Vijay K Chakka - Academia.edu (original) (raw)

Papers by Vijay K Chakka

In this paper, a new algorithm is presented to adaptively estimate the direction of arrivals (DOA... more In this paper, a new algorithm is presented to adaptively estimate the direction of arrivals (DOAs) of multiple moving targets when linear equispaced sensor array is used for making the measurements. This algorithm is based on an extension of differential MUSIC method. It ...

2022 IEEE 19th India Council International Conference (INDICON), Nov 24, 2022

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)

2016 International Conference on Signal Processing and Communication (ICSC)

3G/4G wireless systems employed multiple antennas at both the transmitter and receiver offered si... more 3G/4G wireless systems employed multiple antennas at both the transmitter and receiver offered significant gains over single-antenna systems. MassiveMIMO (MM) is one of the 5G technologies, which supports to increase the demand for high speed data, capacity, energy efficiency and spectral efficiency. It is known that space-time multiplexing and/or coding offer attractive means of combating fading and increases the capacity in a multi-antenna communication. In this paper, MM downlink scenario is considered. Performance analysis using conventional precoders like Matched Filter (MF), Zero Forcing (ZF), Regularized Beamforming (RBF) and Minimum Mean Square Error (MMSE) with Singular Value Decomposition (SVD) based balanced equalizer are studied in spatio-temporal environment. Signal to Interference Ratio (SINR) and Bit-Error Rate (BER) are used as performance measures. Monte-Carlo simulations are carried out to compute BER and SINR in MATLAB.

TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)

MassiveMIMO(MaMI) is one of the fiery topics in 5G wireless communications. It is introduced base... more MassiveMIMO(MaMI) is one of the fiery topics in 5G wireless communications. It is introduced based on the fact that the channel vectors becomes asymptotically orthogonal (as antenna elements tending to infinity). Convergence of frequency selective channel (quasi static block fading) characteristics in terms of favorable propagation and channel hardening (asymptotically orthogonal) conditions are studied using metrics like Eigenvalue Ratio (EVR), Mean Absolute Deviation (MAD) and Diagonal Dominance (DD). Finally, simulations results for convergence metrics EVR, MAD and DD are compared with its limiting behavior. The effect of time delay spread on the channel convergence is studied based on the condition number of channel matrix.

2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), 2020

Epileptic Seizure is a chronic nervous system disorder which is analyzed using Electroencephalogr... more Epileptic Seizure is a chronic nervous system disorder which is analyzed using Electroencephalogram (EEG) signals. This paper proposes a Graph Signal Processing technique called Graph Discrete Fourier Transform (GDFT) for the detection of epilepsy. EEG data points are projected on the Eigen space of Laplacian matrix of graph to produce GDFT coefficients. The Laplacian matrix is generated from weighted visibility graph constructed from EEG signals. It proposes Gaussian kernel based edge weights between the nodes. The proposed GDFT based feature vectors are then used to detect the seizure class from the given EEG signal using a crisp rule based classification. Simulation results show that the proposed GDFT based features from Gaussian Weighted Visibility Graph (VG) can detect epileptic seizure with 100 % accuracy.

IEEE Sensors Letters, 2021

Epilepsy is a chronic brain disorder that is characterized by intermittent epileptic seizures tha... more Epilepsy is a chronic brain disorder that is characterized by intermittent epileptic seizures that can be identified in an electroencephalogram (EEG) signal. This letter proposes a low computational complex method to classify epileptic EEG signals by using a suitable Ramanujan periodic subspace (RPS). Initially, this method divides the given single-channel EEG signal into multiple nonoverlapping EEG blocks, which are projected onto a particular RPS. The energy of the projection is used as a feature to classify each block into epileptic or nonepileptic, using an SVM binary classifier. Here, in order to choose that particular RPS, a few sample blocks from the epileptic (ictal), interictal, and healthy EEG signal are projected onto the divisor RPSs of that block. The difference between the average subspace energy of healthy versus ictal or interictal versus ictal blocks is used as a measure to choose the suitable RPS. Finally, the class of each block of the EEG signal is combined using the majority voting scheme to classify the epileptic EEG signal. A publicly available benchmark EEG database from Bonn University, Germany, is used to evaluate the performance of the proposed method. Furthermore, the EEG signals are added with white Gaussian noise and ocular artifact for testing the robustness of the method against noise. Evaluation results in terms of accuracy, sensitivity, specificity, and F-score demonstrate that the proposed method is comparable with the state-of-the-art techniques and also robust against artifacts and noise.

