background: in recent years, adipose tissue, due to the stem cells contained within, has found a new special place in laboratory and clinical applications. these adipose‑derived stem cells (adscs) have the same characteristics of bone marrow mesenchymal stem cells (bmscs). although bone marrow (bm) is not easily accessible and its procurements may be painful, most patients possess excess fat wh...

A central characteristic of ad hoc mobile networks is the frequent changes of their topology. This is the source of many problems that need to be solved. AODV is an on-demand routing protocol for decreasing maintenance overhead in ad hoc networks. However, some path breaks can cause significant overhead and transmission delays. If the maintenance overhead of the routing table can be reduced, ta...

In this paper, we propose a novel sparse source separation method that can estimate the number of sources and time-frequency masks simultaneously, even when the spatial aliasing problem exists. Recently, many sparse source separation approaches with time-frequency masks have been proposed. However, most of these approaches require information on the number of sources in advance. In our proposed...

In the research project called !fA Guide map for Finnish Rural Areas" we have developed an entirely new map production process as well as the map output. The goal was to develop a map for different distribution purposes, for emergency vehicle use as well as for general transportation. The source information was gathered as follows: land use information was interpreted from Landsat satellite ima...

A recent result of J. Damon’s [4] relates the Ae-versal unfoldings of a map-germ f with the KD(G)-versal unfoldings of an associated map germ which induces f from a stable map G. We extend this result to the case where the source is a complete intersection with an isolated singularity. In a similar vein, we also relate the bifurcation theoretic versal deformation of a bifurcation problem (map-g...

Quantized maximum a posteriori (Q-MAP) is a recently-proposed Bayesian compressed sensing algorithm that, given the source distribution, recovers X from its linear measurements Y m = AX, where A ∈ R denotes the known measurement matrix. On the other hand, Lagrangian minimum entropy pursuit (L-MEP) is a universal compressed sensing algorithm that aims at recovering X from its linear measurements...