Showing papers on "Fast Fourier transform published in 2013"
TL;DR: The Smart Fusion algorithm is being implemented in a mobile phone pulse oximeter device to facilitate the diagnosis of severe childhood pneumonia in remote areas and shows trends of improved estimation.
Abstract: We present a novel method for estimating respiratory rate in real time from the photoplethysmogram (PPG) obtained from pulse oximetry. Three respiratory-induced variations (frequency, intensity, and amplitude) are extracted from the PPG using the Incremental-Merge Segmentation algorithm. Frequency content of each respiratory-induced variation is analyzed using fast Fourier transforms. The proposed Smart Fusion method then combines the results of the three respiratory-induced variations using a transparent mean calculation. It automatically eliminates estimations considered to be unreliable because of detected presence of artifacts in the PPG or disagreement between the different individual respiratory rate estimations. The algorithm has been tested on data obtained from 29 children and 13 adults. Results show that it is important to combine the three respiratory-induced variations for robust estimation of respiratory rate. The Smart Fusion showed trends of improved estimation (mean root mean square error 3.0 breaths/min) compared to the individual estimation methods (5.8, 6.2, and 3.9 breaths/min). The Smart Fusion algorithm is being implemented in a mobile phone pulse oximeter device to facilitate the diagnosis of severe childhood pneumonia in remote areas.
384 citations
11 Aug 2013
TL;DR: A novel randomized tensor product technique, called Tensor Sketching, is proposed for approximating any polynomial kernel in O(n(d+D \log{D})) time, and achieves higher accuracy and often runs orders of magnitude faster than the state-of-the-art approach for large-scale real-world datasets.
Abstract: Approximation of non-linear kernels using random feature mapping has been successfully employed in large-scale data analysis applications, accelerating the training of kernel machines. While previous random feature mappings run in O(ndD) time for $n$ training samples in d-dimensional space and D random feature maps, we propose a novel randomized tensor product technique, called Tensor Sketching, for approximating any polynomial kernel in O(n(d+D \log{D})) time. Also, we introduce both absolute and relative error bounds for our approximation to guarantee the reliability of our estimation algorithm. Empirically, Tensor Sketching achieves higher accuracy and often runs orders of magnitude faster than the state-of-the-art approach for large-scale real-world datasets.
371 citations
TL;DR: The proposed fault diagnosis technique based on acoustic emission (AE) analysis with the Hilbert-Huang Transform (HHT) and data mining tool can increase reliability for the faults diagnosis of ball bearing.
Abstract: This paper presents a fault diagnosis technique based on acoustic emission (AE) analysis with the Hilbert-Huang Transform (HHT) and data mining tool HHT analyzes the AE signal using intrinsic mode functions (IMFs), which are extracted using the process of Empirical Mode Decomposition (EMD) Instead of time domain approach with Hilbert transform, FFT of IMFs from HHT process are utilized to represent the time frequency domain approach for efficient signal response from rolling element bearing Further, extracted statistical and acoustic features are used to select proper data mining based fault classifier with or without filter K-nearest neighbor algorithm is observed to be more efficient classifier with default setting parameters in WEKA APF-KNN approach, which is based on asymmetric proximity function with optimize feature selection shows better classification accuracy is used Experimental evaluation for time frequency approach is presented for five bearing conditions such as healthy bearing, bearing with outer race, inner race, ball and combined defect The experimental results show that the proposed method can increase reliability for the faults diagnosis of ball bearing
245 citations
TL;DR: The proposed method for classification of fault and prediction of degradation of components and machines in manufacturing system and the result indicates its higher efficiency and effectiveness comparing to traditional methods.
Abstract: This paper proposes a method for classification of fault and prediction of degradation of components and machines in manufacturing system. The analysis is focused on the vibration signals collected from the sensors mounted on the machines for critical components monitoring. The pre-processed signals were decomposed into several signals containing one approximation and some details using Wavelet Packet Decomposition and, then these signals are transformed to frequency domain using Fast Fourier Transform. The features extracted from frequency domain could be used to train Artificial Neural Network (ANN). Trained ANN could predict the degradation (Remaining Useful Life) and identify the fault of the components and machines. A case study is used to illustrate the proposed method and the result indicates its higher efficiency and effectiveness comparing to traditional methods.
196 citations
TL;DR: A review of non-model based methodologies applied to diagnosis of Proton Exchange Membrane Fuel Cell (PEMFC) system is presented and hybrid approaches resulting from integration of different methods are believed to be promising.
