Fourier Magnitude Spectrum Research Articles

Fast CNN-Based Electrocardiogram Signal Quality Assessment Using Fourier Magnitude Spectrum for Resource-Constrained ECG Diagnosis Devices

Fast CNN-Based Electrocardiogram Signal Quality Assessment Using Fourier Magnitude Spectrum for Resource-Constrained ECG Diagnosis Devices

Speech Emotion Recognition Based on Voice Fundamental Frequency

The human voice is one of the basic means of communication, thanks to which one also can easily convey the emotional state. This paper presents experiments on emotion recognition in human speech based on the fundamental frequency. AGH Emotional Speech Corpus was used. This database consists of audio samples of seven emotions acted by 12 different speakers (6 female and 6 male). We explored phrases of all the emotions – all together and in various combinations. Fast Fourier Transformation and magnitude spectrum analysis were applied to extract the fundamental tone out of the speech audio samples. After extraction of several statistical features of the fundamental frequency, we studied if they carry information on the emotional state of the speaker applying different AI methods. Analysis of the outcome data was conducted with classifiers: K-Nearest Neighbours with local induction, Random Forest, Bagging, JRip, and Random Subspace Method from algorithms collection for data mining WEKA. The results prove that the fundamental frequency is a prospective choice for further experiments.

Novel angular velocity estimation technique for plasma filaments.

Magnetic field aligned filaments such as blobs and edge localized mode filaments carry significant amounts of heat and particles to the plasma facing components and they decrease their lifetime. The dynamics of these filaments determine at least a part of the heat and particle loads. These dynamics can be characterized by their translation and rotation. In this paper, we present an analysis method novel for fusion plasmas, which can estimate the angular velocity of the filaments on frame-by-frame time resolution. After pre-processing, the frames are two-dimensional (2D) Fourier-transformed, then the resulting 2D Fourier magnitude spectra are transformed to log-polar coordinates, and finally the 2D cross-correlation coefficient function (CCCF) is calculated between the consecutive frames. The displacement of the CCCF's peak along the angular coordinate estimates the angle of rotation of the most intense structure in the frame. The proposed angular velocity estimation method is tested and validated for its accuracy and robustness by applying it to rotating Gaussian-structures. The method is also applied to gas-puff imaging measurements of filaments in National Spherical Torus Experiment plasmas.

FPGA-Based Semisupervised Multifusion RDCNN of Process Robust VMD Data With Online Kernel RVFLN for Power Quality Events Recognition

The improved particle swarm optimization algorithm is integrated with variational mode decomposition (VMD) to extract the efficient band-limited intrinsic mode function (BLIMF) of the single and combined power quality events (PQEs). The selected BLIMF of the robust VMD (RVMD) and the privileged Fourier magnitude spectrum (FMS) information are fed to the proposed reduced deep convolutional neural network (RDCNN) for the extraction of the most discriminative unsupervised features. The RVMD-FMS-RDCNN method shows minimum feature overlapping compared with RDCNN and RVMD-RDCNN methods. The feature vector is imported to the novel supervised online kernel random vector functional link network (OKRVFLN) for quick and accurate categorization of complex PQEs. The proposed RVMD-FMS-RDCNN-OKRVFLN method produces excellent recognition capability over RDCNN, RVMD-RDCNN, and RVMD-RDCNN-OKRVFLN methods in noise-free and noisy environments. The unique BLIMF selection, clear detection, descriptive feature extraction, higher learning speed, superior classification accuracy, and robust antinoise performances are considerable importance of the proposed RVMD-FMS-RDCNN-OKRVFLN method. Finally, the proposed method architecture is developed and implemented in a very-high-speed ML506 Virtex-5 FPGA to text, examine, and validate the feasibility, performances, and practicability for online monitoring of the PQEs.

Fourier Spectrum Features for Face Recognition

The objective is to introduce a novel approach which deals with the challenges: uneven illumination and partial occlusion. This method performs face recognition by extracting the magnitude spectra features. At each point on the face, largest matching areas were found. Thus robustness is achieved using Fourier magnitude spectra feature extraction and largest matching area comparison. This method performs competitively with corrupted images and other unsupervised methods. The proposed approach is experimented on Yale B and AR datasets.

