Reconnaissance mapping and management of salt-affected soils using satellite images

The Indo-Gangetic alluvial plain is subjected to large scale soil alkalization. In order to map and characterize salt-affected soils, with the aim of applying management techniques, Etah district in Uttar Pradesh, located between 26o^o45' to 28^o02' N and 78^o15' to 79^o20' E was selected. Multidate, high resolution, IRS-LISS II, geocoded FCC images on 1550 000 scale were used. Integrating visual image interpretation, physiographic analysis, ground data and laboratory analysis of soil samples, a legend for mapping salt-affected soils (SAS) was formulated. Based on variations in physicochemical properties: nature, intensity and depth wise distribution of salts, five categories of SAS requiring specific reclamation measures were identified. Soil categories S2, S3 and S4 have a gypsum requirement (GR) of 20, 12 and 4t/ha^-1 respectively. Reclamation of medium to heavy textured highly alkali soils requires the addition of amendments and a rice-wheat rotation for the initial 3-4 years. Under resource constraints, pit planting of Prosopis juliflora can bring about slow but effective reclamation. The soil category S5 is slightly alkali in the substratum, needing only biological reclamation by growing salt tolerant varieties of rice and wheat crops. Soils of category S1 are saline and need management by hydrological treatments. Incorporation of village boundaries on a map showing SAS would facilitate decision taking in planning reclamation projects and accelarate management operations directly at village level.     

Robust object tracking with crow search optimized multi-cue particle filter

Pattern Analysis and Applications - Particle filter is used extensively for estimation of target nonlinear and non-Gaussian state. However, its performance suffers due to its inherent problem of...     

Design and development of guided four beam cantilever type MEMS based piezoelectric energy harvester

Microsystem Technologies - MEMS devices have found applicability in remote area of operation such as temperature monitoring in extreme climates, structural health monitoring, and car tire pressure...     

Mobile Network Operator and Mobile User Cooperation for Customized D2D Data Services

The explosive growth of smart devices has led to the evolution of multimedia data (mainly video) services in mobile networks. It attracted many mobile network operators (MNO) to deploy novel network architectures and develop effective economic policies. Mobile data offloading through smart devices (SDs) by exploring device-to-device (D2D) communications can significantly reduce network congestion and enhance quality of service at a lower cost, which is the key requirement of upcoming 5G networks. This reasonable cost solution is useful for attracting mobile users to participate in the offloading process by paying them proper incentives. It is beneficial for MNOs as well as mobile users. Moreover, D2D communications promise to be one of the prominent services for 5G networks. In this paper, we present a combinatorial optimal reverse auction (CORA) mechanism, which efficiently selects and utilizes available high-end SDs on the basis of available resources for offloading purposes. It also decides the optimal pricing policy for the selected SDs. The efficiency of CORA has been realized in terms of bandwidth and storage demand. Subsequently, we implement caching in SDs, eNodeB (eNB), and evolved packet core (EPC) with the help of our novel video dissemination cache update algorithm to solve the latency or delay issues in the offloading process. Due to high popularity, we specifically focus on video data. Simulation results show that the proposed SD caching scenario curtails the delay up to 75% and the combined cache (CC) scenario slashes the delay varying from 15 to 57%. We also scruitinize the video downloading time performance of various cache scenarios (i.e., CC, EPC cache, eNB cache, and SD cache scenarios).     