2017 4th International Conference on Signal Processing and Integrated Networks (SPIN), 2017

Ramanujan Periodic Transform (RPT) is the newly emerging transform to identify periodicities in t... more Ramanujan Periodic Transform (RPT) is the newly emerging transform to identify periodicities in the given data. RPT represents the given finite length sequence into a weighted linear combination of signals from Ramanujan subspaces. RPT has the inability of handling the frequency components with in the Ramanujan subspace. This is due to the basis function (Ramanujan sum) used in RPT. To overcome this, a new mixed basis representation is proposed which uses both the sequences from Ramanujan subspace and complex exponentials as basis and both the basis are orthogonal to each other. Using this mixed basis representation, the problem of removing Power Line Interference (PLI) in ECG data is addressed. The methodology is tested on the MIT-BIH Arrhythmia database and the obtained results are competitive when compared with other techniques.

2021 IEEE 18th India Council International Conference (INDICON), 2021

This work considers the 5G new radio (5G-NR) frame structure through the 3GPP channel model in Ru... more This work considers the 5G new radio (5G-NR) frame structure through the 3GPP channel model in Rural-Macro (RMa) scenario. A graph-based channel-coding scheme to ensure data-security to the intended user/destination in a MIMO-cooperative communication network in presence of an external passive eavesdropper is proposed. For this purpose, two graphs: mathcalGM(mathrmVM,mathcalEM,mathrmWM)\mathcal{G}_{M}(\mathrm{V}_{M},\ \mathcal{E}_{M},\ \mathrm{W}_{M})mathcalGM(mathrmVM,mathcalEM,mathrmWM), mathcalGE(mathrmVE,mathcalEE,mathrmWE)\mathcal{G}_{E}(\mathrm{V}_{E},\ \mathcal{E}_{E},\ \mathrm{W}_{E})mathcalGE(mathrmVE,mathcalEE,mathrmWE) corresponding to the source-relay-destination, source-relay-eavesdropper are considered in MIMO-channels environment respectively. The channel coding scheme, based on the inverse graph Fourier transformation (IGFT) of the mathcalGM\mathcal{G}_{M}mathcalGM, is proposed to create increased bit/symbol errors at the eavesdropper along with the MIMO-diversity gain at the main user. The GFT of mathcalGM\mathcal{G}_{M}mathcalGM based postcoder is used at the destination to detect the transmitted symbols whereas GFT of mathcalGE\mathcal{G}_{E}mathcalGE with/without postcoder at the eavesdropper is used to estimates the signal. Physical layer security (PLS) scheme based on the difference of BERs between destination and the eavesdropper is proposed.

2021 IEEE 18th India Council International Conference (INDICON), 2021

Graph learning from the brain signals deals with capturing the changes in functional relationship... more Graph learning from the brain signals deals with capturing the changes in functional relationship between the brain regions during mental active and relaxed states. This paper investigates different graph learning techniques, namely geometry, signal similarity, and Graphical LASSO based methods for the classification of mental task from electroencephalogram (EEG) signals. Graph spectral energy based metric using Graph Signal Processing (GSP) technique is presented to classify mental active state from relaxed state. A binary KNN classifier is used to analyse each graph learning technique on publicly available Keirn and Aunon mental task EEG database. Performance of different graphs is then analysed and compared using classification Accuracy and F-Score.

2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2017

This paper proposes the precoder for Multi User-MassiveMIMO (MU-MaMI) frequency selective broadca... more This paper proposes the precoder for Multi User-MassiveMIMO (MU-MaMI) frequency selective broadcast channels with few User Terminals(UTs) having single antenna each. The frequency selective effect of the MaMI channel will be manifested as ISI and IUI. In order to mitigate these interferences and to eliminate the group level decoding of UTs, a precoder is proposed utilizing the block time Channel Impulse Responses (CIRs). This proposed precoder is designed based on the following approaches, Matched Filter(MF), Zero Forcing (ZF), Minimum Mean Square Error (MMSE) and Regularized Channel Inversion (RCI). The performance of these precoders is evaluated using single symbol time (one time symbol) and block time symbols (N time symbols) of each UT. The optimal length N block time symbols and minimum number of Base Station (BS) antennas required for a given N block time and multipath L is evaluated with respect to the Signal-to-Interference Noise Ratio (SINR) asymptotic limits.