184 citations
TL;DR: The aim of this study is to classify alert and drowsy driving events using the wavelet transform of HRV signals over short time periods and to compare the classification performance of this method with the conventional method that uses fast Fourier transform (FFT)-based features.
Abstract: Driving while fatigued is just as dangerous as drunk driving and may result in car accidents. Heart rate variability (HRV) analysis has been studied recently for the detection of driver drowsiness. However, the detection reliability has been lower than anticipated, because the HRV signals of drivers were always regarded as stationary signals. The wavelet transform method is a method for analyzing non-stationary signals. The aim of this study is to classify alert and drowsy driving events using the wavelet transform of HRV signals over short time periods and to compare the classification performance of this method with the conventional method that uses fast Fourier transform (FFT)-based features. Based on the standard shortest duration for FFT-based short-term HRV evaluation, the wavelet decomposition is performed on 2-min HRV samples, as well as 1-min and 3-min samples for reference purposes. A receiver operation curve (ROC) analysis and a support vector machine (SVM) classifier are used for feature selection and classification, respectively. The ROC analysis results show that the wavelet-based method performs better than the FFT-based method regardless of the duration of the HRV sample that is used. Finally, based on the real-time requirements for driver drowsiness detection, the SVM classifier is trained using eighty FFT and wavelet-based features that are extracted from 1-min HRV signals from four subjects. The averaged leave-one-out (LOO) classification performance using wavelet-based feature is 95% accuracy, 95% sensitivity, and 95% specificity. This is better than the FFT-based results that have 68.8% accuracy, 62.5% sensitivity, and 75% specificity. In addition, the proposed hardware platform is inexpensive and easy-to-use.
177 citations
TL;DR: In this paper, a fast version of the Cadzow reduced-rank reconstruction method is implemented by embedding 4D spatial data into a level-four block Toeplitz matrix.
Abstract: Rank reduction strategies can be employed to attenuate noise and for prestack seismic data regularization. We present a fast version of Cadzow reduced-rank reconstruction method. Cadzow reconstruction is implemented by embedding 4D spatial data into a level-four block Toeplitz matrix. Rank reduction of this matrix via the Lanczos bidiagonalization algorithm is used to recover missing observations and to attenuate random noise. The computational cost of the Lanczos bidiagonalization is dominated by the cost of multiplying a level-four block Toeplitz matrix by a vector. This is efficiently implemented via the 4D fast Fourier transform. The proposed algorithm significantly decreases the computational cost of rank-reduction methods for multidimensional seismic data denoising and reconstruction. Synthetic and field prestack data examples are used to examine the effectiveness of the proposed method.
151 citations
TL;DR: The principles of one-dimensional FFT-based autoindexing of diffraction images are described and a procedure for indexing multiple lattices as implemented in iMosflm is presented.
Abstract: An overview of autoindexing diffraction images based on one-dimensional fast Fourier transforms is presented. The implementation of the algorithm in the Mosflm/iMosflm program suite is described with a discussion of practical issues that may arise and ways of assessing the success or failure of the procedure. Recent developments allow indexing of images that show multiple lattices, and several examples demonstrate the success of this approach in real cases.
148 citations
TL;DR: In this paper, the authors extend ADMM-based image deconvolution to the more realistic scenario of unknown boundary, where the observation operator is modeled as the composition of a convolution with arbitrary boundary conditions, and a spatial mask that keeps only pixels that do not depend on the unknown boundary.
Abstract: The alternating direction method of multipliers (ADMM) has recently sparked interest as a flexible and efficient optimization tool for inverse problems, namely, image deconvolution and reconstruction under non-smooth convex regularization. ADMM achieves state-of-the-art speed by adopting a divide and conquer strategy, wherein a hard problem is split into simpler, efficiently solvable sub-problems (e.g., using fast Fourier or wavelet transforms, or simple proximity operators). In deconvolution, one of these sub-problems involves a matrix inversion (i.e., solving a linear system), which can be done efficiently (in the discrete Fourier domain) if the observation operator is circulant, i.e., under periodic boundary conditions. This paper extends ADMM-based image deconvolution to the more realistic scenario of unknown boundary, where the observation operator is modeled as the composition of a convolution (with arbitrary boundary conditions) with a spatial mask that keeps only pixels that do not depend on the unknown boundary. The proposed approach also handles, at no extra cost, problems that combine the recovery of missing pixels (i.e., inpainting) with deconvolution. We show that the resulting algorithms inherit the convergence guarantees of ADMM and illustrate its performance on non-periodic deblurring (with and without inpainting of interior pixels) under total-variation and frame-based regularization.
