Efficient Random Grid Visual Cryptographic Schemes having Essential Members

In Conway’s Game of Life [2], if one starts with a large array of randomly set cells, then after around twenty thousand generations one will see that all motion has died down, and only stationary objects of low period remain, providing a final density of about .0287. No methods are known for proving rigorously that this behavior should occur, but it is reliably observed in simulations. This brings up several interesting related questions. Why does this “freezing” occur? After everything has frozen, what is the remaining debris composed of? Is there some construction that can “eat through” the debris? If we start with an infinitely large random grid, so that all constructions appear somewhere, what will the long term behavior be? It seems clear that knowing the composition of typical debris is central to many such questions. Much effort has gone into analyzing the objects that occur in such stationary debris, as well as into determining what stationary objects can exist at all in Life [4, 8], Both of these endeavors depend on having some notion of what an “object” is in the first place. One simple notion is that of an island, a maximal set of live cells connected to each other by paths of purely live cells. But many common objects, such as the “aircraft carrier,” are not connected so strongly. They are composed of more than one island, but we think of them as a single object anyway, since their constituent islands are not separately stable. Any pattern that is stable (has period one, i.e., does not change over time) is called a still life. Since a collection of stable objects can satisfy this definition, the term strict still life is used to refer to a single indivisible stable object, and pseudo still life is used to refer to a stable pattern that is composed of distinct strict still lifes. For example, the bi-block is a pseudo still life, since it is composed of two blocks, but the aircraft carrier is a strict still life, since its islands are not stable on their own.

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Random Grid and Reversible Watermarking-Based On Verifiable Secret Sharing for Outsourcing Images in Cloud

International Journal of Digital Crime and Forensics ◽

10.4018/ijdcf.2018010103 ◽

2018 ◽

Vol 10 (1) ◽

pp. 24-39 ◽

Cited By ~ 3

Author(s):

Hang Gao ◽

Mengting Hu ◽

Tiegang Gao ◽

Renhong Cheng

Keyword(s):

Secret Sharing ◽

Reversible Watermarking ◽

Initial Value ◽

Verifiable Secret Sharing ◽

Authorized User ◽

Secret Image ◽

Data Owner ◽

Xor Operation ◽

Random Grid ◽

Cloud Server

A novel random grid and reversible watermarking based verifiable secret sharing scheme for outsourcing image in cloud is proposed in the paper. In the proposed scheme, data owner firstly embeds the hash value of the secret image into the secret image itself using reversible watermarking algorithm; then, watermarked image is divided into $n$ sub image. Secondly, the hash of n sub image is calculated, and then the hash value is transformed into the initial value of hyper-chaos, thus n random grids are generated by different hyper-chaos. Lastly, after expanding the sub-image to the same size with the original secret image, it is performer XOR operation with the corresponding random grid, this will accordingly produce $n$ sharing secret. In order to securely outsource the image in the cloud, the generated shares are issued to the $n$ different cloud server. For authorized user, (s)he can get shares from different cloud server, and then can recover the original secret image through a series of decryption operations and extraction of reversible watermarking. The proposed scheme can losslessly restore the original secret image, and have the double verification ability, that is to say, it can verify whether the anyone of the sharing is modified, and it can also verify whether the original secret image is completely reconstructed. Some analysis and comparisons are given to show the security and effectiveness of proposed scheme.

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An Approach to Analyse Errors Introduced in the Random Grid Strain Measurement Method

Strain ◽

10.1111/j.1475-1305.2008.00444.x ◽

2008 ◽

Vol 46 (3) ◽

pp. 258-266 ◽

Cited By ~ 5

Author(s):

A. P. Iliopoulos ◽

N. P. Andrianopoulos

Keyword(s):

Strain Measurement ◽

Measurement Method ◽

Random Grid

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Random-Grid Based Region Incrementing Visual Secret Sharing

Fundamenta Informaticae ◽

10.3233/fi-2015-1185 ◽

2015 ◽

Vol 137 (3) ◽

pp. 369-386

Author(s):

Sachin Kumar ◽

Rajendra Kumar Sharma

Keyword(s):

Secret Sharing ◽

Visual Secret Sharing ◽

Random Grid ◽

Grid Based

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The Research of Depth Perception Method Based on Sparse Random Grid

2018 11th International Workshop on Human Friendly Robotics (HFR) ◽

10.1109/hfr.2018.8633523 ◽

2018 ◽

Author(s):

Dongxue Li ◽

Fang Xu ◽

Fengshan Zou ◽

Pei Di ◽

Hongyu Wang

Keyword(s):

Depth Perception ◽

Random Grid

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Recurrent Neural Network for Inertial Gait User Recognition in Smartphones

Sensors ◽

10.3390/s19184054 ◽

2019 ◽

Vol 19 (18) ◽

pp. 4054 ◽

Cited By ~ 4

Author(s):

Fernandez-Lopez ◽

Liu-Jimenez ◽

Kiyokawa ◽

Wu

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Inertial Sensors ◽

Selection Process ◽

Gait Recognition ◽

Recognition Algorithm ◽

Tuning Method ◽

Random Grid ◽

Hyperparameter Selection ◽

User Recognition

In this article, a gait recognition algorithm is presented based on the information obtained from inertial sensors embedded in a smartphone, in particular, the accelerometers and gyroscopes typically embedded on them. The algorithm processes the signal by extracting gait cycles, which are then fed into a Recurrent Neural Network (RNN) to generate feature vectors. To optimize the accuracy of this algorithm, we apply a random grid hyperparameter selection process followed by a hand-tuning method to reach the final hyperparameter configuration. The different configurations are tested on a public database with 744 users and compared with other algorithms that were previously tested on the same database. After reaching the best-performing configuration for our algorithm, we obtain an equal error rate (EER) of 11.48% when training with only 20% of the users. Even better, when using 70% of the users for training, that value drops to 7.55%. The system manages to improve on state-of-the-art methods, but we believe the algorithm could reach a significantly better performance if it was trained with more visits per user. With a large enough database with several visits per user, the algorithm could improve substantially.

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