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

In this paper, we propose a multimodal biometrics authentication method using features of an utterance. The proposed authentication method authenticates persons using image and voice signals. Hence, the proposed method can be realized with only a camera and microphone to extract the lip area and voice without the special equipment used in other personal authentication methods and can easily change the registration data. Moreover, the proposed authentication method can provide a key function to the registered phrase of the utterance. In the proposed method, the edges and texture in the mouth are used as image features, and pitch and spectrum envelope are used as voice features. Authentication is realized by classifiers generated by AdaBoost, classifiers are generated for the voice- and image-processing parts. Moreover, each classifier is weighted according to the corresponding confidence and then the final authentication score is calculated. Hence, the proposed method can provide valid authentication results in various environments. Experimental results demonstrate that multimodal processing in the proposed method is more effective than monomodal (only image or voice) processing.

Multimodal person authentication system using features of utterance

This paper proposes a detection method for carotid artery calcification in dental panoramic radiographs. The existence of the carotid artery calcification as an index for arteriosclerosis attracts a great deal of attention. The carotid artery calcification has brighter intensity compared to the neighborhood region and its fact is utilized in the proposed method. To compensate the bad contrast of the dental panoramic radiographs, we also employ the fuzzy image contrast enhancement and algebraic image operator to the proposed method. From experimental results, the carotid artery calcification region can extract from several dental panoramic radiographs.

A Detection Method for Carotid Artery Calcification in Dental Panoramic Radiographs

This paper considers the construction of two-dimensional (2-D) adaptive state-space filters using the least mean squares (LMS) method. The approach is based on the Roesser local state-space model. First, the relation between the coefficient sensitivities and the intermediate transfer functions is investigated. Then, after introducing new systems for generating the gradient vectors, an adaptive algorithm is developed using the LMS method.

Next, a 2-D adaptive state-space filter with separable denominator is introduced to reduce the amount of computation in the adaptive process. Finally, a numerical example is given in which the 2-D adaptive state-space filters are used to design 2-D digital filters in the spatial domain.

Two‐dimensional adaptive state‐space filters using LMS method

In offside detection during a football match, afterimage and occlusion confuse the linesman and the referee, and this confusion can cause judgment errors. This paper proposes a method of football-offside detection using multiple cameras in an automatic linesman assistance system. In this method, multiple cameras and offside detection units are installed as a hardware system, and offside will be alerted when at least one of the offside detection units detects offside. In an offside detection unit, when the offside position is determined, offside is immediately judged in the case in which the ball is passed from an attacker to another attacker who is located nearer the opponent-goal line than the most-back defender and the ball. The vertical lines that represent these attackers, defenders and ball are used as their positions for logically judging the offside. Experiments were performed using video clips from 381 tournaments of English Premier Leagues from 2016 to 2017. The results showed 98.50% accuracy compared with the expert.

A Method of Football-Offside Detection Using Multiple Cameras for an Automatic Linesman Assistance System

In this paper, we propose a biometrics authentication method using lip motion in utterance. The proposed authentication method authenticates persons using lip shape (physical trait) and lip motion accompanying utterance (behavioral trait). Hence, the proposed method can be realized with only a camera extracting lip area without the special equipment used in other personal authentication methods and can easily change the registration data. Conventional methods using a lip image use time differential coefficients of horizontal and vertical lip ranges, and their corresponding rates. However, these features cannot describe the lip motion in detail because the extraction of the exact lip shape is difficult. Therefore, the proposed authentication method uses features concerned with lip shape, which are extracted from a divided lip region, and extracts lip motion by dynamic time warping (DTW). Experimental results demonstrate that the proposed method can achieve an authentication rate of more than 99.5 %.

Mitsuji Muneyasu

Papers

Multimodal person authentication system using features of utterance

A Detection Method for Carotid Artery Calcification in Dental Panoramic Radiographs

Two‐dimensional adaptive state‐space filters using LMS method

A Method of Football-Offside Detection Using Multiple Cameras for an Automatic Linesman Assistance System

Biometrics authentication method using lip motion in utterance