Presence Of High-frequency Noise Research Articles

A Fitted empirical demodulation for low frequency electrical signal evaluation

Empirical Demodulation (ED) is a technique used to build a discrete signal, called an empirical envelope, from a modulated time sequence. For example, in three-phase induction motors, this envelope can carry fault frequency data that allows the machine health status to be evaluated during a spectral analysis. However, due to mathematical reasons, the method is very sensitive to the amplitude oscillations within the signal. When these oscillations are unwanted, as in the presence of measurement noise, the results can be strongly affected. This work proposes an iterative and adjustable version of the ED that considerably reduces its sensitivity to the presence of high frequency noise, thus eliminating the need for signal pre-filtering. To prove the effectiveness of the Fitted Empirical Demodulation, the authors applied the new tool in motor current signals for analysis of the rotor bars conditions.

Functional Sensitivity of 2D Simultaneous Multi-Slice Echo-Planar Imaging: Effects of Acceleration on g-factor and Physiological Noise.

Accelerated data acquisition with simultaneous multi-slice (SMS) imaging for functional MRI studies leads to interacting and opposing effects that influence the sensitivity to blood oxygen level-dependent (BOLD) signal changes. Image signal to noise ratio (SNR) is decreased with higher SMS acceleration factors and shorter repetition times (TR) due to g-factor noise penalties and saturation of longitudinal magnetization. However, the lower image SNR is counteracted by greater statistical power from more samples per unit time and a higher temporal Nyquist frequency that allows for better removal of spurious non-BOLD high frequency signal content. This study investigated the dependence of the BOLD sensitivity on these main driving factors and their interaction, and provides a framework for evaluating optimal acceleration of SMS-EPI sequences. functional magnetic resonance imaging (fMRI) data from a scenes/objects visualization task was acquired in 10 healthy volunteers at a standard neuroscience resolution of 3 mm on a 3T MRI scanner. SMS factors 1, 2, 4, and 8 were used, spanning TRs of 2800 ms to 350 ms. Two data processing methods were used to equalize the number of samples over the SMS factors. BOLD sensitivity was assessed using g-factors maps, temporal SNR (tSNR), and t-score metrics. tSNR results show a dependence on SMS factor that is highly non-uniform over the brain, with outcomes driven by g-factor noise amplification and the presence of high frequency noise. The t-score metrics also show a high degree of spatial dependence: the lower g-factor noise area of V1 shows significant improvements at higher SMS factors; the moderate-level g-factor noise area of the parahippocampal place area shows only a trend of improvement; and the high g-factor noise area of the ventral-medial pre-frontal cortex shows a trend of declining t-scores at higher SMS factors. This spatial variability suggests that the optimal SMS factor for fMRI studies is region dependent. For task fMRI studies done with similar parameters as were used here (3T scanner, 32-channel RF head coil, whole brain coverage at 3 mm isotropic resolution), we recommend SMS accelerations of 4x (conservative) to 8x (aggressive) for most studies and a more conservative acceleration of 2x for studies interested in anterior midline regions.

Quantum Metrology Enhanced by Repetitive Quantum Error Correction.

We experimentally demonstrate the protection of a room-temperature hybrid spin register against environmental decoherence by performing repeated quantum error correction whilst maintaining sensitivity to signal fields. We use a long-lived nuclear spin to correct multiple phase errors on a sensitive electron spin in diamond and realize magnetic field sensing beyond the time scales set by natural decoherence. The universal extension of sensing time, robust to noise at any frequency, demonstrates the definitive advantage entangled multiqubit systems provide for quantum sensing and offers an important complement to quantum control techniques.

Brain Electrical Activity Obeys Benford’s Law

Monitoring and automated online analysis of brain electrical activity are frequently used for verifying brain diseases and for estimating anesthetic depth in subjects undergoing surgery. However, false diagnosis with potentially catastrophic consequences for patients such as intraoperative awareness may result from unnoticed irregularities in the process of signal analysis. Here we ask whether Benford's Law can be applied to detect accidental or intended modulation of neurophysiologic signals. This law states that the first digits of many datasets such as atomic weights or river lengths are distributed logarithmically and not equally. In particular, we tested whether data obtained from electrophysiological recordings of human patients representing global activity and organotypic slice cultures representing pure cortical activity follow the predictions of Benford's Law in the absence and in the presence of an anesthetic drug. Electroencephalographic (EEG) recordings from human subjects and local field potential recordings from cultured cortical brain slices were obtained before and after administration of sevoflurane. The first digit distribution of the datasets was compared with the Benford distribution. All datasets showed a Benford-like distribution. Nevertheless, distributions belonging to different anesthetic levels could be distinguished in vitro and in human EEGs. With sevoflurane, the first digit distribution of the in vitro data becomes steeper, while it flattens for EEG data. In the presence of high frequency noise, the Benford distribution falls apart. In vitro and EEG data show a Benford-like distribution which is altered by sevoflurane or destroyed by noise used to simulate artefacts. These findings suggest that algorithms based on Benford's Law can be successfully used to detect sevoflurane-induced signal modulations in electrophysiological recordings.

