Abstract As the demand for cloud computing infrastructure increased, availability and reliability have become more important as they are major features in real‐time computing systems. The chances of failure in a cloud computing system increase compared to other computing systems. To achieve high reliability and availability for the cloud computing infrastructure fault tolerance is one of the most essential modules. For a cloud computing infrastructure, a proactive fault tolerance algorithm with load balancing is proposed in this paper. The proposed methodology tolerates virtual machine CPU faults to achieve maximum cloud computing infrastructure reliability and availability. CPU faults can occur during virtual machine operation. The proposed model's main objective is to track changes in CPU utilization and make a decision once a high value of CPU utilization is identified. The decision is either to migrate the defective virtual machine to another “destination host”, or to manage loads on the destination host so that the faulty virtual machine can be migrated. A load is calculated for the destination host; if the destination host is overloaded, the subroutine for load balancing is launched. Load balancing is based on the technique of choosing a virtual machine.

This work presents a new approach for crowd counting and classification based upon human thermal and motion features. The technique is efficient for automatic crowd density estimation and type of motion determination. Crowd density is measured without any need for camera calibration or assumption of prior knowledge about the input videos. It does not need any human intervention so it can be used successfully in a fully automated crowd control systems. Two new features are introduced for crowd counting purpose: the first represents thermal characteristics of humans and is expressed by the ratio between their temperature and their ambient environment temperature. The second describes humans motion characteristics and is measured by the ratio between humans motion velocity and the ambient environment rigidity. Each ratio should exceed a certain predetermined threshold for human beings. These features have been investigated and proved to give accurate crowd counting performance in real time. Moreover, the two features are combined and used together for crowd classification into one of the three main types, which are: fully mobile, fully static, or mix of both types. Last but not least, the proposed system offers several advantages such as being a privacy preserving crowd counting system, reliable for homogeneous and inhomogeneous crowds, does not depend on a certain direction in motion detection, has no restriction on crowd size. The experimental results demonstrate the effectiveness of the approach.

ABSTRACT In this era, hacking techniques have become more dangerous and more widely used across the Internet. If we want to send and receive a trusted email, our current techniques would not be efficient. In this paper, we propose a new protocol for trusting our emails based on Trusted Computing. We combine the hardware‐based cryptography of the Trusted Platform Module and the Remote Attestation with the email protocols. The details of the proposed protocol and its security analysis are discussed in this paper. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, a genetic algorithm (GA)-based approach to estimate the fundamental matrix is presented. The aim of the proposed GA-based algorithm is to reduce the effect of noise and outliers in the corresponding points which affect the accuracy of the estimated fundamental matrix. Although in the proposed approach the GA is allowed to select the significant among all detected points, on the average half of the matched points have been determined to give optimum estimation of the fundamental matrix. Experiments with synthetic and real data show that the proposed approach is accurate especially in the presence of a high percentage of outliers. The proposed GA can always obtain good results in both high and low detailed images. Even for low detailed images which have a small number of matched points available to estimate the fundamental matrix, the proposed GA outperformed other methods.

Several attempts have been made to enhance PSO performance by combining it with a local search method. Following the same track, we present in this paper local search in PSO performed by smaller independent swarms of PSO producing PSO2. Different modifications are made to help basic PSO2 enhance performance. PSO2-RS and PSO2-SA are 2 modified versions of PSO2 that targeted to increase the swarm diversity. Increasing the local search swarms sizes as the search progresses is another modification made to basic PSO2 in order to change the algorithm behavior to be more exploitive. The final algorithm is examined against 4 functions of the CEC-2005 benchmark suite and results are reported.

In this paper, we propose a rule-based system for semantically understanding and analyzing the motion of the trajectories of the human activity. The proposed system can be used as a preprocessing phase for enhancing the object detection process. Detected trajectories are classified into three categories; normal, semi-normal and abnormal trajectories according to the distances between their adjacent points. Abnormal trajectories are removed from the trajectory space. Semi-normal trajectories are broken into small normal trajectories that are linked later to form a longer normal trajectory. The proposed system does not assume a specific trajectory length and hence is more generic than similar trajectory enhancement approaches. The effectiveness of the proposed approach is demonstrated through several experimental results using known human motion datasets.

The paper surveys the developments of the last 10 years in the area of camera self-calibration. Self-calibration is an attempt to calibrate camera by finding intrinsic parameters that are consistent with the underlying projective geometry of a sequence of images. In order to solve this problem, the camera intrinsic constraints have been used separately and in conjunction with camera motion constraints or scene constraints. Most self-calibration algorithms are concerned with unknown but constant intrinsic camera parameters. Recently, camera self-calibration in the case of varying intrinsic camera parameters was also studied. We present the basic theories behind the different self-calibration techniques and discuss the ideas behind most of the self-calibration algorithms.

We consider the problem of computing the 3D shape of an unknown, arbitrary-shaped object. Starting with an initial surface, larger than the scene, we refine it using the object's projections taken at known but arbitrarily distributed viewpoints. These projections are used independently in a recursive manner. Thus, using more projections improves the reconstruction with a slight increase in the computation time. The technique generates an approximate voxelized representation of the object. We describe the proposed technique and outline the algorithm. Rendered images of voxel spaces recovered from synthetic and real observation images are shown.

We present an algorithm to solve the sensor planning problem for a trinocular, active vision system. This algorithm uses an iterative optimization method to first solve for the translation between the three cameras and then uses this result to solve for parameters such as pan, tilt angles of the cameras and zoom setting.

Camera calibration is a primary crucial step in many computer vision tasks. We present a new neural approach for camera calibration. Unlike some existing neural approaches, our calibrating network can tell the perspective-projection-transformation matrix between the world 3D points and the corresponding 2D image pixels. Starting from random initial weights, the net can specify the camera model parameters satisfying the orthogonality constraints on the rotational transformation. The neurocalibration technique is shown to solve four different types of calibration problems that are found in computer vision applications. Moreover, it can be extended to the more difficult problem of calibrating cameras with automated active lenses. The validity and performance of our technique are tested with both synthetic data under different noise conditions and with real images. Experiments have shown the accuracy and the efficiency of our neurocalibration technique.

This paper presents the neurocalibration approach as a new neural-based solution for the problem of camera calibration. Unlike some existing neural approaches, our calibrating network can match the perspective-projection-transformation matrix between the world 3D points and the corresponding 2D image pixels. Starting from random initial weights, the net can specify the camera model parameters satisfying the orthogonality constraints on the rotational transformation. In order to improve the accuracy of calibration results, the paper demonstrates the application of the neurocalibration technique to multi-image camera calibration. In such a case, many images are taken by the same camera but from different (rotated and/or translated) positions. Experiments have shown the accuracy and the efficiency of our neurocalibration technique.

We present a trinocular stereo vision technique that can be used to build a fast, robust description of indoor objects. The proposed technique utilizes the geometrical constraints of the trinocular vision system and considers the uncertainty of image measurements. The technique has been successfully applied to several indoor scenes. The reliability and speed of the proposed system make it particularly suitable for real-time applications such as robot navigation.

A novel approach is proposed to obtain a record of the patient's occlusion using computer vision. Data acquisition is obtained using intra-oral video cameras. The technique utilizes shape from shading to extract 3D information from 2D views of the jaw, and a novel technique for 3D data registration using genetic algorithms. The resulting 3D model can be used for diagnosis, treatment planning, and implant purposes. The overall purpose of this research is to develop a model-based vision system for orthodontics to replace traditional approaches. This system will be flexible, accurate, and will reduce the cost of orthodontic treatments.