Using segmented objects in ostensive video shot retrieval

This paper presents a system for video shot retrieval in which shots are retrieved based on matching video objects using a combination of colour, shape and texture. Rather than matching on individual objects, our system supports sets of query objects which in total reflect the user’s object-based information need. Our work also adapts to a shifting user information need by initiating the partitioning of a user’s search into two or more distinct search threads, which can be followed by the user in sequence. This is an automatic process which maps neatly to the ostensive model for information retrieval in that it allows a user to place a virtual checkpoint on their search, explore one thread or aspect of their information need and then return to that checkpoint to then explore an alternative thread. Our system is fully functional and operational and in this paper we illustrate several design decisions we have made in building it

Load Balancing via Random Local Search in Closed and Open systems

In this paper, we analyze the performance of random load resampling and migration strategies in parallel server systems. Clients initially attach to an arbitrary server, but may switch server independently at random instants of time in an attempt to improve their service rate. This approach to load balancing contrasts with traditional approaches where clients make smart server selections upon arrival (e.g., Join-the-Shortest-Queue policy and variants thereof). Load resampling is particularly relevant in scenarios where clients cannot predict the load of a server before being actually attached to it. An important example is in wireless spectrum sharing where clients try to share a set of frequency bands in a distributed manner.Comment: Accepted to Sigmetrics 201

Distributed PC Based Routers: Bottleneck Analysis and Architecture Proposal

Recent research in the different functional areas of modern routers have made proposals that can greatly increase the efficiency of these machines. Most of these proposals can be implemented quickly and often efficiently in software. We wish to use personal computers as forwarders in a network to utilize the advances made by researchers. We therefore examine the ability of a personal computer to act as a router. We analyze the performance of a single general purpose computer and show that I/O is the primary bottleneck. We then study the performance of distributed router composed of multiple general purpose computers. We study the performance of a star topology and through experimental results we show that although its performance is good, it lacks flexibility in its design. We compare it with a multistage architecture. We conclude with a proposal for an architecture that provides us with a forwarder that is both flexible and scalable.© IEE

Clustering algorithm for local area network design in a CIM environment

Communication systems have come to be regarded as being one of the most important aspects in manufacturing enterprises in recent years. It continues to offer enormous potential for improving the efficiency of manufacturing enterprises through Computer Integrated Manufacturing (CIM). It is true however that this potential cannot be realized by developments on the communications front alone. Strategic and economic considerations in installing a communications system in a manufacturing environment is of vital significance. This thesis, at the outset attempts to bring out some of the issues in the area of communications (research), specifically in relation to CIM. A perception of prior research to some of these issues has been provided. The main focus of this thesis is placed on developing a methodology for designing an optimal flow local area network (LAN) for a CIM communications system using analytical models for resource allocation. A new representation of information flow load among the various nodes in a network is conceived which helps to determine an optimal communication traffic matrix. A decision logic to generate a system architecture based on the above information flow is designed. The various factors and variables involved in a communication network are identified. For the purpose of this study however, the most critical variables /factors of these are considered. Definitions of the various terminologies associated with these factors are provided. Experimentations are performed using the simulation approach. The main objective of this study is to help LAN designers to suggest effective configurations of the nodes to be networked so that the installation costs and propagation delays are reduced

Molecular detection and characterization of phytoplasma associated with China aster (Callistephus chinensis) phyllody in India

China aster (Callistephus chinensis L.) is one of the most popular annual flowering plant grown through-out the world. Phyllody disease of China aster is a phytoplasma associated disease that induces severe economic losses. Phytoplasmal disease in China aster was assessed for phytoplasma by direct polymerase chain reaction primed by using phytoplasma universal primer pairs PI/P7. A 1.8 Kb DNA fragments encoding the portion of phyto-plasma 16SrDNA amplified by PCR was cloned and sequenced. Sequencing of the PCR product and BLAST analy-sis indicated that China aster phyllody phytoplasma strain shared maximum sequence identity (99%) with strains of Peanut Witches’ broom (16SrII) phytoplasma group. Phylogenetic relationship of 16SrDNA sequence of China aster phyllody phytoplasma strain in the present study confirmed association of Peanut Witches’ broom (16SrII) group of phytoplasmas with China aster phyllody disease in India

Accurate 3D Cell Segmentation using Deep Feature and CRF Refinement

We consider the problem of accurately identifying cell boundaries and labeling individual cells in confocal microscopy images, specifically, 3D image stacks of cells with tagged cell membranes. Precise identification of cell boundaries, their shapes, and quantifying inter-cellular space leads to a better understanding of cell morphogenesis. Towards this, we outline a cell segmentation method that uses a deep neural network architecture to extract a confidence map of cell boundaries, followed by a 3D watershed algorithm and a final refinement using a conditional random field. In addition to improving the accuracy of segmentation compared to other state-of-the-art methods, the proposed approach also generalizes well to different datasets without the need to retrain the network for each dataset. Detailed experimental results are provided, and the source code is available on GitHub.Comment: 5 pages, 5 figures, 3 table

Microarray spot partitioning by autonoumsly organising maps thorugh contour model

In cDNA microarray image analysis, classification of pixels as forefront area and the area covered by background is very challenging. In microarray experimentation, identifying forefront area of desired spots is nothing but computation of forefront pixels concentration, area covered by spot and shape of the spots. In this piece of writing, an innovative way for spot partitioning of microarray images using autonomously organizing maps (AOM) method through C-V model has been proposed. Concept of neural networks has been incorpated to train and to test microarray spots.In a trained AOM the comprehensive information arising from the prototypes of created neurons are clearly integrated to decide whether to get smaller or get bigger of contour. During the process of optimization, this is done in an iterative manner. Next using C-V model, inside curve area of trained spot is compared with test spot finally curve fitting is done.The presented model can handle spots with variations in terms of shape and quality of the spots and meanwhile it is robust to the noise. From the review of experimental work, presented approach is accurate over the approaches like C-means by fuzzy, Morphology sectionalization