Trend removal is an important problem in most communication systems. Here, we show a proposed
algorithm for trend (background) removal from Raman Spectra by merging local variance estimation and
cubic spline interpolation methods. We found that Raman spectrum does not need a smoothing process to
remove trend from noisy signals. Employing this technique results in more speedy and noiseless systems
than other techniques that use wavelet transformation to suppress noise.
Reduction of Azimuth Uncertainties in SAR Images Using Selective RestorationIJTET Journal
Abstract— A framework is proposed for reduction of azimuth uncertainty space borne strip map synthetic aperture radar (SAR) images. In this paper, the azimuth uncertainty in SAR images is identified by using a local average SAR image, system parameter, and a distinct metric derived from azimuth antenna pattern. The distinct metric helps isolate targets lying at locations of uncertainty. The method for restoration of uncertainty regions is selected on the basis of the size of uncertainty regions. A compressive imaging technique is engaged to bring back isolated ambiguity regions (smaller regions of interrelated pixels), clustered regions (relatively bigger regions of interrelated pixels) are filled by using exemplar-based in-painting. The recreation results on a real Terra SAR-X data set established that the proposed method can effectively remove azimuth uncertainties and enhance SAR image quality.
These slides deal with the basic problem of channel equalization and exposes the issue related to it and shows how it can be balanced by the usage of effective and robust algorithms.
Reducting Power Dissipation in Fir Filter: an AnalysisCSCJournals
In this paper, three existing techniques, Signed Power-of-Two (SPT), Steepest decent and Coefficient segmentation, for power reduction of FIR filters are analyzed. These techniques reduce switching activity which is directly related to the power consumption of a circuit. In an FIR filter, the multiplier consumes maximum power. Therefore, power consumption can be reduced either by by making the filter multiplier-less or by minimizing hamming distance between the coefficients of this multiplier as it directly translates into reduction in power dissipation [8]. The results obtained on four filters (LP) show that hamming distance can be reduced upto 26% and 47% in steepest decent and coefficient segmentation algorithm respectively. Multiplier-less filter can be realized by realizing coefficients in signed power-of-two terms, i.e. by shifting and adding the coefficients, though at the cost of shift operation overhead.
Reduction of Azimuth Uncertainties in SAR Images Using Selective RestorationIJTET Journal
Abstract— A framework is proposed for reduction of azimuth uncertainty space borne strip map synthetic aperture radar (SAR) images. In this paper, the azimuth uncertainty in SAR images is identified by using a local average SAR image, system parameter, and a distinct metric derived from azimuth antenna pattern. The distinct metric helps isolate targets lying at locations of uncertainty. The method for restoration of uncertainty regions is selected on the basis of the size of uncertainty regions. A compressive imaging technique is engaged to bring back isolated ambiguity regions (smaller regions of interrelated pixels), clustered regions (relatively bigger regions of interrelated pixels) are filled by using exemplar-based in-painting. The recreation results on a real Terra SAR-X data set established that the proposed method can effectively remove azimuth uncertainties and enhance SAR image quality.
These slides deal with the basic problem of channel equalization and exposes the issue related to it and shows how it can be balanced by the usage of effective and robust algorithms.
Reducting Power Dissipation in Fir Filter: an AnalysisCSCJournals
In this paper, three existing techniques, Signed Power-of-Two (SPT), Steepest decent and Coefficient segmentation, for power reduction of FIR filters are analyzed. These techniques reduce switching activity which is directly related to the power consumption of a circuit. In an FIR filter, the multiplier consumes maximum power. Therefore, power consumption can be reduced either by by making the filter multiplier-less or by minimizing hamming distance between the coefficients of this multiplier as it directly translates into reduction in power dissipation [8]. The results obtained on four filters (LP) show that hamming distance can be reduced upto 26% and 47% in steepest decent and coefficient segmentation algorithm respectively. Multiplier-less filter can be realized by realizing coefficients in signed power-of-two terms, i.e. by shifting and adding the coefficients, though at the cost of shift operation overhead.
A Combined Voice Activity Detector Based On Singular Value Decomposition and ...CSCJournals
voice activity detector (VAD) is used to separate the speech data included parts from silence parts of the signal. In this paper a new VAD algorithm is represented on the basis of singular value decomposition. There are two sections to perform the feature vector extraction. In first section voiced frames are separated from unvoiced and silence frames. In second section unvoiced frames are silence frames. To perform the above sections, first, windowing the noisy signal then Hankel’s matrix is formed for each frame. The basis of statistical feature extraction of purposed system is slope of singular value curve related to each frame by using linear regression. It is shown that the slope of singular values curve per different SNRs in voiced frames is more than the other types and this property can be to achieve the goal the first part can be used. High similarity between feature vector of unvoiced and silence frame caused to approach for separation of the two categories above cannot be used. So in the second part, the frequency characteristics for identification of unvoiced frames from silent frames have been used. Simulation results show that high speed and accuracy are the advantages of the proposed system.
Cyclostationary analysis of polytime coded signals for lpi radarseSAT Journals
Abstract In Radars, an electromagnetic waveform will be sent, and an echo of the same signal will be received by the receiver. From this received signal, by extracting various parameters such as round trip delay, doppler frequency it is possible to find distance, speed, altitude, etc. However, nowadays as the technology increases, intruders are intercepting transmitted signal as it reaches them, and they will be extracting the characteristics and trying to modify them. So there is a need to develop a system whose signal cannot be identified by no cooperative intercept receivers. That is why LPI radars came into existence. In this paper a brief discussion on LPI radar and its modulation (Polytime code (PT1)), detection (Cyclostationary (DFSM & FAM) techniques such as DFSM, FAM are presented and compared with respect to computational complexity.
