Spectrum Estimation Techniques Research Articles

3D Imaging With Moving Fringe Structured Illumination Microscopy

Structured Light Illumination Microscopy (SIM) has proved itself as a very effective method to improve the resolution of a widefield (WF) fluorescent microscope. In this paper, we demonstrate a new approach to three-dimensional (3D) imaging with the SIM, using a moving fringe (MF) illumination pattern. Instead of the standard three-beam standing wave illumination pattern, our method requires a two-beam one, varying along the optical axis. Each axial layer of the MF illumination pattern contains single-spatial-frequency interference fringes, traversing the space with its own speed (temporal frequency), proportional to the axial offset of such layer from the excitation plane. The different axial layers of a fluorescent object, excited with the MF illumination, will emit a continuous amplitude modulated fluorescent signal with the frequency of modulation proportional to the temporal frequency of the moving fringe pattern. The fine 3D image reconstruction is achieved via extracting the spatial location of the fluorescent object from the temporal frequency of amplitude modulated signal emitted by it. Since in our approach the problem of 3D image reconstruction is reduced to the problem of accurate temporal frequency estimation, any of the well-known spectrum estimation techniques can be applied to the problem, allowing the axial resolution improvement far beyond the limits of the classical 3D SIM. In this research, we suggest using the Minimum-norm method for the proposed MF SIM system, which gives a superior resolving power in spectrum estimation. Simulation results show that such a simple and rapid hardware implementation in combination with a straightforward signal processing method can, however, deliver an improvement in axial resolution far beyond the classical 3D SIM approach.

Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach

With the fast development of Fifth-/Sixth-Generation (5G/6G) communications and the Internet of Video Things (IoVT), a broad range of mega-scale data applications emerge (e.g., all-weather all-time video). These network-based applications highly depend on reliable, secure, and real-time audio and/or video streams (AVSs), which consequently become a target for attackers. While modern Artificial Intelligence (AI) technology is integrated with many multimedia applications to help enhance its applications, the development of General Adversarial Networks (GANs) also leads to deepfake attacks that enable manipulation of audio or video streams to mimic any targeted person. Deepfake attacks are highly disturbing and can mislead the public, raising further challenges in policy, technology, social, and legal aspects. Instead of engaging in an endless AI arms race “fighting fire with fire”, where new Deep Learning (DL) algorithms keep making fake AVS more realistic, this paper proposes a novel approach that tackles the challenging problem of detecting deepfaked AVS data leveraging Electrical Network Frequency (ENF) signals embedded in the AVS data as a fingerprint. Under low Signal-to-Noise Ratio (SNR) conditions, Short-Time Fourier Transform (STFT) and Multiple Signal Classification (MUSIC) spectrum estimation techniques are investigated to detect the Instantaneous Frequency (IF) of interest. For reliable authentication, we enhanced the ENF signal embedded through an artificial power source in a noisy environment using the spectral combination technique and a Robust Filtering Algorithm (RFA). The proposed signal estimation workflow was deployed on a continuous audio/video input for resilience against frame manipulation attacks. A Singular Spectrum Analysis (SSA) approach was selected to minimize the false positive rate of signal correlations. Extensive experimental analysis for a reliable ENF edge-based estimation in deepfaked multimedia recordings is provided to facilitate the need for distinguishing artificially altered media content.

Target Altitude Estimation in Over-the-Horizon Radar

Doppler signatures of local multipath signals provide useful information for target altitude estimation in over-the-horizon radar surveillance. In this paper, we develop a method to improve the resolution of these multipath Doppler signatures and enable enhanced altitude estimation of aircraft target which maintains a constant altitude. Moreover, we consider the impact of ionospheric layer motion on target parameter estimation and show that target parameters can be estimated under both stationary as well as time-varying ionospheric layer conditions. In order to improve the resolution and estimation accuracy of the target parameters and ionosphere velocity with a significantly reduced complexity, we exploit a frequency focused transform to the de-chirped target signals for dimension reduction before applying a least absolute shrinkage and selection operator (LASSO)-based high-resolution spectrum estimation technique. The proposed strategy outperforms fractional Fourier transform and classical subspace-based frequency estimation methods with a much lower computational complexity. The effectiveness of the proposed approach is especially evident for challenging cases where the multipath signal components have spectrally close Doppler signatures. Simulation results confirm the effectiveness of the proposed method.

