Simulation and hardware implementation of Adaptive algorithms on tms320 c6713 digital signal processor

A
Dissertation Presentation
on

“Simulation and Hardware
Implementation of NLMS algorithm
on TMS320C6713 Digital Signal
Processor”
Presented by:
Raj Kumar Thenua
M.Tech IV Sem.
Roll No. 07ESODC611

Under Guidance of:
Mr. S.K. Agrawal
Associate Professor
Deptt. of ECE

Sobhasaria Engineering College, Sikar, Rajasthan

Presentation Outline

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Introduction
Adaptive Noise Cancellation System
Adaptive Algorithms
MATLAB Simulation Results Analysis
Performance comparison of various adaptive algorithms
Experimental Setup using DSP hardware
Simulink Model
DSP Processor Results Analysis
Conclusions
Future Scope
Applications
Publications
References
2

INTRODUCTION


During transmission of information, noise gets added to
the signal from the surroundings automatically .



Reduces the perceived quality or intelligibility.



Effective removal of noise is an active area of research .



Adaptive filters are capable of learning from the
statistics of current conditions.



Change filter coefficients in order to achieve a certain
goal.



Can work effectively in unknown environment.

3

Adaptive Filter
Three Major Specification
 Input
 Filter structure
 Algorithm

FIR
IIR
NLMS
TVLMS
ECLMS

LMS
RLS
FT RLS

Fig.1 (a): Basic block diagram of an adaptive filter
4

Adaptive Noise Cancellation System

Adaptive
Algorithm

Fig.1(b): Block diagram of an adaptive noise cancellation system

5

Least Mean Squared (LMS) Algorithm


Filter weights are updated by the following formula for
each iteration:
w ( n  1)  w ( n )  2  e ( n ) x ( n )

Here x(n) is the input vector of time delayed input
values,
x ( n )  [ x ( n ) x ( n  1) x ( n  2 )..... x ( n  N  1)]






T

w(n) represents the coefficients of the adaptive FIR
filter tap weight vector at time n.
μ is known as the step size, (fixed).
If μ is too small ;converge on the optimal solution will
be too long;
if μ is too large; the adaptive filter becomes unstable
and its output diverges.
6

Normalized Least Mean Squared (NLMS)
Algorithm




The step size is normalized by the input signal
power.
The NLMS is always the favorable choice of
algorithm for fast convergence speed and for nonstationary input.
w ( n  1)  w ( n ) 





c  x(n)

2

e(n) x(n)

α: NLMS adaption constant, which optimize the
convergence rate of algorithm.



0<α<2 (practically <1)



c : constant term for normalization
7

Recursive Least Square (RLS) Algorithm






RLS algorithms are known for excellent performance
when working in time varying environments.
Increased computational complexity and some
stability problems.
Filter tap weight vector is updated using equation
w n   w



T

 n  1   k  n e n 1  n 

Intermediate gain vector



k n   u n  /   x
1



T

 n u  n 

u ( n )  w  ( n  1) x ( n )
The filter output is calculated using the filter tap weights from
the previous iteration and the current input vector.
y n 1  n   w

T

n  1 x n 

e n 1  n   d  n   y n 1  n 
8

Results Analysis


MATLAB Simulation





Tone Signal analysis( LMS, NLMS,RLS)
Performance Analysis of discussed algorithms

DSP Processor Implementation



Tone Signal Analysis( NLMS)
ECG Signal Analysis (LMS, NLMS)

9

Result Analysis (MATLAB Simulation)

Fig.2: MATLAB simulation for LMS algorithm; N=19, step size=0.001
10


Fig.3: MSE versus Step-size (µ) for LMS algorithm
11

Table 1
MSE VERSUS STEP-SIZE (µ) FOR LMS ALGORITHM
S.N.

Step-size(µ)

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.

0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0.0007
0.0008
0.0009
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
0.009
0.01

Mean Squared
Error(MSE)
0.1281
0.0738
0.0516
0.0404
0.0340
0.0300
0.0275
0.0259
0.0249
0.0244
0.0308
.0448
0.0618
0.0805
0.1005
0.1215
0.1437
0.1671
0.1918
12

Result Analysis (MATLAB Simulation) Contd...

Fig.4: MATLAB simulation for NLMS algorithm; N=19, step size=0.001
13


Fig.5: MATLAB simulation for RLS algorithm; N=19
14

Table 2
PERFORMANCE COMPARISON OF VARIOUS ADAPTIVE ALGORITHMS

S.N Algorithm

1.

