Sampling Rate Of Signal Research Articles

A Distribution Line Fault Location Estimation Algorithm Based on Electromagnetic Time-Reversal Method Calculated in the Finite Difference Time Domain

The distribution line is an important part of a power system. How to quickly eliminate faults is of great significance to improve the power supply reliability. Based on electromagnetic time-reversal (EMTR), this article proposes a fault location estimation algorithm calculated by the finite-difference time-domain (FDTD) method to solve two problems. One is the calculation efficiency of the fault current based method in the EMTR, and the other is the fault location accuracy of the EMTR-based method when the measurement device has a low sampling rate in the direct time. We first derived the FDTD updated equation of the node voltage, which is at the discontinuous point of the lossless single conductor transmission line. The algorithm obtains the fault current at any transverse branch of the distribution line through one-time simulation, which improves the calculation efficiency. Furthermore, considering the sampling rate of the transient signals, we analyzed the impact of the spectrum range of the transient signal, the interval of the guessed fault location, the length of the time-reversal window, the fault impedance, and the length of the line. The results showed that when the transient signal is measured at a high sampling rate (10 MHz), the proposed algorithm can be used to locate the fault with a minimum time-reversal window of 1 ms. When the sampling rate is as low as 100 kHz, the proposed algorithm can achieve accurate fault location estimation compared to the algorithm in the other time-domain electromagnetic transient simulation. The error of the proposed location algorithm is less than 100 m.

Some features of Hilbert transform and their use in energy informatics

Information-measuring technologies (IMT) are an important instrument for solving problems of energy informatics. They allow to form primary information based on the interaction of energy facilities with IMT sensors that form information signals. In many practical applications, the constructive model of information signals is the model of narrowband signals. The article summarizes the features of the discrete Hilbert transform and its application to obtain the primary characteristics of information signals – bypass and phase as functions of time. The main advantages of using the discrete Hilbert transform in signal processing for energy informatics are considered, including the consistency of obtaining frequency and time characteristics, high information content, the ability to analyze the dynamics of changes in signal characteristics, the possibility of obtaining samples of characteristics of information signals of significant volumes, etc. It is proposed to use a phase characteristic to select the time interval that limits the signal sample and sets it to a multiple of the signal period, and the sampling rate of information signals to reduce the errors in estimating their spectrum. The possibility of obtaining on their basis secondary deterministic (voltage level, voltage deviations from the nominal level, attenuation coefficient, signal period, signal phase shift, oscillation frequency, etc.) and statistical (sample characteristic, sample variance, sample median, sample circular variance, sample circular median, sample circular kurtosis, etc.) of signal information characteristics, which allows more complete to use their information resource. These characteristics can be used both for assessing power quality characteristics and for monitoring and diagnosing of energy facilities. Keywords: energy informatics, information signals, signal processing, discrete Hilbert transform, amplitude signal characteristics, phase signal characteristics

Linking the sampling frequency with multiscale entropy to classify mitoBK patch-clamp data

We analyze the activity of large-conductance voltage- and Ca2+-activated potassium channels located in the inner mitochondrial membrane (mitoBK) from human dermal fibroblast cells. The ion current activity registered via the patch-clamp technique was taken into consideration. At the preliminary stage, we performed an in-depth analysis of the signal power spectrum to find an optimal sampling frequency and study the impact of different sampling on changes in the information hidden in the signal. We found the optimal 10 kHz sampling frequency for the fibroblast’s mitoBK currents sequences. Interestingly, as the signal sampling rate increases, we can observe a decrease in entropy values. The application of Multiscale Entropy analysis enabled a practical classification of single-channel current traces at various membrane potentials. Using the machine learning techniques such as K-Nearest Neighbors and Support Vector Machine, optimized by the Stochastic Gradient Descent algorithm with Sample Entropy values as inputs, allowed us to assess the more outstanding accuracy scores for the chosen classifiers at membrane the hyperpolarization than at its depolarization.

