Development of new encryption system using Brownian motion based diffusion

Multimedia Tools and Applications - A new cryptographic model is proposed incorporating intertwining logistic map based confusion process and two dimensional Brownian Motion based diffusion...     

Low intensity visual data improvement using DWT and LWT based regularizer

Image Completion plays a vital role in compressed sensing, machine learning, and computer vision applications. The Rank Minimization algorithms are used to perform the image completion. The major problem with rank minimization algorithms is the loss of information in the recovered image at high corruption ratios. To overcome this problem Lifting wavelet transform based Rank Minimization (LwRM), and Discrete wavelet transform based Rank Minimization (DwRM) methods are proposed, which can recover the image, if the corrupted observations are more than 80%. The evaluation of the proposed methods are accomplished by Full Reference Image Quality Assessment (FRIQA) and No Reference Image Quality Assessment (NR-IQA) metrics. The simulation results of proposed methods are superior to state-of-the-art methods.

     

Filtering Data Streams for Entity-Based Continuous Queries

The idea of allowing query users to relax their correctness requirements in order to improve performance of a data stream management system (e.g., location-based services and sensor networks) has been recently studied. By exploiting the maximum error (or tolerance) allowed in query answers, algorithms for reducing the use of system resources have been developed. In most of these works, however, query tolerance is expressed as a numerical value, which may be difficult to specify. We observe that in many situations, users may not be concerned with the actual value of an answer, but rather which object satisfies a query (e.g., "who is my nearest neighbor?”). In particular, an entity-based query returns only the names of objects that satisfy the query. For these queries, it is possible to specify a tolerance that is "nonvalue-based.” In this paper, we study fraction-based tolerance, a type of nonvalue-based tolerance, where a user specifies the maximum fractions of a query answer that can be false positives and false negatives. We develop fraction-based tolerance for two major classes of entity-based queries: 1) nonrank-based query (e.g., range queries) and 2) rank-based query (e.g., k-nearest-neighbor queries). These definitions provide users with an alternative to specify the maximum tolerance allowed in their answers. We further investigate how these definitions can be exploited in a distributed stream environment. We design adaptive filter algorithms that allow updates be dropped conditionally at the data stream sources without affecting the overall query correctness. Extensive experimental results show that our protocols reduce the use of network and energy resources significantly.     

Vectorial scale-based fuzzy-connected image segmentation

This paper presents an extension of previously published theory and algorithms for fuzzy-connected image segmentation. In this approach, a strength of connectedness is assigned to every pair of image elements. This is done by finding the strongest among all possible connecting paths between the two elements in each pair. The strength assigned to a particular path is defined as the weakest affinity between successive pairs of elements along the path. Affinity specifies the degree to which elements hang together locally in the image. A scale is determined at every element in the image that indicates the size of the largest homogeneous hyperball region centered at the element. In determining affinity between any two elements, all elements within their scale regions are considered. This method has been effectively utilized in several medical applications. In this paper, we generalize this method from scalar images to vectorial images. In a vectorial image, scale is defined as the radius of the largest hyperball contained in the same homogeneous region under a predefined condition of homogeneity of the image vector field. Two different components of affinity, namely homogeneity-based affinity and object-feature-based affinity, are devised in a fully vectorial manner. The original relative fuzzy connectedness algorithm is utilized to delineate a specified object via a competing strategy among multiple objects. We have presented several studies to evaluate the performance of this method based on simulated MR images, 20 clinical MR images, and 250 mathematical phantom images. These studies indicate that the fully vectorial fuzzy connectedness formulation has generally overall better accuracy than the method using some intermediate ad hoc steps to fit the vectorial image to a scalar fuzzy connectedness formulation, and precision and efficiency are similar for these two methods.     

