Optimized S-box and Mix-Columns for AES Architecture for live IP video Encryption and Decryption by Key Generation Through face Recognition

This paper proposes an AES based Encryption and Decryption of a live IP video used for security in surveillance systems. Here, the key is generated based on neural networks techniques for facial recognition. Principal component analysis and Eigen vector algorithms are used to extract biometric facial features which are used to train the neural network. At the receiver side, the original video plays only if the user is authenticated or else it plays an encrypted video. This work proposes an AES architecture based on optimizing timing in terms of adding inner and outer pipeline registers for each round and Key Expansions. Further by optimizing the Crypto Multiplication for Mix columns via LUT based approach aid in further optimization in terms of timing. LUT and Pipelined based implementation techniques are optimal for FPGA based implementations. ROM table and pipelining are the two techniques used to implement AES. Result indicates that with the combination of pipelined architecture and Distributed/Split LUTPipelined techniques, the encryption has higher throughput and speed

Higher-order Rotation Vector Attitude Updating Algorithm

Rotation vector-based attitude updating algorithms have been used as the mainstream attitude computation algorithms for many years. The most popular methodology for designing the rotation vector algorithm is by leveraging multiple samples of gyro integrated angular rate measurements. However, it has been pointed out by many researchers that the attitude updating accuracy is limited when using the multiple samples rotation vector algorithms, especially when the platforms work under high rate manoeuvres. The third-, fourth-, fifth- and sixth-order Picard component solutions of the rotation vector differential equation are given in this paper. A new design methodology for rotation vector-based attitude updating algorithms is proposed. Different vibratory dynamics and high rate manoeuvre roller coaster experiments were conducted to validate the effectiveness of the new algorithm. The results demonstrate the high accuracy of the new algorithm compared with conventional coning correction methods. The proposed algorithm can also be used in high accuracy attitude computation of a post-processing system, especially when the output frequency of the gyro is limited.

Comparative analysis of vector algorithms for statistical modelling of polarized radiative transfer process

The comparative efficiency of different algorithms of statistical modelling of polarized radiation transfer process is studied for the problem with molecular matrix of scattering. The vector illumination and brightness are calculated for passing and reflected radiation. A statistical nuclear estimator is developed for evaluation of the corresponding angular distributions taking into account the weights of registered quanta.

Distance-Vector Algorithms for Distributed Shortest Paths on Dynamic Power-Law Networks

Efficiently solving the problem of computing, in a distributed fashion, the shortest paths of a graph whose topology dynamically changes over time is a core functionality of many today’s digital infrastructures, probably the most prominent example being communication networks. Many solutions have been proposed over the years for this problem that can be broadly classified into two categories, namely Distance-Vector and Link-State algorithms. Distance-Vector algorithms are widely adopted solutions when scalability and reliability are key issues or when nodes have either limited hardware resources, as they result in being very competitive approaches in terms of both the memory and the computational point of view. In this paper, we first survey some of the most established solutions of the Distance-Vector category. Then, we discuss some recent algorithmic developments in this area. Finally, we propose a new experimental study, conducted on a prominent category of network instances, namely generalized linear preference (GLP) power-law networks, to rank the performance of such solutions.

Support Vector Algorithms for Optimizing the Partial Area under the ROC Curve

The area under the ROC curve (AUC) is a widely used performance measure in machine learning. Increasingly, however, in several applications, ranging from ranking to biometric screening to medicine, performance is measured not in terms of the full area under the ROC curve but in terms of the partial area under the ROC curve between two false-positive rates. In this letter, we develop support vector algorithms for directly optimizing the partial AUC between any two false-positive rates. Our methods are based on minimizing a suitable proxy or surrogate objective for the partial AUC error. In the case of the full AUC, one can readily construct and optimize convex surrogates by expressing the performance measure as a summation of pairwise terms. The partial AUC, on the other hand, does not admit such a simple decomposable structure, making it more challenging to design and optimize (tight) convex surrogates for this measure. Our approach builds on the structural SVM framework of Joachims ( 2005 ) to design convex surrogates for partial AUC and solves the resulting optimization problem using a cutting plane solver. Unlike the full AUC, where the combinatorial optimization needed in each iteration of the cutting plane solver can be decomposed and solved efficiently, the corresponding problem for the partial AUC is harder to decompose. One of our main contributions is a polynomial time algorithm for solving the combinatorial optimization problem associated with partial AUC. We also develop an approach for optimizing a tighter nonconvex hinge loss–based surrogate for the partial AUC using difference-of-convex programming. Our experiments on a variety of real-world and benchmark tasks confirm the efficacy of the proposed methods.