Rangeland ecosystems shift across dynamic thresholds between differentecological states in response to natural or human-induced factors. These differentecological states are the result of interactions among climate, soils, grazing history,and management practices. The notion of a single ‘‘pristine’’ final state is onlyconceptual in nature, and because of this, dynamic thresholds and the effects...

To provide services according to user behavior, parameters should be adapted appropriately for the precise recognition of user behavior. In particular, the threshold value which is used to create behavioral patterns matched for behavior recognition impacts accuracy of behavior recognition. Because the threshold value is common to all users in the conventional model, the threshold setting unsu...

In dynamic networks with flexible memberships, group signatures and distributed signatures are an important problem. Dynamic threshold cryptosystems are best suited to realize distributed signatures in dynamic (e.g. meshed) networks. Without a group manager or a trusted third party even more flexible scenarios can be realized. Gennaro et al. [6] showed, it is possible to dynamically increase th...

Unlike localized communication failures that occur on a fixed (although a priori unknown) set of links, dynamic faults can occur on any link. Known also as mobile or ubiquitous faults, their presence makes many tasks difficult if not impossible to solve even in synchronous systems. Their analysis and the development of fault-tolerant protocols have been carried out under two main models. In thi...

The use of neural networks for recognition application is generally constrained by their inherent parameters inflexibility after the training phase. This means no adaptation is accommodated for input variations that have any influence on the network parameters. Attempts were made in this work to design a neural network that includes an additional mechanism that adjusts the threshold values acco...

We discuss a general approach to dynamic sparsity modeling in multivariate time series analysis. Time-varying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing natural mechanisms for dynamic variable inclusion/selection. We discuss Bayesian model specification, analysis and prediction in dynamic regressions, timevarying vector autoregress...

An operational scheme for cloud detection for FY-2C data is presented. The scheme consists of two major parts: total cloud detection and high cloud detection. Cloud detection separates cloud from underline surface, for every pixel, it is either clear pixel or is cloudy pixel. High cloud detection separates high cloud from other cloud types.

Vision-based systems for remote surveillance usually involve change detection algorithms for intruders, obstacles or irregularities detection. Real time applications require simple, fast and reliable algorithms for change detection methodology. Morphological change detection algorithm satisfies these requirements. Threshold is a very important issue for morphological change detection. Threshold...

Detection of faults, such as clogged valves, broken bearings or biased sensors, has beenbrought more and more into focus during the last few decades. There are two mainreasons why faults are important to detect at an early stage. Firstly, faults in safetycritical applications, such as aircraft, nuclear reactors, cars and trains, may create risks ofpersonal injuries. Secondly, fa...