Characterization and Modeling of Laser Micromachined Periodically Corrugated Metallic Terahertz Wire Waveguides

Finite element method simulations of periodically corrugated metal terahertz wire waveguides have been conducted with concurrent analysis done on both the near-field confinement properties and the far-field emission properties at the end of the waveguides. This modeling was used to guide the choice of design parameters for the fabrication of waveguides with laser micromachining. The waveguides were characterized with a fiber-coupled terahertz time-domain spectroscopy and imaging system. The propagation properties as well as the frequency dependent diffraction at the end of the wire waveguides were examined and compared to straight, non-engineered metallic wire waveguides.     

A multiple circular path convolution neural network system for detection of mammographic masses

A multiple circular path convolution neural network (MCPCNN) architecture specifically designed for the analysis of tumor and tumor-like structures has been constructed. We first divided each suspected tumor area into sectors and computed the defined mass features for each sector independently. These sector features were used on the input layer and were coordinated by convolution kernels of different sizes that propagated signals to the second layer in the neural network system. The convolution kernels were trained, as required, by presenting the training cases to the neural network. In this study, randomly selected mammograms were processed by a dual morphological enhancement technique. Radiodense areas were isolated and were delineated using a region growing algorithm. The boundary of each region of interest was then divided into 36 sectors using 36 equi-angular dividers radiated from the center of the region. A total of 144 Breast Imaging-Reporting and Data System-based features (i.e., four features per sector for 36 sectors) were computed as input values for the evaluation of this newly invented neural network system. The overall performance was 0.78-0.80 for the areas (Az) under the receiver operating characteristic curves using the conventional feed-forward neural network in the detection of mammographic masses. The performance was markedly improved with Az values ranging from 0.84 to 0.89 using the MCPCNN. This paper does not intend to claim the best mass detection system. Instead it reports a potentially better neural network structure for analyzing a set of the mass features defined by an investigator.     

Multicast-based inference of network-internal delay distributions

Packet delay greatly influences the overall performance of network applications. It is therefore important to identify causes and locations of delay performance degradation within a network. Existing techniques, largely based on end-to-end delay measurements of unicast traffic, are well suited to monitor and characterize the behavior of particular end-to-end paths. Within these approaches, however, it is not clear how to apportion the variable component of end-to-end delay as queueing delay at each link along a path. Moreover, there are issues of scalability for large networks. In this paper, we show how end-to-end measurements of multicast traffic can be used to infer the packet delay distribution and utilization on each link of a logical multicast tree. The idea, recently introduced in Caceres et al. (1999), is to exploit the inherent correlation between multicast observations to infer performance of paths between branch points in a tree spanning a multicast source and its receivers. The method does not depend on cooperation from intervening network elements; because of the bandwidth efficiency of multicast traffic, it is suitable for large-scale measurements of both end-to-end and internal network dynamics. We establish desirable statistical properties of the estimator, namely consistency and asymptotic normality. We evaluate the estimator through simulation and observe that it is robust with respect to moderate violations of the underlying model.     

Single frequency inverse obstacle scattering: a sparsity constrained linear sampling method approach

The linear sampling method (LSM) offers a qualitative image reconstruction approach, which is known as a viable alternative for obstacle support identification to the well-studied filtered backprojection (FBP), which depends on a linearized forward scattering model. Of practical interest is the imaging of obstacles from sparse aperture far-field data under a fixed single frequency mode of operation. Under this scenario, the Tikhonov regularization typically applied to LSM produces poor images that fail to capture the obstacle boundary. In this paper, we employ an alternative regularization strategy based on constraining the sparsity of the solution's spatial gradient. Two regularization approaches based on the spatial gradient are developed. A numerical comparison to the FBP demonstrates that the new method's ability to account for aspect-dependent scattering permits more accurate reconstruction of concave obstacles, whereas a comparison to Tikhonov-regularized LSM demonstrates that the proposed approach significantly improves obstacle recovery with sparse-aperture data.     

Cryptanalysis of Hsiang‐Shih's authentication scheme for multi‐server architecture

From user point of view, password‐based remote user authentication technique is one of the most convenient and easy‐to‐use mechanisms to provide necessary security on system access. As the number of computer crimes in modern cyberspace has increased dramatically, the robustness of password‐based authentication schemes has been investigated by industries and organizations in recent years. In this paper, a well‐designed password‐based authentication protocol for multi‐server communication environment, introduced by Hsiang and Shih, is evaluated. Our security analysis indicates that their scheme is insecure against session key disclosure, server spoofing attack, and replay attack and behavior denial. Copyright © 2010 John Wiley & Sons, Ltd.     