Keywords—LPI Radar, Polytime codes, Cyclostationary DFSM, and FAM
Fourier Filtering Denoising Based on Genetic Algorithmsijtsrd
As the traditional filtering methods may cause the problem of blur when applied on stripe-lihike images, this paper raised a new method intending to solve the problem, and provided the process of the method development. The method, based on genetic algorithms as well as two-dimensional Fourier transform, offered a new perspective to denoise an image with abundant components of high frequency, and is of certain significance. Jiahe Shi"Fourier Filtering Denoising Based on Genetic Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-5 , August 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2420.pdf http://www.ijtsrd.com/engineering/telecommunications/2420/fourier-filtering-denoising-based-on-genetic-algorithms/jiahe-shi
non parametric methods for power spectrum estimatonBhavika Jethani
non-parametric methods for power spectrum estimation which includes bartlett method, welch method , blackman and tukey methods and also the comparision of all these methods
Three Element Beam forming Algorithm with Reduced Interference Effect in Sign...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Chaotic Secure Communication Using Iterated Filtering Method P. Karthik -Assistant Professor,
D. Gokul Prashanth -UG Scholar,
T. Gokul - UG Scholar,
Department of Electronics and Communication Engineering,
SNS College of Engineering, Coimbatore, India.
Time alignment techniques for experimental sensor dataIJCSES Journal
Experimental data is subject to data loss, which presents a challenge for representing the data with a
proper time scale. Additionally, data from separate measurement systems need to be aligned in order to
use the data cooperatively. Due to the need for accurate time alignment, various practical techniques are
presented along with an illustrative example detailing each step of the time alignment procedure for actual
experimental data from an Unmanned Aerial Vehicle (UAV). Some example MATLAB code is also
provided.
Blind, Non-stationary Source Separation Using Variational Mode Decomposition ...CSCJournals
The Fourier Transform (FT) is the single best-known technique for viewing and reconstructing signals. It has many uses in all realms of signal processing, communications, image processing, radar, optics, etc. The premise of the FT is to decompose a signal into its frequency components, where a coefficient is determined to represent the amplitude of each frequency component. It is rarely ever emphasized, however, that this coefficient is a constant. The implication of that fact is that Fourier Analysis (FA) is limited in its accuracy at representing signals that are time-varying, e.g. non-stationary. Another novel technique called empirical mode decomposition (EMD) was introduced in the late 1990s to overcome the limits of FA, but the EMD was shown to have stability issues in reconstructing non-stationary signals in the presence of noise or sampling errors. More recently, a technique called variational mode decomposition (VMD) was introduced that overcomes the limitations of both aforementioned methods. This is a powerful technique that can reconstruct non-stationary signals blindly. It is only limited in the choice of the number of modes, K, in the decomposition. In this paper, we discuss how K may be determined a priori, using several examples. We also present a new approach that applies VMD to the problem of blind source separation (BSS) of two signals, estimating the strong powered signal, termed the interferer, first and then extracting the weaker one, termed the signal-of-interest (SOI). The baseline approach is to use all the predetermined K modes to reconstruct the interferer and then subtract its estimate from the received signal to estimate the SOI. We then devise an approach to choose a subset of the K modes to better estimate the interferer, termed culling, based on a very rough a priori frequency estimate of the weak SOI. We show that the VMD method with culling results in improvement in the mean-square error (MSE) of the estimates over the baseline approach by nearly an order of magnitude.
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.
At this present scenario, the demand of the system capacity is very high in wireless network. MIMO
technology is used from the last decade to provide this requirement for wireless network antenna
technology. MIMO channels are mostly used for advanced antenna array technology. But it is most
important to control the error rate with enhanced system capacity in MIMO for present-day progressive
wireless communication. This paper explores the frame error rate with respect to different path gain of
MIMO channel. This work has been done in different fading scenario and produces a comparative analysis
of MIMO on the basis of those fading models in various conditions. Here, it is to be considered that
modulation technique as QPSK to observe these comparative evaluations for different Doppler frequencies.
From the comparative analysis, minimum amount of frame error rate is viewed for Rician distribution at
LOS path Doppler shift of 0 Hz. At last, this work is concluded with a comparative bit error rate study on
the basis of singular parameters at different SNR levels to produce the system performance for uncoded
QPSK modulation.
Design of Linear Array Transducer Using Ultrasound Simulation Program Field-IIinventy
This paper analyze the effect of number of elements of linear array and frequency influence the
image quality in a homogenous medium. Linear arrays are most common for conventional ultrasound imaging,
because of the advantages of electronic focusing and steering. Propagation of ultrasound in biological tissues is
of nonlinear in nature. But linear approximation in far-field is promising solution to model and simulate the
real time ultrasound wave propagation. The simulation of ultrasound imaging using linear acoustics has been
most widely used for understanding focusing, image formation and flow estimation, and it has become a
standard tool in ultrasound research. . In this paper the ultrasound field generated from linear array transducer
and propagation through biological tissues is modeled and simulated using FIELD II program.
