Joint Time Delay and Frequency Estimation (JTDFE) problem of complex sinusoidal signals received at
two separated sensors is an attractive problem that has been considered for several engineering
applications. In this paper, a high resolution null (noise) subspace method without eigenvalue
decomposition is proposed. The direct data Matrix is replaced by an upper triangular matrix obtained from
Rank-Revealing LU (RRLU) factorization. The RRLU provides accurate information about the rank and the
numerical null space which make it a valuable tool in numerical linear algebra.The proposed novel method
decreases the computational complexity of JTDFE approximately to the half compared with RRQR
methods. The proposed method generates estimates of the unknown parameters which are based on the
observation and/or covariance matrices. This leads to a significant improvement in the computational load.
Computer simulations are included in this paper to demonstrate the proposed method.
A Comparative Study of DOA Estimation Algorithms with Application to Tracking...sipij
Tracking the Direction of Arrival (DOA) Estimation of a moving source is an important and challenging
task in the field of navigation, RADAR, SONAR, Wireless Sensor Networks (WSNs) etc. Tracking is carried
out starting from the estimation of DOA, considering the estimated DOA as an initial value, the Kalman
Filter (KF) algorithm is used to track the moving source based on the motion model which governs the
motion of the source. This comparative study deals with analysis, significance of Non-coherent,
Narrowband DOA (Direction of Arrival) Estimation Algorithms in perception to tracking. The DOA
estimation algorithms Multiple Signal Classification (MUSIC), Root-MUSIC& Estimation of Signal
Parameters via Rotational Invariance Technique (ESPRIT) are considered for the purpose of the study, a
comparison in terms of optimality with respect to Signal to Noise Ratio (SNR), number of snapshots and
number of Antenna elements used and Computational complexity is drawn between the chosen algorithms
resulting in an optimum DOA estimate. The optimum DOA Estimate is taken as an initial value for the
Kalman filter tracking algorithm. The Kalman filter algorithm is used to track the optimum DOA Estimate.
This document summarizes a research paper that proposes a novel architecture for implementing a 1D lifting integer wavelet transform (IWT) using residue number system (RNS). The key aspects covered are:
1) RNS offers advantages over binary representations for digital signal processing by avoiding carry propagation. A ROM-based approach is proposed for RNS division.
2) The lifting scheme for discrete wavelet transforms is summarized, including split, predict, and update stages.
3) A novel RNS-based architecture is proposed using three main blocks - split, predict, and update - that repeat at each decomposition level. Pipelined implementations of the predict and update blocks are detailed.
Adaptive blind multiuser detection under impulsive noise using principal comp...csandit
The document describes an adaptive blind multiuser detection method for asynchronous code division multiple access (CDMA) systems using principal component analysis (PCA) in impulsive noise environments. PCA is used to extract the principal components from the received signal without requiring training sequences or prior knowledge of channel characteristics. The PCA blind multiuser detector provides robust performance compared to knowledge-based detectors when signature waveforms and timing offsets of users are unknown. Simulation results show the proposed PCA method offers substantial gains over traditional subspace methods for multiuser detection.
ADAPTIVE BLIND MULTIUSER DETECTION UNDER IMPULSIVE NOISE USING PRINCIPAL COMP...csandit
In this paper we consider blind signal detection for an asynchronous code division multiple access (CDMA) system with Principal component analysis (PCA) in impulsive noise. The blind multiuser detector requires no training sequences compared with the conventional multiuser detection receiver. The proposed PCA blind multiuser detector is robust when compared with knowledge based signature waveforms and the timing of the user of interest. PCA is a statistical method for reducing the dimension of data set, spectral decomposition of the covariance matrix of the dataset i.e first and second order statistics are estimated.
Principal component analysis makes no assumption on the independence of the data vectors PCA searches for linear combinations with the largest variances and when several linear combinations are needed, it considers variances in decreasing order of importance. PCA
improves SNR of signals used for differential side channel analysis. In different to other approaches, the linear minimum mean-square-error (MMSE) detector is obtained blindly; the detector does not use any training sequence like in subspace methods to detect multi user
receiver. The algorithm need not estimate the subspace rank in order to reduce the computational complexity. Simulation results show that the new algorithm offers substantial performance gains over the traditional subspace methods.
Compressive Data Gathering using NACS in Wireless Sensor NetworkIRJET Journal
The document proposes a Neighbor-Aided Compressive Sensing (NACS) scheme for efficient data gathering in wireless sensor networks. NACS exploits both spatial and temporal correlations in sensor data to reduce data transmissions compared to existing compressive sensing models like Kronecker Compressive Sensing (KCS) and Structured Random Matrix (SRM). In NACS, each sensor node sends its raw sensor readings to a uniquely selected nearest neighbor node, which then applies compressive sensing measurements and sends the compressed data to the sink node. Simulation results show NACS achieves better data recovery performance using fewer transmissions than KCS and SRM, improving energy efficiency for data gathering in wireless sensor networks.
Recurrence Quantification Analysis :Tutorial & application to eye-movement dataDeb Aks
This document provides an overview of recurrence quantification analysis (RQA) and its application to analyzing eye movement data. RQA uses time series analysis techniques like phase space reconstruction to detect recurring patterns in complex systems. It was applied to study whether the recurring dynamics of eye movements can serve as a memory to sustain object tracking over time and during interruptions. The document reviews key concepts in RQA like delay coordinates, embedding dimension estimation, recurrence plots, and measures like determinism, laminarity, and trapping time. It includes examples of RQA applied to simulated sine waves and analyses the steps involved in conducting RQA on human eye tracking data.
This document compares the performance of conventional and efficient square root algorithms for detection in vertical Bell Laboratories layered space-time (V-BLAST) multiple-input multiple-output (MIMO) wireless communications. The efficient square root algorithm aims to reduce computational complexity by avoiding matrix inversion and squaring operations through orthogonal and unitary transformations. Simulation results show that the efficient square root algorithm achieves a reduction of 1x106 floating point operations compared to the conventional algorithm employing minimum mean square error detection with 16 transmitting and receiving antennas, while maintaining performance. The efficient square root algorithm also exhibits better bit error rate and symbol error rate at lower modulation schemes and increased number of antennas compared to zero-forcing detection.
A Comparative Study of DOA Estimation Algorithms with Application to Tracking...sipij
Tracking the Direction of Arrival (DOA) Estimation of a moving source is an important and challenging
task in the field of navigation, RADAR, SONAR, Wireless Sensor Networks (WSNs) etc. Tracking is carried
out starting from the estimation of DOA, considering the estimated DOA as an initial value, the Kalman
Filter (KF) algorithm is used to track the moving source based on the motion model which governs the
motion of the source. This comparative study deals with analysis, significance of Non-coherent,
Narrowband DOA (Direction of Arrival) Estimation Algorithms in perception to tracking. The DOA
estimation algorithms Multiple Signal Classification (MUSIC), Root-MUSIC& Estimation of Signal
Parameters via Rotational Invariance Technique (ESPRIT) are considered for the purpose of the study, a
comparison in terms of optimality with respect to Signal to Noise Ratio (SNR), number of snapshots and
number of Antenna elements used and Computational complexity is drawn between the chosen algorithms
resulting in an optimum DOA estimate. The optimum DOA Estimate is taken as an initial value for the
Kalman filter tracking algorithm. The Kalman filter algorithm is used to track the optimum DOA Estimate.
This document summarizes a research paper that proposes a novel architecture for implementing a 1D lifting integer wavelet transform (IWT) using residue number system (RNS). The key aspects covered are:
1) RNS offers advantages over binary representations for digital signal processing by avoiding carry propagation. A ROM-based approach is proposed for RNS division.
2) The lifting scheme for discrete wavelet transforms is summarized, including split, predict, and update stages.
3) A novel RNS-based architecture is proposed using three main blocks - split, predict, and update - that repeat at each decomposition level. Pipelined implementations of the predict and update blocks are detailed.
Adaptive blind multiuser detection under impulsive noise using principal comp...csandit
The document describes an adaptive blind multiuser detection method for asynchronous code division multiple access (CDMA) systems using principal component analysis (PCA) in impulsive noise environments. PCA is used to extract the principal components from the received signal without requiring training sequences or prior knowledge of channel characteristics. The PCA blind multiuser detector provides robust performance compared to knowledge-based detectors when signature waveforms and timing offsets of users are unknown. Simulation results show the proposed PCA method offers substantial gains over traditional subspace methods for multiuser detection.
ADAPTIVE BLIND MULTIUSER DETECTION UNDER IMPULSIVE NOISE USING PRINCIPAL COMP...csandit
In this paper we consider blind signal detection for an asynchronous code division multiple access (CDMA) system with Principal component analysis (PCA) in impulsive noise. The blind multiuser detector requires no training sequences compared with the conventional multiuser detection receiver. The proposed PCA blind multiuser detector is robust when compared with knowledge based signature waveforms and the timing of the user of interest. PCA is a statistical method for reducing the dimension of data set, spectral decomposition of the covariance matrix of the dataset i.e first and second order statistics are estimated.
Principal component analysis makes no assumption on the independence of the data vectors PCA searches for linear combinations with the largest variances and when several linear combinations are needed, it considers variances in decreasing order of importance. PCA
improves SNR of signals used for differential side channel analysis. In different to other approaches, the linear minimum mean-square-error (MMSE) detector is obtained blindly; the detector does not use any training sequence like in subspace methods to detect multi user
receiver. The algorithm need not estimate the subspace rank in order to reduce the computational complexity. Simulation results show that the new algorithm offers substantial performance gains over the traditional subspace methods.
Compressive Data Gathering using NACS in Wireless Sensor NetworkIRJET Journal
The document proposes a Neighbor-Aided Compressive Sensing (NACS) scheme for efficient data gathering in wireless sensor networks. NACS exploits both spatial and temporal correlations in sensor data to reduce data transmissions compared to existing compressive sensing models like Kronecker Compressive Sensing (KCS) and Structured Random Matrix (SRM). In NACS, each sensor node sends its raw sensor readings to a uniquely selected nearest neighbor node, which then applies compressive sensing measurements and sends the compressed data to the sink node. Simulation results show NACS achieves better data recovery performance using fewer transmissions than KCS and SRM, improving energy efficiency for data gathering in wireless sensor networks.
Recurrence Quantification Analysis :Tutorial & application to eye-movement dataDeb Aks
This document provides an overview of recurrence quantification analysis (RQA) and its application to analyzing eye movement data. RQA uses time series analysis techniques like phase space reconstruction to detect recurring patterns in complex systems. It was applied to study whether the recurring dynamics of eye movements can serve as a memory to sustain object tracking over time and during interruptions. The document reviews key concepts in RQA like delay coordinates, embedding dimension estimation, recurrence plots, and measures like determinism, laminarity, and trapping time. It includes examples of RQA applied to simulated sine waves and analyses the steps involved in conducting RQA on human eye tracking data.
This document compares the performance of conventional and efficient square root algorithms for detection in vertical Bell Laboratories layered space-time (V-BLAST) multiple-input multiple-output (MIMO) wireless communications. The efficient square root algorithm aims to reduce computational complexity by avoiding matrix inversion and squaring operations through orthogonal and unitary transformations. Simulation results show that the efficient square root algorithm achieves a reduction of 1x106 floating point operations compared to the conventional algorithm employing minimum mean square error detection with 16 transmitting and receiving antennas, while maintaining performance. The efficient square root algorithm also exhibits better bit error rate and symbol error rate at lower modulation schemes and increased number of antennas compared to zero-forcing detection.