2.5×10-2

NLMS

2.1×10-2

91.62%
93.85%

2N+1

Stability

(39)

2.

LMS

Mean
% Noise
Complexity
Squared
Reduction
(No. of
Error (MSE)
multiplications per
iteration)

Highly
Stable

3N+1

Stable

(58)

3.

RLS

1.7×10-2

98.78%

4N2

less Stable

(1444)

15


N=19

Fig.6: MSE versus filter order(N) for LMS ,NLMS & RLS
16

DSP Processor Experimental Setup

Fig.7: Experimental Setup for DSP processor
17

Fig.8: DSP Processor interfaced with CRO and PC
18

Hardware Implementation Experimental Setup

Fig.9: Real-time Experimental Setup for DSP processor
19

Simulink Model

Fig.10: ANC simulink model
20

Result Analysis (DSP Processor)


Tone Signal Analysis:
A clean tone ( Sinusoidal) signal of 2-dB power and
1 KHz frequency is generated using function generator.

Fig.11: Clean and Noisy tone signals
21

Result Analysis (DSP Processor) Contd...



Fig.12: Filtered output
22




Fig.13: Time delay in filtered signal
23



Tone Signal Analysis at various Frequencies:

Fig.14: Filtered output at 2kHz,3kHz,4kHz and 5kHz frequencies
24



Tone Signal Analysis at various Amplitudes:

Fig.15: Filtered outputs for 2V,3V,4V and 5V input signals
25



Tone Signal Analysis at High Noise Environment:

Fig.16: Filtered output at high noise
26



Tone Signal Analysis
Table 3
SNR IMPROVEMENT VERSUS VOLTAGE AND FREQUENCY

S.N.

Amplitude
(V)

Frequency
(kHz)

1.
2.
3.
4.
5.
6.
7.
8.

2
3
4
5
2
2
2
2

1
1
1
1
2
3
4
5

SNR
Improvement
(dB)
11.00
11.52
11.93
12.80
11.58
11.93
12.08
11.66
27



Tone Signal Analysis
Table 4
SNR IMPROVEMENT VERSUS NOISE LEVEL FOR A TONE SIGNAL

S.N.

Noise Level

Noise
Variance

1.
2.
3.

Low
Medium
High

0.02
0.05
0.15

SNR
Improvement
(dB)
13
12
10

28



ECG Signal Analysis:
A clean (amplified) ECG signal with 1000 sample values, of
amplitude 260mV and frequency 35 Hz, generated through
twelve lead configurations, sampled at a frequency of 1.5 kHz.

Fig.17: Clean ECG signal
29



ECG Signal Analysis: (Low level noise)

(a) LMS

(b) NLMS
Fig.18: Filter output at low noise level
30



ECG Signal Analysis: (Medium level noise)

(a) LMS

(b) NLMS
Fig.19: Filter output at medium level noise
31



ECG Signal Analysis: (High level noise)

(a) LMS

(b) NLMS
Fig.20: Filter output at high level noise
32



ECG Signal Analysis:
Table 5
SNR IMPROVEMENT VERSUS NOISE LEVEL FOR AN ECG SIGNAL

S.N. Noise Level

1.
2.
3.

Low
Medium
High

Sampling
Rate (kHz)
1.5
1.5
1.5

SNR
Improvement
NLMS (dB)
9.89
8.62
6.38

SNR
Improvement
LMS (dB)
8.85
7.55
5.12

33





The above results justify that the proposed real-time
hardware implementation of NLMS algorithm shows a
considerable improvement in the SNR of a noisy signal.
And the performance of the proposed system is better than
the LMS based systems.
The hardware implementation of NLMS algorithm enables
one to work with real-time biomedical and other kind of
signals whereas simulation does not provide real-time
working environment.

34

Conclusions










The three adaptive filter algorithms; LMS, NLMS & RLS are
implemented on MATLAB and the simulation results are
analyzed.
A fair performance comparison is presented among the
discussed algorithms based the various performance
parameters like MSE, convergence speed, computational
complexity etc.
The resulted best algorithm (i.e. NLMS) is implemented on
the TMS320C6713 processor for real-time noise
cancellation.
The real-time processor results are analyzed for two types
of signals; tone signal and ECG signal, with the help of DSO.
Filter performance is measured in terms of SNR
improvement.
35

Future Scope








Considering the nature of the experiment, there are a number
of viable directions for the extension of the research.
An interesting extension would be implementing different
kind of adaptive filters that will perform with faster
convergence and better noise reduction.
The system was implemented using auto C code generation
which takes more memory on the board which limits the
hardware performance. Coding in assembler could allow for
further optimization and an improvement in the systems
performance.
The implemented noise cancellation system is mainly
analysed for tone signals and ECG signals. However someone
can also analysed it for other kind of noise corrupted signals.

