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Showing papers on "Fast Fourier transform published in 2021"


Journal ArticleDOI
TL;DR: An algorithm is presented that computes the product of two n-bit integers in O(n log n) bit operations, thus confirming a conjecture of Schonhage and Strassen from 1971, and using a novel “Gaussian resampling” technique that enables the integer multiplication problem to be reduced to a collection of multidimensional discrete Fourier transforms over the complex numbers.
Abstract: We present an algorithm that computes the product of two n-bit integers in O(n log n) bit operations, thus confirming a conjecture of Schonhage and Strassen from 1971. Our complexity analysis takes place in the multitape Turing machine model, with integers encoded in the usual binary representa- tion. Central to the new algorithm is a novel “Gaussian resampling” technique that enables us to reduce the integer multiplication problem to a collection of multidimensional discrete Fourier transforms over the complex numbers, whose dimensions are all powers of two. These transforms may then be evaluated rapidly by means of Nussbaumer’s fast polynomial transforms.

140 citations


Journal ArticleDOI
TL;DR: In this paper, the authors proposed a new feature engineering model which combines Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT) and statistical features of raw signals.

95 citations


Journal ArticleDOI
TL;DR: A condensed overview of results scattered throughout the literature is provided and guides the reader to the current state of the art in nonlinear computational homogenization methods using the fast Fourier transform.
Abstract: Since their inception, computational homogenization methods based on the fast Fourier transform (FFT) have grown in popularity, establishing themselves as a powerful tool applicable to complex, digitized microstructures. At the same time, the understanding of the underlying principles has grown, in terms of both discretization schemes and solution methods, leading to improvements of the original approach and extending the applications. This article provides a condensed overview of results scattered throughout the literature and guides the reader to the current state of the art in nonlinear computational homogenization methods using the fast Fourier transform.

72 citations


Journal ArticleDOI
TL;DR: The Weak SINDy framework is extended to the setting of partial differential equations (PDEs), and the elimination of pointwise derivative approximations via the weak form enables effective machine-precision recovery of model coefficients from noise-free data and robust identification of PDEs in the large noise regime.

70 citations


Journal ArticleDOI
TL;DR: A new method to predict the RUL of bearings based on the convolutional neural network (CNN) using the 3 sigma criterion and the NASA IMS dataset is utilized to assess the preprocessing method, DCNN accuracy and generalization ability.

41 citations


Journal ArticleDOI
TL;DR: In this paper, the authors provide a new efficient implementation of Iterative Filtering algorithm, called fast iterative filtering, which reduces the original iterative algorithm computational complexity by utilizing, in a nontrivial way, Fast Fourier Transform in the computations.
Abstract: The development of methods able to extract hidden features from non-stationary and non-linear signals in a fast and reliable way is of high importance in many research fields. In this work we tackle the problem of further analyzing the convergence of the Iterative Filtering method both in a continuous and a discrete setting in order to provide a comprehensive analysis of its behavior. Based on these results we provide a new efficient implementation of Iterative Filtering algorithm, called Fast Iterative Filtering, which reduces the original iterative algorithm computational complexity by utilizing, in a nontrivial way, Fast Fourier Transform in the computations.

40 citations


Journal ArticleDOI
TL;DR: A reduced 2D Hermite interpolation-enhanced approach is developed to further improve the efficiency of SRM in simulating fully non-stationary wind fields and provides a desired level of simulation accuracy when appropriate interpolation interval is selected.

39 citations


Journal ArticleDOI
TL;DR: A fast convolution-based method for solving linear and a certain class of nonlinear peridynamic transient diffusion problems in 1D, 2D, and 3D and a new “embedded constraint” (EC) strategy allows using the Fourier transform on irregular domains and imposing arbitrary nonlocal boundary conditions.

35 citations


Journal ArticleDOI
TL;DR: A new method for electroencephalogram (EEG) signal classification based on deep learning model, by which relevant features are automatically learned in a supervised learning framework, which exhibits better stability across different classification cases or patients, indicates the worth in practical applications for diagnostic reference in clinics.