This document summarizes a research paper that designed and implemented sphere decoding (SD) for multiple-input multiple-output (MIMO) systems using an FPGA. It used Newton's iterative method to calculate the matrix inverse as part of the SD algorithm, which reduces complexity compared to direct matrix inversion. The authors implemented SD for a 2x2 MIMO system with 4-QAM modulation. Simulation results showed that Newton's method converged after 7 iterations, and SD successfully calculated the minimum Euclidean distance vector.
This paper proposes modeling and identification of dynamical systems in delta
domain using neural network. The properties of delta operator are used such as greater
numerical robustness in computation and superior coefficients representation in finite word
length in implementation and well ensured numerical conditioning at high sampling
frequency. To formulate the identification scheme delta operator model is recasted into a
realizable neural network structure using the properties of inverse delta operator.
TIME OF ARRIVAL BASED LOCALIZATION IN WIRELESS SENSOR NETWORKS: A LINEAR APPR...sipij
This document describes localization techniques for wireless sensor networks based on time of arrival (TOA) measurements. It introduces four linear localization approaches: Linear Least Squares (LLS), Subspace Approach (SA), Weighted Linear Least Squares (WLLS), and Two-step WLS. It derives the Cramer-Rao Lower Bound (CRLB) for position estimation and compares the mean square position error of the four approaches through simulation. The results show that the Two-step WLS approach achieves the highest localization accuracy.
Comparison of Different Methods for Fusion of Multimodal Medical ImagesIRJET Journal
This document compares different methods for fusing multimodal medical images, including PCA, DCT, SWT, and DWT. It provides an overview of each method, including formulations, process flow diagrams, algorithms, and advantages/disadvantages. PCA uses eigenvectors to reveal internal data structure and remove redundancy. DCT expresses image blocks as sums of cosine functions. SWT is a translation-invariant modification of DWT that does not decimate coefficients. DWT decomposes images into coarse and detailed frequency subbands using wavelet transforms. The document reviews each method for fusing medical images from different modalities to extract complementary information.
In this paper, a new algorithm for a high resolution
Direction Of Arrival (DOA) estimation method for multiple
wideband signals is proposed. The proposed method proceeds
in two steps. In the first step, the received signals data is
decomposed in a Toeplitz form using the first-order statistics.
In the second step, The QR decomposition is applied on the
constructed Toeplitz matrix. Compared with existing schemes,
the proposed scheme provides several advantages. First, it
requires computing the triangular matrix R or the orthogonal
matrix Q to find the DOA; these matrices can be computed
with O(n2) operation. However, most of the existing schemes
required eignvalue decomposition (EVD) for the covariance
matrix or singular value decomposition (SVD) for the data
matrix; using EVD or SVD requires much more complex
computational O(n3) operation. Second, the proposed scheme
is more suitable for high-speed communication since it
requires first-order statistics and a single snapshot. Third,
the proposed scheme can estimate the correlated wideband
signals without using spatial smoothing techniques; whereas,
already-existing schemes do not. Accuracy of the proposed
wideband DOA estimation method is evaluated through
computer simulation in comparison with a conventional
method.
The variational Gaussian process (VGP), a Bayesian nonparametric model which adapts its shape to match com- plex posterior distributions. The VGP generates approximate posterior samples by generating latent inputs and warping them through random non-linear mappings; the distribution over random mappings is learned during inference, enabling the transformed outputs to adapt to varying complexity.
Advanced Support Vector Machine for classification in Neural NetworkAshwani Jha
This paper proposes a method to classify EEG signals using a support vector machine (SVM) classifier combined with a quicksort sorting algorithm (SVM-Q). The method involves extracting phase locking value (PLV) features from EEG data, sorting the feature vectors using quicksort, and then applying SVM classification. When tested on two BCI competition datasets, SVM-Q achieved classification accuracies of 86% and 77%, outperforming plain SVM which achieved 62% and 54% respectively. The paper concludes that adding a sorting algorithm to SVM can improve its classification performance for brain-computer interface applications.
New Data Association Technique for Target Tracking in Dense Clutter Environme...CSCJournals
Improving data association process by increasing the probability of detecting valid data points (measurements obtained from radar/sonar system) in the presence of noise for target tracking are discussed in manuscript. We develop a novel algorithm by filtering gate for target tracking in dense clutter environment. This algorithm is less sensitive to false alarm (clutter) in gate size than conventional approaches as probabilistic data association filter (PDAF) which has data association algorithm that begin to fail due to the increase in the false alarm rate or low probability of target detection. This new selection filtered gate method combines a conventional threshold based algorithm with geometric metric measure based on one type of the filtering methods that depends on the idea of adaptive clutter suppression methods. An adaptive search based on the distance threshold measure is then used to detect valid filtered data point for target tracking. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithm.
The document discusses convolution and its applications in digital signal processing. It begins with an introduction to convolution and its mathematical definitions for both continuous and discrete time signals. It then discusses various types of convolution including linear and circular convolution. The properties of convolution such as commutativity, associativity and distributivity are also covered. Applications of convolution in areas such as statistics, optics, acoustics, electrical engineering and digital signal processing are summarized. Finally, the document discusses symmetric convolution and its advantages over traditional convolution methods.
The document provides an overview of self-organizing maps (SOM). It defines SOM as an unsupervised learning technique that reduces the dimensions of data through the use of self-organizing neural networks. SOM is based on competitive learning where the closest neural network unit to the input vector (the best matching unit or BMU) is identified and adjusted along with neighboring units. The algorithm involves initializing weight vectors, presenting input vectors, identifying the BMU, and updating weights of the BMU and neighboring units. SOM can be used for applications like dimensionality reduction, clustering, and visualization.
This is a presentation that I gave to my research group. It is about probabilistic extensions to Principal Components Analysis, as proposed by Tipping and Bishop.
A Novel Algorithm to Estimate Closely Spaced Source DOA IJECEIAES
In order to improve resolution and direction of arrival (DOA) estimation of two closely spaced sources, in context of array processing, a new algorithm is presented. However, the proposed algorithm combines both spatial sampling technic to widen the resolution and a high resolution method which is the Multiple Signal Classification (MUSIC) to estimate the DOA of two closely spaced sources impinging on the far-field of Uniform Linear Array (ULA). Simulations examples are discussed to demonstrate the performance and the effectiveness of the proposed approach (referred as Spatial sampling MUSIC SS-MUSIC) compared to the classical MUSIC method when it’s used alone in this context.
IRJET- Clustering the Real Time Moving Object Adjacent TrackingIRJET Journal
This paper proposes a new algorithm for clustering the trajectories of moving objects in real-time based on sensor data. The algorithm represents each object's trajectory as a series of time-stamped positions. It aims to reduce data storage and transmission costs by clustering objects with similar movements together and sending updates only when objects change clusters. The key aspects of the algorithm are using a metric called M that measures how well an object fits in a cluster based on its predicted future trajectory, and updating clusters and transmitting changes when this metric exceeds a threshold for an object.
Anchor Positioning using Sensor Transmission Range Based Clustering for Mobil...ijdmtaiir
This document summarizes and compares two algorithms for selecting anchor points for mobile data collection in wireless sensor networks: Square Grid Clustering (SGC) and Sensor Transmission based Clustering (STC). SGC divides the deployment area into a grid and uses the centroid of each grid cell as an anchor point. STC clusters sensors based on their transmission range and uses the centroid of each cluster as an anchor point. The document finds that STC typically results in fewer anchor points than SGC and lower round-trip times for the mobile data collector. An analysis of the algorithms using sample sensor networks shows that STC outperforms SGC in anchor point selection and data collection efficiency.
Fixed Point Realization of Iterative LR-Aided Soft MIMO Decoding AlgorithmCSCJournals
Multiple-input multiple-output (MIMO) systems have been widely acclaimed in order to provide high data rates. Recently Lattice Reduction (LR) aided detectors have been proposed to achieve near Maximum Likelihood (ML) performance with low complexity. In this paper, we develop the fixed point design of an iterative soft decision based LR-aided K-best decoder, which reduces the complexity of existing sphere decoder. A simulation based word-length optimization is presented for physical implementation of the K-best decoder. Simulations show that the fixed point result of 16 bit precision can keep bit error rate (BER) degradation within 0.3 dB for 8×8 MIMO systems with different modulation schemes.
Exact network reconstruction from consensus signals and one eigen valueIJCNCJournal
The basic inverse problem in spectral graph theory consists in determining the graph given its eigenvalue
spectrum. In this paper, we are interested in a network of technological agents whose graph is unknown,
communicating by means of a consensus protocol. Recently, the use of artificial noise added to consensus
signals has been proposed to reconstruct the unknown graph, although errors are possible. On the other
hand, some methodologies have been devised to estimate the eigenvalue spectrum, but noise could interfere
with the elaborations. We combine these two techniques in order to simplify calculations and avoid
topological reconstruction errors, using only one eigenvalue. Moreover, we use an high frequency noise to
reconstruct the network, thus it is easy to filter the control signals after the graph identification. Numerical
simulations of several topologies show an exact and robust reconstruction of the graphs.
A C OMPARATIVE A NALYSIS A ND A PPLICATIONS O F M ULTI W AVELET T RANS...IJCI JOURNAL
In the era of telemedicine a large amount of medica
l information is exchanged via electronic media mos
tly
in the form of medical images, to improve the accur
acy and speed of diagnosis process. Medical Image
denoising has the basic importance in image analysi
s as these algorithm and procedures affects the eff
icacy
of medical diagnostic. In this paper focus is on Mu
lti wavelets based Image denoising techniques, beca
use
they provide the possibility of designing wavelets
systems which are orthogonal, symmetric and compact
ly
supported, simultaneously. Performance of Discrete
Multi Wavelet Transform and Discrete Wavelet
Transform based denoising methods are compared on t
he basis of PSNR
This document describes using backpropagation neural networks (BPNN) and nonlinear autoregressive models with exogenous inputs (NARX) to forecast wheat prices. Historical price data from 1978-2012 for wheat, barley, oats, and soybeans was used. Correlation analysis found wheat price positively correlated to the prices of the other grains. BPNN and NARX models were developed and compared. The best BPNN model used 11 hidden nodes and predicted wheat price from the prices of barley, oats, and soybeans. The best NARX model used 8 hidden nodes and 4 tapped delay lines to predict wheat price. NARX was identified as an alternative model for wheat price prediction.
A fuzzy logic controllerfora two link functional manipulatorIJCNCJournal
This paper presents a new approach for designing a Fuzzy Logic Controller "FLC"for a dynamically multivariable nonlinear coupling system. The conventional controller with constant gains for different operating points may not be sufficient to guarantee satisfactory performance for Robot manipulator. The Fuzzy Logic Controller utilizes the error and the change of error as fuzzy linguistic inputs to regulate the system performance. The proposed controller have been developed to simulate the dynamic behavior of A
Two-Link Functional Manipulator. The new controller uses only the available information of the input-output for controlling the position and velocity of the robot axes of the motion of the end effectors
Dcf learn and performance analysis of 802.11 b wireless networkIJCNCJournal
Though WLAN wireless network has been widely deployed as the main split-flow deployment of the
communication network, little study emphasizes its performance as WLAN protocols were only designed for
the public communicating conveniently with each other. Actually that too much wireless access points
assembling together will cause self-interference to the whole WLAN network. This paper investigates the
distributed coordination function (DCF) learn and the performance study of 802.11b networks. Firstly, our
study illustrates the performance of its MAC layer and its fairness issues related to DCF. Next we propose
the details which should be paid attention to in deploying network services. Then, performance analyses
are evaluated by simulation and real test for a dense wireless network. Our main goal is to give proposals
to network operators how to design a WLAN network more standardized and orderly.