A Combined Voice Activity Detector Based On Singular Value Decomposition and ...CSCJournals
voice activity detector (VAD) is used to separate the speech data included parts from silence parts of the signal. In this paper a new VAD algorithm is represented on the basis of singular value decomposition. There are two sections to perform the feature vector extraction. In first section voiced frames are separated from unvoiced and silence frames. In second section unvoiced frames are silence frames. To perform the above sections, first, windowing the noisy signal then Hankel’s matrix is formed for each frame. The basis of statistical feature extraction of purposed system is slope of singular value curve related to each frame by using linear regression. It is shown that the slope of singular values curve per different SNRs in voiced frames is more than the other types and this property can be to achieve the goal the first part can be used. High similarity between feature vector of unvoiced and silence frame caused to approach for separation of the two categories above cannot be used. So in the second part, the frequency characteristics for identification of unvoiced frames from silent frames have been used. Simulation results show that high speed and accuracy are the advantages of the proposed system.
Cyclostationary analysis of polytime coded signals for lpi radarseSAT Journals
Abstract In Radars, an electromagnetic waveform will be sent, and an echo of the same signal will be received by the receiver. From this received signal, by extracting various parameters such as round trip delay, doppler frequency it is possible to find distance, speed, altitude, etc. However, nowadays as the technology increases, intruders are intercepting transmitted signal as it reaches them, and they will be extracting the characteristics and trying to modify them. So there is a need to develop a system whose signal cannot be identified by no cooperative intercept receivers. That is why LPI radars came into existence. In this paper a brief discussion on LPI radar and its modulation (Polytime code (PT1)), detection (Cyclostationary (DFSM & FAM) techniques such as DFSM, FAM are presented and compared with respect to computational complexity.
Keywords—LPI Radar, Polytime codes, Cyclostationary DFSM, and FAM
Fourier Filtering Denoising Based on Genetic Algorithmsijtsrd
As the traditional filtering methods may cause the problem of blur when applied on stripe-lihike images, this paper raised a new method intending to solve the problem, and provided the process of the method development. The method, based on genetic algorithms as well as two-dimensional Fourier transform, offered a new perspective to denoise an image with abundant components of high frequency, and is of certain significance. Jiahe Shi"Fourier Filtering Denoising Based on Genetic Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-5 , August 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2420.pdf http://www.ijtsrd.com/engineering/telecommunications/2420/fourier-filtering-denoising-based-on-genetic-algorithms/jiahe-shi
non parametric methods for power spectrum estimatonBhavika Jethani
non-parametric methods for power spectrum estimation which includes bartlett method, welch method , blackman and tukey methods and also the comparision of all these methods
Three Element Beam forming Algorithm with Reduced Interference Effect in Sign...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Chaotic Secure Communication Using Iterated Filtering Method P. Karthik -Assistant Professor,
D. Gokul Prashanth -UG Scholar,
T. Gokul - UG Scholar,
Department of Electronics and Communication Engineering,
SNS College of Engineering, Coimbatore, India.
Time alignment techniques for experimental sensor dataIJCSES Journal
Experimental data is subject to data loss, which presents a challenge for representing the data with a
proper time scale. Additionally, data from separate measurement systems need to be aligned in order to
use the data cooperatively. Due to the need for accurate time alignment, various practical techniques are
presented along with an illustrative example detailing each step of the time alignment procedure for actual
experimental data from an Unmanned Aerial Vehicle (UAV). Some example MATLAB code is also
provided.
Blind, Non-stationary Source Separation Using Variational Mode Decomposition ...CSCJournals
The Fourier Transform (FT) is the single best-known technique for viewing and reconstructing signals. It has many uses in all realms of signal processing, communications, image processing, radar, optics, etc. The premise of the FT is to decompose a signal into its frequency components, where a coefficient is determined to represent the amplitude of each frequency component. It is rarely ever emphasized, however, that this coefficient is a constant. The implication of that fact is that Fourier Analysis (FA) is limited in its accuracy at representing signals that are time-varying, e.g. non-stationary. Another novel technique called empirical mode decomposition (EMD) was introduced in the late 1990s to overcome the limits of FA, but the EMD was shown to have stability issues in reconstructing non-stationary signals in the presence of noise or sampling errors. More recently, a technique called variational mode decomposition (VMD) was introduced that overcomes the limitations of both aforementioned methods. This is a powerful technique that can reconstruct non-stationary signals blindly. It is only limited in the choice of the number of modes, K, in the decomposition. In this paper, we discuss how K may be determined a priori, using several examples. We also present a new approach that applies VMD to the problem of blind source separation (BSS) of two signals, estimating the strong powered signal, termed the interferer, first and then extracting the weaker one, termed the signal-of-interest (SOI). The baseline approach is to use all the predetermined K modes to reconstruct the interferer and then subtract its estimate from the received signal to estimate the SOI. We then devise an approach to choose a subset of the K modes to better estimate the interferer, termed culling, based on a very rough a priori frequency estimate of the weak SOI. We show that the VMD method with culling results in improvement in the mean-square error (MSE) of the estimates over the baseline approach by nearly an order of magnitude.
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.
At this present scenario, the demand of the system capacity is very high in wireless network. MIMO
technology is used from the last decade to provide this requirement for wireless network antenna
technology. MIMO channels are mostly used for advanced antenna array technology. But it is most
important to control the error rate with enhanced system capacity in MIMO for present-day progressive
wireless communication. This paper explores the frame error rate with respect to different path gain of
MIMO channel. This work has been done in different fading scenario and produces a comparative analysis
of MIMO on the basis of those fading models in various conditions. Here, it is to be considered that
modulation technique as QPSK to observe these comparative evaluations for different Doppler frequencies.