Testbed Implementation of a Scalable ARIMA Model for Spectrum Estimation in Cognitive Radio-A Null Hypothesis Approach

A two-stage data-driven Spectrum Estimation (SE) technique has been introduced for a Cognitive Radio (CR) system involving Null-Hypothesis approach using robust Chi-Square Goodness of Fit (GoF) for confirmation of the desired signal. Development of optimized scalable ARIMA model concerning data length, lag order and AIC-BIC for frugal SE with minimum response time has been the main contribution. The implementation of the model and subsequent validation have been performed in WARP testbed with an optimized data length of only 250 in ARIMA (3,1,2) model. A response time of 6.25 μ S provides an improvement in peak PSD from existing −40 dB to +45 dB.

Coprime array‐adaptive beamforming via atomic‐norm‐based sparse recovery

Coprime arrays (CPAs) have been found to be an effective configuration for adaptive beamforming (ABF). However, most ABF methods for CPAs are embedded with the spatial spectrum estimation technique, and therefore they usually deteriorate in the case of low signal-to-noise ratio (SNR), especially without prior information of interferences. To address this issue, a robust ABF method for CPAs via atomic-norm-based sparse recovery is proposed. This method begins with initialised virtual array interpolation to avoid aperture loss caused by ‘holes’, which is subsequently transformed to a matrix completion and denoising issue via a Toeplitz step. With the matrix’s Hermitian Toeplitz structure and the intrinsic sparsity of interferences, sparse recovery for the interference covariance matrix is introduced by subtracting the target signal and noise from the received signal and implemented by atomic-norm minimisation. Concretely, this non-convex process is decomposed into two convex steps and solved iteratively. Unlike previous methods, this method can interpolate the virtual array and estimate the steering vector of the target and the noise power simultaneously; consequently, the interference-plus-noise covariance matrix is reconstructed with merely some trivial entry selections without the need for the spatial spectrum. Furthermore, the optimality conditions and boundedness in this method are proven theoretically. Simulation results demonstrate the effectiveness of the proposed method and its advantages over previous methods under those non-ideal conditions.

Investigation on Spectrum Estimation Methods for Bimodal Sea State Conditions.

The reliable monitoring of sea state parameters is a key factor for weather forecasting, as well as for ensuring the safety and navigation of ships. In the current analysis, two spectrum estimation techniques, based on the Welch and Thomson methods, were applied to a set of random wave signals generated from a theoretical wave spectrum obtained by combining wind sea and swell components with the same prevailing direction but different combinations of significant wave heights, peak periods, and peak enhancement factors. A wide benchmark study was performed to systematically apply and compare the two spectrum estimation methods. In this respect, different combinations of wind sea spectra, corresponding to four grades of the Douglas Scale, were combined with three swell spectra corresponding to different swell categories. The main aim of the benchmark study was to systematically investigate the effectiveness of the Welch and Thomson methods in terms of spectrum restitution and the assessment of sea state parameters. The spectrum estimation methods were applied to random wave signals with different durations, namely 600 s (short) and 3600 s (long), to investigate how the record length affected the assembled sea state parameters, which, in turn, were assessed by the nonlinear least square method. Finally, based on the main outcomes of the benchmark study, some suggestions are provided to select the most suitable spectrum reconstruction method and increase the effectiveness of the assembled sea state parameters.

Time-series data optimized AR/ARMA model for frugal spectrum estimation in Cognitive Radio

Wideband and agile Spectrum Estimation (SE) is a fundamental component of the Cognitive Radio (CR) system. However, CR systems generally utilize the classical sensing techniques for SE due to heteroscedasticity of the available spectrum. Unfortunately, analysis of the Time-series data for SE using a testbed is rare to find out. A novel Goodness-of-Fit (GoF) based accurate SE technique for CR system has been proposed in this work involving Time-series data samples generated from Field Programmable Gate Array (FPGA) based Wireless open Access Radio Protocol (WARP) testbed having a sampling frequency of 40 MHz. Anderson–Darling (AD) rejection based Null-Hypothesis testing has been employed to implement the CR system within a frequency range of 9 kHz to 10 MHz. 1 MHz sinusoidal signal has been generated by the testbed for digital transmission/reception through Radio Board 1 and 3. Statistical parameters like Mean Square Error (MSE), Final Prediction Error (FPE), Loss Function and Fit(%) of the received samples adjudicate the Convex optimization of the data length. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) are responsible for the selection of the Auto Regressive Moving Average (ARMA) (3,2) model for optimal signal processing. Finally, the Power Spectral Density (PSD) confirms the superiority of the proposed work with the most optimized data length and lag order in real-time. Computation of complexities of the proposed algorithms also indicates a parsimonious choice of the model.