2020 International Conference on Signal Processing and Communications (SPCOM), 2020

In signal processing applications the information about the signal such as frequency (or) period ... more In signal processing applications the information about the signal such as frequency (or) period is known a prior for most of the practical signals like ECG, EEG, speech, etc. Inspired by this, in this paper, we propose a new signal representation to estimate the period and frequency information of a given signal with low computational complexity. We achieve this by representing a finite-length discrete-time signal as a linear combination of signals belongs to Ramanujan subspaces. Further, we evaluate the performance of the proposed representation with a simulated example and also by addressing the problem of reducing Power Line Interference (PLI) in an ECG signal. Finally, for a given integer-valued signal, we show that the computational complexity of the proposed transform is quite low in comparison with the existing transforms, and it is quite comparable for a given real (or) complex-valued signal.

2020 IEEE 17th India Council International Conference (INDICON), 2020

The non-orthogonal multiple access (NOMA) scheme has been considered as a study item in 3rd gener... more The non-orthogonal multiple access (NOMA) scheme has been considered as a study item in 3rd generation partnership project (3GPP) for its possible use in fifth-generation new radio (5G NR) and beyond. In this paper, we explore the available scalable bandwidth feature of 5G NR and propose a hybrid NOMA-OMA scheme based downlink data transmission in 5G NR. The hybrid NOMA-OMA based seamless data transmission over the same 5G NR frame is carried out with help of different numerology factor which indirectly controls the subcarrier spacing, without affecting the conventional OMA based transmission. Performance of the proposed method in conventional 5G NR system is analyzed in terms of the average bit error rate (BER) and ergodic capacity performance for various modulation schemes. The results are illustrated for different µ used in 5G NR specifications. Moreover, the obtained results are corroborated with the analytically obtained expressions.

2017 Fourth International Conference on Image Information Processing (ICIIP), 2017

This paper proposes a method for gesture classification based on Graph Fourier transform (GFT) co... more This paper proposes a method for gesture classification based on Graph Fourier transform (GFT) coefficients. GFT coefficients are the projection of image pixel block onto the eigenvectors of a Laplacian matrix. This Laplacian matrix is generated from undirected graph, representing a spatial connectedness between each pixel within an image block. This work proposes a method for generating an undirected graph by using edge information of the image. Edge information of the image is obtained by average sum of absolute difference between the current pixel and its neighboring pixels by using an appropriate threshold. The resulting GFT based feature vector is formed by concatenating GFT coefficients of each block. The resultant feature vector is applied to linear Support Vector Machine (SVM) classifier to predict the gesture class. For NTU and Massey hand gesture datasets, threshold value 30 gives maximum prediction accuracy. We compare the results of the proposed GFT based descriptor appr...

IEEE Sensors Letters, 2021

This letter presents a small-scale wireless distributed cooperative secure-communication network ... more This letter presents a small-scale wireless distributed cooperative secure-communication network (WDCSN) design using a novel spectral graph FIR filter to achieve desired secrecy capacity at the intended receiver in the presence of single and multiple eavesdroppers. An undirected weighted graph <inline-formula><tex-math notation="LaTeX">$\mathcal {G} (V, \mathcal {E}, W)$</tex-math></inline-formula>, with relays, eavesdroppers, source, and destination are as nodes (<inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula>), and circularly symmetric complex Gaussian random variables as edge weights (<inline-formula><tex-math notation="LaTeX">$W$</tex-math></inline-formula>) are considered to represent the WDCSN. A graph Laplacian is used as a graph shift operator to design the proposed filter. Filter coefficients are calculated by using the least squared error as a criterion. Simulations are conducted for a two-way WDCSN to achieve the desired secrecy capacity using different graph structures considered for WDCSN. The proposed filter design's performance is quantified by using the secrecy outage probability metric. The results show that desired secrecy capacity with SOP of <inline-formula><tex-math notation="LaTeX">$10^{-2}$</tex-math></inline-formula> is achievable using the proposed methodology, irrespective of graph structures with variable complexity. An SOP performance gain of <inline-formula><tex-math notation="LaTeX">$(60\!-\!80) \%$</tex-math></inline-formula> is achieved over the SOP reported in the literature.

