Quantum Mechanical Study on Tunnelling and Ballistic Transport of Nanometer Si MOSFETs

Using self-consistent calculations of million-atom Schrodinger-Poisson equations, we investigate the I-V characteristics of tunnelling and ballistic transport of nanometer metal oxide semiconductor field effect transistors (MOSFET) based on a full 3-D quantum mechanical simulation under nonequilibtium condition. Atomistic empirical pseudopotentials are used to describe the device Hamiltonian and the underlying bulk band structure. We find that the ballistic transport dominates the I-V characteristics, whereas the effects of tunnelling cannot be neglected with the maximal value up to 0.8mA/mu m when the channel length of MOSFET scales down to 25 nm. The effects of tunnelling transport lower the threshold voltage V-t. The ballistic current based on fully 3-D quantum mechanical simulation is relatively large and has small on-off ratio compared with results derived from the calculation methods of Luo et al.

A fully three-dimensional atomistic quantum mechanical study on random dopant-induced effects in 25-nm MOSFETs

A fully 3-D atomistic quantum mechanical simulation is presented to study the random dopant-induced effects in nanometer metal-oxide-semiconductor field-effect transistors. The empirical pseudopotential is used to represent the single particle Hamiltonian, and the linear combination of bulk band method is used to solve the million atom Schrodinger equation. The gate threshold fluctuation and lowering due to the discrete dopant configurations are studied. It is found that quantum mechanical effects increase the threshold fluctuation while decreasing the threshold lowering. The increase of threshold fluctuation is in agreement with the researchers' early study based on an approximated density gradient approach. However, the decrease in threshold lowering is in contrast with the density gradient calculations.

Design, fabrication, and characterization of an ultracompact low-loss photonic crystal corner mirror

An ultracompact, low-loss, and broad-band corner mirror, based on photonic crystals, is investigated in this paper. Based on the theoretical analysis of the loss mechanism, the boundary layers of the photonic crystal region are revised to improve the extra losses, and the transmission characteristics are evaluated by using the 3-D finite-difference time-domain method. The device with optimized structure was fabricated on silicon-on-insulator substrate by using electron-beam lithography and inductively coupled plasma etching. The measured extra losses are about 1.1 +/- 0.4 dB per corner mirror for transverse-electronic polarization for the scanning wavelength range of 1510-1630 nm. Dimensions of the achieved PC corner mirror are less than ;7 x 7 mu m(2), which are only about one tenth of conventional wave-guide corner mirrors.

Application of ICA in on-line verification of the phase difference of the current sensor

The performance of the current sensor in power equipment may become worse affected by the environment. In this paper, based on ICA, we propose a method for on-line verification of the phase difference of the current sensor. However, not all source components are mutually independent in our application. In order to get an exact result, we have proposed a relative likelihood index to choose an optimal result from different runs. The index is based on the maximum likelihood evaluation theory and the independent subspace analysis. The feasibility of our method has been confirmed by experimental results.

Study on text-dependent speaker recognition based on Biomimetic Pattern Recognition

We studied the application of Biomimetic Pattern Recognition to speaker recognition. A speaker recognition neural network using network matching degree as criterion is proposed. It has been used in the system of text-dependent speaker recognition. Experimental results show that good effect could be obtained even with lesser samples. Furthermore, the misrecognition caused by untrained speakers occurring in testing could be controlled effectively. In addition, the basic idea "cognition" of Biomimetic Pattern Recognition results in no requirement of retraining the old system for enrolling new speakers.

High-Dimensional Space Geometrical Informatics and its applications to image restoration

With a view to solve the problems in modern information science, we put forward a new subject named High-Dimensional Space Geometrical Informatics (HDSGI). It builds a bridge between information science and point distribution analysis in high-dimensional space. A good many experimental results certified the correctness and availability of the theory of HDSGI. The proposed method for image restoration is an instance of its application in signal processing. Using an iterative "further blurring-debluring-further blurring" algorithm, the deblured image could be obtained.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

A novel image restoration algorithm based on high-dimensional space geometry

A novel image restoration approach based on high-dimensional space geometry is proposed, which is quite different from the existing traditional image restoration techniques. It is based on the homeomorphisms and "Principle of Homology Continuity" (PHC), an image is mapped to a point in high-dimensional space. Begin with the original blurred image, we get two further blurred images, then the restored image can be obtained through the regressive curve derived from the three points which are mapped form the images. Experiments have proved the availability of this "blurred-blurred-restored" algorithm, and the comparison with the classical Wiener Filter approach is presented in final.

Continuous speech research based on HyperSausage Neuron

In this paper, we presents HyperSausage Neuron based on the High-Dimension Space(HDS), and proposes a new algorithm for speaker independent continuous digit speech recognition. At last, compared to HMM-based method, the recognition rate of HyperSausage Neuron method is higher than that of in HMM-based method.

