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In the manufacturing process of ternary cathode material, batching, mixing, loading and sintering procedures play decisive roles on product quality, and data generated by these procedures are collected by the manufacturing process of ternary cathode material. However, data of the process are heterogeneous and have different properties, and sampling of key quality variables is difficult. On this basis, on-line evaluation on product quality is hard. In this paper, a data fusion based online product quality evaluation method for the ternary cathode material is proposed. Data is collected from an enterprise manufactures ternary cathode material. Then, to establish the model between the extracted data features and the key quality variables such as particle size and surface-free lithium content, a semi-supervised double-weighted probabilistic principal component regression is proposed. After that, a distance cost index is brought to cluster and grade the two quality variables, which are predicted by the semi-supervised model. Therefore, the on-line evaluation of product performance is achieved by setting rule table in accordance with production experience. Finally, based on data collected from an enterprise manufactures ternary cathode material, the proposed method is verified to be effective and accurate.

Time-frequency analysis is the important tool in signal processing, especially for non-stationary signals. When the interesting signals contaminated by impulsive noises, conventional time-frequency analysis method degenerate seriously because of lacking of limited second moment. In order to solve the degeneration of the estimation algorithm under non-Gaussian impulsive noise environment, the improved time delay estimation algorithm using fractional lower order Wigner-Ville distribution is proposed in this paper. Theoretical analysis and computer experiment demonstrates that the new algorithm has improved performance compared with conventional time-frequency analysis method.

An improved mixed cryptosystem is presented by combining both random phase encoding technique based chaos and public key cryptography. The scheme provides not only good robustness of random phase encoding technique, but also a new solution of key distribution for symmetric cryptography algorithms. In additions, computer simulation is illustrated in detail. The tolerance to data loss of the encoded barcode is also studied particularly. The results show that the presented encoding method has advantage of robustness and high security, and very convenient to be popularized in practice.

Many signals encountered in wireless communication, telemetry, radar and sonar systems exhibit a cyclostationarity (or spectral correlation) property, as a result of periodically carrier modulating, multiplexing and coding operations. By exploiting cyclostationarity, i.e., evaluating the cyclic correlation or cyclic spectrum of the received signals at certain cycle frequency, we can extract the measures of the signals with different cycle frequency. Thus, the signal processing algorithms which based on cyclic statistics are highly tolerant to the interference and noise. However, most of the cyclostationarity based methods assume that the noise is Gaussian noise. Hence, their performance can be degraded in real applications where the noises contain non-Gaussian impulsive components. In this paper, we propose a new approach for exploiting cyclostationarity of signals in alpha-stable impulsive noise. Simulation results indicate a significantly better performance for the new statistics in some environments.

In some communication systems the transmitted signal is contaminated by impulsive noise with a non-Gaussian distribution. Non-Gaussian noise caused significant performance degradation to communication receivers. The new constant modulus blind equalizer based on fractional lower-order statistics of the equalizer input, referred to as FLOS_CMA, is able to mitigate impulsive channel noise while restoring the constant modulus character of the transmitted communication signal. However, like the constant modulus algorithm (CMA), the steady-state mean square error of the FLOS_CMA algorithm may not be sufficiently low for the system to obtain adequate performance. This paper proposes a concurrent equalizer, in which a decision-directed Least Mean p Norm (DD_LMP) equalizer operates cooperatively with a FLOS_CMA equalizer, controlled through a nonlinear link. Simulation results using M-QAM signaling have shown that the concurrent FLOS_CMA and DD_LMP blind equalizer achieves a dramatic improvement in equalization performance over the FLOS_CMA approach.

The capability of CDMA system is restricted by MAI and Near-far effect problems. Two blind multi-user detector algorithms based on Improved-Infomax and FastICA are proposed and compared in the paper, which could detect target user effectively. These two detectors solve the order indeterminacy of ICA problem by initiating the demix vector with the target user's spreading codes sequence. Simulation results show that new detectors have good performances in BER(bit error rate), convergence speed and user capability by comparing with traditional method.