From the comparative analysis, minimum amount of frame error rate is viewed for Rician distribution at
LOS path Doppler shift of 0 Hz. At last, this work is concluded with a comparative bit error rate study on
the basis of singular parameters at different SNR levels to produce the system performance for uncoded
QPSK modulation.
Design of Linear Array Transducer Using Ultrasound Simulation Program Field-IIinventy
This paper analyze the effect of number of elements of linear array and frequency influence the
image quality in a homogenous medium. Linear arrays are most common for conventional ultrasound imaging,
because of the advantages of electronic focusing and steering. Propagation of ultrasound in biological tissues is
of nonlinear in nature. But linear approximation in far-field is promising solution to model and simulate the
real time ultrasound wave propagation. The simulation of ultrasound imaging using linear acoustics has been
most widely used for understanding focusing, image formation and flow estimation, and it has become a
standard tool in ultrasound research. . In this paper the ultrasound field generated from linear array transducer
and propagation through biological tissues is modeled and simulated using FIELD II program.
Cooperative Spectrum Sensing Technique Based on Blind Detection MethodINFOGAIN PUBLICATION
Spectrum sensing is a key task for cognitive radio. Our motivation is to increase the probability of detection of spectrum sensing in cognitive radio. The spectrum-sensing algorithms are proposed based on the statistical methods like EVD,CVD of a covariance matrix. In this Two test statistics are then extracted from the sample covariance matrix. The decision on the signal presence is made by comparing the two test statistics.The Detection probability and the associated threshold are found based on the statistical theory. In this paper, we study the collaborative sensing as a means to improve the performance of the proposed spectrum sensing technique and show their effect on cooperative cognitive radio network. Simulations results and performances evaluation are done in Matlab and the results are tabulated.
3D METALLIC PLATE LENS ANTENNA BASED BEAMSPACE CHANNEL ESTIMATION TECHNIQUE F...ijwmn
Beamspace channel estimation mechanism for massive MIMO (multiple input multiple output) antenna
system presents a major process to compensate the 5G spectrum challenges caused by the proliferation of
information from mobile devices. However, this estimation is required to ensure the perfect channel state
information (CSI) for lower amount of Radio Frequency (RF) chains for each beam. In addition, phase
shifter (PS) components used in this estimation need high power to select the beam in the desired direction.
To overcome these limitations, in this work, we propose Regular Scanning Support Detection (RSSD)
based channel estimation mechanism. Moreover, we utilise a 3D lens antenna array having metallic plate
and a switch in our model which compensates the limitation of phase shifters. Simulation results show that
the proposed RSSD based channel estimation surpasses traditional technique and SD based channel
estimation even in lower SNR area which is highly desirable in the millimeter wave (mmWave) massive
MIMO systems.
3D METALLIC PLATE LENS ANTENNA BASED BEAMSPACE CHANNEL ESTIMATION TECHNIQUE F...ijwmn
Beamspace channel estimation mechanism for massive MIMO (multiple input multiple output) antenna
system presents a major process to compensate the 5G spectrum challenges caused by the proliferation of
information from mobile devices. However, this estimation is required to ensure the perfect channel state
information (CSI) for lower amount of Radio Frequency (RF) chains for each beam. In addition, phase
shifter (PS) components used in this estimation need high power to select the beam in the desired direction.
To overcome these limitations, in this work, we propose Regular Scanning Support Detection (RSSD)
based channel estimation mechanism. Moreover, we utilise a 3D lens antenna array having metallic plate
and a switch in our model which compensates the limitation of phase shifters. Simulation results show that
the proposed RSSD based channel estimation surpasses traditional technique and SD based channel
estimation even in lower SNR area which is highly desirable in the millimeter wave (mmWave) massive
MIMO systems.
A New Approach for Speech Enhancement Based On Eigenvalue Spectral SubtractionCSCJournals
In this paper, a phase space reconstruction-based method is proposed for speech enhancement. The method embeds the noisy signal into a high dimensional reconstructed phase space and uses Spectral Subtraction idea. The advantages of the proposed method are fast performance, high SNR and good MOS. In order to evaluate the proposed method, ten signals of TIMIT database mixed with the white additive Gaussian noise and then the method was implemented. The efficiency of the proposed method was evaluated by using qualitative and quantitative criteria.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, due to the coherent nature of scattering phenomena. In this paper, a novel algorithm capable of suppressing speckle noise using Particle Swarm Optimization (PSO) technique is presented. The algorithm initially identifies homogenous region from the corrupted image and uses PSO to optimize the Thresholding of curvelet coefficients to recover the original image. Average Power Spectrum Value (APSV) has been used as objective function of PSO. The Proposed algorithm removes Speckle noise effectively and the performance of the algorithm is tested and compared with Mean filter, Median filter, Lee filter, Statistic Lee filter, Kuan filter, frost filter and gamma filter., outperforming conventional filtering methods.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, due to the coherent nature of scattering phenomena. In this paper, a novel algorithm capable of suppressing speckle noise using Particle Swarm Optimization (PSO) technique is presented. The algorithm initially identifies homogenous region from the corrupted image and uses PSO to optimize the Thresholding of curvelet coefficients to recover the original image. Average Power Spectrum Value (APSV) has been used as objective function of PSO. The Proposed algorithm removes Speckle noise effectively and the performance of the algorithm is tested and compared with Mean filter, Median filter, Lee filter, Statistic Lee filter, Kuan filter, frost filter and gamma filter., outperforming conventional filtering methods.
