Software-Based Failure Detection and Recovery in Programmable Network Interfaces

Emerging network technologies have complex network interfaces that have renewed concerns about network reliability. In this paper, we present an effective low-overhead fault tolerance technique to recover from network interface failures. Failure detection is based on a software watchdog timer that detects network processor hangs and a self-testing scheme that detects interface failures other than processor hangs. The proposed self-testing scheme achieves failure detection by periodically directing the control flow to go through only active software modules in order to detect errors that affect instructions in the local memory of the network interface. Our failure recovery is achieved by restoring the state of the network interface using a small backup copy containing just the right amount of information required for complete recovery. The paper shows how this technique can be made to minimize the performance impact to the host system and be completely transparent to the user.     

Deciphering robust reactor kinetic data using mutual information

Experimentalists use Chauvenets’s criterion to check the quality of any measured data. Based on this criterion they rejected data having high degree of correlation. Multivariate techniques like principal component analysis used for analysis of these correlated data, does not provide any scope to minimize the effect of correlation. We propose a novel method using information theory and the technique of determinant inequalities developed by us to reduce the effect of correlation among these data without summarily rejecting them. We demonstrate the utility of our technique in transient measurements of kinetic parameters performed on the commercially advanced gas cooled reactor (CAGCR).     

Midwavelength Infrared Avalanche Photodiode Using InAs–GaSb Strain Layer Superlattice

InAs-GaSb strain layer superlattice p⁺-n^--n avalanche photodiodes (APDs) are fabricated using a newly introduced electron-beam aided zinc sulfide deposition. Temperature-dependent measurements were performed on 300 times 300 mum² mesa etched APDs. The effect of passivation was also studied on the diode characteristics and APD performances. Temperature-dependent gain strongly correlates with avalanche mechanism.     

Optimum lipid production using agro-industrial wastewater treated microalgae as biofuel substrate

Abundant wastewater discharges from palm oil industries in tropical nations being a valuable resource of biodiesel need proper exploration. Research hinted that such wastewater as economical nutrient source or substrate can support the cultivation of microalgae. In this experiment, we have tested the growth and lipid production of five different microalgal strains in palm oil mill effluent (POME). POME as a biofuel substrate is demonstrated to be lucrative for microalgae-assisted lipids production. POME is rich in macro- and micronutrients can be used as a growth medium for algal growth in order to reduce the growth medium cost and environmental pollutions. Among the five microalgal strains tested, Chlorella sorokiniana revealed optimum biomass and lipid production. The productivity was evaluated in terms of chlorophyll content, growth rate, biomass, and lipid content, which discerned to be 0.099/day, 8.0 mg/L day and 2.68 mg/mg cell dry weight (CDW). Furthermore, in this study, an optimization study was carried out to enhance the microalgae to produce high lipid content using carbon-to-nitrogen ratio and different light/dark periods. The presence of nitrogen combined glucose (with a carbon-to-nitrogen ratio 100:7) as an alternative source to carbon displayed higher lipid production of 2.68 (mg/mg CDW) by C. sorokiniana. This study confirms that 8:16 h light/dark condition at C:TN ratio of 100:7 supported to produce high lipid content of 17 mg lipid/mg CDW. The above results revealed that POME could be a suitable growth media for the alga C. sorokiniana to improve the maximum lipid yield for biofuels production.     

Performance analysis for queueing systems with close down periods and server under maintenance

A single server queue subject to maintenance of the server and the close down period is considered. We obtain explicit expressions for the transient probabilities of the system size, the server under maintenance state and the close down period. The time-dependent performance measures of the system and the probability density function of the first-passage-time to reach the maintenance state are discussed. The corresponding steady state analysis and key performance measures of the system are also presented. Finally, the effect of various parameters on system performance measures is demonstrated by a numerical example.     

Integration of IRS-1A L2 data by fuzzy logic approaches for landuse classification

A methodology has been formulated to integrate images from IRS-1A LISS II of two dates for landuse/landcover classification. The methodology developed includes image classification by fuzzy k-means clustering and fusion of memberships by fuzzy set theoretic operators. The two date images have been geometrically coregistered and classified for the identification of land classes individually. The fuzzy memberships of the classified output images have been integrated by using fuzzy logic operators like algebraic sum and gamma (gamma) operator. The classification accuracy of the resultant land classes in the integrated images was verified with the ground data collected in situ. The resultant images have been evaluated by kappa (kappa) statistic and it was found that output from the image of fuzzy algebraic sum operator scored high in generating the land classes, with an overall accuracy of 95%.     

