down each input image into its fundamental components: reflectance and illumination. Introducing an innovative super-resolution technique for multi-resolution multispectral image fusion, rooted in Smoothing Filter-based Intensity Modulation (SFIM), the procedure begins by identifying the most suitable guide images to enhance the high-resolution counterparts of each band, aligning with the low-resolution images of the same scene. Subsequently, the super-resolution image is upsampled and sliced, with the average gray value of each slice computed to determine the appropriate guide image slice, guided by a formulated degree value. Following this, the SFIM-enhanced algorithm is applied to the image slices, which are then integrated to generate the final enhanced output. Experimental validation conducted on the Sentinel-2 flood dataset demonstrates the effectiveness of this approach for multispectral image fusion super-resolution. Comparative analysis against alternative image super-resolution methods highlights its superior subjective and objective performance, as well as computational efficiency, establishing its superiority over existing approaches.

关键词： Visualization Smoothing methods Superresolution signal processing algorithms signal processing Image decomposition Computational efficiency Sensors Floods Image fusion

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Unified Structure to Generate Sparsity-Inducing Regularizers for Enhanced Low-Rank Matrix Completion

Unified Structure to Generate Sparsity-Inducing Regularizers...

引用

Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Zhi-Yong Wang Hing Cheung So Department of Electrical Engineering City University of Hong Kong Hong Kong China

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Applying half-quadratic optimization to loss functions can generate the associated regularizers, while these reg-ularizers are usually not able to give a sparse solution. To address it, we put forth an unified structure to produce sparsity-promoting regularizers with closed-form proximity operators. In addition, three commonly-used loss functions are adopted in our structure to generate the corresponding sparsity-inducing regu-larizers, which are then employed as nonconvex rank surrogates to achieve enhanced low-rank matrix completion. Furthermore, algorithms with convergence guarantees are developed, and numerical results demonstrate the effectiveness of our methods in terms of recovery performance and runtime.

关键词： Tensors Runtime Closed-form solutions signal processing algorithms signal reconstruction Sparse matrices Optimization Convergence

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Autonomous TDOA Localization via Swarm UAV Concept

Autonomous TDOA Localization via Swarm UAV Concept

引用

IEEE signal processing and Communications Applications (SIU)

作者： Atakan Yiğit Ali Osman Kalaycý Eren Akdemir ASELSAN A.Ş. Ankara Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Geolocation studies with distributed sensors have an important place in the Electronic Warfare concept. In this study, a Swarm UAV (Unmanned Aerial Vehicle) with TDOA (Time Difference of Arrival) capability was used to determine passive emitter locations in noisy environment. The aim of the study is to increase the accuracy of location estimation on a target land. In this regard, the formation of the Swarm UAV during autonomous flight and the effects of single-step, two-step and multi-measurement, multi-step and multi-measurement position estimations on its performance were analyzed. An autonomous working algorithm that maximizes the location estimation accuracy is provided as the result of analysis.

关键词： Location awareness Accuracy Time difference of arrival Geology Estimation signal processing algorithms signal processing

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Variable Selection for Max-Affine Regression via Sparse Gradient Descent

Variable Selection for Max-Affine Regression via Sparse Grad...

引用

Sensor Array and Multichannel signal processing Workshop

作者： Haitham Kanj Seonho Kim Kiryung Lee Department of Electrical and Computer Engineering The Ohio State University Columbus OH USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Sparse max-affine regression is introduced as a solution to variable selection for non-linear learning problems. A non-asymptotic local convergence analysis for Sparse Gradient Descent (Sp-GD) is presented when covariates are independent and identically distributed Gaussian random vectors. If at most $s$ out of $d$ covariates are actively contributing to the explanation of the target variable and the max-affine model combines a fixed number of linear models, then a suitably initialized Sp-GD linearly approaches the optimal solution and the true active covariates given $n=\mathcal{O}(s\log[(n\vee ed)/s])$ noise-free samples. Numerical Monte Carlo results corroborate these theoretical findings on the phase transition of exact parameter recovery.

关键词： Monte Carlo methods Input variables Conferences signal processing algorithms Vectors Numerical models Complexity theory

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Two-order Wavelet Transform-Based Dual-Tamper Detection and Self-Restoration

Two-order Wavelet Transform-Based Dual-Tamper Detection and ...