32 citations


Journal ArticleDOI
TL;DR: An aliasing error estimate is proved which bounds the error of the one-dimensional NUFFT of types 1 and 2 in exact arithmetic and new connections are drawn between the above kernel, Kaiser–Bessel, and prolate spheroidal wavefunctions of order zero, which all appear to share an optimal exponential convergence rate.

31 citations


Journal ArticleDOI
TL;DR: A sub-Sub-inline-formula for always-ON keyword spotting with LaTeX notation is proposed, which is mainly composed of a neural network and a feature extraction circuit for audio wake-up systems.
Abstract: We propose a sub- $\mu \text{W}$ always-ON keyword spotting ( $\mu $ KWS) chip for audio wake-up systems. It is mainly composed of a neural network (NN) and a feature extraction (FE) circuit. For significantly reducing the memory footprint and computational load, four techniques are used to achieve ultra-low-power consumption: 1) a serial-FFT-based Mel-frequency cepstrum coefficient circuit is designed for FE, instead of the common parallel FFT. 2) A small-sized binarized depthwise separable convolutional NN (DSCNN) is designed as the classifier. 3) A framewise incremental computation technique is devised in contrast to the conventional whole-word processing. 4) Reduced computation allows a low system clock frequency, which enables near-threshold voltage operation, and low leakage memory blocks are designed to minimize the leakage power. Implemented in 28-nm CMOS technology, this $\mu $ KWS consumes $0.51~\mu \text{W}$ at a 40-kHz frequency and a 0.41-V supply, with an area of 0.23 mm2. Using the Google speech command data set, 97.3% accuracy is reached for a one-word KWS task and 94.6% for a two-word task.

Journal ArticleDOI
TL;DR: This letter adopts a frequency-modulated continuous wave (FMCW) radar to achieve short-range hand gesture sensing and recognition and shows that the recognition accuracy is higher than the existing alternatives with low computational complexity.
Abstract: With the development of the radar sensing technology, hand gesture sensing and recognition has attracted much attention. This letter adopts a frequency-modulated continuous wave (FMCW) radar to achieve short-range hand gesture sensing and recognition. Specifically, the range, Doppler, and angle parameters of hand gestures are measured by fast Fourier transformation (FFT) and multiple signal classification (MUSIC) algorithm, respectively. The mixup (MP) algorithm combined with augmentation (AU) algorithm using a weight factor is applied to expand the hand gesture data. Then, a complementary multidimensional feature fusion network-based hand gesture recognition (CMFF-HGR) is designed to extract the features and achieve HGR. Finally, a series of experiments are carried out to verify the effectiveness of the proposed approach, and the results show that the recognition accuracy is higher than the existing alternatives with low computational complexity.


Journal ArticleDOI
30 Jan 2021-Energies
TL;DR: This paper focuses on the possibility of detecting permanent magnet synchronous motors by analysing mechanical vibrations supported by shallow neural networks, and compared the effectiveness of the analysed NN structures from the point of view of the influence of the network architecture and various parameters of the learning process.
Abstract: Permanent magnet synchronous motors (PMSMs) are becoming more popular, both in industrial applications and in electric and hybrid vehicle drives. Unfortunately, like the others, these are not reliable drives. As in the drive systems with induction motors, the rolling bearings can often fail. This paper focuses on the possibility of detecting this type of mechanical damage by analysing mechanical vibrations supported by shallow neural networks (NNs). For the extraction of diagnostic symptoms, the Fast Fourier Transform (FFT) and the Hilbert transform (HT) were used to obtain the envelope signal, which was subjected to the FFT analysis. Three types of neural networks were tested to automate the detection process: multilayer perceptron (MLP), neural network with radial base function (RBF), and Kohonen map (self-organizing map, SOM). The input signals of these networks were the amplitudes of harmonic components characteristic of damage to bearing elements, obtained as a result of FFT or HT analysis of the vibration acceleration signal. The effectiveness of the analysed NN structures was compared from the point of view of the influence of the network architecture and various parameters of the learning process on the detection effectiveness.