Pipeline Architecture and Parallel Computation-Based Real-Time Stereovision Tracking System for Surgical Navigation

This paper studies the development of a real-time stereovision system to track multiple infrared markers attached to a surgical instrument. Multiple stages of pipeline in field-programmable gate array (FPGA) are developed to recognize the targets in both left and right image planes and to give each target a unique label. The pipeline architecture includes a smoothing filter, an adaptive threshold module, a connected component labeling operation, and a centroid extraction process. A parallel distortion correction method is proposed and implemented in a dual-core DSP. A suitable kinematic model is established for the moving targets, and a novel set of parallel and interactive computation mechanisms is proposed to position and track the targets, which are carried out by a cross-computation method in a dual-core DSP. The proposed tracking system can track the 3-D coordinate, velocity, and acceleration of four infrared markers with a delay of 9.18 ms. Furthermore, it is capable of tracking a maximum of 110 infrared markers without frame dropping at a frame rate of 60 f/s. The accuracy of the proposed system can reach the scale of 0.37 mm RMS along the x- and y-directions and 0.45 mm RMS along the depth direction (the depth is from 0.8 to 0.45 m). The performance of the proposed system can meet the requirements of applications such as surgical navigation, which needs high real time and accuracy capability.

Pipeline Architecture and Parallel Computation-Based Real-Time Stereovision Tracking System for Surgical Navigation

This paper studies the development of a real-time stereovision system to track multiple infrared markers attached to a surgical instrument. Multiple stages of pipeline in field-programmable gate array (FPGA) are developed to recognize the targets in both left and right image planes and to give each target a unique label. The pipeline architecture includes a smoothing filter, an adaptive threshold module, a connected component labeling operation, and a centroid extraction process. A parallel distortion correction method is proposed and implemented in a dual-core DSP. A suitable kinematic model is established for the moving targets, and a novel set of parallel and interactive computation mechanisms is proposed to position and track the targets, which are carried out by a cross-computation method in a dual-core DSP. The proposed tracking system can track the 3-D coordinate, velocity, and acceleration of four infrared markers with a delay of 9.18 ms. Furthermore, it is capable of tracking a maximum of 110 infrared markers without frame dropping at a frame rate of 60 f/s. The accuracy of the proposed system can reach the scale of 0.37 mm RMS along the x- and y-directions and 0.45 mm RMS along the depth direction (the depth is from 0.8 to 0.45 m). The performance of the proposed system can meet the requirements of applications such as surgical navigation, which needs high real time and accuracy capability.

基于信息融合的网络安全态势评估模型

安全态势评估是近年来国内外在网络安全领域的研究热点之一.对已有的安全态势评估方法进行了详细分析和比较,针对网络安全中多数据源的特点,提出基于信息融合的网络安全态势评估模型,引入改进的D-S证据理论将多数据源信息进行融合,利用漏洞信息和服务信息,经过态势要素融合和节点态势融合计算网络安全态势,绘制安全态势曲线图,同时对态势计算结果进行时间序列分析,从而实现网络安全趋势的预测.最后利用网络实例数据,对所提出的网络安全态势评估模型和算法进行了验证,结果表明该模型比已有成果更加有效和准确.

Synthesis and random laser application of ZnO nano-walls: a review

This review paper summarises briefly some important achievements of our recent research on the synthesis and novel applications of nanostructure ZnO such as honeycomb shaped 3-D (dimension) nano random-walls. A chemical reaction/vapour transportation deposition technique was employed to fabricate this structure on ZnO/SiO2/Si substrate without any catalyst and additive in a simple tube furnace to aim the low-cost and high qualified samples. Random laser action with strong coherent feedback at the wavelength between 375 nm and 395 nm has been firstly observed under 355 nm optical excitation with threshold pumping intensity of 0.38 MW/cm(2).

草地植被恢复对次降雨土壤水分动态的影响分析

通过对宁南典型草原植被恢复过程次降雨土壤水分动态的研究,阐明植被恢复对次降雨后土壤水分的影响及机理。结果表明,次降雨提高了土壤含水率和贮水量,均表现出1 d>3 d>7 d。草地封育能够提高次降雨资源化效率,随封育时间延长,次降雨后0-60 cm土壤含水率和0-100 cm土壤贮水量不断提高。降雨对封育草地土壤水分的影响范围在100 cm土层内,100 cm以下不能得到降雨的补充。封育时间延长土壤水分活跃层加深,坡耕地仅为40 cm,封育17 a后达到60 cm。土壤持水能力越强,表层土壤饱和导水率越大,雨后1 d在0-100 cm土壤贮水量越大。地上生物量愈大,雨后1~7 d在0-200 cm土壤耗水量越大。