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

Deterministic Multi-step Crossover Fusion (dMSXF) is a promising crossover method in combinatorial problems, which performs multi-step searches focusing on inheritance of parents' characteristics using a neighborhood structure and a distance measure. DMSXF shows good search ability in various kinds of combinatorial problem. In this paper, we apply dMSXF to unsupervised design of morphological filters based on the opening operation for noise removal. We estimate suitable structuring elements (SE) for texture images corrupted by impulse noise. Here we fix the shape of SE and optimize pixel values of each element of SE. Through the experiments, it is shown that dMSXF works very well on estimating optimal SEs just like typical combinatorial problems.

Application of genetic multi-step search to unsupervised design of morphological filters for noise removal

Analysis and Evaluation of Visual Identification of Texture Using Image Processing Methods

To recover texture images from impulse noise by the opening operator which is one of morphological operations, a suitable structuring element (SE) has to be estimated. In this paper, we apply a Genetic Algorithm (GA) to an unsupervised design problem of SEs. In previous work, it was shown that deterministic Multi-step Crossover Fusion (dMSXF) which is a promising interpolation-directed crossover method worked very well on the design of SEs. However, dMSXF does not work effectively when parents' characteristics are extremely similar to each other, and an extrapolation search method which explores outside the distribution of the population is required. Here, we introduce deterministic Multi-step Mutation Fusion (dMSMF) as a complementary search of dMSXF for exploring the extrapolation domain to improve the search performance. Through experiments, it is shown that dMSXF+dMSMF can design effective SEs stably.

Effectiveness of extrapolation-directed multistep search in unsupervised design for morphological filters for noise removal

In JPEG2000 coding, SNR scalability on the basis of regions of interests (ROI) is considered as one of the main features However, the image quality of the background region is severely degraded in this case This paper proposes an image control technique for ROI coding with consideration of SNR scalability by separating the ROI coding and the rate-control operation The proposed method can improve the image quality for the background region, without severely degrading the image quality of the ROI An application of the proposed technique to motion pictures is also considered

An image quality control technique for the ROI coding in JPEG2000 and its application to the motion pictures

The state-space realization of two-dimensional separable-denominator digital filters with real and complex poles is discussed. A new realization is achieved which is suitable for real-time processing in a multiprocessor system. This is done by maximizing parallelism and pipelining, and by minimizing data throughput delay. The performance of the realization is evaluated in terms of real-time processing and multiprocessor implementation. The number of required processors and the efficiency measure are also clarified for the realization. >

Mitsuji Muneyasu

Papers

Application of genetic multi-step search to unsupervised design of morphological filters for noise removal

Analysis and Evaluation of Visual Identification of Texture Using Image Processing Methods

Effectiveness of extrapolation-directed multistep search in unsupervised design for morphological filters for noise removal

An image quality control technique for the ROI coding in JPEG2000 and its application to the motion pictures

On the multiprocessor implementation of 2-D separable-denominator digital filters