Evaluation of Arable Land Reserve Resources and Analysis of Restrictive Factors: A Case Study of Hangjin Banner in Inner Mongolia

Taking land available for cultivation and mining land available for reclamation in Hangjin Banner of Inner Mongolia Autonomous Region, using land use database of 2012 as evaluation base, it made evaluation of arability of 677 021.40 hm2 reserve land resources by the restrictive factor evaluation method. Besides, it analyzed main restrictive factors of arable land reserve resources in Hangjin Banner. Results indicate that the total area arable land reserve resources is 52 200.02 hm2, accounting for 7.71% of total area evaluated. Irrigation condition and soil thickness are major factors restricting development of arable land reserve resources in the study area. It is expected to provide reference for development of arable land reserve resources and land consolidation project in Hangjin Banner

A New Color Image Encryption Scheme Using CML and a Fractional-Order Chaotic System

Funding: This research was jointly supported by the National Natural Science Foundation of China (No. 61004006, http://www.nsfc.gov.cn), China Postdoctoral Science Foundation(No. 2013M530181, http://res.chinapostdoctor.org.cn/BshWeb/index.shtml), the Natural Science Foundation of Henan Province, China (No. 13230010254, http://www.hnkjt.gov.cn/, Program for Science & Technology Innovation Talents in Universities of Henan Province, China (Grant No 14HASTIT042, http://rcloud.edu.cn), the Foundation for University Young Key Teacher Program of Henan Province, China (No. 2011GGJS-025, http://www.haedu.gov.cn/), Shanghai Postdoctoral Scientific Program (No. 13R21410600, http://www.21cnhr.gov.cn/doctorarea/), the Science & Technology Project Plan of Archives Bureau of Henan Province (No. 2012-X-62, http://www.hada.gov.cn/) and the Natural Science Foundation of Educational Committee of Henan Province, China (No. 13A520082, http://www.haedu.gov.cn/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

An ASIFT-based local registration method for satellite imagery

Imagery registration is a fundamental step, which greatly affects later processes in image mosaic, multi-spectral image fusion, digital surface modelling, etc., where the final solution needs blending of pixel information from more than one images. It is highly desired to find a way to identify registration regions among input stereo image pairs with high accuracy, particularly in remote sensing applications in which ground control points (GCPs) are not always available, such as in selecting a landing zone on an outer space planet. In this paper, a framework for localization in image registration is developed. It strengthened the local registration accuracy from two aspects: less reprojection error and better feature point distribution. Affine scale-invariant feature transform (ASIFT) was used for acquiring feature points and correspondences on the input images. Then, a homography matrix was estimated as the transformation model by an improved random sample consensus (IM-RANSAC) algorithm. In order to identify a registration region with a better spatial distribution of feature points, the Euclidean distance between the feature points is applied (named the S criterion). Finally, the parameters of the homography matrix were optimized by the Levenberg–Marquardt (LM) algorithm with selective feature points from the chosen registration region. In the experiment section, the Chang’E-2 satellite remote sensing imagery was used for evaluating the performance of the proposed method. The experiment result demonstrates that the proposed method can automatically locate a specific region with high registration accuracy between input images by achieving lower root mean square error (RMSE) and better distribution of feature points

Parametric Reshaping of Portraits in Videos

Sharing short personalized videos to various social media networks has become quite popular in recent years. This raises the need for digital retouching of portraits in videos. However, applying portrait image editing directly on portrait video frames cannot generate smooth and stable video sequences. To this end, we present a robust and easy-to-use parametric method to reshape the portrait in a video to produce smooth retouched results. Given an input portrait video, our method consists of two main stages: stabilized face reconstruction, and continuous video reshaping. In the first stage, we start by estimating face rigid pose transformations across video frames. Then we jointly optimize multiple frames to reconstruct an accurate face identity, followed by recovering face expressions over the entire video. In the second stage, we first reshape the reconstructed 3D face using a parametric reshaping model reflecting the weight change of the face, and then utilize the reshaped 3D face to guide the warping of video frames. We develop a novel signed distance function based dense mapping method for the warping between face contours before and after reshaping, resulting in stable warped video frames with minimum distortions. In addition, we use the 3D structure of the face to correct the dense mapping to achieve temporal consistency. We generate the final result by minimizing the background distortion through optimizing a content-aware warping mesh. Extensive experiments show that our method is able to create visually pleasing results by adjusting a simple reshaping parameter, which facilitates portrait video editing for social media and visual effects

Regulation of B cell fate by chronic activity of the IgE B cell receptor.