The document summarizes a study on simulating a cognitive radio system using different wireless channel models. The study evaluated the performance of an energy detector for spectrum sensing in the cognitive radio system. Three channel models were considered: (1) Additive White Gaussian Noise channel, (2) Rayleigh fading channel, and (3) Rician fading channel. The simulation results showed that the detection capability of the energy detector improved with increasing signal-to-noise ratio, and the probabilities of false alarm and missed detection decreased. The Rician fading model, which includes a line-of-sight path, had a significant impact on energy detector performance compared to the other models.
Different date block size using to evaluate the performance between different...IJCNCJournal
The different computer networks whether wired or wireless are becoming more popular with its high
security aspect. Different security algorithms and technique are using to avoid any aforementioned attacks.
One of these technique is a cryptography technique that makes the data as unreadable during the transfer
hence; there is no chance to reclaim the information. Presently, most of the users are using various media
types and internet to transfer the data but, it has the chance to retrieve the data by using these media types.
The perfect solution for this problem is to provide security on time-to-time basis; this stage is always
significant to the security related community discussions. This paper explains the comparison between the
run time of three different encryption algorithms which are DES, AES and Blowfish The compression
includes using different modes, data block size and different operation modes. As a result, Blowfish
algorithm followed by AES take less time for running compared to DES.
This document summarizes a research paper that designed and implemented sphere decoding (SD) for multiple-input multiple-output (MIMO) systems using an FPGA. It used Newton's iterative method to calculate the matrix inverse as part of the SD algorithm, which reduces complexity compared to direct matrix inversion. The authors implemented SD for a 2x2 MIMO system with 4-QAM modulation. Simulation results showed that Newton's method converged after 7 iterations, and SD successfully calculated the minimum Euclidean distance vector.
This paper proposes modeling and identification of dynamical systems in delta
domain using neural network. The properties of delta operator are used such as greater
numerical robustness in computation and superior coefficients representation in finite word
length in implementation and well ensured numerical conditioning at high sampling
frequency. To formulate the identification scheme delta operator model is recasted into a
realizable neural network structure using the properties of inverse delta operator.
TIME OF ARRIVAL BASED LOCALIZATION IN WIRELESS SENSOR NETWORKS: A LINEAR APPR...sipij
This document describes localization techniques for wireless sensor networks based on time of arrival (TOA) measurements. It introduces four linear localization approaches: Linear Least Squares (LLS), Subspace Approach (SA), Weighted Linear Least Squares (WLLS), and Two-step WLS. It derives the Cramer-Rao Lower Bound (CRLB) for position estimation and compares the mean square position error of the four approaches through simulation. The results show that the Two-step WLS approach achieves the highest localization accuracy.
Comparison of Different Methods for Fusion of Multimodal Medical ImagesIRJET Journal
This document compares different methods for fusing multimodal medical images, including PCA, DCT, SWT, and DWT. It provides an overview of each method, including formulations, process flow diagrams, algorithms, and advantages/disadvantages. PCA uses eigenvectors to reveal internal data structure and remove redundancy. DCT expresses image blocks as sums of cosine functions. SWT is a translation-invariant modification of DWT that does not decimate coefficients. DWT decomposes images into coarse and detailed frequency subbands using wavelet transforms. The document reviews each method for fusing medical images from different modalities to extract complementary information.
In this paper, a new algorithm for a high resolution
Direction Of Arrival (DOA) estimation method for multiple
wideband signals is proposed. The proposed method proceeds
in two steps. In the first step, the received signals data is
decomposed in a Toeplitz form using the first-order statistics.
In the second step, The QR decomposition is applied on the
constructed Toeplitz matrix. Compared with existing schemes,
the proposed scheme provides several advantages. First, it
requires computing the triangular matrix R or the orthogonal
matrix Q to find the DOA; these matrices can be computed
with O(n2) operation. However, most of the existing schemes
required eignvalue decomposition (EVD) for the covariance
matrix or singular value decomposition (SVD) for the data
matrix; using EVD or SVD requires much more complex
computational O(n3) operation. Second, the proposed scheme
is more suitable for high-speed communication since it
requires first-order statistics and a single snapshot. Third,
the proposed scheme can estimate the correlated wideband
signals without using spatial smoothing techniques; whereas,
already-existing schemes do not. Accuracy of the proposed
wideband DOA estimation method is evaluated through
computer simulation in comparison with a conventional
method.
The variational Gaussian process (VGP), a Bayesian nonparametric model which adapts its shape to match com- plex posterior distributions. The VGP generates approximate posterior samples by generating latent inputs and warping them through random non-linear mappings; the distribution over random mappings is learned during inference, enabling the transformed outputs to adapt to varying complexity.
Advanced Support Vector Machine for classification in Neural NetworkAshwani Jha
This paper proposes a method to classify EEG signals using a support vector machine (SVM) classifier combined with a quicksort sorting algorithm (SVM-Q). The method involves extracting phase locking value (PLV) features from EEG data, sorting the feature vectors using quicksort, and then applying SVM classification. When tested on two BCI competition datasets, SVM-Q achieved classification accuracies of 86% and 77%, outperforming plain SVM which achieved 62% and 54% respectively. The paper concludes that adding a sorting algorithm to SVM can improve its classification performance for brain-computer interface applications.
New Data Association Technique for Target Tracking in Dense Clutter Environme...CSCJournals
Improving data association process by increasing the probability of detecting valid data points (measurements obtained from radar/sonar system) in the presence of noise for target tracking are discussed in manuscript. We develop a novel algorithm by filtering gate for target tracking in dense clutter environment. This algorithm is less sensitive to false alarm (clutter) in gate size than conventional approaches as probabilistic data association filter (PDAF) which has data association algorithm that begin to fail due to the increase in the false alarm rate or low probability of target detection. This new selection filtered gate method combines a conventional threshold based algorithm with geometric metric measure based on one type of the filtering methods that depends on the idea of adaptive clutter suppression methods. An adaptive search based on the distance threshold measure is then used to detect valid filtered data point for target tracking. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithm.
The document discusses convolution and its applications in digital signal processing. It begins with an introduction to convolution and its mathematical definitions for both continuous and discrete time signals. It then discusses various types of convolution including linear and circular convolution. The properties of convolution such as commutativity, associativity and distributivity are also covered. Applications of convolution in areas such as statistics, optics, acoustics, electrical engineering and digital signal processing are summarized. Finally, the document discusses symmetric convolution and its advantages over traditional convolution methods.
The document provides an overview of self-organizing maps (SOM). It defines SOM as an unsupervised learning technique that reduces the dimensions of data through the use of self-organizing neural networks. SOM is based on competitive learning where the closest neural network unit to the input vector (the best matching unit or BMU) is identified and adjusted along with neighboring units. The algorithm involves initializing weight vectors, presenting input vectors, identifying the BMU, and updating weights of the BMU and neighboring units. SOM can be used for applications like dimensionality reduction, clustering, and visualization.
This is a presentation that I gave to my research group. It is about probabilistic extensions to Principal Components Analysis, as proposed by Tipping and Bishop.
A Novel Algorithm to Estimate Closely Spaced Source DOA IJECEIAES
In order to improve resolution and direction of arrival (DOA) estimation of two closely spaced sources, in context of array processing, a new algorithm is presented. However, the proposed algorithm combines both spatial sampling technic to widen the resolution and a high resolution method which is the Multiple Signal Classification (MUSIC) to estimate the DOA of two closely spaced sources impinging on the far-field of Uniform Linear Array (ULA). Simulations examples are discussed to demonstrate the performance and the effectiveness of the proposed approach (referred as Spatial sampling MUSIC SS-MUSIC) compared to the classical MUSIC method when it’s used alone in this context.
IRJET- Clustering the Real Time Moving Object Adjacent TrackingIRJET Journal
This paper proposes a new algorithm for clustering the trajectories of moving objects in real-time based on sensor data. The algorithm represents each object's trajectory as a series of time-stamped positions. It aims to reduce data storage and transmission costs by clustering objects with similar movements together and sending updates only when objects change clusters. The key aspects of the algorithm are using a metric called M that measures how well an object fits in a cluster based on its predicted future trajectory, and updating clusters and transmitting changes when this metric exceeds a threshold for an object.
Anchor Positioning using Sensor Transmission Range Based Clustering for Mobil...ijdmtaiir
This document summarizes and compares two algorithms for selecting anchor points for mobile data collection in wireless sensor networks: Square Grid Clustering (SGC) and Sensor Transmission based Clustering (STC). SGC divides the deployment area into a grid and uses the centroid of each grid cell as an anchor point. STC clusters sensors based on their transmission range and uses the centroid of each cluster as an anchor point. The document finds that STC typically results in fewer anchor points than SGC and lower round-trip times for the mobile data collector. An analysis of the algorithms using sample sensor networks shows that STC outperforms SGC in anchor point selection and data collection efficiency.
Fixed Point Realization of Iterative LR-Aided Soft MIMO Decoding AlgorithmCSCJournals
Multiple-input multiple-output (MIMO) systems have been widely acclaimed in order to provide high data rates. Recently Lattice Reduction (LR) aided detectors have been proposed to achieve near Maximum Likelihood (ML) performance with low complexity. In this paper, we develop the fixed point design of an iterative soft decision based LR-aided K-best decoder, which reduces the complexity of existing sphere decoder. A simulation based word-length optimization is presented for physical implementation of the K-best decoder. Simulations show that the fixed point result of 16 bit precision can keep bit error rate (BER) degradation within 0.3 dB for 8×8 MIMO systems with different modulation schemes.
Exact network reconstruction from consensus signals and one eigen valueIJCNCJournal
The basic inverse problem in spectral graph theory consists in determining the graph given its eigenvalue
spectrum. In this paper, we are interested in a network of technological agents whose graph is unknown,
communicating by means of a consensus protocol. Recently, the use of artificial noise added to consensus
signals has been proposed to reconstruct the unknown graph, although errors are possible. On the other
hand, some methodologies have been devised to estimate the eigenvalue spectrum, but noise could interfere
with the elaborations. We combine these two techniques in order to simplify calculations and avoid
topological reconstruction errors, using only one eigenvalue. Moreover, we use an high frequency noise to
reconstruct the network, thus it is easy to filter the control signals after the graph identification. Numerical
simulations of several topologies show an exact and robust reconstruction of the graphs.
