Susceptibility of penicillin-susceptible and -resistant pneumococci to CFC-222, a new fluoroquinolone

Rotavirus (RV) strains infecting newborns often have unique neutralization antigens (P serotypes) on their outer capsids that are distinct from those found on RV strains that cause diarrhea in older children. We examined the hypothesis that unusual RV strains preferentially infect newborns because the newborns lack maternal neutralizing antibodies to these strains. To test this hypothesis, sera and saliva samples collected from neonates infected with 116E-like (P[11]G9) strains in the maternity ward of the All India Institute of Medical Sciences (AIIMS) hospital in New Delhi were tested for neutralizing antibodies against common RV strains and those infecting newborns and these titers were compared with those of newborns who did not become infected (controls). The infected neonates had significantly lower levels of cord blood neutralizing antibodies to 116E than the controls, suggesting that immunity to neonatal RV infection is acquired transplacentally through maternal antibodies. Further, this study confirmed the immunogenicity of the AIIMS neonatal strain 116E, a vaccine candidate, in its ability to evoke a potent RV-specific immunoglobulin A and neutralizing antibody response in serum and saliva among the infected babies. Our findings have important implications for the development of an effective RV vaccine. In India, where G9 strains are common in the community, the use of 116E as a vaccine, together with the rhesus tetravalent vaccine, may provide a broader protection against all the circulating RV serotypes, including serotype G9, which is not represented in the current rhesus RV tetravalent vaccine (G1-G4).     

Efficient scheduling of transmissions in optical broadcast networks

All-optical networks (AONs) with a broadcast-star based physical topology offer the possibility of transmission scheduling to resolve channel and receiver conflicts. This paper considers the problem of scheduling packet transmissions in a wavelength-division multiplexed (WDM) optical network with tunable transmitters and fixed-tuned receivers. The scheduling problem is complicated by tuning latency, a limited number of channels, and arbitrary traffic demands. We first analyze scheduling all-to-all packet transmissions and obtain a new lower bound for the schedule length. The lower bound is achieved by an algorithm proposed by Pieris and Sasaki (1994). We then extend the analysis to the case of arbitrary traffic demands and obtain lower bounds for the schedule length. Two constructions for scheduling algorithms are provided through list scheduling and multigraphs. The upper bounds so obtained not only provide performance guarantees with arbitrary demands, but also nearly meet the lower bound in simulations     

A programmable analog VLSI neural network processor forcommunication receivers

An analog VLSI neural network processor was designed and fabricated for communication receiver applications. It does not require prior estimation of the channel characteristics. A powerful channel equalizer was implemented with this processor chip configured as a four-layered perceptron network. The compact synapse cell is realized with an enhanced wide-range Gilbert multiplier circuit. The output neuron consists of a linear current-to-voltage converter and a sigmoid function generator with a controllable voltage gain. Network training is performed by the modified Kalman neuro-filtering algorithm to speed up the convergence process for intersymbol interference and white Gaussian noise communication channels. The learning process is done in the companion DSP board which also keeps the synapse weight for later use of the chip. The VLSI neural network processor chip occupies a silicon area of 4.6 mmx6.8 mm and was fabricated in a 2-mum double-polysilicon CMOS technology. System analysis and experimental results are presented.     

Multitask learning for health condition identification and remaining useful life prediction: deep convolutional neural network approach

Predicting remaining useful life (RUL) is crucial for system maintenance. Condition monitoring makes not only degradation data available for RUL estimation but also categorized health status data for health state identification. However, RUL prediction has been treated as an independent process in most cases even though potential relevance exists with health status detection process. In this paper, we propose a convolution neural network based multi-task learning method to reflect the relatedness of RUL estimation with health status detection process. The proposed method applied to the C-MAPSS dataset for aero-engine unit prognostics supported superior performances to existing baseline models.

     

Robust moving object detection against fast illumination change

To solve the problem due to fast illumination change in a visual surveillance system, we propose a novel moving object detection algorithm for which we develop an illumination change model, a chromaticity difference model, and a brightness ratio model. When fast illumination change occurs, background pixels as well as moving object pixels are detected as foreground pixels. To separate detected foreground pixels into moving object pixels and false foreground pixels, we develop a chromaticity difference model and a brightness ratio model that estimates the intensity difference and intensity ratio of false foreground pixels, respectively. These models are based on the proposed illumination change model. Based on experimental results, the proposed method shows excellent performance under various illumination change conditions while operating in real-time.     

Robust pose invariant face recognition using coupled latent space discriminant analysis

We propose a novel pose-invariant face recognition approach which we call Discriminant Multiple Coupled Latent Subspace framework. It finds the sets of projection directions for different poses such that the projected images of the same subject in different poses are maximally correlated in the latent space. Discriminant analysis with artificially simulated pose errors in the latent space makes it robust to small pose errors caused due to a subject’s incorrect pose estimation. We do a comparative analysis of three popular latent space learning approaches: Partial Least Squares (PLSs), Bilinear Model (BLM) and Canonical Correlational Analysis (CCA) in the proposed coupled latent subspace framework. We experimentally demonstrate that using more than two poses simultaneously with CCA results in better performance. We report state-of-the-art results for pose-invariant face recognition on CMU PIE and FERET and comparable results on MultiPIE when using only four fiducial points for alignment and intensity features.     

Performance of TZCD-MBOK watermarking scheme in T-DMB systems

Recently, watermarking based data transmission techniques using terrestrial digital TV signal have been proposed since they are also cost-free and can overcome the limitations of GPS. However, in the previous watermarking based methods, the detection accuracy is low and additional data rate is too low. Thus, we propose the throughput enhancement method by employing the TZCD-MBOK watermarking technique in T-DMB system. By applying the proposed scheme to T-DMB, it allows additional data transmission for disaster broadcasting and improves efficiency of data transmission in shadow region and indoor to mobile environment through watermarking spread code. From the simulation results, we confirm the proposed watermarking scheme affected on the existing T-DMB signal. Moreover, it was also confirmed that the system capacity increases as the power of additional watermarking signal rose. The results of the paper can be applied to wireless multimedia digital broadcasting systems.     

Path planning for an autonomous mobile robot considering a region with a velocity constraint in a real environment

Recently, many research projects and competitions have attempted to find an autonomous mobile robot that can drive in the real world. In this article, we consider a path-planning method for an autonomous mobile robot that would be safe in a real environment. In such a case, it is very important for the robot to be able to identify its own position and orientation in real time. Therefore, we applied a localization method based on a particle filter. Moreover, in order to improve the safety of such autonomous locomotion, we improved the path-planning algorithm and the generation of the trajectory so that it can consider a region with a limited maximum velocity. In order to demonstrate the validity of the proposed method, we participated in the Real World Robot Challenge 2010. The experimental results are given.     

A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis

Many online shopping malls in which explicit rating information is not available still have difficulty in providing recommendation services using collaborative filtering (CF) techniques for their users. Applying temporal purchase patterns derived from sequential pattern analysis (SPA) for recommendation services also often makes users unhappy with the inaccurate and biased results obtained by not considering individual preferences. The objective of this research is twofold. One is to derive implicit ratings so that CF can be applied to online transaction data even when no explicit rating information is available, and the other is to integrate CF and SPA for improving recommendation quality. Based on the results of several experiments that we conducted to compare the performance between ours and others, we contend that implicit rating can successfully replace explicit rating in CF and that the hybrid approach of CF and SPA is better than the individual ones.