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.     

Simple substitution distance and metamorphic detection

To evade signature-based detection, metamorphic viruses transform their code before each new infection. Software similarity measures are a potentially useful means of detecting such malware. We can compare a given file to a known sample of metamorphic malware and compute their similarity—if they are sufficiently similar, we classify the file as malware of the same family. In this paper, we analyze an opcode-based software similarity measure inspired by simple substitution cipher cryptanalysis. We show that the technique provides a useful means of classifying metamorphic malware.     

Mixing matrix estimation using discriminative clustering for blind source separation

Mixing matrix estimation in instantaneous blind source separation (BSS) can be performed by exploiting the sparsity and disjoint orthogonality of source signals. As a result, approaches for estimating the unknown mixing process typically employ clustering algorithms on the mixtures in a parametric domain, where the signals can be sparsely represented. In this paper, we propose two algorithms to perform discriminative clustering of the mixture signals for estimating the mixing matrix. For the case of overdetermined BSS, we develop an algorithm to perform linear discriminant analysis based on similarity measures and combine it with K-hyperline clustering. Furthermore, we propose to perform discriminative clustering in a high-dimensional feature space obtained by an implicit mapping, using the kernel trick, for the case of underdetermined source separation. Using simulations on synthetic data, we demonstrate the improvements in mixing matrix estimation performance obtained using the proposed algorithms in comparison to other clustering methods. Finally we perform mixing matrix estimation from speech mixtures, by clustering single source points in the time-frequency domain, and show that the proposed algorithms achieve higher signal to interference ratio when compared to other baseline algorithms.     

Improving the performance of speaker and language identification tasks using unique characteristics of a class

In classification tasks, the error rate is proportional to the commonality among classes. In conventional GMM-based modeling technique, since the model parameters of a class are estimated without considering other classes in the system, features that are common across various classes may also be captured, along with unique features. This paper proposes to use unique characteristics of a class at the feature-level and at the phoneme-level, separately, to improve the classification accuracy. At the feature-level, the performance of a classifier has been analyzed by capturing the unique features while modeling, and removing common feature vectors during classification. Experiments were conducted on speaker identification task, using speech data of 40 female speakers from NTIMIT corpus, and on a language identification task, using speech data of two languages (English and French) from OGI_MLTS corpus. At the phoneme-level, performance of a classifier has been analyzed by identifying a subset of phonemes, which are unique to a speaker with respect to his/her closely resembling speaker, in the acoustic sense, on a speaker identification task. In both the cases (feature-level and phoneme-level) considerable improvement in classification accuracy is observed over conventional GMM-based classifiers in the above mentioned tasks. Among the three experimental setup, speaker identification task using unique phonemes shows as high as 9.56 % performance improvement over conventional GMM-based classifier.     

Probabilistic Health-lnformatics and Bioterrorism

In this article, new probabilistic health-informatics indices connecting probabilities： Pr（A）,Pr（B）,Pr（A IB） and P r（A ]B-） are discovered, where .4 and B denote respectively the ＂ability of a hospital to treat anthrax patients＂ and ＂whether a hospital drilled to be prepared to deal with an adverse bioterrorism＂. These probabilistic informatics are not seen in any textbooks or journal articles and yet, they are too valuable to be unnoticed to comprehend the hospitals＇ preparedness to treat anthrax patients in an outbreak of bioterrorism. A demonstration of this new probabilistic informatics is made in this article with the data in the U.S. Government＇s General Accounting Office＇s report GA0-03-924. Via this example, this article advocates the importance of the above mentioned probabilistic-informatics for health professionals to understand and act swiftly to deal with public health emergencies.     

Design of high-speed data transfer turbo code for body channel communication transceivers

In this work, a digital differential transmitter based on low-power wireless compensation transceiver for body channel communication (BCC) is proposed. Further, the proposed transceiver is composed of Touch Status Detection Unit (TSDU), Wireless Status Compensation Unit (WSCU), and a reconfigurable preamplifier. Initially, the human body channel environment for wireless communication is investigated based on properties from 1 to 100 MHz. Further, the turbo code-based encoding scheme is used to encode the data before transferring the data on the transmitter side. Also, the proposed error-correcting parallel turbo decoder using a modified step-by-step algorithm is presented. The turbo code-based decoding scheme is used to recover the error-free transmitted data at the receiver side. Results demonstrate that the proposed BCC transceiver is designed using 90 nm CMOS technology and it is observed that the proposed BCC transceiver has utilized an area of 600mm². Also, the maximum data rate achieved by a proposed BCC transceiver was 100 Mbps, and the overall transceiver power consumption is 0.42 mW, and energy for communication is 0.02 nj/b.     

