Methods for detection and characterization of signals in noisy data with the Hilbert-Huang Transform

The Hilbert-Huang Transform is a novel, adaptive approach to time series analysis that does not make assumptions about the data form. Its adaptive, local character allows the decomposition of non-stationary signals with hightime-frequency resolution but also renders it susceptible to degradation from noise. We show that complementing the HHT with techniques such as zero-phase filtering, kernel density estimation and Fourier analysis allows it to be used effectively to detect and characterize signals with low signal to noise ratio.Comment: submitted to PRD, 10 pages, 9 figures in colo

Newly uncovered physics of MHD instabilities using 2-D electron cyclotron emission imaging system in toroidal plasmas

Validation of physics models using the newly uncovered physics with a 2-D electron cyclotron emission imaging (ECEi) system for magnetic fusion plasmas has either enhanced the confidence or substantially improved the modeling capability. The discarded "full reconnection model" in sawtooth instability is vindicated and established that symmetry and magnetic shear of the 1/1 kink mode are critical parameters in sawtooth instability. For the 2/1 instability, it is demonstrated that the 2-D data can determine critical physics parameters with a high confidence and the measured anisotropic distribution of the turbulence and its flow in presence of the 2/1 island is validated by the modelled potential and gyro-kinetic calculation. The validation process of the measured reversed-shear Alfveneigenmode (RSAE) structures has improved deficiencies of prior models. The 2-D images of internal structure of the ELMs and turbulence induced by the resonant magnetic perturbation (RMP) have provided an opportunity to establish firm physics basis of the ELM instability and role of RMPs. The importance of symmetry in determining the reconnection time scale and role of magnetic shear of the 1/1 kink mode in sawtooth instability may be relevant to the underlying physics of the violent kink instability of the filament ropes in a solar flare

Investigation of Magnetohydrodynamic Fluctuation Modes in the STOR-M Tokamak

While magnetohydrodynamic (MHD) instabilities are considered one of the intriguing topics in tokamak physics, a feasibility study was conducted in the Saskatchewan Torus-Modified (STOR-M) tokamak to investigate the global MHD activities during the normal (L-mode) and improved (H-mode) confinement regimes. The experimental setup consists of 32 discrete Mirnov coils arranged into four poloidal arrays and mounted on STOR-M at even toroidal distances. The perturbed magnetic field fluctuations during STOR-M discharges were acquired and processed by the Fourier transform (FT), the wavelet analysis and the singular value decomposition (SVD) techniques. In L-mode discharges, the poloidal MHD mode numbers varied from 2 to 4 with peak frequencies in the range 20-40 kHz. The dominant toroidal modes were reported between 1 and 2 oscillating at frequencies 15-35 kHz. In another experiment, a noticeable MHD suppression was observed during the H-mode-like phase induced by the compact torus (CT) injection into STOR-M. However, a burst-like mode called the gong mode was triggered prior to the H-L transition, followed by coherent Mirnov oscillations. Mirnov oscillations with strong amplitude modulations were observed in the STOR-M tokamak. Correlations between Mirnov signals and soft x-ray (SXR) signals were found

Non-Contact Detection of Vital Signs Based on Improved Adaptive EEMD Algorithm (July 2022)

Non-contact vital sign detection technology has brought a more comfortable experience to the detection process of human respiratory and heartbeat signals. Ensemble empirical mode decomposition (EEMD) is a noise-assisted adaptive data analysis method which can be used to decompose the echo data of frequency modulated continuous wave (FMCW) radar and extract the heartbeat and respiratory signals. The key of EEMD is to add Gaussian white noise into the signal to overcome the mode aliasing problem caused by original empirical mode decomposition (EMD). Based on the characteristics of clutter and noise distribution in public places, this paper proposed a static clutter filtering method for eliminating ambient clutter and an improved EEMD method based on stable alpha noise distribution. The symmetrical alpha stable distribution is used to replace Gaussian distribution, and the improved EEMD is used for the separation of respiratory and heartbeat signals. The experimental results show that the static clutter filtering technology can effectively filter the surrounding static clutter and highlight the periodic moving targets. Within the detection range of 0.5 m similar to 2.5 m, the improved EEMD method can better distinguish the heartbeat, respiration, and their harmonics, and accurately estimate the heart rate

Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection

Price manipulation refers to the activities of those traders who use carefully designed trading behaviors to manually push up or down the underlying equity prices for making profits. With increasing volumes and frequency of trading, price manipulation can be extremely damaging to the proper functioning and integrity of capital markets. The existing literature focuses on either empirical studies of market abuse cases or analysis of particular manipulation types based on certain assumptions. Effective approaches for analyzing and detecting price manipulation in real time are yet to be developed. This paper proposes a novel approach, called adaptive hidden Markov model with anomaly states (AHMMAS) for modeling and detecting price manipulation activities. Together with wavelet transformations and gradients as the feature extraction methods, the AHMMAS model caters to price manipulation detection and basic manipulation type recognition. The evaluation experiments conducted on seven stock tick data from NASDAQ and the London Stock Exchange and 10 simulated stock prices by stochastic differential equation show that the proposed AHMMAS model can effectively detect price manipulation patterns and outperforms the selected benchmark models

Sub-pixel Registration In Computational Imaging And Applications To Enhancement Of Maxillofacial Ct Data

In computational imaging, data acquired by sampling the same scene or object at different times or from different orientations result in images in different coordinate systems. Registration is a crucial step in order to be able to compare, integrate and fuse the data obtained from different measurements. Tomography is the method of imaging a single plane or slice of an object. A Computed Tomography (CT) scan, also known as a CAT scan (Computed Axial Tomography scan), is a Helical Tomography, which traditionally produces a 2D image of the structures in a thin section of the body. It uses X-ray, which is ionizing radiation. Although the actual dose is typically low, repeated scans should be limited. In dentistry, implant dentistry in specific, there is a need for 3D visualization of internal anatomy. The internal visualization is mainly based on CT scanning technologies. The most important technological advancement which dramatically enhanced the clinician\u27s ability to diagnose, treat, and plan dental implants has been the CT scan. Advanced 3D modeling and visualization techniques permit highly refined and accurate assessment of the CT scan data. However, in addition to imperfections of the instrument and the imaging process, it is not uncommon to encounter other unwanted artifacts in the form of bright regions, flares and erroneous pixels due to dental bridges, metal braces, etc. Currently, removing and cleaning up the data from acquisition backscattering imperfections and unwanted artifacts is performed manually, which is as good as the experience level of the technician. On the other hand the process is error prone, since the editing process needs to be performed image by image. We address some of these issues by proposing novel registration methods and using stonecast models of patient\u27s dental imprint as reference ground truth data. Stone-cast models were originally used by dentists to make complete or partial dentures. The CT scan of such stone-cast models can be used to automatically guide the cleaning of patients\u27 CT scans from defects or unwanted artifacts, and also as an automatic segmentation system for the outliers of the CT scan data without use of stone-cast models. Segmented data is subsequently used to clean the data from artifacts using a new proposed 3D inpainting approach