Identification of Agricultural Row Features Using Optical Data for Scattering and Reflectance Modeling Over Periodic Surfaces

Various forward models have been developed for measuring agricultural biophysical parameters from the microwave or optical data, but their application to periodic surfaces (e.g., periodic crop rows and plowed soil rows) still suffers from the need of row-feature descriptions. Accordingly, an operational method was proposed herein to estimate the orientation and period of row features from very high spatial resolution optical data, as input to multi-scale scattering models. The periodic features were estimated from the frequency domain using the Fourier magnitude spectrum by scanning in azimuth to identify the presence/absence of periodic features and the main direction. The frequency representing the dominant periodic features was then determined from the main direction of the magnitude spectrum. Moreover, the tillage type of the bare soil surface was classified into one of three types (sinusoidal, sinusoidal bench, or bench) according to the magnitudes of dominant periodic features in the main direction. The method was evaluated using two data sets consisting of various agricultural features in regional Melbourne, Australia. The retrieval Root Mean Square Error (RMSE) in orientation and period was 95% and 70.59%, respectively. The proposed method and the retrieved periodic features are expected to partly solve the ill-posed nature of microwave data inversion over periodic surfaces.

The Saliency of Angular Shapes in Threatening and Nonthreatening Faces.

Several lines of evidence suggest that angularity and curvilinearity are relied upon to infer the presence or absence of threat. This study examines whether angular shapes are more salient in threatening compared with nonthreatening emotionally neutral faces. The saliency of angular shapes was measured by the amount of local maxima in S(θ), a function that characterizes how the Fourier magnitude spectrum varies along specific orientations. The validity of this metric was tested and supported with images of threatening and nonthreatening real-world objects and abstract patterns that have predominantly angular or curvilinear features (Experiment 1). This metric was then applied to computer-generated faces that maximally correlate with threat (Experiment 2a) and to real faces that have been rated according to threat (Experiment 3). For computer-generated faces, angular shapes became increasingly salient as the threat level of the faces increased. For real faces, the saliency of angular shapes was not predictive of threat ratings after controlling for other well-established threat cues, however, other facial features related to angularity (e.g., brow steepness) and curvilinearity (e.g., round eyes) were significant predictors. The results offer preliminary support for angularity as a threat cue for emotionally neutral faces.

Exact Phase Retrieval in Principal Shift-Invariant Spaces

We address the problem of phase retrieval from Fourier transform magnitude spectrum for continuous-time signals that lie in a shift-invariant space spanned by integer shifts of a generator kernel. The phase retrieval problem for such signals is formulated as one of reconstructing the combining coefficients in the shift-invariant basis expansion. We develop sufficient conditions on the coefficients and the bases to guarantee exact phase retrieval, by which we mean reconstruction up to a global phase factor. We present a new class of discrete-domain signals that are not necessarily minimum-phase, but allow for exact phase retrieval from their Fourier magnitude spectra. We also establish Hilbert transform relations between log-magnitude and phase spectra for this class of discrete signals. It turns out that the corresponding continuous-domain counterparts need not satisfy a Hilbert transform relation; notwithstanding, the continuous-domain signals can be reconstructed from their Fourier magnitude spectra. We validate the reconstruction guarantees through simulations for some important classes of signals such as bandlimited signals and piecewise-smooth signals. We also present an application of the proposed phase retrieval technique for artifact-free signal reconstruction in frequency-domain optical-coherence tomography (FDOCT).

Structured prediction for differentiating between normal rhythms, ventricular tachycardia, and ventricular fibrillation in the ECG.

Recent studies have been performed on feature selection for diagnostics between non-ventricular rhythms and ventricular arrhythmias, or between non-ventricular fibrillation and ventricular fibrillation. However they did not assess classification directly between non-ventricular rhythms, ventricular tachycardia and ventricular fibrillation, which is important in both a clinical setting and preclinical drug discovery. In this study it is shown that in a direct multiclass setting, the selected features from these studies are not capable at differentiating between ventricular tachycardia and ventricular fibrillation. A high dimensional feature space, Fourier magnitude spectra, is proposed for classification, in combination with the structured prediction method conditional random fields. An improvement in overall accuracy, and sensitivity of every category under investigation is achieved.