Study on the annoyance of high frequency noise at industrial workstations

The aim of the study was subjective assessment of the acoustic environment of persons exposed to high frequency noise at workstations. The study based on the survey regarding the annoyance of this type of noise which was conducted among 52 operators of equipment for the production of platform gratings. The study covered workstations, where acoustic measurements confirmed the presence of high frequency noise (in the frequency range from 10 to 20 kHz). During work at these workstations, the value of the equivalent sound pressure level in one-third octave-bands center frequencies of 10, 12.5, and 16 kHz occurs within the range of 81–103 dB and exceeds the admissible value defined for these frequency bands. The results of the study indicated that 92% of respondents are exposed to noise the whole time of the shift. All respondents wear hearing protection. Most of the employees describe the noise as: buzzing, insistent, high-pitched squeaky, and whistling. Respondents unanimously consider related noise levels as: loud, impeding communication, highly strenuous, and tiring. The highest number of points on a scale corresponding to noise annoyance was achieved by terms: horrible, very, persistently, and firmly.

Dependency on the silicon detector working bias for proton–deuteron particle identification at low energies

Proton–deuteron identification at energies between 2.5MeV and 6MeV has been studied as a function of the detector working bias. Digital pulse shape analysis (DPSA) has been used to perform the separation from the two mono-energetic beams. The technique makes use of the current signal delivered by a 500μm neutron transmutation doped (NTD) silicon detector, which was setup for low-field injection. It is shown that identification of the H isotopes is better when the detector working bias is close to the depletion voltage rather than over-depletion. The presence of high frequency noise diminished the possibility of identification, however, the use of a simple triangular smoothing algorithm counteracted this.

Automatic modulation classification of digital modulations in presence of HF noise

Designing an automatic modulation classifier (AMC) for high frequency (HF) band is a research challenge. This is due to the recent observation that noise distribution in HF band is changing over time. Existing AMCs are often designed for one type of noise distribution, e.g., additive white Gaussian noise. This means their performance is severely compromised in the presence of HF noise. Therefore, an AMC capable of mitigating the time-varying nature of HF noise is required. This article presents a robust AMC method for the classification of FSK, PSK, OQPSK, QAM, and amplitude-phase shift keying modulations in presence of HF noise using feature-based methods. Here, extracted features are insensitive to symbol synchronization and carrier frequency and phase offsets. The proposed AMC method is simple to implement as it uses decision-tree approach with pre-computed thresholds for signal classification. In addition, it is capable to classify type and order of modulation in both Gaussian and non-Gaussian environments.

Transducers and signal conditioners of the RFX new magnetic measurement system

Abstract RFX is the largest magnetic confinement fusion machine operating with the reversed field pinch (RFP) configuration. The RFX magnetic measurement system has been completely redesigned in order to meet the electromagnetic, mechanical and thermal requirements of the new toroidal assembly, with the thinner shell and the new system of coils for MHD modes control. In total, 744 independent measurements are available, 712 probes are placed in between the vacuum vessel and the new thin shell, where an air gap of only 6 mm thickness is available. Temperature can rise up to 200 °C and a voltage up to 2000 V can develop between shell and vessel during machine operation. To comply with such extreme requirements, specific techniques were developed for probes and connections. Conditioning electronics have been completely redesigned to improve accuracy and interferences immunity. Integrator drifts were lowered by an order of magnitude with respect to the previous system, and a new front-end was designed to cope with extended signal bandwidths and the presence of high frequency noise due to new powerful switching power supplies. Mechanical characteristics of conditioning channels were changed to improve modularity and reliability. This paper describes the design of all integral probes the tools and procedures adopted for installation and tests of the whole probe system, and the design and realisation of the conditioning electronics.

950-95 Enhanced Detection of Endocardial Motion by Spatiotemporal Fourier Filtration of 2-D Echocardiographic Acoustic Quantification Images

Automated detection of endocardial borders by 2-D echocardiography can be useful for the assessment of regional wall motion. Although Acoustic Quantification (AQ) currently provides on-line endocardial edge detection, the presence of high frequency noise in AQ images limits its utility for this purpose. We propose that spatiotemporal Fourierfiltration (FF) of AQ images may improve signal-to-noise ratio and enhance accuracy of analysis of endocardial motion. We stored digitally the 2-D AQ echocardiographic images obtained from the parasternal short axis view in 10 study subjects. These were processed using a stepwise computer algorithm that included smoothing using FF with spatial and temporal cutoff of 10 and 5 harmonics, respectively. We tested the accuracy of the FF against the raw AQ images by 1) comparing the vertical axis diameters of the raw AQ and FF images with the measurements obtained from digitized M-Mode tracings, and 2) counting the number of LV internal diameter reversals during each cardiac cycle along the vertical and horizontal axis. Five successive frames of endocardial borders from a loop of raw AQ and after FF are superimposed below. 1) The correlation between M-mode and raw AQ was 0.86, vs. 0.95 between M-mode and FF (p < 0.001). 2) The number of reversals per cycle was 13 ± 3.5 for AQ vS. 3.1 ± 3 for FF (p < 0.001). By eliminating high frequency noise from the echocardiographic AQ images, spatiotemporal Fourier filtration provides a more accurate representation of the endocardial edge motion.