Secure and efficient privacy-preserving public auditing scheme for cloud storage

Cloud computing poses many challenges on integrity and privacy of users’ data though it brings an easy, cost-effective and reliable way of data management. Hence, secure and efficient methods are needed to ensure integrity and privacy of data stored at the cloud. Wang et al. proposed a privacy-preserving public auditing protocol in 2010 but it is seriously insecure. Their scheme is vulnerable to attacks from malicious cloud server and outside attackers regarding to storage correctness. So they proposed a scheme in 2011 with an improved security guarantee but it is not efficient. Thus, in this paper, we proposed a scheme which is secure and with better efficiency. It is a public auditing scheme with third party auditor (TPA), who performs data auditing on behalf of user(s). With detail security analysis, our scheme is proved secure in the random oracle model and our performance analysis shows the scheme is efficient.     

Betweenness estimation in OLSR-based multi-hop networks for distributed filtering

In traditional networks special efforts are put to secure the perimeter with firewalls: particular routers that analyze and filter the traffic to separate zones with different levels of trust. In wireless multi-hop networks the perimeter is a concept extremely hard to identify, thus, it is much more effective to enforce control on the nodes that will route more traffic. But traffic filtering and traffic analysis are costly activities for the limited resources of mesh nodes, so a trade-off must be reached limiting the number of nodes that enforce them. This work shows how, using the OLSR protocol, the centrality of groups of nodes with reference to traffic can be estimated with high accuracy independently of the network topology or size. We also show how this approach greatly limits the impact of an attack to the network using a number of firewalls that is only a fraction of the available nodes.     

HEGM: A hierarchical elastic graph matching for hand gesture recognition

A hierarchical scheme for elastic graph matching applied to hand gesture recognition is proposed. The proposed algorithm exploits the relative discriminatory capabilities of visual features scattered on the images, assigning the corresponding weights to each feature. A boosting algorithm is used to determine the structure of the hierarchy of a given graph. The graph is expressed by annotating the nodes of interest over the target object to form a bunch graph. Three annotation techniques, manual, semi-automatic, and automatic annotation are used to determine the position of the nodes. The scheme and the annotation approaches are applied to explore the hand gesture recognition performance. A number of filter banks are applied to hand gestures images to investigate the effect of using different feature representation approaches. Experimental results show that the hierarchical elastic graph matching (HEGM) approach classified the hand posture with a gesture recognition accuracy of 99.85% when visual features were extracted by utilizing the Histogram of Oriented Gradient (HOG) representation. The results also provide the performance measures from the aspect of recognition accuracy to matching benefits, node positions correlation and consistency on three annotation approaches, showing that the semi-automatic annotation method is more efficient and accurate than the other two methods.     

3D fingerprint reconstruction system using feature correspondences and prior estimated finger model

The paper studies a 3D fingerprint reconstruction technique based on multi-view touchless fingerprint images. This technique offers a solution for 3D fingerprint image generation and application when only multi-view 2D images are available. However, the difficulties and stresses of 3D fingerprint reconstruction are the establishment of feature correspondences based on 2D touchless fingerprint images and the estimation of the finger shape model. In this paper, several popular used features, such as scale invariant feature transformation (SIFT) feature, ridge feature and minutiae, are employed for correspondences establishment. To extract these fingerprint features accurately, an improved fingerprint enhancement method has been proposed by polishing orientation and ridge frequency maps according to the characteristics of 2D touchless fingerprint images. Therefore, correspondences can be established by adopting hierarchical fingerprint matching approaches. Through an analysis of 440 3D point cloud finger data (220 fingers, 2 pictures each) collected by a 3D scanning technique, i.e., the structured light illumination (SLI) method, the finger shape model is estimated. It is found that the binary quadratic function is more suitable for the finger shape model than the other mixed model tested in this paper. In our experiments, the reconstruction accuracy is illustrated by constructing a cylinder. Furthermore, results obtained from different fingerprint feature correspondences are analyzed and compared to show which features are more suitable for 3D fingerprint images generation.     

Gravity optimised particle filter for hand tracking

This paper presents a gravity optimised particle filter (GOPF) where the magnitude of the gravitational force for every particle is proportional to its weight. GOPF attracts nearby particles and replicates new particles as if moving the particles towards the peak of the likelihood distribution, improving the sampling efficiency. GOPF is incorporated into a technique for hand features tracking. A fast approach to hand features detection and labelling using convexity defects is also presented. Experimental results show that GOPF outperforms the standard particle filter and its variants, as well as state-of-the-art CamShift guided particle filter using a significantly reduced number of particles.     

A portable wireless body sensor data logger and its application in video capsule endoscopy

Due to the advancement of low power miniature electronic devices, there is growing interest of physiological data sensing and recording using wireless body sensor networks. The paper presents the design of a portable physiological data logger that includes rechargeable battery, wireless radio frequency and Bluetooth connectivity, and graphical display with touch screen capability. The target application is video capsule endoscopy. Image data are logged in micro SD cards which can be easily transferred to PC or Smartphone using SD card reader, USB interface or Bluetooth wireless link. The hardware design is general and can be used in various medical or industrial applications by changing only the firmware of the microcontroller. The design is prototyped in 109 × 107 × 20 mm printed circuit board (PCB). Tests with animal tissues have been conducted to demonstrate the performance advantages of the data logger. A demonstration of wireless heart pulse monitoring and data logging is also presented.     

A GPU-based parallel method for evolutionary tree construction

Evolutionary trees are widely applied in various applications to show the inferred evolutionary relationships among species or entities. Neighbor-Joining is one solution for data-intensive and time-consuming evolutionary tree construction, with polynomial time complexity. However, its performance becomes poorer with the growth of massive datasets. Graphics Processing Units (GPUs) have brought about new opportunities for these time-consuming applications. Based on its high efficiency, a GPU-based parallel Neighbor-Joining method is proposed, and two efficient parallel mechanisms, data segmentation with asynchronous processing and the minimal chain model with bitonic sort, are put forward to speed up the processing. The experimental results show that an average speedup of 25.1 is achieved and even approximately 30 can be obtained with a sequence dataset ranging from 16,000 to 25,000. Moreover, the proposed parallel mechanisms can be effectively exploited in some other high performance applications.     

Robust passive autofocus system for mobile phone camera applications

A robust autofocus system is a ubiquitous function in today’s mobile phone camera applications. However, due to the power consumption and size requirements, it is difficult for the autofocus function to be implemented into the design of mobile phone cameras. This paper presents a passive autofocus system with low computational complexity. This system uses a novel contrast measurement to determine degree of image sharpness, which can better reflect the information about image discontinuities. In order to gauge the performance of this measurement, a modified peak search strategy is used in the experiments. The experimental results from several typical image sequences validate the effectiveness of the proposed method.     

A Non-Line-of-Sight mitigation localization algorithm for sensor networks using clustering analysis

Non-Line-of-Sight propagation of wireless signal has an impact on measured distances in range-based localization and will bias the final localization results. A new localization algorithm is proposed in this paper to mitigate Non-Line-of-Sight errors when there are more than enough anchor nodes deployed around the node to be located. This algorithm utilizes multi-round clustering analysis to filter the pre-located estimators which derive from all possible subsets of measured distances. In each round, the method density-based spatial clustering of applications with noise is adopted. Simulations show that the proposed algorithm can effectively improve localization accuracy not only when the measured distances with Non-Line-of-Sight error are minor but also under the condition that all of them suffer random Non-Line-of-Sight error.