mobile sampling of sensor field data using controlled broadcast full documentation ....etc

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The physical and chemical characteristics of drinking water vary from top to bottom of depth of land, as well as with time as from morning to evening. It therefore becomes difficult to obtain a truly representative sample. ....etc

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Mobile Sampling of Sensor Field Data Using Controlled Broadcast

ABSTRACT:
Mobile objects can be used to gather samples from a sensor field. Civilian vehicles or even human beings equipped with proper wireless communication devices can be used as mobile sinks that retrieve sensor-data from sampling points within a large sensor field. A key challenge is how to gather the sensor data in a manner that is energy efficient with respect to the sensor nodes that serve as sources of the sensor data. In this paper, an alg ....etc

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Abstract.
We present a randomized procedure named Hierarchical Sampling from Sketches (HSS) that can be used for estimating a class of functions over the frequency vector f of update streams of the form (S) = Pn i=1 (|fi|).We illustrate this by applying the HSS technique to design nearly space-optimal algorithms for estimating the pth moment of the frequency vector, for real p _ 2 and for estimating the entropy of a data stream. 3
1 Introduction
A variety of applications in diverse areas, such as, networking, database sys ....etc

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my project topic impact of sampling jittter in uwb communication. using matlab of 7.6..i want project ideas . existing work and proposed work of uwb in my paper.. full report also.. ....etc

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Procedure: -
1. Select a frequency of ‘f’ Hz
2. To generate a sine wave of F Hz, define a closely spaced time vector(to represent analog signal, at least 1000 samples pr cycle)t=0:1/1000:1;
3. Generate the sinusoid, and plot the signal x_analog=sin(2*pi*F*t)
4. Select a sampling frequency Fs=10Fsamples/sec,generate a suitable time scale for this sampling signal n=0:1/Fs:1;%vector to define the sampling instants
5. Sample the analog signal at the instants specified by ’n ’
x_discreate =sin (2*pi*F*n) plot this using separa ....etc

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Sampling Moments and Reconstructing Signals

Consider the problem of sampling signals which are not bandlimited, but still have a finite number of degrees of freedom per unit of time, such as, for example, piecewise polynomial or piecewise sinusoidal signals, and call the number of degrees of freedom per unit of time the rate of innovation. Classical sampling theory does not enable a perfect reconstruction of such signals since the band is not limited.

Recently, it was shown that by using an adeq ....etc

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