A novel fast balance technique for the digital AC bridge

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     

Fusion of soft and hard computing: multi-dimensional categorization of computationally intelligent hybrid systems

The concept of fusion of soft computing and hard computing has rapidly gained importance over the last few years. Soft computing is known as a complementary set of techniques such as neural networks, fuzzy systems, or evolutionary computation which are able to deal with uncertainty, partial truth, and imprecision. Hard computing, i.e., the huge set of traditional techniques, is usually seen as the antipode of soft computing. Fusion of soft and hard computing techniques aims at exploiting the particular advantages of both realms. This article introduces a multi-dimensional categorization scheme for fusion techniques and applies it by analyzing several fusion techniques where the soft computing part is realized by a neural network. The categorization scheme facilitates the discussion of advantages or drawbacks of certain fusion approaches, thus supporting the development of novel fusion techniques and applications.     

Tachometer signal smoothing with analog discrete-time polynomial estimators

The design of sampled-data polynomial estimators for noise reduction in industrial instrumentation applications is discussed. Unlike conventional lowpass filters, an estimator causes no delay on the polynomial-like primary signal. A general purpose design approach is described, incorporating notch frequencies for removal of narrow-band noise components, such as the 50/60 Hz line frequency. A 24-tap FIR estimator is optimized for tachometer signal smoothing in motor control systems. An analog circuit architecture, targeted for silicon CMOS implementation, is described and simulated.<>     

Prefiltering approach for optimal polynomial prediction

A prefiltering approach for optimal prediction of polynomial signals is proposed. The new scheme enables the use of an arbitrary stable prefilter for which an optimal FIR postfilter is designed such that polynomial signals of given order are predicted unchanged. Additional degrees of freedom are used for noise suppression. The advantages of the approach are demonstrated with examples employing a first-order recursive prefilter     

Classification of polynomial-shaped measurement signals using abackpropagation neural network

Smoothly varying signals are frequently encountered in the field of instrumentation and measurement, and they can be accurately modeled by low-order polynomials. The order identification is difficult when the measured noisy signal has frequent order variations in the underlying polynomial. In this paper, we introduce a flexible real-time order estimator, which is based on a backpropagation neural network     

ADC characterization based on singular value decomposition

A new method for ADC characterization, based on singular value decomposition, is introduced in this paper. Here, the singular values of the sampled data matrix, directly derived from the measured input data, are used to characterize the signal-to-noise ratio (SNR), and further to estimate the number of effective bits. Various input signals, such as single-tone, dual-tone, or multitone, can be used to obtain accurate estimation results. In addition, the new method avoids the difficulties and problems of the earlier characterization methods such as the sensitivity to the applied sinewave frequency and sampled data sizes, and the spectral leakage. Extensive simulations indicate that the proposed method provides excellent performance with single-tone, dual-tone, and multitone test signals. The proposed method also shows remarkable robustness over a truly wide SNR range: from 5 dB to 200 dB     

Distributed architecture of an adaptive positioning servo

A distributed computer architecture which offers high precision and high-speed computing capabilities that are needed when complex system identification and controller adaptation algorithms are implemented in real time. The distributed implementation offers high immunity against external disturbances. An elevator control application is considered. However, the principles presented are applicable to different types of industrial automation problems. The individual computer units in this system are programmable VLSI signal processors (e.g. TMS-320C25). All the precision-sensitive computations are performed using simulated single-precision floating-point arithmetic (IEEE Standard 754). This is possible because of the high speed of the signal processor and low sampling rate (i.e. ⩽100 Hz) of the entire positioning servo. The communication between the master controller and the slave units is coordinated by a simple communication protocol that utilizes a prediction-type transmission error correction principle     

Polynomial predictive filtering in control instrumentation: areview

Additional delay is an unavoidable drawback of conventional filters used frequently in industrial electronics. This delay is particularly harmful if the filtered primary signal is to be used for time-critical feedback or synchronization purposes. Therefore, predictive signal processing methods can offer significant advantages for these real-time applications. Polynomial predictive filters are specified without explicit passbands and stopbands, and they are behaving delaylessly or predictively for smoothly varying signal components. The degree of smoothness of the incoming signal sets the requirements for the applied filtering scheme and its parameters. Smoothness of a signal is a fuzzy and application-specific concept: the degree of smoothness depends on the ratio of the bandwidth of the primary signal and the applied sampling rate, as well as the noise component. In this paper, the authors review the most important polynomial predictive filtering methods and algorithms, their design and implementation techniques, and a collection of successful applications     

Analysis of second-order harmonic distortion of ADC usingbispectrum

This paper presents a new frequency-domain approach to test the second-order harmonic distortion of an analog-to-digital converter (ADC). It uses triple correlation and its Fourier transform, bispectrum, to suppress quantization noise and improve the testing accuracy of weak second-order harmonics. Our approach is independent of the triggering delay that is a known weakness of the spectral averaging method. The new method is also less sensitive to the quantization noise than the power spectrum averaging method. Both theoretical analyses and illustrative computer simulations are provided