Image fusion is becoming one of the hottest techniques in image processing. Its performance evaluation method that can compare and analyze different fusion techniques is an essential part of image fusion techniques. In this paper, we proposed two intuitive schemes—correlation and information deviation schemes—for evaluating the performances of image fusion techniques. The former scheme is a fast and compact version of the quantitative correlation analysis (QCA). The latter scheme is an information deviation analysis. Using the two schemes, the performances of different fusion techniques can be compared directly and quantitatively based on the source images and the fused images with a faster speed than the QCA method. Two multiresolution analysis-based image fusion methods, wavelet transform-based fusion and pyramid transform-based fusion, which operate on typical hyperspectral image sets [airborne visible/infrared imaging spectrometer (AVIRIS)], are evaluated by the proposed schemes. The simulation results show the correctness and effectiveness of the proposed two schemes.

This paper presents a wavelet transform-based data fusion algorithm for multisensor systems.With this algorithm, the optimum estimate of a measurand can be obtained in terms of minimum mean square error (MMSE). The variance of the optimum estimate is not only smaller than that of each observation sequence but also smaller than the arithmetic average estimate. To implement this algorithm, the variance of each observation sequence is estimated using the wavelet transform, and the optimum weighting factor to each observation is obtained accordingly. Since the variance of each observation sequence is estimated only from its most recent data of a predetermined length, the algorithm is selfadaptive. This algorithm is applicable to both static and dynamic systems including time-invariant and time-varying processes. The effectiveness of the algorithm is demonstrated using a piecewisesmooth signal and an actual time-varying flow signal.

In order to develop an intelligent flow sensor which can be validated by itself, a wavelet-transform-based new method for assessing blockage of the sensing line in a differential-pressure flow sensor is proposed in this paper. The wavelet transform, a powerful means of signal processing for analysing the characteristic of a signal if there is little prior knowledge about it, is used to decompose the sensor-output signal into some elementary building blocks. The energy distributions in these building blocks reveal the effects of blockage of the sensing line of the sensor and then are exploited to determine their existence. Experimental results show that the incipience of the sensing-line blockage can be detected by this method.

Sinewave fit is a fundamental task in many test and measurement systems. The characterizations of analog–digital converters and digital oscilloscopes are two examples. In this paper, we present a high-performance (i.e., high-precision and high-speed) algorithm to estimate the four parameters of a sinewave from a sample data record. By the use of trigonometric identity, we propose a frequency estimator that turns the nonlinear estimation problem into a linear one. Thus, the difficulty of the traditional nonlinear least-squares sinewave fit method is attenuated. The total least-squares method is used to estimate four parameters of a sinewave in order to minimize the estimation errors in the sense of l2 norm. Simulation results exhibit that the proposed method gives superior performance over traditional ones and achieves excellent estimation of the true resolution of the simulated ideal ADC. This new algorithm is noniterative and gives swift and consistent results.

The development of a reliable, cost-effective instrument for on-line particle sizing remains a challenging area worldwide. This paper presents the design and evaluation of a new electrostatic sensor combined with a novel digital signal processing algorithm for the on-line continuous measurement of the mass median size of particles in a dilute-phase pneumatic suspension. Key design aspects of the constituent elements of the sensor, including a wire-mesh electrode, signal conditioning electronics and digital signal processing algorithm are described. Experimental results obtained using a typical range of particulate materials demonstrate that, under constant mass flow conditions, the processed output of the sensor is capable of indicating the mass median particle size with a relative error no greater than 15%. A critical appraisal of the technique, including advantages and limitations, is also included.

This paper describes a novel wavelet-based approach to the abrupt fault detection and diagnosis of sensors. By the use of wavelet transforms that accurately localize the characteristics of a signal both in the time and frequency domains, the occurring instants of abnormal status of a sensor in the output signal can be identified by the multiscale representation of the signal. Once the instants are detected, the distribution differences of the signal energy on all decomposed wavelet scales of the signal before and after the instants are used to claim and classify the sensor faults. Synthetic data simulated by means of a computer using real-word data from a general-purpose pressure sensor have verified the effectiveness of the proposed method.

In this paper, a new decoupling measure approach, which can avoid the shortcomings of the paper sheet basis-weight and ash-content sensors available now, is proposed. A new type of sensor is designed and produced. A test has been performed, and the experimental results show that the new type sensor gives excellent performance.

It is understood that the eigenvalues of a covariance matrix, deriving from the data of multiple sinusoidal signals in white Gaussian noise, can be divided into two groups when the dimensions of the covariance matrix are larger than the number of sinusoidal signals. One is composed of the sinusoidal signals plus the noise. Another is called 'small eigenvalues' which is produced by the noise only. It is also known that the small eigenvalues are an asymptotic Gaussian random process. Based on these facts, an eigenvalue-based residuum criterion for the detection of the number of sinusoids in white Gaussian noise is presented in this paper. Using this criterion, the small eigenvalues of the covariance matrix can gradually be ruled out. While the eigenvalues which belong to the signal and noise are separated, the number of sinusoids is detected successfully. Simulation results exhibit that the proposed method gives a superior performance over the Akaike information criterion (AIC) and the minimum description length principle (MDL), especially for situations where the analyzed signals exhibit a low signal-to-noise ratio (SNR), short data records, and a high number of sinusoids. In addition, the implementation of the new criterion is simpler and faster.

In this paper, a wavelet-based method for measuring the particulate velocities in a duct via an active electrostatic sensor is presented. The main motivation behind this paper is to avoid the known problems which exist with cross-correlation signal processing techniques; i.e., they are sensitive to the distortion of the output waveforms and the variance of the output pulse width from the detecting electrode of the sensor. Instead of cross-correlation techniques, the signal rising edges from the wavelet transforms of the signals of the electric charger and the detecting electrode of the sensor are used to determine the particulate velocity in the duct. As a consequence, the proposed method is robust to the distortion of the output waveforms and the variance of the output pulsewidth, so that it is specifically suitable for measuring the particulate velocity with the spread of velocities within a conveyor. Experimental results confirm the effectiveness of the proposed method and show that the new method can achieve a better measured result than that of the cross-correlation techniques.

A wide range, fast balance technique for digital AC bridges is proposed in this paper. We introduce a novel method that can give the balance parameters of a digital AC bridge in a closed form. Unlike the traditional heuristic methods used to balance AC bridges, the proposed method only needs to arbitrarily change one adjustable parameter two times including the setting of original values. The balance parameters can then be computed using two closed-form formulas. Finally, the applicability of the proposed theory is discussed, and simulation examples are given and analyzed. Our algorithm is noniterative and precise and offers clear benefits for real-world applications.