Proof of Retrievability for Image Data in Industrial Cloud Platform

2020 ◽  
Author(s):  
Guangchen Yu ◽  
Juan Ma ◽  
Ji Wang
Keyword(s):  
Image Data ◽  
Cloud Platform ◽  
Industrial Cloud ◽  
Proof Of Retrievability

scholarly journals BARcode DEmixing through Non-negative Spatial Regression (BarDensr)

2021 ◽  
Vol 17 (3) ◽  
pp. e1008256
Author(s):  
Shuonan Chen ◽  
Jackson Loper ◽  
Xiaoyin Chen ◽  
Alex Vaughan ◽  
Anthony M. Zador ◽  
...  
Keyword(s):  
State Of The Art ◽  
Spatial Regression ◽  
Image Data ◽  
Real Data ◽  
Optimization Methods ◽  
Spatial Density ◽  
Signal Recovery ◽  
Cloud Platform ◽  
Rna Transcripts ◽  
Different Types

Modern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many voxels; unfortunately, current analysis methods do not perform robustly in this highly-mixed setting. Here we develop a new analysis approach, BARcode DEmixing through Non-negative Spatial Regression (BarDensr): we start with a generative model of the physical process that leads to the observed image data and then apply sparse convex optimization methods to estimate the underlying (demixed) rolony densities. We apply BarDensr to simulated and real data and find that it achieves state of the art signal recovery, particularly in densely-labeled regions or data with low spatial resolution. Finally, BarDensr is fast and parallelizable. We provide open-source code as well as an implementation for the ‘NeuroCAAS’ cloud platform.

Study on the Data Mining of Image Storage on the Hadoop Cloud Platform

2014 ◽  
Vol 543-547 ◽  
pp. 3667-3670 ◽  
Author(s):  
Jian Xin Zhu
Keyword(s):  
Data Mining ◽  
Satellite Remote Sensing ◽  
Medical Image ◽  
Rapid Development ◽  
Image Data ◽  
Challenging Problem ◽  
Cloud Platform ◽  
Huge Number ◽  
Image Storage ◽  
Usable Information

Along with the rapid development of image acquisition and image storage, a huge number of usable image data are obtained by people, such as satellite remote sensing image data, medical image data, etc. Data mining of images is to analyze these useful images and extract the usable information from them. How to effectively store rapidly make data mining for the increasing images has become the most challenging problem faced by people. This paper focuses on data mining of the massive images with the help of the Hadoop cloud platform.

scholarly journals BUILDING SPATIOTEMPORAL CLOUD PLATFORM FOR SUPPORTING GIS APPLICATION

2015 ◽  
Vol II-4/W2 ◽  
pp. 55-62 ◽  
Author(s):  
W. W. Song ◽  
B. X. Jin ◽  
S. H. Li ◽  
X. Y. Wei ◽  
D. Li ◽  
...  
Keyword(s):  
High Performance ◽  
Image Data ◽  
Geospatial Data ◽  
Cloud Platform ◽  
Geospatial Information ◽  
Gis Application ◽  
Government Decision ◽  
Auto Scaling ◽  
Scaling Algorithms ◽  
Thematic Data

Traditional geospatial information platforms are built, managed and maintained by the geoinformation agencies. They integrate various geospatial data (such as DLG, DOM, DEM, gazetteers, and thematic data) to provide data analysis services for supporting government decision making. In the era of big data, it is challenging to address the data- and computing- intensive issues by traditional platforms. In this research, we propose to build a spatiotemporal cloud platform, which uses HDFS for managing image data, and MapReduce-based computing service and workflow for high performance geospatial analysis, as well as optimizing auto-scaling algorithms for Web client users’ quick access and visualization. Finally, we demonstrate the feasibility by several GIS application cases.

scholarly journals Enhanced Framework for Ensuring Privacy Preserving Image Retrieval in Cloud

2019 ◽  
Vol 8 (12) ◽  
pp. 2127-2132
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Research Work ◽  
Image Data ◽  
Cloud Service ◽  
Sensitive Information ◽  
Cloud Platform ◽  
Privacy And Security ◽  
Computing Platform ◽  
Computing Platforms

Large scale of images data sets are being produced every day by various digital devices. Due to huge computational jobs make people seizure to cloud platforms for their efficient & economical reckoning resources. These computing platforms in which assets are provided as services of the internet. Sensitive information stored in cloud makes more challenging in data security and access control. Once data is uploaded to cloud-platform, the privacy and security of image-data fully depend and believe upon cloud service provider honesty. Our proposed work deals with securing image where high protections are applied on multimedia contents. This paper deals with studies security challenges algorithms lies in image at the time of constructing cloud platform. In this a new enhanced security technique investigated, includes secure by using computation and encryption, act as a security information guard for high secrecy in cloud platform data storage areas. In our research work, cipher-text image is created and performing encryption-decryption at User level. Data hiding and ECC (Elliptic curve cryptosystem) based watermarking technique at cloud computing platform.

Dynamical Scattering in Crystalline Biological Specimens. I. Criteria of Validity for Scattering Approximations

1975 ◽  
Vol 33 ◽  
pp. 192-193
Author(s):  
Robert M. Glaeser ◽  
Bing K. Jap
Keyword(s):  
Biological Sciences ◽  
Electron Microscopy ◽  
Electron Microscope ◽  
Electron Diffraction ◽  
Born Approximation ◽  
Materials Science ◽  
Image Data ◽  
Dynamical Effect ◽  
Crystallographic Structure ◽  
Electron Microscope Image

The dynamical scattering effect, which can be described as the failure of the first Born approximation, is perhaps the most important factor that has prevented the widespread use of electron diffraction intensities for crystallographic structure determination. It would seem to be quite certain that dynamical effects will also interfere with structure analysis based upon electron microscope image data, whenever the dynamical effect seriously perturbs the diffracted wave. While it is normally taken for granted that the dynamical effect must be taken into consideration in materials science applications of electron microscopy, very little attention has been given to this problem in the biological sciences.

