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130 results for “BITs”
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 2. Image stored in cloud each year (Perret, 2017)
<p>Currently, 4.7 trillion of photos are saved in the cloud (Perret, 2017). And only a few percentage are called to use again. So less used files can be stores in a compression technique which can save more space than time and make the cloud system faster as memory redundancy time will be reduced.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 1. Photo amount by year (Perret, 2017)
<p>Revolution of a portable camera with computer started to produce an exponential rate of media files, and users are sharing these files with everyone. So, using the cloud to store images is becoming a favorite choice for users. But cloud does not only store huge files which are approximately 1.2 trillion in 2017 (Perret, 2017), it also has to transfer these files to a different network to serve users. To reduce load, the cloud system started to use different compression algorithm. These algorithms have a tradeoff between time and space. Most of these have better time complexity than space. But as the cloud has powerful and distributed computing power, it may be better to focus on saving space. As data transfer takes more time than processing same data in the cloud. A perfect use case is a mobile sending the large image to the cloud takes more data transfer time than the compression and decompression process in the cloud. So, in this age of the distributed computer, it is better to reduce size as computation time is less important than network data transfer time.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 9. Resized image and extracted data for a 100*100 size image
<p>For the extraction, our goal is to divide an image into smaller blocks and keep the row and column data for these blocks. But for our experiment we used a single block, which means taking the full image as a single block. For bigger image we should always divide the image in separate blocks and work on them par rally. As in figure 8, after extracting the data we can add the row and column bits information in the resized image or saved in a separate file. For proof of concept we saved it in a text file. And later that file is used to feed GA to make the fitness function, in figure 9 an extraction has been shown. In upper and side textbox containing the information which later is saved in a text file.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 7. Basic Methodology
<p>As in figure 7 we are storing the extra data which is look like figure 8. Where a 20*20 size image of alphabet ‘A’ data has been stored. When we regenerate image, we are using these data.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 5. Image down sample
<p>Our proposal is to store each column and row bits count in a separate file and used that to reproduce the image using genetic algorithm.If we take 10% of an image size and the row and column image hamming bit count our total size will be approximately below 15% of the actual image size. We proposed a method to reproduce original image from using this 15% information.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 8. Sample Data Extraction for a 20*20 size image
<p>As in figure 7 we are storing the extra data which is look like figure 8. Where a 20*20 size image of alphabet ‘A’ data has been stored. When we regenerate image, we are using these data.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 4. Image regeneration using GA
<p>In 2008, Roger Johansson was able to regenerate a Mona Lisa image from random sampling (Roger Johansson, 2017). It uses a genetic algorithm to model a population of individuals, each containing a string of DNA which can be visualized in the form of an image (Grow Your Own Picture Genetic Algorithms & Generative Art, 2017).</p> <p>By starting with a population consisting of a randomly generated gene pool, each individual is compared to the reference image (the one on the left), and the individuals can then be ranked by their likeness to it, known as their "fitness", with the best fit being displayed on the output image (the one on the right) (Grow Your Own Picture Genetic Algorithms & Generative Art, 2017). By breeding the fittest individuals from the population, the DNA which produces the most accurate representation of the reference image is selected over successive generations, effectively demonstrating the power of a natural selection process to produce the best candidate for any given environment (Grow Your Own Picture Genetic Algorithms & Generative Art, 2017).</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 3. Data compression and image reconstruction (55:148 Digital Image Processing, 2017)
<p>There are several techniques which are normally divided into two categories lossy and lossless image compressions. In lossy compression, after recovery there are negligible difference present where lossless gives accurate image. Huffman encoding is very well known, which can provide optimal compression and decompression without error (55:148 Digital Image Processing, 2017). The basic idea of Huffman coding is to represent data by number of variable size, where more frequent info being represented by shorter number (55:148 Digital Image Processing, 2017). Currently the Lempel-Ziv (or Lempel-Ziv-Welch, LZW) algorithm for dictionary-based coding has got attention as a better compression algorithm (55:148 Digital Image Processing, 2017).</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 12. After few generation
<p>In figure 11 it is the initial population showed and figure 12 the population started to change and figure 13 we reached a convergence.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 13. Reached convergence
<p>In figure 11 it is the initial population showed and figure 12 the population started to change and figure 13 we reached a convergence.</p>
Biased RNG Bit Sequences, Dependent RNG Bit Sequences
<p>Matlab PRNG generated biased bit sequences without correlations and unbiased bit sequences with bit correlations; used for validation of the tests in Publication: https://doi.org/10.5281/zenodo.1286723</p>
Programmatic Image Sequences for Multi-Disciplinary Applications: 16 Bit TIFF Sequences, HDR FULL RANGE
<p>This dataset is a part of the paper "Defining and Characterizing Programmatic Image Sequences for Multi-Disciplinary Applications". This download is an extended part of the "Spatio-Temporal Noise Sequences: Multipurposed Pseudo-Random Visual Test Signals" project. The main project for this can be found on GitHub <a href="https://github.com/FloFriedrich/STnoise">https://github.com/FloFriedrich/STnoise</a></p>
Linked collectors and determiners for: Aggregated occurrence records of invasive European frog-bit (Hydrocharis morsus-ranae L.) across North America.
