Sampling of Graph Signals With Successive Local Aggregations

Spectral Domain Sampling of Graph Signals

Sampling methods for graph signals in the graph spectral domain are proposed. Conventional sampling of graph signals can be regarded as sampling in the graph vertex domain, but it does not have desired characteristics in regard to the graph spectral domain. Downand upsampled graph signals by using our methods inherit the frequency domain characteristics of sampled signals defined in the time/sp...

Structured sampling and fast reconstruction of smooth graph signals

This work concerns sampling of smooth signals on arbitrary graphs. We first study a structured sampling strategy for such smooth graph signals that consists of a random selection of few pre-defined groups of nodes. The number of groups to sample to stably embed the set of k-bandlimited signals is driven by a quantity called the group graph cumulative coherence. For some optimised sampling distr...

dynamic coloring of graph

در این پایان نامه رنگ آمیزی دینامیکی یک گراف را بیان و مطالعه می کنیم. یک –kرنگ آمیزی سره ی رأسی گراف g را رنگ آمیزی دینامیکی می نامند اگر در همسایه های هر رأس v?v(g) با درجه ی حداقل 2، حداقل 2 رنگ متفاوت ظاهر شوند. کوچکترین عدد صحیح k، به طوری که g دارای –kرنگ آمیزی دینامیکی باشد را عدد رنگی دینامیکی g می نامند و آنرا با نماد ?_2 (g) نمایش می دهند. مونت گمری حدس زده است که تمام گراف های منتظم ...

Successive Sampling and Software Reliability

Inference in Successive Sampling Discovery

A variety of practical problems of nite population inference can be addressed in the framework of successive sampling discovery models { population units are assumed drawn from a superpopulation distribution and then successively sampled according to a speciied`size-biased' selection mechanism. Formal statistical analysis of discovery data under such models is technically challenging, as exempl...