Aiming at the large calculation workload of adaptive algorithm in adaptive filter based on wavelet transform, affecting the filtering speed, a wavelet-based neural network adaptive filter is constructed in this paper. Since the neural network has the ability of distributed storage and fast self-evolution, use Hopfield neural network to implement adaptive filter LMS algorithm in this filter so as to improve the speed of operation. The simulation results prove that, the new filter can achieve rapid real-time denoising.
Abstract
Vibration predictions for rotating machinery with high-speed flexible rotors must account for the methods and limitations of the balance test process which determine the residual rotor unbalance. Vibration predictions based on finite element analysis (FEA) methods are highly dependent upon the assumed rotor unbalance amplitude and phase. The actual residual unbalance distribution depends upon the measured influence coefficients and the least-mean-square (LMS) algorithm used to calculate balance correction weights. Repeatability of the vibration measurements is a key factor in successful balancing. The vibration predictions described in this paper use estimates of final residual unbalance obtained by simulating the balance test process. The simulation uses FEA based influence coefficients, a test based measurement uncertainty (repeatability) model, and LMS balance weight calculations including the specified vibration target levels. The simulations can be used to predict the limit of balance performance of the machinery and to evaluate design options for impact on residual unbalance levels.
Network traffic has increased rapidly in recent years, mainly associated with the massive growth of various applications on mobile devices. Named data networking (NDN) technology has been proposed as a future Internet architecture for effectively handling this ever-increasing network traffic. In order to realize the NDN, high-speed lookup algorithms for a forwarding information base (FIB) are crucial. This paper proposes a level-priority trie (LPT) and a 2-phase Bloom filter architecture implementing the LPT. The proposed Bloom filters are sufficiently small to be implemented with on-chip memories (less than 3 MB) for FIB tables with up to 100,000 name prefixes. Hence, the proposed structure enables high-speed FIB lookup. The performance evaluation result shows that FIB lookups for more than 99.99% of inputs are achieved without needing to access the database stored in an off-chip memory.
The Ultra High Speed LMS Algorithm Implemented on Parallel Architecture Suitable for Multidimensional Adaptive Filtering