A control chart for detecting shifts in the variance of a process is developed for the case where the nominal value of the variance is unknown. As our approach does not require that the in-control variance be known a priori, it avoids the need for a lengthy Phase I data-gathering step before charting can begin. The method is a variance-change-point model, based on the likelihood ratio test for a change in variance with the conventional Bartlett correction, adapted for repeated sequential use. The chart may be used alone in settings where one wishes to monitor one-degree-of-freedom chi-squared variates for departure from control; or it may be used together with a parallel change-point methodology for the mean to monitor process data for shifts in mean and/or variance. In both the solo use and as the scale portion of a combined scheme for monitoring changes in mean and/or variance, the approach has good performance across the range of possible shifts.

A change-point model for a shift in variance

Focusing on generalized sensor combinations, this paper deals with the attitude estimation problem using a linear complementary filter (CF). The quaternion observation model is obtained via a gradient descent algorithm. An additive measurement model is then established according to derived results. The filter is named as the generalized CF where the observation model is simplified as a linear one that is quite different from previous-reported brute-force nonlinear results. Moreover, we prove that representative derivative-based optimization algorithms are essentially equivalent to each other. Derivations are given to establish the state model based on the quaternion kinematic equation. The proposed algorithm is validated under several experimental conditions involving the free-living environment, harsh external field disturbances, and aerial flight test aided by robotic vision. Using the specially designed experimental devices, data acquisition and algorithm computations are performed to give comparisons on accuracy, robustness, time-consumption, and so on with representative methods. The results show that not only the proposed filter can give fast, accurate, and stable estimates in terms of various sensor combinations but also produces robust attitude estimation in the scenario of harsh situations, e.g., irregular magnetic distortion. Note to Practitioners —Multisensor attitude estimation is a crucial technique in robotic devices. Many existing methods focus on the orientation fusion of specific sensor combinations. In this paper, we make the problem more concise. The results given in this paper are very general and can significantly decrease the space consumption and computation burden without losing the original estimation accuracy. Such performance will be of benefit to robotic platforms requiring flexible and easy-to-tune attitude estimation in the future.

https://hal.archives-ouvertes.fr/hal-01958652/document

Generalized Linear Quaternion Complementary Filter for Attitude Estimation From Multisensor Observations: An Optimization Approach

40 years of sensor fusion for orientation tracking via magnetic and inertial measurement units: Methods, lessons learned, and future challenges

In current healthcare systems, patients use various types of medical Internet of Things devices for monitoring their health conditions The collected information (personal health records) will be sent back to hospitals for diagnosis and quick responses However, severe security and privacy leakages with regard to data privacy and identity authentication are incurred because the monitored health data contains sensitive information Therefore, the data should be well protected from unauthorized entities Unfortunately, traditional cryptographic approaches or password-based mechanisms cannot fulfill the privacy and security demands in health monitoring due to their low efficiency and knowledge-based property Biometric authentication overcomes these deficiencies and successfully verifies the inherent characteristics of humans Among all biometrics, the electrocardiogram (ECG) signal is the most suitable one due to its medical properties However, the security and privacy objectives of ECG-based authentication usually fail in practice due to the noise interferences in the collected ECG data and the privacy breach of the ECG database In this paper, we propose a practical scheme that can reliably authenticate patients with noisy ECG signals and provide differentially private protection simultaneously The effectiveness and efficiency of our scheme are thoroughly analyzed and evaluated over online datasets We also conduct a pilot study on human subjects experiencing different exercise levels to validate our scheme

https://par.nsf.gov/servlets/purl/10108265

Practical Privacy-Preserving ECG-Based Authentication for IoT-Based Healthcare

Multiple Model Adaptive Complementary Filter for Attitude Estimation

With rapid advancements in the area of mobile robotics and industrial automation, a growing need has arisen towards accurate navigation and localization of moving objects. Camera based motion estimation is one such technique which is gaining huge popularity owing to its simplicity and use of limited resources in generating motion path. In this paper, an attempt is made to introduce this topic for beginners covering different aspects of vision based motion estimation task. The evolution of VO schemes over last few decades is discussed under two broad categories, that is, geometric and non-geometric approaches. The geometric approaches are further detailed under three different classes, that is, feature-based, appearance-based, and a hybrid of feature and appearance based schemes. The non-geometric approach is one of the recent paradigm shift from conventional pose estimation technique and is thus discussed in a separate section. Towards the end, a list of different datasets for visual odometry and allied research areas are provided for a ready reference.

Evolution of Visual Odometry Techniques.

Window based Multiple Model Adaptive Estimation for Navigational Framework

https://isiarticles.com/bundles/Article/pre/pdf/53022.pdf

Adaptive Sliding Kalman Filter using Nonparametric Change Point Detection

Motion estimation from inertial sensors is an art of computing translation and rotation about each axis of the vehicle to which it is strapped to. These inertial sensors, however, suffer from errors and noise which need to be calibrated and compensated, respectively. With growing use of low cost MEMS based inertial sensors in autonomous vehicles, it is desired to calibrate these systems online and infield without requiring any external equipment. The in-field calibration schemes are based on the simple principle that the norms of the accelerometer triads equals the magnitude of the Earth's gravity. In this paper, a particle swarm optimization scheme and few of its variants are used for estimating bias, scale and non-orthogonality parameter for an uncalibrated accelerometer. The proposed scheme provides a matrix of unknown parameters which can be fed to obtain calibrated sensor readings. And finally, an improved version of PSO has been shown to provide better calibration results as compared to other variants of particle swarm algorithms.

Rahul Kottath

Papers

Multiple Model Adaptive Complementary Filter for Attitude Estimation

Evolution of Visual Odometry Techniques.

Window based Multiple Model Adaptive Estimation for Navigational Framework

Adaptive Sliding Kalman Filter using Nonparametric Change Point Detection

Adaptive parameter based Particle Swarm Optimisation for accelerometer calibration