International Journal of Engineering Trends and Technology (IJETT) – Volume22 Number 5- April2015
Digital Signal Processing On Embedded Processors
M.Sandeep Sarma
Electronics & Instrumentation Department
Gitam University
Visakhapatnam, India
K.V.Ravi Kumar
EIE Dept.
Gitam University
Visakhapatnam
B.Rajesh Kumar
EIE Dept.
Gitam University
Visakhapatnam
Abstract—This paper discusses how to effectively utilize the
embedded processor by stretching it to the different
applications and thereby obtaining the important qualities of
digital electronics we strive for i.e. improving performance and
power with cutting down the price. With the advent of System
on Chip (SoC), we are able to integrate many functional blocks
on a single chip, but by making the SoC to execute different
applications also leads to the hardware optimization thereby
meeting the above digital electronics requirements. In this
paper we discuss how to run different filters to remove the
electrical interferences, baseline wandering and algorithms to
detect the QRS peak i.e. performing Digital Signal Processing
applications on the Cortex M3 microcontroller, which is an
embedded processor not designed mainly for signal processing
applications.
P.H.S.T.Murthy
EIE Dept.
Gitam University
Visakhapatnam
incorporating Digital Signal processing applications on
Cortex M3 microcontroller in processing the ECG signal,
the controller Cortex M3 can be further used to transmit the
ECG processed data by using the core controller operations.
Designing a notch filter helps to overcome the electrical line
interference on the signal. High-frequency components
which are produced by chest and extremity muscles can be
removed by implementing a low pass FIR filter.
Keywords— Embedded Systems; Digital Signal Processing;
MATLAB; Cortex; ECG (Electrocardiogram).
Introduction
In this Digital world, every day in the process of improving
the performance of the device we strive for optimizing the
existing techniques, methods or technologies to reduce the
software overhead and to reduce the hardware. Many
different optimizing techniques are adopted while coding
the software which can efficiently reduce the code length
and thus resulting in the reduction of the overhead provided
by the code. Reduction in the hardware can be obtained
even by implementing more number of functions on the
existing chip. Today the trend is to perform both the Digital
Signal Processing (DSP) and the Microcontroller operations
on the single embedded processor. The present practice is to
have a Digital Signal Processor and the Microcontroller
interfaced together to perform the functionality of an
application, where the Digital signal processing and the core
microcontroller operations are individually performed by
respective controllers, but due to the interface between them
we need to compromise on the performance.
But with the recent advancements in the System on Chip
technologies we can incorporate application-specific Digital
Signal Processors with ARM cores on the die of SoCs to
offload the data i.e. demanded from the CPU in order to
optimize power consumption and performance.
This technique of performing different operations like signal
processing and core microcontroller operations on a single
embedded processor (Cortex M3 in this paper) is best
suitable to the applications where some basic signal
processing is required with no need of stringent
computations. Though this paper discusses only about
ISSN: 2231-5381
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ECG SIGNAL WITH AND WITHOUT NOISE
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International Journal of Engineering Trends and Technology (IJETT) – Volume22 Number 5- April2015
I.
MODELLING
-1
-1
-1
-1
x(n)
A. Step to run a DSP application on an Embedded
Processor:
Mathematical
Coefficients.
analysis:
To
find
out
the
x
Code Conversion techniques to the required
Hardware.
Compiling, Linking, Executing and Dumping
using respective Integrated Development
Environment
In this paper, we observe the results of the low pass FIR
filter step by step right from the mathematical analysis to the
final ARM Cortex M3 compatible code.
B. Why to Model a FIR filter:
The powerful design algorithms existed has made FIR filters
to be extensively used along with the inherent stability they
obtained when implemented in non-recursive form. The
other reasons are that we can attain linear phase with ease
and their facile extensibility to multi-rate cases along with
the ample hardware support existed for them.
The amazing property of FIR filters is linear phase i.e. the
phase is linear function of the frequency. The phase
distortion can be eliminated by delaying signals of all
frequencies with the same amount of time. In many audio
signal processing applications the concept of linear phase is
strictly adopted.
II.