A C OMPARATIVE A NALYSIS A ND A PPLICATIONS O F M ULTI W AVELET T RANS...IJCI JOURNAL
In the era of telemedicine a large amount of medica
l information is exchanged via electronic media mos
tly
in the form of medical images, to improve the accur
acy and speed of diagnosis process. Medical Image
denoising has the basic importance in image analysi
s as these algorithm and procedures affects the eff
icacy
of medical diagnostic. In this paper focus is on Mu
lti wavelets based Image denoising techniques, beca
use
they provide the possibility of designing wavelets
systems which are orthogonal, symmetric and compact
ly
supported, simultaneously. Performance of Discrete
Multi Wavelet Transform and Discrete Wavelet
Transform based denoising methods are compared on t
he basis of PSNR
This document describes using backpropagation neural networks (BPNN) and nonlinear autoregressive models with exogenous inputs (NARX) to forecast wheat prices. Historical price data from 1978-2012 for wheat, barley, oats, and soybeans was used. Correlation analysis found wheat price positively correlated to the prices of the other grains. BPNN and NARX models were developed and compared. The best BPNN model used 11 hidden nodes and predicted wheat price from the prices of barley, oats, and soybeans. The best NARX model used 8 hidden nodes and 4 tapped delay lines to predict wheat price. NARX was identified as an alternative model for wheat price prediction.
A fuzzy logic controllerfora two link functional manipulatorIJCNCJournal
This paper presents a new approach for designing a Fuzzy Logic Controller "FLC"for a dynamically multivariable nonlinear coupling system. The conventional controller with constant gains for different operating points may not be sufficient to guarantee satisfactory performance for Robot manipulator. The Fuzzy Logic Controller utilizes the error and the change of error as fuzzy linguistic inputs to regulate the system performance. The proposed controller have been developed to simulate the dynamic behavior of A
Two-Link Functional Manipulator. The new controller uses only the available information of the input-output for controlling the position and velocity of the robot axes of the motion of the end effectors
Dcf learn and performance analysis of 802.11 b wireless networkIJCNCJournal
Though WLAN wireless network has been widely deployed as the main split-flow deployment of the
communication network, little study emphasizes its performance as WLAN protocols were only designed for
the public communicating conveniently with each other. Actually that too much wireless access points
assembling together will cause self-interference to the whole WLAN network. This paper investigates the
distributed coordination function (DCF) learn and the performance study of 802.11b networks. Firstly, our
study illustrates the performance of its MAC layer and its fairness issues related to DCF. Next we propose
the details which should be paid attention to in deploying network services. Then, performance analyses
are evaluated by simulation and real test for a dense wireless network. Our main goal is to give proposals
to network operators how to design a WLAN network more standardized and orderly.
The document summarizes a study on simulating a cognitive radio system using different wireless channel models. The study evaluated the performance of an energy detector for spectrum sensing in the cognitive radio system. Three channel models were considered: (1) Additive White Gaussian Noise channel, (2) Rayleigh fading channel, and (3) Rician fading channel. The simulation results showed that the detection capability of the energy detector improved with increasing signal-to-noise ratio, and the probabilities of false alarm and missed detection decreased. The Rician fading model, which includes a line-of-sight path, had a significant impact on energy detector performance compared to the other models.
Different date block size using to evaluate the performance between different...IJCNCJournal
The different computer networks whether wired or wireless are becoming more popular with its high
security aspect. Different security algorithms and technique are using to avoid any aforementioned attacks.
One of these technique is a cryptography technique that makes the data as unreadable during the transfer
hence; there is no chance to reclaim the information. Presently, most of the users are using various media
types and internet to transfer the data but, it has the chance to retrieve the data by using these media types.
The perfect solution for this problem is to provide security on time-to-time basis; this stage is always
significant to the security related community discussions. This paper explains the comparison between the
run time of three different encryption algorithms which are DES, AES and Blowfish The compression
includes using different modes, data block size and different operation modes. As a result, Blowfish
algorithm followed by AES take less time for running compared to DES.
A novel scheme to improve the spectrum sensing performanceIJCNCJournal
Due to limited availability of spectrum for license
d users only, the need for secondary access by unli
censed
users is increasing. Cognitive radio turns out to b
e helping this situation because all that is needed
is a
technique that could efficiently detect the empty s
paces and provide them to the secondary devices wit
hout
causing any interference to the primary (licensed)
users. Spectrum sensing is the foremost function of
the
cognitive radio which senses the environment for wh
ite spaces. Energy detection is one of the various
spectrum sensing techniques that are under research
. Earlier it was shown that energy detection works
better under AWGN channel as compared to Rayleigh c
hannel, however the conventional spectrum sensing
techniques have a high probability of false alarm a
nd also show a better probability of detection for
higher
values of SNR. There is a need for a new technique
that shows a reduced probability of false alarm as
well
as an increase in the probability of detection for
lower values of SNR. In the present work the conven
tional
energy detection technique has been enhanced to get
better results.
OPTIMIZING VOIP USING A CROSS LAYER CALL ADMISSION CONTROL SCHEMEIJCNCJournal
This document discusses optimizing VoIP quality over wireless networks using a cross-layer call admission control scheme. It proposes monitoring real-time control protocol reports and data rates at the MAC layer to determine when quality is degraded. When quality degrades due to issues like network congestion or variable transmission rates, the solution is to adapt the packet size or codec type. The proposed scheme is simulated using a wireless campus network model to improve performance.
The document summarizes a proposed scheme for rapid signal acquisition in direct sequence spread spectrum (DSSS) communication systems operating in high Doppler environments. The scheme uses time domain correlation of differential signals to estimate pseudo-noise (PN) code phase, followed by fast Fourier transform (FFT) to precisely estimate Doppler frequency. An area-efficient field programmable gate array (FPGA) architecture is presented that combines time and frequency domain approaches. The architecture achieves 52% area occupancy when synthesized for a Virtex-6 FPGA and operates at 134 MHz, allowing for fast signal acquisition needed in applications like missile guidance systems.
NODE FAILURE TIME AND COVERAGE LOSS TIME ANALYSIS FOR MAXIMUM STABILITY VS MI...IJCNCJournal
This document analyzes and compares two algorithms for data gathering in mobile sensor networks:
1) Maximum Stability Spanning Tree-based Data Gathering (Max.Stability-DG) which determines data gathering trees that exist for the longest time by assuming knowledge of future topology changes.
2) Minimum Distance Spanning Tree-based Data Gathering (MST-DG) which determines data gathering trees based on the minimum distance spanning tree at each current time instant.
An exhaustive simulation study is conducted to analyze the impact of these algorithms on node lifetime, network lifetime, and coverage loss time due to node failures in mobile sensor networks.
PAPR REDUCTION OF OFDM SIGNAL BY USING COMBINED HADAMARD AND MODIFIED MEU-LAW...IJCNCJournal
Orthogonal frequency division multiplexing (OFDM) is a technique which gives high quality of service (QOS) to the users by mitigating the fading signals as well as high data rates in multimedia services. However, the peak-to-average power ratio (PAPR) is a technical challenge that reduces the efficiency of RF power amplifiers. In this paper, we propose the combined Hadamard transform and modified meu-law companding transform method in order to lessen the effects of the peak-to-average power ratio of the
OFDM signal. Simulation results show that the proposed scheme reduces PAPR compared to other companding techniques as well as the Hadamard transform technique when used on its own.
On the development of methodology for planning and cost modeling of a wide ar...IJCNCJournal
The most important stages in designing a
computer
network
in a
wider geographical area include:
definition of requirements, topological description
,
identification and calculation of relevant parameters
(
i
.
e
.
traffic matrix
)
, determining the shortest path between nodes, quantification of the effect of various
levels
of technical and technological development of urban areas involved, the cost of technology
,
and the
cost of services. The
se
parameters differ for WAN networks in different regions
–
their calculation depends
directly
on
the data “
i
n the field
”
: number of inhabitants, distance between populated areas,
network
traffic
density
,
as well as
available
bandwidth
. The
main
reason for identification and evaluation of these
parameters
is
to develop a model that could
meet the
constraints
im
posed by poten
tial beneficiaries.
In this
paper
,
we develop a methodology for planning and cost
-
modeling of a wide area network
and
validate it
in
a case study,
under the
supposition
that
behavioral interactions of individuals and groups play a significant
role and have
to be taken into consideration
by employing either simple or composite indicators of
socioeconomic status
.
Key management in information centric networkingIJCNCJournal
Information centric networking (ICN) has been in the spotlight of recent research. It is an emerging
communication paradigm that relays on the concept of publish and subscribe. It aims to revise the current
Internet with a new clean slate architecture where the design is completely different from today’s location
based model. To secure the forwarding plan in this network, it is vital to have a time based transient
forwarding identifiers by periodically changing the network link identifiers. This assumes shared keys to be
distributed prior the communications between an entity termed topology manager (TM) and each forwarder
in the network. Exchanging and sharing a secret key between two parties is one of most critical functions in
cryptography that needs to be more concerned when integrating cryptographic functions into the system. As
ICN is brand new Internet architecture, many existing cryptography protocols may need to be redesigned
to fit this new architecture. Therefore, this paper focuses on the security aspect of ICN and proposes an
initial design to deploy the integrated Diffie-Hellman-DSA key exchange protocol as a key distributions
mechanism.
This document summarizes a research paper that proposes a new approach for complex encryption and decryption of data. The approach uses a combination of public key infrastructure and RC6 algorithm. It divides plaintext into blocks, uses one block as an encryption key, and inserts the key into the ciphertext based on a private position. Performance analysis shows the proposed approach encrypts and decrypts data faster than the AES algorithm. Security analysis indicates the approach is secure against known attacks based on correlation analysis and information entropy tests. The approach provides improved security and performance for encrypting network data.
A review study of handover performance in mobile ipIJCNCJournal
The Mobile Internet Protocol (Mobile IP) is an extension to the Internet Protocol proposed by the Internet
Engineering Task Force (IETF) that addresses the mobility issues. In order to support un-interrupted
services and seamless mobility of nodes across the networks (and/or sub-networks) with permanent IP
addresses, handover is performed in mobile IP enabled networks. Handover in mobile IP is source cause of
performance degradation as it results in increased latency and packet loss during handover. Other issues
like scalability issues, ordered packet delivery issues, control plane management issues etc are also
adversely affected by it. The paper provides a constructive survey by classifying, discussing and comparing
different handover techniques that have been proposed so far, for enhancing the performance during
handovers. Finally some general solutions that have been used to solve handover related problems are
briefly discussed.
LTE QOS DYNAMIC RESOURCE BLOCK ALLOCATION WITH POWER SOURCE LIMITATION AND QU...IJCNCJournal
3GPP has defined the long term evolution (LTE) for 3G radio access in order to maintain the future
competitiveness for 3G technology, the system provides the capability of supporting a mixture of services
with different quality of service (QoS) requirements. This paper proposes a new cross-layer scheduling
algorithm to satisfy better QoS parameters for real time applications. The proposed algorithm takes care of
allocating resource blocks (RBs) with different modulation and coding schemes (MCS) according to target
bit error rate (BER), user equipment supportable MCS, queue stability constraints and available transmit
power constraints. The proposed algorithm has been valued, compared with an earlier allocation algorithm
in terms of service rate and packet delay and showed better performance regards the real time
applications.
Framework for wireless network security using quantum cryptographyIJCNCJournal
Data that is transient over an unsecured wireless network is always susceptible to being intercepted by
anyone within the range of the wireless signal. Hence providing secure communication to keep the user’s
information and devices safe when connected wirelessly has become one of the major concerns. Quantum cryptography provides a solution towards absolute communication security over the network by encoding
information as polarized photons, which can be sent through the air. This paper explores on the aspect of
application of quantum cryptography in wireless networks.
