32 Bit Pc Games Highly Compressed Download
Arleen Jerdee
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Ammonia is an important compound with many uses, such as in the manufacture of fertilizers, explosives and pharmaceuticals. As an archetypal hydrogen-bonded system, the properties of ammonia under pressure are of fundamental interest, and compressed ammonia has a significant role in planetary physics. We predict new high-pressure crystalline phases of ammonia (NH(3)) through a computational search based on first-principles density-functional-theory calculations. Ammonia is known to form hydrogen-bonded solids, but we predict that at higher pressures it will form ammonium amide ionic solids consisting of alternate layers of NH(4)(+) and NH(2)(-) ions. These ionic phases are predicted to be stable over a wide range of pressures readily obtainable in laboratory experiments. The occurrence of ionic phases is rationalized in terms of the relative ease of forming ammonium and amide ions from ammonia molecules, and the volume reduction on doing so. We also predict that the ionic bonding cannot be sustained under extreme compression and that, at pressures beyond the reach of current static-loading experiments, ammonia will return to hydrogen-bonded structures consisting of neutral NH(3) molecules.
Your real compression performance will probably depend a lot on the data you are putting in. Is it all geometries? If you have a lot of non-spatial data (or a lot of text attributes for spatial points), then it doesn't really matter what you do the geometries - you need to find some way to compress that data instead.
As others have said, I think you are going to struggle to find a format that meets your compression requirements. You would have to create your own custom format, which given your requirement to use commercial software is not going to be viable.
I think you need to possibly first consider how you can make your data models more efficient, then look at the compression aspects. For example, do you have a lot of repetition of geometry? You could then have a base set of geometry layers with unique IDs and then separate attribute data sets that reference the geometry by ID - that way you can have multiple views of the same geometry serving specific functions. Most decent software packages will then allow you to create joins or relates in order to create the unified view for a layer.
GML is a good example of a format that supports this kind of relational data model, though being a verbose format file sizes will be large. You can compress GML using gzip compression and can potentially get a 20:1 ratio but then you are relying on the software being able to support compressed GML.
Regardless, I would urge you to first look at your data models and see where there could be savings to be had. FME from Safe Software is your best bet if you need to start manipulating your data models.
To achieve that sort of ratio, you could use some sort of lossy compression, but I don't know of anything that uses it, and although I have a couple of ideas on how one might implement it, it would be far from standard. It would be much much cheaper to kit your server out with a 1TB disk than to spend time and money developing a custom solution.
You are also confusing data storage with data representation. Your 4th point mentions being able to view the data at different scales, but this is a function of your renderer, not the format per se. Again, a hypothetical lossily compressed file could store data at various resolutions in a sort of LoD structure, but that is likely to increase data size if anything.
If your data is to be on a server somewhere accessible by mobile applications, you're far better off using existing tools that have been designed for the purpose. A WFS server (such as GeoServer or MapServer) is ideally suited to this sort of application. The client makes a request for data of a specific area, normally that covered by the screen, and the WFS sends vector data for just that area, so all the heavy lifting is done by the server. It's then up to the application to render that data. An alternative would be to use the WMS features of MapServer and GeoServer, in which all the rendering is done by the server, and then it sends an image tile to the client. This enables features such as server-side caching of tiles, as well as scale-dependent rendering, with the minimum of work by you. They both read myriad formats, so you can author your data exactly how you like, and store it where you like, and they do all the cool stuff. Quantum GIS also has a WMS server, so you can author and serve data all in the same application.
Compressed sensing (CS) is a recent mathematical technique that leverages the sparsity in certain sets of data to solve an underdetermined system and recover a full set of data from a sub-Nyquist set of measurements of the data. Given the size and sparsity of the data, radar has been a natural choice to apply compressed sensing to, typically in the fast-time and slow-time domains. Polarimetric synthetic aperture radar (PolSAR) generates a particularly large amount of data for a given scene; however, the data tends to be sparse. Recently a technique was developed to recover a dropped PolSAR channel by leveraging antenna crosstalk information and using compressed sensing. In this dissertation, we build upon the initial concept of the dropped-channel PolSAR CS in three ways. First, we determine a metric which relates the measurement matrix to the l2 recovery error. The new metric is necessary given the deterministic nature of the measurement matrix. We then determine a range of antenna crosstalk required to recover a dropped PolSAR channel. Second, we propose a new antenna design that incorporates the relatively high levels of crosstalk required by a dropped-channel PolSAR system. Finally, we integrate fast- and slow-time compression schemes into the dropped-channel model in order to leverage sparsity in additional PolSAR domains and overall increase the compression ratio. The completion of these research tasks has allowed a more accurate description of a PolSAR system that compresses in fast-time, slow-time, and polarization; termed herein as highly compressed PolSAR. The description of a highly compressed PolSAR system is a big step towards the development of prototype hardware in the future.
The development of equations-of-state and transport models in areas such as shock compression and fusion energy science is critical to DOE programs. Notable shortcomings in these activities are phase transitions in highly compressed metals. Fully characterizing high energy density phenomena using pulsed power facilities is possible only with complementary numerical modeling for design, diagnostics, and data interpretation.
This team constructed a multiscale simulation framework based on a combination of high-fidelity electronic structure data, ML, and molecular dynamics enabling quantum-accurate, computationally efficient predictions. This provides kinetics of magneto-structural phase transitions along shock Hugoniots and ramp compression paths in the equations of state, and transport properties such as viscosity, electrical and thermal conductivities. Findings from this project were published in the Journal of Material Science and npj Computational Materials.
The single-pixel imaging technique uses multiple patterns to modulate the entire scene and then reconstructs a two-dimensional (2-D) image from the single-pixel measurements. Inspired by the statistical redundancy of natural images that distinct regions of an image contain similar information, we report a highly compressed single-pixel imaging technique with a decreased sampling ratio. This technique superimposes an occluded mask onto modulation patterns, realizing that only the unmasked region of the scene is modulated and acquired. In this way, we can effectively decrease 75% modulation patterns experimentally. To reconstruct the entire image, we designed a highly sparse input and extrapolation network consisting of two modules: the first module reconstructs the unmasked region from one-dimensional (1-D) measurements, and the second module recovers the entire scene image by extrapolation from the neighboring unmasked region. Simulation and experimental results validate that sampling 25% of the region is enough to reconstruct the whole scene. Our technique exhibits significant improvements in peak signal-to-noise ratio (PSNR) of 1.5 dB and structural similarity index measure (SSIM) of 0.2 when compared with conventional methods at the same sampling ratios. The proposed technique can be widely applied in various resource-limited platforms and occluded scene imaging.
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