P-Wave Onset Point Detection for Seismic Signal Using Bhattacharyya DistanceCSCJournals
In seismology Primary p-wave arrival identification is a fundamental problem for the geologist worldwide. Several numbers of algorithms that deal with p-wave onset detection and identification have already been proposed. Accurate p- wave picking is required for earthquake early warning system and determination of epicenter location etc. In this paper we have proposed a novel algorithm for p-wave detection using Bhattacharyya distance for seismic signals. In our study we have taken 50 numbers of real seismic signals (generated by earthquake) recorded by K-NET (Kyoshin network), Japan. Our results show maximum standard deviation of 1.76 sample from true picks which gives better accuracy with respect to ratio test method.
Ill-posedness formulation of the emission source localization in the radio- d...Ahmed Ammar Rebai PhD
To contact the authors : tarek.salhi@gmail.com and ahmed.rebai2@gmail.com
In the field of radio detection in astroparticle physics, many studies have shown the strong dependence of the solution of the radio-transient sources localization problem (the radio-shower time of arrival on antennas) such solutions are purely numerical artifacts. Based on a detailed analysis of some already published results of radio-detection experiments like : CODALEMA 3 in France, AERA in Argentina and TREND in China, we demonstrate the ill-posed character of this problem in the sens of Hadamard. Two approaches have been used as the existence of solutions degeneration and the bad conditioning of the mathematical formulation problem. A comparison between experimental results and simulations have been made, to highlight the mathematical studies. Many properties of the non-linear least square function are discussed such as the configuration of the set of solutions and the bias.
Path Loss Prediction by Robust Regression Methodsijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Fault Tolerant Matrix Pencil Method for Direction of Arrival Estimationsipij
Continuing to estimate the Direction-of-arrival (DOA) of the signals impinging on the antenna array, even when a few elements of the underlying Uniform Linear Antenna Array (ULA) fail to work will be of practical interest in RADAR, SONAR and Wireless Radio Communication Systems. This paper proposes a new technique to estimate the DOAs when a few elements are malfunctioning. The technique combines Singular Value Thresholding (SVT) based Matrix Completion (MC) procedure with the Direct Data Domain (D3) based Matrix Pencil (MP) Method. When the element failure is observed, first, the MC is performed to recover the missing data from failed elements, and then the MP method is used to estimate the DOAs. We also, propose a very simple technique to detect the location of elements failed, which is required to perform MC procedure. We provide simulation studies to demonstrate the performance and usefulness of the proposed technique. The results indicate a better performance, of the proposed DOA estimation scheme under different antenna failure scenarios.
Enhanced Mobile Node Tracking With Received Signal Strength in Wireless Senso...IOSR Journals
Abstract : Node localization is important parameter in WSN. Node localization is required to report origin of
events which makes it one of the important challenges in WSN. Received signal strength (RSS) is used to
calculate distance between mobile node and reference node. The position of the mobile node is calculated using
multilateration algorithm (MA). Extended Kalman filter (EKF) is utilized to estimate the actual position. In this
paper, the implementation and enhancement of a tracking system based on RSS indicator with the aid of an
Extended Kalman Filter (EKF) is described and an adaptive filter is derived.
Keywords - Extended Kalman filter (EKF), mobile node tracking, multilateration algorithm (MA), received
signal strength (RSS), Wireless sensor networks (WSN)
Enhanced Mobile Node Tracking With Received Signal Strength in Wireless Senso...IOSR Journals
Node localization is important parameter in WSN. Node localization is required to report origin of
events which makes it one of the important challenges in WSN. Received signal strength (RSS) is used to
calculate distance between mobile node and reference node. The position of the mobile node is calculated using
multilateration algorithm (MA). Extended Kalman filter (EKF) is utilized to estimate the actual position. In this
paper, the implementation and enhancement of a tracking system based on RSS indicator with the aid of an
Extended Kalman Filter (EKF) is described and an adaptive filter is derived.
Similar to Trend removal from raman spectra with local variance estimation and cubic spline interpolation (20)
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
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Trend removal from raman spectra with local variance estimation and cubic spline interpolation
1. Circuits and Systems:An International Journal (CSIJ), Vol.2, No.1 , January 2015
1
TREND REMOVAL FROM RAMAN SPECTRA WITH
LOCAL VARIANCE ESTIMATION AND CUBIC
SPLINE INTERPOLATION
Bidaa Mortada1
, El-Sayed M. El-Rabaie, Mohamad F. El-Kordy, Osama
Zahran, and Fathi E. Abd El-Samie
1
Department of Electronics and Electrical and Communications Engineering, Faculty of
Electronic Engineering, Menofia University, Menouf, EGYPT.
Abstract
Trend removal is an important problem in most communication systems. Here, we show a proposed
algorithm for trend (background) removal from Raman Spectra by merging local variance estimation and
cubic spline interpolation methods. We found that Raman spectrum does not need a smoothing process to
remove trend from noisy signals. Employing this technique results in more speedy and noiseless systems
than other techniques that use wavelet transformation to suppress noise.