Dynamic Modeling and Simulation of Impact in Tether Net/Gripper systems

The objective of this paper is to study the dynamic modeling andsimulation of a tether-net/gripper system during an impact, while it isbeing deployed or retrieved by a winch on a satellite orbiting aroundearth. We stick to Tether-Net system but the analysis is applicable toTether-Gripper systems too. We assume that the net is deployed from thesatellite in orbit and the motion is restricted to the orbital plane.This net captures a second satellite and tows it. The motion of atether-net system can be broken down into the following phases: (i)Phase 1: Net is shot out from the satellite with the tether completelyslack, (ii) Phase 2: Net comes to a location where the tether is tautwhile the drum on the orbiter is locked, (iii) Phase 3: Drum is unlockedand the net moves with the tether, (iv) Phase 4: Net captures a body.The continua (tether) is modeled using mode functions and coordinates.The theory of impulse and momentum can be used to model Phases 1, 2, and4 of motion of the tether-net system. The dynamics of the motion of thesystem in phase 3 is characterized by differential and algebraicequations (DAEs). Matlab ODE solvers were used to solve these DAEs.     

Optimizing functionally graded nickel–zirconia coating profiles for thermal stress relaxation

The investigations on optimization of composite composition of nickel–zirconia for the functionally graded layered thermal barrier coating for the lowest but uniform stress field under thermal loading is presented. The procedure for obtaining temperature- and composition-dependent thermal and mechanical properties of various coating compositions is discussed. These material parameters were used in thermo-mechanical finite element stress analyses of a nickel substrate with the coating. The results showed that the Von-Mises stresses in the substrate and the interfaces were the lowest with the coating profile that followed a concave power law relationship with the index n ≈ 2.65.     

Assessment of diverse image encryption mechanisms under prevalent invasion

Multimedia Tools and Applications - Image encryption mechanisms provide confidentiality and concealment of information (image) in transmission over the alleyway, susceptible to prevalent invasions....     

Distributed algorithms for barrier coverage using relocatable sensors

We study the barrier coverage problem using relocatable sensor nodes. We assume each sensor can sense an intruder or event inside its sensing range. Sensors are initially located at arbitrary positions on the barrier and can move along the barrier. The goal is to find final positions for sensors so that the entire barrier is covered. In recent years, the problem has been studied extensively in the centralized setting. In this paper, we study a barrier coverage problem in the distributed and discrete setting. We assume that we have n identical sensors located at grid positions on the barrier, and that each sensor repeatedly executes a Look-Compute-Move cycle: based on what it sees in its vicinity, it makes a decision on where to move, and moves to its next position. We make two strong but realistic restrictions on the capabilities of sensors: they have a constant visibility range and can move only a constant distance in every cycle. In this model, we give the first two distributed algorithms that achieve barrier coverage for a line segment barrier when there are enough nodes in the network to cover the entire barrier. Our algorithms are synchronous, and local in the sense that sensors make their decisions independently based only on what they see within their constant visibility range. One of our algorithms is oblivious whereas the other uses two bits of memory at each sensor to store the type of move made in the previous step. We show that our oblivious algorithm terminates within \(\varTheta (n^2)\) steps with the barrier fully covered, while the constant-memory algorithm is shown to take \(\varTheta (n)\) steps to terminate in the worst case. Since any algorithm in which a sensor can only move a constant distance in one step requires \(\varOmega (n)\) steps on some inputs, our second algorithm is asymptotically optimal.     

Sequential pattern mining in databases with temporal uncertainty

Temporally uncertain data widely exist in many real-world applications. Temporal uncertainty can be caused by various reasons such as conflicting or missing event timestamps, network latency, granularity mismatch, synchronization problems, device precision limitations, data aggregation. In this paper, we propose an efficient algorithm to mine sequential patterns from data with temporal uncertainty. We propose an uncertain model in which timestamps are modeled by random variables and then design a new approach to manage temporal uncertainty. We integrate it into the pattern-growth sequential pattern mining algorithm to discover probabilistic frequent sequential patterns. Extensive experiments on both synthetic and real datasets prove that the proposed algorithm is both efficient and scalable.     

A characterization of partial blocks in weighted graphs

Some of the classical connectivity concepts in Graph theory are generalized in this article. Strong and strongest strong cycles are introduced. Partial blocks are characterized using strongest paths. Some necessary and sufficient conditions for a weighted graph to be a partial block are also presented.