Optimal phases for a family of quadriphase CDMA sequences

Signature sequences with good even even and odd (or polyphase) correlations are crucial for asynchronous code-division multiple access (CDMA). When the data sequence is random, the even and odd (or polyphase) correlations are equally important. However, for most known signature sequences, only their even correlations were analyzed. It appears that determining the odd (or the polyphase) correlations is generally a very hard problem since the odd (or the polyphase) correlations depend on the phases of the signature sequences. Sole (1989), Boztas, Hammons, and Kumar (1992) found a family of quadriphase sequences that are asymptotically optimal. These sequences gain a factor √2 in terms of their maximum periodic even correlations when compared with the best possible binary phase-shift keying (BPSK) sequences. We find the optimal phases of these sequences. The optimality is in the sense that at these phases, the mean square values of the even, odd, and the polyphase correlations are minimal, and achieve the Welch (1974) bound-equality simultaneously. Furthermore, we show that at these phases, the average user interference of these sequences is always smaller than that of the ideal random signature sequences. Comprehensive analytical and numerical results show that good phase sequences can offer a nonnegligible amount of gain over bad phase sequences at modest and high signal-to-noise ratios     

Analogue implementation of a neural network controller for UPSinverter applications

An analogue neural-network controller for UPS inverter applications is presented. The proposed neural-network controller is trained off-line using patterns obtained from a simulated controller, which had an idealized load-current-reference. Simulation results show that the proposed neural-network controller can achieve low total harmonic distortion under nonlinear loading condition and good dynamic responses under transient loading condition. To verify the performance of the proposed NN controller, a hardware inverter with an analogue neural network (NN) controller (using mainly operational amplifiers and resistors) is built. Additionally, for comparison purposes, a PI controller with optimized parameters is built. Experimental results confirm the simulation results and show the superior performance of the NN controller especially under rectifier-type loading condition. Implementing the analogue neural-network controller using programmable integrated circuits is also discussed     

Feature Reduction via Generalized Uncorrelated Linear Discriminant Analysis

High-dimensional data appear in many applications of data mining, machine learning, and bioinformatics. Feature reduction is commonly applied as a preprocessing step to overcome the curse of dimensionality. Uncorrelated Linear Discriminant Analysis (ULDA) was recently proposed for feature reduction. The extracted features via ULDA were shown to be statistically uncorrelated, which is desirable for many applications. In this paper, an algorithm called ULDA/QR is proposed to simplify the previous implementation of ULDA. Then, the ULDA/GSVD algorithm is proposed, based on a novel optimization criterion, to address the singularity problem which occurs in undersampled problems, where the data dimension is larger than the sample size. The criterion used is the regularized version of the one in ULDA/QR. Surprisingly, our theoretical result shows that the solution to ULDA/GSVD is independent of the value of the regularization parameter. Experimental results on various types of data sets are reported to show the effectiveness of the proposed algorithm and to compare it with other commonly used feature reduction algorithms.     

Nonregular feedback linearization: a nonsmooth approach

In this note, we address the problem of exact linearization via nonsmooth nonregular feedback. A criterion of nonregular static state feedback linearizability is presented for a class of nonlinear affine systems with two control inputs, and its application to nonholonomic systems is briefly discussed.     

Synthesis and comparative molecular field analysis (CoMFA) of antitumor 3-arylisoquinoline derivatives

In this study a series of 3-arylisoquinoline derivatives were synthesized and cytotoxicity against human melanoma tumor cell evaluated, and a three dimensional quantitative structure-activity relationship was investigated using the comparative molecular field analysis (CoMFA). The results suggested that the electrostatic, steric and hydrophobic factors of 3-arylisoquinolines were strongly correlated with the antitumor activity. Considerable predictive ability (cross-validated r2 as high as 0.721) was obtained through CoMFA.     

温度,压力对漂移室气体增益的影响

研究了温度及气体压力对漂移室（工作于正比区）气体放大倍数的影响，温度和气体压力的变化范围分别是５￣４０℃及９０￣１０５ｋＰａ。用实验数据拟合得到了函数关系并测量了几种饱和度下函数中的参数值。     

RADIOACTIVE OR NATURAL TRACER。TECHNIQUES FOR LEAK DETERMINING OF DAM ABUTMENT

Infiltration and localization of preferential infiltration zones at the dam abutment are measured using radioactive tracer tests of flow in boreholes,meanwhile interconnection between boreholes and the energing water points is analysed.The theory and practice of radioactive tracer synthetic detective method are described to give methods and calculation formulae used under the condition of stable flow in single well to measure permeability coefficient and hydrostatic heads.Major single hole techniques including measurement for seepage line,velocity,rate of seepage flow and relationship of recharge of groundwater in aquifers are introduced briefly.The possibilities offered by natureal tracers are analysed,uincluding electric-conduct,pH-value and temperature of water as well as stable isotopes(D,^18O) and tritium.Furthermore,the sensibilities of this theory and methods were confirmed by detecting seepage flow field of Xinanjiang Dam.     

服务器虚拟化带来的信息安全风险以及影响策略

服务器虚拟化大大提高了服务器的资源利用率,对客户来说是十分有利的。但是,也带来了一些诸如破坏正常的网络架构、可能致使系统服务器超载等信息安全风险,需要采取一定的影响策略予以解决。能采取的影响策略有:安装兼容性杀毒软件、不间断地进行服务器硬件的容量分析等。     

GRU-based capsule network with an improved loss for personnel performance prediction

Applied Intelligence - Personnel performance is a key factor to maintain core competitive advantages. Thus, predicting personnel future performance is a significant research domain in human...     

Adaptive synthesis of distance fields

We address the computational resource requirements of 3D example-based synthesis with an adaptive synthesis technique that uses a tree-based synthesis map. A signed-distance field (SDF) is determined for the 3D exemplars, and then new models can be synthesized as SDFs by neighborhood matching. Unlike voxel synthesis approach, our input is posed in the real domain to preserve maximum detail. In comparison to straightforward extensions to the existing volume texture synthesis approach, we made several improvements in terms of memory requirements, computation times, and synthesis quality. The inherent parallelism in this method makes it suitable for a multicore CPU. Results show that computation times and memory requirements are very much reduced, and large synthesized scenes exhibit fine details which mimic the exemplars.