Keywords
Raman spectroscopy, Background correction method, Local variance, Cubic spline interpolation.
1. Introduction
Spectroscopy is the study of the interaction between matter (a particle that has rest mass) and
radiated energy. Spectroscopic data is often represented by a spectrum, a (frequency & intensity)
which is the response of intensity to the frequency [1].
Raman spectroscopy is an application of spectroscopy, and it has a lot of advantages. It can be
used with solids and liquids. There is no need for sample preparation. It is non-destructive, and it
is acquired quickly within seconds. Also, Raman spectroscopy has some disadvantages. It cannot
be used for metals or alloys. The detection process needs a sensitive instrumentation. The Sample
can be destroyed by heating through the intense laser radiation, and the fluorescence of impurities
in the sample itself can hide the Raman spectrum [2].
Raman spectrum is defined as a plot of the intensity of Raman scattered radiation as a function of
its frequency difference from the incident radiation. Due to the existence of the background
affecting the main spectrum, the detection becomes very difficult. So, it is necessary for applying
2. Circuits and Systems:An International Journal (CSIJ), Vol.2, No.1 , January 2015
2
a background correction method (BCM) to the spectrum before performing analysis of the spectra
obtained from Raman spectroscopy [3].
In signal processing software, it is necessary to be able to distinguish noise and background from
the original signal. To express this mathematically, a sampled signal can be considered as an
array (S) that can be given as:
S = (ps+ B) + N (1)
Where, ps , B and N refer to the noiseless signal without background, background, and noise,
respectively.
It is important to remove noise and background signals from the experimental spectrum. In most
cases, it is necessary to apply a proper background correction algorithm in order to increase the
effective resolution for quantitative analyses [4].
The BCM algorithms can be categorized into two major groups, based on the type of information
which needs to be extracted from original signals. The first group of BCMs includes methods
requiring knowledge about background, blurring effect and noise that often deal with signals by
using knowledge about the signal components such as background shape, position and SNR. This
category includes the noise median method [5], signal removal method (SRM) [6] and threshold-
based classification (TBC) [7].
The second group of BCMs includes those requiring knowledge about frequency of signal
components, as it is well known that the noise and background would have completely different
characteristics, because noise is generally a high-frequency phenomenon, while background has a
low-frequency component of the signal. This type of signal processing includes Fourier transform
(FT) [8] and wavelet transforms (WT) method [9].
In signal removal methodology (SRM), peaks are removed from the spectrum using the
derivative of the spectrum to understand the position, starting, and finishing points of
these peaks. We can use continuous WT (CWT) and discrete WT (DWT) as alternative
approaches to get derivatives of noisy signals [10–13].
There are several algorithms used to solve the background problem. One of these algorithms is
based on combining SRM and CWT methodologies. This algorithm is developed by Kandjanih et
al. [3]. This algorithm starts with employing CWT to calculate the second derivative of the noisy
signal, which identifies signal peak positions in the experimental spectrum. To remove the signal
peak component of the spectrum and fit the reminiscent spectrum the SRM method to be used to
find the background, which is further subtracted from the original spectrum to obtain a
background corrected signal.
In this paper, we present a proposed algorithm based on using the local variance estimation and
cubic spline interpolation to make background correction, wherein the error was found
considerably as the best value reported so far for similar studies. The major advantage of the
3. Circuits and Systems:An International Journal (CSIJ), Vol.2, No.1 , January 2015
3
current approach is that it does not involve any smoothing step which is a major challenge in
obtaining background-corrected spectra.
We organize the paper as follows. Section two includes the problem formulation. Section 3
introduces the method of solution of the trend problem. Section 4 includes the discussion of the
local variance and cubic spline interpolation concepts, and then an explanation of how to generate
a simulated Raman spectrum with the types of trends (linear, sigmoidal, and sinusoidal) is given
in section 5. Section 6 shows the results. Finally, section 7 gives the concluding remarks.
2. Problem Formulation
We aim to remove a type of noise called trend or background from noisy signal and extract the
original noiseless signal with minimum error, high performance and less data processing time.
This will be applied on Raman spectrum using local variance estimation and cubic spline
interpolation to detect trend, and hence make some calculations to estimate variance and apply
interpolation to remove the trend.
3. Method of Solution
We present a smoothing algorithm for trend removal by carrying out some calculations using the
Matlab package starting from generating simulated Raman spectrum by Matlab, adding three
types of trend (linear, sigmoidal, and sinusoidal) to the original simulated spectrum, estimating
local variance to determine peak values and their vicinity to remove them, and applying cubic
spline interpolation in the removed regions from the spectra determine the trend and subtract it.
We can summarize the proposed method in the following steps:
• Simulate Raman spectrum.
• Add one of three types of trends (linear, sigmoidal and sinusoidal trend).
• Estimate local variance to estimate the peak regions for removal.
• Apply cubic spline interpolation to interpolate in removed peak regions.
• Subtract the estimated trend without peaks from the noise spectrum.
• Estimate RMSE between an original spectral without trend and the spectrum after
trend removal.
4.Local Variance and Cubic Spline Interpolation Concepts
As mentioned in theory of probability and statistics, variance measures how far a set of numbers
is spread out. When variance is equal to zero, it indicates that all the values are identical. Also,
when variance has a small value, it indicates that the data is very close to the mean (expected
value) and hence to each other, and a high value indicates that the data is very spreading out
around the mean and from each other [14].