Effects of using different algorithms and fiducial points for the detection of interbeat intervals, and different sampling rates on the assessment of pulse rate variability from photoplethysmography

ObjectivePulse Rate Variability (PRV) has been widely used as a surrogate of Heart Rate Variability (HRV). However, there are several technical aspects that may affect the extraction of PRV information from pulse wave signals such as the photoplethysmogram (PPG). The aim of this study was to evaluate the effects of changing the algorithm and fiducial points used for determining inter-beat intervals (IBIs), as well as the PPG sampling rate, from simulated PPG signals with known PRV content. MethodsPPG signals were simulated using a proposed model, in which PRV information can be modelled. Two independent experiments were performed. First, 5 IBIs detection algorithms and 8 fiducial points were used for assessing PRV information from the simulated PPG signals, and time-domain and Poincaré plot indices were extracted and compared to the expected values according to the simulated PRV. The best combination of algorithms and fiducial points were determined for each index, using factorial designs. Then, using one of the best combinations, PPG signals were simulated with varying sampling rates. PRV indices were extracted and compared to the expected values using Student t-tests or Mann-Whitney U-tests. ResultsFrom the first experiment, it was observed that AVNN and SD2 indices behaved similarly, and there was no significant influence of the fiducial points used. For other indices, there were several combinations that behaved similarly well, mostly based on the detection of the valleys of the PPG signal. There were differences according to the quality of the PPG signal. From the second experiment, it was observed that, for all indices but SDNN, the higher the sampling rate the better. AVNN and SD2 showed no statistical differences even at the lowest evaluated sampling rate (32 Hz), while RMSSD, pNN50, S, SD1 and SD1/SD2 showed good performance at sampling rates as low as 128 Hz. ConclusionThe best combination of IBIs detection algorithms and fiducial points differs according to the application, but those based on the detection of the valleys of the PPG signal tend to show a better performance. The sampling rate of PPG signals for PRV analysis could be lowered to around 128 Hz, although it could be further lowered according to the application. SignificanceThe standardisation of PRV analysis could increase the reliability of this signal and allow for the comparison of results obtained from different studies. The obtained results allow for a first approach to establish guidelines for two important aspects in PRV analysis from PPG signals, i.e. the way the IBIs are segmented from PPG signals, and the sampling rate that should be used for these analyses. Moreover, a model for simulating PPG signals with PRV information has been proposed, which allows for the establishing of these guidelines while controlling for other variables, such as the quality of the PPG signal.

Influence of Analog-To-Digital Conversion and Decimation Parameters on the Signal-To-Noise Ratio in the Radar Path

Introduction. The digital representation of received radar signals has provided a wide range of opportunities for their processing. However, the used hardware and software impose some limits on the number of bits and sampling rate of the signal at all conversion and processing stages. These limitations lead to a decrease in the signal-to-interference ratio due to quantization noise introduced by powerful components comprising the received signal (interfering reflections; active noise interference), as well as the attenuation of a low-power reflected signal represented by a limited number of bits. In practice, the amplitude of interfering reflections can exceed that of the signal reflected from the target by a factor of thousands.Aim. In this connection, it is essential to take into account the effect of quantization noise on the signal-tointerference ratio.Materials and methods. The article presents expressions for calculating the power and power spectral density (PSD) of quantization noise, which take into account the value of the least significant bit of an analog-to-digital converter (ADC) and the signal sampling rate. These expressions are verified by simulating 4-, 8- and 16-bit ADCs in the Mathcad environment.Results. Expressions are derived for calculating the quantization noise PSD of interfering reflections, which allows the PSD to be taken into account in the signal-to-interference ratio at the output of the processing chain. In addition, a comparison of decimation options (by discarding and averaging samples) is performed drawing on the estimates of the noise PSD and the signal-to-noise ratio.Conclusion. Recommendations regarding the ADC bit depth and sampling rate for the radar receiver are presented.

