Digital Signal processing technology is enabling cost effective and energy efficient control syst... more Digital Signal processing technology is enabling cost effective and energy efficient control system design. The performance of DSP architecture allows an intelligent approach to reduce the complete system costs of digital motion control applications using cheaper electrical motors, fewer sensors, and smaller sizes of EMI filters. The new - so called "DSPController" delivers the real-time MIPS and the tightly integrated peripherals to implement optimal control algorithms with no cost penalty. This project deals with a DSP solution in digital motor control. In this paper, the application of DSP in motor control is realized with TMS320LF2407 DSP Processor. A Permanent Magnet DC motor adjustable speed drive PI (proportional + Integral) control is implemented with hardware setup and software program in Assembly code. The main feature used in DSP is their peripherals to realize pulse width modulation. One chip and re-programmable ROM replaces the conventional complicated circuit...
For classification of patterns, various neural networks related to Fuzzy Min-Max (FMM) have been ... more For classification of patterns, various neural networks related to Fuzzy Min-Max (FMM) have been studied. An Enhanced Fuzzy Min-Max (EFMM) neural network is most recent. EFMM Neural Network classifier that utilizes fuzzy sets as pattern classes has been studied. The contribution of EFMM is ability to overcome a number of limitations of the original FMM network and improve its classification performance. The key contributions are three heuristic rules to enhance the learning algorithm of FMM. First, a new hyperbox expansion rule to eliminate the overlapping problem during the hyperbox expansion process is suggested. Second, the existing hyperbox overlap test rule is extended to discover other possible overlapping cases. Third, a new hyperbox contraction rule to resolve possible overlapping cases is provided. A survey on Pattern Classification based on Fuzzy Min-Max Neural Network has been done and presented
The paper represents analysis Bit Error Rate (BER) performance of various modulation techniques. ... more The paper represents analysis Bit Error Rate (BER) performance of various modulation techniques. There are various modulation schemes such as Binary Phase Shit Keying (BPSK) and Quadrature Phase Shift Keying (QPSK). The performance in between these modulation techniques is analysed and best suited with respect to low Bit Error Rate (BER) is transmitted. Simulation is carried out on the software named MATLAB.
PREFACE : Radio Frequency Identification (RFID) provides contactless or distant identification. T... more PREFACE : Radio Frequency Identification (RFID) provides contactless or distant identification. There are basically two main components of any RFID system. One is Tag or Transponder and other is Reader or Interrogator. Each tag contains the information that uniquely points to a person, an object or anything to be uniquely identified. The conversation of a reader with a tag takes place through an RF signal of fixed frequency, to which all system components are tuned. All RF data exchange between the tag and reader is encrypted using a secure algorithm. So the data cant be either read or altered. RFID technology has been used in the access control arena maintain the security in the organisation and monitor by providing identity of employee, his incoming and outgoing time, absentee, location etc. The RFID tag transmits a unique ID to sensors, which identify the persons location in real time.
2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2009
... [9] Ajit P. Ramesh Gupta and Firdaus E. Udwadia, “Use of the fast Fourier transform in the fr... more ... [9] Ajit P. Ramesh Gupta and Firdaus E. Udwadia, “Use of the fast Fourier transform in the frequency analysis of the second heart sound in normal man”. Medical and Biological Engineering, pp.455-460, July 1976. [10] KPSoman and KI Ramachandran, “Insight into wavelets ...
2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2009
ABSTRACT A robust feature extraction technique using Teager Energy Operator (TEO) for Isolated Wo... more ABSTRACT A robust feature extraction technique using Teager Energy Operator (TEO) for Isolated Word Recognition (IWR) has been proposed in this paper. A feature extraction algorithm is motivated by the enhanced discrimination capability TEO that estimates the true energy of the source of a resonance. The robustness is further added using Cepstral Mean Normalization (CMN) on the estimated features. The robust features are computed from the speech signal of a given frame through a series of steps. First, the short time spectrum of each frame of speech signal is calculated. Second, the frame spectrum is passed through a Mel scaled triangular filter bank. Then, the average of absolute values of sequence obtained after applying TEO on each filter output is estimated. Finally, the cepstral coefficients are extracted by applying discrete cosine transform on the estimated averages. These coefficients are further normalized using CMN to get the final features denoted as Normalized Teager Energy Coefficient (NTEC) features. The effectiveness of this technique has been tested on TI-20 isolated word database in presence of white noise. The experimental results show the superiority of the proposed technique over conventional MFCC, Spectral Subtraction (SS) and CMN methods.
