Combined effect of gamma-irradiation and conventional cooking onAeromonas hydrophila in meatball

Irradiation combined with a conventional cooking procedure was applied to meatball and the effects on bacterial load and inoculatedAeromonas hydrophila were determined. Meatball samples were irradiated by using a⁶⁰Co source at the dose levels of 0, 0.30,0.75,1.50,2.50 kGy and cold stored at 4±1°C for 7 days. Bacterial load and the count ofA. hydrophila decreased when the irradiation dose level increased. A minimum inhibition effect was found at the dose of 0.30 kGy. Irradiation in combination with a conventional cooking procedure was found to be more effective in reducingA. hydrophila and the bacterial load in meatball. This study indicated that a dose of 0.75 kGy was sufficient to destroy approximately 10⁴ cfu/g ofA. hydrophila in meatball.     

Motion correction for coronary stent reconstruction from rotational X-ray projection sequences

This paper presents a new method for 3-D tomographic reconstruction of stent in X-ray cardiac rotational angiography. The method relies on 2-D motion correction from two radiopaque markerballs located on each side of the stent. The two markerballs are on a guidewire and linked to the balloon, which is introduced into the artery. Once the balloon has been inflated, deflated, and the stent deployed, a rotational sequence around the patient is acquired. Under the assumption that the guidewire and the stent have the same 3-D motion during rotational acquisition, we developed an algorithm to correct cardiac stent motion on the 2-D X-ray projection images. The 3-D image of the deployed stent is then reconstructed with the Feldkamp algorithm using all the available projections. Although the correction is an approximation, we show that the intrinsic geometrical error of our method has no visual impact on the reconstruction when the 2-D markerball centers are exactly detected and the markerballs have the same 3-D motion as the stent. Qualitative and quantitative results on simulated sequences under different realistic conditions demonstrate the robustness of the method. Finally, results from animal data acquired on a rotational angiography device are presented.     

In vivo measurement of the brain and skull resistivities using an EIT-based method and realistic models for the head

In vivo measurements of equivalent resistivities of skull (rho(skull)) and brain (rho(brain)) are performed for six subjects using an electric impedance tomography (EIT)-based method and realistic models for the head. The classical boundary element method (BEM) formulation for EIT is very time consuming. However, the application of the Sherman-Morrison formula reduces the computation time by a factor of 5. Using an optimal point distribution in the BEM model to optimize its accuracy, decreasing systematic errors of numerical origin, is important because cost functions are shallow. Results demonstrate that rho(skull)/rho(brain) is more likely to be within 20 and 50 rather than equal to the commonly accepted value of 80. The variation in rho(brain)(average = 301 omega x cm, SD = 13%) and rho(skull)(average = 12230 omega x cm, SD = 18%) is decreased by half, when compared with the results using the sphere model, showing that the correction for geometry errors is essential to obtain realistic estimations. However, a factor of 2.4 may still exist between values of rho(skull)/rho(brain) corresponding to different subjects. Earlier results show the necessity of calibrating rho(brain) and rho(skull) by measuring them in vivo for each subject, in order to decrease errors associated with the electroencephalogram inverse problem. We show that the proposed method is suited to this goal.     

Scalable Data Processing for Community Sensing Applications

Participatory Sensing is a new computing paradigm that aims to turn personal mobile devices into advanced mobile sensing networks. For popular applications, we can expect a huge number of users to both contribute with sensor data and request information from the system. In such scenario, scalability of data processing becomes a major issue. In this paper, we present a system for supporting participatory sensing applications that leverages cluster or cloud infrastructures to provide a scalable data processing infrastructure. We propose and evaluate three strategies for data processing in this architecture.     

Digital signature to help network management using flow analysis

Because of constant growth in proportion and complexity of networks, flow analysis has become an indispensable tool for network management mechanisms. Through this resource, a traffic characterization, called digital signature of network segment using flow analysis (DSNSF), is accomplished. The models used for this purpose are the ant colony optimization metaheuristic, the Holt–Winters forecasting method and the statistical procedure, principal component analysis. The obtained DSNSF by each model is compared with the actual traffic of packets and bits and then subjected to specific evaluations in order to measure its accuracy. The experimental results show that the three methods could achieve good correlation indices and low normalized mean square error values between the DSNSF curve and the real traffic movement, indicating a good adaptability and efficiency in characterizing a network traffic segment. Copyright © 2015 John Wiley & Sons, Ltd.     

