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No abstract uploaded!

In this report, we have mainly developed an abstract framework of k bit (t,n) secret sharing framework for cloud storage. Such framework is clean and it can be implemented. A successful implementation of the framework would provide users with protection when the system is under the attack on its confidentiality,integrity and reliability. Furthermore, such system has its own encryption by using permutations and tailor made error detection, location and data rescue. We make use of Lagrange polynomials and take the advantages of the algebraic property “t distinct points on the plane can uniquely determine a polynomial function of degree t-1” to design a k bit (t,n) -secret sharing distributed storage. We employ the set with unique factorization property (UFP) so that we simply need to calculate the y intercept of a Lagrange polynomial and then use a look up table to recover a secret.Moreover, the set which has minimum UFP would help us to design storage with smallest containers. In addition to the algebraic methods, we can utilize the geometric facts that three non collinear points determine a unique circle and four non coplanar points determine a unique sphere to construct k bit (3,n) and k bit (4,n) secret sharing storage respectively. To generalize to arbitrary case, it is straight forward if we have defined the Haar measure on the higher dimensional unit sphere. The last method is an application of Chinese Remainder Theorem (CRT) and we have designed k bit (t,n) secret sharing distributed storage and one of the designs can produce containers with the half size of the original secret. However, such k is no longer unrestricted and it has to be chosen from an interval. We have developed a C program for implementing both algebraic, geometric k bit (3,n) secret sharing distributed storages as well as the CRT method. The performances of both algebraic and geometric designs are satisfactory in term of processing time and compressed container size. The container size is even half of the size of the original secret in the CRT case and it is also very speedy.

No abstract uploaded!

Sign language bridges the communication between the deaf-mute and the rest of the world. With the development of computer technology, the research on sign language recognition is prospering. Currently, there are two kinds of research methods. One is based on machine vision, the other is based on wearable input devices. Comparing with machine vision, wearable devices have the advantage of being able to get real-time information of the bend of the fingers and the movement of the hand. The essay applies recent technology in the field of voice recognition, and aims at finding out the best algorithm for medium vocabulary continuous sign language recognition. By building a hybrid Deep Neural Network-Hidden Markov Model, and designing and making a low-cost digital glove, the essay successfully compares the features between Dynamic Time Warping (DTW), Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) and Deep Neural Network-Hidden Markov Model (DNN-HMM) as of solving the problem of sign language recognition. Tests show that in terms of calculating the observation probability, DNN has a much better performance than HMM, especially when syntax is not provided, meaning that DNN better suits the developing trend of sign language recognition. Real-time recognition is achieved on the intelligent terminal with an accuracy of over 97%, using the trained model and a decoding program.