2009 Second International Conference on Emerging Trends in Engineering & Technology, 2009
... [9] Ajit P. Ramesh Gupta and Firdaus E. Udwadia, “Use of the fast Fourier transform in the fr... more ... [9] Ajit P. Ramesh Gupta and Firdaus E. Udwadia, “Use of the fast Fourier transform in the frequency analysis of the second heart sound in normal man”. Medical and Biological Engineering, pp.455-460, July 1976. [10] KPSoman and KI Ramachandran, “Insight into wavelets ...
Proceedings of the International Conference on Advances in Computing, Communication and Control - ICAC3 '09, 2009
ABSTRACT In this paper a new robust feature extraction method for speech recognition, has been pr... more ABSTRACT In this paper a new robust feature extraction method for speech recognition, has been proposed. The features are obtained from Cepstral Mean Normalized reduced order Linear Predictive Coding (LPC) coefficients derived from the speech frames decomposed using Discrete Wavelet Transform (DWT). In the literature it is assumed that the speech frame of size 10 msec to 30 msec is stationary, however, in practice different parts of the speech signal may convey different amount of information (hence may not be perfectly stationary). LPC coefficients derived from wavelet decomposed subbands of speech frame provide better representation than modeling the frame directly. Experimentally it has been shown that, the proposed approach provides effective (better recognition rate), efficient (reduced feature vector dimension) and robust features. The speech recognition system using the Continuous Density Hidden Markov Model (CDHMM) has been implemented. The proposed algorithm is evaluated using NIST TI-46 isolated-word database.
2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems, 2008
ABSTRACT This paper presents the use of auditory perception based admissible wavelet packet tree ... more ABSTRACT This paper presents the use of auditory perception based admissible wavelet packet tree (WPT) for partitioning of speech frequencies into different bands based on the Mel scale or the Bark Scale. The proposed WPTs selected using root mean square error (RMSE) criterion mimic the Mel scale or the bark scale more accurately and hence the human auditory system. Performance of the features obtained from the proposed WPTs is compared with Mel frequency cepstral coefficients (MFCC). The algorithms are evaluated using NIST TI-46 isolated-word database using hidden Markov model (HMM) as a classifier. Experimental results show that the performance of proposed features is better than MFCC and other wavelet features for isolated word recognition (IWR).
2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems, 2008
Feature extraction in noisy condition is one of the most important issues in the speech recogniti... more Feature extraction in noisy condition is one of the most important issues in the speech recognition system. There are two dominant approaches of acoustic measurement. First is in temporal domain called parametric approach like linear prediction (LP) and second is in frequency domain called nonparametric approach like Mel frequency cepstral coefficients (MFCC) based on human auditory perception system. It is widely accepted that incorporating perceptual information in the feature extraction process leads to improve accuracy and robustness. MFCC is widely used due to low complexity, good performance for automatic speech recognition (ASR) under clean environment. In this paper features derived from the power spectrum difference (PSD) and Teager energy operator (TEO) abbreviated as PSDTE-MFCC have been proposed to improve the robustness of speech recognizer in presence of white noise. Noise filtering capability of TEO and noise reduction due to PSD improves the performance of proposed features in noisy environment. We demonstrate the effectiveness of the newly derived feature set for isolated word recognition (IWR) in noisy environment. The results are compared using hidden Markov model (HMM) and found superior than MFCC.
TENCON 2008 - 2008 IEEE Region 10 Conference, 2008
In this paper a new feature extraction methods, which utilize reduced order Linear Predictive Cod... more In this paper a new feature extraction methods, which utilize reduced order Linear Predictive Coding (LPC) coefficients for speech recognition, have been proposed. The coefficients have been derived from the speech frames decomposed using Discrete Wavelet Transform (DWT). In the literature it is assumed that the speech frame of size 10 msec to 30 msec is stationary, however, in practice different parts of the speech signal may convey different amount of information (hence may not be perfectly stationary). LPC coefficients derived from subband decomposition of speech frame provide better representation than modeling the frame directly. Experimentally it has been shown that, the proposed approaches provide effective (better recognition rate) and efficient (reduced feature vector dimension) features. The speech recognition system using the continuous Hidden Markov Model (HMM) has been implemented. The proposed algorithms are evaluated using NIST TI-46 isolated-word database.