E-Commerce Logistics System Based on Internet of Things

In order to improve the efficiency of the e-commerce logistics system, this paper analyzes the application of dynamic bandwidth resource allocation algorithm in intelligent logistics tracking scenarios and distribution scenarios, and achieves the purpose of optimizing the allocation of bandwidth resources by changing the sampling rate of the control signal. Moreover, this paper designs and implements an e-commerce logistics information system based on the Internet of Things, and starts with each functional module to introduce in detail the various functions realized by the system in this paper. This paper changes the traditional logistics operation mode, through the realization of various functional modules, users can grasp personal historical orders and view the list of historical orders. Finally, this paper analyzes the performance of this system through experimental research. The results of the research show that the e-commerce logistics system based on the Internet of Things proposed in this paper is effective.

Electric Power Steering Power Circuit Health Assessment and Mitigation Strategy

With recent developments of energy efficient design and control for electric motors, electrical subsystems and components have become integral parts of main actuators in vehicle systems (e.g., steering and propulsion systems). To ensure proper vehicle operations, it is important to make sure that electrical power is properly transmitted through the power circuit from vehicle power source to the electric motor. However, degradation in the power circuit health, which often manifests itself as increased resistance, may affect power transmission and degrade the system performance. For example, in Electric Power Steering (EPS) systems, if the EPS power circuit resistance is increased and the EPS is drawing power to assist the driver, voltage at the EPS module will drop significantly, causing the EPS to reset and, consequently, Loss of Assist (LOA) incidents. Due to compliance in the steering system and suspension design, drivers often feel that the steering system is fighting back when an LOA incident occurs. While previous work has partially addressed this issue by developing algorithms that estimate resistance increase in EPS power circuits, this paper further validates and refines the algorithms for vehicle on-board and off-board implementations using test drive data collected. Since on-board and off-board implementations impose different limits on signal sampling rates, a total of 250 and 465 minutes of data are respectively collected with various vehicle speeds and steering maneuvers. Moreover, a supervisory control solution, referred to as EPS Anti-Loss-of-Assist (ALOA), is proposed that gradually and proactively reduces EPS torque assist as resistance in the EPS power circuit increases so that the EPS voltage is kept above a resetting threshold. Stationary steering tests of the proposed solution as well as demonstrations on parking lot maneuvers at General Motors Milford Proving Grounds are conducted. The stationary steering tests and demonstrations show that, with the proposed supervisory control, negative effects of increased EPS power circuit resistance can be mitigated without noticeable changes in normal driving experience.

Real-time filtering adaptive algorithms for non-stationary noise in electrocardiograms

A non-stationary noise with a previously unknown intensity level often contaminates electrocardiogram (ECG) signals. Therefore, provision of high quality suppression of the non-stationary noise in ECG is a vital task to be performed. A new lightweight adaptive method has been proposed for real-time filtering of non-stationary (from the point of view of its variance) noise in ECG with noise- and signal-dependent switching filters, appropriate for processing a local vicinity of the current input signal sample. This method does not require time for filter parameters adaptation and a priori information about the noise variance. A one- and a two-pass algorithm on the simple optimal Savitzky & Golay filters and on the linear averaging filter have been developed on the basis of the proposed method. There is also an algorithm suggested applying a re-filtering only when the identifiers used in the method define a not low noise level. The integral and local statistical estimates of filters' efficiency have been obtained from numerical simulations over mean-square error (MSE), maximum absolute error (MAE), and signal-to-noise ratio (SNR) for a model ECG signal under different levels of Gaussian noise. Filtering efficiency was estimated with the real signals taken from physionet.org database. The filter parameters were chosen by numerical simulations for a typical P-QRS-T cycle with corresponding signal sampling rate and scale considered. For a wide range from low to high noise levels (input SNR belongs to the interval from 25 to 0 dB), the statistical estimates of efficiency have been obtained as follows: for an ECG sampled at 360 Hz (taken from NSTDB), inside QRS-complex, the SNR increases by 2.5–6.7 dB, the MSE decreases in 1.7–4.3 times and the MAE decreases in 1.3–2.2 times; inside the segments prior to and following QRS-complex, the SNR, on an average, increase by 8.6–13.2 dB and the MSE decreases in 7.1–19.2 times, and the MAE decreases in 2.4–5.1 times. For an ECG sampled at 1 kHz (taken from PTB), inside QRS-complex, the SNR increases by 5.2–8 dB, the MSE and the MAE decrease in 3.2–6 times and 1.9–2.7 times, respectively; outside QRS, the SNR increases by 10.2–15.8 dB, the MSE and the MAE decrease in 10.5–36 times and in 2.6–5.2 times, respectively. For an ECG sampled at 250 Hz (from CUDB), the local indicators of efficiency are: inside QRS-complex, the SNR increases by 2.9–6.6 dB, the MSE and the MAE decrease in 1.8–4 times and in 1.4–2.3 times, respectively; outside QRS-complex, the SNR, on an average, increase by 4.7–11.6 dB, the MSE and the MAE decrease in 3.3–10.9 times and in1.7–3.9 times. Additionally, the filters' efficiency has been estimated as to suppression of real electromyographic (EMG) noise with significantly different variance and the proposed algorithms have been compared with other filters. A noise-free ECGs during 5 min sampled at 360 Hz were contaminated with highly non-stationary EMG noise from a muscle artifact (MA) record of different intensity (input SNR varies from 20 to −5 dB), the SNR improvement at the proposed algorithm output is 10–14 dB. The calculated quantitative estimates of efficiency confirm the high quality of non-stationary EMG noise suppression obtained with the adaptive algorithms suggested. Minute signal distortions and a high degree of noise suppression have been demonstrated. Good performance and high filter quality for various real signals with non-stationary EMG noise have been shown. ECG amplitude-time parameters and waveforms, including pathological changes, are shown to be well-preserved.

