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With the development of science and technology in recent years, many operating machines have become sources of noise affecting quality of life. Hence, the topic of noise diminution using an active noise control (ANC) system has attracted many researchers. This paper develops a new adaptive fuzzy feedback neural network controller (AFFNNC) to improve the performance for narrowband active noise control (NANC) systems. The proposed controller combines fuzzy inference and adaptive feedback neural network controllers that are based on the filtered-s least mean square (FSLMS) algorithm. The AFFNNC comprises five network layers, in which the output layer of the controller uses an adaptive algorithm to tune directly the parameters of filters without prior training. The computational complexity, convergence and stability of the AFFNNC are analyzed. Evaluations are performed on both linear and nonlinear NANC systems with a recorded noise signal that was obtained from a transformer. Numerical simulations confirm the efficiency of the proposed controller compared with other ANC controllers.

Audio-integrated feedback active noise control (AFANC) systems deliver wideband audio signals and cancel low frequency narrowband noises simultaneously. The conventional AFANC system uses single-rate processing with fullband adaptive active noise control (ANC) filter for generating anti-noise signal and fullband audio cancelation filter for audio-interference cancelation. The conventional system requires a high sampling rate for audio processing. Thus, the fullband adaptive filters require long filter lengths, resulting in high computational complexity and impracticality in real-time system. This paper proposes a multirate AFANC system using decimated-band adaptive filters (DAFs) to decrease the required filter lengths. The decimated-band adaptive ANC filter is updated by the proposed decimated filtered-X least mean square (FXLMS) algorithm, and the decimated-band audio cancelation filter can be obtained by the proposed on-line and off-line decimated secondary-path modeling algorithms. The computational complexity can be decreased significantly in the proposed AFANC system with good enough noise reduction and fast convergence speed, which were verified in the analysis and computer simulations. The proposed AFANC system was implemented for an active headrest system, and the real-time performances were tested in real-time experiments.

Snake robots have come to represent a new subfield of bionic robot research in recent years. A snake robot comprises many modules and performs various movements in arranged connections. The structure of a snake body enables it to move smoothly in narrow spaces or pipes with high stability and reliability. This article studies the application of a snake robot on a large-scale nuclear power facility to sense in pipe components. Therefore, a snake robot must move in pipes in which high radiation is present to explore the surrounding environment and take samples. A simple but effective method of locomotion is developed and executed to confirm feasibility of motion, especially in narrow space. A sampling mechanism with a storage box is designed at the tail of the snake to take and keep the samples well at designated locations. We built a pipe system which has two right-angled turns to simulate the pipes of a large-scale nuclear power facility. A user interface helps operators to manipulate the snake robot.

Radar is a type of wireless, noncontact sensor that does not need to be placed on or near a test object for detection. A key component of any radar sensor is the antenna. Among different types of antennas, the linear tapered slot antenna (LTSA) is a wideband antenna that has the advantages of small size, design simplicity, and easy adaptation to an array. This study examined and analyzed the 10 primary parameters that define the LTSA design when operated in the ultra-wideband (UWB) frequency range. The study method involved varying each of the 10 parameters to discern how the variations impact the three critical characteristics of an antenna, namely, (1) return loss, (2) the far field radiation pattern on the E-plane, and (3) the far field radiation pattern on the H-plane. By analyzing the changes in these critical characteristics, a set of design recommendations for the 10 parameters was developed for the LTSA.

This paper presents the development of active noise control (ANC) for light-weight earphones, and proposes using music or natural sound to estimate the critical secondary path model instead of extra random noise. Three types of light-weight ANC earphones including in-ear, earbud, and clip phones are developed. Real-time experiments are conducted to evaluate their performance using the built-in microphone inside KEMAR’s ear and to compare with commercially-available ANC headphones and earphones. Experimental results show that the developed light-weight ANC earphones achieve higher noise reduction than the commercial ANC headphones and earphones, and the in-ear ANC earphone has the best noise reduction performance.

Temperature control in etching process is important for semiconductor manufacturing technology. However, pressure variations in vacuum chamber results in a change in temperature, worsening the accuracy of the temperature of the wafer and the speed and quality of the etching process. This work develops an adaptive network-based fuzzy inference system (ANFIS) using a field-programmable gate array (FPGA) to improve the effectiveness. The proposed method adjusts every membership function to keep the temperature in the chamber stable. The improvement of the proposed algorithm is confirmed using a medium vacuum (MV) inductively-coupled plasma- (ICP-) type etcher.