2016 IEEE Annual India Conference (INDICON), 2016

Ramanujan Periodic Transform (RPT) is a newly emerging transformation technique in the field of s... more Ramanujan Periodic Transform (RPT) is a newly emerging transformation technique in the field of signal processing. It uses an integer bases (obtained from Ramanujan sum) for transformation. A recorded ECG signal often contains artifacts (bioelectric signals) namely, baseline wander, muscle artifacts (EMG-Electromyogram), motion artifacts, powerline interference (PLI) and its harmonics. With certain precautions during signal recording we can avoid both muscle and motion artifacts. The other noises can be reduced by preprocessing of the recorded ECG signal. In this paper, RPT is used for preprocessing, to reduce baseline wander noise, PLI and its harmonics. The proposed methodology is tested on a record from MIT-BIH Arrhythmia database for different block sizes. A sum (E) of Euclidean errors per block (ei-ith block), is used as a measure to compare the results of RPT with notch filter technique. From the results, the RPT is reducing the noise with minimum error (E), when compared with notch filter technique.

2016 IEEE Region 10 Conference (TENCON), 2016

This study investigates the diagnostic sensitivity of gamma/beta and alpha/gamma ratios in discri... more This study investigates the diagnostic sensitivity of gamma/beta and alpha/gamma ratios in discriminating dementia patients from healthy subjects. Our analysis uses EEG based Multivariate Fourier Decomposition method to extract the features of brain rhythms (alpha, gamma and beta) and their respective ratios for characterization. These features are analyzed using correlation in the sub-band of the envelope. Based on the results of this study, we propose that features gamma/beta and alpha/gamma ratios can be further analyzed using an extension of this method to fully evaluate their potential in the characterization of dementia.

2015 Advances in Wireless and Optical Communications (RTUWO), 2015

In this paper we propose an adaptive channel estimation scheme for Amplify and Forward (AF) two-w... more In this paper we propose an adaptive channel estimation scheme for Amplify and Forward (AF) two-way relay systems with frequency selective and fast varying channels. The modulation used is Orthogonal Frequency Division Multiplexing (OFDM). This adaptive filter is called Block Fast Array Multichannel 2D Recursive Least Square (BFAM 2D-RLS) filter. The complexity of this adaptive filter is comparable to that of Least Mean Square (LMS) algorithm while maintaining similar convergence rate as that of the standard Recursive Least Mean Square (RLS) algorithm. Computer simulation using Matlab is performed to analyse the performance of BFAM 2D-RLS filter in estimating fast varying Vehicular-A (Veh-A) channel. The results obtained is compared with slow varying channels.

2013 International Conference on Communication and Signal Processing, 2013

ABSTRACT Beacon receiver with better power estimation accuracy is the need for the propagation mo... more ABSTRACT Beacon receiver with better power estimation accuracy is the need for the propagation model development for Ka Band propagation studies experiment. This paper proposes the design philosophy of Digital Receiver, signal processing steps &amp; design of window to achieve the beacon power estimation accuracy of +/- 0.5 dB with measurement rate of 1 Hz up to 30 dBHz C/N0 of a single channel Ka-band beacon receiver working with IPSTAR Ka-band on-board beacon. A custom window with intelligent signal processing steps resulted into improved beacon power measurement accuracy of +/- 0.5 dB over a large programmable bandwidth which is better than the available beacon receiver developed so far under similar signal conditions. A performance comparison with different window functions is also presented. The present receiver design works in closed loop with L-band downconverter to achieve better power estimation accuracy and suitable for unmanned operation. The designed receiver is tested within lab setup with simulated input and also tested in field with 20.199827 GHz beacon onboard IPSTAR.

2014 International Conference on Communication and Signal Processing, 2014

This paper proposes a Compressive Signal Processing Receiver based on Modulated Wideband Converte... more This paper proposes a Compressive Signal Processing Receiver based on Modulated Wideband Converter (MWC) sensing architecture and power spectrum estimation method in compressive domain for interference detection and filtering. The proposed receiver uses the signal processing method capable of reconstructing the unknown power spectrum of a signal from the MWC output samples in compressive domain. Interference detection achieved with respect to ITU or service provider defined PFD limits for operating frequency band on per Hz basis for a given receive system. Interference filtering by a novel method of compressive domain filtering which exploits the conjugate symmetry of DFT matrix for modifying the sensing matrix to achieve the sub-band filtering in compressive domain for interference removal is proposed. Proposed receiver is suitable for next generation of wideband satellite transponders. Matlab simulation to verify the proposed algorithm and results are presented.