A Fuzzy Interpolation Cross-Correlation Method for Time-Delay Estimation of Partial Discharge UHF Pulse Signals

Time-delay estimation of partial discharge (PD) ultrahigh-frequency (UHF) pulse signals is one of the effective means to diagnose the local defect of electrical equipment. In order to improve the time-delay estimation accuracy of a multielement PD UHF pulse signal sensor array and reduce the cost of the PD detection system, a fuzzy interpolation cross-correlation method for time-delay estimation of PD UHF pulse signals was proposed in this paper. Improving the signal sampling rate is an effective way to improve the time-delay estimation accuracy. In this work, the fuzzy interpolation inference method was applied to interpolate intermediate values into the feature area of the PD UHF signal to improve the sampling rate, and then the cross-correlation method was used to estimate the time delay. This method not only improves the system sampling rate by fuzzy interpolation inference, which can make up for the deficiency of system equipment sampling performance, but also reduces the estimation error caused by noninteger multiple sampling intervals of signal time delay. The comparative experiment results demonstrate the power of the proposed method in improving the accuracy of the time-delay estimation of PD UHF pulse signals.

Online signature verification using signature down-sampling and signer-dependent sampling frequency

AbstractOnline signature verification considers signatures as time sequences of different measurements of the signing instrument. These signals are captured on digital devices and therefore consist of a discrete number of samples. To enrich or simplify this information, several verifiers employ resampling and interpolation as a preprocessing step to improve their results; however, their design decisions may be difficult to generalize. This study investigates the direct effect of the sampling rate of the input signals on the accuracy of online signature verification systems without using interpolation techniques and proposes a novel online signature verification system based on a signer-dependent sampling frequency. Twenty verifier configurations were created for five different public signature databases and a variety of popular preprocessing approaches and evaluated for 20–40 different sampling rates. Our results show that there is an optimal range for the sampling frequency and the number of sample points that minimizes the error rate of a verifier. A sampling frequency range of 15–50 Hz and a signature point count of 60–240 provided the best accuracies in our experiments. As expected, lower ranges showed inaccurate results; interestingly, however, higher frequencies often decreased the verification accuracy. The results show that one can achieve better or at least the same verification accuracies faster by down-sampling the online signatures before further processing. The proposed system achieved competitive results to state-of-the-art systems for different databases by using the optimal sampling frequency. We also studied the effect of choosing individual sampling frequencies for each signer and proposed a signature verification system based on signer-dependent sampling frequency. The proposed system was tested using 500 different verification methods and improved the accuracy in 92% of the test cases compared to the usage of the original frequency.