144 citations
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TL;DR: This approach formulates the spatio-temporal relationships between the object of interest and its local context based on a Bayesian framework, which models the statistical correlation between the low-level features from the target and its surrounding regions.
Abstract: In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its local context based on a Bayesian framework, which models the statistical correlation between the low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is posed by computing a confidence map, and obtaining the best target location by maximizing an object location likelihood function. The Fast Fourier Transform is adopted for fast learning and detection in this work. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness.
143 citations
TL;DR: A parallel FFT framework that is based on a combination of local FFTs, local data permutations, and global data transpositions is proposed that can be generalized to arbitrary multidimensional data and process meshes.
Abstract: We present an MPI based software library for computing fast Fourier transforms (FFTs) on massively parallel, distributed memory architectures based on the Message Passing Interface standard (MPI). Similar to established transpose FFT algorithms, we propose a parallel FFT framework that is based on a combination of local FFTs, local data permutations, and global data transpositions. This framework can be generalized to arbitrary multidimensional data and process meshes. All performance-relevant building blocks can be implemented with the help of the FFTW software library. Therefore, our library offers great flexibility and portable performance. Similarly to FFTW, we are able to compute FFTs of complex data, real data, and even- or odd-symmetric real data. All the transforms can be performed completely in place. Furthermore, we propose an algorithm to calculate pruned FFTs more efficiently on distributed memory architectures. For example, we provide performance measurements of FFTs of sizes between $512^3$ ...
TL;DR: In this article, an improved Hilbert method was proposed by conjugating the Hilbert transform and ESPRIT together to detect rotor bar faults in induction motors, where the estimation of signal parameters via rotational invariance technique (ESPRIT) was introduced to replace FFT.
Abstract: The traditional Hilbert method to detect broken rotor bar fault in induction motors is reviewed and its major drawbacks are clearly revealed, namely, deteriorating or even completely failing when a motor operating at low slip due to the fixed constraints of fast Fourier transform (FFT) is used in this method. To overcome this, the estimation of signal parameters via rotational invariance technique (ESPRIT) is then introduced to replace FFT, and an improved Hilbert method is thus presented by conjugating the Hilbert transform and ESPRIT together. Experimental results of a small motor in a laboratory and a large motor operating on an industrial site are reported to demonstrate the effectiveness of the improved Hilbert method.
01 Oct 2013
TL;DR: A new algorithm to estimate a signal from its short-time Fourier transform modulus (STFTM) shows not only significant improvement in speed of convergence but it does as well recover the signals with a smaller error than the traditional GLA.
Abstract: In this paper, we present a new algorithm to estimate a signal from its short-time Fourier transform modulus (STFTM). This algorithm is computationally simple and is obtained by an acceleration of the well-known Griffin-Lim algorithm (GLA). Before deriving the algorithm, we will give a new interpretation of the GLA and formulate the phase recovery problem in an optimization form. We then present some experimental results where the new algorithm is tested on various signals. It shows not only significant improvement in speed of convergence but it does as well recover the signals with a smaller error than the traditional GLA.
TL;DR: A novel method is presented for the parallelization of electromagnetic pseudo-spectral solvers that requires only local FFTs and exchange of local guard cell data between neighboring regions, by taking advantage of the properties of DFTs, the linearity of Maxwell's equations and the finite speed of light.
TL;DR: In this article, the authors proposed a method based on a Newton-Raphson algorithm to solve the problem of a heterogeneous unit-cell submitted to periodic boundary conditions, which is of great interest in the context of numerical homogenization.
TL;DR: A fast polygon-based method based on two-dimensional Fourier analysis of 3D affine transformation that could reconstruct the 3D scene with the solid effect and without the depth limitation is proposed.