In this paper, a new algorithm is presented to adaptively estimate the direction of arrivals (DOA... more In this paper, a new algorithm is presented to adaptively estimate the direction of arrivals (DOAs) of multiple moving targets when linear equispaced sensor array is used for making the measurements. This algorithm is based on an extension of differential MUSIC method. It ...

2022 IEEE 19th India Council International Conference (INDICON), Nov 24, 2022

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)

2016 International Conference on Signal Processing and Communication (ICSC)

3G/4G wireless systems employed multiple antennas at both the transmitter and receiver offered si... more 3G/4G wireless systems employed multiple antennas at both the transmitter and receiver offered significant gains over single-antenna systems. MassiveMIMO (MM) is one of the 5G technologies, which supports to increase the demand for high speed data, capacity, energy efficiency and spectral efficiency. It is known that space-time multiplexing and/or coding offer attractive means of combating fading and increases the capacity in a multi-antenna communication. In this paper, MM downlink scenario is considered. Performance analysis using conventional precoders like Matched Filter (MF), Zero Forcing (ZF), Regularized Beamforming (RBF) and Minimum Mean Square Error (MMSE) with Singular Value Decomposition (SVD) based balanced equalizer are studied in spatio-temporal environment. Signal to Interference Ratio (SINR) and Bit-Error Rate (BER) are used as performance measures. Monte-Carlo simulations are carried out to compute BER and SINR in MATLAB.

TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)

MassiveMIMO(MaMI) is one of the fiery topics in 5G wireless communications. It is introduced base... more MassiveMIMO(MaMI) is one of the fiery topics in 5G wireless communications. It is introduced based on the fact that the channel vectors becomes asymptotically orthogonal (as antenna elements tending to infinity). Convergence of frequency selective channel (quasi static block fading) characteristics in terms of favorable propagation and channel hardening (asymptotically orthogonal) conditions are studied using metrics like Eigenvalue Ratio (EVR), Mean Absolute Deviation (MAD) and Diagonal Dominance (DD). Finally, simulations results for convergence metrics EVR, MAD and DD are compared with its limiting behavior. The effect of time delay spread on the channel convergence is studied based on the condition number of channel matrix.

2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), 2020

Epileptic Seizure is a chronic nervous system disorder which is analyzed using Electroencephalogr... more Epileptic Seizure is a chronic nervous system disorder which is analyzed using Electroencephalogram (EEG) signals. This paper proposes a Graph Signal Processing technique called Graph Discrete Fourier Transform (GDFT) for the detection of epilepsy. EEG data points are projected on the Eigen space of Laplacian matrix of graph to produce GDFT coefficients. The Laplacian matrix is generated from weighted visibility graph constructed from EEG signals. It proposes Gaussian kernel based edge weights between the nodes. The proposed GDFT based feature vectors are then used to detect the seizure class from the given EEG signal using a crisp rule based classification. Simulation results show that the proposed GDFT based features from Gaussian Weighted Visibility Graph (VG) can detect epileptic seizure with 100 % accuracy.

IEEE Sensors Letters, 2021

Epilepsy is a chronic brain disorder that is characterized by intermittent epileptic seizures tha... more Epilepsy is a chronic brain disorder that is characterized by intermittent epileptic seizures that can be identified in an electroencephalogram (EEG) signal. This letter proposes a low computational complex method to classify epileptic EEG signals by using a suitable Ramanujan periodic subspace (RPS). Initially, this method divides the given single-channel EEG signal into multiple nonoverlapping EEG blocks, which are projected onto a particular RPS. The energy of the projection is used as a feature to classify each block into epileptic or nonepileptic, using an SVM binary classifier. Here, in order to choose that particular RPS, a few sample blocks from the epileptic (ictal), interictal, and healthy EEG signal are projected onto the divisor RPSs of that block. The difference between the average subspace energy of healthy versus ictal or interictal versus ictal blocks is used as a measure to choose the suitable RPS. Finally, the class of each block of the EEG signal is combined using the majority voting scheme to classify the epileptic EEG signal. A publicly available benchmark EEG database from Bonn University, Germany, is used to evaluate the performance of the proposed method. Furthermore, the EEG signals are added with white Gaussian noise and ocular artifact for testing the robustness of the method against noise. Evaluation results in terms of accuracy, sensitivity, specificity, and F-score demonstrate that the proposed method is comparable with the state-of-the-art techniques and also robust against artifacts and noise.