Journal ArticleDOI
TL;DR: In this paper, the authors used Fast Fourier Transform (FFT) spectrum analysis and Nonlinear Least Square Fitting (NLSF) to calculate the vertical height from the antenna phase center to the reflection surface.

Journal ArticleDOI
TL;DR: DFTpy as discussed by the authors is an open source software implementing orbital-free DFT written entirely in Python 3 and outsourcing the computationally expensive operations to third-party modules, such as NumPy and SciPy When fast simulations are in order, DFTpy exploits the fast Fourier transforms (FFTs) from PyFFTW.
Abstract: In silico materials design is hampered by the computational complexity of Kohn-Sham DFT, which scales cubically with the system size Owing to the development of new-generation kinetic energy density functionals (KEDFs), orbital-free DFT (OFDFT, a linear-scaling method) can now be successfully applied to a large class of semiconductors and such finite systems as quantum dots and metal clusters In this work, we present DFTpy, an open source software implementing OFDFT written entirely in Python 3 and outsourcing the computationally expensive operations to third-party modules, such as NumPy and SciPy When fast simulations are in order, DFTpy exploits the fast Fourier transforms (FFTs) from PyFFTW New-generation, nonlocal and density-dependent-kernel KEDFs are made computationally efficient by employing linear splines and other methods for fast kernel builds We showcase DFTpy by solving for the electronic structure of a million-atom system of aluminum metal which was computed on a single CPU The Python 3 implementation is object-oriented, opening the door to easy implementation of new features As an example, we present a time-dependent OFDFT implementation (hydrodynamic DFT) which we use to compute the spectra of small metal cluster recovering qualitatively the time-dependent Kohn-Sham DFT result The Python code base allows for easy implementation of APIs We showcase the combination of DFTpy and ASE for molecular dynamics simulations (NVT) of liquid metals DFTpy is released under the MIT license

Journal ArticleDOI
TL;DR: A novel multi- task domain adaptation framework, called deep multi-scale separable convolutional network with triple attention mechanism (MSSCN-TAM), is established, and the multi-task cross-domain transfer diagnosis results show that it has superior transferability and stability.
Abstract: Rotating components, as the core functional part of rotating machinery, their performance directly determines the stability, reliability, and safety of the equipment operation. Effective intelligent fault identification techniques are being developed as a promising tool for perceiving the state of rotating elements. However, the domain shift phenomenon caused by internal and external interference inevitably exists in practical application scenarios, which significantly deteriorates the performances of the intelligent diagnosis model. Besides, most of the existing intelligent fault diagnosis models are constructed mainly for the single task attribute, that is, the established model can only meet the requirements of a single task, such as the identification of different fault severities or the monitoring of different fault locations. To overcome these challenges, a novel multi-task domain adaptation framework, called deep multi-scale separable convolutional network with triple attention mechanism (MSSCN-TAM), is established in this paper. First, the condition monitoring data preprocessed based on Fast Fourier Transform (FFT) is fed into the improved separable convolution (ISC) module, in which depth-attention and point-attention are introduced to make it self-adjusting. Then, combined with the scale-attention mechanism, which determines the contribution of each branch, the output nodes of each ISC module are connected across scales and treated as the common input of the subsequent two task-specific discriminators. Finally, the weighted Multi-Kernel Maximum Mean Discrepancies (MK-MMD) is adopted to the proposed MSSCN-TAM model to align the distribution and extract domain-invariant features. A total of twenty transfer scenarios based on three rotating component datasets are employed for performance validation of the proposed MSSCN-TAM model, and the multi-task cross-domain transfer diagnosis results show that it has superior transferability and stability.

