Limited feedback SDMA schemes based on statistical CSI

This paper investigates the multi-beam selection algorithms for transmit correlation channels by using statistical channel state information （SCSI） and instantaneous channel state information. Unlike the conventional codebook-based transmission scheme, the proposed multi-beam selection with the single channel quality indicator （CQI） feedback （MBS- SCF） algorithm determines the preferred beam vector by exploiting the SCSI and only feeds back CQI at each timeslot. The performance of the MBS-SCF algorithm is nearly the same as that of the conventional scheme. In order to further improve the average sum rate, a novel multi-beam selection with the dual CQIs feedback （MBS-DCF） algorithm is proposed, which determines dual preferred statistical eigen- directions and feeds back dual CQIs at each timeslot. The theoretical analysis and simulation results demonstrate that the MBS-DCF algorithm can increase the multiuser diversity and multiplexing gain and exhibits a higher average sum rate.

Evolution of a Thermo Vacuum State in a Single-Mode Amplitude Dissipative Channel

We investigate how an initial thermo vacuum state, in the context of thermo field dynamics, evolves in a single-mode amplitude dissipative channel, and find that in this process the thermo squeezing effect decreases while the fictitious-mode vacuum becomes chaotic.

Optimal transmission strategy for spatially correlated MIMO systems with channel statistical information

In real multiple-input multiple-output (MIMO) systems, the perfect channel state information (CSI) may be costly or impossible to acquire. But the channel statistical information can be considered relatively stationary during long-term transmission. The statistical information can be obtained at the receiver and fed back to the transmitter and do not require frequent update. By exploiting channel mean and covariance information at the transmitter simultaneously, this paper investigates the optimal trans- mission strategy for spatially correlated MIMO channels. An upper bound of ergodic capacity is derived and taken as the per- formance criterion. Simulation results are also given to show the performance improvement of the optimal transmission strategy.

Improved Rate Control Using Statistical Information in Unit-Layer for H.264/AVC

The purpose of this paper is to improve allocation of the number of bits by estimating the target bits in H.264/AVC rate control.In the scheme,an enhancement method of the target unit-layer bit allocation is proposed,which uses a frame and unit complexity estimation to improve the existing mean absolute difference(MAD)complexity measurement.Using the statistical characteristics,we obtain change of occurrence bit about QP to apply the bit amount by QP from the video characteristics in the estimated bit amount of the current frame.Simulation results show that not only the proposed rate control scheme could achieve time saving of more than 99% over existing rate control algorithm,but also PSNR and bit rate were almost the same as the performance in all the sequences.

Macedonia Demographic Aging

In this paper, it explained the process of demographic aging in Republic of Macedonia. There is relevant information for the gradually process of aging. At the end of the text, there are few charts. Macedonia gained independence in 1991, in the course of transition from planned to market economy, many socio-economic parameters changed： Demography become evident that fertility rate dropped significantly. In the previous 20 years, number of infants born decreased somewhat 5-6 times compared to 1980. Number of divorce grew rapidly. Also, in the Republic of Macedonia, there followed a parallel process of immigration to western countries, especially western Europe, USA and Australia as well as other countries from the developed world. This migration happened as a result of mainly economic reasons, i.e., countries in which Macedonians migrate have much higher real incomes when compared with Macedonia, and comparatively much higher living standard. One also can find reason for migration in political reasons also, and family reunification. Low fertility is associated with low income and unemployment. People in Macedonia had suffered greatly in transition times with high unemployment, low wages, and corruption, which largely derogated their chances of better future and career and contributed to the demographic aging and low fertility rates.

Statistical Interaction Term of One-Dimensional Anyon Models

A form of statistical interaction term of one-dimensional anyons is introduced, based on which one-dimensional anyon models are theoretically realized, and the statistical transmutation between bosons （or fermions） and anyons is established in quantum mechanics formalism. Two kinds of anyon models which are being studied are recovered and reexplained naturally in our formalism.

Security Analysis of a Block Encryption Algorithm Based on Dynamic Sequences of Multiple Chaotic Systems

Recently, the cryptosystem based on chaos has attracted much attention. Wang and Yu （Commun. Nonlin. Sci. Numer. Simulat. 14（2009）574） proposed a block encryption algorithm based on dynamic sequences of multiple chaotic systems. We analyze the potential flaws in the algorithm. Then, a chosen-plaintext attack is presented. Some remedial measures are suggested to avoid the flaws effectively. Furthermore, an improved encryption algorithm is proposed to resist the attacks＂ and to keep all the merits of the original cryptosystem.