Phase Retrieval of Complex OFDM Signal by Solving Temporal Transport-of-Intensity Equation

We simulate the complex signal reconstruction by solving the temporal transport-of-intensity equation (TIE) in a direct detection orthogonal frequency division multiplexing (OFDM) system. At the receiver, by measuring two intensities before and after a dispersive element, we construct a TIE and retrieve the phase information of the complex OFDM signal by solving it. We investigate the influence of the carrier-signal power ratio (CSPR), the value of the dispersion, signal baud rate, sampling rate, laser linewidth, and frequency offset on the performance of phase retrieval (PR). In a 20 Gbaud 16 quadrature-amplitude-modulation (16QAM) OFDM system, the TIE-based direct-detection requires a 12 dB CSPR, which is comparable to Kramers–Kronig (KK)-based receiver and the single carrier TIE scheme. A proof-of-concept adaptive modulation scheme has also been applied to the TIE-based OFDM system.

An Improved Background Subtraction Method for Adaptive Rate Compressive Sensing

In Compressive Video Sensing application, the using of Adaptive Rate Compressive Sensing (ARCS) method can predict and adjust the sampling rate for each video frame, reduce the total sampling rate and improve the quality of reconstructed image. In order to use the inter-frame correlation of the video signal efficiently and reduce the sampling rate of the whole signal, an Improved Background Subtraction (IBS) method is proposed in this paper. By alternately using the frame without any foreground objects and the previous frame as the background of the signal, the sparsity of the foreground signal is improved and the total sampling rate is reduced. Experimental results show that, compare with the traditional background subtraction method, the IBS method can significantly reduce the total video sampling rate without apparent degradation of reconstructed image quality under the same ARCS method.

A Low-Processing-Rate Dual BPSK Tracking Method for BOC Modulated Signals

Among the existing wideband processing methods for binary offset carrier (BOC) signals, dual binary phase-shift keying (BPSK) tracking (DBT) features the interoperability with traditional BPSK tracking and flexible switching between single-sideband and wideband tracking modes. However, due to the large bandwidth of entire BOC signal, the DBT method requires a high signal sampling and processing rate, resulting in high computational burden. This letter proposes a low-processing-rate dual BPSK tracking (LPR-DBT) method to tackle this problem. A preprocessing unit is proposed to be added between the ADC and DBT correlator channels under the constraint of retaining the subcarrier ranging information. It converts both the upper and lower sidebands of BOC signal to near-zero frequency, respectively, and then decimates them. This significantly reduces the correlator channel processing rate without ranging performance loss. It is worth emphasizing that the delays and phase shifts introduced by the preprocessing to each sideband are carefully considered and dealt with to ensure the relationship between two sidebands required by the DBT method is not destroyed.

The minimal sampling frequency of the photoplethysmogram for accurate pulse rate variability parameters in healthy volunteers

ObjectiveToday mobile health-monitoring devices calculate heart rate and its variability mostly from the photoplethysmogram (PPG). Minimizing the power consumption is crucial, one option is optimizing the signal sampling rate. Present study aimed to determine the minimal sampling frequency of the PPG signal, which is sufficient for accurate heart rate variability (HRV) analysis. Methods57 high-quality, 5-minute PPG signals from healthy volunteers sampled at 1 kHz (master) were decimated by a factor of 2, 5, 10, 20, 50, 100, 200, 500, then cubic spline and parabola interpolated back to 1 ms resolution. The mean pulse rate, its standard deviation (SDNN), root mean square of successive RR-differences (RMSSD), Porta and Guzik indices (PI, GI) were calculated. Their relative accuracy error (RAE) was determined, RAE<5% was acceptable. Also, the processing times were measured. Results200ms sampling interval without interpolation is sufficient to calculate mean pulse rate with RAE<0.05%. SDNN and RMSSD require at least 20 ms sampling interval without interpolation (RAE: 1.28±0.96% and 4.25±3.59%, respectively). By both interpolations, these sampling intervals can be increased to 100 ms and 50 ms, respectively. Also, the accuracy of GI and PI can be improved by interpolation, here parabola approximation was better (GI: 20 ms versus 100 ms, PI: 50 ms versus 100 ms). Parabola approximation needs significantly less computation than cubic spline interpolation. ConclusionsFor monitoring the average heart rate, 5 Hz sampling frequency can be sufficient without interpolation in healthy subjects. Correct HRV analysis requires higher sampling rates depending on the parameter. Interpolation can improve HRV accuracy from lower temporal resolution PPGs.