Application of an image management system in microscopy

1992 ◽  
Vol 50 (2) ◽  
pp. 1052-1053
Author(s):  
Richard S. Chemock
Keyword(s):  
Data Storage ◽  
Cost Savings ◽  
Image Data ◽  
Analytical Techniques ◽  
Operational Mode ◽  
Fine Grained ◽  
Image Recording ◽  
Primary Output ◽  
Capacity Data ◽  
Image Management System

One of the most common tasks in a typical analysis lab is the recording of images. Many analytical techniques (TEM, SEM, and metallography for example) produce images as their primary output. Until recently, the most common method of recording images was by using film. Current PS/2R systems offer very large capacity data storage devices and high resolution displays, making it practical to work with analytical images on PS/2s, thereby sidestepping the traditional film and darkroom steps. This change in operational mode offers many benefits: cost savings, throughput, archiving and searching capabilities as well as direct incorporation of the image data into reports.The conventional way to record images involves film, either sheet film (with its associated wet chemistry) for TEM or PolaroidR film for SEM and light microscopy. Although film is inconvenient, it does have the highest quality of all available image recording techniques. The fine grained film used for TEM has a resolution that would exceed a 4096x4096x16 bit digital image.

Digital differential hysteresis iimage processing displays what the microscope acquires but the eye can't see

1995 ◽  
Vol 53 ◽  
pp. 642-643
Author(s):  
Klaus-Ruediger Peters
Keyword(s):  
Image Processing ◽  
Visual System ◽  
High Precision ◽  
Image Data ◽  
Processing Technology ◽  
Output Data ◽  
Contrast Resolution ◽  
Pixel Intensity ◽  
Data Reading ◽  
Hysteresis Properties

Differential hysteresis processing is a new image processing technology that provides a tool for the display of image data information at any level of differential contrast resolution. This includes the maximum contrast resolution of the acquisition system which may be 1,000-times higher than that of the visual system (16 bit versus 6 bit). All microscopes acquire high precision contrasts at a level of <0.01-25% of the acquisition range in 16-bit - 8-bit data, but these contrasts are mostly invisible or only partially visible even in conventionally enhanced images. The processing principle of the differential hysteresis tool is based on hysteresis properties of intensity variations within an image.Differential hysteresis image processing moves a cursor of selected intensity range (hysteresis range) along lines through the image data reading each successive pixel intensity. The midpoint of the cursor provides the output data. If the intensity value of the following pixel falls outside of the actual cursor endpoint values, then the cursor follows the data either with its top or with its bottom, but if the pixels' intensity value falls within the cursor range, then the cursor maintains its intensity value.

SPECTRA: A program for processing electron images of crystals

1992 ◽  
Vol 50 (1) ◽  
pp. 132-133
Author(s):  
M.F. Schmid ◽  
R. Dargahi ◽  
M. W. Tam
Keyword(s):  
Lattice Distortion ◽  
Data Transfer ◽  
Reciprocal Lattice ◽  
Data Entry ◽  
Image Data ◽  
Distortion Correction ◽  
Specimen Preparation ◽  
Electron Image ◽  
Computer Controlled ◽  
Program Modules

Electron crystallography is an emerging field for structure determination as evidenced by a number of membrane proteins that have been solved to near-atomic resolution. Advances in specimen preparation and in data acquisition with a 400kV microscope by computer controlled spot scanning mean that our ability to record electron image data will outstrip our capacity to analyze it. The computed fourier transform of these images must be processed in order to provide a direct measurement of amplitudes and phases needed for 3-D reconstruction.In anticipation of this processing bottleneck, we have written a program that incorporates a menu-and mouse-driven procedure for auto-indexing and refining the reciprocal lattice parameters in the computed transform from an image of a crystal. It is linked to subsequent steps of image processing by a system of data bases and spawned child processes; data transfer between different program modules no longer requires manual data entry. The progress of the reciprocal lattice refinement is monitored visually and quantitatively. If desired, the processing is carried through the lattice distortion correction (unbending) steps automatically.

Information and estimation in image processing

1987 ◽  
Vol 45 ◽  
pp. 14-17
Author(s):  
B. Roy Frieden
Keyword(s):  
Image Processing ◽  
Optical System ◽  
Large Amplitude ◽  
Communication Theory ◽  
Image Data ◽  
Direct Approach ◽  
Ill Posed

Despite the skill and determination of electro-optical system designers, the images acquired using their best designs often suffer from blur and noise. The aim of an “image enhancer” such as myself is to improve these poor images, usually by digital means, such that they better resemble the true, “optical object,” input to the system. This problem is notoriously “ill-posed,” i.e. any direct approach at inversion of the image data suffers strongly from the presence of even a small amount of noise in the data. In fact, the fluctuations engendered in neighboring output values tend to be strongly negative-correlated, so that the output spatially oscillates up and down, with large amplitude, about the true object. What can be done about this situation? As we shall see, various concepts taken from statistical communication theory have proven to be of real use in attacking this problem. We offer below a brief summary of these concepts.

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