Abstract: In the holographic three-dimensional (3D) display, the numerical synthesis of the computer-generated holograms needs tremendous calculation. To solve the problem, a fast polygon-based method based on two-dimensional Fourier analysis of 3D affine transformation is proposed. From one primitive polygon, the proposed method calculates the diffracted optical field of each arbitrary polygon in the 3D model, where the pseudo-inverse matrix, the interpolation, and the compensation of the power spectral density are employed. The proposed method could save the computation time in the hologram synthesis since it does not need the fast Fourier transform for each polygonal surface and the additional diffusion computation. The numerical simulation and the optical experimental results are presented to demonstrate the effectiveness of the method. The results reveal the proposed method could reconstruct the 3D scene with the solid effect and without the depth limitation. The factors that influence the image quality are discussed, and the thresholds are proposed to ensure the reconstruction quality.
TL;DR: An multipath delay commutator (MDC)-based architecture and memory scheduling to implement fast Fourier transform (FFT) processors for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems with variable length is presented.
Abstract: This paper presents an multipath delay commutator (MDC)-based architecture and memory scheduling to implement fast Fourier transform (FFT) processors for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems with variable length. Based on the MDC architecture, we propose to use radix-Ns butterflies at each stage, where Ns is the number of data streams, so that there is only one butterfly needed in each stage. Consequently, a 100% utilization rate in computational elements is achieved. Moreover, thanks to the simple control mechanism of the MDC, we propose simple memory scheduling methods for input data and output bit/set-reversing, which again results in a full utilization rate in memory usage. Since the memory requirements usually dominate the die area of FFT/inverse fast Fourier transform (IFFT) processors, the proposed scheme can effectively reduce the memory size and thus the die area as well. Furthermore, to apply the proposed scheme in practical applications, we let Ns=4 and implement a 4-stream FFT/IFFT processor with variable length including 2048, 1024, 512, and 128 for MIMO-OFDM systems. This processor can be used in IEEE 802.16 WiMAX and 3GPP long term evolution applications. The processor was implemented with an UMC 90-nm CMOS technology with a core area of 3.1 mm2. The power consumption at 40 MHz was 63.72/62.92/57.51/51.69 mW for 2048/1024/512/128-FFT, respectively in the post-layout simulation. Finally, we analyze the complexity and performance of the implemented processor and compare it with other processors. The results show advantages of the proposed scheme in terms of area and power consumption.
TL;DR: This paper proposes a family of front-end receiver structures that utilize multiple-resampling (MR) branches, each matched to the Doppler scaling factor of a particular user and/or path, and proposes a gradient-descent approach to refine the channel estimates obtained by standard sparse channel estimators.
Abstract: In this paper, we focus on orthogonal frequency-division multiplexing (OFDM) receiver designs for underwater acoustic (UWA) channels with user- and/or path-specific Doppler scaling distortions. The scenario is motivated by the cooperative communications framework, where distributed transmitter/receiver pairs may experience significantly different Doppler distortions, as well as by the single-user scenarios, where distinct Doppler scaling factors may exist among different propagation paths. The conventional approach of front-end resampling that corrects for common Doppler scaling may not be appropriate in such scenarios, rendering a post-fast-Fourier-transform (FFT) signal that is contaminated by user- and/or path-specific intercarrier interference. To counteract this problem, we propose a family of front-end receiver structures that utilize multiple-resampling (MR) branches, each matched to the Doppler scaling factor of a particular user and/or path. Following resampling, FFT modules transform the Doppler-compensated signals into the frequency domain for further processing through linear or nonlinear detection schemes. As part of the overall receiver structure, a gradient-descent approach is also proposed to refine the channel estimates obtained by standard sparse channel estimators. The effectiveness and robustness of the proposed receivers are demonstrated via simulations, as well as emulations based on real data collected during the 2010 Mobile Acoustic Communications Experiment (MACE10, Martha's Vineyard, MA) and the 2008 Kauai Acomms MURI (KAM08, Kauai, HI) experiment.
TL;DR: In this article, the authors considered a multidimensional convolution model for which they provided adaptive anisotropic kernel estimators of a signal density $f$ measured with additive error.
Abstract: In this paper, we consider a multidimensional convolution model for which we provide adaptive anisotropic kernel estimators of a signal density $f$ measured with additive error. For this, we generalize Fan's~(1991) estimators to multidimensional setting and use a bandwidth selection device in the spirit of Goldenschluger and Lepski's~(2011) proposal fr density estimation without noise. We consider first the pointwise setting and then, we study the integrated risk. Our estimators depend on an automatically selected random bandwidth. We assume both ordinary and super smooth components for measurement errors, which have known density. We also consider both anisotropic H\"{o}lder and Sobolev classes for $f$. We provide non asymptotic risk bounds and asymptotic rates for the resulting data driven estimator, which is proved to be adaptive. We provide an illustrative simulation study, involving the use of Fast Fourier Transform algorithms. We conclude by a proposal of extension of the method to the case of unknown noise density, when a preliminary pure noise sample is available.
