Piecewise Cubic Hermite Interpolation Research Articles

Hybrid technique for crop image compression using discrete wavelet transform and sparse singular vector reconstruction

In this paper, a hybrid compression technique, based on discrete wavelet transform (DWT) and singular vector sparse reconstruction (SVSR), is proposed. In the initial phase, an original image has been divided into four sub-band by using the DWT technique. After that, SVSR is utilized for boosting the compression ratio and to improve reconstruction using piecewise cubic Hermite interpolation (PCHIP). Inverse discrete wavelet transform (IDWT) process has been applied to obtain a compressed crop image. The first time, this developed technique has been applied for compression. The performance of proposed hybrid techniques has been assessed in terms of several evaluation parameters, which are compression ratio (CR), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean square error (MSE) on ten crop image data sets. It is observed from the results that the proposed technique is quite effective in compression of different types of crop images. Further, the experimental results show that the proposed hybrid method achieved significantly higher compression over the existing techniques. The proposed technique with Haar wavelet provides 412.5% and 94.97% higher CR as compared to Singular Value Decomposition (SVD), and SVSR respectively with desirable image quality. Further, other wavelets Db4 provide 396.48% and 88.88% and Bior3.5 also provides 306.74% and 54.74% higher CR as compared to SVD, and SVSR respectively with similar image quality.

Power quality disturbance detection based on IEWT

With the continuous development of distributed generation, power quality problems are more complex. In order to ensure that the power quality of the active distribution network meets the requirements of users, it is of great significance to accurately obtain the power quality disturbance parameters. For the problem that the traditional empirical wavelet transform needs to manually select the number of frequency bands when analyzing unknown power quality disturbances, this paper proposes an improved empirical wavelet transform (IEWT) algorithm. The algorithm uses the Piecewise Cubic Hermite Interpolation (PCHIP) method and the improvement of the transition region to achieve adaptive frequency band division and noise interference reduction, and different amplitude (AM) and pitch (FM) components can be obtained. Then the Normalized Direct Quadrature (NDQ) algorithm and Singular Value Decomposition (SVD) algorithm are used to extract frequency, amplitude and time parameters of the AM-FM components. Simulation and experimental results show that the power quality disturbance parameters extracted by the proposed method are more accurate and less affected by noise.

Modeling, Simulation, and Cruise Characteristics of Wingtip-Jointed Composite Aircraft

In this paper, multibody dynamic modeling and a simulation method for the wingtip-jointing process of a new-concept composite aircraft system are investigated. When the wingtips of two aircraft are jointed, the resultant wingtip-jointed aircraft is regarded as variable-geometry multiple rigid bodies, and a seven-degree-of-freedom non-linear dynamic model is established by mathematical derivation. The slip-meshing method is adopted to analyze the unsteady aerodynamic influence. We also present specific aerodynamic database acquisition methods under the quasi-steady assumption. Based on this, the simulation results indicate that the longitudinal and lateral movements are highly jointed and complex. A new composite aircraft system is investigated, in order to meet the balance requirement. With the lift–drag ratio (K) considered, the piecewise cubic Hermite interpolation (PCHIP) method, with a sufficient sample size, was utilized to help the cruise strategy optimization analysis under fixed altitude and speed conditions. Meanwhile, distribution of cruise characteristics with different sampling values of composite flight characteristic parameters were also analyzed. The research results can be used as a reference for new-concept composite aircraft model establishment, simulation, and multibody dynamic characteristic investigation.

The genetic algorithm-radial basis function neural network to quickly predict aerodynamic performance of compressors

In this paper, compressor aerodynamic performance has been predicted based on throughflow theory, combined with a surrogate model, which is a combination of the Genetic Algorithm (GA) and generalized Radial Basis function (RBF) neural network. And the predicting results have been compared with those from the traditional models and spanwise mixing model, which still widely be used to predict the aerodynamic performance. We first predicted the deviation angle and total-pressure loss coefficient (TPLC) by the surrogate model, and then using these two intermediate variables connected the model with throughflow theory. The pressure ratio and efficiency, representing the compressors’ total performance parameters, are predicted and compared with experimental data. In order to increase the accuracy of prediction, a data augmentation method based on the piecewise cubic Hermite interpolation (PCHIP) algorithm is introduced to enlarge the training database. At the same time, considering the vast differences of deviation angle and loss in different working conditions as well as aerodynamic and geometric differences of rotor and stator, the database and the network should be split into six components based on the choke, the normal and the stall conditions as well as rotor and stator. Then, the performance curves of pressure ratio and efficiency can be determined by an iteration process. The predicting results are compared with experimental data, which shows that the surrogate model matches experiments much better than those from the traditional models and spanwise mixing model.

