In the classical problem of quickest change detection, a decision maker observes a sequence of random variables. At some point in time, the distribution of the random variables changes abruptly. The objective is to detect this change in distribution with minimum possible delay, subject to a constraint on the false alarm rate. In many applications of quickest change detection, e.g., where the ch...

The literature displays change point detection problems in the context of one of the key issues that belong to testing statistical hypotheses. The main focus in this article is to review recent retrospective change point policies and propose new relevant procedures. Commonly applied practical quality control purposes have declared statements of the change point problems. Various biostatistical ...

The new generation of Synthetic Aperture Radar (RADARSAT-2, TerraSAR-X, ALOS, . . . ) allows us to capture Earth surface images with very high resolution. Therefore the possibility to characterize objects has become more and more attainable. Moreover, the short revisit time propertie of these satellites enables the development of techniques of change detection and their applications. Sphericall...

groundwater drought denotes the condition and hazard during a prolonged meteorological drought when groundwater resources decline and become unavailable or inaccessible for human use. the aim of this study is to identify the influencial factors on groundwater drought in hamadan- bahar watershed,iran, to understand the forcing mechanisms. the standardised precipitation index (spi) has been used ...

land use may be regarded as one of the most important factors affecting theenvironment with respect to human activities. so far, destroying the rangelands andchanging them into the waste lands and poor rangelands has been proposed as the mostsignificant variations of land use done by human beings. this paper has been conducted toevaluate the variations of vegetation percentage and land uses in ...

PURPOSE Signal detection methods have been used extensively in postmarket surveillance to identify elevated risks of adverse events associated with medical products (drugs, vaccines, and devices). However, current popular disproportionality methods ignore useful information such as trends when the data are aggregated over time for signal detection. METHODS In this paper, we applied change poi...

Non-neutral speech data has a strong negative impact on speech processing systems such as Automatic Speech Recognition (ASR) or speaker ID systems [1]. It is therefore necessary to detect and segment non-neutral speech data before further processing steps. Alternatively, the detection and segmentation of non-neutral speech segments from an input speech stream can be used in speech analysis and ...

A number of real world problems in many domains (e.g. sociology, biology, political science and communication networks) can be modeled as dynamic networks with nodes representing entities of interest and edges representing interactions among the entities at different points in time. A common representation for such models is the snapshot model where a network is defined at logical time-stamps. ...

A core problem in time series data is learning when things change. This problem is especially challenging when changes appear gradually and at varying timescales, such as in health. Convolutional Neural Networks (CNNs) have the potential to recognize and localize complex patterns, but are sensitive to scale. We propose a new class of scale and shift invariant neural networks that augment CNNs w...