TL;DR: Wang et al. as mentioned in this paper proposed a new method based on the layered clustering algorithm to diagnose multiple faults of an aircraft hydraulic pump that occur simultaneously. But, most existing diagnosis methods can only distinguish pump faults that occur individually, therefore, new method needs to be developed to realize effective diagnosis of simultaneous multiple faults on aircraft hydraulic piston pump.
TL;DR: The new molecular dynamics simulation program, MODYLAS, is a general-purpose program appropriate for very large physical, chemical, and biological systems and enables investigations of large-scale real systems such as viruses, liposomes, assemblies of proteins and micelles, and polymers.
Abstract: Our new molecular dynamics (MD) simulation program, MODYLAS, is a general-purpose program appropriate for very large physical, chemical, and biological systems. It is equipped with most standard MD techniques. Long-range forces are evaluated rigorously by the fast multipole method (FMM) without using the fast Fourier transform (FFT). Several new methods have also been developed for extremely fine-grained parallelism of the MD calculation. The virtually buffering-free methods for communications and arithmetic operations, the minimal communication latency algorithm, and the parallel bucket-relay communication algorithm for the upper-level multipole moments in the FMM realize excellent scalability. The methods for blockwise arithmetic operations avoid data reload, attaining very small cache miss rates. Benchmark tests for MODYLAS using 65 536 nodes of the K-computer showed that the overall calculation time per MD step including communications is as short as about 5 ms for a 10 million-atom system; that is, 3...
TL;DR: The accuracy of digital in-line holography to detect particle position and size within a 3D domain is evaluated with particular focus placed on detection of nonspherical particles and a new hybrid method is proposed.
Abstract: The accuracy of digital in-line holography to detect particle position and size within a 3D domain is evaluated with particular focus placed on detection of nonspherical particles. Dimensionless models are proposed for simulation of holograms from single particles, and these models are used to evaluate the uncertainty of existing particle detection methods. From the lessons learned, a new hybrid method is proposed. This method features automatic determination of optimum thresholds, and simulations indicate improved accuracy compared to alternative methods. To validate this, experiments are performed using quasi-stationary, 3D particle fields with imposed translations. For the spherical particles considered in experiments, the proposed hybrid method resolves mean particle concentration and size to within 4% of the actual value, while the standard deviation of particle depth is less than two particle diameters. Initial experimental results for nonspherical particles reveal similar performance.
TL;DR: A variation of chirped-pulse Fourier transform spectroscopy that significantly reduces the technical requirements on high-speed digital electronics and the data throughput, with no reduction in the broadband spectral coverage and no increase in the time required to reach a given sensitivity level.
Abstract: Chirped-pulse Fourier transform spectroscopy has recently been extended to millimeter wave spectroscopy as a technique for the characterization of room-temperature gas samples. Here we present a variation of this technique that significantly reduces the technical requirements on high-speed digital electronics and the data throughput, with no reduction in the broadband spectral coverage and no increase in the time required to reach a given sensitivity level. This method takes advantage of the frequency agility of arbitrary waveform generators by utilizing a series of low-bandwidth chirped excitation pulses paired in time with a series of offset single frequency local oscillators, which are used to detect the molecular free induction decay signals in a heterodyne receiver. A demonstration of this technique is presented in which a 67 GHz bandwidth spectrum of methanol (spanning from 792 to 859 GHz) is acquired in 58 μs.
TL;DR: Large-scale direct numerical simulations of homogeneous-isotropic fluid turbulence, achieving sustained performance of 1.08 petaflop/s on gpu hardware using single precision, exceeds by an order of magnitude the largest vortex-method calculations to date.
TL;DR: Based on a parallel scalable library for Coulomb interactions in particle systems, a comparison between the fast multipole method (FMM), multigrid-based methods, fast Fourier transform (FFT), and a Maxwell solver is provided for the case of three-dimensional periodic boundary conditions suggest that the FMM- and FFT- based methods are most efficient in performance and stability.