IMPLEMENTATION
A. Mathematical analysis: To find out the filter Coefficients
and filter lenth
Figure 1 shows the basic block diagram for a FIR filter of
length N. To operate on the prior input samples we
introduce the delays. The output y(n) at any time n is the
summation of all the delayed samples multiplied by
appropriate coefficients bk .
ISSN: 2231-5381
z
z
b0
b1
b2
x
+
Performing Soft Computing: Using Signal
Processing tools like MATLAB.
Installing hardware packages: from Math
works.
z
x
+
z
x
bN-1
+
y(n)
Figure 1
The process of selecting the filter's length and coefficients is
called filter design. The goal is to set those parameters such
that certain desired low pass band parameters will result
from running the filter. We have considered 63 as the filter
order in this paper and it varies depending on the desired
processing on the signal.
The differential equation for the finite impulse response is
given as
In the process of finding the system response if we replace
x[n] with ẟ [n] then,
.
Finally, we find the coefficients to be
b0 = h [0],b1 = h [1] and bk = h [k].
B. Performing soft computing using MATLAB:
In the below pseudo code, we have added two sinusoidal
signals of 1 kHz and 50Hz frequencies with a random noise
signal and the resulted signal is processed through a FIR
filtering function.
NN = 2000;
for k = 1:NN
x2k = step(hsin1);
% generate 2K Hz sine wave
X50 = step(hsin2);
% generate 50Hz sine wave
n1 = randn(size(x1k),'single')*sqrt(.05); % generate noise
signal
u1 = x2k+x50+n1;
y1 = ex_fircmsis_tut_ml(u1);
step(hs,[u1,y1]);
end
FIR filter function ex_fircmsis_tut_ml(u1) order is set to 63
and wn to 0.33 and these values can be varied with our
desired response. Below is the coding for the function
function y1 = ex_fircmsis_tut_ml(u1)
persistent h;
if isempty(h)
h=dsp.FIRFilter('Numerator',fir1(63,0.33);
end
y1 = step(h, u1);
end
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International Journal of Engineering Trends and Technology (IJETT) – Volume22 Number 5- April2015
FIGURE 3: TAB IN WHICH SPECIFICATIONS TO BE ENTERED IN
CONVERTING THE CODE
FIGURE 4: TAB IN WHICH THE CONVERTED CODE
AVAILABLE WITH HEADER FILES.
FIGURE 2: SPECTRUM ANALYZER PLOTTING INPUT SIGNALS ON
CHANNEL 1 AND FILTERED OUTPUT ON
CHANNEL 2
After running the code in MATLAB, the output can be
observed on the spectrum analyzer as shown in figure 2 and
the order of the filter can be modified until the desired
response is observed on the spectrum analyzer.
E. Final execution on an Embedded Hardware:
The generated code can be compiled and executed on ARM
Cortex-M target by using a user selected Integrated
Development Environment, for example, ARM® KEIL™
uVision® IDE.
III.
C. Installing Hardware Packages from Math Works:
DSP System Toolbox Support Package is available for
ARM Cortex – M Processors and it provides Optimized C
code generation for ARM Cortex–M processors from
System objects with MATLAB Coder and Embedded Coder
and supports fixed-point data type support for FIR filter, in
ARM Cortex-M support package.
D. Code conversion techniques to the required hardware:
After once the desired response is obtained on the spectrum
analyzer with the code, we can convert the code into the
necessary format by modifying the hardware configurations
shown in figure 3 that implies selecting type of the
microcontroller on which the converted code is to be
dumped.
CONCLUSION
We fail to effectively utilize the hardware in many real-time
systems. But it would help us to achieve few digital
electronics Constraints like performance and power. The
theme of this paper concentrates on how to incorporate a
Digital Signal Processing application on a core Microcontroller as it is required to process Digital signals in many
applications. This paper discusses and makes it conspicuous
with ECG signal processing application though it can be
stretched to any applications where the Digital Signal
Processing is required.
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ISSN: 2231-5381
IS
IEEE Transactions on information technology In biomedicine, vol. 9,
no. 1, march 2005.
IEEE Transactions on information technology in biomedicine, vol.
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IEEE Transactions on information technology in biomedicine, vol.
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