We can estimate the local variance of a signal x(n) as follows [15]:
( )22
)(ˆ)(
)12(
1
)(ˆ ∑
+
−=
−
+
=
kn
knk
x
nxkx
k
nσ (2)
4. Circuits and Systems:An International Journal (CSIJ), Vol.2, No.1 , January 2015
4
where (2K+1) is the number of samples in the short segment used in the estimation and is the
local mean defined as:-
∑
+
−=+
=
kn
knk
kx
K
nx )(
)12(
1
)(ˆ (3)
Interpolation is defined simply as it is an informed estimate of the unknown. It is defined also as
a model based recovery of continuous data from discrete data within a known range of abscissa.
In digital signal processing, interpolation is considered as a digital convolution operation. This
convolution operation can be implemented using the digital filtering approach, row by row and
then column by column, separately.
Spline interpolation is a type of interpolation where the interpolant is a special type of
piecewise polynomial called a spline. We preferred Spline interpolation over
polynomial interpolation as it has a small interpolation error even when using low
degree polynomials for the spline. Also, spline interpolation avoids the problem of
Runge's phenomenon, which occurs only in high degree polynomials [15]. Figure 1
shows the shape of spline.
Fig.1 Spline shape
5.Generation of Simulated Spectra
We utilize in this paper Matlab for MS Windows, version 7.10 (R2010b) for the experimental
steps. First, Raman peaks were simulated using a Gaussian function and it is expressed as:
݂ሺݔሻ = ܽ. ݁
ሺ
షబ.ఱሺೣషሻమ
మ ሻ
(4)
where a is the intensity controller, c and σ are mean and variance of the Gaussian peak,
respectively as shown in Fig 2.
5. Circuits and Systems:An International Journal (CSIJ), Vol.2, No.1 , January 2015
5
Fig. 2: Simulated Raman spectrum
For accuracy, we simulated Gaussian peaks of variable quantity with random positions, intensity
and width with three trends as linear, sigmoid and sinusoid forms and variable background
constants. Then we added a trend (background).
A.Linear Trend:
Trend = a.x+b (5)
where, a is the slope of the linear trend and b is a constant. This is shown in fig 3.
Fig. 3: Spectrum with linear trend
B.Sigmoidal Trend:
Background =
ଵ
ଵାୣ୶୮ሺିሺ௫ିሻሻ
ܫ + ܱ (6)
where a defines the gradient at the inflection point, c defines the location of the inflection point, I
defines the intensity controller and O defines the offset, and this is shown in next figure.
6. Circuits and Systems:An International Journal (CSIJ), Vol.2, No.1 , January 2015
6
Fig. 4: Spectrum with sigmoidal trend
C.Sinusoidal trend:
Background = ݔଵ.ହ
sinሺ
௫
ሻ. ܫ + ܱ (7)
Fig. 5: Spectrum with sinusoidal trend.
6.Trend Removal Results
After these calculations, we can extract the trend from the noisy spectra.
A.Linear Trend
The estimated linear trend is shown in Fig. (6).
7. Circuits and Systems:An International Journal (CSIJ), Vol.2, No.1 , January 2015
7
Fig 6: Estimated linear trend.
Then remove the estimated linear trend from the spectrum with linear trend fig (3), we use
subtraction and get the spectrum with trend removal in Fig 7.
Fig7: Spectrum after trend removal for the linear case, MSE= 0.0015.
B.Sigmoidal Trend
The estimated sinusoidal trend is shown in Fig. (8).
8. Circuits and Systems:An International Journal (CSIJ), Vol.2, No.1 , January 2015
8
Fig8. Estimated sigmoidal trend
After trend removal, we get the spectrum in Fig 9.
Fig9: Spectrum after trend removal for the sigmoidal case, MSE=0.001
9. Circuits and Systems:An International Journal (CSIJ), Vol.2, No.1 , January 2015
9
C.Sinusoidal Trend:
The estimated sinusoidal trend is shown in Fig. (10).
Fig10. Estimated sinusoidal trend
After trend removal, we get the spectrum in Fig 11.
Fig11 .Spectrum after trend removal for the sinusoidal trend, MSE=0.0019
10. Circuits and Systems:An International Journal (CSIJ), Vol.2, No.1 , January 2015
10
8. Conclusions
This paper presented an efficient trend removal algorithm from Raman spectra. This
algorithm is based simply on local variance estimation, and cubic spline interpolation.
Simulation results have revealed the success of the proposed trend removal algorithm
with various types of trends and various levels of peaks in spectra. As compared to the
Kandjani′s algorithm which achieves MSE values in the range of 0.1 to 0.2, the proposed
algorithm achieves MSE values in the range of 0.001 to 0.002.
References
[1] John Wiley & Sons Ltd, ” MODERN SPECTROSCOPY Fourth Edition”, The Atrium, Southern
Gate, Chichester, West Sussex PO19 8SQ, England,2004.
[2] Wei-Chuan Shih1, Kate L. Bechtel2, and Michael S. Feld, "Intrinsic Raman spectroscopy for
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[3] Ahmad EsmaielzadehKandjani, MatthewJ. Griffin, Rajesh Ramanathan, Samuel J. Ippolito, Suresh K.
