Vision Algorithms For VLSI Wafer Probing

Large numbers of sensors networked together form a powerful infrastructure for a wide variety of applications in health, military, home, manufacturing, and disaster areas. Networked video sensors over a geographical area can automatically detect the objects in that geographical area. However, real-time central processing of the large amount of data generated by the individual image sensors places significant demands on the bandwidth and the central processor.In this paper, we address this issue by introducing a novel concept of super-sensor that is based on selforganization and collaboration between several tens of sensors. As an example, we describe a histogram calculation that uses recursive doubling for global collaboration between sensors. We compare the performance of our networked super-sensor with conventional image processing algorithms run on a single processor.

show abstract

“…This is done through a form of recursive doubling [3]. Suppose that there are B grey levels in the image.…”

Section: Iteration 4 Transfer Of Vector Of X Elementsmentioning

confidence: 99%

Collaborative vision using networked sensors

Dantu

Joglekar

2004

International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004.

View full text Add to dashboard Cite

show abstract

“…Each strip is allocated to a sensor that calculates a partial histogram of the strip assigned to it. These partial histograms are merged using recursive doubling [5].…”

Section: Analytic Model For Histogrammingmentioning

confidence: 99%

IEEE Global Telecommunications Conference

2004

IEEE Global Telecommunications Conference 2004 GLOBECOM 04 GLOCOM-04

View full text Add to dashboard Cite

Meteorological and hydrological sensors deployed over several hundred kilometers of geographical area comprise an environmental sensor network. Large amounts of data need to be processed in minimal time and transmitted over the available low speed and low bandwidth links. This paper describes algorithms for optimal data collection and data fusion. An inductive model using exponential back-off policy is used to collect optimal amount of data. The data measurements for temperature, pH and specific conductance collected for a year from the sensors deployed at Lake Lewisville are used to test the inductive model. Energy savings of 90% are achieved even with 1% of degree of tolerance. The problem of data fusion is addressed by the introduction of a novel concept of a super-sensor, based on self-organization and collaboration among sensors. A histogram application is described that uses recursive doubling for global collaboration between sensors. The performance of the networked supersensor in comparison to a centralized polling approach is analyzed for optimality on two different geographical areas. I.

show abstract

Vision Algorithms For VLSI Wafer Probing

Cited by 2 publications

References 0 publications

Collaborative vision using networked sensors

Collaborative vision using networked sensors

IEEE Global Telecommunications Conference

Contact Info

Product

Resources

About