IgE can trigger potent allergic responses, yet the mechanisms regulating IgE production are poorly understood. Here we reveal that IgE+ B cells are constrained by chronic activity of the IgE B cell receptor (BCR). In the absence of cognate antigen, the IgE BCR promoted terminal differentiation of B cells into plasma cells (PCs) under cell culture conditions mimicking T cell help. This antigen-independent PC differentiation involved multiple IgE domains and Syk, CD19, BLNK, Btk, and IRF4. Disruption of BCR signaling in mice led to consistently exaggerated IgE+ germinal center (GC) B cell but variably increased PC responses. We were unable to confirm reports that the IgE BCR directly promoted intrinsic apoptosis. Instead, IgE+ GC B cells exhibited poor antigen presentation and prolonged cell cycles, suggesting reduced competition for T cell help. We propose that chronic BCR activity and access to T cell help play critical roles in regulating IgE responses

Evaluation of positioning error-induced pixel shifts on satellite linear push-broom imagery

Georeferencing is one of the major tasks of satellite-borne remote sensing. Compared to traditional indirect methods, direct georeferencing through a Global Positioning System/inertial navigation system requires fewer and simpler steps to obtain exterior orientation parameters of remotely sensed images. However, the pixel shift caused by geographic positioning error, which is generally derived from boresight angle as well as terrain topography variation, can have a great impact on the precision of georeferencing. The distribution of pixel shifts introduced by the positioning error on a satellite linear push-broom image is quantitatively analyzed. We use the variation of the object space coordinate to simulate different kinds of positioning errors and terrain topography. Then a total differential method was applied to establish a rigorous sensor model in order to mathematically obtain the relationship between pixel shift and positioning error. Finally, two simulation experiments are conducted using the imaging parameters of Chang’ E-1 satellite to evaluate two different kinds of positioning errors. The experimental results have shown that with the experimental parameters, the maximum pixel shift could reach 1.74 pixels. The proposed approach can be extended to a generic application for imaging error modeling in remote sensing with terrain variation

Machine Vision based Grabbing Objects with Manipulator System Design

In recent years, machine vision technology and robot control technology have attracted lots attention of the researchers. They provide people with fast and efficient services in many fields, which have an increasingly important impact on the modern manufacturing industry and the inspection industry. In this paper, a mechanical vision-based grab control system based on machine vision is developed and analyzed accordingly. This design employs industrial cameras with Gigabit Ethernet ports, six-degree-of-freedom servo drive robots. The Host computer control software is designed on the development platform provided by Microsoft and processed in machine vision image processing. The software has implemented an image processing algorithm. It aims to combine machine vision, robot control and other technologies to achieve precise positioning, recognition and capture of targets. In the end, the proposed method is displayed in the upper computer accordingly

Panoramic mosaics from Chang’E-3 PCAM images at Point A

This paper presents a unique approach for panoramic mosaics based on Moon surface images from the Chang’E-3 (CE-3) mission, with consideration of the exposure time and external illumination changes in CE-3 Panoramic Camera (PCAM) imaging. The engineering implementation involves algorithms of image feature points extraction by using Speed-Up Robust Features (SURF), and a newly defined measure is used to obtain the corresponding points in feature matching. Then, the transformation matrix is calculated and optimized between adjacent images by the Levenberg–Marquardt algorithm. Finally, an image is reconstructed by using a fade-in-fade-out method based on linear interpolation to achieve a seamless mosaic. The developed algorithm has been tested with CE-3 PCAM images at Point A (one of the rover sites where the rover is separated from the lander). This approach has produced accurate mosaics from CE-3 PCAM images, as is indicated by the value of the Peak Signal to Noise Ratio (PSNR), which is greater than 31 dB between the overlapped region of the images before and after fusion

Mining the bitter melon (momordica charantia l.) seed transcriptome by 454 analysis of non-normalized and normalized cDNA populations for conjugated fatty acid metabolism-related genes

Abstract Background Seeds of Momordica charantia (bitter melon) produce high levels of eleostearic acid, an unusual conjugated fatty acid with industrial value. Deep sequencing of non-normalized and normalized cDNAs from developing bitter melon seeds was conducted to uncover key genes required for biotechnological transfer of conjugated fatty acid production to existing oilseed crops. It is expected that these studies will also provide basic information regarding the metabolism of other high-value novel fatty acids. Results Deep sequencing using 454 technology with non-normalized and normalized cDNA libraries prepared from bitter melon seeds at 18 DAP resulted in the identification of transcripts for the vast majority of known genes involved in fatty acid and triacylglycerol biosynthesis. The non-normalized library provided a transcriptome profile of the early stage in seed development that highlighted the abundance of transcripts for genes encoding seed storage proteins as well as for a number of genes for lipid metabolism-associated polypeptides, including Δ12 oleic acid desaturases and fatty acid conjugases, class 3 lipases, acyl-carrier protein, and acyl-CoA binding protein. Normalization of cDNA by use of a duplex-specific nuclease method not only increased the overall discovery of genes from developing bitter melon seeds, but also resulted in the identification of 345 contigs with homology to 189 known lipid genes in Arabidopsis. These included candidate genes for eleostearic acid metabolism such as diacylglycerol acyltransferase 1 and 2, and a phospholipid:diacylglycerol acyltransferase 1-related enzyme. Transcripts were also identified for a novel FAD2 gene encoding a functional Δ12 oleic acid desaturase with potential implications for eleostearic acid biosynthesis. Conclusions 454 deep sequencing, particularly with normalized cDNA populations, was an effective method for mining of genes associated with eleostearic acid metabolism in developing bitter melon seeds. The transcriptomic data presented provide a resource for the study of novel fatty acid metabolism and for the biotechnological production of conjugated fatty acids and possibly other novel fatty acids in established oilseed crops.</p