In this paper we present a methodology for integrating quantum cryptography and security of IEEE 802.11 wireless networks in terms of distribution of the encryption keys.
A New Programming Model to Simulate Wireless Sensor Networks : Finding The Be...IJCNCJournal
This document summarizes a research paper that proposes a new programming model for wireless sensor networks. The programming model aims to find the best routing path between sensor nodes by coding the actual sensor nodes to perform tasks. The paper reviews existing programming models and requirements for sensor network programming such as energy efficiency, scalability, localization, and time synchronization. It then describes the proposed model and compares results from applying the model in different network topologies using multiple routing algorithms.
The document analyzes the performance of IEEE 802.16d under the Stanford University Interim (SUI) channel model. It investigates the bit error rate of IEEE 802.16d using different SUI channel models and digital modulations like BPSK and QPSK. The results show the BER performance for different SUI channel models and modulations.
ETOR-Efficient Token based Opportunistic RoutingIJCNCJournal
This paper proposes an Efficient Token based Opportunistic Routing called ETOR, which is an
improvement to the token based coordination approach for opportunistic routing proposed by Economy[1].
In Economy, method used for finding the connected candidate order chooses neighbor as the next
candidate by considering ETX of that neighbor towards the source but it does not consider the link
probability between the relay candidate and neighbor to be selected. ETOR proposes variant methods for
finding the connected candidate order in token based opportunistic routing by considering both the ETX
of the neighbor towards source as well as ETX of the relay towards sending candidate which avoids weaker
links between its intermediate nodes thereby improving the throughput and reducing the AA Ratio. We also
propose a solution for reducing the number of hops traversed by the token, which in turn increases the
token generation speed. Simulation results show that the proposed ETOR approaches perform better than
Economy approach in terms of AA Ratio, number of hops traversed by the token and number of token
traversals.
CHANNEL ESTIMATION AND MULTIUSER DETECTION IN ASYNCHRONOUS SATELLITE COMMUNIC...ijwmn
In this paper, we propose a new method of channel estimation for asynchronous additive white Gaussian noise channels in satellite communications. This method is based on signals correlation and multiuser interference cancellation which adopts a successive structure. Propagation delays and signals amplitudes are jointly estimated in order to be used for data detection at the receiver. As, a multiuser detector, a single stage successive interference cancellation (SIC) architecture is analyzed and integrated to the channel estimation technique and the whole system is evaluated. The satellite access method adopted is the direct sequence code division multiple access (DS CDMA) one. To evaluate the channel estimation and the detection technique, we have simulated a satellite uplink with an asynchronous multiuser access.
Massive MIMO for Cooperative Network ApplicationVARUN KUMAR
This document summarizes a research paper on analyzing the achievable rate and power efficiency of massive MIMO in cooperative networks using zero-forcing receivers. It outlines the problem definition, system model, and simulation results. The system model considers uplink transmission from mobile users to a base station via relay stations. It analyzes the observed signal-to-noise ratio in direct and cooperative transmission and defines achievable rate metrics for direct, non-cooperative, and cooperative decoding schemes. Power efficiency is defined as the achievable rate divided by the total transmit power of all nodes.
Time of arrival based localization in wireless sensor networks a non linear ...sipij
In this paper, we aim to obtain the location information of a sensor node deployed in a Wireless Sensor Network (WSN). Here, Time of Arrival based localization technique is considered. We calculate the position information of an unknown sensor node using the non- linear techniques. The performances of the techniques are compared with the Cramer Rao Lower bound (CRLB). Non-linear Least Squares and the Maximum Likelihood are the non-linear techniques that have been used to estimate the position of the unknown sensor node. Each of these non-linear techniques are iterative approaches, namely, Newton
Raphson estimate, Gauss Newton Estimate and the Steepest Descent estimate for comparison. Based on the
results of the simulation, the approaches have been compared. From the simulation study, Localization
based on Maximum Likelihood approach is having higher localization accuracy.
ADAPTIVE BLIND MULTIUSER DETECTION UNDER IMPULSIVE NOISE USING PRINCIPAL COMP...cscpconf
The document describes an adaptive blind multiuser detection method for an asynchronous code division multiple access (CDMA) system using principal component analysis (PCA) in impulsive noise. PCA is used to reduce the dimensionality of the received signal data set without much loss of information. The key steps are to calculate the covariance matrix of the received signal, perform singular value decomposition to obtain the principal components which are ordered by decreasing variance, and select the top principal components which describe most of the variance in the original data to reduce dimensionality. Simulation results showed the proposed PCA blind multiuser detection method offers substantial performance gains over traditional subspace methods.
Channel and clipping level estimation for ofdm in io t –based networks a reviewIJARIIT
Internet of Things (IoT) is the idea to connect all devices to the internet. To implement such systems, we need to design
low cost and less complex transmitters and make the receiver side complex. Now days OFDM is mainly used for communication
due to its great advantages. But it faces the main problem such as PAPR due to the non-linear performance of High power
amplifiers. There are so many methods are available to reduce the effect of PAPR in OFDM transmission, among this clipping
is the simplest one. In this paper, we propose two algorithms to find the clipping level as well as the channel estimation. The
efficiency of these algorithms is evaluated by using CLRB calculation.
The document proposes a stochastic modeling approach to analyze the time-domain variability of general linear systems with uncertain parameters. It uses a polynomial chaos expansion of the scattering parameters to build an "augmented system" that relates the polynomial chaos coefficients of the input and output port signals. A vector fitting algorithm is then used to obtain a stable and passive state-space model of the augmented system. This provides an efficient way to perform time-domain variability analysis with one simulation, avoiding the computational cost of Monte Carlo analysis which requires many simulations. The approach is demonstrated on a microstrip bandstop filter example.
This document proposes a stochastic modeling approach to analyze the time-domain variability of general linear systems with uncertain parameters. It uses a polynomial chaos expansion of the scattering parameters to build an "augmented system" described by a deterministic matrix. The Galerkin projection method is used to relate the polynomial chaos coefficients of the input/output port signals. A Vector Fitting algorithm then generates a stable and passive state-space model of the augmented system. This allows time-domain variability analysis to be performed with one simulation, demonstrating computational efficiency over conventional Monte Carlo methods. The approach is validated on a microstrip bandstop filter with random width and permittivity parameters.
Cyclostationary analysis of polytime coded signals for lpi radarseSAT Journals
This document discusses cyclostationary analysis of polytime coded signals for low probability of intercept (LPI) radars. It begins with an introduction to LPI radars and their modulation and detection techniques, focusing on polytime codes. It then describes cyclostationary signal processing methods like the direct frequency smoothing method (DFSM) and fast Fourier transform accumulation method (FAM) that can be used to extract parameters from polytime coded signals. The document analyzes example polytime coded signals with and without noise using these cyclostationary techniques and accurately extracts key parameters like carrier frequency, bandwidth, and code rate. It finds the FAM method has better computational efficiency than DFSM for long signals.
Performance Comparison of Energy Detection Based Spectrum Sensing for Cogniti...irjes
With the rapid deployment of new wireless devices and applications, the last decade has witnessed a growing
demand for wireless radio spectrum. However, the policy of fixed spectrum assignment produces a bottleneck for more
efficient spectrum utilization, such that a great portion of the licensed spectrum is severely under-utilized. So the concept of
cognitive radio was introduced to address this issue.The inefficient usage of the limited spectrum necessitates the
development of dynamic spectrum access techniques, where users who have no spectrum licenses, also known as secondary
users, are allowed to use the temporarily unused licensed spectrum. For this purpose we have to know the presence or
absence of primary users for spectrum usage. So spectrums sensing is one of the major requirements of cognitive radio.Many
spectrum sensing techniques have been developed to sense the presence or absence of a licensed user. This paper evaluates
the performance of the energy detection based spectrum sensing technique in noisy and fading environments.The
performance of the energy detection technique will be evaluated by use of Receiver Operating Characteristics (ROC) curves
over additive white Gaussian noise (AWGN) and fading channels.
ECG Classification using Dynamic High Pass Filtering and Statistical Framewor...CSCJournals
This paper presents a technique for detection of R peak from ECG using statistics of the input signal. In this method, high pass filter is derived from the statistics of given signal. Using Minimum Mean Square Error (MMSE) approach, filter parameters are estimated. For estimation of filter parameters, autocorrelation is used. Then further processing is done on the output of high pass filter to detect R peaks and analysis is carried out from the series of R-R intervals to estimate the time domain and frequency domain parameters. From these parameters, ECG classification is done as Normal Sinus Rhythm and Supraventricular tachycardia (SVT).
Reduction of PAPR and Efficient detection ordering scheme for MIMO Transmissi...IJERA Editor
The technical challenges for communication engineers is the development of best performance wireless
networks with negligible amount of distortions. We have to consider multipath propagation attenuation and
radio spectrum inefficiency. Now a days, In MIMO (Multi Input Multi Output) systems there is a huge demand
for the networks with the high transmission rates and better quality of service which are having low PAPR ratio.
Instead of OFDMA, filter banks are used in massive MIMO to reduce the complexity. But they are error prone
to noise. This base paper discusses about PAPR reduction in MIMO systems using different precoding based
OFDM systems. Mainly, minimization of multi-antenna systems by controlling the transmission power and
reduction of PAPR using ZC (Zadoff-Chu) matrix transform.
Discrete-wavelet-transform recursive inverse algorithm using second-order est...TELKOMNIKA JOURNAL
The recursive-least-squares (RLS) algorithm was introduced as an alternative to LMS algorithm with enhanced performance. Computational complexity and instability in updating the autocolleltion matrix are some of the drawbacks of the RLS algorithm that were among the reasons for the intrduction of the second-order recursive inverse (RI) adaptive algorithm. The 2nd order RI adaptive algorithm suffered from low convergence rate in certain scenarios that required a relatively small initial step-size. In this paper, we propose a newsecond-order RI algorithm that projects the input signal to a new domain namely discrete-wavelet-transform (DWT) as pre step before performing the algorithm. This transformation overcomes the low convergence rate of the second-order RI algorithm by reducing the self-correlation of the input signal in the mentioned scenatios. Expeirments are conducted using the noise cancellation setting. The performance of the proposed algorithm is compared to those of the RI, original second-order RI and RLS algorithms in different Gaussian and impulsive noise environments. Simulations demonstrate the superiority of the proposed algorithm in terms of convergence rate comparedto those algorithms.
Turbo Detection in Rayleigh flat fading channel with unknown statisticsijwmn
The turbo detection of turbo coded symbols over correlated Rayleigh flat fading channels generated
according to Jakes’ model is considered in this paper. We propose a method to estimate the channel
signal-to-noise ratio (SNR) and the maximum Doppler frequency. These statistics are required by
the linear minimum mean squared error (LMMSE) channel estimator. To improve the system convergence, we redefine the channel reliability factor by taking into account the channel estimation
error statistics. Simulation results for rate 1/3 turbo code and two different normalized fading rates
show that the use of the new reliability factor greatly improves the performance. The improvement
is more substantial when channel statistics are unknown.