Low Complexity Channel Estimation for MC-CDMA System Based Chaos under Fast Multipath Channel

Based on interpolation methods, a low complexity channel estimation method for MC-CDMA system based chaos under fast fading multipath channel is proposed.Different interpolation methods are used; Linear interpolation (LI), cubic spline interpolation (CSI), and piecewise cubic Hermite interpolation (PCHI). The multi-carrier scheme is established by using a chaotic generator with different initial conditions, while the pilots are established using another chaotic generator with another different initial conditions. The proposed channel estimator without and with interpolation methods for single user and multiusers is tested under fast frequency selective Rayleigh fading channel. Simulation results demonstrate that the channel estimation of MC-CDMA system based chaos using interpolation methods and when the pilots are chaos signal too, achieves better performance improvement in fast multipath environment compared to the conventional MC-CDMA system when the pilots are considered as Walsh- Hadamard codes. The proposed system is also built and tested using Simulink program.

Enhancement of low sampling frequency recordings for ECG biometric matching using interpolation

Electrocardiogram (ECG) based biometric matching suffers from high misclassification error with lower sampling frequency data. This situation may lead to an unreliable and vulnerable identity authentication process in high security applications. In this paper, quality enhancement techniques for ECG data with low sampling frequency has been proposed for person identification based on piecewise cubic Hermite interpolation (PCHIP) and piecewise cubic spline interpolation (SPLINE). A total of 70 ECG recordings from 4 different public ECG databases with 2 different sampling frequencies were applied for development and performance comparison purposes. An analytical method was used for feature extraction. The ECG recordings were segmented into two parts: the enrolment and recognition datasets. Three biometric matching methods, namely, Cross Correlation (CC), Percent Root-Mean-Square Deviation (PRD) and Wavelet Distance Measurement (WDM) were used for performance evaluation before and after applying interpolation techniques. Results of the experiments suggest that biometric matching with interpolated ECG data on average achieved higher matching percentage value of up to 4% for CC, 3% for PRD and 94% for WDM. These results are compared with the existing method when using ECG recordings with lower sampling frequency. Moreover, increasing the sample size from 56 to 70 subjects improves the results of the experiment by 4% for CC, 14.6% for PRD and 0.3% for WDM. Furthermore, higher classification accuracy of up to 99.1% for PCHIP and 99.2% for SPLINE with interpolated ECG data as compared of up to 97.2% without interpolation ECG data verifies the study claim that applying interpolation techniques enhances the quality of the ECG data.

Time-domain modeling of electromagnetic diffusion with a frequency-domain code

We modeled time-domain EM measurements of induction currents for marine and land applications with a frequency-domain code. An analysis of the computational complexity of a number of numerical methods shows that frequency-domain modeling followed by a Fourier transform is an attractive choice if a sufficiently powerful solver is available. A recently developed, robust multigrid solver meets this requirement. An interpolation criterion determined the automatic selection of frequencies. The skin depth controlled the construction of the computational grid at each frequency. Tests of the method against exact solutions for some simple problems and a realistic marine example demonstrate that a limited number of frequencies suffice to provide time-domain solutions after piecewise-cubic Hermite interpolation and a fast Fourier transform.

Intelligent condition monitoring of a gearbox using artificial neural network

This paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a multi-layer perceptron neural network. Feature vector which is one of the most significant parameters to design an appropriate neural network was innovated by standard deviation of wavelet packet coefficients. The gear conditions were considered to be normal gearbox and slight- and medium-worn and broken-teeth gears faults and a general bearing fault which were five neurons of output layer with the aim of fault detection and identification. A downscaled 2-layer multi-layer perceptron neural-network-based system with great accuracy was designed to carry out the task. In this research, vibration signals were recognised as the most reliable source to extract the feature vector which were synchronised by piecewise cubic hermite interpolation (PCHI) and pre-processed using the standard deviation of wavelet packet coefficients.