Advanced endoscopic methods in gastrointestinal diseases: a systematic review.

Endoscopic imaging is the main method for detecting gastrointestinal diseases, which adversely affect human health. White light endoscopy (WLE) was the first method used for endoscopic examination and is still the preliminary step in the detection of gastrointestinal diseases during clinical examination. However, it cannot accurately diagnose gastrointestinal diseases owing to its poor correlation with histopathological diagnosis. In recent years, many advanced endoscopic methods have emerged to improve the detection accuracy by endoscopy. Chromoendoscopy (CE) enhances the contrast between normal and diseased tissues using biocompatible dye agents. Narrow band imaging (NBI) can improve the contrast between capillaries and submucosal vessels by changing the light source acting on the tissue using special filters to realize the visualization of the vascular structure. Flexible spectral imaging color enhancement (FICE) technique uses the reflectance spectrum estimation technique to obtain individual spectral images and reconstructs an enhanced image of the mucosal surface using three selected spectral images. The i-Scan technology takes advantage of the different reflective properties of normal and diseased tissues to obtain images, and enhances image contrast through post-processing algorithms. These abovementioned methods can be used to detect gastrointestinal diseases by observing the macroscopic structure of the digestive tract mucosa, but the ability of early cancer detection is limited with low resolution. However, based on the principle of confocal imaging, probe-based confocal laser endomicroscopy (pCLE) can enable cellular visualization with high-performance probes, which can present cellular morphology that is highly consistent with that shown by biopsy to provide the possibility of early detection of cancer. Other endoscopic imaging techniques including endoscopic optical coherence tomography (EOCT) and photoacoustic endoscopy (PAE), are also promising for diagnosing gastrointestinal diseases. This review focuses on these technologies and aims to provide an overview of different technologies and their clinical applicability.

Fast FRFT-Based Algorithm for 3-D LFM Source Localization With Uniform Circular Array

Localization of linear frequency modulation (LFM) source based on combination of time–frequency analysis and a spatial spectrum estimation technique has received extensive research. However, this scheme is always confined to far-field sources and suffers from high computational cost. In this paper, by performing the fractional Fourier transform to multiple incoherent LFM signals, a fast and closed-form algorithm based on phase difference is proposed for 3-D source localization under a uniform circular array. First, the phase relationship among sensors’ maximum outputs in fractional Fourier domain is exploited to construct indefinite equations, and then the least square method is employed to estimate the source’s azimuth angle, elevation angle, and range simultaneously. Finally, comparing with current approaches, the satisfactory performance of our proposed algorithm is demonstrated, and the effectiveness for dealing with mixed far-field and near-field sources is also confirmed.

Underdetermined passive localisation of emitters based on multi‐dimensional spectrum estimation techniques

One-step passive localisation methods are verified to outperform the conventional two-step methods in terms of estimation accuracy and identifiability. However, the existing one-step algorithms still cannot work in the scenario where the number of emitters exceeds the total number of sensors from all the base stations. Based on a spatio-temporal processing procedure, the authors propose a novel localisation model, which has the form of multi-dimensional harmonic retrieval. By utilising multi-dimensional spectrum estimation techniques, the underdetermined localisation problem can be handled thanks to the increase of model's degree of freedom. To further improve the identifiability, a nested-array-based localisation model is also given based on the multi-dimensional processing framework. Simulation results demonstrate that the novel-model-based Capon algorithm can achieve higher localisation accuracy without the prior knowledge about the number of emitters, and is robust to the correlated noise.

Online Detection of Broken Rotor Bar Fault in Induction Motors by Combining Estimation of Signal Parameters via Min-norm Algorithm and Least Square Method

Current research in broken rotor bar (BRB) fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estimation cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the frequencies of the fundamental and fault characteristic components with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.