Thermal expansion of Chromium (Cr) to melting temperature

In situ x-ray data on molar volumes of chromium have been collected over the temperature range from 300 K to melting. The sample was heated to melting by passing an electrical current through the sample (the technique of electrical resistance wire heating). The sample consists of a tungsten wire as a heater and a mixture of Cr and W powder, which is placed in a hole of 250 μm diameter. Tungsten was used as an internal standard for temperature determination. In order to prevent the specimen from oxidation, the experiments were carried out in an argon flow. Unit cell parameters of Cr at different temperatures were calculated using (110), (200), (211), and (220) reflections. Precision of determination of lattice parameter is 10⁻⁴ A at 300 K and 5 10⁻⁴ A at 2000 K. Thermal expansion of Cr increases rapidly at temperatures higher than 1200 K. The linear thermal expansion () of chromium between 300 and 2130 K is given by: = 1.220(5) 10⁻⁵ − 1.150(6) 10⁻⁸ T + 1.132(8) 10⁻⁶ T² − 0.507(7)/T² (T, K). In our experiments, Cr melted between 2120 and 2150 K.     

Design, implementation, and evaluation of differentiated caching services

With the dramatic explosion of online information, the Internet is undergoing a transition from a data communication infrastructure to a global information utility. PDAs, wireless phones, Web-enabled vehicles, modem PCs, and high-end workstations can be viewed as appliances that "plug-in" to this utility for information. The increasing diversity of such appliances calls for an architecture for performance differentiation of information access. The key performance accelerator on the Internet is the caching and content distribution infrastructure. While many research efforts addressed performance differentiation in the network and on Web servers, providing multiple levels of service in the caching system has received much less attention. It has two main contributions. First, we describe, implement, and evaluate an architecture for differentiated content caching services as a key element of the Internet content distribution architecture. Second, we describe a control-theoretical approach that lays well-understood theoretical foundations for resource management to achieve performance differentiation in proxy caches. An experimental study using the Squid proxy cache shows that differentiated caching services provide significantly better performance to the premium content classes.     

An intelligent wavelet transform-based framework to detect subsurface fires with NOAA–AVHRR images

Subsurface coal fires (in this article, termed as hotspots), responsible for atmospheric pollution, human fatalities and perilous land subsidence, pose a big threat to major coal-producing countries in the world. The majority of the research performed to date has focused on providing hotspot allocation information for a specific region of interest and most has explored quite expensive high-resolution Landsat Thematic Mapper (TM) satellite images for the same. This article aims to investigate the applicability of a wavelet transform-based model to detect subsurface fires (hotspots) with freely available National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA–AVHRR) images and find out the possibility of extracting novel hotspot features by applying a wavelet transform-based analysis technique. The proposed feature vector consists of wavelet variance coefficients (WVCs) obtained from scale-by-scale decomposition of the AVHRR image variance and builds up a strong base for designing an accurate classification system. Furthermore, the support vector machine (SVM), an efficient machine learning tool, is applied to the proposed feature vector in order to develop a classification model. The demonstrated results successfully prove the effectiveness of the proposed framework as the classified images show a good correspondence with records of subsurface fires mapped by the Bharat Coking Coal Limited (BCCL), India. The effectiveness of the SVM method is also evaluated in comparison with the classical neural network-based approach.     

A semi-supervised domain adaptation assembling approach for image classification

Automatic annotation of images is one of the fundamental problems in computer vision applications. With the increasing amount of freely available images, it is quite possible that the training data used to learn a classifier has different distribution from the data which is used for testing. This results in degradation of the classifier performance and highlights the problem known as domain adaptation. Framework for domain adaptation typically requires a classification model which can utilize several classifiers by combining their results to get the desired accuracy. This work proposes depth-based and iterative depth-based fusion methods which are basically rank-based fusion methods and utilize rank of the predicted labels from different classifiers. Two frameworks are also proposed for domain adaptation. The first framework uses traditional machine learning algorithms, while the other works with metric learning as well as transfer learning algorithm. Motivated from ImageCLEF’s 2014 domain adaptation task, these frameworks with the proposed fusion methods are validated and verified by conducting experiments on the images from five domains having varied distributions. Bing, Caltech, ImageNet, and PASCAL are used as source domains and the target domain is SUN. Twelve object categories are chosen from these domains. The experimental results show the performance improvement not only over the baseline system, but also over the winner of the ImageCLEF’s 2014 domain adaptation challenge.