wireless sensor network includes a large number of nodes which are distributed in a geographical location. the essential fact about wsn is that energy of nodes is limited. therefore, presenting proper solutions as an optimized routing is crucial to equally use energy of all nodes. in this paper we propose a method which performs routing in wsns using greedy approach. it is able to choose optimu...

We introduce and study the notion of weak semi-greedy systems—which is inspired in concepts branch systems thresholding sets-, prove that infinite dimensional Banach spaces, notions semi-greedy, almost greedy Markushevich bases are all equivalent. This completes extends some results from (Berná J Math Anal Appl 470:218–225, 2019; Dilworth et al. Studia 159:67–101, 2003; Funct 263:3900–3921, 201...

In recent years, power management and power reduction have become critical issues in portable systems that are designed for real-time use. In this paper, we study the problem of statically allocating a set of independent real-time tasks to a system consisting of heterogeneous processing elements, each enabled with discrete Dynamic Voltage Scaling. The goal is to minimize the overall energy diss...

In this contribution we define a new class of non-linear integrals based on decomposition integrals. These integrals are motivated by greediness of many real-life situations. Another view on this new class of integrals is that it is a generalization of both the Shilkret and PAN integrals. Moreover, it can be seen as an iterated Shilkret integral. Also, an example in time-series analysis is prov...

To address the difficulty of separating background materials from similar associated with use “single-spectral information” for hyperspectral anomaly detection, a fast detection algorithm based on what we term “greedy bilateral smoothing and extended multi-attribute profile” (GBSAED) method is proposed to improve precision operation efficiency. This utilizes smoothing” decompose low-rank part i...

Deep networks have been widely used in many domains in recent years. However, the pre-training of deep networks is time consuming with greedy layer-wise algorithm, and the scalability of this algorithm is greatly restricted by its inherently sequential nature where only one hidden layer can be trained at one time. In order to speed up the training of deep networks, this paper mainly focuses on ...