An analytical method to simulate the H i 21-cm visibility signal for intensity mapping experiments

Simulations play a vital role in testing and validating H i 21-cm power spectrum estimation techniques. Conventional methods use techniques like N-body simulations to simulate the sky signal which is then passed through a model of the instrument. This makes it necessary to simulate the H i distribution in a large cosmological volume, and incorporate both the light-cone effect and the telescope's chromatic response. The computational requirements may be particularly large if one wishes to simulate many realizations of the signal. In this paper, we present an analytical method to simulate the H i visibility signal. This is particularly efficient if one wishes to simulate a large number of realizations of the signal. Our method is based on theoretical predictions of the visibility correlation which incorporate both the light-cone effect and the telescope's chromatic response. We have demonstrated this method by applying it to simulate the H i visibility signal for the upcoming Ooty Wide Field Array Phase I.

A general sequential delay-Doppler estimation scheme for sub-Nyquist pulse-Doppler radar

Sequential estimation of the delay and Doppler parameters for sub-Nyquist radars by analog-to-information conversion (AIC) systems has received wide attention recently. However, the estimation methods reported are AIC-dependent and have poor performance for off-grid targets. This paper develops a general estimation scheme in the sense that it is applicable to all AICs regardless whether the targets are on or off the grids. The proposed scheme estimates the delay and Doppler parameters sequentially, in which the delay estimation is formulated into a beamspace direction-of- arrival problem and the Doppler estimation is translated into a line spectrum estimation problem. Then the well-known spatial and temporal spectrum estimation techniques are used to provide efficient and high-resolution estimates of the delay and Doppler parameters. In addition, sufficient conditions on the AIC to guarantee the successful estimation of off-grid targets are provided, while the existing conditions are mostly related to the on-grid targets. Theoretical analyses and numerical experiments show the effectiveness and the correctness of the proposed scheme.

A two-stage time domain subspace method for identification of nonlinear vibrating structures

To identify multiple degree-of-freedom vibrating structures with local nonlinearities, a two-stage time domain approach based on the subspace method is proposed in this study. The locally nonlinear system to-be-identified is divided into an underlying linear part described by the FRF and a local nonlinear part described by nonlinear coefficients. The identification process of the proposed approach is not the same as the existing single-stage method. It identifies the underlying linear system before the local nonlinearities. The proposed approach reduces the dimensions of matrices which are used to calculate the system's state-space model and also gives a more appropriate estimation of the order of the underlying linear system with the utilization of classical spectrum estimation techniques. Both numerical and experimental examples are given to verify the performance of the method. Results show that the method is more accurate and reliable than the single-stage method, especially in a noisy environment.

Nested Periodic Matrices and Dictionaries: New Signal Representations for Period Estimation

In this paper, we propose a new class of techniques to identify periodicities in data. We target the period estimation directly rather than inferring the period from the signal’s spectrum. By doing so, we obtain several advantages over the traditional spectrum estimation techniques such as DFT and MUSIC. Apart from estimating the unknown period of a signal, we search for finer periodic structure within the given signal. For instance, it might be possible that the given periodic signal was actually a sum of signals with much smaller periods. For example, adding signals with periods 3, 7, and 11 can give rise to a period 231 signal. We propose methods to identify these “hidden periods” 3, 7, and 11. We first propose a new family of square matrices called Nested Periodic Matrices (NPMs), having several useful properties in the context of periodicity. These include the DFT, Walsh–Hadamard, and Ramanujan periodicity transform matrices as examples. Based on these matrices, we develop high dimensional dictionary representations for periodic signals. Various optimization problems can be formulated to identify the periods of signals from such representations. We propose an approach based on finding the least $l_{2}$ norm solution to an under-determined linear system. Alternatively, the period identification problem can also be formulated as a sparse vector recovery problem and we show that by a slight modification to the usual $l_{1}$ norm minimization techniques, we can incorporate a number of new and computationally simple dictionaries.

Efficient finite-difference time-domain computation of resonant frequencies of rectangular Lamé mode resonators via a combination of the symmetry boundary conditions and the Padé approximation

Resonant frequency analysis of the fundamental and higher-order modes of Lamé mode resonators on a lossless isotropic solid is carried out by the finite-difference time-domain (FD-TD) method with the staggered grid with collocated grid points of velocities (SGCV). The symmetry boundary condition is implemented to reduce the size of the computational domain for the FD-TD method with the SGCV. One spectrum estimation technique based on the Padé approximation is employed to effectively extract the resonant frequencies from a spectrum transformed from the time series data calculated by the FD-TD method. Numerical results show the validity and efficiency of these techniques.

