Collision avoidance and resolution multiple access (CARMA) protocols establish a three-way handshake between sender and receiver to attempt to avoid collisions, and resolve those collisions that occur. This paper describes and analyzes CARMA protocols that resolve collisions up to the first success obtained by running a tree-splitting algorithm for collision resolution. An upper bound is derive...

Nowadays many enterprises have to face a real problem in their operation processes, which is mismatch between active demands and resource. To solve this problem, traditional literatures employed Resource aggregation of Cloud Computing. However the most of these researches focus on computing resources but only few on application resources. And there does not exist a complete solution. In this pa...

Some computational aspects of the continuous-time ARMA, CARMA are reviewed. Methods of simulation and estimation have been implemented in an R-package, ctarma. The simulations can be either frequency-domain based or time-domain based. Several approaches of simulating CARMA processes with time-and frequency-domain methods are implemented in the package. The estimation is based on numerically max...

Many complex physical systems such as biological systems are characterized both by incomplete models and limited empirical data. Accurate prediction of the behavior of such systems requires exploitation of multiple, individually incomplete, knowledge sources. Our approach, called approximate-model-based adaptation, utilizes case-based reasoning to provide an approximate solution and model-based...

Continuous-time autoregressive moving average (CARMA) processes have recently been used widely in the modeling of non-uniformly spaced data and as a tool for dealing with high-frequency data of the form Yn∆, n = 0, 1, 2, . . ., where ∆ is small and positive. Such data occur in many fields of application, particularly in finance and the study of turbulence. This paper is concerned with the chara...

We propose an online partial counting algorithm based on statistical inference that approximates itemset frequencies from data streams. The space complexity of our algorithm is proportional to the number of frequent itemsets in the stream at any time. Furthermore, the longer an itemset is frequent the closer is the approximation to its frequency, implying that the results become more precise as...

Cosmological parameters encoding our understanding of the expansion history Universe can be constrained by accurate estimation time delays arising in gravitationally lensed systems. We propose TD-CARMA, a Bayesian method to estimate cosmological modelling observed and irregularly sampled light curves as realizations Continuous Auto-Regressive Moving Average (CARMA) process. Our model accounts f...