2017 4th International Conference on Signal Processing and Integrated Networks (SPIN), 2017

Ramanujan Periodic Transform (RPT) is the newly emerging transform to identify periodicities in t... more Ramanujan Periodic Transform (RPT) is the newly emerging transform to identify periodicities in the given data. RPT represents the given finite length sequence into a weighted linear combination of signals from Ramanujan subspaces. RPT has the inability of handling the frequency components with in the Ramanujan subspace. This is due to the basis function (Ramanujan sum) used in RPT. To overcome this, a new mixed basis representation is proposed which uses both the sequences from Ramanujan subspace and complex exponentials as basis and both the basis are orthogonal to each other. Using this mixed basis representation, the problem of removing Power Line Interference (PLI) in ECG data is addressed. The methodology is tested on the MIT-BIH Arrhythmia database and the obtained results are competitive when compared with other techniques.

2021 IEEE 18th India Council International Conference (INDICON), 2021

This work considers the 5G new radio (5G-NR) frame structure through the 3GPP channel model in Ru... more This work considers the 5G new radio (5G-NR) frame structure through the 3GPP channel model in Rural-Macro (RMa) scenario. A graph-based channel-coding scheme to ensure data-security to the intended user/destination in a MIMO-cooperative communication network in presence of an external passive eavesdropper is proposed. For this purpose, two graphs: mathcalGM(mathrmVM,mathcalEM,mathrmWM)\mathcal{G}_{M}(\mathrm{V}_{M},\ \mathcal{E}_{M},\ \mathrm{W}_{M})mathcalGM(mathrmVM,mathcalEM,mathrmWM), mathcalGE(mathrmVE,mathcalEE,mathrmWE)\mathcal{G}_{E}(\mathrm{V}_{E},\ \mathcal{E}_{E},\ \mathrm{W}_{E})mathcalGE(mathrmVE,mathcalEE,mathrmWE) corresponding to the source-relay-destination, source-relay-eavesdropper are considered in MIMO-channels environment respectively. The channel coding scheme, based on the inverse graph Fourier transformation (IGFT) of the mathcalGM\mathcal{G}_{M}mathcalGM, is proposed to create increased bit/symbol errors at the eavesdropper along with the MIMO-diversity gain at the main user. The GFT of mathcalGM\mathcal{G}_{M}mathcalGM based postcoder is used at the destination to detect the transmitted symbols whereas GFT of mathcalGE\mathcal{G}_{E}mathcalGE with/without postcoder at the eavesdropper is used to estimates the signal. Physical layer security (PLS) scheme based on the difference of BERs between destination and the eavesdropper is proposed.

2021 IEEE 18th India Council International Conference (INDICON), 2021

Graph learning from the brain signals deals with capturing the changes in functional relationship... more Graph learning from the brain signals deals with capturing the changes in functional relationship between the brain regions during mental active and relaxed states. This paper investigates different graph learning techniques, namely geometry, signal similarity, and Graphical LASSO based methods for the classification of mental task from electroencephalogram (EEG) signals. Graph spectral energy based metric using Graph Signal Processing (GSP) technique is presented to classify mental active state from relaxed state. A binary KNN classifier is used to analyse each graph learning technique on publicly available Keirn and Aunon mental task EEG database. Performance of different graphs is then analysed and compared using classification Accuracy and F-Score.

2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2017

This paper proposes the precoder for Multi User-MassiveMIMO (MU-MaMI) frequency selective broadca... more This paper proposes the precoder for Multi User-MassiveMIMO (MU-MaMI) frequency selective broadcast channels with few User Terminals(UTs) having single antenna each. The frequency selective effect of the MaMI channel will be manifested as ISI and IUI. In order to mitigate these interferences and to eliminate the group level decoding of UTs, a precoder is proposed utilizing the block time Channel Impulse Responses (CIRs). This proposed precoder is designed based on the following approaches, Matched Filter(MF), Zero Forcing (ZF), Minimum Mean Square Error (MMSE) and Regularized Channel Inversion (RCI). The performance of these precoders is evaluated using single symbol time (one time symbol) and block time symbols (N time symbols) of each UT. The optimal length N block time symbols and minimum number of Base Station (BS) antennas required for a given N block time and multipath L is evaluated with respect to the Signal-to-Interference Noise Ratio (SINR) asymptotic limits.

