A Dynamic Inertial Weight Strategy in Micro PSO for Swarm Robots

Wireless Personal Communications - A relatively new area of research and development is Swarm Robotics. It is a part of the swarm intelligence field. In the proposed paper, we shall use swarm...     

Inference of message sequence charts

Software designers draw message sequence charts for early modeling of the individual behaviors they expect from the concurrent system under design. Can they be sure that precisely the behaviors they have described are realizable by some implementation of the components of the concurrent system? If so, can we automatically synthesize concurrent state machines realizing the given MSCs? If, on the other hand, other unspecified and possibly unwanted scenarios are "implied" by their MSCs, can the software designer be automatically warned and provided the implied MSCs? In this paper, we provide a framework in which all these questions are answered positively. We first describe the formal framework within which one can derive implied MSCs and then provide polynomial-time algorithms for implication, realizability, and synthesis.     

High-utility and diverse itemset mining

High-utility Itemset Mining (HUIM) finds patterns from a transaction database with their utility no less than a user-defined threshold. The utility of an itemset is defined as the sum of the utilities of its items. The utility notion enables a data analyst to associate a profit score with each item and thereof to a pattern. We extend the notion of high-utility with diversity to define a new pattern type called High-utility and Diverse pattern (HUD). The notion of diversity of a pattern captures the extent of the different categories covered by the selected items in the pattern. An application of diverse-pattern lies in the recommendation task where a system can recommend to a customer a set of items from a new class based on her previously bought items. Our notion of diversity is easy to compute and also captures the basic essence of a previously proposed diversity notion. The existing algorithm to compute frequent-diverse patterns is 2-phase, i.e., in the first phase, frequent patterns are computed, out of which diverse patterns are filtered out in the second phase. We, in this paper, give an integrated algorithm that efficiently computes high-utility and diverse patterns in a single phase. Our experimental study shows that our proposed algorithm is very efficient as compared to a 2-phase algorithm that extracts high-utility itemsets in the first phase and filters out the diverse itemsets in the second phase.

     

Favorable influence of ssDNA-functionalized SWCNT on the navigation pattern of C. elegans

Microsystem Technologies - Single-Walled Carbon Nanotubes (SWCNTs) are widely used as potential carriers in drug delivery systems. The objective of this work was to observe the effects of pristine,...     

A finite element alternative to infinite elements

In this paper, a simple idea based on midpoint integration rule is utilized to solve a particular class of mechanics problems; namely static problems defined on unbounded domains where the solution is required to be accurate only in an interior (and not in the far field). By developing a finite element mesh that approximates the stiffness of an unbounded domain directly (without approximating the far-field displacement profile first), the current formulation provides a superior alternative to infinite elements (IEs) that have long been used to incorporate unbounded domains into the finite element method (FEM). In contrast to most IEs, the present formulation (a) requires no new shape functions or special integration rules, (b) is proved to be both accurate and efficient, and (c) is versatile enough to handle a large variety of domains including those with anisotropic, stratified media and convex polygonal corners. In addition to this, the proposed model leads to the derivation of a simple error expression that provides an explicit correlation between the mesh parameters and the accuracy achieved. This error expression can be used to calculate the accuracy of a given mesh a-priori. This in-turn, allows one to generate the most efficient mesh capable of achieving a desired accuracy by solving a mesh optimization problem. We formulate such an optimization problem, solve it and use the results to develop a practical mesh generation methodology. This methodology does not require any additional computation on the part of the user, and can hence be used in practical situations to quickly generate an efficient and near optimal finite element mesh that models an unbounded domain to the required accuracy. Numerical examples involving practical problems are presented at the end to illustrate the effectiveness of this method.     

Using In-frame Shear Constraints for Monocular Motion Segmentation of Rigid Bodies

It is a well known result in the vision literature that the motion of independently moving objects viewed by an affine camera lie on affine subspaces of dimension four or less. As a result a large number of the recently proposed motion segmentation algorithms model the problem as one of clustering the trajectory data to its corresponding affine subspace. While these algorithms are elegant in formulation and achieve near perfect results on benchmark datasets, they fail to address certain very key real-world challenges, including perspective effects and motion degeneracies. Within a robotics and autonomous vehicle setting, the relative configuration of the robot and moving object will frequently be degenerate leading to a failure of subspace clustering algorithms. On the other hand, while gestalt-inspired motion similarity algorithms have been used for motion segmentation, in the moving camera case, they tend to over-segment or under-segment the scene based on their parameter values. In this paper we present a principled approach that incorporates the strengths of both approaches into a cohesive motion segmentation algorithm capable of dealing with the degenerate cases, where camera motion follows that of the moving object. We first generate a set of prospective motion models for the various moving and stationary objects in the video sequence by a RANSAC-like procedure. Then, we incorporate affine and long-term gestalt-inspired motion similarity constraints, into a multi-label Markov Random Field (MRF). Its inference leads to an over-segmentation, where each label belongs to a particular moving object or the background. This is followed by a model selection step where we merge clusters based on a novel motion coherence constraint, we call in-frame shear, that tracks the in-frame change in orientation and distance between the clusters, leading to the final segmentation. This oversegmentation is deliberate and necessary, allowing us to assess the relative motion between the motion models which we believe to be essential in dealing with degenerate motion scenarios.We present results on the Hopkins-155 benchmark motion segmentation dataset [27], as well as several on-road scenes where camera and object motion are near identical. We show that our algorithm is competitive with the state-of-the-art algorithms on [27] and exceeds them substantially on the more realistic on-road sequences.     

Hybrid NSCT domain multiple watermarking for medical images

Multimedia Tools and Applications - Medical image watermarking is a challenging area of research. High bandwidth, secure transmission of patient’s data among hospitals and hiding capacity are...     

A new representation and associated algorithms for generalized planning

Constructing plans that can handle multiple problem instances is a longstanding open problem in AI. We present a framework for generalized planning that captures the notion of algorithm-like plans and unifies various approaches developed for addressing this problem. Using this framework, and building on the TVLA system for static analysis of programs, we develop a novel approach for computing generalizations of classical plans by identifying sequences of actions that will make measurable progress when placed in a loop. In a wide class of problems that we characterize formally in the paper, these methods allow us to find generalized plans with loops for solving problem instances of unbounded sizes and also to determine the correctness and applicability of the computed generalized plans. We demonstrate the scope and scalability of the proposed approach on a wide range of planning problems.     

Integral invariants for shape matching

For shapes represented as closed planar contours, we introduce a class of functionals which are invariant with respect to the Euclidean group and which are obtained by performing integral operations. While such integral invariants enjoy some of the desirable properties of their differential counterparts, such as locality of computation (which allows matching under occlusions) and uniqueness of representation (asymptotically), they do not exhibit the noise sensitivity associated with differential quantities and, therefore, do not require presmoothing of the input shape. Our formulation allows the analysis of shapes at multiple scales. Based on integral invariants, we define a notion of distance between shapes. The proposed distance measure can be computed efficiently and allows warping the shape boundaries onto each other; its computation results in optimal point correspondence as an intermediate step. Numerical results on shape matching demonstrate that this framework can match shapes despite the deformation of subparts, missing parts and noise. As a quantitative analysis, we report matching scores for shape retrieval from a database.     

2010 CAV award announcement

The 2010 CAV (Computer-Aided Verification) award was awarded to Kenneth L. McMillan of Cadence Research Laboratories for a series of fundamental contributions resulting in significant advances in scalability of model checking tools. The annual award recognizes a specific fundamental contribution or a series of outstanding contributions to the CAV field.