On quantifying the uncertainty of stochastic process power spectrum estimates subject to missing data

The issue of quantifying the uncertainty in stochastic process power spectrum estimates based on realisations with missing data is addressed. In this regard, relying on relatively relaxed assumptions for the missing data, utilising fundamental concepts from probability theory, and resorting to Fourier and harmonic wavelets based representations of stationary and non-stationary stochastic processes, respectively, a closed-form expression is derived for the probability density function (PDF) of the power spectrum value corresponding to a specific frequency. The significance of the derived PDF relates to cases where incomplete process realisations are available for power spectrum estimation applications. In this setting, standard power spectrum estimation techniques subject to missing data typically provide with a deterministic estimate for the power spectrum. Thus, no information is provided concerning the uncertainty in the estimates. Numerical examples herein demonstrate the large extent to which any given single estimate may be unrepresentative of the target spectrum.

Minimum Utilization of Electromagnetic (2G, 3G, 4G) Spectrum in Seamless Mobility Based on Various Estimation Methods

AbstractWireless Cellular Seamless Mobility is the most important feature of a wireless cellular communication system. Basically cell phones are used for communication purpose and therefore good cell coverage is needed to make and receive calls. Usually, continuous service is achieved by supporting handover from one cell to another. Handover is the process of changing the channel associated with the current connection while a call is in progress. It is often initiated either by crossing a cell boundary or by deterioration in quality of the signal in the current channel. Mobile IP, too, has to support redirection of data to a new foreign agent after the change of network access. In this case the additional delay caused by the redirection. The shorter the interruption of the service is -up to the ideal case of no service interruption. Minimizing the Delay based on some prior estimation methods in handover is better for quality of seamless connectivity anytime; anywhere across communication networks. All mobile phone system supports the seamless handover between base stations. To avoid the additional delay and wastage of Electromagnetic Spectrum, Mobile IP may use Optimizations such as rerouting of the whole packet in the flow of efficiently transmission of data with short interrupted service by Time series based Spectrum Estimation Technique. Due to the utilization of seamless handover mechanism it reduces the unwanted usage of Spectrum.

A Novel Method of Ambient Interferences Suppressing for In Situ Electromagnetic Radiated Emission Test

A measurement method for adaptive interference suppression in in situ radiated emission test is proposed. First, the method obtained multichannel data to achieve the signal directions of arrival from equipment under test (EUT) and interference sources by the spatial spectrum estimation technique based on the multiple signal classification algorithm. Second, broadband minimum variance distortionless response (MVDR) beamforming algorithm combined with short time Fourier transform (STFT) was taken to form the beam pattern null traps in the directions of interferences, thus realizing the suppression on interferences. The factors affecting the accuracy of spatial spectrum estimation and the angular resolution of beamforming were discussed, and the suppression effects on different kinds of interferences were simulated. The simulation results show that the method can suppress multiple interferences, which are either narrowband or broadband, continuous or transient, and avoid little distortion to the time-frequency characteristics of EUT signal simultaneously. The method performance was evaluated by experiments. The results show that sine wave and pulse modulation interferences can both be suppressed remarkably, which proves the effectiveness and advantage of this new method for electromagnetic compatibility in situ test.

A Study of the Potential of EIIP Mapping Method in Exon Prediction Using the Frequency Domain Techniques

Recently, a number of numerical DNA sequence representations have evolved in order to transform the DNA sequence analysis problems from the traditional string processing domain to the discrete signal processing domain. On the other hand, the coding regions (exons) detection problem has received a special attention due to the 3-base periodicity property of exons which can be easily detected using simple discrete signal processing techniques. The 3-base periodicity in the nucleotide arrangement is evidenced as a sharp peak at frequency f =1/3 in the frequency domain power spectrum. In this paper, we exploit the 3-base periodicity property of a set of the Electron-Ion Interaction Pseudopotential (EIIP) coded DNA sequences by employing a frequency domain power spectrum estimation techniques as Short Time Fourier Transform (STFT),Auto Regressive (AR), Singular Vector Decomposition (SVD) and Digital filtering methods. Also, we give a brief comparison of these methods In order to enhance the coding prediction performance as well as the computa- tional complexity. Results provided that both STFT and digital filtering techniques for EIIP coded sequences performs with highest accuracy compared with AR and SVD methods.