Number Of Sub-signals Research Articles

A double-float structured triboelectric nanogenerator for wave hydrological monitoring

Triboelectric nanogenerator (TENG) is one technique for real-time monitoring of wave hydrology. However, the influence of environmental factors is a key problem for the practical application of TENGs. In this study, a TENG with a simple but durable double-float structure (DF-TENG) for wave hydrological monitoring is proposed and analyzed. Different with traditional TENGs, the frequency-domain parameter Ns (number of sub-signals) is utilized. Experiments indicate that the designed DF-TENG is effective to measure the wave hydrological information (wave height, motion frequency, motion period and vertical speed) with great robustness even under the influence of environmental factors (humidity, temperature and wind force). In the case that wave height increases gradually from Hr = 35 mm to Hr = 145 mm, Ns raises up from Ns = 4.5 to Ns = 20. A linear relationship between Ns and Hr is obtained as Ns = 0.1346Hr − 0.2821, with a perfect correlation of R2 = 0.99. More importantly, the proposed Ns shows great robustness and the fluctuation of it is almost zero even under different environmental factors. This study confirms the feasibility of the DF-TENG for continuous monitoring of wave hydrology, which is meaningful for the real application of TENGs in the future.

Concurrent Dual-Band Heterodyne Interferometric Receiver for Multistandard and Multifunction Wireless Systems

Radio frequency (RF), microwave, and millimeter-wave (mmW) multiport interferometric receivers present a competitive solution for the front-end reception of low-power multistandard and multifunction wireless applications. The systematic generation of rectified waves in a conventional direct-conversion interferometric receiver is highly related to temporal and power changes and the number of subsignals in the frequency band of interest. It can cause the receiver to saturate and, thus, requires auxiliary power-hungry tunable amplifiers and a data converter for compensation. This work presents a heterodyne detection in an RF/microwave interferometric receiver, the first of its kind, which separates the desired frequency signals from the rectified wave components with two output ports through a frequency offset. This architecture also makes use of the field-effect transistor (FET)-based power detectors to combine the received and reference signals, which reduces a theoretical multiport junction loss of 6 dB to 3 dB. In this way, the proposed receiver is set to support the development of an ultralow-power concurrent dual-band wireless system using a single wave-correlator circuit. A mathematical model of the proposed system is derived and subsequently prototyped using off-the-shelf components to evaluate its carrier-to-noise power ratio (CNR) and error vector magnitude (EVM) performances, which can be extended for integrated circuits (ICs) implementation, such as CMOS and III–V ICs. The power detectors only require a bias voltage at gate terminals with zero direct current (dc) power consumption for optimum voltage responsivity and CNR performances. The demodulation of various modulated digital signals, including QPSK, QAM-16, QAM-32, and QAM-64, has been successfully demonstrated in the 24- and 28-GHz mmW frequency bands at 4 MSps of the symbol rate with a maximum EVM of 11.43% root-mean-square (rms) free from any postprocessing linearization and calibration.

Noise suppression for magnetotelluric using variational mode decomposition and detrended fluctuation analysis

Noise suppression is significant for magnetotelluric (MT) data analysis, especially in the face of strong human electromagnetic interference. Variational mode decomposition (VMD) is a novel signal processing method, and the selection of proper decomposition layers plays a key role in its signal processing effects. In view of the fact that detrended fluctuation analysis (DFA) is an evaluation criterion capable of selecting the appropriate number of sub-signals, we propose a noise suppression method for MT by combing VMD with DFA. We firstly estimate the scaling exponent range of MT noisy data and decompose it into a given series of K-order intrinsic mode functions (IMFs). Then, a criterion based on DFA is designed to select the mode number K with the purpose to improve the decomposition accuracy of VMD. The scaling exponent of DFA is used to define the relevant modes for the construction of filtered signals. In this research, the MT data is collected from Qinghai test site and added with strong interference for comprehensive analysis by the remote reference method, VMD method, and the proposed VMD-DFA method. The results have shown that the proposed method could greatly improve the accuracy of signal decomposition and the reliability of the reconstructed signal by adaptively selecting suitable K-order IMFs for different types of interference. Consequently, strong interference is effectively eliminated, and the obtained apparent resistivity-phase curve is stable and smooth. Moreover, the denoised MT data will further strengthen the interpretability of geoelectric information.

