The problem of acoustic noise is becoming increasingly serious with the growing use of industrial and medical equipment, appliances, and consumer electronics. Active noise control (ANC), based on the principle of superposition, was developed in the early 20th century to help reduce noise. However, ANC is still not widely used owing to the effectiveness of control algorithms, and to the physical and economical constraints of practical applications. In this paper, we briefly introduce some fundamental ANC algorithms and theoretical analyses, and focus on recent advances on signal processing algorithms, implementation techniques, challenges for innovative applications, and open issues for further research and development of ANC systems.

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Recent Advances on Active Noise Control: Open Issues and Innovative Applications

FIRE operators for image processing

We propose a new edge enhancer based on a modified multilayer neural network, which is called a neural edge enhancer (NEE), for enhancing the desired edges clearly from noisy images. The NEE is a supervised edge enhancer: Through training with a set of input noisy images and teaching edges, the NEE acquires the function of a desired edge enhancer. The input images are synthesized from noiseless images by addition of noise. The teaching edges are made from the noiseless images by performing the desired edge enhancer. To investigate the performance, we carried out experiments to enhance edges from noisy artificial and natural images. By comparison with conventional edge enhancers, the following was demonstrated: The NEE was robust against noise, was able to enhance continuous edges from noisy images, and was superior to the conventional edge enhancers in similarity to the desired edges. To gain insight into the nonlinear kernel of the NEE, we performed analyses on the trained NEE. The results suggested that the trained NEE acquired directional gradient operators with smoothing. Furthermore, we propose a method for edge localization for the NEE. We compared the NEE, together with the proposed edge localization method, with a leading edge detector. The NEE was proven to be useful for enhancing edges from noisy images.

/pdf/neural-edge-enhancer-for-supervised-edge-enhancement-from-1n8x1nx2jy.pdf

Neural edge enhancer for supervised edge enhancement from noisy images

Active noise control (ANC) has been widely applied in industry to reduce environmental noise and equipment vibrations. Most available control algorithms require the identification of the secondary path, which increases the control system complexity, contributes to an increased residual noise power, and can even cause the control system to fail if the identified secondary path is not sufficiently close to the actual path. In this paper, based on the geometric analysis and the strict positive real (SPR) property of the filtered-x LMS algorithm, we introduce a new ANC algorithm suitable for single-tone noises as well as some specific narrowband noises that does not require the identification of the secondary path, though its convergence can be very slow in some special cases. We are able to extend the developed ANC algorithm to the case of active control of broadband noises through our use of a subband implementation of the ANC algorithm. Compared to other available control algorithms that do not require secondary path identification, our developed method is simple to implement, yields good performance, and converges quickly. Simulation results confirm the effectiveness of our proposed algorithm

A New Active Noise Control Algorithm That Requires No Secondary Path Identification Based on the SPR Property

The paper developed a reasonable and practical method for identifying the useful information from the signal that has been contaminated by noise, so that to provide a feasible tool for vibration analysis. A new concept namely the Singular Entropy (SE) was proposed based on the singular value decomposition technique. With the aid of the SE, a series of investigations were done for discovering the distribution characteristics of noise contaminated and pure signals, and consequently an advanced noise reduction method was developed. The experiments showed that the proposed method was not only applied for dealing with the stationary signals but also applied for dealing with the non-stationary signals.

https://msvlab.hre.ntou.edu.tw/NDT-Yang-2003.pdf

Development of an advanced noise reduction method for vibration analysis based on singular value decomposition

Image Regularization with Morphological Gradient Priors Using Optimization of Structure Element

When multivalued images are transformed to binary images, the error diffusion method is often used because it is excellent in gray scale representation. When the error diffusion method is applied by means of a raster scan, equal-color regions with all black pixels or all white pixels occur along the edges. This phenomenon has the effect of emphasizing the edges, and the edges may be visually emphasized excessively. In this paper, in order to freely control the equal-color regions, a method that adaptively adjusts the filter coefficients in the error diffusion method based only on the difference from the points to be processed is presented. The filter coefficients are computed by using fuzzy reasoning. According to the proposed method, the width of the equal-color region near a step edge can be controlled adequately by providing appropriate values. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(2): 82–90, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.1135

Adaptive error diffusion using fuzzy reasoning for step edges

This paper presents a novel fuzzy filter structure for edge-preserving smoothing of an image corrupted by impulsive and white Gaussian noise. This filter structure is expressed as an adaptive weighted mean filter that uses fuzzy control. The coefficients of the proposed filter can be varied adaptively by fuzzy control laws based on differences between pixels in the window. In the setting of fuzzy control laws, the method takes the distance between the center and each pixel in the window into account. Finally, simulation results demonstrate the effectiveness of the proposed technique.

An edge-preserving fuzzy filter based on differences between pixels

Based on the Fornasini-Marchesini second model, a technique is developed for implementing two-dimensional (2D) adaptive state-space filters. First, the relationship between the coefficient sensitivities and the intermediate transfer functions is investigated for the Fornasini-Marchesini second model. A least mean square (LMS) adaptive algorithm is then presented by using new systems that generate the gradient vectors. Finally, a 2-D adaptive line enhancer is constructed by using the 2-D adaptive state-space filter to illustrate the utility of the proposed technique.

http://ieeexplore.ieee.org/iel4/82/13459/00618042.pdf

2-D adaptive state-space filters based on the Fornasini-Marchesini second model

A hybridized classification system of the logistic discriminant analysis and the three-layer neural network is proposed. This system is basically a linear discrimination and is assisted by the neural network only for the cases that are difficult to be classified by linear methods. This system presents a simple discrimination structure given by linear methods, and its computational cost is much lower than the exclusive use of the neural network while the misclassification rate is as low as the neural network. The ability of this system is shown experimentally in the case of applying it to image identification problems. The computation time for the learning process is reduced to one-fifth by this method in this experiment, while the misclassification rate remains almost the same.

Mitsuji Muneyasu

Papers

Image Regularization with Morphological Gradient Priors Using Optimization of Structure Element

Adaptive error diffusion using fuzzy reasoning for step edges

An edge-preserving fuzzy filter based on differences between pixels

2-D adaptive state-space filters based on the Fornasini-Marchesini second model

An experimental study of the hybridization of logistic discriminant analysis and multilayer neural network for image identification