Network Lifetime Enhancement of Wireless Sensor Networks Using EFRP Protocol

This paper presents a novel transceiver architecture for in-band full duplex radio. A transceiver for full duplex radio requires a self-interference (SI) canceler to remove the SI occurring from the transmitter to the receiver, and a full duplex transceiver generally has two SI cancelers: one at the analog RF stage and the other at the baseband stage. The output from the SI canceler at the RF stage includes much residual SI, and it decreases the number of bits allocated to the analog baseband signal at the analog-to-digital converter. A 1-tap analog baseband SI canceler that uses a replica signal including only the direct path component of the residual SI has been presented for preventing degradation. However, the architecture cannot remove the SI well due to the high Ricial K-factor. To address the problem, the presented architecture has an SI canceler at the analog baseband stage, and this canceler employs a replica signal that is output from a digital-to-analog converter. Because the replica signal is generated in the digital domain, the architecture can generate a multipath replica signal, and improved performance can be expected. Numerical and theoretical analyses are shown to validate the effectiveness of the presented architecture.

     

Parameter-uniform hybrid numerical scheme for time-dependent convection-dominated initial-boundary-value problems

In this paper, we propose a numerical scheme which is almost second-order spatial accurate for a one-dimensional singularly perturbed parabolic convection-diffusion problem exhibiting a regular boundary layer. The proposed numerical scheme consists of classical backward-Euler method for the time discretization and a hybrid finite difference scheme for the spatial discretization. We analyze the scheme on a piecewise-uniform Shishkin mesh for the spatial discretization to establish uniform convergence with respect to the perturbation parameter. Numerical results are presented to validate the theoretical results.     

Automated dynamic detection of busy–wait synchronizations

With the advent of multicores, multithreaded programming has acquired increased importance. In order to obtain good performance, the synchronization constructs in multithreaded programs need to be carefully implemented. These implementations can be broadly classified into two categories: busy–wait and schedule‐based. For shared memory architectures, busy–wait synchronizations are preferred over schedule‐based synchronizations because they can achieve lower wakeup latency, especially when the expected wait time is much shorter than the scheduling time. While busy–wait synchronizations can improve the performance of multithreaded programs running on multicore machines, they create a challenge in program debugging, especially in detecting and identifying the causes of data races. Although significant research has been done on data race detection, prior works rely on one important assumption—the debuggers are aware of all the synchronization operations performed during a program run. This assumption is a significant limitation as multithreaded programs, including the popular SPLASH‐2 benchmark have busy–wait synchronizations such as barriers and flag synchronizations implemented in the user code. We show that the lack of knowledge of these synchronization operations leads to unnecessary reporting of numerous races. To tackle this problem, we propose a dynamic technique for identifying user‐defined synchronizations that are performed during a program run. Both software and hardware implementations are presented. Furthermore, our technique can be easily exploited by a record/replay system to significantly speedup the replay. It can also be leveraged by a transactional memory system to effectively resolve a livelock situation. Our evaluation confirms that our synchronization detector is highly accurate with no false negatives and very few false positives. We further observe that the knowledge of synchronization operations results in 23% reduction in replay time. Finally, we show that using synchronization knowledge livelocks can be efficiently avoided during runtime monitoring of programs. Copyright © 2009 John Wiley & Sons, Ltd.     

Richardson extrapolation technique for singularly perturbed parabolic convection–diffusion problems

This paper deals with the study of a post-processing technique for one-dimensional singularly perturbed parabolic convection–diffusion problems exhibiting a regular boundary layer. For discretizing the time derivative, we use the classical backward-Euler method and for the spatial discretization the simple upwind scheme is used on a piecewise-uniform Shishkin mesh. We show that the use of Richardson extrapolation technique improves the ε-uniform accuracy of simple upwinding in the discrete supremum norm from O (N ⁻¹ ln N + Δt) to O (N ⁻² ln² N + Δt ²), where N is the number of mesh-intervals in the spatial direction and Δt is the step size in the temporal direction. The theoretical result is also verified computationally by applying the proposed technique on two test examples.