2020 International Conference on Signal Processing and Communications (SPCOM), 2020

In signal processing applications the information about the signal such as frequency (or) period ... more In signal processing applications the information about the signal such as frequency (or) period is known a prior for most of the practical signals like ECG, EEG, speech, etc. Inspired by this, in this paper, we propose a new signal representation to estimate the period and frequency information of a given signal with low computational complexity. We achieve this by representing a finite-length discrete-time signal as a linear combination of signals belongs to Ramanujan subspaces. Further, we evaluate the performance of the proposed representation with a simulated example and also by addressing the problem of reducing Power Line Interference (PLI) in an ECG signal. Finally, for a given integer-valued signal, we show that the computational complexity of the proposed transform is quite low in comparison with the existing transforms, and it is quite comparable for a given real (or) complex-valued signal.

2020 IEEE 17th India Council International Conference (INDICON), 2020

The non-orthogonal multiple access (NOMA) scheme has been considered as a study item in 3rd gener... more The non-orthogonal multiple access (NOMA) scheme has been considered as a study item in 3rd generation partnership project (3GPP) for its possible use in fifth-generation new radio (5G NR) and beyond. In this paper, we explore the available scalable bandwidth feature of 5G NR and propose a hybrid NOMA-OMA scheme based downlink data transmission in 5G NR. The hybrid NOMA-OMA based seamless data transmission over the same 5G NR frame is carried out with help of different numerology factor which indirectly controls the subcarrier spacing, without affecting the conventional OMA based transmission. Performance of the proposed method in conventional 5G NR system is analyzed in terms of the average bit error rate (BER) and ergodic capacity performance for various modulation schemes. The results are illustrated for different µ used in 5G NR specifications. Moreover, the obtained results are corroborated with the analytically obtained expressions.

2017 Fourth International Conference on Image Information Processing (ICIIP), 2017

This paper proposes a method for gesture classification based on Graph Fourier transform (GFT) co... more This paper proposes a method for gesture classification based on Graph Fourier transform (GFT) coefficients. GFT coefficients are the projection of image pixel block onto the eigenvectors of a Laplacian matrix. This Laplacian matrix is generated from undirected graph, representing a spatial connectedness between each pixel within an image block. This work proposes a method for generating an undirected graph by using edge information of the image. Edge information of the image is obtained by average sum of absolute difference between the current pixel and its neighboring pixels by using an appropriate threshold. The resulting GFT based feature vector is formed by concatenating GFT coefficients of each block. The resultant feature vector is applied to linear Support Vector Machine (SVM) classifier to predict the gesture class. For NTU and Massey hand gesture datasets, threshold value 30 gives maximum prediction accuracy. We compare the results of the proposed GFT based descriptor appr...

IEEE Sensors Letters, 2021

This letter presents a small-scale wireless distributed cooperative secure-communication network ... more This letter presents a small-scale wireless distributed cooperative secure-communication network (WDCSN) design using a novel spectral graph FIR filter to achieve desired secrecy capacity at the intended receiver in the presence of single and multiple eavesdroppers. An undirected weighted graph <inline-formula><tex-math notation="LaTeX">$\mathcal {G} (V, \mathcal {E}, W)$</tex-math></inline-formula>, with relays, eavesdroppers, source, and destination are as nodes (<inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula>), and circularly symmetric complex Gaussian random variables as edge weights (<inline-formula><tex-math notation="LaTeX">$W$</tex-math></inline-formula>) are considered to represent the WDCSN. A graph Laplacian is used as a graph shift operator to design the proposed filter. Filter coefficients are calculated by using the least squared error as a criterion. Simulations are conducted for a two-way WDCSN to achieve the desired secrecy capacity using different graph structures considered for WDCSN. The proposed filter design's performance is quantified by using the secrecy outage probability metric. The results show that desired secrecy capacity with SOP of <inline-formula><tex-math notation="LaTeX">$10^{-2}$</tex-math></inline-formula> is achievable using the proposed methodology, irrespective of graph structures with variable complexity. An SOP performance gain of <inline-formula><tex-math notation="LaTeX">$(60\!-\!80) \%$</tex-math></inline-formula> is achieved over the SOP reported in the literature.