Adaptive Channel Equalization using Multilayer Perceptron Neural Networks wit...IOSRJVSP
This document presents a neural network approach to channel equalization using a multilayer perceptron with a variable learning rate parameter. Specifically, it proposes modifying the backpropagation algorithm to allow the learning rate to adapt at each iteration in order to achieve faster convergence. The equalizer structure is a decision feedback equalizer modeled as a neural network with an input, hidden and output layer. Simulation results show the proposed variable learning rate approach improves bit error rate and convergence speed compared to a standard backpropagation algorithm.
This document summarizes a research paper on using wavelet neural networks (WNNs) for adaptive equalization in digital communication systems. The paper proposes using WNNs structured with wavelet basis functions as the activation functions. The orthogonal least squares (OLS) algorithm is then used to update the weighting matrix and select the most important wavelet basis units, reducing redundancy. The experimental results showed that a WNN equalizer using OLS outperformed conventional neural network equalizers in terms of signal-to-noise ratio and ability to handle non-linear channels.
This document summarizes a research paper on using wavelet neural networks (WNNs) for adaptive equalization in digital communication systems. The paper proposes using WNNs structured with wavelet basis functions as the activation functions. The orthogonal least squares (OLS) algorithm is then used to update the weighting matrix and select the most important wavelet basis units, reducing redundancy. The experimental results showed that a WNN equalizer using OLS outperformed conventional neural network equalizers in terms of signal-to-noise ratio and ability to handle non-linear channels.
Projected Barzilai-Borwein Methods Applied to Distributed Compressive Spectru...Polytechnique Montreal
Cognitive radio allows unlicensed (cognitive) users to use licensed frequency bands by exploiting spectrum sensing techniques to detect whether or not the licensed (primary) users are present. In this paper, we present a compressed sensing applied to spectrum-occupancy detection in wide-band applications. The collected analog signals from each cognitive radio (CR) receiver at a fusion center are transformed to discrete-time signals by using analog-to-information converter (AIC) and then employed to calculate the autocorrelation. For signal reconstruction, we exploit a novel approach to solve the optimization problem consisting of minimizing both a quadratic (l2) error term and an l1-regularization term. In specific, we propose the Basic gradient projection (GP) and projected Barzilai-Borwein (PBB) algorithm to offer a better performance in terms of the mean squared error of the power spectrum density estimate and the detection probability of licensed signal occupancy.
A novel delay dictionary design for compressive sensing-based time varying ch...TELKOMNIKA JOURNAL
Compressive sensing (CS) is a new attractive technique adopted for Linear Time Varying channel estimation. orthogonal frequency division multiplexing (OFDM) was proposed to be used in 4G and 5G which supports high data rate requirements. Different pilot aided channel estimation techniques were proposed to better track the channel conditions, which consumes bandwidth, thus, considerable data rate reduced. In order to estimate the channel with minimum number of pilots, compressive sensing CS was proposed to efficiently estimate the channel variations. In this paper, a novel delay dictionary-based CS was designed and simulated to estimate the linear time varying (LTV) channel. The proposed dictionary shows the suitability of estimating the channel impulse response (CIR) with low to moderate Doppler frequency shifts with acceptable bit error rate (BER) performance.
Investigation of repeated blasts at Aitik mine using waveform cross correlationIvan Kitov
We present results of signal detection from repeated events at the Aitik and Kiruna mines in Sweden as based on waveform cross correlation. Several advanced methods based on tensor Singular Value Decomposition is applied to waveforms measured at seismic array ARCES, which consists of three-component sensors.
Fpga Design Of Clutter Generator For Radar Testingcsijjournal
Detection of weak target echo in the presence of strong clutter is the main objective of any RADAR. To
evaluate the performance of RADAR it is required to generate the clutter of various types including land and sea. This clutter is of non Gaussian distribution such as lognormal, Weibull and k-type. In this project it is proposed to develop a clutter generation algorithm of given distribution type. This process consists of
random number generator with Gaussian distribution converting into a non Gaussian using ZMNL (Zero
Memory Nonlinearity Transformation) technique. It also includes amplitude shaping and addition other interference signals. The complete algorithm is first simulated using Xilinx ISE 9.2i and would be implemented in VIRTEX-V FPGA.T
Similar to Further results on the joint time delay and frequency estimation without eigendecomposition (20)
Rendezvous Sequence Generation Algorithm for Cognitive Radio Networks in Post...IJCNCJournal
Recent natural disasters have inflicted tremendous damage on humanity, with their scale progressively increasing and leading to numerous casualties. Events such as earthquakes can trigger secondary disasters, such as tsunamis, further complicating the situation by destroying communication infrastructures. This destruction impedes the dissemination of information about secondary disasters and complicates post-disaster rescue efforts. Consequently, there is an urgent demand for technologies capable of substituting for these destroyed communication infrastructures. This paper proposes a technique for generating rendezvous sequences to swiftly reconnect communication infrastructures in post-disaster scenarios. We compare the time required for rendezvous using the proposed technique against existing methods and analyze the average time taken to establish links with the rendezvous technique, discussing its significance. This research presents a novel approach enabling rapid recovery of destroyed communication infrastructures in disaster environments through Cognitive Radio Network (CRN) technology, showcasing the potential to significantly improve disaster response and recovery efforts. The proposed method reduces the time for the rendezvous compared to existing methods, suggesting that it can enhance the efficiency of rescue operations in post-disaster scenarios and contribute to life-saving efforts.
Blockchain Enforced Attribute based Access Control with ZKP for Healthcare Se...IJCNCJournal
The relationship between doctors and patients is reinforced through the expanded communication channels provided by remote healthcare services, resulting in heightened patient satisfaction and loyalty. Nonetheless, the growth of these services is hampered by security and privacy challenges they confront. Additionally, patient electronic health records (EHR) information is dispersed across multiple hospitals in different formats, undermining data sovereignty. It allows any service to assert authority over their EHR, effectively controlling its usage. This paper proposes a blockchain enforced attribute-based access control in healthcare service. To enhance the privacy and data-sovereignty, the proposed system employs attribute-based access control, zero-knowledge proof (ZKP) and blockchain. The role of data within our system is pivotal in defining attributes. These attributes, in turn, form the fundamental basis for access control criteria. Blockchain is used to keep hospital information in public chain but EHR related data in private chain. Furthermore, EHR provides access control by using the attributed based cryptosystem before they are stored in the blockchain. Analysis shows that the proposed system provides data sovereignty with privacy provision based on the attributed based access control.
EECRPSID: Energy-Efficient Cluster-Based Routing Protocol with a Secure Intru...IJCNCJournal
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Advanced Privacy Scheme to Improve Road Safety in Smart Transportation SystemsIJCNCJournal
In -Vehicle Ad-Hoc Network (VANET), vehicles continuously transmit and receive spatiotemporal data with neighboring vehicles, thereby establishing a comprehensive 360-degree traffic awareness system. Vehicular Network safety applications facilitate the transmission of messages between vehicles that are near each other, at regular intervals, enhancing drivers' contextual understanding of the driving environment and significantly improving traffic safety. Privacy schemes in VANETs are vital to safeguard vehicles’ identities and their associated owners or drivers. Privacy schemes prevent unauthorized parties from linking the vehicle's communications to a specific real-world identity by employing techniques such as pseudonyms, randomization, or cryptographic protocols. Nevertheless, these communications frequently contain important vehicle information that malevolent groups could use to Monitor the vehicle over a long period. The acquisition of this shared data has the potential to facilitate the reconstruction of vehicle trajectories, thereby posing a potential risk to the privacy of the driver. Addressing the critical challenge of developing effective and scalable privacy-preserving protocols for communication in vehicle networks is of the highest priority. These protocols aim to reduce the transmission of confidential data while ensuring the required level of communication. This paper aims to propose an Advanced Privacy Vehicle Scheme (APV) that periodically changes pseudonyms to protect vehicle identities and improve privacy. The APV scheme utilizes a concept called the silent period, which involves changing the pseudonym of a vehicle periodically based on the tracking of neighboring vehicles. The pseudonym is a temporary identifier that vehicles use to communicate with each other in a VANET. By changing the pseudonym regularly, the APV scheme makes it difficult for unauthorized entities to link a vehicle's communications to its real-world identity. The proposed APV is compared to the SLOW, RSP, CAPS, and CPN techniques. The data indicates that the efficiency of APV is a better improvement in privacy metrics. It is evident that the AVP offers enhanced safety for vehicles during transportation in the smart city.
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Further results on the joint time delay and frequency estimation without eigendecomposition
1. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.6, November 2013
FURTHER RESULTS ON THE JOINT TIME DELAY AND
FREQUENCY ESTIMATION WITHOUT EIGENDECOMPOSITION
Qasaymeh M. M.
Department of Electrical Engineering, Tafila Technical University, Tafila, Jordan
ABSTRACT
Joint Time Delay and Frequency Estimation (JTDFE) problem of complex sinusoidal signals received at
two separated sensors is an attractive problem that has been considered for several engineering
applications. In this paper, a high resolution null (noise) subspace method without eigenvalue
decomposition is proposed. The direct data Matrix is replaced by an upper triangular matrix obtained from
Rank-Revealing LU (RRLU) factorization. The RRLU provides accurate information about the rank and the
numerical null space which make it a valuable tool in numerical linear algebra.The proposed novel method
decreases the computational complexity of JTDFE approximately to the half compared with RRQR
methods. The proposed method generates estimates of the unknown parameters which are based on the
observation and/or covariance matrices. This leads to a significant improvement in the computational load.
Computer simulations are included in this paper to demonstrate the proposed method.
KEYWORDS
Time delay, Frequency estimation, Subspace estimation method, Rank revealing, Matrix decomposition, LU
factorization, QR factorization, Signal space, Null (noise) space, MUSIC, root-MUSIC.
1.
INTRODUCTION
Aprecise Time Delay Estimation (TDE) between two or morenoisy versions of the same signal
received at spatially separated sensors is an essentialsubject that has been used in many
applications such that positioning and tracking, speed sensing, direction finding, biomedicine,
exploration geophysics, etc[1], [2].Similarly, frequency estimation [3], [4] has been
comprehensively addressed in signal processing literature. Recently, these twoproblems were
joined as a Joint Time Delay and Frequency Estimation problem (JTDFE)[5], [6], which appeared
in manyapplications like synchronization in Code Division Multiple Access (CDMA)
systems,speech enhancement, and pitch estimation using a microphone array.
A Discrete-Time Fourier Transform (DTFT) based method has been derived for estimating the
time difference of arrival between sinusoidal signals received at two separated sensors[7]. A
subspace algorithm based on State-Space Realization (SSR) has been proposed for JTDFE [8]. In
SSR the frequency estimates is obtained directly from the eigenvalues of the state transition
matrix, while the delay is determined using the observation matrix and the estimated
DOI : 10.5121/ijcnc.2013.5616
243
2. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.6, November 2013
frequenciesitself.Now, the super-resolution technologies are mainly based on subspace fitting
algorithms, such as Multiple Signal Classification (MUSIC) [9], Estimation of Signal Parameters
via Rotational Invariance Techniques (ESPRIT) [10] and Generalized Eigen values Utilizing
Signal Subspace Eigen vectors (GEESE) [11]. It has been proved that the performance of MUSIC
and ESPRIT is inferior to the Maximum Likelihood (ML) estimation algorithm when they are
used in the arrival angle estimation in the antennal array [12]. Besides the subspace fitting
algorithms, there is another super-resolution algorithm which is based on the spectral moment
estimation [13], and the performance is also superior to the MUSIC and ESPRIT. However, it is
only used in the arrival angle estimation [14], and has not been introduced into the application of
time-delay estimation.The null space of the JTDEF was extracted by applying the Propagator
Method (PM) [15], Rank Revealing QR factorization (RRQR) [16] and an extra step was made in
[16] to convert the complex matrix to a real data one via the unitary transformation of a square
Toepltiz complex data matrix.