Posted Content
TL;DR: In this article, an efficient non-Cartesian unrolled neural network-based reconstruction and an accurate approximation for backpropagation through the non-uniform fast Fourier transform (NUFFT) operator are used to accurately reconstruct and backpropagate multi-coil non-cartesian data.
Abstract: Optimizing k-space sampling trajectories is a challenging topic for fast magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction algorithm and sampling trajectories jointly concerning image reconstruction quality. We parameterize trajectories with quadratic B-spline kernels to reduce the number of parameters and enable multi-scale optimization, which may help to avoid sub-optimal local minima. The algorithm includes an efficient non-Cartesian unrolled neural network-based reconstruction and an accurate approximation for backpropagation through the non-uniform fast Fourier transform (NUFFT) operator to accurately reconstruct and back-propagate multi-coil non-Cartesian data. Penalties on slew rate and gradient amplitude enforce hardware constraints. Sampling and reconstruction are trained jointly using large public datasets. To correct the potential eddy-current effect introduced by the curved trajectory, we use a pencil-beam trajectory mapping technique. In both simulations and in-vivo experiments, the learned trajectory demonstrates significantly improved image quality compared to previous model-based and learning-based trajectory optimization methods for 20x acceleration factors. Though trained with neural network-based reconstruction, the proposed trajectory also leads to improved image quality with compressed sensing-based reconstruction.

Journal ArticleDOI
Rui Guo1, Tao Shan1, Xiaoqian Song1, Maokun Li1, Fan Yang1, Shenheng Xu1, Aria Abubakar 
TL;DR: In this paper, the authors used the Fast Fourier Transform (FFT) to accelerate the computation of volume integrations, where the global influence of all points in the space is compressed into a layer by the volume integration.
Abstract: The volume integral equation (VIE) that describes the forward scattering problem is generally solved by iterative methods, such as the conjugate gradient method. In this work, we unfold the conjugate gradient method into an iterative deep neural network to accelerate solving the VIE. After the dielectric scatterer’s relative permittivity and the incident field are input into the network, the total field is trained to converge to the ground truth iteratively. In the neural network, the Green’s function is taken as an explicit operator to describe wave physics, and the Fast Fourier Transform (FFT) is applied to accelerate the computation of volume integrations. The global influence of all points in the space is compressed into a layer by the volume integration. In numerical tests, we validate the accuracy, efficiency, and generalization ability of the proposed neural network, and investigate the feasibility of changing the input size and the frequency in the prediction. Results show that the network is scale-independent, and adaptable to predict fields in a narrow frequency band. This work provides us a new perspective of incorporating both learned parameters and physics into numerical algorithms for fast computation, and has the potential of being applied in deep-learning-based inverse scattering problems.

Journal ArticleDOI
29 Mar 2021
TL;DR: In this article, Fourier decomposition of non-stationary EEG signals has been used for the diagnosis of epilepsy using fast Fourier transform (FFT) algorithm and support vector machine (SVM).
Abstract: Epilepsy is a disease recognized as the chronic neurological dysfunction of the human brain which is described by the sudden and excessive electrical discharges of the brain cells Electroencephalogram (EEG) is a prime tool applied for the diagnosis of epilepsy In this study, a novel and effective approach is introduced to decompose the non-stationary EEG signals using the Fourier decomposition method The concept of position, velocity, and acceleration has been employed on the EEG signals for feature extraction using $$L^p$$ norms computed from Fourier intrinsic band functions (FIBFs) The proposed scheme comprises three main sections In the first section, the EEG signal is decomposed into a finite number of FIBFs In the second stage, the features are extracted from FIBFs and relevant features are selected by using the Kruskal–Wallis test In the last stage, the significant features are passed on to the support vector machine (SVM) classifier By applying 10-fold cross-validation, the proposed method provides better results in comparison to the state-of-the-art methods discussed in the literature, with an average classification accuracy of 9996% and 9994% for classification of EEG signals from the BONN dataset and the CHB-MIT dataset, respectively It can be implemented using the computationally efficient fast Fourier transform (FFT) algorithm