... Navnath S. Nehe JSPM'S NTC, Pune 411 041, Maharashtra, India ... between centers of two ... more ... Navnath S. Nehe JSPM'S NTC, Pune 411 041, Maharashtra, India ... between centers of two eyes is calculated by multiplying the distance between eye and the centre by 2. It has been observed that the distance between either of the eyes and the lower face edge is approximately ...
2009 International Conference on Advances in Recent Technologies in Communication and Computing, 2009
ABSTRACT This paper describes polynomial kernel subspace approach to Isolated Word Recognition (I... more ABSTRACT This paper describes polynomial kernel subspace approach to Isolated Word Recognition (IWR) systems. Linear Predictive Coding (LPC) coefficients derived from wavelet sub-bands of speech frame were used as features. This approach represents mapping of speech features (input space) into a feature space via a non-linear mapping onto the principal components called Kernel Linear Discriminant Analysis (KLDA). The non-linear mapping between the input space and the feature space is implicitly performed using the kernel-trick. This nonlinear mapping using KLDA increases the discrimination ability of a pattern classifier. The use of wavelet sub-band based LPC features (WLPC) provide low dimensional features which reduce the memory requirement and KLDA provides the fast classification and recognition. Experimental results obtained on isolated word database show that the proposed technique is computationally efficient and performs well with less training data.
In this paper a new efficient feature extraction methods for speech recognition have been propose... more In this paper a new efficient feature extraction methods for speech recognition have been proposed. The features are obtained from Cepstral Mean Normalized reduced order Linear Predictive Coding (LPC) coefficients derived from the speech frames decomposed using Discrete Wavelet Transform (DWT). In the literature it is assumed that the speech frame of size 10 msec to 30 msec is stationary, however, in practice different parts of the speech signal may convey different amount of information (hence may not be perfectly stationary). LPC coefficients derived from wavelet-decomposed subbands of speech frame provide better representation than modeling the frame directly. Experimentally it has been shown that, the proposed approach provides effective (better recognition rate), efficient (reduced feature vector dimension) features. The speech recognition system using the Continuous Density Hidden Markov Model (CDHMM) has been implemented. The proposed algorithms were evaluated using isolated ...
This paper presents an isolated word recognition using polynomial classifier. Along with the high... more This paper presents an isolated word recognition using polynomial classifier. Along with the high accuracy, speech recognition applications also required the low complexity and less storage space, which is achieved using the polynomial classifier. Speech features used are the well-known Mel-Frequency Cepstral Coefficient (MFCC). The performance of the said classifier is tested for MFCC of size 12 to 22 and the best one is selected for the further analysis. The effect of % overlap between the two frames is also evaluated. We also provide the performance comparison of polynomial classifier with the other classifiers like Vector Quantizer (VQ) and Dynamic Time Warping (DTW). The recognition using polynomial classifier is found faster than the VQ and DTW and also requires less storage space, however it is found that the recognition rate using polynomial classifier is slightly less than the two.
Proceedings of the International Conference & Workshop on Emerging Trends in Technology - ICWET '11, 2011
This paper, evaluates the performance of two ad-hoc multicast routing protocols under varying tra... more This paper, evaluates the performance of two ad-hoc multicast routing protocols under varying traffic, density and mobility conditions. We observe that a large fraction of the traffic is being carried on the Internet today by TCP. Thus internet traffic has inherently different characteristics than CBR traffic, which is commonly used traffic type for evaluating MANET routing protocols performance. Previous efforts to evaluate performance of TCP and CBR in tree-based multicast routing protocol (MAODV) are done. But these tree based protocols face lot of problem like single path property, vulnerable to high mobility and large group, single point of failure which are removed in mesh-based multicast routing protocol like ODMRP, ADMR etc. It is observe that mesh based protocol are robust enough and performance of CBR is more in mesh based protocol as compare to TCP.