Sparse discrete linear canonical transform and its applications

The linear canonical transform (LCT) is a powerful tool for non-stationary signals in signal processing. In this paper, we propose a sparse discrete linear canonical transform (SDLCT) algorithm to solve the high sampling rate and large data calculation of non-stationary signals. In detail, we introduce permutation of spectra and window function to ensure the sparsity of the frequency domain. To get rid of the redundant data of the signal and improve the processing efficiency, we compress the high-dimensional signal into the low-dimensional signal by down-sampling. Then the low dimensional signal is mapped to a linear canonical domain. The above processes effectively reduce the amount of data and computation for signal processing. Then, we apply the SDLCT to the pulse compression of the linear frequency modulation (LFM) signal. The comparison between SDLCT and LCT shows that SDLCT is hardly affected by noise, it can accurately obtain the target positions at a low signal-to-noise ratio (SNR). Furthermore, we apply this algorithm to moving target detection for synthetic aperture radar. The comparison between LCT, sparse Fourier transform (SFT), sparse discrete fraction Fourier transform (SDFrFT), and SDLCT shows that SDLCT has stronger signal detection performance and can detect all the signals that affect each other.

Adaptive delay lines for absolute distance measurements in high-speed long-range frequency scanning interferometry

The application of frequency scanning interferometry to long-range (∼10 m) high-speed (upwards of 105 coordinates s−1) absolute distance measurement is currently impractical at reasonable cost due to the extremely high modulation frequencies (typically 100 GHz or more). A solution is proposed here based on an adaptive delay line architecture, in which the reference beam passes through a series of N switchable delay lines, with exponentially-growing delays. The benefits include a reduction by a factor of 2 N in the required signal sampling rate, in the size of dataset to be processed, and in minimum allowable source coherence length, thus paving the way for the use of fast sweeping sources such as vertical-cavity surface-emitting lasers (VCSELs) and Fourier-domain mode-locked (FDML) lasers for long-range lidars. The validity of the principle has been demonstrated experimentally by means of a three-switch prototype.

Precise wide-band electrical impedance spectroscopy measurement via an ADC operated below the Nyquist sampling rate

Usually, an analog-to-digital converter (ADC) used for electrical impedance measurement is required to operate above the Nyquist sampling rate of excitation signals to ensure the accurate discretization. This paper proposed a robust impedance measurement method in wide-band spectrum via using compressive sensing to eliminate this requirement. The excitation signals were compressed by a random-modulator and a low-pass filter with bandwidth below the excitation frequency, then impedance is recovered by the reconstruction algorithm with the filter parameter identified on-line and the data recorded by the ADC operated just above the Nyquist rate of the filter bandwidth. In the simulations and experiments, a sparse wide-band impedance spectroscopy from 0.01 to 1.05 MHz was sampled by an ADC operated with fixed sampling rate 400 kHz, and the results is comparable to the impedance analyzer. Impedance imaging experiment results demonstrated that the impedance measurement could be precisely achieved for both single- and multi- frequencies.