36

Future Scope Contd...




This Dissertation dealt with transversal FIR adaptive
filters, this is only one of many methods of digital filtering.
Other techniques such as infinite impulse response (IIR) or
lattice filtering may prove to be more effective in an
adaptive noise reduction application but asking for enough
memory requirements.
Wavelet transforms could give advantages or
disadvantages in different aspects of the application. Such
an experiment could be done on the same or similar
hardware as the one used in this experiment.

37

Real Time Applications of ANC System:
Application 1:
 To measure the ECG of a pregnant lady and her
baby which is to be born, measurement of mother’s
ECG is simple but it is very difficult for the baby, here
the concept of adaptive filtering is used. The
mother’s heart sound acts as a noise signal and we
have to cancel that noise using various adaptive
algorithms.

38

Application 2:


Motion
artifact
spectroscopy

cancellation

in

NIR

39

Application-3:


The noise removal from the pilot’s microphone in the
airplane. Due to the high environmental noise produced by
the airplane engines, the pilot’s voice in the microphone is
distorted with a high amount of noise, and can be very
difficult to understand. In order to overcome the problem, an
adaptive filter can be used.

40

Application-4:
 Improving the Response of Accelerometers for
Automotive Applications

41

Publications
International/ National Journals
[1]

Raj kumar Thenua and S.K. Agarwal
“Simulation and Performance Analysis
of
Adaptive
Filter
in
Noise
Cancellation”, International Journal of
Engineering Science and Technology
(IJEST), ISSN: 0975-5462, Vol. 2(9),
2010, Page no. 4374-4379. (Published)

42

Publications Contd...
International/ National Conferences
[2] Raj kumar Thenua and S.K. Agarwal, “Hardware Implementation
of Adaptive algorithms for Noise Cancellation”, IEEE
International Conference on Network Communication and Computer
(ICNCC 2011), 21st -23rd Mar 2011, organized by International
Association of Computer Science and Information Technology
(IACSIT) and Singapore Institute of Electronics (SIE) at New Delhi,
India. (Published)
[3] Raj kumar Thenua, S.K. Agarwal and Ayub khan “Performance
analysis of Adaptive Noise Canceller for an ECG signal”
International Conference on Recent Trends in Engineering,
Technology and Management, 26th -27th Feb 2011 at BIET,
Jhansi. (Published).
[4] Raj kumar Thenua and S.K. Agarwal, “Hardware Implementation of
NLMS Algorithm for Adaptive Noise Cancellation”, National
Conference on Electronics and Communication (NCEC-2010)
on 22nd -24th December 2010 at MITS, Gwalior. (Published)

43

Publications Contd...
International/ National Conferences
[5] Raj kumar Thenua and S.K. Agarwal “Real-time Noise
Cancellation using Digital Signal Processor” National
conference on Electronics, Computers and Communications
(NCECC-2010) on 06th -07th March 2010 at MITS, Gwalior.
(Published)

44

References
[1] Bernard Widrow, John R. Glover, John M. Mccool, John Kaunitz, Charles S.
Williams, Robert H. Hean, James R. Zeidler, Eugene Dong, Jr. and Robert C.
Goodlin, “Adaptive Noise Cancelling: Principles and Applications”, Proceedings
of the IEEE, 1975, Vol.63 , No. 12 , Page(s): 1692 – 1716.
[2] Slock, D.T.M., “On the convergence behavior of the LMS and the normalized
LMS algorithms”, IEEE Transactions on Signal Processing, 1993, Vol. 41,
Issue 9, pp. 2811-2825.
[3] Abhishek Tandon, M. Omair Ahmad, “An efficient, low-complexity, normalized
LMS algorithm for echo cancellation” The 2nd Annual IEEE Northeast
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[4] Sanaullah Khan, M.Arif and T.Majeed, “Comparison of LMS, RLS and Notch
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15, 2008, Page(s): 5 – 8.
45

References
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46

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47

References
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49

Simulation and hardware implementation of Adaptive algorithms on tms320 c6713 digital signal processor

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