An Efficient and Flexible Window Function for Memristor Model and its Analog Circuit Application

The memristor is a nanostructure resistive tuning two terminal novel electronics device that has been widely explored in the area of neuromorphic computing systems, memories, digital circuits, analog circuits and many more new applications. In this article an efficient and flexible window function is presented for linear drift memristor model. Propose window function provides a unique feature (controllable window function discontinuity) to linear drift memristor model by which DPHL (Distorted Pinched Hysteresis Loop) problem is resolved and also improved the programming resistance state of the memristor. Five control parameters are introduced in the presented window function, in order to fix the pre-existing problem (like boundary effect, boundary lock and inflexibility) and make it more flexible. The programmable analog gain amplifier circuit is ultimately executed to instantiate the utilization of evolved memristor model.

Feature mining method of information system operation and maintenance big data based on window function

In this paper, a window function based feature mining method for the operation and maintenance of big data in information system is proposed. The time clustering feature vector is combined with window function to reduce the dimension of operation and maintenance data of high-dimensional information system. The operation and maintenance data feature subset is segmented according to the similar feature level, and the redundant features of operation and maintenance data are removed to complete the information system operation and maintenance big data feature mining. The simulation results show that the proposed method has better clustering effect, fewer iterations and shorter mining time.

Uniform error estimates for nonequispaced fast Fourier transforms

In this paper, we study the error behavior of the nonequispaced fast Fourier transform (NFFT). This approximate algorithm is mainly based on the convenient choice of a compactly supported window function. So far, various window functions have been used and new window functions have recently been proposed. We present novel error estimates for NFFT with compactly supported, continuous window functions and derive rules for convenient choice from the parameters involved in NFFT. The error constant of a window function depends mainly on the oversampling factor and the truncation parameter.

Using Windowed Interpolation Method to Suppress Power Frequency Noise of Vibration Signal

Power frequency noise interference is widespread. In order to suppress power frequency noise coupled into vibration signal, windowed interpolating method was studied. Firstly, three typical window functions were compared, including rectangular window, Hanning window and six-term cosine window. Secondly, the numerical simulation method was used to analyze the influence of window function on the interpolation results of power frequency noise, finding that different window function should be chose when the spectrum spacing between power frequency noise and useful components of vibration signal is different. Finally, the windowed interpolation method was applied to actual engineering vibration signal to suppress its power frequency noise, with the result that the interpolation result of vibration signal is the most accurate with Hanning window. Numerical simulation and engineering application show that the windowed interpolation method is effective for power frequency noise suppression.

Unified galaxy power spectrum measurements from 6dFGS, BOSS, and eBOSS

We make use of recent developments in the analysis of galaxy redshift surveys to present an easy to use matrix-based analysis framework for the galaxy power spectrum multipoles, including wide-angle effects and the survey window function. We employ this framework to derive the deconvolved power spectrum multipoles of 6dFGS DR3, BOSS DR12 and the eBOSS DR16 quasar sample. As an alternative to the standard analysis, the deconvolved power spectrum multipoles can be used to perform a data analysis agnostic of survey specific aspects, like the window function. We show that in the case of the BOSS dataset, the Baryon Acoustic Oscillation (BAO) analysis using the deconvolved power spectra results in the same likelihood as the standard analysis. To facilitate the analysis based on both the convolved and deconvolved power spectrum measurements, we provide the window function matrices, wide-angle matrices, covariance matrices and the power spectrum multipole measurements for the datasets mentioned above. Together with this paper we publish a Python-based toolbox to calculate the different analysis components. The appendix contains a detailed user guide with examples for how a cosmological analysis of these datasets could be implemented. We hope that our work makes the analysis of galaxy survey datasets more accessible to the wider cosmology community.

Theoretical Demonstration of Security Improvement of Optical Phased Array Based on Optically Injection-Locked Lasers

The high security of optical phased array (OPA) signals is an important requirement for OPA-based optical wireless communication (OWC). We propose a method for improving the security of OPA-based OWC systems using optically injection-locked (OIL) semiconductor lasers. We theoretically demonstrate the amplitude and phase modulation of OIL-OPA elements by controlling the injection-locking parameters of the OIL lasers. When a Taylor window function is applied as the amplitude profile of the OPA transmitter, the sidelobe level decreases by 22 dB and the unsecured distance reduces 10 times compared to the case without the Taylor window function. In addition, the unsecured area factor becomes 0.8%.

Staring Spotlight SAR with Nonlinear Frequency Modulation Signal and Azimuth Non-Uniform Sampling for Low Sidelobe Imaging

In synthetic aperture radar (SAR) imaging, geometric resolution, sidelobe level (SLL) and signal-to-noise ratio (SNR) are the most important parameters for measuring the SAR image quality. The staring spotlight mode continuously transmits signals to a fixed area by steering the azimuth beam to acquire azimuth high geometric resolution, and its two-dimensional (2D) impulse response with the low SLL is usually obtained from the 2D weighted power spectral density (PSD) by the selected weighting window function. However, this results in the SNR reduction due to 2D amplitude window weighting. In this paper, the staring spotlight SAR with nonlinear frequency modulation (NLFM) signal and azimuth non-uniform sampling (ANUS) is proposed to obtain high geometric resolution SAR images with the low SLL and almost without any SNR reduction. The NLFM signal obtains non-equal interval frequency sampling points under uniform time sampling by adjusting the instantaneous chirp rate. Its corresponding PSD is similar to the weighting window function, and its pulse compression result without amplitude window weighting has low sidelobes. To obtain a similar Doppler frequency distribution for low sidelobe imaging in azimuth, the received SAR echoes are designed to be non-uniformly sampled in azimuth, in which the sampling sequence is dense in middle and sparse in both ends, and azimuth compression result with window weighting would also have low sidelobes. According to the echo model of the proposed imaging mode, both the back projection algorithm (BPA) and range migration algorithm (RMA) are modified and presented to handle the raw data of the proposed imaging mode. Both imaging results on simulated targets and experimental real SAR data processing results of a ground-based radar validate the proposed low sidelobe imaging mode.

Power Quality Disturbances Recognition Using Modified S-Transform Based on Optimally Concentrated Window with Integration of Renewable Energy

To meet power quality requirements, it is necessary to classify and identify the power quality of the power grid connected with renewable energy generation. S-transform (ST) is an effective method to analyze power quality in time and frequency domains. ST is widely used to detect and classify various kinds of non-stationary power quality disturbances. However, the long taper and scaling criteria of the Gaussian window in standard ST (SST) will lead to poor time domain resolution at low frequency and poor frequency resolution at high frequency. To solve the discrete side effects, it is necessary to select the optimal window function to locate the time frequency accurately. This paper proposes a modified ST (MST) method. In this method, an improved window function of energy concentration in time-frequency distribution is introduced to optimize the shape of each window function. This method determines the parameters of Gaussian window to maximize the product of energy concentration in a time-frequency domain within a given time and frequency interval, so as to improve the energy concentration. The result shows that compared with the SST with Gaussian window, ST based on the optimally concentrated window proposed in this paper has better energy concentration in time-frequency distribution.