Assessment of Dual-Tree Complex Wavelet Transform to improve SNR in collaboration with Neuro-Fuzzy System for Heart Sound Identification

none6siThe research paper proposes a novel denoising method to improve the outcome of heartsound (HS)-based heart-condition identification by applying the dual-tree complex wavelet transform (DTCWT) together with the adaptive neuro-fuzzy inference System (ANFIS) classifier. The method consists of three steps: first, preprocessing to eliminate 50 Hz noise; second, applying four successive levels of DTCWT to denoise and reconstruct the time-domain HS signal; third, to evaluate ANFIS on a total of 2735 HS recordings from an international dataset (PhysioNet Challenge 2016). The results show that the signal-to-noise ratio (SNR) with DTCWT was significantly improved (p < 0.001) as compared to original HS recordings. Quantitatively, there was an 11% to many decibel (dB)-fold increase in SNR after DTCWT, representing a significant improvement in denoising HS. In addition, the ANFIS, using six time-domain features, resulted in 55–86% precision, 51–98% recall, 53–86% f-score, and 54–86% MAcc compared to other attempts on the same dataset. Therefore, DTCWT is a successful technique in removing noise from biosignals such as HS recordings. The adaptive property of ANFIS exhibited capability in classifying HS recordings.Special Issue “Biomedical Signal Processing”, Section BioelectronicsopenBassam Al-Naami, Hossam Fraihat, Jamal Al-Nabulsi, Nasr Y. Gharaibeh, Paolo Visconti, Abdel-Razzak Al-HinnawiAl-Naami, Bassam; Fraihat, Hossam; Al-Nabulsi, Jamal; Gharaibeh, Nasr Y.; Visconti, Paolo; Al-Hinnawi, Abdel-Razza

Dynamic gas temperature measurement system, volume 1

A gas temperature measurement system with compensated frequency response of 1 kHz and capability to operate in the exhaust of a gas turbine engine combustor was developed. A review of available technologies which could attain this objective was done. The most promising method was identified as a two wire thermocouple, with a compensation method based on the responses of the two different diameter thermocouples to the fluctuating gas temperature field. In a detailed design of the probe, transient conduction effects were identified as significant. A compensation scheme was derived to include the effects of gas convection and wire conduction. The two wire thermocouple concept was tested in a laboratory burner exhaust to temperatures of about 3000 F and in a gas turbine engine to combustor exhaust temperatures of about 2400 F. Uncompensated and compensated waveforms and compensation spectra are presented

ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing

We address the problem of finding realistic geometric corrections to a foreground object such that it appears natural when composited into a background image. To achieve this, we propose a novel Generative Adversarial Network (GAN) architecture that utilizes Spatial Transformer Networks (STNs) as the generator, which we call Spatial Transformer GANs (ST-GANs). ST-GANs seek image realism by operating in the geometric warp parameter space. In particular, we exploit an iterative STN warping scheme and propose a sequential training strategy that achieves better results compared to naive training of a single generator. One of the key advantages of ST-GAN is its applicability to high-resolution images indirectly since the predicted warp parameters are transferable between reference frames. We demonstrate our approach in two applications: (1) visualizing how indoor furniture (e.g. from product images) might be perceived in a room, (2) hallucinating how accessories like glasses would look when matched with real portraits.Comment: Accepted to CVPR 2018 (website & code: https://chenhsuanlin.bitbucket.io/spatial-transformer-GAN/

Application of fault detection techniques to spiral bevel gear fatigue data

Results of applying a variety of gear fault detection techniques to experimental data is presented. A spiral bevel gear fatigue rig was used to initiate a naturally occurring fault and propagate the fault to a near catastrophic condition of the test gear pair. The spiral bevel gear fatigue test lasted a total of eighteen hours. At approximately five and a half hours into the test, the rig was stopped to inspect the gears for damage, at which time a small pit was identified on a tooth of the pinion. The test was then stopped an additional seven times throughout the rest of the test in order to observe and document the growth and propagation of the fault. The test was ended when a major portion of a pinion tooth broke off. A personal computer based diagnostic system was developed to obtain vibration data from the test rig, and to perform the on-line gear condition monitoring. A number of gear fault detection techniques, which use the signal average in both the time and frequency domain, were applied to the experimental data. Among the techniques investigated, two of the recently developed methods appeared to be the first to react to the start of tooth damage. These methods continued to react to the damage as the pitted area grew in size to cover approximately 75% of the face width of the pinion tooth. In addition, information gathered from one of the newer methods was found to be a good accumulative damage indicator. An unexpected result of the test showed that although the speed of the rig was held to within a band of six percent of the nominal speed, and the load within eighteen percent of nominal, the resulting speed and load variations substantially affected the performance of all of the gear fault detection techniques investigated

Injected and Delivered: Fabricating Implicit Control over Actuation Systems by Spoofing Inertial Sensors

