Least Mean Square

A method is presented for the recovery of optical flow. The key idea is that the local spatial structure of optical flow, with the exception of surface boundaries, is usually rather coherent and can thus be appropriately approximated by a linear vector field. According to the proposed method, the optical flow components and their first order spatial derivatives are computed

In this work an automatic impedance matching for wireless communication systems is proposed. A Takagi-Sugeno (T-S) fuzzy logic controller is used to change adaptively the value of one capacitor in the π matching network. Asymptotic stability is established via a common Lyapunov function for all the subsystems of the T-S fuzzy system. Results obtained using Verilog-A and a Least Mean Square (LMS) impedance matching approach are presented.

Wireline transceivers for high-speed data transmission through backplane and ethernet cables are important applications for microelectronic and nanoelectronic CMOS technologies. Although many circuit simulators provide correct system-level and transistor-level simulations, they usually fail to give correct results for many highly lossy or/and highly dispersive channels. This paper discusses the advanced simulator that we developed. It can give correct simulation results

Abstract—This paper proposes two modifications of the filtered-x least mean squares (FxLMS) algorithm with improved con-vergence behavior albeit at the same computational cost of 2M operations per time step as the original FxLMS update. The paper further ...

Ultrasound transmission tomography is a potentially promising alternative to standard X-Ray imaging in medi- cal diagnosis, especially in mammography. The reconstruc- tion of the local attenuation coefficient from the measured signals can be formulated as a large overdetermined system of linear equations based on a simplified ultrasound trans- mission model. It can be solved by means of the Kaczmarz algebraic reconstruction technique. The algorithm succes- sively iterates through the equations and computes the cor- rections of the initial solution estimates. Because the orig- inal version of the algorithm does not guarantee conver- gence to the optimum, an extended version of the method is used here. It has been shown previously to converge to the least-mean-squares optimum. Both the original and ex- tended algorithms are strictly sequential since the compu- tation in the particular iteration depends on the correc- tions from the previous step. To enable parallelization of the metho...

The conjugate gradient is the most popular optimization method for solving large systems of linear equations. In a system identification problem, for example, where very large impulse response is involved, it is necessary to apply a particular strategy which diminishes the delay, while improving the convergence time. In this paper we propose a new scheme which combines frequency-domainadaptive filtering with a conjugate gradient technique in order to solve a high order multichannel daptive filter, while being delayless and guaranteeing a very short convergence time.

Ultrasonic non-destructive testing has been widely used in assessing the integrity of engineering materials such as high-temperature alloys and structures such as pipelines, bridges, and other load-bearing structures. The ultrasonic signals received from these structures are often noisy. Effective noise-reduction techniques are needed in order to accurately assess their condition. This paper presents a new digital signal processing method for

An automatic ac bridge method employing the principle of balancing by means of a stochastic gradient search algorithm has been presented. Among the various gradient search techniques, the Widrow-Hoff LMS technique has been chosen as it involves low computational burden. The LMS algorithm has been used by the authors for operation of an ac bridge operating with continuous variables. The relevant operation has been verified by simulation. It can measure negative impedance. A relation has been established between parameters to be used in LMS algorithm for discrete and continuous versions of the bridge. The convergence time for a relatively simple form of the LMS bridge, namely the R-R type bridge has been theoretically established and experimentally verified both by simulation and real time implementation. The frequency response of the LMS algorithm has also been determined

This paper considers Least Mean Square (LMS) algorithm to subsequently modify adaptive filter wieghts. The corresponding filter is used to cancel noise caused by powerline interference contained in Electrocardiograph (ECG) signal. The design is then implemented on LabView software. The simulation shows that the design can reduce powerline interference. Average powerline noise power contained in the signal is attenuated by more than 20 times.

In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. This study proposes hybrid learning of RBF Network with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. The hybrid learning of RBF Network involves two phases. The first phase is a structure identification, in which unsupervised learning is exploited to determine the RBF centers and widths. This is done by executing different algorithms such as k-mean clustering and standard derivation respectively. The second phase is parameters estimation, in which supervised learning is implemented to establish the connections weights between the hidden layer and the output layer. This is done by performing different algorithms such as Least Mean Squares (LMS) and gradient based methods. The incorporation of PSO in hybrid learning of RBF Network is accomplished by optimizing the centers, the widths and the weights of RBF Network. The results for training, testing and validation of five datasets (XOR, Balloon, Cancer, Iris and Ionosphere) illustrate the effectiveness of PSO in enhancing RBF Network learning compared to conventional Backpropogation.