A Robust and Sensitive Metric for Quantifying Movement Smoothness

The need for movement smoothness quantification to assess motor learning and recovery has resulted in various measures that look at different aspects of a movement's profile. This paper first shows that most of the previously published smoothness measures lack validity, consistency, sensitivity, or robustness. It then introduces and evaluates the spectral arc-length metric that uses a movement speed profile's Fourier magnitude spectrum to quantify movement smoothness. This new metric is systematically tested and compared to other smoothness metrics, using experimental data from stroke and healthy subjects as well as simulated movement data. The results indicate that the spectral arc-length metric is a valid and consistent measure of movement smoothness, which is both sensitive to modifications in motor behavior and robust to measurement noise. We hope that the systematic analysis of this paper is a step toward the standardization of the quantitative assessment of movement smoothness.

A New Technique for Digital Image Watermarking

In this paper, a new technique is proposed for rotation, scaling and translation (RST) invariant image watermarking based on log-polar mappings (LPM) and phase-only filtering (POF). The watermark is embedded in the LPM of Fourier magnitude spectrum of the original image, and a small portion of resulting LPM spectrum is used to calculate the watermark positions. This technique avoids computing inverse log-polar mapping (ILPM) to preserve the quality of the watermarked image, and avoids exhaustive search to save computation time and reduce false detection. Experimental results demonstrate that the digital watermarking technique is invariant and robust to rotation, scaling, and translation transformation.

Correlation-based watermarking method for image authentication applications

We propose a correlation-based digital watermarking technique for robust image pattern authentication. We hide a phase-based signature of the image back into its Fourier magnitude spectrum in the embedding stage. The detector computes the Fourier transform of the watermarked image and extracts the embedded signature. Authentication performance is measured by a correlation test of the extracted signature and the signature computed from the watermarked image. The quality of the watermarked image is obtained from the peak signal-to-noise ratio metric. We also furnish simulation results to show the robustness of our approach to typical image processing as found in JPEG compression

Dynamic-Absorption Spectral Contours: Vibrational Phase-Dependent Resolution of Low-Frequency Coherent Wave-Packet Motion of IR144 on the Ground-State and Excited-State π → π* Surfaces

We have characterized the femtosecond dynamic-absorption spectrum from the tricarbocyanine dye IR144 with the aid of a new approach that uses time-probe-wavelength contour lines to project the phase relationships between the coherent wave-packet motions on the ground-state and excited-state potential-energy surfaces. The distinct phase of the waveform carried by a dynamic-absorption contour line reports the motion of a single wave packet. The spectrum from IR144 exhibits four regions of alternating contour phase; simulations show that this interference pattern results from the antiphase motion and partial spectral overlap of the ground-state depletion and stimulated-emission spectra and the narrowing of the spectra that occurs when the excited-state and ground-state wave packets reach their turning points. The Fourier-magnitude spectra of contour lines and intensity transients observed in the spectral region, which are assigned to the turning point of the ground-state wave packet, have been compared to those obtained in the long-wavelength-limit spectral region that is associated with the excited-state wave packet. The intensities and frequencies of eleven modes observed over the 15-634 cm - 1 region suggest that the excited-state wave packet rapidly moves from the Franck-Condon geometry to a region of the potential-energy surface that is relatively flat with respect to the global normal coordinates that bend and twist the conjugated polyene backbone of IR144. These coordinates are evidently anharmonically coupled to high-frequency modes that are not impulsively excited by the 12-fs pulses used in the dynamic-absorption experiments.

RST-invariant digital image watermarking based on log-polar mapping and phase correlation

Based on log-polar mapping (LPM) and phase correlation, the paper presents a novel digital image watermarking scheme that is invariant to rotation, scaling, and translation (RST). We embed a watermark in the LPMs of the Fourier magnitude spectrum of an original image, and use the phase correlation between the LPM of the original image and the LPM of the watermarked image to calculate the displacement of watermark positions in the LPM domain. The scheme preserves the image quality by avoiding computing the inverse log-polar mapping (ILPM), and produces smaller correlation coefficients for unwatermarked images by using phase correlation to avoid exhaustive search. The evaluations demonstrate that the scheme is invariant to rotation and translation, invariant to scaling when the scale is in a reasonable range, and very robust to JPEG compression.