Complex discrete samples in electrocardiosignal processing tasks

A typical task of preprocessing an electrocardiosignal is to eliminate the isoline drift. The presence of the isoline drift changes the position of the ST segment relative to the zero line, which leads to a distortion of the informative parameters of the ST segment. Modern electrocardiographs are equipped with high-pass filters for isoline drift removal. In this case, along with the isoline drift, a part of the spectral components of the useful signal is removed from the electrocardiogram signal. Preservation of the components of the spectrum of the electrocardiogram signal is possible by interpolating discrete samples of the isoline drift signal taken on the PQ segment or the TP interval. In this case, the sampling rate of the isoline drift signal is determined by the heart rate. Consequently, when the frequency of the isoline drift signal changing increases, the accuracy of its recovery deteriorates, and when half of the heart rate is reached, recovery becomes impossible. Purpose – the working purpose is to find ways to eliminate the isoline drift even in the presence of components in its spectrum with frequencies reaching the heart rate, while preserving the informative spectral components of the electrocardiosignal. This purpose can be achieved by converting the samples of the isoline drift signal taken at the TP interval of the electrocardiosignal into a group of samples forming a complex discrete sample (CDS). In the spectrum of the CDS sequence, the specified spectral zones can be suppressed. It is sufficient to suppress the first spectral zone at the sampling rate equal to the average heart rate to isolate the isoline drift. In this case, the frequency of the isolated isoline drift signal can theoretically be increased to the sampling rate, which is up to the heart rate. Due to the heart rate variability, the isoline drift signal samples taken at the TP interval will have a varying repetition period (sampling period). To take this fact into account, the following tasks have been solved. The analysis of the spectral composition of the sequences of samples, from which complex discrete samples are formed, has been carried out with a changing sampling rate. Pulse-frequency modulation is used as a mathematical model of the sample sequence. It has been found that with a changing sampling rate, the side components remain non-suppressed in the suppressed spectral zone at the frequencies that differ from the sampling rate by the frequency of sampling rate change. The conditions for the equality of all spectral components in the suppressed spectral zone to zero have been determined. Mathematical expressions that describe these conditions and allow us to determine the amplitude-time parameters of the CDS necessary for the implementation of these conditions have been obtained. Mathematical simulation of the CDS spectra, which confirmed the workability of the proposed description of the CDS with a changing sampling period, has been carried out. The obtained mathematical expressions allow us to determine the structure and amplitude-time parameters of complex discrete samples that provide suppression of the specified spectral components with a changing sampling period, which provides an extension of the frequency range of the isolated isoline drift signal while preserving the informative components of the electrocardiosignal.

An Adaptive-Resolution Quasi-Level-Crossing Delta Modulator With VCO-Based Residue Quantizer

This brief introduces an adaptive-resolution (AR) quasi-level-crossing delta modulator ADC with a voltage-controlled oscillator (VCO)-based residue quantizer for Internet-of-Things (IoT) wireless sensor nodes. The residue voltage signal is digitized by a VCO-based residue quantizer, thus leading to a straightforward implementation of level-crossing (LC) and AR algorithms in digital domain. The inherent and mismatch-induced dithering provided by the VCO-based residue quantizer increases the proposed modulator’s dynamic range and average sampling rate for slowly-varying input signals, enabling accurate sampling and conversion. For fast-varying signals, the AR algorithm is adopted to reduce the average sampling rate by a factor of three compared to the straightforward LC algorithm at the edge of the modulator’s signal bandwidth. Fabricated in 28-nm CMOS, the proposed modulator achieves a peak SNDR of 53dB over a signal bandwidth of 1.78MHz.

A New Acoustic Emission Sensor Based Gear Fault Detection Approach

In order to reduce wind energy costs, prognostics and health management (PHM) of wind turbine is needed to reduce operations and maintenance cost of wind turbines. The major cost on wind turbine repairs is due to gearbox failure. Therefore, developing effective gearbox fault detection tools is important in the PHM of wind turbine. PHM system allows less costly maintenance because it can inform operators of needed repairs before a fault causes collateral damage happens to the gearbox. In this paper, a new acoustic emission (AE) sensor based gear fault detection approach is presented. This approach combines a heterodyne based frequency reduction technique with time synchronous average (TSA) and spectral kurtosis (SK) toprocess AE sensor signals and extract features as condition indictors for gear fault detection. Heterodyne techniques commonly used in communication are used to preprocess the AE signals before sampling. By heterodyning, the AE signal frequency is down shifted from MHz to below 50 kHz. This reduced AE signal sampling rate is comparable to that of vibration signals. The presented approach is validated using seeded gear tooth crack fault tests on a notational split torque gearbox. The approach presented in this paper is physics based and the validation results have showed that it could effectively detect the gear faults.