Motion detection

Summary

Motion detection is the process of detecting a change in the position of an object relative to its surroundings or a change in the surroundings relative to an object. It can be achieved by either mechanical or electronic methods. When it is done by natural organisms, it is called motion perception. Methods Motion detector#Technology Motion can be detected by monitoring changes in: *Infrared light (passive and active sensors) *Visible light (video and camera systems) *Radio frequency energy (radar, microwave and tomographic motion detection) *Sound (microphones, other acoustic sensors) *Kinetic energy (triboelectric, seismic, and inertia-switch sensors) *Magnetism (magnetic sensors, magnetometers) *Wi-Fi Signals (WiFi Sensing) Mechanical The most basic forms of mechanical motion detection utilize a switch or trigger. For example, the keys of a typewriter use a mechanical method of detecting motion, where each key is a switch that is either off or on, and each le

Official source

https://en.wikipedia.org/wiki/Motion_detection

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Nonlinear Filtering for Switch Detection in Hybrid Models of Genetic Regulatory Networks

Giancarlo Ferrari Trecate

2006

This paper considers piecewise affine models of genetic regulatory networks and focuses on the problem of detecting switches among different modes of operation in gene expression data. This task constitutes the first step of a procedure for the complete identification of the network and complements the algorithms proposed in (Drulhe et al., 2006). We propose two methods for switch detection. The first one is based on the computation of suitable indexes that emphasize the occurrence of switches in the data. The second one exploits nonlinear identification techniques in order to recast switch detection into an hypothesis testing problem. In both cases we assume that the expression of individual genes obeys to an output-error piecewise affine dynamics and we study the performance of the proposed algorithms for different noise levels. We also illustrate the application of our methods to the reconstruction of switching times in data produced by a piecewise affine model of the network regulating the carbon starvation response in Escherichia coli.

2006

Magnetic resonance imaging (MRI) is highly susceptible to subject's motion and can significantly degrade image quality. In brain MRI exams, involuntary head movements can affect the sampled k-space data. Such unintended alterations may result in visible image artifacts such as blurring, ghosting and others, and therefore potentially disqualify the image from diagnostic purposes. Methods to characterize motion in order to mitigate its impact on image quality exist and range from MR sequence based techniques to scanner independent tracking systems. Although, many motion detection and correction strategies have been proposed in the past, a universal solution to the problem does not exist yet. The work of this thesis was focused on the exploitation of the motion information from a multi-channel Free Induction Decay Navigator (FID) to develop and to optimize motion detection and correction methods in structural brain MRI. After a short introduction to the motion problem in MRI the fundamental methodology behind FID based motion detection is presented and used in this thesis. Considerable work has already been done in the field of motion correction for MRI that is summarized by reviewing the most recent literature, which allowed to reveal some pitfalls in the present approaches and to demonstrate the motivation behind an FID-based method for motion correction in MRI. The first study was conducted to demonstrate that substantial motion information is contained in the multi-channel FID signal, whereby the FID signal is correlated with motion parameters that were obtained from a highly accurate optical tracking system. This work was able to confirm the theoretical foundations from the Biot-Savart law, however, also revealed that a pure FID-based method is not sufficient to exactly calculate the underlying motion trajectory. It is speculated that scanner and subject related information might lead to a closed form solution, yet it was not possible to derive one due to a high dimensionality of the motion problem. Therefore, two alternative approaches were developed to utilize the FID signal for motion detection and correction in MRI. First, a prospective motion correction strategy for an MP-RAGE sequence is demonstrated, whereby the FID signal is used to trigger a prospective motion correction mechanism. The second alternative approach describes how the FID signal can be used to evaluate the quality of an already acquired image and how the FID signal can be used as an optimizer for an autofocusing based retrospective motion correction technique.

EPFL2016