In this paper, the problem of estimating time delay and frequencies of received signal using Rank
Revealing LU(RRLU) matrix decomposition [17], [18] in combination with the well-known
MUSIC/root-MUSIC algorithm is addressed. It is well-known that the computational load of the
LU-based method is significantly lower, as it does not involve eigenvalue decomposition (EVD)
or singular value decomposition (SVD) of the cross-spectral matrix (CSM) of received signals.
In [17], [19] two theoretical approximations forcomputing the numerical rank of a triangular
matrix were introduced. This triangular matrix can be obtained by means of the LU factorization.
LU factorization implementation includes several improvements over the QR algorithm [20].
Specifically, an incremental condition estimator is employed to reduce the implementation cost.
The principle is based on a RRLU factorization [17], [18]-[19] which allows extraction from the
CSM, necessary information to estimate the subspace noise.
This paper is structured as follows. In Section 2, the system model and the problem formulation
is presented which is similar to the models [6], [15]. The development of the proposed method is
presented in Section 3. In Section 4, the performance of the method is illustrated through
MATLAB simulations. A comparison with the RRQR and SSR is made. Finally, some
concluding remarks follow in Section 5.
2.
PROBLEM FORMULATION
Consider the discrete-time sinusoidal signals ݔሺ݊ሻ and ݕሺ݊ሻwhich are two sensors measurements
that satisfying
where
ݔሺ݊ሻ ൌ ݏሺ݊ሻ ݑሺ݊ሻ
ݕሺ݊ሻ ൌ ݏሺ݊ െ ܦሻ ݖሺ݊ሻ, ݊ ൌ 0,1, … , ܰ െ 1
(1)
ݏሺ݊ሻ ൌ ܽ ݁ ఠ ሺ2ሻ
ୀଵ
The source signal ݏሺ݊ሻis demonstrated by a sum of P complex sinusoids where the amplitudes
(ܽ ) are unknown and complex-valued constants. The normalized radian frequencies (߱ ) are
different for every i and has been arranged in ascending order as ߱ଵ ൏ ߱ଶ … ൏ ߱ without loss of
244
3. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.6, November 2013
generality.To simplify the problem,it is assumed that, the number of sources Peither known or pre
estimated [21]. The two terms ݑሺ݊ሻand ݖሺ݊ሻare representingthe two zero mean, additive white
complex Gaussian noise processes independent of each other. Also,parameters Nrepresentthe
number ofsamples collected at each channel. The variable D is the delay between the received
copies of the signalݏሺ݊ሻ at the two separated sensors, which is unknown and is to be estimated.
The received data at the first and the second sensor with N available sampleswill be collected in
the vectors࢞ and ࢟ respectively, so the problembecomes a problem of estimationof both the
frequencies and the time delay from two N-points vectorsgiven by:
࢞ ൌ ሾݔሺ0ሻ, ݔሺ1ሻ, … … … ݔሺܰ െ 1ሻሿ் ሺ3ሻ
࢟ ൌ ሾݕሺ0ሻ, ݕሺ1ሻ, … … … ݕሺܰ െ 1ሻሿ் ሺ4ሻ
3.
DEVELOPMENT OF PROPOSED METHOD
The development of the proposed method is divided into two parts. In the first part, the
frequencies are estimated using the received data at the first sensor and by applying the RRLU
method withthe root-MUSIC [22] algorithm. In the second part, the received data at the both
sensors and the estimated frequencies inthe first partare used to extract the time delay information
by calculatingP eigenvalues using RRLU one more time.
3.1Frequency Estimation
Using the received data at the first sensor given by (3), a Hankel Matrix of size ܮൈ ሺܰ െ ܮ 1ሻis
constructed as:
ݔሺ0ሻ
ݔሺ1ሻ …
ݔሺܰ െ ܮሻ
ݔሺ1ሻ
ݔሺ2ሻ …
ݔሺܰ െ ܮ 1ሻ
ࢄൌ൦
൪
ڭ
ڭ
ڰ
ڭ
ݔሺ ܮെ 1ሻ ݔሺܮሻ
… ݔሺܰ െ 1ሻ
(5)
The parameter L which controls the number of rows and columns in the matrix X should satisfy:
ܲ1 ܮܰെܲെ1
ሺ6ሻ
In other words the row rank and the column rank should be at leastܲ 1. The matrixࢄcan be
rewritten as
ࢄ ൌ ሾ࢞
࢞
࢞
ࡸିࡺ࢞ ڮሿ
Wherethe ݅ ௧ column of ࢄ is given by:
And it can be written as:
ሺ7ሻ
࢞ ൌ ሾݔሺ݅ሻ, ݔሺ݅ሻ, … … … ݔሺ ܮ ݅ െ 1ሻሿ் ሺ8ሻ
࢞ ൌ ࡸ ሺ࣓ሻሺ࣐ሺ࣓ሻሻ ࢇ ࢛ (9)
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4. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.6, November 2013
where:݅ ൌ 0,1, … … ܰ െ ܮ
ࡸ ሺ࣓ሻ ൌ
݁
ڭ
1
݁ ఠభ
݁
݁
ሺିଵሻఠభ
1
ఠమ
ڭ
ሺିଵሻఠమ
…
1
ఠು
… ݁
(10)
ڰ
ڭ
… ݁ ሺିଵሻఠು
࣐ሺ࣓ሻ ൌ ݀݅ܽ݃൫ ݁ ఠభ ݁ ఠమ … … ݁ ఠು ൯ (11)
ࢇ ൌ ሾܽଵ , ܽଶ , … … ܽ ሿ்
(12)
࢛ ൌ ሾݑሺ݅ሻ ݑሺ݅ 1ሻ … … ݑሺ݅ ܮെ 1ሻሿ்
The received data matrix can be expressed as
ࢄ ൌ ቂࡸ ሺ࣓ሻࢇ ࡸ ሺ࣓ሻ࣐ሺ࣓ሻࢇ … … ࡸ ሺ࣓ሻ൫࣐ሺ࣓ሻ൯
ࡺିࡸ
(13)
ࢇቃ ሾ࢛ ࢛ … … ࢛ࡺିࡸ ሿ
Or simply:
ࢄ ൌ ࡸ ሺ࣓ሻ ቂࡵ
࣐ሺ࣓ሻ൫࣐ሺ࣓ሻ൯ … ൫࣐ሺ࣓ሻ൯
ࡺିࡸ
ቃ ࢇ ሾ࢛ ࢛ … … ࢛ࡺିࡸ ሿ
Matrix ࢄ can be expressed as product of a lower triangular matrix L (with 1’s on the diagonal) of
dimension L × L, and an upper triangular matrix U of dimension ܮൈ ሺܰ െ ܮ 1ሻusing LU
decompositionas shown
ࢁ
൨ .
ࡸ
ࢁ
൨ ሺ14ሻ
ࢁ
ࡸ
෨෩
ࢄ ൎ ࡸࢁ ൌ ൨ . ሾࢁ
ࡸ
ࢁ ሿሺ15ሻ
ࢄ ൌ ࡸࢁ ൌ
ࡸ
ࡸ
Here the sub matrixࢁ is an upper triangular matrix of sizeሺܲ ൈ ܲሻand the sub matrix ࢁ is of
size൫ܲ ൈ ሺܰ െ ܮ 1ሻ൯.Since the norm of ࢁ is small norm, the basis of the noise space can be
෩
easily extracted using the sub matrixࢁ ൌ ሾࢁ ࢁ ሿ.
෩
Here the matrix ࢁ is defined as the signal space of ࢄ. Obviously, any vector belongs to null
spaceࡳ should satisfy
Or simply:
ࡸ
෨෩
ࢄ. ࡳ ൎ ࡸࢁ. ࡳ ൌ ൨ . ሾࢁ
ࡸ
ሾࢁ
ࢍ
ࢁ ሿ ቂࢍ ቃ ൌ
ࢍ
ࢁ ሿ ቂࢍ ቃ ൌ
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5. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.6, November 2013
ࢁ ࢍ ࢁ ࢍ =0
Since ࢁ is a nonsingular matrix, ࢍ can be written in terms of ࢍ as
ࢍ ൌ െࢁି ࢁ ࢍ
Rewrite matrixࡳas
ࢍ
െࢁି ࢁ
ࡳ ൌ ቂࢍ ቃ ൌ ቈ ࢍ ൌ ࡴࢍ
ࡵሺିሻ
(16)
ࡽ ൌ ࡴሺࡴࡴ ࡴሻି ࡴࡴ
(17)
෩
So, ࢁ. ࡴ ൌ . It can be observed here is that the columns of the matrix that represent the null
space ࡴare not orthonormal. To fulfillorthonormality,the orthogonal projection onto this subspace
has been used in order to improve the performance by making the columns of the null space of ࡴ
orthonormal.
Apply MUSIClike search
followingestimation function
algorithm
[12]
ܲெ ሺ݁ ఠ ሻ ൌ
to
ࡸ
estimate
1
ሺ࣓ሻࡴ ࡽ
the
frequencies
using
the
ሺ18ሻ
ࡸ ሺ࣓ሻ
A root-MUSIC may be used instead of searching for the peaks of the estimation function in (9).
The frequency estimates may be taken to be the angles of the p roots of the polynomial D(z) that
are closest to the unit circle
ࡸି
ܦሺݖሻ ൌ ࢂ ሺݖሻࢂ כሺ1⁄ כ ݖሻ ሺ19ሻ
ୀ
whereࢂ ሺݖሻ is the z-transform of the ith column of the projection matrix ࡽ[12].