Digital Signal processing technology is enabling cost effective and energy efficient control syst... more Digital Signal processing technology is enabling cost effective and energy efficient control system design. The performance of DSP architecture allows an intelligent approach to reduce the complete system costs of digital motion control applications using cheaper electrical motors, fewer sensors, and smaller sizes of EMI filters. The new - so called "DSPController" delivers the real-time MIPS and the tightly integrated peripherals to implement optimal control algorithms with no cost penalty. This project deals with a DSP solution in digital motor control. In this paper, the application of DSP in motor control is realized with TMS320LF2407 DSP Processor. A Permanent Magnet DC motor adjustable speed drive PI (proportional + Integral) control is implemented with hardware setup and software program in Assembly code. The main feature used in DSP is their peripherals to realize pulse width modulation. One chip and re-programmable ROM replaces the conventional complicated circuit...
For classification of patterns, various neural networks related to Fuzzy Min-Max (FMM) have been ... more For classification of patterns, various neural networks related to Fuzzy Min-Max (FMM) have been studied. An Enhanced Fuzzy Min-Max (EFMM) neural network is most recent. EFMM Neural Network classifier that utilizes fuzzy sets as pattern classes has been studied. The contribution of EFMM is ability to overcome a number of limitations of the original FMM network and improve its classification performance. The key contributions are three heuristic rules to enhance the learning algorithm of FMM. First, a new hyperbox expansion rule to eliminate the overlapping problem during the hyperbox expansion process is suggested. Second, the existing hyperbox overlap test rule is extended to discover other possible overlapping cases. Third, a new hyperbox contraction rule to resolve possible overlapping cases is provided. A survey on Pattern Classification based on Fuzzy Min-Max Neural Network has been done and presented
The paper represents analysis Bit Error Rate (BER) performance of various modulation techniques. ... more The paper represents analysis Bit Error Rate (BER) performance of various modulation techniques. There are various modulation schemes such as Binary Phase Shit Keying (BPSK) and Quadrature Phase Shift Keying (QPSK). The performance in between these modulation techniques is analysed and best suited with respect to low Bit Error Rate (BER) is transmitted. Simulation is carried out on the software named MATLAB.
PREFACE : Radio Frequency Identification (RFID) provides contactless or distant identification. T... more PREFACE : Radio Frequency Identification (RFID) provides contactless or distant identification. There are basically two main components of any RFID system. One is Tag or Transponder and other is Reader or Interrogator. Each tag contains the information that uniquely points to a person, an object or anything to be uniquely identified. The conversation of a reader with a tag takes place through an RF signal of fixed frequency, to which all system components are tuned. All RF data exchange between the tag and reader is encrypted using a secure algorithm. So the data cant be either read or altered. RFID technology has been used in the access control arena maintain the security in the organisation and monitor by providing identity of employee, his incoming and outgoing time, absentee, location etc. The RFID tag transmits a unique ID to sensors, which identify the persons location in real time.
2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2009
... [9] Ajit P. Ramesh Gupta and Firdaus E. Udwadia, “Use of the fast Fourier transform in the fr... more ... [9] Ajit P. Ramesh Gupta and Firdaus E. Udwadia, “Use of the fast Fourier transform in the frequency analysis of the second heart sound in normal man”. Medical and Biological Engineering, pp.455-460, July 1976. [10] KPSoman and KI Ramachandran, “Insight into wavelets ...
2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2009
ABSTRACT A robust feature extraction technique using Teager Energy Operator (TEO) for Isolated Wo... more ABSTRACT A robust feature extraction technique using Teager Energy Operator (TEO) for Isolated Word Recognition (IWR) has been proposed in this paper. A feature extraction algorithm is motivated by the enhanced discrimination capability TEO that estimates the true energy of the source of a resonance. The robustness is further added using Cepstral Mean Normalization (CMN) on the estimated features. The robust features are computed from the speech signal of a given frame through a series of steps. First, the short time spectrum of each frame of speech signal is calculated. Second, the frame spectrum is passed through a Mel scaled triangular filter bank. Then, the average of absolute values of sequence obtained after applying TEO on each filter output is estimated. Finally, the cepstral coefficients are extracted by applying discrete cosine transform on the estimated averages. These coefficients are further normalized using CMN to get the final features denoted as Normalized Teager Energy Coefficient (NTEC) features. The effectiveness of this technique has been tested on TI-20 isolated word database in presence of white noise. The experimental results show the superiority of the proposed technique over conventional MFCC, Spectral Subtraction (SS) and CMN methods.
