Design, implementation, and evaluation of differentiated caching services

With the dramatic explosion of online information, the Internet is undergoing a transition from a data communication infrastructure to a global information utility. PDAs, wireless phones, Web-enabled vehicles, modem PCs, and high-end workstations can be viewed as appliances that "plug-in" to this utility for information. The increasing diversity of such appliances calls for an architecture for performance differentiation of information access. The key performance accelerator on the Internet is the caching and content distribution infrastructure. While many research efforts addressed performance differentiation in the network and on Web servers, providing multiple levels of service in the caching system has received much less attention. It has two main contributions. First, we describe, implement, and evaluate an architecture for differentiated content caching services as a key element of the Internet content distribution architecture. Second, we describe a control-theoretical approach that lays well-understood theoretical foundations for resource management to achieve performance differentiation in proxy caches. An experimental study using the Squid proxy cache shows that differentiated caching services provide significantly better performance to the premium content classes.     

Fabrication and characterization of nanostructured (Sn–Ti)O<Subscript>2</Subscript> pellets and films for liquefied petroleum gas sensing

Present paper reports the synthesis of nanostructured (Sn–Ti)O₂ via physicochemical method, its characterization and performance as liquefied petroleum gas (LPG) sensor. The synthesized material was characterized using XRD that confirmed the formation of (Sn–Ti)O₂ nanocomposite. Minimum crystallite size was found as 7 nm. The material was also investigated through SEM, DSC, FTIR, PL and UV–Vis spectrophotometer. Further, the pellet, thick and thin films were fabricated for the sensing analysis. Pellets (9 mm diameter, 4 mm thickness) of (Sn–Ti)O₂ nanocomposite were made by hydraulic pressing machine by applying uniaxial pressure of 616 MPa, thick films (thickness ~2 µm) were made by screen printing technique and thin films were prepared using a Photo resist spinner unit. Further at room temperature, the pellet and films were exposed to LPG in a gas chamber under controlled conditions at room temperature and variations in resistance with the concentrations of LPG were observed. The maximum value of sensitivity of solid state pellet, thick and thin films based sensors were found 7, 9 and 39 for 5 vol% of LPG, respectively. Sensing characteristics were found to be reproducible, after 6 months of their fabrication, indicating the stability of the sensors.     

Design and development of guided four beam cantilever type MEMS based piezoelectric energy harvester

Microsystem Technologies - MEMS devices have found applicability in remote area of operation such as temperature monitoring in extreme climates, structural health monitoring, and car tire pressure...     

An intelligent wavelet transform-based framework to detect subsurface fires with NOAA–AVHRR images

Subsurface coal fires (in this article, termed as hotspots), responsible for atmospheric pollution, human fatalities and perilous land subsidence, pose a big threat to major coal-producing countries in the world. The majority of the research performed to date has focused on providing hotspot allocation information for a specific region of interest and most has explored quite expensive high-resolution Landsat Thematic Mapper (TM) satellite images for the same. This article aims to investigate the applicability of a wavelet transform-based model to detect subsurface fires (hotspots) with freely available National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA–AVHRR) images and find out the possibility of extracting novel hotspot features by applying a wavelet transform-based analysis technique. The proposed feature vector consists of wavelet variance coefficients (WVCs) obtained from scale-by-scale decomposition of the AVHRR image variance and builds up a strong base for designing an accurate classification system. Furthermore, the support vector machine (SVM), an efficient machine learning tool, is applied to the proposed feature vector in order to develop a classification model. The demonstrated results successfully prove the effectiveness of the proposed framework as the classified images show a good correspondence with records of subsurface fires mapped by the Bharat Coking Coal Limited (BCCL), India. The effectiveness of the SVM method is also evaluated in comparison with the classical neural network-based approach.     

Adaptive Discretization for Computerized Tomography

Two adaptive discretization frameworks are tested for computerized tomography (CT) data reconstruction. Removal of inactive pixels is primary motivation. Efficient and user independent entropy optimized masking is employed for spatial filtering purposes. Density of nodes at high gradient of reconstructed physical property is used as adaptation criterion. An alternative option, independent from noisy projection data and nature of the physical properties, is also discussed. Sensitivity analysis between the uniform and nonuniform (evolved via adaptive route) reconstruction grid reveals the utility of nonuniform grids. Iterative and transform based reconstruction techniques are used. Outcomes are tested successfully on three real world projection data from two different compact CT setups and one commercial high-resolution micro-CT scanner.     

Alginate gel particles–A review of production techniques and physical properties

The application of hydrocolloid gel particles is potentially useful in food, chemical, and pharmaceutical industries. Alginate gel particles are one of the more commonly used hydrocolloid gel particles due to them being biocompatible, nontoxic, biodegradable, cheap, and simple to produce. They are particularly valued for their application in encapsulation. Encapsulation in alginate gel particles confers protective benefits to cells, DNA, nutrients, and microbes. Slow release of flavors, minerals, and drugs can also be achieved by encapsulation in gel particles. The particle size and shape of the gel particles are crucial for specific applications. In this review, current methods of producing alginate gel particles will be discussed, taking into account their advantages, disadvantages, scalability, and impact on particle size. The physical properties of alginate gel particles will determine the effectiveness in different application conditions. This review will cover the current understanding of the alginate biopolymer, gelation mechanisms and factors affecting release properties, gel strength, and rheology of the alginate gel particle systems.     

Strategies for software-based hybrid business models

The open source approach to software development has been used by software organizations in tandem with their existing business models, which are based on proprietary software licensing. This led to the creation of hybrid business models that merge open source and proprietary paradigms. This paper explores the practices used by software product vendors using hybrid business models and proposes strategies emerging out of these practices using interpretive, single case study research design.     

Make3D: Learning 3D Scene Structure from a Single Still Image

We consider the problem of estimating detailed 3D structure from a single still image of an unstructured environment. Our goal is to create 3D models that are both quantitatively accurate as well as visually pleasing. For each small homogeneous patch in the image, we use a Markov Random Field (MRF) to infer a set of "plane parameters” that capture both the 3D location and 3D orientation of the patch. The MRF, trained via supervised learning, models both image depth cues as well as the relationships between different parts of the image. Other than assuming that the environment is made up of a number of small planes, our model makes no explicit assumptions about the structure of the scene; this enables the algorithm to capture much more detailed 3D structure than does prior art and also give a much richer experience in the 3D flythroughs created using image-based rendering, even for scenes with significant nonvertical structure. Using this approach, we have created qualitatively correct 3D models for 64.9 percent of 588 images downloaded from the Internet. We have also extended our model to produce large-scale 3D models from a few images.