Natural history specimen data linked to collectors and determiners held within, "Aggregated occurrence records of invasive European frog-bit (Hydrocharis morsus-ranae L.) across North America". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/71454d8a-6e9c-49f5-bf37-353f9ad2e2b9">https://bionomia.net/dataset/71454d8a-6e9c-49f5-bf37-353f9ad2e2b9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/71454d8a-6e9c-49f5-bf37-353f9ad2e2b9">https://gbif.org/dataset/71454d8a-6e9c-49f5-bf37-353f9ad2e2b9</a>. Formatted as a Frictionless Data package.
Data used in "Qubit Readout Error Mitigation with Bit-flip Averaging"
<p>This data supports "Qubit Readout Error Mitigation with Bit-flip Averaging" by Alistair Smith, Kiran Khosla, Chris Self, and M. S. Kim (arXiv:2106.05800). </p> <p>Included are the measurement results obtained from 1-8 qubit calibration measurements on the <em>ibmq_manhattan</em> device (on 1st Dec 2020), which were used to define the "exact" device response matrices used in the paper. Also included are the results of the numerical simulations used to generate the data for figures 3, 4, and 5.</p>
KeyNet: An Open Source Dataset of Key Bittings
<p>This repository introduces a dataset of obverse and reverse images of 319 unique Schlage SC1 keys, labeled with each key's bitting code. </p> <p>We make our data accessible in an HDF5 format, through arrays aligned where the Nth index of each array represents the Nth key, with keys sorted ascending by bitting code:</p> <ul> <li><code>/bittings</code>: Each keys 1-9 bitting code, recorded from shoulder through the tip of the key, <code>uint8</code> of shape (319,5).</li> <li><code>/obverse</code>: Obverse image of each key, <code>uint8</code> of shape (319,512,512,3).</li> <li><code>/reverse</code>: Reverse image of each key, <code>uint8</code> of shape (319,512,512,3).</li> </ul> <p>Full dataset details available on <a href="https://github.com/alexxke/keynet">GitHub</a></p>
[ELMI2023] BioImage Town (BIT) FAIR Data Metro Map
<p>Figures created collaboratively by the presenters of the Data Management and Analysis session of ELMI2023 (https://elmi2023.eu/) for their presentations. They represent an idealized metro through which data ("the passengers") travel between various solutions ("the stops") within bioimaging ("BioImage Town"), but also connecting to IT solutions, metadata, and other areas, though of course the real situation is much more complicated. Working together, we should be able to the improve the number of easy-to-use, performant, and complete solutions through BioImage Town for the benefit of the community.</p>
[HCB] BioImage Town (BIT) FAIR Data Metro Map
<p>Figures created collaboratively by the presenters of the Data Management and Analysis session of ELMI2023 (https://elmi2023.eu/) for their presentations. They represent an idealized metro through which data ("the passengers") travel between various solutions ("the stops") within bioimaging ("BioImage Town"), but also connecting to IT solutions, metadata, and other areas, though of course the real situation is much more complicated. Working together, we should be able to the improve the number of easy-to-use, performant, and complete solutions through BioImage Town for the benefit of the community.</p> <p>See previous version at https://zenodo.org/record/8019760</p> <p> </p>
After a catastrophe, a little bit of sex is better than nothing: genetic consequences of a major earthquake on asexual and sexual populations
<p>Catastrophic events can have profound effects on the demography of a population and consequently, on genetic diversity. The dynamics of post-catastrophic recovery as well as the role of sexual versus asexual reproduction in buffering the effects of massive perturbations remain poorly understood, in part because the opportunity to document genetic diversity before and after such events is rare. Six natural (purely sexual) and seven cultivated (mainly clonal due to farming practices) populations of the red alga Agarophyton chilense were surveyed along the Chilean coast before, in the days after and two years after the 8.8 magnitude earthquake in 2010. The genetic diversity of sexual populations appeared sensitive to this massive perturbation, notably through the loss of rare alleles immediately after the earthquake. By 2012, the levels of diversity returned to those observed before the catastrophe, probably due to migration. In contrast, enhanced rates of clonality in cultivated populations conferred a surprising ability to buffer the instantaneous loss of diversity. After the earthquake, farmers increased the already high rate of clonality to maintain the few surviving beds, but most of them collapsed rapidly. Contrasting fates between sexual and clonal populations suggest that betting on strict clonality to sustain production is risky, probably because this extreme strategy hampered adaptation to the brutal environmental perturbation induced by the catastrophe.</p>
Bit Pot
Made for Gigital Lab of Humanity Institute of Siberian Federal University Source: Objaverse 1.0 / Sketchfab
Ethiopian Pot (needs bit of interior work)
Its imperfection is its perfection as it shows that they are so poor they cannot make perfect pots as they cannot afford the equipment to do so. Source: Objaverse 1.0 / Sketchfab
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