Journal ArticleDOI
TL;DR: The manifold separation steering vector modeling technique is used to develop a maximum likelihood method for joint direction of arrival (DOA) and polarization estimation that can obtain DOA and polarization estimates based on very small-size primary data samples, even with a single sample, which makes the proposed method more suitable for nonstationary target polarization.
Abstract: The use of the polarization diversity of a target signal at a polarization-sensitive antenna array can enhance the target detection and tracking capabilities of a radar. In this article, the manifold separation steering vector modeling technique is used to develop a maximum likelihood method for joint direction of arrival (DOA) and polarization estimation. Manifold separation can incorporate antenna array nonideal characteristics (e.g., cross polarization, mutual coupling) into the estimation algorithm using array calibration measurements. In the proposed technique, the estimation problem is formulated as a generalized Rayleigh quotient minimization problem that is transformed into a determinant minimization problem. Both the azimuth and elevation angles are estimated using the fast Fourier transform. Unlike the existing manifold separation based polarimetric element space (PES) multiple signal classification method and the PES Capon method, the proposed method can obtain DOA and polarization estimates based on very small-size primary data samples, even with a single sample, which makes the proposed method more suitable for nonstationary target polarization. The performance of the proposed method is demonstrated through simulations. The Cramer–Rao lower bound for joint DOA and polarization is also used for comparison with empirical errors.

Journal ArticleDOI
TL;DR: The goal of this research is to design time-domain measurement-based admittance identification methods so that event data can be utilized for online admittance formation and accurate stability prediction can be made.
Abstract: Unprecedented dynamic phenomena appear in power grids due to integration of more and more inverter-based resources (IBR). A major challenge is that inverter models are proprietary information and usually only real code models are provided to grid operators. Thus, measurement based characterization of IBR is a popular approach to find the frequency-domain measurements of an IBR's admittance or impedance. The predominant methods rely on injecting perturbation and extracting frequency-domain information via fast Fourier transform (FFT). The goal of this research is to design time-domain measurement-based admittance identification methods so that event data can be utilized for online admittance identification. The proposed method has a key step: converting $dq$ -frame voltage and current transient responses into $s$ -domain expressions using eigensystem realization algorithm (ERA) or dynamic mode decomposition (DMD). From there, $s$ -domain admittance or frequency-domain admittance measurement will be computed. Proof of concept is first demonstrated using the data generated from an analytical model representing a grid-integrated IBR. The identified admittance is shown to be the exact match of the known admittance in the subsynchronous frequency range. The identified $s$ -domain admittance is employed for eigenvalue analysis and accurate stability prediction can be made. The tool is then tested on two electromagnetic transient (EMT) computer simulation testbeds: a PV grid-integration system and a type-4 wind grid-integration system. In the first case, offline admittance identification is demonstrated. In the second case, online admittance identification using data from two events is demonstrated.

Journal ArticleDOI
05 Apr 2021
TL;DR: This article proposed an adaptive frequency-split-based quantitative power allocation strategy that provides an improved performance in suppressing the dc bus voltage fluctuations and protecting batteries when compared with existing methods.
Abstract: As the two classical power allocation methods in battery-supercapacitor hybrid energy storage systems, split-frequency methods and power-level methods have been developed separately for many years In this article, we made an attempt to integrate the advantages of the two methods and proposed an adaptive frequency-split-based quantitative power allocation strategy First, an adaptive power preallocation is quantitatively determined according to the state of charge (SoC) of batteries and supercapacitors Then, a windowed fast Fourier transform (FFT)-based power spectrum calculation algorithm is designed to derive the power level corresponding to each sampling frequency By mapping the preallocated power to the power spectrum, the split frequency is adaptively computed Finally, the power allocation is implemented through a low-pass filter (LPF) with the derived split frequency Extensive experiments verify that the proposed method provides an improved performance in suppressing the dc bus voltage fluctuations and protecting batteries when compared with existing methods

