Nanoindentation measurement of core–skin interphase viscoelastic properties in a sandwich glass composite

Mechanics of Time-Dependent Materials - Debonding at the core–skin interphase region is one of the primary failure modes in core sandwich composites under shear loads. As a result, the...     

Large-scale biomedical image analysis in grid environments

This paper presents the application of a component-based Grid middleware system for processing extremely large images obtained from digital microscopy devices. We have developed parallel, out-of-core techniques for different classes of data processing operations employed on images from confocal microscopy scanners. These techniques are combined into a data preprocessing and analysis pipeline using the component-based middleware system. The experimental results show that: 1) our implementation achieves good performance and can handle very large datasets on high-performance Grid nodes, consisting of computation and/or storage clusters and 2) it can take advantage of Grid nodes connected over high-bandwidth wide-area networks by combining task and data parallelism.     

MiniPCI、PCMCIA模块打头阵：WiMAX推进商品化速度

在硝烟弥漫的宽带战场,强大的网络接入能力、低成本、高收益等优点无疑是竞争力所在.WiMAX可作为无线DSL或无线Cable技术来弥补有线技术的不足,同时也被部分业界人士视作新一移动基础设施或第四代无线移动应用的互补技术.目前OEM、ODM相关发展厂商正通过采用具弹性系统架构的ASIC以降低成本,加速WiMAX技术推向市场的时间.     

Weight loss in frankfurters during thermal processing

A simplified two-term model was developed to predict the weight loss of frankfurters during thermal processing at various process conditions and with various product compositions. The model was validated with the experimental data. The moisture loss rate was found to be proportional to product temperature and inversely proportional to the fat-protein ratio of the product.     

The Asymmetric Golden Code for Fast Decoding on Time-Varying Channels

The golden code is a full-rate full-diversity space–time code for the two-input two-output channel with good performance but high decoding complexity. The overlaid Alamouti codes were recently proposed as an alternative; in exchange for a slight performance penalty, they have lower decoding complexity on quasistatic channels with QAM alphabets. However, the complexity advantage of the overlaid codes vanishes for time-varying channels. This paper proposes the asymmetric golden code, a novel full-rate and full-diversity space–time code for the two-input two-output channel that offers reduced-complexity decoding on both quasistatic and time-varying channels.     

The design and evaluation of an end-to-end handoff management protocol

We introduce an efficient protocol for end-to-end handoff management in heterogeneous wireless IP-based networks. The protocol is based on the stream control transmission protocol (SCTP), and employs a soft-handoff mechanism that uses end-to-end semantics for signaling handoffs and for transmitting control messages. The design goal of this protocol is twofold—first, to reduce the home registration delay, and, second, to eliminate the tunnelling cost which exists in current proposals, such as Mobile IP and its derivatives. Furthermore, we propose successive enhancements to the initial mobility management framework for achieving better scalability. We present strong analytical and simulation-based results that show performance improvements over existing approaches. Antonios Argyriou is a Ph.D. candidate in the school of electrical and computer engineering, Georgia Institute of Technology. He received his M.S. degree from the Georgia Institute of Technology in 2003, and the diploma from Democritus University of Thrace in 2001, both in electrical and computer engineering. His research interests spawn in all aspects of computer networking while specific interests include wireless networks and multimedia communications. He is a student member of IEEE and ACM. Vijay Madisetti is a professor of electrical and computer engineering at the Georgia Institute of Technology. He splits his time among teaching, research and entrepreneurship. His interests are design, prototyping, and packaging of electronic systems, virtual prototyping, embedded software systems, and computer networks. He obtained his Ph.D. in electrical engineering and computer science from the University of California at Berkeley. He is a member of the IEEE and the Computer Society.     

Parameterized key assignment for confidential communication in wireless networks

Predistribution of cryptographic keys is a widely used approach for establishing secure communication between severely resource-constrained nodes with limited or no access to network infrastructure. Many proposed key predistribution schemes make the implicit assumption that message contents need not be kept private from nodes other than the intended recipient. Messages in such schemes are not guaranteed to be confidential—they may be read by nodes within the network other than the intended recipient. In this paper, we present TASK—a symmetric key predistribution scheme that enables secure and confidential communication within wireless networks. TASK distributes keys by generating and reinforcing a series of template key assignment instances. It is parameterized, which allows it to make use of key storage capacities that other recently proposed schemes cannot. We show, through analysis and simulation, that TASK can achieve a level of security superior to that of two recently proposed schemes that also provide confidentiality. We also demonstrate that the techniques used in TASK (namely parameterization, templatization, and selective reinforcement) can be applied to other key assignment schemes for star or bipartite networks.     

Semi-supervised annotation of faces in image collection

The objective of this work is to correctly detect and recognize faces in an image collection using a database of known faces. This has applications in photo-tagging, video indexing, surveillance and recognition in wearable computers. We propose a two-stage approach for both detection and recognition tasks. In the first stage, we generate a seed set from the given image collection using off-the-shelf face detection and recognition algorithms. In the second stage, the obtained seed set is used to improve the performance of these algorithms by adapting them to the domain at hand. We propose an exemplar-based semi-supervised framework for improving the detections. For recognition of images, we use sparse representation classifier and generate seed images based on a confidence measure. The labels of the seed set are then propagated to other faces using label propagation framework by imposing appropriate constraints. Unlike traditional approaches, our approach exploits the similarities among the faces in collection to obtain improved performance. We conduct extensive experiments on two real-world photo-album and video collections. Our approach consistently provides an improvement of \({\sim } 4\)% for detection and \(5{-}9\)% for recognition on all these datasets.     

Pulse oximetry theory and calibration for low saturations

Pulse oximetry is a widely used technique in biomedical optics, but currently available pulse oximeters rely on empirical calibration approaches, which perform poorly at low saturations. We present an exact solution for pulse oximetry and show how this can be used as the basis for the development of a semiempirical calibration approach that may be useful, especially at low saturations and variable probe geometries. This new approach was experimentally tested against traditional empirical calibration techniques on transmission pulse oximetry for monitoring of fetal sheep using a minimally invasive spiral probe. The results open the way for the development of more accurate pulse oximetry.