Design of Analog Filters

Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of machine learning assisted PQDs management. Based on the conventional processing theory, the existing PQDs identification is time-invariant. It can result in a huge amount of unnecessary information being collected, processed, and transmitted. Consequently, needless processing activities, power consumption and latency can occur. In this paper, a novel combination of signal-piloted acquisition, adaptive-rate segmentation and time-domain features extraction with machine learning tools is suggested. The signal-piloted acquisition and processing brings real-time compression. Therefore, a remarkable reduction can be secured in the data storage, processing and transmission requirement towards the post classifier. Additionally, a reduced computational cost and latency of classifier is promised. The classification is accomplished by using robust machine learning algorithms. A comparison is made among the k-Nearest Neighbor, Naive Bayes, Artificial Neural Network and Support Vector Machine. Multiple metrics are used to test the success of classification. It permits to avoid any biasness of findings. The applicability of the suggested approach is studied for automated recognition of the power signal’s major voltage and transient disturbances. Results show that the system attains a 6.75-fold reduction in the collected information and the processing load and secures the 98.05% accuracy of classification.

/pdf/signal-piloted-processing-and-machine-learning-based-2f6zkbmbtt.pdf

Signal-piloted processing and machine learning based efficient power quality disturbances recognition.

Reference evapotranspiration (ET0) is an important part of the water and energy cycles during crop growth. Understanding the influencing factors and spatiotemporal variations of ET0 is of positive significance for guiding regional water-saving irrigation and regulating agricultural production. Data for daily meteorological observations of temperature, relative humidity, wind speed, and sunshine hours from 40 surface meteorological stations and the methods of climate tendency rate, Morlet wavelet, M-K mutation, path analysis, sensitivity analysis, and contribution rate analysis were utilized, to analyze the spatiotemporal distribution characteristics and influencing factors in the Beijing–Tianjin–Hebei region from 1990 to 2019. The ET0 from 1990 to 2019 was 958.9 mm, and there was a significant downward trend in the climate tendency rate of −3.07 mm/10 a. The ET0 presents a spatial distribution pattern decreasing from southwest to northeast. A change in the Beijing–Tianjin–Hebei region’s interannual ET0 occurred in 2016, with a decrease of 41.12 mm since then. The ET0 was positively correlated with temperature, wind speed, and sunshine hours, and negatively correlated with relative humidity; among those, wind speed and temperature are the dominant factors affecting the change of ET0. This study provides a scientific basis for the regulation and control of agricultural production in the Beijing–Tianjin–Hebei region.

/pdf/spatiotemporal-distribution-characteristics-and-influencing-4qwydczl.pdf

Spatiotemporal Distribution Characteristics and Influencing Factors Analysis of Reference Evapotranspiration in Beijing–Tianjin–Hebei Region from 1990 to 2019 under Climate Change

PurposeThis study aims to reveal the essential characteristics of nonstationary signals and explore the high-concentration representation in the joint time–frequency (TF) plane.Design/methodology/approachIn this paper, the authors consider the effective TF analysis for nonstationary signals consisting of multiple components.FindingsTo make it, the authors propose the combined multi-window Gabor transform (CMGT) under the scheme of multi-window Gabor transform by introducing the combination operator. The authors establish the completeness utilizing the discrete piecewise Zak transform and provide the perfect-reconstruction conditions with respect to combined TF coefficients. The high-concentration is achieved by optimization. The authors establish the optimization function with considerations of TF concentration and computational complexity. Based on Bergman formulation, the iteration process is further analyzed to obtain the optimal solution.Originality/valueWith numerical experiments, it is verified that the proposed CMGT performs better in TF analysis for multi-component nonstationary signals.

High-concentration time–frequency analysis for multi-component nonstationary signals based on combined multi-window Gabor transform

Continuous wavelet transform (CWT) has been proven to be an effective tool in feature extraction of non-stationary bio-signals. Therefore, hardware implementation of CWT has been widely investigated in wearable sensor integrated with local intelligence algorithm. To realize the feature extraction from low-frequency bio-signals, the design method of low-frequency Gm-C wavelet filter used in wearable sensor has been proposed in this paper. To alleviate the power constraint by wearable device, the Leap-Frog multiple-loop feedback filter structure is employed, which has low circuit complexity and sensitivity. Also, the transconductor consisting of simple differential pair is employed as Gm cell. By using low level bias current and deep weak inversion, low transconductance can be achieved to realize low frequency operation. A sixth-order Gaussian wavelet filter is designed in 0.18 $$\upmu$$
 m CMOS process. Simulation results show that power consumption is only 392 pW at center frequency of 5.9 Hz, corresponding to dynamic range of 42.7 dB and figure-of-merit of 2.59 $$\times$$
 10
 $$^{-13}$$
 . Experiment result shows that the proposed wavelet filter can be used for accurate extraction of transient features in biomedical signal processing.

A 392-pW 42.7-dB Gm-C wavelet filter for low-frequency feature extraction used for wearable sensor

In this paper, a novel methodology is proposed to implement the Morlet wavelet transform in an analog circuit. Under the proposed scheme, the impulse response of the linear time-invariant system is used to approximate the Morlet wavelet function. The approximation accuracy is guaranteed by the constrained L
 2
 -norm method, which reduces the approximation error by decreasing free parameters in searching routines. Due to the complex wavelet function, a common-pole strategy is presented for filter construction. With the common poles in real and imaginary parts, partial components can be shared during the analog implementation of the wavelet transform. With the reasonably good approximation for Morlet wavelet function, an analog continuous-time filter is designed based on the Gm-C integrator and orthonormal ladder topology. Application examples are introduced to illustrate the superior performance of the proposed filter. It is shown that the designed analog filter approximates the ideal Morlet wavelet function well in both time and frequency.

/pdf/analog-continuous-time-filter-designing-for-morlet-wavelet-2qv1cmtto5.pdf

Analog Continuous-Time Filter Designing for Morlet Wavelet Transform Using Constrained L2-Norm Approximation

In this paper, we consider the problem of analog-to-information conversion for nonstationary signals, which exhibit time-varying properties with respect to spectral contents. Nowadays, sampling for nonstationary signals is mainly based on Nyquist sampling theorem or signal-dependent techniques. Unfortunately, in the context of the efficient ‘blind’ sampling, these methods are infeasible. To deal with this problem, we propose a novel analog-to-information conversion architecture to achieve the sub-Nyquist sampling for nonstationary signals. With the proposed scheme, we present a multi-channel sampling system to sample the signals in time-frequency domain. We analyze the sampling process and establish the reconstruction model for recovering the original signals. To guarantee the wide application, we establish the completeness under the frame theory. Besides, we provide the feasible approach to simplify the system construction. The reconstruction error for the proposed system is analyzed. We show that, with the consideration of noises and mismatch, the total error is bounded. The effectiveness of the proposed system is verified in the numerical experiments. It is shown that our proposed scheme outperforms the other sampling methods state-of-the-art.

/pdf/analog-to-information-conversion-for-nonstationary-signals-4f22h5d7y4.pdf

Analog-to-Information Conversion for Nonstationary Signals

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Analog Continuous-Time Filter Designing for Morlet Wavelet Transform Using Constrained L2-Norm Approximation

Analog-to-Information Conversion for Nonstationary Signals