Journal ArticleDOI
TL;DR: A survey that includes the main advances in the field related to architectures for complex input data and power-of-two FFT sizes and divides the architectures into serial and parallel.
Abstract: The field of pipelined FFT hardware architectures has been studied during the last 50 years. This paper is a survey that includes the main advances in the field related to architectures for complex input data and power-of-two FFT sizes. Furthermore, the paper is intended to be educational, so that the reader can learn how the architectures work. Finally, the paper divides the architectures into serial and parallel. This classification puts together those architectures that are conceived for a similar purpose and, therefore, are comparable.

Journal ArticleDOI
TL;DR: In this article, the authors present a method for computing the small-scale power spectrum and bispectrum in cosmological simulations. But the method is not applicable to any tracer; simulation particles, halos or galaxies, and take advantage of the simple geometry of the box and periodicity to remove almost all dependence on large random particle catalogs.
Abstract: We present $\mathcal{O}(N^2)$ estimators for the small-scale power spectrum and bispectrum in cosmological simulations. In combination with traditional methods, these allow spectra to be efficiently computed across a vast range of scales, requiring orders of magnitude less computation time than Fast Fourier Transform based approaches alone. These methods are applicable to any tracer; simulation particles, halos or galaxies, and take advantage of the simple geometry of the box and periodicity to remove almost all dependence on large random particle catalogs. By working in configuration-space, both power spectra and bispectra can be computed via a weighted sum of particle pairs up to some radius, which can be reduced at larger $k$, leading to algorithms with decreasing complexity on small scales. These do not suffer from aliasing or shot-noise, allowing spectra to be computed to arbitrarily large wavenumbers. The estimators are rigorously derived and tested against simulations, and their covariances discussed. The accompanying code, HIPSTER, has been publicly released, incorporating these algorithms. Such estimators will be of great use in the analysis of large sets of high-resolution simulations.

Journal ArticleDOI
13 Apr 2021-Sensors
TL;DR: In this paper, a non-contact heartbeat/respiratory rate monitoring system was designed using narrow beam millimeter wave radar, which is equipped with a special low sidelobe and small-sized antenna lens at the front end of the receiving and transmitting antennas in the 120 GHz band of frequency modulated continuous-wave (FMCW) system.
Abstract: A non-contact heartbeat/respiratory rate monitoring system was designed using narrow beam millimeter wave radar. Equipped with a special low sidelobe and small-sized antenna lens at the front end of the receiving and transmitting antennas in the 120 GHz band of frequency-modulated continuous-wave (FMCW) system, this sensor system realizes the narrow beam control of radar, reduces the interference caused by the reflection of other objects in the measurement background, improves the signal-to-clutter ratio (SCR) of the intermediate frequency signal (IF), and reduces the complexity of the subsequent signal processing. In order to solve the problem that the accuracy of heart rate is easy to be interfered with by respiratory harmonics, an adaptive notch filter was applied to filter respiratory harmonics. Meanwhile, the heart rate obtained by fast Fourier transform (FFT) was modified by using the ratio of adjacent elements, which helped to improve the accuracy of heart rate detection. The experimental results show that when the monitoring system is 1 m away from the human body, the probability of respiratory rate detection error within ±2 times for eight volunteers can reach 90.48%, and the detection accuracy of the heart rate can reach 90.54%. Finally, short-term heart rate measurement was realized by means of improved empirical mode decomposition and fast independent component analysis algorithm.