Strain Anisotropies and Self‐Limiting Capacities in Single‐Crystalline 3D Silicon Microstructures: Models for High Energy Density Lithium‐Ion Battery Anodes

This study examines the crystallographic anisotropy of strain evolution in model, single‐crystalline silicon anode microstructures on electrochemical intercalation of lithium atoms. The 3D hierarchically patterned single‐ crystalline silicon microstructures used as model anodes were prepared using combined methods of photolithography and anisotropic dry and wet chemical etching. Silicon anodes, which possesses theoretically ten times the energy density by weight compared to conventional carbon anodes, reveal highly anisotropic but more importantly, variably recoverable crystallographic strains during cycling. Model strain‐limiting silicon anode architectures that mitigate these impacts are highlighted. By selecting a specific design for the silicon anode microstructure, and exploiting the crystallographic anisotropy of strain evolution upon lithium intercalation to control the direction of volumetric expansion, the volume available for expansion and thus the charging capacity of these structures can be broadly varied. We highlight exemplary design rules for this self‐strain‐limited charging in which an anode can be variably optimized between capacity and stability. Strain‐limited capacities ranging from 677 mAhg^?1 to 2833 mAhg^?1 were achieved by constraining the area available for volumetric expansion via the design rules of the microstructures.     

2008年一季度元器件价格将快速下跌

当元器件市场的季节性需求高峰过去以后,大多数供货商们仍然握有大量的存货。许多供货商对今年的消费需求持高度谨慎的态度。目前,过剩的库存是他们今年的主要问题之一。事实上,由于需求的不确定性和产能扩张,库存过剩是2008年1季度的现实问题。假日需求的情况仍然是一个未知数,iSuppli公司预测,电子元器件的供货将足以满足市场的需求。     

Rapid thermal annealing effects on the photoluminescence properties of molecular beam epitaxy-grown GaIn(N)As quantum wells with Ga(N)As spacers and barriers

This article describes the effects of rapid thermal annealing (RTA) on the photoluminescence (PL) emission from a series of GaIn(N)As quantum wells. Indium compositions of both 20% and 32% were examined with nominal N compositions of 1% or 2%. The N location was varied within our quantum structure, which can be divided into three regions: (1) quantum well, (2) Ga(N)As spacer layers at the barrier-to-well interface and well-to-barrier interface, and (3) barriers surrounding each quantum well. Eight combinations of samples were examined with varying In content, Ga(N)As spacer layer thickness, N content, and N location in the structure. In the best cases, the presence of these Ga(N)As spacer layers improves the PL properties, due to annealing, with a reduction in the emission wavelength blueshift by ～400 Å, a reduction of the decrease in the full-width at half-maximum (FWHM) by ～5 meV, and a threefold reduction of the increase in integrated intensity. It was also observed that relocating N from the quantum wells to the barriers produces a comparable emission wavelength both before and after annealing. Our results further show that the composition of incorporated N in the material is most influential during the stages of RTA in which relatively small amounts of thermal energy is present from our lower annealing times and temperatures. Hence, we believe a low thermal-energy anneal is responsible for the recovery of the plasma-related crystal damage that was incurred during its growth. However, the In composition in the quantum well is most influential during the latter stages of thermal annealing, at increased times and temperatures, where the wavelength blueshift was roughly independent of the amount of incorporated N. As a result, our investigations into the effects of RTA on the PL properties support other reports that suggest the wavelength blueshift is not due to N diffusion.     

Polarity dependence of charge to breakdown and interface state generation of oxynitride gate dielectrics prepared by rapid thermal processing

MOS devices were fabricated with dry thermal oxides, nitrided oxides and annealed nitrided oxides. The anneals were performed in O/sub 2/ or N/sub 2/ ambients using rapid thermal processing. Charge to breakdown, Q/sub bd/, and interface state generation, Delta D/sub it/, for these devices were studied using Fowler-Nordheim electron injection. The gate bias polarity dependence of Q/sub bd/ and Delta D/sub it/ was investigated. A model is proposed to explain the observed dependence of these quantities on the polarity of injection and process parameters.<>     

Power Efficient SDR Implementation of IEEE 802.11a/p Physical Layer

Software defined physical layer modems can be considered the new trend in the field of communications. Differently from dedicated hardware, software can be easily modified to implement a large variety of standards on the same platform. The use of software can significantly reduce development costs, but generally comes at the price of an increase in silicon area and power consumption. For different reasons, this price is something that is not always convenient or even possible to pay, as in the case of low-cost ICs implementing a single waveform, or even multi-mode modems embedding legacy IPs already available in hardware. In particular, power consumption overhead can be prohibitive for mobile terminals or in general for battery-powered devices. The very first challenge for a computing fabric to be competitive is to find and implement the right trade-off between flexibility and performance. This was the guideline for the design of the Block Processing Engine (BPE), a template architecture conceived for power-efficient baseband processing. The BPE core feature is a mixed-grain instruction set balancing general-purpose fine-grain instructions with more specific coarse-grain instructions wrapping custom hardware modules. To further limit the power consumption, the BPE also implements instruction-pipelining, variable-size SIMD and multi-task support. To prove the efficiency of such an approach, a dual-mode IEEE 802.11a/p receiver has been implemented.