Nordin Zakaria

Markerless Human Motion Tracking Using Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization

The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches—Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.

Quantum Network via Partial Entangled State

In this article we designed a quantum network consists of four nodes using pairs of partial entangled state (Werner-state). The nodes of this network are connected via Dzyaloshiniskii-Moriya (DM) interaction. The entanglement is quantified between all different nodes using Wootters concurrence. It is shown that there is a maximum entangled state generated between two nodes which are connected indirectly. The degree of entanglement depends on the direction of switching the interaction. Index Terms—Entanglement, quantum network, dzyaloshiniskii-Moriya (DM) interaction, Entangled State

Reversible circuit synthesis by genetic programming using dynamic gate libraries

We have defined a new method for automatic construction of reversible logic circuits by using the genetic programming approach. The choice of the gate library is 100% dynamic. The algorithm is capable of accepting all possible combinations of the following gate types: NOT TOFFOLI, NOT PERES, NOT CNOT TOFFOLI, NOT CNOT SWAP FREDKIN, NOT CNOT TOFFOLI SWAP FREDKIN, NOT CNOT PERES, NOT CNOT SWAP FREDKIN PERES, NOT CNOT TOFFOLI PERES and NOT CNOT TOFFOLI SWAP FREDKIN PERES. Our method produced near optimum circuits in some cases when a particular subset of gate types was used in the library. Meanwhile, in some cases, optimal circuits were produced due to the heuristic nature of the algorithm. We compared the outcomes of our method with several existing synthesis methods, and it was shown that our algorithm performed relatively well compared to the previous synthesis methods in terms of the output efficiency of the algorithm and execution time as well.

K-NN classifier for data confidentiality in cloud computing

Securing the data in cloud is still a challenging issue. In cloud, many techniques are used to secure the data. Data encryption is a data security technique which is widely used for data security. Deciding data security approach for the data without understanding the security needs of the data is not a technically valid approach. Before applying any security on data in cloud, it is best to know the security needs of the data. What data need security and what data do not need security. In this paper, we propose a data classification approach based on data confidentiality. K-NN data classification technique is modulated in the cloud virtual environment. The aim to use K-NN is to classify the data based on their security needs. The data is classified into two classes, sensitive and non-sensitive (public) data. After data classification we got that what data need security and what data does not need security. The RSA algorithm is used to encrypt the sensitive data to keep it secure. We used CloudSim simulator to find the results of proposed approach in cloud. The proposed approach will easily decide the security needs of the data. After the data classification, it is easy to select an appropriate security for data according to the need of data. The results show that this approach is appropriate as compared to store data in cloud without understanding the security requirements of data.

DYNAMICAL PROPERTIES OF MULTI-PHOTON INTERACTION BETWEEN A CAVITY FIELD AND A SINGLE-QUBIT

We study the dynamical properties of a cavity field coupling to a Cooper pair box (CPB). We assumed that the CPB is prepared initially in a mixed state with a coherent state for the field. By solving the time-dependent equations using the evolution operator, it shows that mean numbers of Cooper pairs is affected by the detuning. The mean number of Cooper pairs is further enhanced by the multi-photon processes in commonly used cavity field.

An unfair semi-greedy real-time multiprocessor scheduling algorithm

Most real-time multiprocessor scheduling algorithms for achieving optimal processor utilization, adhere to the fairness rule. Accordingly, tasks are executed in proportion to their utilizations at each time quantum or at the end of each time slice in a fluid schedule model. Obeying the fairness rule results in a large number of scheduling overheads, which affect the practicality of the algorithm. This paper presents a new algorithm for scheduling independent real-time tasks on multiprocessors, which produces very few scheduling overheads while maintaining high schedulability. The algorithm is designed by totally relaxing the fairness rule and adopting a new semi-greedy criterion instead. Simulations have shown promising results, i.e. the scheduling overheads generated by the proposed algorithm are significantly fewer than those generated by state-of-the-art algorithms. Although the proposed algorithm sometimes misses a few deadlines, these are sufficiently few to be tolerated in view of the considerable reduction achieved in the scheduling overheads.

Performance comparison of least slack time based heuristics for job scheduling on computational grid

In recent years, increasing demand for computing has led to the development of computational grid. Typically scheduling challenges tend to be NP-hard problems where there is no optimal solution. The research reported here therefore is focused on the development of hybrids scheduling algorithms based on deadline and slack time parameters and its variations, using the concept of optimization techniques. An extensive performance comparison has been presented using real workload traces as benchmark on a grid computational environment. The results were compared with some baseline scheduling approaches in extant literature. The results have shown that the performances of grid scheduling algorithms developed and reported in this paper give good results in most of the cases and also support true scalability, when in the scenario of increasing workload and number of processors on a computational grid environment.

Effect of the Spin–Orbit Interaction on Partial Entangled Quantum Network

Dzyaloshiniskii–Moriya (DM) interaction is used to generate entangled network from partially entangled states in the presence of the spin–orbit coupling. The effect of the spin coupling on the entanglement between any two nodes of the network is investigated. It is shown that the entanglement decays as the coupling increases. For larger values of the spin coupling, the entanglement oscillates between upper and lower bounds. For initially entangled channels, the upper bound does not exceed its initial value, whereas for the channels generated via indirect interaction, the entanglement reaches its maximum value.

Statistical properties of a Raman three-level atom interacting with a cavity field

We investigate the linear entropy of a degenerate Raman process involving two degenerate Rydberg energy levels of an atom interacting with a cavity field. It is shown that a long living entanglement can be obtained for particular initial state settings. We demonstrate how these states can be observed experimentally and study the feasibility of the implementation with present day technology.

A quantitative analysis of cloud users' satisfaction and data security in cloud models

The advancement in cloud infrastructure and services is speeding up the rate for organizations to sell their services on a user demand basis. The migration of enterprises to cloud has the same trend as grid and utility computing. However, the cloud infrastructure provides more robust virtualized services despite some security issues such as virtualization vulnerability. Therefore all public clouds are incorporating virtualization technology in their services. There are many cloud service providers available in the market. The new users may suffer from ambiguity as to which cloud will best fit their business needs. We conducted a quantitative survey with the aim to help new users in selecting the best cloud to fit their needs especially for the required services and security. This survey is based on cloud user Small and Medium Enterprises (SMEs) feedback on their existing hired clouds. The users disclosed the pros and cons of their clouds. Aim of this study is to provide information about market oriented cloud to new users which are willing to get cloud services for their SMEs.