Abstract

Most queries in wireless sensor network (WSN) applications are issued at the point of the base station or gateway node of the network. However, for critical applications of WSNs there is a great need to access the real-time data inside the WSN from the nodes, because the real-time data may no longer be accessed through the base station only. So, the real-time data can be given access directly to the external users (parties) those who are authorized to access data as and when they demand. The user authentication plays a vital role for this purpose. In this paper, we propose a new password-based user authentication scheme in hierarchical wireless sensor networks. Our proposed scheme achieves better security and efficiency as compared to those for other existing password-based approaches. In addition, our scheme has merit to change dynamically the user's password locally without the help of the base station or gateway node. Furthermore, our scheme supports dynamic nodes addition after the initial deployment of nodes in the existing sensor network.

Abstract

Several key management schemes for dynamic access control in a user hierarchy are proposed in the literature based on elliptic curve cryptosystem (ECC) and polynomial interpolation. Since the elliptic curve scalar multiplication and construction of interpolating polynomials are time-consuming operations, most of the proposed schemes require high storage and computational complexity. Further, most of the proposed schemes are vulnerable to different attacks including the man-in-the-middle attacks. In this paper, we propose a novel key management scheme for hierarchical access control based on linear polynomials only. We show that our scheme is secure against different attacks including the man-in-the-middle attack, which are required for an idle access control scheme. Moreover, the computational cost and the storage space are significantly reduced in our scheme while compared to the recently proposed related schemes.

Abstract

In a key management scheme for hierarchy based access control, each security class having higher clearance can derive the cryptographic secret keys of its other security classes having lower clearances. In 2008, Chung et al. proposed an efficient scheme on access control in user hierarchy based on elliptic curve cryptosystem [Information Sciences 178 (1) (2008) 230–243]. Their scheme provides solution of key management efficiently for dynamic access problems. However, in this paper, we propose an attack on Chung et al.’s scheme to show that Chung et al.’s scheme is insecure against the exterior root finding attack. We show that under this attack, an attacker (adversary) who is not a user in any security class in a user hierarchy attempts to derive the secret key of a security class by using the root finding algorithm. In order to remedy this attack, we further propose a simple improvement on Chung et al.’s scheme. Overall, the main theme of this paper is very simple: a security flaw is presented on Chung et al.’s scheme and then a fix is provided in order to remedy the security flaw found in Chung et al.’s scheme.

Abstract

The objective of this paper is to suggest a fresh approach to introductory programming curricula in the Indian school and engineering college context. The approach allows the student to connect high-school (up to 10+2) mathematics to the fundamentals of computing, algorithms and problem solving. The bridge connecting algebra and computing is functional programming, a paradigm confined over forty years to the computer science research community but now gaining popularity in industry as well as undergraduate education in some schools across the world. We show, using several examples, why and how functional programming is easier to master than traditional imperative programming. We conclude with the results of our attempts so far at introducing functional programming to students in IT colleges in India.

Abstract

3D textures are often described by parametric functions for each pixel, that models the variation in its appearance with respect to varying lighting direction. However, parametric models such as Polynomial Texture Maps (PTMs) tend to smoothen the changes in appearance. We propose a technique to effectively model natural material surfaces and their interactions with changing light conditions. We show that the direct and global components of the image have different nature, and when modeled separately, leads to a more accurate and compact model of the 3D surface texture. For a given lighting position, both components are computed separately and combined to render a new image. This method models sharp shadows and specularities, while preserving the structural relief and surface color. Thus rendered image have enhanced photorealism as compared to images rendered by existing single pixel models such as PTMs.