Abstract: Based on a parallel scalable library for Coulomb interactions in particle systems, a comparison between the fast multipole method (FMM), multigrid-based methods, fast Fourier transform (FFT)-based methods, and a Maxwell solver is provided for the case of three-dimensional periodic boundary conditions. These methods are directly compared with respect to complexity, scalability, performance, and accuracy. To ensure comparable conditions for all methods and to cover typical applications, we tested all methods on the same set of computers using identical benchmark systems. Our findings suggest that, depending on system size and desired accuracy, the FMM- and FFT-based methods are most efficient in performance and stability.
20 Aug 2013
TL;DR: This paper presents extremely fast algorithms for code-based public-key cryptography, including full protection against timing attacks, and achieves a reciprocal decryption throughput of just 60493 cycles on a single Ivy Bridge core.
Abstract: This paper presents extremely fast algorithms for code-based public-key cryptography, including full protection against timing attacks. For example, at a 2128 security level, this paper achieves a reciprocal decryption throughput of just 60493 cycles (plus cipher cost etc.) on a single Ivy Bridge core. These algorithms rely on an additive FFT for fast root computation, a transposed additive FFT for fast syndrome computation, and a sorting network to avoid cache-timing attacks.
TL;DR: The spectral expansion of the three-layered medium dyadic Green's function is employed to derive a linear relationship between the spatial Fourier transforms of the image and the scattered field under Born approximation and the image can be efficiently reconstructed with inverse fast Fourier transform (IFFT).
Abstract: In this paper, a 3-D diffraction tomographic algorithm is proposed for real-time through-the-wall radar imaging (TWRI). The spectral expansion of the three-layered medium dyadic Green's function is employed to derive a linear relationship between the spatial Fourier transforms of the image and the scattered field under Born approximation. Then, the image can be efficiently reconstructed with inverse fast Fourier transform (IFFT). The linearization of the inversion scheme and the easy implementation of the algorithm with FFT/IFFT make the diffraction tomographic TWRI algorithm suitable for on-site applications. The 3-D polarimetric TWRI is investigated using the proposed algorithm for the enhanced target detection and feature extraction as well as mitigation of the wall effect in the cross-polarization. Numerical and experimental results are presented to show the effectiveness and high efficiency of the proposed algorithm for 3-D real-time TWRI.
TL;DR: In this article, the aliasing conditions of shifted-Fresnel diffraction with fast Fourier transform (FFT) have been investigated, and the effect of aliasing error in a short propagation distance has been investigated.
Abstract: Numerical simulation of Fresnel diffraction with fast Fourier transform (FFT) is widely used in optics, especially computer holography. Fresnel diffraction with FFT cannot set different sampling rates between source and destination planes, while shifted-Fresnel diffraction can set different rates. However, an aliasing error may be incurred in shifted-Fresnel diffraction in a short propagation distance, and the aliasing conditions have not been investigated. In this paper, we investigate the aliasing conditions of shifted-Fresnel diffraction and improve its properties based on the conditions.
TL;DR: In this paper, the aliasing conditions of shifted-Fresnel diffraction with fast Fourier transform (FFT) have been investigated, and the effect of aliasing error in a short propagation distance has been investigated.
Abstract: Numerical simulation of Fresnel diffraction with fast Fourier transform (FFT) is widely used in optics, especially computer holography. Fresnel diffraction with FFT cannot set different sampling rates between source and destination planes, while shifted-Fresnel diffraction can set different rates. However, an aliasing error may be incurred in shifted-Fresnel diffraction in a short propagation distance, and the aliasing conditions have not been investigated. In this paper, we investigate the aliasing conditions of shifted-Fresnel diffraction and improve its properties based on the conditions.
TL;DR: In this paper, the authors observed that high-power pulsed magnetron plasmas show instabilities in the form of rotating emission structures, which are correlated with modulation of the floating potential and changes in the ion saturation current of a Langmuir probe.
Abstract: We observed that high-power pulsed magnetron plasmas show instabilities in the form of rotating emission structures. Fast CCD camera measurements show motion into the E × B direction. Their characteristic frequencies are in the 100 kHz range. They are correlated with a modulation of the floating potential and changes in the ion saturation current of a Langmuir probe. Rotation velocities are of the order of 10 km s−1. The azimuthal mode number, i.e. the number of emission nodes depends on current. The fast Fourier transform (FFT) analysis of the frequency spectra indicates that the structures condense out of broad band fluctuations suggesting transition from stochastic to periodic behaviour. FFT of the Langmuir probe signals also identifies other types of instabilities at around 2.4 MHz, which is corresponding to the frequency of a modified two-stream instability.