Evaluation on Filter Performance of Variational Mode Decomposition and Its Application in Separating Closely Spaced Modes

The equivalent filter characteristics of variational mode decomposition (VMD) are fully evaluated when applied to the fractional Gaussian noise (fGn) and the application in separating closely spaced modes of vibration system is performed in this paper. VMD is a newly proposed signal decomposition technique, which nonrecursively decomposes a signal into a given number of subsignals (modes), and each mode is mostly compact around a center pulsation. The filter performance of VMD is largely dependent on the constraint parameter and the initialization of center frequencies. In order to extract the desired modes, criteria for the determination of decomposition parameters are established. The initial center frequencies could be simply determined by prior estimated modal frequencies of the analyzed signal, while the constraint parameter is optimized utilizing a genetic algorithm (GA). A two-degree-of-freedom parametric system is considered to evaluate the capability of VMD in the separation of closely spaced modes. Compared with the noise-assisted versions of empirical mode decomposition (EMD) and wavelet packet transform (WPT), the parameter-optimized VMD can successfully separate the closely spaced modes while recovering the most modal information simultaneously. When introduced to the ground vibration test (GVT) of a horizontal tail, the proposed method successfully extracted the first five oscillation modes and identified the modal parameters accurately.

Analysis and Classification of Cold Speech Using Variational Mode Decomposition

This paper presents analysis and classification of a pathological speech called cold speech, which is recorded when the person is suffering from common cold. Nose and throat are affected by the common cold. As nose and throat play an important role in speech production, the speech characteristics are altered during this pathology. In this work, variational mode decomposition (VMD) is used for analysis and classification of cold speech. VMD decomposes the speech signal into a number of sub-signals or modes. These sub-signals may better exploit the pathological information for characterization of cold speech. Various statistics, mean, variance, kurtosis and skewness are extracted from each of the decomposed sub-signals. Along with those statistics, center frequency, energy, peak amplitude, spectral entropy, permutation entropy and Renyi's entropy are evaluated, and used as features. Mutual information (MI) is further employed to assign the weight values to the features. In terms of classification rates, the proposed feature outperforms the linear prediction coefficients (LPC), mel frequency cepstral coefficients (MFCC), Teager energy operator (TEO) based feature and ComParE feature sets (IS09-emotion and IS13-ComParE). The proposed feature shows an average recognition rate of 90.02 percent for IITG cold speech database and 66.84 percent for URTIC database.

System identification method by using inverse solution of equations of motion in time domain and noisy condition

In direct system identification (DSI) method, inverse solution of the equations of motion is applied to obtain physical parameters of a linear system (structural mass, damping, and stiffness matrices). With the small error in measurement, caused by sensor drift or signal noise, these physical parameters are identified with a significant drift. In this paper, a new simple post-processing method is proposed, for error reduction in DSI in the time domain in a manner that structural properties are identified without applying the optimization process. In noisy condition, baseline correction is applied through a nonic curve-fitting approach. The modified signals are partitioned into a number of sub-signals, and their mean is applied for system identification. The number of sub-signals is optimized so that the root mean square (RMS) of off-diagonal components of the mass matrix becomes near zero. Seeking more precision, the residual force in every time step at all degrees of freedom (DOFs) is minimized. The validity of the proposed method is tested on a multistory structure, subjected to a random force at the foundation level. In terms of incomplete measurement and 5% noise, identified parameters including mass, stiffness and damping matrices, have satisfying mean errors of 0.094%, 0.545%, and 4.42% with the coefficient of variations of 0.35%, 1.38% and 6.98%, respectively. The sensitivity of the proposed method to the noise level is also investigated. It is observed that the baseline slop of velocity and displacement signals are sharply increased when the noise level goes beyond 3%.