22-05-2012, 11:35 AM
Modeling and Simulation of a wireless sensor data acquisition system using PCM algorithms
[attachment=22669]
INTRODUCTION
Recent advances in integrated sensors have made a
realization of smart microsystems combining a large
mixture of micro-sensors and signal processing circuitries.
This will have a significant impact on a variety of
applications such as health care, consumer electronics,
environmental monitoring and medical diagnosis [1,2].
Such systems are required to have many attributes, such as
low cost, robusmess and real-time data processing.
System Specifications
Figure.2 shows the main task and functions that should be
implemented by the proposed system. In the first stage, two
different analog signals obtained from a real data [7] are
simulated and encoded into digital signal. The sources of the
two signals are a pH and pressure sensors that have been
used for medical applications. As shown in Figures 3 and 4,
the rate of change of the two analog data are quite different,
such that pressure signal is rapidly changing in a random
way, while the pH taking a relatively stable values over a
certain periods.
Pulse Code Modulation Algorithms (PCM)
PCM is the most obvious method developed for digital
coding of waveforms and it is often used for speech signals,
which have a non-stationary nature. Essentially it refers to
the process of quantizing the samples of discrete-time
signal, so that both time and amplitude are represented in a
discrete form. Three different PCM methods have been
adapted in the proposed system, these are [8,9]:
Uniform Pulse Code Modulation (UPCM)
It is a digital representation of an analog signal where the
magnitude of the signal is sampled regularly at uniform
intervals of duration. Every sample is quantized to a series
of symbols in a digital code, which is usually a binary code.
A uniform quantizer with an even number of outputs values
is employed in this method. For a uniformaly distributed
input variable x with standard deviation U and zero mean,
the probability density function (PDF) is:
Differential Pulse Coding Modulation (DPCM)
DPCM is based on the notion of quantizing the predictionerror
signal. In many signal sources, samples are correlated
with their neighbours, so the cnrrent sample can be easily
predicted from the past history by forming the predictionerror
signal. By quantizing the prediction error, a higher
signal-to-noise ratio can be achieved for a given resolution.
This method is presented in Figure 5, where the prediction
error e[n], obtained by subtracting the input x[n] from the
prediction xp[n], is quantized. "be indices at the output of
the quantizer's encoder represent the DPCM bit stream. The
DPCM decoder works in a similar fashion to the encoder in
order to obtain the quantized samples from the indices [SI.
[attachment=22669]
INTRODUCTION
Recent advances in integrated sensors have made a
realization of smart microsystems combining a large
mixture of micro-sensors and signal processing circuitries.
This will have a significant impact on a variety of
applications such as health care, consumer electronics,
environmental monitoring and medical diagnosis [1,2].
Such systems are required to have many attributes, such as
low cost, robusmess and real-time data processing.
System Specifications
Figure.2 shows the main task and functions that should be
implemented by the proposed system. In the first stage, two
different analog signals obtained from a real data [7] are
simulated and encoded into digital signal. The sources of the
two signals are a pH and pressure sensors that have been
used for medical applications. As shown in Figures 3 and 4,
the rate of change of the two analog data are quite different,
such that pressure signal is rapidly changing in a random
way, while the pH taking a relatively stable values over a
certain periods.
Pulse Code Modulation Algorithms (PCM)
PCM is the most obvious method developed for digital
coding of waveforms and it is often used for speech signals,
which have a non-stationary nature. Essentially it refers to
the process of quantizing the samples of discrete-time
signal, so that both time and amplitude are represented in a
discrete form. Three different PCM methods have been
adapted in the proposed system, these are [8,9]:
Uniform Pulse Code Modulation (UPCM)
It is a digital representation of an analog signal where the
magnitude of the signal is sampled regularly at uniform
intervals of duration. Every sample is quantized to a series
of symbols in a digital code, which is usually a binary code.
A uniform quantizer with an even number of outputs values
is employed in this method. For a uniformaly distributed
input variable x with standard deviation U and zero mean,
the probability density function (PDF) is:
Differential Pulse Coding Modulation (DPCM)
DPCM is based on the notion of quantizing the predictionerror
signal. In many signal sources, samples are correlated
with their neighbours, so the cnrrent sample can be easily
predicted from the past history by forming the predictionerror
signal. By quantizing the prediction error, a higher
signal-to-noise ratio can be achieved for a given resolution.
This method is presented in Figure 5, where the prediction
error e[n], obtained by subtracting the input x[n] from the
prediction xp[n], is quantized. "be indices at the output of
the quantizer's encoder represent the DPCM bit stream. The
DPCM decoder works in a similar fashion to the encoder in
order to obtain the quantized samples from the indices [SI.