引用

Communication, Computing and signal processing (IICCCS), IEEE International Conference on

作者： Pavan A. C M. T. Somashekara Department of Computer Science and Applications Bangalore University Bengaluru India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

In this research, we propose a neat dual-tampering detection approach to further retrieve the forged content of 1080p images. The recovering of actual information is derived from the low-frequency components of the second-order wavelet. The procedure initiates with the segmentation of the image into a non-underlying section. To make better the quality of the recuperate image, a radially symmetrical mapping technique is employed to safeguard the recovery bits. The most notable bit-plane of each segment is utilized to generate the first component authentication key through the MD5 algorithm, while the second component authentication key is produced using SHA1 based on the recovered data. Subsequently, the watermarked image is formed by embedding the recovery bits and the dual authentication keys into the least significant bits of the original image. During the tampering detection phase, the calculated authentication keys are compared against the retrieved keys. Any discrepancies result in the block being classified as tampered. Following this, centrosymmetric mapping is applied to extract the low-frequency coefficients of the second-order wavelet for the purpose of image self-recovery. The inverse wavelet transform is then executed to restore the tampered regions with the recovery data, thereby finalizing the self-recovery process. Our experimental findings indicate that this approach yields enhanced recovery quality and more effectively conceals the embedded information.

关键词： Wavelet transforms Resistance Location awareness Image segmentation Databases Authentication signal processing algorithms signal processing Image restoration Telecommunication computing

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A Fast and Robust MUSIC Algorithm for Estimating Multiple Coherent signals

A Fast and Robust MUSIC Algorithm for Estimating Multiple Co...

引用

Communication, Image and signal processing (CCISP), International Conference on

作者： Mingyang Zhang Lihai Ji Pengyan Jia Shanghai Zhangjiang Institute of Mathematics Shanghai China Institute of Applied Physics and Computational Mathematics and Shanghai Zhangjiang Institute of Mathematics Beijing China

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

The conventional MUSIC algorithm demonstrates subpar performance in estimating coherent signals from multiple targets and yields unreliable results. Moreover, it suffers from high computational complexity and sluggish processing speed when applied to extensive datasets involving multiple sensors. In order to tackle these challenges, this paper presents an enhanced and expedient version of the MUSIC algorithm for estimating multiple coherent signals. Drawing upon the ROOT-MUSIC algorithm, which is based on the propagation operator, this algorithm introduces a spatial smoothing technique by substituting the original covariance matrix with the average of subarray covariance matrices. Simulation results demonstrate that the proposed algorithm not only resolves the issue of estimating multiple coherent signals but also achieves exceptional performance in terms of robustness and computational speed, even under low signal-to-noise ratio conditions.

关键词： Smoothing methods Simulation signal processing algorithms Robustness Multiple signal classification Sensors Covariance matrices Spatial resolution signal resolution signal to noise ratio

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Sleep Stage Classification Using EEG signals: A Empirical Wavelet Transform-Based Decomposition Approach

Sleep Stage Classification Using EEG Signals: A Empirical Wa...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Rajveer Singh Lalawat Varun Bajaj Prabin Kumar Padhy Department of Electronics and Communication Engineering Indian Institute of Information Technology Design and Manufacturing Jabalpur Jabalpur India Department of Electronics and Communication Maulana Azad National Institute of Technology Bhopal Bhopal India

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

The objective of signal processing using transformation techniques, is to accurately detect the frequency decomposition and extract small variations within the signals. Previous studies have explored various algorithms aiming to decompose signals and classify sleep stages based on EEG signals, offering valuable insights. However, there is still scope for improvement in handling highly non-stationary signals across all sleep stages. The article proposes employing spectrum partitioning from six sleep phases and the Empirical Wavelet Transform (EWT) to properly decompose extremely non-stationary EEG data. This approach uses an adaptive filter bank to control dynamic data in the frequency domain to accurately distinguish sleep stages. Our experiments demonstrate that, the Support Vector Machines (SVM) classifier outperforms Random Forest (RF) and k-Nearest Neighbors (KNN) classifiers, and it achieves notable classification accuracy for all sleep stages. This method enhances the performance of medical devices and benefits for their use in healthcare.

关键词： Support vector machines Wavelet transforms Sleep signal processing algorithms Nearest neighbor methods Feature extraction Brain modeling Electroencephalography Data models Classification algorithms

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