2009 Second International Conference on Emerging Trends in Engineering & Technology, 2009
... [9] Ajit P. Ramesh Gupta and Firdaus E. Udwadia, “Use of the fast Fourier transform in the fr... more ... [9] Ajit P. Ramesh Gupta and Firdaus E. Udwadia, “Use of the fast Fourier transform in the frequency analysis of the second heart sound in normal man”. Medical and Biological Engineering, pp.455-460, July 1976. [10] KPSoman and KI Ramachandran, “Insight into wavelets ...
Proceedings of the International Conference on Advances in Computing, Communication and Control - ICAC3 '09, 2009
ABSTRACT In this paper a new robust feature extraction method for speech recognition, has been pr... more ABSTRACT In this paper a new robust feature extraction method for speech recognition, has been proposed. The features are obtained from Cepstral Mean Normalized reduced order Linear Predictive Coding (LPC) coefficients derived from the speech frames decomposed using Discrete Wavelet Transform (DWT). In the literature it is assumed that the speech frame of size 10 msec to 30 msec is stationary, however, in practice different parts of the speech signal may convey different amount of information (hence may not be perfectly stationary). LPC coefficients derived from wavelet decomposed subbands of speech frame provide better representation than modeling the frame directly. Experimentally it has been shown that, the proposed approach provides effective (better recognition rate), efficient (reduced feature vector dimension) and robust features. The speech recognition system using the Continuous Density Hidden Markov Model (CDHMM) has been implemented. The proposed algorithm is evaluated using NIST TI-46 isolated-word database.
2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems, 2008
ABSTRACT This paper presents the use of auditory perception based admissible wavelet packet tree ... more ABSTRACT This paper presents the use of auditory perception based admissible wavelet packet tree (WPT) for partitioning of speech frequencies into different bands based on the Mel scale or the Bark Scale. The proposed WPTs selected using root mean square error (RMSE) criterion mimic the Mel scale or the bark scale more accurately and hence the human auditory system. Performance of the features obtained from the proposed WPTs is compared with Mel frequency cepstral coefficients (MFCC). The algorithms are evaluated using NIST TI-46 isolated-word database using hidden Markov model (HMM) as a classifier. Experimental results show that the performance of proposed features is better than MFCC and other wavelet features for isolated word recognition (IWR).
2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems, 2008
Feature extraction in noisy condition is one of the most important issues in the speech recogniti... more Feature extraction in noisy condition is one of the most important issues in the speech recognition system. There are two dominant approaches of acoustic measurement. First is in temporal domain called parametric approach like linear prediction (LP) and second is in frequency domain called nonparametric approach like Mel frequency cepstral coefficients (MFCC) based on human auditory perception system. It is widely accepted that incorporating perceptual information in the feature extraction process leads to improve accuracy and robustness. MFCC is widely used due to low complexity, good performance for automatic speech recognition (ASR) under clean environment. In this paper features derived from the power spectrum difference (PSD) and Teager energy operator (TEO) abbreviated as PSDTE-MFCC have been proposed to improve the robustness of speech recognizer in presence of white noise. Noise filtering capability of TEO and noise reduction due to PSD improves the performance of proposed features in noisy environment. We demonstrate the effectiveness of the newly derived feature set for isolated word recognition (IWR) in noisy environment. The results are compared using hidden Markov model (HMM) and found superior than MFCC.
TENCON 2008 - 2008 IEEE Region 10 Conference, 2008
In this paper a new feature extraction methods, which utilize reduced order Linear Predictive Cod... more In this paper a new feature extraction methods, which utilize reduced order Linear Predictive Coding (LPC) coefficients for speech recognition, have been proposed. The coefficients have been derived from the speech frames decomposed using Discrete Wavelet Transform (DWT). In the literature it is assumed that the speech frame of size 10 msec to 30 msec is stationary, however, in practice different parts of the speech signal may convey different amount of information (hence may not be perfectly stationary). LPC coefficients derived from subband decomposition of speech frame provide better representation than modeling the frame directly. Experimentally it has been shown that, the proposed approaches provide effective (better recognition rate) and efficient (reduced feature vector dimension) features. The speech recognition system using the continuous Hidden Markov Model (HMM) has been implemented. The proposed algorithms are evaluated using NIST TI-46 isolated-word database.
