Oxidation and sulfidation behavior of Fe-20Cr-16Ni-4Al-1Y2O3 oxide-dispersion-strengthened alloy

High-temperature oxidation and sulfidation studies were conducted on an oxide-dispersion-strengthened alloy of composition Fe-20Cr-16Ni-4Al-1Y₂O₃. The oxidation studies were conducted in air and low-PO₂ environments over a temperature range of 650–1200°C. Results are also reported on the sulfidation resistance of preformed oxide scales and the influence of reoxidation of preformed sulfide scales. Detailed microstructural results and x-ray diffraction analysis data are presented to substantiate the corrosion behavior of the alloy over the wide range of conditions anticipated in fossil-energy systems. Data are also presented on the corrosion behavior of the alloy in oxygen/sulfur mixed gas atmospheres, and the results are used to compare the corrosion behavior of the present alloy with other chromia- and alumina-forming alloys.     

Adaptive image denoising using cuckoo algorithm

This paper presents a novel denoising approach based on smoothing linear and nonlinear filters combined with an optimization algorithm. The optimization algorithm used was cuckoo search algorithm and is employed to determine the optimal sequence of filters for each kind of noise. Noises that would be eliminated form images using the proposed approach including Gaussian, speckle, and salt and pepper noise. The denoising behaviour of nonlinear filters and wavelet shrinkage threshold methods have also been analysed and compared with the proposed approach. Results show the robustness of the proposed filter when compared with the state-of-the-art methods in terms of peak signal-to-noise ratio and image quality index. Furthermore, a comparative analysis is provided between the said optimization algorithm and the genetic algorithm.     

Peer to peer trade in HTM5 meta model for agent oriented cloud robotic systems

Cloud computing is a methodology and not a technology. Adaptation of cloud computing services for robotic applications is relatively straightforward while adaptation of underlying ideas will require a new design attitude. Cloud computing is a cost-effective and dynamic business model. Currently cloud robotics is understood as a client server methodology which enables robots utilize resources and services placed at centralized servers. These cloud servers treat robots as any other client computer offering them platform, infrastructure, process or algorithm as a service. HTM5 is an OMG MDA based multi-view meta-model for agent oriented development of cloud robotic systems. HTM5 encourages design of peer-to-peer service ecosystems based on an open registry and matchmaking mechanism. In peer-to-peer cloud robotics, a robot can trade its hardware, software and functional resources as a service to other robots in the ecosystem. The peer-to-peer trade in such systems may be driven by contracts and relationships between its member agents. This article discusses trade-view model of HTM5 methodology and its use in developing a cloud robotic ecosystem that implements peer-to-peer, contract based economy. The article also presents a case study with experiments that implement distributed artificial intelligence and peer-to-peer service oriented trade on simulated and real robot colonies.     

SRA-MSDU: Enhanced A-MSDU frame aggregation with selective retransmission in 802.11n wireless networks

The main goal of the IEEE 802.11n standard is to achieve more than 100 Mbps of throughput at the MAC service access point. This high throughput has been achieved via many enhancements in both the physical and MAC layers. One of the MAC enhancements is the frame aggregation in which multiple frames are concatenated into a single large frame before being transmitted. The 802.11n MAC layer defines two types of aggregation, aggregate MAC service data unit (A-MSDU) and aggregate MAC protocol data unit (A-MPDU). The A-MPDU outperforms A-MSDU due to its large aggregation size and the subframes retransmission in erroneous channels. However, in error free channels and under the same aggregation size the A-MSDU performs better than the A-MPDU due to its smaller headers. Thus, adding a selective retransmission capability to the A-MSDU would improve the system performance. In this paper, we have proposed an MSDU frame aggregation scheme that enables selective retransmission at the MSDU level without altering the original MAC header. In this proposed scheme an implicit sequence control mechanism has been introduced in order to keep the frames in sequence and preserve their correct order at the receiver side. The results show that the proposed scheme improves the system performance in terms of throughput and delay even under highly erroneous channels.     

New Hybrid Access Method for Femtocell through Adjusting QoS

The increasing number of mobile users raises issues about coverage extension in some areas such as rural zones, indoor or underground locations： one of suggestion solution to accommodate this growing of mobile user is femtocell. Femtocell have been attracting considerable attention in mobile communications, it is a small base station that install to improve the indoor coverage of a given cellular and to enhance user＇s capacity. Call admission control and resource allocation infemtocell＇s hybrid mode are an essential performance promotion issue. Developing methods for femtocell utilization is very comparative nowadays. The two major limitations of wireless communication are capacity and range. The main contribution of our paper is developing resource allocation scheme that can manage the femocell resources between subscriber （femtocell user） and non-subscriber （macrocell user in order to maximizing the system utilizations, we provide a mechanism that leads to serve more users by admitting more subscribers basing on adjusting QoS of the connected users.     

A probabilistic alternative to regression suites

Automated regression suites are essential in developing large applications, while maintaining reasonable quality and timetables. The main argument against the automation of regression suites, in addition to the cost of creation and maintenance, is the observation that if you run the same test many times, it becomes increasingly less likely to find bugs. To alleviate such problems, a new regression suite practice, using random test generators to create regression suites on-the-fly, is becoming more common. In this practice, instead of maintaining tests, we generate test suites on-the-fly by choosing several specifications and generating a number of tests from each specification.     

Learning to Locate Informative Features for Visual Identification

Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize object identification. First, inter-object variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many different instances of the category but few or just one positive “training” examples per object instance. Because variation among object instances may be small, a solution must locate possibly subtle object-specific salient features, like a door handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however, standard modeling and feature selection techniques cannot be used. We describe an on-line algorithm that takes one image from a known category and builds an efficient “same” versus “different” classification cascade by predicting the most discriminative features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered sequence of discriminative features specific to the given image. Learned stopping thresholds make the identifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.     

Home Automation Using Augmented Reality (HAAR)

Wireless Personal Communications - The development of Smart Home Controllers has seen rapid growth in recent years, especially for smart devices, that can utilize the Internet of Things (IoT)....     

Rapid and multimodal <Emphasis Type="Italic">in vivo</Emphasis> bioimaging of cancer cells through <Emphasis Type="Italic">in situ</Emphasis> biosynthesis of Zn&;Fe nanoclusters

Early diagnosis remains highly important for efficient cancer treatment,and hence,there is significant interest in the development of effective imaging strategies.This work reports a new multimodal bioimaging method for accurate and rapid diagnosis of cancer cells by introducing aqueous Fe2+ and Zn2+ ions into cancer cells (i.e.,HeLa,U87,and HepG2 cancer cells).We found that the biocompatible metal ions Fe2+ and Zn2+ forced the cancer cells to spontaneously synthesize fluorescent ZnO nanoclusters and magnetic Fe3O4 nanoclusters.These clusters could then be used for multimodal cancer imaging by combining fluorescence imaging with magnetic resonance imaging and computed tomography imaging.Meanwhile,for normal cells (i.e.,L02) and tissues,neither fluorescence nor any other obvious difference could be detected between preand post-injection.This multimodal bioimaging strategy based on the in situ biosynthesized Zn&Fe oxide nanoclusters might therefore be useful for early cancer diagnosis and therapy.