Abstract

Glaucoma is an eye disorder that causes irreversible loss of vision and is prevalent in the aging population. Glaucoma is indicated both by structural changes and presence of atrophy in retina. In retinal images, these appear in the form of subtle variation of local intensities. These variations are typically described using local shape based statistics which are prone to error. We propose an automated, global feature based approach to detect glaucoma from images. An image representation is devised to accentuate subtle indicators of the disease such that global image features can discriminate between normal and glaucoma cases effectively.The proposed method is demonstrated on a large image dataset annotated by 3 medical experts. The results show the method to be effective in detecting subtle glaucoma indi-cators. The classification performance on a dataset of 1186color retinal images containing a mixture of normal, suspect and confirmed cases of glaucoma is 97 percent sensitivity at 87 percent specificity. This improves further when the suspect cases are removed from the abnormal cases. Thus,the proposed method offers a good solution for glaucoma screening from retinal images.

Abstract

The blood perfusion measurement is done by injecting a bolus of contrast agent in the brain followed by imaging over a period of time (scan). This process can extend into minutes and hence any patient motion mid-scan results in corrupted data. This is often observed in dynamic magnetic resonance(MR) imaging for both susceptibility (DSC) and contrast enhanced (DCE) scans. Motion correction done after scanning is typically the most time-intensive step in the entire measurement process since it involves registering each volume in the time-series to a reference volume. We argue that detecting the presence of motion prior to correction can mitigate this problem by reducing the number of volumes to be corrected. The challenge in motion detection is that the injected contrast alters the signal intensity as a function of time leading to false alarms. We present a robust multi-stage method: subdivision of the time series data into bolus and non bolus phases; clustering-based identification of bolus-affected pixels followed by correction of their intensity using a Gamma variate function fitting-based method and a 2Dblock-wise phase correlation for detecting motion between adjacent volumes in DSC-MRI data. The proposed method was tested on a DSC MR sequence with simulated motion of varying degrees. The experimental results show that the entropy of the derived motion fields is a good metric for detecting and categorizing the motion. The proposed scheme when applied prior to correction can achieve on average a37% reduction in the time required for motion correction

Abstract

In this paper, we propose a novel identity-based random key pre-distribution scheme called the identity based key pre-distribution using a pseudo random function (IBPRF), which has better trade-off between communication overhead, network connectivity and resilience against node capture compared to the other existing key predistribution schemes. IBPRF always guarantees that no matter how many sensor nodes are captured, the secret communication between non-compromised sensor nodes are still secure. We then propose an improved version of our scheme in a large-scale hierarchical wireless sensor network. This improved approach has better trade off among network connectivity, security, communication, computational and storage overheads, and scalability than the existing random key pre-distribution schemes. The strength of the proposed IBPRF scheme and its improved approach is establishing pairwise secret keys between neighboring nodes with scantling communication and computational overheads. The improved IBPRF approach further supports a large-scale sensor network for the network connectivity. Through the analysis we show that the improved IBPRF scheme provides better security and lower overheads than other existing schemes.

Abstract

Several symmetric-key distribution mechanisms are proposed in the literature, but most of them are not scalable or they are vulnerable to a small number of captured nodes. In this paper, we propose a new dynamic random key establishment mechanism in large-scale distributed sensor networks, which supports deployment of sensor nodes in multiple phases. In the existing random key distribution schemes, nodes do not refresh their own keys, and thus, the keys in their key rings remain static throughout the lifetime of the network. One good property of our dynamic key distribution scheme is that the already deployed nodes in a deployment phase refresh their own keys in key rings before another deployment phase occurs. The strength of the proposed scheme is that it provides high resilience against node capture as compared to that for the other existing random key distribution schemes. Through analysis and simulation results, we show that our scheme achieves better network performances as compared to those for the existing random key distribution schemes. Finally, we propose an extended version of our scheme for practical usefulness to support high network connectivity and resilience against node capture

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.