Modeling of Noise Power Spectral Density Analysis for GaN/AlGaN HEMT

Nano Technology is the branch of technology that deals with dimensions and tolerances in terms of nanometers. In this paper, the electrical characteristics analysis is determined for the Nano-GaN HEMT and Micro-GaN HEMT and also power spectrum density is determined for GaN Nano-HEMT by reducing the gate length Lg in nm range. The GaN Nano HEMT is producing high current comparing to Micro GaN HEMT. Accuracy of the proposed analytical model results is verified with simulation results.

Dual transponder ranging performance in the presence of oscillator phase noise

A dual transponder carrier ranging method can be used to measure inter-satellite distance with high precision by combining the reference and the to-and-fro measurements. Based on the differential techniques, the oscillator phase noise, which is the main error source for microwave ranging systems, can be significantly attenuated. Further, since the range measurements are derived on the same satellite, the dual transponder ranging system does not need a time tagging system to synchronize the two satellites. In view of the lack of oscillator noise analysis on the dual transponder ranging model, a comprehensive analysis of oscillator noise effects on ranging accuracy is provided. First, the dual transponder ranging system is described with emphasis on the detailed analysis of oscillator noise on measurement precision. Then, a high-fidelity numerical simulation approach based on the power spectrum density of an actual ultra-stable oscillator is carried out in both frequency domain and time domain to support the presented theoretical analysis. The simulation results under different conditions are consistent with the proposed concepts, which makes the results reliable. Besides, the results demonstrate that a high level of accuracy can be achieved by using this oscillator noise cancelation-oriented ranging method.

Nonstationary probability densities of system response of strongly nonlinear single-degree-of-freedom system subject to modulated white noise excitation

The nonstationary probability densities of system response of a single-degree- of-freedom system with lightly nonlinear damping and strongly nonlinear stiffness subject to modulated white noise excitation are studied. Using the stochastic averaging method based on the generalized harmonic functions, the averaged Fokl^er-Planck-Kolmogorov equation governing the nonstationary probability density of the amplitude is derived. The solution of the equation is approximated by the series expansion in terms of a set of properly selected basis functions with time-dependent coefficients. According to the Galerkin method, the time-dependent coefficients can be solved from a set of first-order linear differential equations. Then, the semi-analytical formulae of the nonstationary probability density of the amplitude response as well as the nonstationary probability density of the state response and the statistic moments of the amplitude response can be obtained. A van der Pol-Duffing oscillator subject to modulated white noise is given as an example to illustrate the proposed procedures. The effects of the system parameters, such as the linear damping coefficient and the nonlinear stiffness coefficient, on the system response are discussed.

Research on Real-time Monitoring of Abnormal Seismic Noise

The noise data in vertical component records of 85 seismic stations in Fujian Province during 2012 is used as the research object in this paper. The noise data is divided into fiveminute segments to calculate the power spectra. The high reference line and low reference line of station are then identified by drawing a probability density function graph( PDF)using the power spectral probability density function. Moreover, according to the anomalies of PDF graphs in 85 seismic stations,the abnormal noise is divided into four categories: dropped packet, low noise, high noise, and median noise anomalies.Afterwards,four selection methods are found by the high or low noise reference line of the stations,and the system of real-time monitoring of seismic noise is formed by combining the four selection methods. Noise records of 85 seismic stations in Fujian Province in July2013 are selected for verification,and the results show that the anomalous noise-recognition system could reach a 90% success rate at most stations and the effect of selection are very good. Therefore,it could be applied to the seismic noise real-time monitoring in stations.

Radar false alarm plots elimination based on multi-feature extraction and classification

Caused by the environment clutter,the radar false alarm plots are unavoidable.Suppressing false alarm points has always been a key issue in Radar plots procession.In this paper,a radar false alarm plots elimination method based on multi-feature extraction and classification is proposed to effectively eliminate false alarm plots.Firstly,the density based spatial clustering of applications with noise(DBSCAN)algorithm is used to cluster the radar echo data processed by constant false-alarm rate(CFAR).The multi-features including the scale features,time domain features and transform domain features are extracted.Secondly,a feature evaluation method combining pearson correlation coefficient(PCC)and entropy weight method(EWM)is proposed to evaluate interrelation among features,effective feature combination sets are selected as inputs of the classifier.Finally,False alarm plots classified as clutters are eliminated.The experimental results show that proposed method can eliminate about 90%false alarm plots with less target loss rate.

An Effective Density Based Approach to Detect Complex Data Clusters Using Notion of Neighborhood Difference

The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.