A practical introduction to signal processing in scientific measurement, for scientists, engineers, researchers, instructors, and students working in academia, industry, environmental, medical, engineering, earth science, space, military, financial, and agriculture. Includes such topics as smoothing, differentiation, peak detection, integration and peak area measurements, harmonic analysis, convolution and deconvolution, Fourier filtering, least-squares curve fitting, multi-component spectroscopy, nonlinear iterative least-squares peak fitting. Provides many demonstrations and real data examples, plus download links to many free Matlab/Octave scripts and functions plus spreadsheet templates which has been widely used and have been cited in over 160 published papers, theses, and patents. Includes an appendix containing case studies, examples, and simulations, plus an alphabetical index.

A Pragmatic Introduction to Signal Processing: with applications in scientific measurement

Based on analysis of basic cubic spline interpolation,the traditional cubic spline interpolation is generalized.The methods are presented on the condition that the first derivative and second derivative of arbitrary node are given.At last,these calculation methods are illustrated through the examples.

Cubic spline interpolation on new condition

Combined vision system is a perspective display concept to enhance a situation awareness of the pilots during aircraft landing, which integrates a real 2-D image captured from forward-looking infra...

https://journals.sagepub.com/doi/pdf/10.1177/1729881419845528

Visual–inertial fusion-based registration between real and synthetic images in airborne combined vision system:

The ambulatory motion capture and gait analysis using wearable MEMS based magnetic-inertial measurement units (MIMUs) is challenging despite multisensor fusion and effective anatomical (sensor-to-segment) calibration. The MEMS based sensors show degraded performance when run for long time, especially indoors. This is due to the fact that assumption of no acceleration except gravity and homogenous magnetic field no longer holds, when the motion is being performed. The rate gyro is used to complement the accelerometer/ magnetometer for orientation estimation, but integration of its residual biases as well as noise eventually causes the sensor fusion estimates to drift. The errors in heading angle or yaw are particular significant due to persistent nature of magnetic inhomogeneity in the environment. This ultimately results in inaccurate and drifting joint angle estimates between body segments that would require some means of correction. In present work, we propose a new adaptive covariance based EKF for sensor fusion which makes it effectively robust to both dynamic body accelerations as well as inhomogeneous magnetic field. The adaptive covariance method penalizes the bad accelerometer and magnetometer measurements and intelligently updates the gyro biases online using only undisturbed readings of accelerometer/magnetometer. Our sensor fusion algorithm provides accurate orientation estimates for each MIMU node over time. In order to account for any residual drift of joint angles, we propose a novel correction term in our anatomical formulation that performs online correction of drift in individual joint angles and updates it as an orientation offset. This offset correction for joint angle is performed automatically when the limb or extended torso are in neutral quasi-static pose and this condition is judged using accelerometers. Overall our approach achieves precise orientation estimates in highly dynamic conditions and avoids drift or error accumulation due to inhomogeneous magnetic fields during inertial motion capture.

Inertial Motion Capture Using Adaptive Sensor Fusion and Joint Angle Drift Correction

This paper proposes a feasible and low-cost solution for designing optical-inertial hybrid motion capture systems by integrating two subsystems: the inertial subsystem and the optical subsystem. We calibrate the relative pose of the optical sensor module and the human joint to establish the transformation between the joint parameters and the optical sensor data, so that the data fusion can be performed under the same reference frame. To construct the above motion capture system, an optical-inertial data fusion scheme is proposed, which leverages the optical data of key nodes of the human body to correct the inertial data with accumulated errors. The proposed system can support long-term drift-free operation with a low-cost hardware setup, avoiding the drift problem of the inertial motion capture system. Simulation experiments have shown that optical data can reduce the drift error to the centimeter level. The practical experiment shows that the virtual character can complete the loop detection after the inertial position is corrected by the optical data. Our motion capture system outputs standard motion capture data, which can be directly imported into engines such as Unity3D and Unreal.

A Low-Cost Drift-Free Optical-Inertial Hybrid Motion Capture System for High-Precision Human Pose Detection

Cameras and Inertial Measurement Units are widely used for motion tracking and general activity recognition. Sensor fusion techniques, which employ both Vision- and IMU-based tracking, rely on their precise synchronization in time and relative pose calibration. In this work, we propose a novel technique for solving both time and relative pose calibration between an optical target (OT) and an inertial measurement unit (IMU). The optical tracking system gathers 6DoF position and rotation data of the OT and the proposed approach uses them to simulate accelerometer and gyroscope readings to compare them against real ones recorded from the IMU. Convergence into the desired result of relative pose calibration is achieved using the adaptive genetic algorithm.

/pdf/relative-translation-and-rotation-calibration-between-35rltf3dm5.pdf

Relative Translation and Rotation Calibration Between Optical Target and Inertial Measurement Unit

Developments of ambient assistance systems and energy consumption optimization in home environments are one of the main goals of ambient intelligent systems. In this work we propose a wearable standalone solution, which combines the assistance task and the energy optimization task. For this purpose we develop a real-time mobile sound-based device and activity recognizer that senses the audible part of the environment to support its owner during his daily tasks and to help him optimize them in terms of resource consumption.

Svilen Dimitrov

Papers

Relative Translation and Rotation Calibration Between Optical Target and Inertial Measurement Unit

Standalone Sound-Based Mobile Activity Recognition for Ambient Assistance in a Home Environment