In conventional active noise control (ANC) system, the primary noise is attenuated over the frequency band of interest based simply on the error signal, which does not take into account the human perception of noise. Hence, researchers have developed psychoacoustic ANC system to improve its noise reduction performance from psychoacoustic point of view. However, in this psychoacoustic ANC system, there may be a disturbance that is uncorrelated with the primary noise at the error sensor, which can severely degrade the system's noise reduction performance. Hence, in this paper, a psychoacoustic hybrid ANC system is proposed, which can simultaneously control both the correlated primary noise and uncorrelated disturbance from psychoacoustic point of view. Loudness is used as the psychoacoustic criterion for evaluating the system's noise reduction performance. Simulation results show the effectiveness of the proposed psychoacoustic hybrid ANC system.

In this paper, two adapting schemes for gain factors of active noise equalizer in application of bass enhancement and engine noise cancellation are presented. The novel system can adaptively equalize the engine noise into a proper enhancement of the bass audio production in automobile cabin. Computer simulations are performed to verify the proposed schemes. In noise cancellation mode, a maximum of 3 dB bass enhancement can be achieved with 6 dB noise suppression. More than 6 dB of bass enhancement can be achieved in the bass enhancement mode. The results showed that the proposed system could be a promising approach for solving the bass audio reproduction, together with the noise control problems in automobile.

An audio enhancement system is presented to improve the sound quality, especially the bass reproduction in the automobile cabin. High quality audio reproduction in cabin can be difficult due to a number of factors, including the noise in the cabin, performance, size of the loudspeakers in the car. The proposed system uses digital signal processing techniques with the built-in car audio system to tune and make use of the engine noise. The main problems are the tracking of the engine noise and tune the noise to match the audio signal. In this paper, we propose a solution based on frequency sampling filters to track and extract the audio signal, and a multi-frequency active noise equalizer to tune the engine noise. The results showed that the proposed system could be a promising approach for solving the bass audio reproduction, together with the noise control problems in automobile

Stochastic timed Petri nets are developed to evaluate the relative performance of distributed shared memory models for scalable multiprocessors, using multithreaded processors as building blocks. Four shared memory models are evaluated: the sequential consistency (SC) model by Lamport (1979), the weak consistency (WC) model by Dubois et al. (1986), the processor consistency (PC) model by Goodman (1989), and the release consistency (RC) model by Gharachorloo et al. (1990). We assumed a scalable network with a sufficient bandwidth to absorb the increased traffic from multithreading, coherent caches, and memory event reordering. The embedded Markov chains are solved to reveal the performance attributes. Under saturated conditions, we find that multithreading contributes more than 50% of the performance improvement, while the improvement from memory consistency models varies between 20% to 40% of the total performance gain. Petri net models are effective to predict the performance of processors with a larger number of contexts than that can be simulated in previous benchmark studies. The accuracy of these memory performance models was validated with the simulation results from Stanford University. Our analytical results reveal the lowest performance of the SC model amongst four memory consistency models. The PC model requires to use larger write buffers, while the WC and RC models require smaller write buffers. The PC model may perform even lower than the SC model, if a small buffer was used. The performance of the WC model depends heavily on the synchronization rate in user code. For a low synchronization rate, the WC model performs as well as the RC model. With sufficient multithreading and network bandwidth, the RC model shows the best performance among the four models. Furthermore, we discovered that cache interferences cause very little performance degradation in all relaxed memory consistency models; as long as the network is contention-free even when multithreading has saturated the system.>

Stochastic timed Petri nets are developed to evaluate the relative performance of distributed shared memory models for scalable multithreaded multiprocessors. The shared memory models evaluated include the Sequential Consistency (SC), the Weak Consistency (WC), the Processor Consistency (PC) and the Release Consistency (RC) models. Under saturated conditions, we found that multithreading contributes more than 50% of the performance improvement, while the improvement from memory consistency models varies between 20% to 40% of the total performance gain. Our analytical results reveal the lowest performance of the SC model. The PC model requires to use larger write buffers and may perform even lower than the SC model if a small buffer was used. The performance of the WC model depends heavily on the synchronization rate in user code. For a low synchronization rate, the WC model performs as well as the RC model. With sufficient multithreading and network bandwidth, the RC model shows the best performance among the four models.