The constant modulus algorithm based on fractional lower-order statistics (FLOS_CMA) has been proven to be an effective blind equalization method under α -stable impulsive noise environment. But, there have been little results in the literature about the performance of this algorithm. In this paper, the steady-state mean- square error (MSE) performance of the FLOS_CMA is studied, and the approximate analytical expressions for real- and complex-valued data under stationary and non- stationary environments are derived, respectively, based on the energy-preserving relation and Taylor series expansion. Finally, the simulation studies are undertaken to support the analysis.

In this paper we propose the analogy measurement of two random variables, and discuss the principle of maximizing nongaussianity of observed data to estimate independent components sequentially based on unsupervised learning neural network. We also prove the non-polynomial moment theorem in a generalized scheme, and reveal the feasibility that replaces the analogy measurement by the expectation of a non-quadratic smooth even function based on the theorem. A formula to compute the sign of above algorithm is given, which overcomes the contradiction between the objective function and the sign computation formula.

Although algorithms based on rough set (RS) theory can extract useful decision rules with the effectiveness in dealing with inexact, uncertain or vague information, the deterministic mechanism for the description of error is very simple and the rules generated by RS are often unstable and have low classification accuracy. Neural networks (NN) are considered the most powerful classifier for their low classification error rates and robustness to noise. But NN usually require long time to train the huge amount of data of large databases and lack explanation facilities for their knowledge. Therefore, we combine RS and NN for autonomous decision-making, with high accuracy, robustness to noise, efficiency, and good understandability. First, generate the decision rules based on RS, then construct the NN with the hidden layer representing decision rules, and learn the arguments of the NN with BP algorithm. With the direction of RS, NN needn’t long time for training, and the knowledge buried in their structures and weights can be well explained by decision rule on RS. The proposed algorithm has been tested on a medical data set for patients with solitary pulmonary nodules (SPN).

Evoked potential (EP) signals have been widely used to quantify neurological system properties. Delay in EP signals latency may be linked to neurological injuries. For abnormal cases where noises in EP signals usually appear impulsive, a novel robust approach, covariation correlation algorithm, is proposed to estimate EP latency delay based on fractional lower order statistics (FLOS) under alpha-stable distribution noise. Firstly, the auto-covariation of reference signal and cross-covariation between reference and detected signals are computed. Secondly, correlation between these two covariations series is calculated. Finally, the estimation value of EP latency delay is obtained from location of this correlation peak Simulation studies show that the estimation precise of this novel algorithm is higher than that of correlation or covariation algorithms especially under lower mixed signal noise ratio (MSNR).

The fractional Fourier transform can be considered as the generalization of Fourier transform. After the real cepstrum theory is reviewed, the fractional cepstrum is presented based on fractional Fourier transform. At the same time, how to use the fractional cepstrum is discussed in blind chirp signal processing. A numerical result confirms that the proposed fractional cepstrum does work even at low signal-to-noise ratios.

It is very difficult to describe a nonstationary random signal, to say nothing of processing it effectively. In recent years, the time-varying parametric model, especially, time-varying auto-regressive parametric model has been used widely. It is well known that a wavelet neural network has very good performance on function approximation. In this paper, the wavelet neural network is introduced into the time-varying auto-regressive parametric model, so a new time-varying auto-regressive parametric model based on wavelet neural network is presented. At the same time, a new algorithm for model parameters estimate is also presented. A few simulations indicate that the performance of the new time-varying auto-regressive parametric model is better than the old one.

This paper analyzes the Kurtosis based ICA method, and presents the geometrical explanation of its principle. Through the analysis of ICA solution process, this paper also explains the reasons for the indeterminacy and probabilistic property of the ICA solution. The analytical results and conclusions can be applied to the studies of other ICA methods.