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

To associate a printed medium with information on the Internet, several data embedding and detecting methods using the camera of a mobile device have been proposed. This paper proposes a new data embedding method to obtain a higher detection rate. A new data embedding positions and a new calculation method of correlations for detecting the data are introduced. We also focus on the capture of versions of the same image simultaneously using a mobile device. We propose a method of selecting the blocks for which a high detection rate can be expected, decoding the embedded data from the blocks, and combining the decoded data. From an experiment, it is shown that the proposed method is effective for improving the detection rate.

New Data Embedding and Detecting Method for Printed Image

Data Extraction Method from Printed Images with Different Formats

SUMMARY

In this paper, we consider a data-embedding method in the discrete wavelet transform (DWT) domain of digital images. A bit pattern embedding method with the human visual system (HVS) and correlation-based detection have been proposed by Kino and Wada based on the watermarking scheme, which is referred to as the conventional method. In the conventional method, the capacity of embedding data depends on the potential correlation between the DWT coefficients of the host image and the watermark signal. We propose a new embedding and detection algorithm not affected by the potential correlation in order to increase the capacity of embedding data. The data-embedding and detection in the proposed method are performed by considering the absolute value of the correlation between the watermarked DWT coefficients and the watermark signal. In simulations, we show that the capacity of the embedding data in the proposed method is increased compared with the conventional method at the same PSNR value (image quality).

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecj.11597

Mitsuji Muneyasu

Papers

New Data Embedding and Detecting Method for Printed Image

Data Extraction Method from Printed Images with Different Formats

Improved Data Embedding Method for Digital Image with Human Visual Systems

An improvement for distributed active noise control systems based on the simultaneous equations method

An adaptive weighted median filter using counterpropagation networks