Efficient implementation of x-ray ghost imaging based on a modified compressive sensing algorithm

Towards efficient implementation of x-ray ghost imaging(XGI),efficient data acquisition and fast image reconstruction together with high image quality are preferred.In view of radiation dose resulted from the incident x-rays,fewer measurements with sufficient signal-to-noise ratio(SNR)are always anticipated.Available methods based on linear and compressive sensing algorithms cannot meet all the requirements simultaneously.In this paper,a method based on a modified compressive sensing algorithm with conjugate gradient descent method(CGDGI)is developed to solve the problems encountered in available XGI methods.Simulation and experiments demonstrate the practicability of CGDGI-based method for the efficient implementation of XGI.The image reconstruction time of sub-second implicates that the proposed method has the potential for real-time XGI.

Monitoring Carbon Dioxide from Space：Retrieval Algorithm and Flux Inversion Based on GOSAT Data and Using CarbonTracker-China

Monitoring atmospheric carbon dioxide（CO_2） from space-borne state-of-the-art hyperspectral instruments can provide a high precision global dataset to improve carbon flux estimation and reduce the uncertainty of climate projection. Here, we introduce a carbon flux inversion system for estimating carbon flux with satellite measurements under the support of ＂The Strategic Priority Research Program of the Chinese Academy of Sciences—Climate Change： Carbon Budget and Relevant Issues＂. The carbon flux inversion system is composed of two separate parts： the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing（IAPCAS）, and Carbon Tracker-China（CT-China）, developed at the Chinese Academy of Sciences. The Greenhouse gases Observing SATellite（GOSAT） measurements are used in the carbon flux inversion experiment. To improve the quality of the IAPCAS-GOSAT retrieval, we have developed a post-screening and bias correction method, resulting in 25%–30% of the data remaining after quality control. Based on these data, the seasonal variation of XCO_2（column-averaged CO_2dry-air mole fraction） is studied, and a strong relation with vegetation cover and population is identified. Then, the IAPCAS-GOSAT XCO_2 product is used in carbon flux estimation by CT-China. The net ecosystem CO_2 exchange is-0.34 Pg C yr^（-1）（±0.08 Pg C yr^（-1））, with a large error reduction of 84%, which is a significant improvement on the error reduction when compared with in situ-only inversion.

Estimation of ocean primary productivity and its spatio-temporal variation mechanism for East China Sea based on VGPM model

According to calculation results of ocean chlorophyll concentration based on SeaWiFS data by SeaBAM model and synchronous ship-measured data, this research set up an improved model for CaseⅠand CaseⅡwater bodies respectively. The monthly chlorophyll distribution in the East China Sea in 1998 was obtained from this improved model on calculation results of SeaBAM. The euphotic depth distribution in 1998 in the East China Sea is calculated by using remote sensing data of K 490 from SeaWiFS according to the relation between the euphotic depth and the oceanic diffuse attenuation coefficient. With data of ocean chlorophyll concentration, euphotic depth, ocean surface photosynthetic available radiation (PAR), daily photoperiod and optimal rate of daily carbon fixation within a water column, the monthly and annual primary productivity spatio-temporal distributions in the East China Sea in 1998 were obtained based on VGPM model. Based on analysis of those distributions, the conclusion can be drawn that there is a clear bimodality character of primary productivity in the monthly distribution in the East China Sea. In detail, the monthly distribution of primary productivity stays the lowest level in winter and rises rapidly to the peak in spring. It gets down a little in summer, and gets up a little in autumn. The daily average of primary productivity in the whole East China Sea is 560.03 mg/m 2 /d, which is far higher than the average of subtropical ocean areas. The annual average of primary productivity is 236.95 g/m 2 /a. The research on the seasonal variety mechanism of primary productivity shows that several factors that affect the spatio-temporal distribution may include the chlorophyll concentration distribution, temperature condition, the Yangtze River diluted water variety, the euphotic depth, ocean current variety, etc. But the main influencing factors may be different in each local sea area.

A modified voice conversion algorithm using compressed sensing

A voice conversion algorithm,which makes use of the information between continuous frames of speech by compressed sensing,is proposed in this paper.According to the sparsity property of the concatenated vector of several continuous Linear Spectrum Pairs（LSP）in the discrete cosine transformation domain,this paper utilizes compressed sensing to extract the compressed vector from the concatenated LSPs and uses it as the feature vector to train the conversion function.The results of evaluations demonstrate that the performance of this approach can averagely improve 3.21%with the conventional algorithm based on weighted frequency warping when choosing the appropriate numbers of speech frame.The experimental results also illustrate that the performance of voice conversion system can be improved by taking full advantage of the inter-frame information,because those information can make the converted speech remain the more stable acoustic properties which is inherent in inter-frames.

Modified optical remote sensing algorithms for the Pearl River Estuary

This study aims to develop new algorithms to retrieve sea surface parameters including concentrations of Chlorophyll a （Chl a） and Suspended Particulate Matter （SPM）, and absorbance of Colored Dissolved Organic Matter （aCDOM） by incorporating the contribution of red bands to make them more adaptable to case 2 waters. Optical remote sensing algorithms have demonstrated efficient retrieval of Chl a, SPM, and aCDOM, yet they are not very accurate especially for coastal areas. It has also been found that the default algorithm has overestimated Chl a in the Pearl River Estuary, and shown poor correlation for CDOM absorbance. By incorporating the red band ratios into the algorithm, a correction effect has been shown, which improves the accuracy of quantifying the actual concentration. Modeling and data fitting of the algorithm have been done based on 61 data samples collected in the Pearl River estuary during a cruise from 3 to 11 May 2014. The study also attempts to modify the aerosol correction bands used in SeaDAS to prevent saturation of these bands. The modified algorithms showed an R-Square value of 0.7289 for Chl a fitting, and 0.7338 for CDOM fitting, and corrected overestimation of Chl a concentration in the Pearl River estuary.

Privacy-preserving human activity sensing:A survey

With the prevalence of various sensors and smart devices in people’s daily lives,numerous types of information are being sensed.While using such information provides critical and convenient services,we are gradually exposing every piece of our behavior and activities.Researchers are aware of the privacy risks and have been working on preserving privacy while sensing human activities.This survey reviews existing studies on privacy-preserving human activity sensing.We first introduce the sensors and captured private information related to human activities.We then propose a taxonomy to structure the methods for preserving private information from two aspects:individual and collaborative activity sensing.For each of the two aspects,the methods are classified into three levels:signal,algorithm,and system.Finally,we discuss the open challenges and provide future directions.