Recently, security in embedded system arises attentions because of modern electronic devices need cautiously either exchange or communicate with the sensitive data. Although security is classical research topic in worldwide communication, the researchers still face the problems of how to deal with these resource constraint devices and enhance the features of assurance and certification. Therefore, some computations of cryptographic algorithms are built on hardware platforms, such as field program gate arrays (FPGAs). The commonly used cryptographic algorithms for digital signature algorithm (DSA) are rivest-shamir-adleman (RSA) and elliptic curve cryptosystems (ECC) which based on the presumed difficulty of factoring large integers and the algebraic structure of elliptic curves over finite fields. Usually, RSA is computed over
In this paper we conduct a tentative study on the requirements and the structure for a quantum computer at the software level. From the software point of view, we describe the methodology used to minimize the decoherence.We construct the quantum instruction set for the higher-level computation. We also study the criteria for designing the quantum programming languages.
Spiking neural (SN) P systems are a class of distributed parallel computing devices inspired by the way neurons communicate by means of spikes. In this work, we investigate reversibility in SN P systems, as well as the computing power of reversible SN P systems. Reversible SN P systems are proved to have Turing creativity, that is, they can compute any recursively enumerable set of non-negative integers by simulating universal reversible register machine.
We address the problem of metric learning for multi-view data. Many metric learning algorithms have been proposed, most of them focus just on single view circumstances, and only a few deal with multi-view data. In this paper, motivated by the co-training framework, we propose an algorithm-independent framework, named co-metric, to learn Mahalanobis metrics in multi-view settings. In its implementation, an off-the-shelf single-view metric learning algorithm is used to learn metrics in individual views of a few labeled examples. Then the most confidently-labeled examples chosen from the unlabeled set are used to guide the metric learning in the next loop. This procedure is repeated until some stop criteria are met. The framework can accommodate most existing metric learning algorithms whether types-ofside- information or example-labels are used. In addition it can naturally deal with semi-supervised circumstances under more than two views. Our comparative experiments demonstrate its competiveness and effectiveness.
Over the last two decades, there has been an extensive study of logical formalisms on specifying and verifying real-time systems. Temporal logics have been an important research subject within this direction. Although numerous logics have been introduced for formal specification of real-time and complex systems, an up to date survey of these logics does not exist in the literature. In this paper we analyse various temporal formalisms introduced for specification, including propositional/first-order linear temporal logics, branching temporal logics, interval temporal logics, real-time temporal logics and probabilistic temporal logics. We give decidability, axiomatizability, expressiveness, model checking results for each logic analysed. We also provide a comparison of features of the temporal logics discussed.
Effective task assignment is essential for achieving high performance in heterogeneous distributed computing systems. This paper proposes a new technique for minimizing the parallel application time cost of task assignment based on the honeybee mating optimization (HBMO) algorithm. The HBMO approach combines the power of simulated annealing, genetic algorithms, and an effective local search heuristic to find the best possible solution to the problem within an acceptable amount of computation time. The performance of the proposed HBMO algorithm is shown by comparing it with three existing task assignment techniques on a large number of randomly generated problem instances. Experimental results indicate that the proposed HBMO algorithm outperforms the competing algorithms.
Real-time multi-media applications are increasingly mapped on modern embedded systems based on multiprocessor systems-on-chip (MPSoC). Tasks of the applications need to be mapped on the MPSoC resources efficiently in order to satisfy their performance constraints. Exploring all the possible mappings, i.e., tasks to resources combinations exhaustively may take days or weeks. Additionally, the exploration is performed at design-time, which cannot handle dynamism in applications and resources’ status. A runtime mapping technique can cater for the dynamism but cannot guarantee for strict timing deadlines due to large computations involved at run-time. Thus, an approach performing feasible compute intensive exploration at design-time and using the explored results at run-time is required. This paper presents a solution in the same direction. Communicationaware design space exploration (CADSE) techniques have been proposed to explore different mapping options to be selected at run-time subject to desired performance and available MPSoC resources. Experiments show that the proposed techniques for exploration are faster over an exhaustive exploration and provides almost the same quality of results.