Step detection

Résumé

In statistics and signal processing, step detection (also known as step smoothing, step filtering, shift detection, jump detection or edge detection) is the process of finding abrupt changes (steps, jumps, shifts) in the mean level of a time series or signal. It is usually considered as a special case of the statistical method known as change detection or change point detection. Often, the step is small and the time series is corrupted by some kind of noise, and this makes the problem challenging because the step may be hidden by the noise. Therefore, statistical and/or signal processing algorithms are often required. The step detection problem occurs in multiple scientific and engineering contexts, for example in statistical process control (the control chart being the most directly related method), in exploration geophysics (where the problem is to segment a well-log recording into stratigraphic zones), in genetics (the problem of separating microarray data i

Source officielle

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

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (10)

Publications associées

Chargement

Personnes associées

Chargement

Unités associées

Chargement

Concepts associés

Chargement

Cours associés (1)

Cours associés

Chargement

Séances de cours associées (1)

Séances de cours associées

Chargement

This project was carried out at Overview Ltd., a London company which specialises in high precision video-surveillance equipment. Overview produces devices that include a camera driven by two stepper motors, and an image-processing unit. Magnetic encoders are used to detect external disturbances and failures. Overview is willing to develop an algorithm capable of detecting lost steps, and in the longer term, capable of giving precise position related information without using encoders.

2007

Chargement

One Step from the Locomotion to the Stepping Pattern

Ronan Boulic, Utku Evci, Eray Molla

The locomotion pattern is characterized by a translation displacement mostly occurring along the forward frontal body direction, whereas local repositioning with large re-orientations, i.e. stepping, may induce translations both along the frontal and the lateral body directions (holonomy). We consider here a stepping pattern with initial and final null speeds within a radius of 40% of the body height and re-orientation up to 180°. We propose a robust step detection method for such a context and identify a consistent intra-subject behavior in terms of the choice of starting foot and the number of steps.

ACM Press2016

Chargement

In this thesis, we deal with one of the facets of the statistical detection problem. We study a particular type of alternative, the mixture model. We consider testing where the null hypothesis corresponds to the absence of a signal, represented by some known distribution, e.g., Gaussian white noise, while in the alternative one assumes that among observations there might be a cluster of points carrying a signal, which is characterized by some distribution G. The main research objective is to determine a detectable set of alternatives in the parameter space combining the parameters of G and the mixture proportion p. We focus is on the finite sample sizes and wish to study the possibility of detecting alternatives for fixed n, given pre-specified error levels. The first part of the thesis covers theoretical results. Specifically, we introduce a parametrization which relates the parameter space of an alternative to the sample size, and present the regions of detectability and non-detectability. The regions of detectability are the subsets of the new parameter space (induced by the parametrization) where for a prespecified type I error rate, the type II error rate of the likelihood ratio test (LRT) is bounded from above by some constant. To move towards the real data applications, we also check the performance of some non-parametric testing procedures proposed for this problem and some widely used distributions. In the second part of the thesis, we use this argument to develop a framework for clinical trial designs aimed at detecting a sensitive-to-therapy subpopulation. The idea of modeling treatment response as a mixture of subpopulations originates from treatment effect heterogeneity. Methods studying the effects of heterogeneity in the clinical data are referred to as subgroup analyses. However, designs accounting for possible response heterogeneity are rarely discussed, though in some cases they might help to avoid trial failure due to the lack of efficacy. In our work, we consider two possible subgroups of patients, drug responders and drug non-responders. Given no preliminary information about patients' memberships, we propose a framework for designing randomized clinical trials that are able to detect a responders' subgroup of desired characteristics. We also propose strategies to minimize the number of enrolled patients whilst preserving the testing errors below given levels and suggest how the design along with all testing metrics can be generalized to the case of multiple centers. The last part of the thesis is not directly related to the preceding parts. We present two supervised classification algorithms for real-data applications.

EPFL2018

Chargement

EPFL2018