3.2 Time Delay Estimation
In this section, a new technique to estimate the time delay using Hankel complex data matrices
from both sensorsis suggested. Estimated frequencies in (18) or (19) from part 3.1 are used to
ே
ே
estimate the time delay information. To proceed in time delay estimation, letቀ ଶ ൈ ଶ ቁHankel
matrix constructed from (4) as:
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6. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.6, November 2013
ݕሺ0ሻ
ݕሺ1ሻ
… ݕሺܰ⁄2 െ 1ሻ
ݕሺ1ሻ
ݕሺ2ሻ
…
ݕሺܰ⁄2ሻ
ࢅൌ൦
൪(20)
ڭ
ڭ
ڰ
ڭ
ݕሺܰ⁄2 െ 1ሻ ݕሺܰ⁄2ሻ
… ݕሺܰ െ 1ሻ
The matrixࢄcan be rewritten as
ࢅ ൌ ሾ࢟
࢟
࢟
Wherethe ݅ ௧ column of ࢅ is given by:
ࡸିࡺ࢟ ڮሿ
and it can be written as:
ሺ21ሻ
࢟ ൌ ሾݕሺ݅ሻ,
… … … ݕሺ ܮ ݅ െ 1ሻሿ் ሺ22ሻ
ݕሺ݅ሻ,
࢟ ൌ ࡸ ሺ࣓ሻࢹሺ࣓, ࡰሻሺ࣐ሺ࣓ሻሻ ࢇ ࢠ (23)
where
ષሺ࣓, ࡰሻ ൌ ݀݅ܽ݃൫ ݁ ିఠభ ݁ ିఠమ … … ݁ ିఠು ൯
்
ࢠ ൌ ቂݖሺ݅ሻ ݖሺ݅ 1ሻ … … ݖቀ݅ ଶ െ 1ቁቃ ,
ே
݅ ൌ 0,1, … ሺ ଶ െ 1ሻ
ே
The received data matrix can be formulatedas
ࢅ ൌ ષࢇ ષ࣐ࢇ … ષ࣐ሺ ିଵሻ ࢇ൨ ሾࢠ ࢠଵ … ࢠሺಿିଵሻ ሿ(24)
ࡺ
మ
Or simply:
ࢅ ൌ ࡸ ሺ࣓ሻࢹ ቂࡵ
࣐ሺ࣓ሻ൫࣐ሺ࣓ሻ൯ … ൫࣐ሺ࣓ሻ൯
ࡺିࡸ
ቃ ࢇ ሾࢠ ࢠଵ … ࢠሺಿିଵሻ ሿ(25)
Data matrix ડ can be created by combining the matrices ࢄ and ࢅ from (3) and (18) as:
మ
ડ ൌ ሾࢄ
Applying LU algorithm to (26)
ࢣ ൌ ࡸࢁ ൌ
ࡸଵଵ
ࡸଶଵ
ࢁ
൨ . ଵଵ
ࡸଶଶ
ࢅሿ(26)
ࢁଵଶ
൨ ሺ27ሻ
ࢁଶଶ
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7. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.6, November 2013
Here the sub matrixࢁ is an upper triangular matrix of size ሺܲ ൈ ܲሻ and the sub matrix ࢁ is of
size ൫ܲ ൈ ሺܰ െ ܮ 1ሻ൯. Since ࢁ has small norm one can easily extract the basis of the noise
෩
space form matrix ࢁ ൌ ሾࢁ ࢁ ሿ.
ࡸ
෨෩
ࢣ ൎ ࡸࢁ ൌ ൨ . ሾࢁ
ࡸ
ࢁ ሿሺ28ሻ
෩
Here the matrix ࢁ is defined as the signal space of ࢄ. As matrix ડ of rank P, the matricesࢁ and
ࢁ are of size ܲ ൈ .ܮFrom (21) the P singular values of the matrix ષ are corresponding to the P
diagonal elements of ષ. Therefore, the time delay estimation can be found as:
ܦൌ
4.
݁ܿܽݎݐ൬݀݅ܽ݃ ቀסሺሺࢁଵଵ ሻற . ࢁଵଶ ሻቁ൰
∑ ߱
ୀଵ ෝ
ሺ29ሻ
SIMULATION RESULTS
In this section, the performance of the proposed method is compared with state-space realization
method in [5]. In the First experiment two-sinusoidal signals with amplitudes a1= a2=1/√2,
߱ଵ ൌ 0.3ߨ rad/s and ߱ଶ ൌ 0.6ߨ rad/s are considered.Thesimulation has been done under AWGN
environment with different SNRs and 500 independent Monte-Carlo realizations. The number of
signal samples was 200 and while L was 100. The MSE of the frequencies is defined as
ܧܵܯܨௗ
ே
1
ଶ
ൌ 10݈݃ଵ ቌ
൫߱ െ ߱ ൯ ቍ
ෝ
ܰ௧ ܲ
ୀଵ ୀଵ
ሺ30ሻ
where߱ is the estimate of ߱ , and ܰ௧ is the number of Monte-Carlo trials. The MSE of the
ෝ
frequencies estimate iscompared with the State-Space Realization (SSR) method in [5].Figure 1
plots the MSE of the frequencies versus SNR. The achieved performance has been significantly
improved, especially at SNR -3 dB compared with SSR method.Figure 2 plots the MSE of the
time delays which is defined as:
ܧܵܯܦௗ
ே
1
෩ ଶ
ൌ 10݈݃ଵ ቌ ൫ ܦെ ܦ൯ ቍ
ܰ௧
ୀଵ
ሺ31ሻ
In the second experiment, only one change has made for the first experiment; the amplitudes are
assumed to be complex with a1= (1+i)/√2 and a2= (1-i)/√2. Figure 3shows that the behavior of
the frequency MSE is similar to Figure 1. An intermediate performance of RRLU between SSR
and RRQR is appeared. Figure 4plots the MSE of the time delay versus SNR for the same case.
A third expermement is done by using only 40 signal samples. The amplitudes is set to (a1=
a2=1/√2). The frequenciesis assumed to be (߱ଵ ൌ 0.3ߨ rad/s and ߱ଶ ൌ 0.6ߨ rad/s). Figure 5
showes the MSE of the frequency estimation. For SNR >10dB RRLU showes an intermediate
performance between both SSR and RRQR. While Figure 6 shows the MSE of the time delay
estimation and for the range of SNR>10dB the three method are mergeing to the same
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8. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.6, November 2013
performance. In many applications and published papers the SNR range is tested from 0dB to
20dB [23]. In this paper, the simulation results have tested a wider range from -10dB to 30dB, to
check the convergence of the proposed method.
10
SSR
0
RRLU
-10
RRQR
-20
-30
-40
-50
-60
-70
-80
-90
-10
-5
0
5
10
15
20
25
30
Figure 1. MSE of frequency estimation versus SNR (a1= a2=1/√2, ߱ଵ ൌ 0.3ߨ rad/s and ߱ଶ ൌ 0.6ߨ rad/s,
N=200)
40
SSR
30
RRLU
20
RRQR
10
0
-10
-20
-30
-40
-50
-60
-10
-5
0
5
10
15
20
25
30
Figure 2. MSE of Delay estimation versus SNR (a1= a2=1/√2, ߱ଵ ൌ 0.3ߨ rad/s and ߱ଶ ൌ 0.6ߨ rad/s,
N=200)
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9. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.6, November 2013
10
SSR
0
LURR
-10
QRRR
-20
-30
-40
-50
-60
-70
-80
-90
-10
-5
0
5
10
15
20
25
30
Figure 3. MSE of frequency estimation versus SNR (a1= (1+i)/√2 and a2= (1-i)/√2, ߱ଵ ൌ 0.3ߨ rad/s and
߱ଶ ൌ 0.6ߨ rad/s, N=200)
30
SSR
20
RRLU
10
RRQR
0
-10
-20
-30
-40
-50
-60
-10
-5
0
5
10
15
20
25
30
Figure 4. MSE of Delay estimation versus SNR (a1= (1+i)/√2 and a2= (1-i)/√2.,߱ଵ ൌ 0.3ߨ rad/s and
߱ଶ ൌ 0.6ߨ rad/s, N=200)
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10. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.6, November 2013
10
SSR
0
RRLU
RRQR
-10
-20
-30
-40
-50
-60
-70
-10
-5
0
5
10
15
20
25
30
Figure 5. MSE of frequency estimation versus SNR (a1= a2=1/√2, ߱ଵ ൌ 0.3ߨ rad/s and ߱ଶ ൌ 0.6ߨ rad/s,
N=40)
40
SSR
30
RRLU
20
RRQR
10
0
-10
-20
-30
-40
-50
-10
-5
0
5
10
15
20
25
30
Figure 6. MSE of time delay estimation versus SNR (a1= a2=1/√2, ߱ଵ ൌ 0.3ߨ rad/s and ߱ଶ ൌ 0.6ߨ rad/s,
N=40)
In the forth experment the amplitudes are assumed complex and set to (a1= (1+i)/√2 and a2=
(1-i)/√2ሻ , and that is the only change that made compared with thired experement. For SNR>5dB
as showen in both Figures 7 and Figure 8 the performance of both frequency and time delay
estimators in the three method are very close to each other, keeping in minde the superiorty of
RRLU as it is less complexity.
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11. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.6, November 2013
10
SSR
0
RRLU
RRQR
-10
-20
-30
-40
-50
-60
-70
-10
-5
0
5
10
15
20
25
30
Figure 7. MSE of frequency estimation versus SNR (a1= (1+i)/√2 and a2= (1-i)/√2., ߱ଵ ൌ 0.3ߨ rad/s and
߱ଶ ൌ 0.6ߨ rad/s, N=40)
30
SSR
20
RRLU
10
RRQR
0
-10
-20
-30
-40
-50
-60
-10
Figure 8. MSE of Delay estimation versus SNR (a1= (1+i)/√2 and a2= (1-i)/√2., ߱ଵ ൌ 0.3ߨ rad/s and
߱ଶ ൌ 0.6ߨ rad/s, N=40)
-5
0
5
10
15
20
25
30
The performanceof the frequency estimator as a function of number of snapshots is illustrated in
Figure 9 with contant SNR of 0 and 10 dB. The number of snapshots is varied from 40 to 400. It
is obvious that a beter performance can be obtained as the number of snapshots is increased.
Figure 10 plots the performance of the time delay estimator.
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12. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.6, November 2013
0
SNR=0dB
-10
SNR=10dB
-20
-30
-40
-50
-60
-70
0
50
100
150
200
250
300
350
400
Figure 9. MSE of frequency estimation versus Number of signal samples (a1= a2=1/√2, ߱ଵ ൌ 0.3ߨ rad/s
and ߱ଶ ൌ 0.6ߨ rad/s)
Figure10. MSE of time delay estimation versus Number of signal samples (a1= a2=1/√2, ߱ଵ ൌ 0.3ߨ rad/s
and ߱ଶ ൌ 0.6ߨ rad/s)
The last experiment is designed to test the performance of the frequency estimator as a function of
the frequency spacing. The first frequency is set to ߱ଵ ൌ 0.3ߨ rad/s, the second frequency is
assumed to be from ߱ଵ 0.01to ߱ଵ 0.04. From figure 11 one can conclude that if the spacing
is greater than 0.15 rad/s the RRLU start showing better performance compared with the SSR.
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13. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.6, November 2013
6
5
4
SSR
3
2
RRLU
1
0
-1
-2
-3
-4
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Figure 11. MSE of frequency estimation versus spacing. SNR (a1= a2=1/√2, ߱ଵ ൌ 0.3ߨ rad/s and ߱ଶ ൌ
݃݊݅ܿܽݏ ߱ଵ rad/s, N=40, SNR=0dB)
5. CONCLUSION
In this paper a new technique is proposed for Joint Time Delay and Frequencies Estimation of
sinusoidal signals received at two separated sensors by applying the RRLU based method. The
frequencies of complex sinusoids are estimated using the received data matrix. Thedeveloped
frequency estimator shows outstanding performance comparedwith the State-Space Realization.
The RRLU time delay estimator remarkablyimproves the performance of the MSEcompared with
SSR estimator and it is very similarto the RRQR estimator.
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Authors
Mahmoud Qasaymehhas Ph.D in ElectricalEngineering / Telecommunications from Wichita
State University(WSU), Kansas 2009. He did his MSc and B.Sc in ElectricalEngineering /
Telecommunications and Electronics at JordanUniversity of Science and Technology. Dr.
Qasaymeh iscurrently working inTafila Technical University, Jordan.
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