Inertial sensors provide crucial feedback for control systems to determine motional status and make timely, automated decisions. Prior efforts tried to control the output of inertial sensors with acoustic signals. However, their approaches did not consider sample rate drifts in analog-to-digital converters as well as many other realistic factors. As a result, few attacks demonstrated effective control over inertial sensors embedded in real systems. This work studies the out-of-band signal injection methods to deliver adversarial control to embedded MEMS inertial sensors and evaluates consequent vulnerabilities exposed in control systems relying on them. Acoustic signals injected into inertial sensors are out-of-band analog signals. Consequently, slight sample rate drifts could be amplified and cause deviations in the frequency of digital signals. Such deviations result in fluctuating sensor output; nevertheless, we characterize two methods to control the output: digital amplitude adjusting and phase pacing. Based on our analysis, we devise non-invasive attacks to manipulate the sensor output as well as the derived inertial information to deceive control systems. We test 25 devices equipped with MEMS inertial sensors and find that 17 of them could be implicitly controlled by our attacks. Furthermore, we investigate the generalizability of our methods and show the possibility to manipulate the digital output through signals with relatively low frequencies in the sensing channel.Comment: Original publication in the proceedings of the 27th USENIX Security Symposium, 201

A Phase-Angle Tracking Method for Synchronization of Single- and Three-Phase Grid-Connected Converters

This thesis proposes a phase-angle tracking method, i.e., based on discrete Fourier transform for synchronization of three-phase and single-phase power-electronic converters under distorted and variable-frequency conditions. The proposed methods are designed based on fixed sampling rate and, thus, they can simply be employed for control applications. For three-phase applications, first, analytical analysis are presented to determine the errors associated with the phasor estimation using standard full-cycle discrete Fourier transform in a variable-frequency environment. Then, a robust phase-angle estimation technique is proposed, which is based on a combination of estimated positive and negative sequences, tracked frequency, and two proposed compensation coefficients. The proposed method has one cycle transient response and is immune to harmonics, noises, voltage imbalances, and grid frequency variations. An effective approximation technique is proposed to simplify the computation of the compensation coefficients. The effectiveness of the proposed method is verified through a comprehensive set of simulations in Matlab software. Simulation results show the robust and accurate performance of the proposed method in various abnormal operating conditions. For single-phase applications, an accurate phasor-estimation method is proposed to track the phase-angle of fundamental frequency component of voltage or current signals. This method can be used in three-phase applications as well. The proposed method is based on a fixed sampling frequency and, thus, it can simply be integrated in control applications of the grid-connected converters. Full-cycle discrete Fourier transform (DFT) is adopted as a base for phasor estimation. Two procedures are taken to effectiveness reduce the phasor estimation error using DFT during o - nominal frequency operation. First, adaptive window length (AWL) is applied to match the window-length of the DFT with respect to the input signal frequency. As AWL can partially reduce the error if sampling rate is not high, phasor compensation is employed to compensate the remaining error in the estimated phasor. Both procedures require system frequency, thus, an effective frequency-estimation technique is proposed to obtain fast and accurate performance. The proposed method has one cycle transient response and is immune to harmonics, noises, and grid frequency variations. The effectiveness of the proposed method is verified through a comprehensive set of simulations in Matlab and hardware implementation test using real-time digital signal processor data acquisition system

Texture to the Rescue : Practical Paper Fingerprinting based on Texture Patterns

In this article, we propose a novel paper fingerprinting technique based on analyzing the translucent patterns revealed when a light source shines through the paper. These patterns represent the inherent texture of paper, formed by the random interleaving of wooden particles during the manufacturing process. We show that these patterns can be easily captured by a commodity camera and condensed into a compact 2,048-bit fingerprint code. Prominent works in this area (Nature 2005, IEEE S&P 2009, CCS 2011) have all focused on fingerprinting paper based on the paper "surface." We are motivated by the observation that capturing the surface alone misses important distinctive features such as the noneven thickness, random distribution of impurities, and different materials in the paper with varying opacities. Through experiments, we demonstrate that the embedded paper texture provides a more reliable source for fingerprinting than features on the surface. Based on the collected datasets, we achieve 0% false rejection and 0% false acceptance rates. We further report that our extracted fingerprints contain 807 degrees of freedom (DoF), which is much higher than the 249 DoF with iris codes (that have the same size of 2,048 bits). The high amount of DoF for texturebased fingerprints makes our method extremely scalable for recognition among very large databases; it also allows secure usage of the extracted fingerprint in privacy-preserving authentication schemes based on error correction techniques

Adaptive noise cancelling applied to machine condition monitoring

Includes bibliography.The objective of this thesis is to determine whether Adaptive Noise Cancelling can be used successfully in determining the state of machine elements. In addition, this thesis was used to gain experience in real-time computing. This was done by designing and building a real-time machine monitoring package using an IBM PC and a TMS 320C25 digital signal-processing chip manufactured by Texas Instruments. To determine which adaptive algorithm should be used in the package, experiments were carried out on a computer with different types of adaptive noise cancelling algorithms, the two main ones being the Least-Mean-Squares (LMS) and Recursive-Least-Squares (RLS) algorithms