Ship interior NOISE produced by engine room disturbs the crews and the workers. Also, it causes lots of complications for the crews and the passengers in the ship. Active NOISE CANCELLATION (ANC) is based on the destructive interference between the primary NOISE and generated NOISE from the secondary source. In this research, performance of the static and dynamic neural networks is evaluated in active CANCELLATION of sound NOISE. For this reason, MLP and RBF are designed and trained as static neural networks. After training, performance of static and dynamic networks in NOISE attenuation are compared. In order to compare the networks appropriately, training and test samples are similar. Moreover, equal number of layers and neurons are considered for the networks. NOISE signals from a SPIB database are used in simulation procedures. The simulation results show that designed neural networks present proper performance in ANC because of using training and validation samples in training process. As it is seen, the trained dynamic network and RBF neural network show better performance in NOISE attenuation than MLP network and achieve 1 dB NOISE attenuation more than MLP network.

Reaction wheels (RWs) used for attitude control of space vehicle systems usually encounter with undesired wide band NOISEs. These NOISEs which significantly affect the performance of regulator controller must tune the review or review rate of RWs. According to wide frequency band of NOISEs in RWs the common approaches of NOISE CANCELLATION cannot conveniently reduce the effects of the NOISE. Therefore, the advanced analyzer of frequency domain signal processing such as wavelets must be used to realize the NOISE CANCELLATION. In this regards, only the online application of wavelets can be practical. In this paper, an online active NOISE canceller based on online wavelet theory is employed to omit the effects of undesired NOISE. In this way, dynamic identification of a RW as well as wavelet delay compensation are used simultaneously to design an active NOISE CANCELLATION system. Numerical and experimental investigations of KNTU test setup as a hard ware in the loop system show the preferences of the proposed method of online NOISE CANCELLATION.

This paper presents a new ultra-wideband LNA which employs the complementary derivative superposition method in NOISE CANCELLATION structure. A pMOS transistor in weak inversion region is utilized for simultaneous second- and third-order distortion CANCELLATION. Source-degeneration technique and two shunt inductors are added to improve the performance at high frequencies. The degeneration inductor resonates atf T/2 and realizes a new input matching technique that widens the bandwidth with decreasing it’s quality factor and input capacitance, while flattens the input resistance and also improves the 1dB Compression Point. The shunt inductors resonate at the center frequency of the band and improve the effective bandwidth of NOISE/distortion CANCELLATION technique. This LNA has been designed in a 0.18μm CMOS process and consumes 8.3 mA from 1.8 V power supply. The chip area is 0.55 mm2. The NOISE figure and voltage gain are 4.48-5.18 dB and 13 dB, respectively. S11 is lower than -13.5 dB over 5.8–10.6 GHz and IIP3 is 14.5–17.5 dBm, IIP2 is 14–15.5 dBm. This technique improves IIP3 more than 9 dB.