Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
Ensuring Software Quality in Engineering Environments
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
This paper presents SSP (Sleep Scheduling Protocol), a centralized scheme for extending the lifetime of densely deployed wireless sensor networks by keeping only a necessary set of sensor nodes active. We present an algorithm for finding out which nodes should be put into sleep mode, and the algorithm preserves coverage and connectivity while trying to put as much nodes as possible into sleep mode. The algorithm is executed at the base station periodically. In this way, the network is reconfigured periodically, which also helps to a more even distribution of energy consumption load to sensor nodes. We evaluated our protocol via simulations and observed a significant increase in the lifetime, depending on the node density, while providing good coverage.
This paper presents a practical implementation of a Wireless Visual Sensor Network (WVSN) with a back-end face detection and tracking system. This back-end processing is based on the method proposed by Viola and Jones. The WVSN is a visually enabled wireless sensor network (WSN). The WVSN consists of visual nodes, network motes, and a base station. The visual nodes are used to capture visual data that is relayed to the base station via network motes or other visual nodes which can act as network motes. The base station acts as a gateway between the WVSN and the back end. To ensure reliable and robust network connectivity, the WVSN is built upon a mesh network allowing automatic signal rerouting if the current path fails. This network is based on the EmberZNet stack. In this implementation, the visual nodes are a combination of a CMOS camera and Xilinx Spartan-3 FPGA upon a wireless ZigBee network using the Ember EM250 System-on-Chip devices.
The mobile ad-hoc networks are more vulnerable to security attacks than their wired counterparts because of their unique characteristics. Cryptographic techniques are essential for the protection against these attacks. For these techniques, cryptographic keys serve as a proof of trustworthiness to authenticate nodes as legitimate members of the network, as well as prerequisite for confidentiality and integrity. So, a secure and efficient management of cryptographic keys is crucial for a reliable network service and thus the success for wireless ad-hoc networks. This paper provides the state of the art of the key management protocols designed for Mobile Ad-hoc Networks according to recent literature. The protocols are subdivided into several groups based on their design strategy and the type of cryptographic system. This paper discusses the advantages and provides comments on the strategy of each group separately. From the review, it is concluded that all the currently available protocols can not guarantee the higher level of security because of the non-hierarchical nature of ad-hoc networks, lack of infrastructure and mobility of participating entities. A comparison between these protocols can provide the basis for future research in this area.
There are two conflicting requirements for choosing the passwords: passwords should be easy to remember and hard to guess. Traditional alphanumeric passwords do not satisfy these requirements and hence, Graphical Passwords are developed as an effort to improve password security by making strong passwords to remember easily. Most of the Graphical Passwords Proposed in the literature are vulnerable to shoulder-surfing attack. Recently, Raj et al. proposed a Novel Cognition based Graphical Password scheme which is resistant to shoulder-surfing attack. This paper discusses the usability study of the scheme proposed by Raj el al. and proposes an improvement to the scheme that reduces the login time further.
As image databases become more and more pervasive, finding the image in large databases becomes a major problem. This thesis intends to give a solution to this problem by proposing a novel neural-fuzzy based approach for identifying the personality by comparing his/her eye image with the eye image extracted from an image database. Image database is in the form of object oriented model. In the object oriented model, operator/operand model is replaced by the object message model. In this model all the information is represented in the form of objects. An object is a self-contained entity consisting of its own private memory, a set of operations which constitute the external interface of the object with the rest of the system. Fuzzy logic provides an effective method for handling the problems with uncertain information or for dealing with the problem of knowledge representation in an uncertain and imprecise environment. Fuzzy logic is used for expressing queries in terms of the natural language. The queries designed are based on the feature “Color”. Since a gray scale image possesses some ambiguity within pixels due to the possible multi-valued levels of brightness and noise, it is justified to apply the concept of fuzzy logic to person identification. Neural network is designed to learn the meaning of the queries raised by fuzzy logic. Neural Network is a learned function that maps a list of real valued inputs to one or more Boolean or real-valued outputs. This thesis uses an Error back propagation algorithm which is used to learn the meaning of queries in fuzzy terms such as “very similar”, “similar” and “some what similar”.
The Transmission Control Protocol (TCP) was designed to provide reliable end-to-end delivery of data over unreliable networks. In practice, most TCP deployments have been carefully designed in the context of wired networks. Ignoring the properties of wireless Ad-hoc Networks can lead to TCP implementations with poor performance. In order to adapt TCP to wireless networks, improvements have been proposed in the literature to help TCP to differentiate between the different types of losses. TCP assumes a relatively underlying network where most packet losses are due to congestion. In a wireless network, however packet losses will occur more often due to unreliable wireless links than due to congestion. When using TCP over wireless links, each packet loss on the wireless link results in congestion control measures being invoked at the source. This causes severe performance degradation. If there is any packet loss in wireless networks, then the reason for that has to be found out and then only congestion control mechanism has to be applied. This paper includes an analysis of the most important issues of TCP over wired and wireless networks.
An Image Retrieval System is the set of techniques for retrieving semantically relevant images from an image database based on either text or automatically derived features. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive. However this approach is clearly impractical in case of very large image databases and its effectiveness is highly dependent on the subjective opinions of the experts, who are also likely to supply different descriptions for the same image. In addition, subjectivity and ambiguity of the description by human perception as well as incompleteness of a limited set of keyword descriptors may significantly reduce the query effectiveness. Using a Content Based Image Retrieval (CBIR), images can be analyzed and indexed automatically by automatic description, which depends on their visual content. Retrieving similar images from image database using automatically derived image features or content for user specified query is an active research area. A visual content descriptor can be either global or local. A global descriptor uses the visual features of the whole image, whereas a local descriptor uses the visual features of regions or objects to describe the image content. To obtain the local visual descriptors, first an image is often divided into parts. The simplest way of dividing an image is to use a partition, which cuts the image into tiles of equal size and shape. A simple partition does not generate perceptually meaningful regions but is a way of representing the global features of the image at a finer resolution. A better method is to divide the image into homogenous regions according to some criterion that are based on color and texture using region segmentation algorithms that have been extensively investigated in computer vision. The main focus of this paper is to improve the capture of regions so as to enhance retrieval performance and also to provide a better similarity distance computation.