Abstract

Biometric identification often involves explicit comparison of a probe against each template stored in a database.This process becomes extremely time-consuming as the size of the database increases. Filtering approaches use a lightweight comparison to select a smaller set of candidate templates from the database for explicit comparison. However,most existing filtering schemes use specific features that are hand-crafted for the biometric trait at each stage of the filtering. In this work, we explore the effectiveness of weak features in a cascade for filtering fingerprint databases. We start with a set of potential indexing features computed from minutiae triplets and minutiae quadruplets. Each stage of filtering consists of projecting the probe onto a specific line and the removal of database samples outside a window around the probe. The critical problem in this process is the selection of lines for projection at each stage of the filtering. We show that by using a set of random lines and the proposed fitness function, one can achieve better results that optimization methods such as PCA or LDA. Experimental results show that using an ensemble of projections we can reduce the penetration to26%at a hit rate of99%. As each stage of the cascade is extremely fast, and filtering is progressive along the cascade, one can terminate the cascade at any point to achieve the desired performance. One can also combine this method with other indexing methods to improve the overall accuracy and speed. We present de-tailed experimental results on various aspects of the process on the FVC 2002 dataset.

Abstract

This paper proposes a hash-based indexing method to speed up fingerprint identification in large databases. For each minutia, its local neighborhood information is com-puted with features defined based on the geometric arrangements of its neighboring minutiae points. The features used are provably invariant to translation, rotation, scale and shear. These features are used to create an affine invariant local descriptor, called an arrangement vector, for each minutia. To account for missing and spurious minutiae, we consider subsets of the neighboring minutiae and hashes of these structures are used in the indexing process. The primary goal of the work is to explore the effectiveness of affine invariant features for representing local minutiae structures. Experiments on FVC 2002 databases show that representation is quite effective even though the technique performs slightly below the state-of-the-art methods. One could use the representation in combination with other techniques to improve the overall performance.

Abstract

This paper addresses the issue of moving object tracking from video. Two steps of processing are PCA reconstruction for classification the area and Optical flow-based tracking of feature points. Optimal flow-based tracking algorithm predicts and restores the feature of area in the real time object tracking. The proposed system is computationally more efficient for learning of dynamic object and object tracking to shape information. PCA based method compare to other methods is powerful algorithm in object detection and the Optical flowbased tracking algorithm could be used for decreasing the false point tracking and remove the error of the motion tracking in real time system tracking. The proposed algorithm tracks a set of feature points; during the tracking, feature is restored inside the predicted region. One important contribution of this work is to provide a restoration process for missing feature points, which occurs at almost every frame under realistic, noisy environment.

Abstract

Algorithms for detection of repeating patterns in images of-ten assume that the repetition is regular and highly similar across the instances. Approximate repetitions are also of interest in many domains such as hand carved sculptures, wall decorations, groups of natural objects, etc. Detection of such repetitive structures can help in applications such as image retrieval, image in painting, 3D reconstruction, etc. In this work, we look at a specific class of approximate repetitions:those in images of hand carved relief structures. We present a robust hierarchical method for detecting such repetitions.Given a single image with reliefs, our algorithm finds dense matches of local features across the image at various scales.The matching features are then grouped based on their ge-ometric configuration to find repeating elements. We also propose a method to group the repeating elements to segment the repetitive patterns in an image. In relief images,foreground and background have nearly the same texture,and matching of a single feature would not provide reliable evidence of repetition. Our grouping algorithm integrates evidences of repetition to reliably find repeating patterns.Input image is processed on a scale-space pyramid to effectively detect all possible repetitions at different scales. Our method has been tested on images with large varieties of complex repetitive patterns and the qualitative results show the robustness of our approach.

Abstract

Texture mapping algorithms use mesh parameterization methods to find an optimal map for the vertices of a 3D model in texture space. These techniques vary in the properties they try to optimize such as stretch and skewness of the texture when mapped onto the surface. While most of them do well in terms of quality, they tend to be computationally intensive for large mesh models, which limits their use in interactive applications. We propose a greedy alternative that is significantly faster than current algorithms and achieves comparable quality. We use a priority queue to store polygons and use tangential vectors to guide the texture over the surface. Our algorithm is simple to implement and can texture over a million polygons per second on a typical desktop.The algorithm does not impose any constraints on the meshtopology and we do not require the model to be cut into patches before texturing. Stretch and distortion measures are stable across models and are comparable to current algorithms. We also propose a method to generate self tileable textures for use in conjunction with our texture mapping algorithm. We present qualitative and quantitative results in comparison with several other texture mapping algorithms.The efficiency and robustness of our algorithm makes it use-ful in interactive modeling applications and texture mapping large mesh models such as heritage monuments.