2016 IEEE Annual India Conference (INDICON), 2016

Ramanujan Periodic Transform (RPT) is a newly emerging transformation technique in the field of s... more Ramanujan Periodic Transform (RPT) is a newly emerging transformation technique in the field of signal processing. It uses an integer bases (obtained from Ramanujan sum) for transformation. A recorded ECG signal often contains artifacts (bioelectric signals) namely, baseline wander, muscle artifacts (EMG-Electromyogram), motion artifacts, powerline interference (PLI) and its harmonics. With certain precautions during signal recording we can avoid both muscle and motion artifacts. The other noises can be reduced by preprocessing of the recorded ECG signal. In this paper, RPT is used for preprocessing, to reduce baseline wander noise, PLI and its harmonics. The proposed methodology is tested on a record from MIT-BIH Arrhythmia database for different block sizes. A sum (E) of Euclidean errors per block (ei-ith block), is used as a measure to compare the results of RPT with notch filter technique. From the results, the RPT is reducing the noise with minimum error (E), when compared with notch filter technique.

2016 IEEE Region 10 Conference (TENCON), 2016

This study investigates the diagnostic sensitivity of gamma/beta and alpha/gamma ratios in discri... more This study investigates the diagnostic sensitivity of gamma/beta and alpha/gamma ratios in discriminating dementia patients from healthy subjects. Our analysis uses EEG based Multivariate Fourier Decomposition method to extract the features of brain rhythms (alpha, gamma and beta) and their respective ratios for characterization. These features are analyzed using correlation in the sub-band of the envelope. Based on the results of this study, we propose that features gamma/beta and alpha/gamma ratios can be further analyzed using an extension of this method to fully evaluate their potential in the characterization of dementia.

2015 Advances in Wireless and Optical Communications (RTUWO), 2015

In this paper we propose an adaptive channel estimation scheme for Amplify and Forward (AF) two-w... more In this paper we propose an adaptive channel estimation scheme for Amplify and Forward (AF) two-way relay systems with frequency selective and fast varying channels. The modulation used is Orthogonal Frequency Division Multiplexing (OFDM). This adaptive filter is called Block Fast Array Multichannel 2D Recursive Least Square (BFAM 2D-RLS) filter. The complexity of this adaptive filter is comparable to that of Least Mean Square (LMS) algorithm while maintaining similar convergence rate as that of the standard Recursive Least Mean Square (RLS) algorithm. Computer simulation using Matlab is performed to analyse the performance of BFAM 2D-RLS filter in estimating fast varying Vehicular-A (Veh-A) channel. The results obtained is compared with slow varying channels.

2013 International Conference on Communication and Signal Processing, 2013

ABSTRACT Beacon receiver with better power estimation accuracy is the need for the propagation mo... more ABSTRACT Beacon receiver with better power estimation accuracy is the need for the propagation model development for Ka Band propagation studies experiment. This paper proposes the design philosophy of Digital Receiver, signal processing steps &amp; design of window to achieve the beacon power estimation accuracy of +/- 0.5 dB with measurement rate of 1 Hz up to 30 dBHz C/N0 of a single channel Ka-band beacon receiver working with IPSTAR Ka-band on-board beacon. A custom window with intelligent signal processing steps resulted into improved beacon power measurement accuracy of +/- 0.5 dB over a large programmable bandwidth which is better than the available beacon receiver developed so far under similar signal conditions. A performance comparison with different window functions is also presented. The present receiver design works in closed loop with L-band downconverter to achieve better power estimation accuracy and suitable for unmanned operation. The designed receiver is tested within lab setup with simulated input and also tested in field with 20.199827 GHz beacon onboard IPSTAR.

2014 International Conference on Communication and Signal Processing, 2014

This paper proposes a Compressive Signal Processing Receiver based on Modulated Wideband Converte... more This paper proposes a Compressive Signal Processing Receiver based on Modulated Wideband Converter (MWC) sensing architecture and power spectrum estimation method in compressive domain for interference detection and filtering. The proposed receiver uses the signal processing method capable of reconstructing the unknown power spectrum of a signal from the MWC output samples in compressive domain. Interference detection achieved with respect to ITU or service provider defined PFD limits for operating frequency band on per Hz basis for a given receive system. Interference filtering by a novel method of compressive domain filtering which exploits the conjugate symmetry of DFT matrix for modifying the sensing matrix to achieve the sub-band filtering in compressive domain for interference removal is proposed. Proposed receiver is suitable for next generation of wideband satellite transponders. Matlab simulation to verify the proposed algorithm and results are presented.