Process Noise Parameters of Beamforming Green Nodes

Signals arrive out of phase at the intended receiver from collaborative beamforming （CB） nodes due to the instability in the output frequency signals of the universal software radio peripheral＇s （USRP） local oscillator （LO）. These nodes including the target must synchronize their oscillator frequencies for coherent signal reception. In order to do this, frequencies and phases of the signals should be estimated in software defined radio （SDR） and smoothen with nonlinear filters such as the extended Kalman filter （EKF）. The process noise parameters of the NI USRP-2920 nodes will have to be calculated and used with the EKF process noise covariance matrix. These nodes are green communication hardware devices where most of the hardware units are now software defined. This article uses the direct spectrum method to obtain the phase noise values at various frequency offsets of the NI USRP-2920 in order to calculate the power spectral density of fractional frequency fluctuation. By applying the power-law noise model to this obtained value, the generated white frequency noise and random walk frequency noise values are q_1=1.93x10^-21 and q_2=5.86x10^-18, respectively.

Hopping Parameter Expansion Technique in Noise Method for Disconnected Quark Loops

We investigate the effectiveness of the hopping parameter expansion（HPE） combined with the Z（2） noise method in the calculation of the trace of the inverse of Wilson＇s Dirac operator and some other disconnected contributions.A numerical comparison of the standard deviation for the Z（2） noise method and HPE with the Z（2） noise method is carried out. It is found that there are noise reductions in all the quantities we calculated using the HPE with the Z（2） noise method. For the trace of the inverse of Wilson＇s Dirac operator, the HPE can reduce the statistical error by about 60%.

Automatic estimation and removal of noise on digital image

An spatially adaptive noise detection and removal algorithm is proposed.Under the assumption that an observed image and its additive noise have Gaussian distribution,the noise parameters are estimated with local statistics from an observed degraded image,and the parameters are used to define the constraints on the noise detection process.In addition,an adaptive low-pass filter having a variable filter window defined by the constraints on noise detection is used to control the degree of smoothness of the reconstructed image.Experimental results demonstrate the capability of the proposed algorithm.

Chaos Control in Electromechanical Seismograph System with Noise Disturbances

In order to eliminate chaotic oscillation of electromechanical characteristics of seismograph system, the complex dynamic the four-dimensional nonlinear equations of seismograph system were analyzed. Sliding mode method was applied to stabilize the chaotic orbits of the eleetromechanieal seismograph system to arbitrary chosen fixed points and periodic orbits precisely, and MATLAB simulations were presented to confirm the validity of the controller. The results show that using sliding mode method can make the system track target orbit strictly and smoothly with short transition time, and its insensitivity to noise disturbances is shown. It also provides reference for relevant chaos control in relevant system.

Combined Signal Processing Based Techniques and Feed Forward Neural Networks for Pathological Voice Detection and Classification

This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks(FFNN).The important pathological voices such as Autism Spectrum Disorder(ASD)and Down Syndrome(DS)are considered for analysis.These pathological voices are known to manifest in different ways in the speech of children and adults.Therefore,it is possible to discriminate ASD and DS children from normal ones using the acoustic features extracted from the speech of these subjects.The important attributes hidden in the pathological voices are extracted by applying different signal processing techniques.In this work,three group of feature vectors such as perturbation measures,noise parameters and spectral-cepstral modeling are derived from the signals.The detection and classification is done by means of Feed For-ward Neural Network(FFNN)classifier trained with Scaled Conjugate Gradient(SCG)algorithm.The performance of the network is evaluated by finding various performance metrics and the the experimental results clearly demonstrate that the proposed method gives better performance compared with other methods discussed in the literature.