Bhargavab and VipulBansala. A new paradigm for signal processing of Raman spectra using a
smoothing free algorithm: Coupling continuous wavelet transform with signal removal method
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[8] Marion D, Bax A. (Baseline correction of 2D FT NMR spectra using a simple linear prediction
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paradigm for signal processing J. Raman Spectroscopy. 2011, 40:45, 1419.
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[12] X. Shao, C. Ma, Chemom. “A general approach to derivative calculation using wavelet
transform”Intell. Lab. Syst. 2003, 69, 157.
[13] A. K. Leung, F. T. Chau, J. B. Gao “wavelet transform a method for derivative calculation in
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[14] Giovani Gomez “Estimating local variance for Gaussian filtering” Dept. of Computing ITESM -
Campus Morelos, gegomez@campus.mor.itesm.mx ,2003.
[15] De Boor, C., “A Practical Guide to Splines, “ Springer-Verlag, 1987.
11. Circuits and Systems:An International Journal (CSIJ), Vol.2, No.1 , January 2015
11
AUTHORS
Bidaa mortada received the B.Sc. degree in communication engineering from Faculty
of Electronic Engineering, Menoufia University, Egypt, in May 2009, and she is
currently working toward the MSc degree in Electrical communication engineering. Her
current research interests are in applications of spectroscopy in different media.
Prof. S. El-Rabaie (Senior Member, IEEE’1992-MIEE-Chartered Electrical Engineer)
was born in Sires Elian (Menoufia), EGYPT in 1953. He received the B. Sc. degree
with honors in radio communications from Tanta University, Egypt, 1976, the M.Sc.
degree in communication systems from Menoufia University, Egypt, 1981, and the
Ph.D. degree in microwave engineering from the Queen’s University of Belfast, 1986.
He was a postdoctoral fellow at Queen’s (Dept. of Electronic Eng.) Up to Feb. 89. In
his doctoral research, he constructed a CAD package used in nonlinear circuit
simulations based on the harmonic balance techniques. He has been involved in
different research areas including CAD of nonlinear microwave circuits, nanotechnology, communication
systems, and digital image processing. He was invited in 1992 as a research fellow in the North Arizona
University (College of Engineering and Technology) and in 1994 as a visiting Prof. in Ecole
Polytechnique de Montreal (Quebec), Canada. Prof. El-Rabaie has authored and co-authored more than
120 papers and technical reports, fifteen books under the titles (Computer Aided Simulation and
Optimization of Nonlinear Active Microwave Circuits, The Whole Dictionary for The Computer and the
Internet Terminologies, Basics and Technologies of Data Communications in Computer Networks,
Technologies and Internet Programming, The Distance Learning and its Technologies on the Third
Millennium, Computer Principles and Their Applications in Education, Software Engineering (1),
Management of Computer Networks(1,2), Advanced Internet Programming, Data-base Principles, Building
of Compilers, Software Engineering (2), Ethics of Profession). In 1993, he was awarded the Egyptian
Academic Scientific Research Award (Salah Amer Award of Electronics) and in 1995, he received the
Award of the Best Researcher on (CAD) from Menoufia University. He has participated in translating the
first part of the Arabic Encyclopedia. Now, he is a professor of Electronics and Communications Eng.,
Faculty of Electronic Engineering, Menoufia University.
Mohammad Elkordy received the B.Sc. (Honors), M.Sc., and PhD. from the Faculty of
Electronic Engineering, Menoufia University, Menouf, Egypt, in 1979, 1985, and 1991,
respectively. He joined the teaching staff of the Department of Electronics and Electrical
Communications, Faculty of Electronic Engineering, Menoufia University, Menouf,
Egypt, in 1991. His current research areas of interest include SAW applications,
radiation applications, image processing, and signal processing.
Osama zahran received the B.Sc. (Honors), M.Sc. from the Faculty of Electronic
Engineering, Menoufia University, Menouf, Egypt, in 1997, 1999 respectively, and the
Ph.D. from Liverpool University, UK. He joined the teaching staff of the Department of
Electronics and Electrical Communications, Faculty of Electronic Engineering,
Menoufia University, Menouf, Egypt. He is a co-author of about 29 papers in national
and international conference proceedings and journals. His current research areas of
interest include Nano-scale devices, expert systems, artificial intelligence and hybrid
intelligent systems.
12. Circuits and Systems:An International Journal (CSIJ), Vol.2, No.1 , January 2015
12
F. E. Abd El-Samie was born in Tanta, Egypt, on May 12, 1975. He received the B.Sc.
degree in communication engineering from Faculty of Electronic Engineering,
Menoufia University, Egypt, in May 1998, the MSc. in Electrical Communications,
Faculty of Electronic Engineering, Menoufia University, 2001. PhD. in Electrical
Communications, Faculty of Electronic Engineering, Menoufia University, 2005. He
has received the most cited paper award from Digital Signal Processing Journal in 2008
for the paper entitled: “Efficient Implementation of Image Interpolation As An Inverse
Problem”, authored and co-authored more than 120 papers and 2 Books, interested in Image Processing: (
Enhancement of old images and images acquired under bad illumination conditions, restoration of
degraded images, restoration of degraded and noisy images, multi-channel image processing, image
interpolation and resizing, super resolution reconstruction of images, color image processing, image
watermarking, encryption, and data hiding) , Signal Processing: (Spectral Estimation, Wavelet Processing,
Signal Separation, and Speech Processing) and Digital Communications (CDMA, OFDM, Dynamic
Spectrum Management, Channel Equalization and Channel Estimation).