Biomass burning and related trace gas emissions from tropical dry deciduous forests of India: A study using DMSP-OLS data and ground-based measurements

Biomass burning is one of the major sources of trace gas emissions in the atmosphere. In India the major sources of biomass burning include deforestation, shifting cultivation, accidental fires, controlled burning, fire wood burning, burning from agricultural residues and burning due to fire lines. Studies on biomass burning practices gain importance due to increasing anthropogenic activities and increasing rates of deforestation. Satellite data have been widely used over the globe to monitor the rates of deforestation and also with respect to biomass burning studies. But, much of the polar orbiting satellites, due to their repetitive cycle, have limitations in observing such events and in the tropics, due to cloud cover, getting a cloud-free image during the daytime is difficult. In this study we used Defence Meteorological Satellite Program Operational Line Scanner (DMSP-OLS) night-time data to study the biomass burning events over a period of 10 years from 1987 to 1998 for the Eastern Ghats region, covering the northern part of Andhra Pradesh, India. Two ground-based experiments were carried out to quantify the emissions from biomass burning practices. The results of the study with respect to trace gases suggested emission ratios for CO, CH4, NOx and N20 during the burning to be about 12.3%, 1.29%, 0.29% and 0.07% at the first site and 12.5%, 1.59%, 0.29% and 0.05% at the second site, suggesting low inter-fire variability between the sites. The variation has been attributed to the fuel load, vegetation characteristics, site conditions and local meteorological parameters affecting the relative amounts of combustion. Using the DMSP OLS derived areal estimates of active fires, the trace gas emissions released from the biomass burning were quantified. The results suggested the emissions of 8.2 2 10 10 g CO 2, 1.8 2 10 8 g CO, 6.0 2 10 6 g N 2 O, 3.0 2 10 6 g NO x and 1.2 2 10 8 8 g CH 4 during March 1987. The emissions increased to 1.0 2 10 11 g CO 2, 2.3 2 10 g CO, 7.8 2 10 6 g N 2 O, 3.9 2 10 7 g NO x and 1.6 2 10 8 g CH 4, over a period of 10 years. The results of the analysis suggest the possible use of monitoring biomass burning events from DMSP-OLS night-time data.     

An effective hybrid approach to remote-sensing image classification

This article presents a hybrid fuzzy classifier for effective land-use/land-cover (LULC) mapping. It discusses a Bayesian method of incorporating spatial contextual information into the fuzzy noise classifier (FNC). The FNC was chosen as it detects noise using spectral information more efficiently than its fuzzy counterparts. The spatial information at the level of the second-order pixel neighbourhood was modelled using Markov random fields (MRFs). Spatial contextual information was added to the MRF using different adaptive interaction functions. These help to avoid over-smoothing at the class boundaries. The hybrid classifier was applied to advanced wide-field sensor (AWiFS) and linear imaging self-scanning sensor-III (LISS-III) images from a rural area in India. Validation was done with a LISS-IV image from the same area. The highest increase in accuracy among the adaptive functions was 4.1% and 2.1% for AWiFS and LISS-III images, respectively. The paper concludes that incorporation of spatial contextual information into the fuzzy noise classifier helps in achieving a more realistic and accurate classification of satellite images.     

Influence of plant pigments on spectral reflectance of maize,groundnut and soybean grown in semi-arid environments

Abstract. Spectral reflectance of leaves is influenced primarily by plant pigments, chlorophyll and carotenoids. Such reflectance can be used to study the changes in chlorophyll content and nitrogen status and in turn measures the amount of biomass accumulation. A field experiment was laid out at the Research Farm of ANGR Agricultural University, Hyderabad. The reflectance observations were taken using a hand-held ground radiometer at an interval of 15 days beginning from 30 days after sowing (DAS) until harvest of the crops. The plant pigments were determined simultaneously using DMSO (dimethyl sulphoxide) method in the laboratory. The experimental results revealed the influence of plant pigments on spectral reflectance of maize, groundnut and soybean. It was observed that there was an increase in chlorophyll- a, chlorophyll- b, total chlorophyll and carotenoid content up to flowering and thereafter chlorophyll- a content declined at a faster rate than chlorophyll- b towards physiological maturity. With the increase in concentrations of chlorophyll and carotenoids, there was a decline in spectral reflectance of the blue band (450-520 nm) and the red band (620-680 nm). Whereas, NIR (near-infrared) reflectance in the case of soybean and groundnut was found to be higher than that of maize by 11% and 2%, respectively. This was attributed to canopy cover of soybean and groundnut crops, where the soil was fully covered with vegetation. In case of maize, due to wider spacing, the soil exposure is greater, which results in low reflectance values of the NIR band. Normalized Difference Vegetation Index (NDVI) is linearly related to total chlorophyll content and the growth stages of a crop up to flowering. The NDVI differs significantly during the peak vegetative growth period among the three crop types. The study revealed that the significant differences in reflectance of maize, groundnut and soybean in the red and NIR bands were influenced by concentrations of chlorophyll- a, chlorophyll- b and carotenoids, which indicates the photosynthetic behaviour of the crops.