Journal ArticleDOI
TL;DR: A novel optical microdisk-based convolutional neural network architecture with joint learnability is proposed as an extension to move beyond Fourier transform and multi-layer perception, enabling hardware-aware ONN design space exploration with lower area cost, higher power efficiency, and better noiserobustness.
Abstract: As a promising neuromorphic framework, the optical neural network (ONN) demonstrates ultrahigh inference speed with low energy consumption. However, the previous ONN architectures have high area overhead which limits their practicality. In this article, we propose an area-efficient ONN architecture based on structured neural networks, leveraging optical fast Fourier transform for efficient computation. A two-phase software training flow with structured pruning is proposed to further reduce the optical component utilization. Experimental results demonstrate that the proposed architecture can achieve 2.2– $3.7\times $ area cost improvement compared with the previous singular value decomposition-based architecture with comparable inference accuracy. A novel optical microdisk-based convolutional neural network architecture with joint learnability is proposed as an extension to move beyond Fourier transform and multilayer perception, enabling hardware-aware ONN design space exploration with lower area cost, higher power efficiency, and better noise-robustness.

Journal ArticleDOI
TL;DR: This scheme improves the PAPR performance by 1.6 dB and has high chaotic initial sensitivity and safety performance, and the proposed high-security OFDM-PON is experimentally verified.
Abstract: This paper proposes a technique for enhancing the reliability and security of the orthogonal frequency division multiplexed passive optical network (OFDM-PON) based on modified Lorenz chaos. The chaotic mapping uses a modified three-dimensional Lorenz mapping, adding feedback factors to the common Lorenz mapping, generates 3 masking factors, and uniformly encrypts the three dimensions (3D) of the constellation diagram, subcarrier frequency, and symbols in OFDM, which effectively improves the security of the system. Owing to the linearity of the (inverse) fast Fourier transform (FFT/IFFT) operation in the OFDM system and the mismatch between the number of subcarriers and the number of Fourier points, the use of idle subcarriers in the FFT/IFFT operation can reduce peak-to-average power ratio (PAPR). The proposed high-security OFDM-PON is experimentally verified. In the experiments, the OFDM signal is modulated by the quadrature amplitude modulation and achieves a 14.7 Gb/s data rate, which is tested against the fiber span of 25 km single-mode fiber (SMF). In comparison with the normal OFDM-PON, concerning the PAPR and security performance, this scheme improves the PAPR performance by 1.6 dB and has high chaotic initial sensitivity and safety performance.

Journal ArticleDOI
TL;DR: Owing to the proposed spectrogram approach, the reading success rate of chipless RFID tag is improved by accurate extraction of identification (ID) based on the extraction of quality factors.
Abstract: In this paper, a novel method for the extraction of aspect-independent parameters (that are analogous to complex natural resonances) of chipless radio frequency (RF) identification (RFID) tags is presented. This method is based on short-time Fourier transform (STFT). The concept is proved by utilizing classical depolarizing tags. Owing to the proposed spectrogram approach, the reading success rate of chipless RFID tag is improved by accurate extraction of identification (ID) based on the extraction of quality factors. With single tag measurement (i.e., without empty measurement) in a practical environment, the performance of the proposed spectrogram method is characterized: 1) at various misalignments of the reader and the tag. 2) at various distances for the tag mounted on numerous objects. 3) at various displacements within 3D zone of reader interrogation. The proposed technique is computationally fast due to the inherent nature of fast Fourier transform (FFT) based STFT.

Proceedings ArticleDOI
29 Mar 2021
TL;DR: In this paper, a low-complexity multidimensional channel parameter estimation via rotational invariance techniques (MD-ESPRIT) is proposed for simultaneous positioning and mapping.
Abstract: Simultaneous localization and communication (SLAC) is a desirable feature of 5G and beyond 5G wireless networks. To be able to implement SLAC, efficient high dimensional channel estimation methods are critical. This work presents a low-complexity multidimensional channel parameter estimation via rotational invariance techniques (MD-ESPRIT). We use both the spatial smoothing and forward-backward averaging techniques to further explore data samples to extract multipath components (MPCs). We propose a one-dimensional Fast-Fourier-Transform- (FFT) and inverse-FFT-based approach to obtain the signal subspaces for angular frequency estimation. The geometry relationship between MPCs and positions is utilized for simultaneous positioning and mapping. Numerical results demonstrate the improved identifiability and low complexity performance of the proposed scheme.