... Navnath S. Nehe JSPM'S NTC, Pune 411 041, Maharashtra, India ... between centers of two ... more ... Navnath S. Nehe JSPM'S NTC, Pune 411 041, Maharashtra, India ... between centers of two eyes is calculated by multiplying the distance between eye and the centre by 2. It has been observed that the distance between either of the eyes and the lower face edge is approximately ...
2009 International Conference on Advances in Recent Technologies in Communication and Computing, 2009
ABSTRACT This paper describes polynomial kernel subspace approach to Isolated Word Recognition (I... more ABSTRACT This paper describes polynomial kernel subspace approach to Isolated Word Recognition (IWR) systems. Linear Predictive Coding (LPC) coefficients derived from wavelet sub-bands of speech frame were used as features. This approach represents mapping of speech features (input space) into a feature space via a non-linear mapping onto the principal components called Kernel Linear Discriminant Analysis (KLDA). The non-linear mapping between the input space and the feature space is implicitly performed using the kernel-trick. This nonlinear mapping using KLDA increases the discrimination ability of a pattern classifier. The use of wavelet sub-band based LPC features (WLPC) provide low dimensional features which reduce the memory requirement and KLDA provides the fast classification and recognition. Experimental results obtained on isolated word database show that the proposed technique is computationally efficient and performs well with less training data.
In this paper a new efficient feature extraction methods for speech recognition have been propose... more In this paper a new efficient feature extraction methods for speech recognition have been proposed. The features are obtained from Cepstral Mean Normalized reduced order Linear Predictive Coding (LPC) coefficients derived from the speech frames decomposed using Discrete Wavelet Transform (DWT). In the literature it is assumed that the speech frame of size 10 msec to 30 msec is stationary, however, in practice different parts of the speech signal may convey different amount of information (hence may not be perfectly stationary). LPC coefficients derived from wavelet-decomposed subbands of speech frame provide better representation than modeling the frame directly. Experimentally it has been shown that, the proposed approach provides effective (better recognition rate), efficient (reduced feature vector dimension) features. The speech recognition system using the Continuous Density Hidden Markov Model (CDHMM) has been implemented. The proposed algorithms were evaluated using isolated ...
This paper presents an isolated word recognition using polynomial classifier. Along with the high... more This paper presents an isolated word recognition using polynomial classifier. Along with the high accuracy, speech recognition applications also required the low complexity and less storage space, which is achieved using the polynomial classifier. Speech features used are the well-known Mel-Frequency Cepstral Coefficient (MFCC). The performance of the said classifier is tested for MFCC of size 12 to 22 and the best one is selected for the further analysis. The effect of % overlap between the two frames is also evaluated. We also provide the performance comparison of polynomial classifier with the other classifiers like Vector Quantizer (VQ) and Dynamic Time Warping (DTW). The recognition using polynomial classifier is found faster than the VQ and DTW and also requires less storage space, however it is found that the recognition rate using polynomial classifier is slightly less than the two.
Proceedings of the International Conference & Workshop on Emerging Trends in Technology - ICWET '11, 2011
This paper, evaluates the performance of two ad-hoc multicast routing protocols under varying tra... more This paper, evaluates the performance of two ad-hoc multicast routing protocols under varying traffic, density and mobility conditions. We observe that a large fraction of the traffic is being carried on the Internet today by TCP. Thus internet traffic has inherently different characteristics than CBR traffic, which is commonly used traffic type for evaluating MANET routing protocols performance. Previous efforts to evaluate performance of TCP and CBR in tree-based multicast routing protocol (MAODV) are done. But these tree based protocols face lot of problem like single path property, vulnerable to high mobility and large group, single point of failure which are removed in mesh-based multicast routing protocol like ODMRP, ADMR etc. It is observe that mesh based protocol are robust enough and performance of CBR is more in mesh based protocol as compare to TCP.
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