ReLoc:Indoor Visual Localization with Hierarchical Sitemap and View Synthesis

Indoor visual localization,i.e.,6 Degree-of-Freedom camera pose estimation for a query image with respect to a known scene,is gaining increased attention driven by rapid progress of applications such as robotics and augmented reality.However,drastic visual discrepancies between an onsite query image and prerecorded indoor images cast a significant challenge for visual localization.In this paper,based on the key observation of the constant existence of planar surfaces such as floors or walls in indoor scenes,we propose a novel system incorporating geometric information to address issues using only pixelated images.Through the system implementation,we contribute a hierarchical structure consisting of pre-scanned images and point cloud,as well as a distilled representation of the planar-element layout extracted from the original dataset.A view synthesis procedure is designed to generate synthetic images as complementary to that of a sparsely sampled dataset.Moreover,a global image descriptor based on the image statistic modality,called block mean,variance,and color(BMVC),was employed to speed up the candidate pose identification incorporated with a traditional convolutional neural network(CNN)descriptor.Experimental results on a popular benchmark demonstrate that the proposed method outperforms the state-of-the-art approaches in terms of visual localization validity and accuracy.

Spatio-Temporal evolutive Data Infrastructure: a Spatial Data Infrastructure for managing data flows of Territorial Statistical Information

The observation of demographical,economical or environmental indicators over time through maps is crucial.It enables analysing territories and helps stakeholders to take decisions.However,the understanding of Territorial Statistical Information(TSI)is compromised unless comprehensive description of both the statistical methodology used and the spatial and temporal references are given.Thus,in this paper,we stress the importance of metadata descriptions and of their quality that helps assessing data reliability.Furthermore,time-series of such TSI are paramount.They enable analysing a territory over a long period of time and likewise judging the effectiveness of reforms.In light of these observations,we present Spatio-Temporal evolutive Data Infrastructure(STeDI)an innovative Spatial Data Infrastructure(SDI)that enriches the description of a Digital Earth,providing a virtual representation of territories and of their evolution through statistics and time.STeDI aims at managing a whole dataflow of multi-dimensional,multi-scale and multi-temporal TSI,from their acquisition to their dissemination to scientists and policy-makers.The content of this SDI evolves autonomously thanks to automated processes and to a Web platform that help improving the quality of datasets uploaded by experts.Then,STeDI allows visualizing up-to-date time-series reflecting the human activities on a given territory.It helps policy-makers in their decision-making process.

Protein Residue Contact Prediction Based on Deep Learning and Massive Statistical Features from Multi-Sequence Alignment

Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3 D structure.An accurate residue-residue contact map is one of the essential elements for current ab initio prediction protocols of 3 D structure prediction.Recently,with the combination of deep learning and direct coupling techniques,the performance of residue contact prediction has achieved significant progress.However,a considerable number of current Deep-Learning(DL)-based prediction methods are usually time-consuming,mainly because they rely on different categories of data types and third-party programs.In this research,we transformed the complex biological problem into a pure computational problem through statistics and artificial intelligence.We have accordingly proposed a feature extraction method to obtain various categories of statistical information from only the multi-sequence alignment,followed by training a DL model for residue-residue contact prediction based on the massive statistical information.The proposed method is robust in terms of different test sets,showed high reliability on model confidence score,could obtain high computational efficiency and achieve comparable prediction precisions with DL methods that relying on multi-source inputs.

Micro-Expression Recognition Algorithm Based on Information Entropy Feature

The intensity of the micro-expression is weak,although the directional low frequency components in the image are preserved by many algorithms,the extracted micro-expression ft^ature information is not sufficient to accurately represent its sequences.In order to improve the accuracy of micro-expression recognition,first,each frame image is extracted from,its sequences,and the image frame is pre-processed by using gray normalization,size normalization,and two-dimensional principal component analysis(2DPCA);then,the optical flow method is used to extract the motion characteristics of the reduced-dimensional image,the information entropy value of the optical flow characteristic image is calculated by the information entropy principle,and the information entropy value is analyzed to obtain the eigenvalue.Therefore,more micro-expression feature information is extracted,including more important information,which can further improve the accuracy of micro-expression classification and recognition;finally,the feature images are classified by using the support vector machine(SVM).The experimental results show that the micro-expression feature image obtained by the information entropy statistics can effectively improve the accuracy of micro-expression recognition.

STATISTICAL INFORMATION OF THE DEVELOPMENT OF CHINA'S HEALTH WORK IN 1994

The following general status concerning nation-wide health institutions, hospital beds, personnel and hospital work as well as causes of diseases and death is briefly reported according to the Information Centre of Health Statistics of the Ministry of Public Health of China. HEALTH INSTITUTION, HOSPITAL BEDS AND PERSONNEL In 1994 there was a slight decrease in the number of China’s health institutions while the increasing rate of hospital beds was slowed down and the nmnber of health personnel continued to increase. Health institutions. In 1994 the number of various health institutions in China totalled 191.7 thousand. with a decrease of 1844 as against 1993: there