Effect of bit-to-bit dependence on packet error rate using asynchronous DS-CDMA for mobile packet radio networks

The probability of packet success for asynchronous direct sequence code division multiple access (DS-CDMA) over the multipath fading channel is investigated under different fading conditions and for different cases. We studied the bit-to-bit dependence caused by the multipath fading and by the multiple access interference (MAI) originating from the chip and phase offsets between the desired and interfering signals. We investigated the effect of using the Gaussian versus the improved Gaussian process on the computation of probability of packet success as well as the effect of the bit-to-bit dependence within a packet. The improved Gaussian approximation provides more accurate values for the probability of bit error for any number of simultaneous users, while it also allows us to incorporate the effects of bit-to-bit dependence into the calculations of the probability of packet success.This paper was presented in part at the 17th Biennial Symposium on Communiations, Kingston, June 1994. This work was supported by a strategic grant (STR-0100720) from the Natural Sciences and Engineering Research Council (NSERC) of Canada.     

Transient-enhanced diffusion in shallow-junction formation

Shallow junctions are formed in crystalline Si by low-energy ion implantation of B⁺, P⁺, or As⁺ species accompanied by electrical activation of dopants by rapid thermal annealing and the special case of spike annealing. Diffusion depths were determined by secondary ion-mass spectroscopy (SIMS). Electrical activation was characterized by sheet resistance, Hall coefficient, and reverse-bias diode-leakage measurements. The B⁺ and P⁺ species exhibit transient-enhanced diffusion (TED) caused by transient excess populations of Si interstitials. The electrically activated fraction of implanted dopants depends mainly on the temperature for B⁺ species, while for P⁺ species, it depends on both temperature and P⁺ dose. The relatively small amount of diffusion associated with As⁺ implants is favorable for shallow-junction formation with spike annealing.     

Design and Test of a Certifiable ASIC for a Safety-Critical Gas Burner Control System

The purpose of this paper is to present a methodology and tools for the design and test of an EN298 compliant ASIC chip for a safety-critical gas burner control system. Safe operation, as far as the critical variable is concerned, is guaranteed in the presence of two simultaneous faults. Emphasis is put on circumventing methodology, EDA (Electronic Design Automation) and foundry limitations and on product certification requirements.     

Adaptive gain and delay mismatch cancellation for LINC transmitter

Linear amplification with Nonlinear Component (LINC) transmitter architecture is an efficient solution for high efficiency amplification of signals. Nonetheless, this solution suffers both from gain impairment and delay mismatch between the two signal paths. Indeed, a mismatch in propagation time between the paths degrades the quality of the transmit signal but also disrupts the convergence of the gain correction algorithm resulting in a degradation of its performance. In this paper, we present an adaptive algorithm based on a gradient descent formulation for the identification and correction of these delays. We also demonstrate its effectiveness when applied prior to the gain adjustment procedure. The identification approach is preferred here, to ensure monitoring facilities.     

Multilevel statistical inference from functional near-infrared spectroscopy data during stroop interference

Functional near-infrared spectroscopy (fNIRS) is an emerging technique for monitoring the concentration changes of oxy- and deoxy-hemoglobin (oxy-Hb and deoxy-Hb) in the brain. An important consideration in fNIRS-based neuroimaging modality is to conduct group-level analysis from a set of time series measured from a group of subjects. We investigate the feasibility of multilevel statistical inference for fNIRS. As a case study, we search for hemodynamic activations in the prefrontal cortex during Stroop interference. Hierarchical general linear model (GLM) is used for making this multilevel analysis. Activation patterns both at the subject and group level are investigated on a comparative basis using various classical and Bayesian inference methods. All methods showed consistent left lateral prefrontal cortex activation for oxy-Hb during interference condition, while the effects were much less pronounced for deoxy-Hb. Our analysis showed that mixed effects or Bayesian models are more convenient for faithful analysis of fNIRS data. We arrived at two important conclusions. First, fNIRS has the capability to identify activations at the group level, and second, the mixed effects or Bayesian model is the appropriate mechanism to pass from subject to group-level inference.