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We all know that in an environment which is highly polluted. Latest survey report by World Health Organization says 9 out of 10 people are affected by air pollution and it also reveals that 7 million people die every year due to air pollution which is affecting human health leading to respiratory infections and diseases such as lung cancer, heart problems and stroke. The presence of various toxic elements(pollutants) in the air such as CO2, SO 2 , NO 2 , CO, Particulate Matter(PM), Lead and Mercury, Ammonia, pongs from Garbage (Solid waste), Sewage and industrial processes has made our lives very miserable. We have very few researchers contributing to solid waste garbage monitoring and hence this research paper focuses on one of the contributors of Air Pollution i, e solid waste garbage which is being produced in higher rates in crowded cities. It is one of the challenges we are facing on daily basis. The research paper aims at providing a cloud based solution to monitor and process solid waste garbage odor in crowded cities. The proposed architecture helps in collecting the data from garbage locations through sensors and processes the data collected using AWS Kinesis. AWS Kinesis is one of the AWS Services which helps us to know the quantity of various pollutants in the solid waste garbage when they are burnt contributing to air pollution.

Agriculture is the main work field in India. Farming industry adopts less innovative technology compared to other industries. Information and Communication Technologies provides simple and cost effective techniques for farmers to enable precision agriculture. The work propose a state of the art model in agriculture field which will guide the rural farmers to use Information and Communication technologies (ICT) in agriculture fields. Big data analytics is used to improve the crop yield. It can be customized for precision agriculture to improve the quality of crops which improves the overall production rate.

Compact exceptionally delegated frameworks (MANETs) are without establishment frameworks prompted by remote adaptable contraptions with kept battery life. This enveloped battery confines in MANETs makes it required to consider the imperativeness mindfulness feature in their layout. In perspective of the way that guiding traditions have central part in MANETs, their essentialness mindfulness grows orchestrate life time by capably utilizing of the open limited imperativeness. TORA is one of these directing traditions that offer abnormal state of flexibility. This paper uses the EBC (Efficient Binary Calculation) to organize the vitality mindfulness feature to the TORA directing tradition. The proposed tradition considers courses length in its course isolate plans and furthermore fuses courses imperativeness level in its figures. It figures the directing issue as a change predicament and thereafter uses EBC to sedative a course that extends a weighted limit of the course length and the course imperativeness level. Expansive amusements in ns-2 test framework condition exhibit that the proposed directing tradition, called EBC-TORA, expands the framework lifetime shockingly and beats TORA to the extent framework life time, structure life time and total appropriated data.

Currently climate research is most priority area as climate change generally affects the society as a whole. So there is requirement to study the weather and climate variability at very high resolution in multiple spatial and temporal scales. Presently climate data are huge in size as more data are being generated compared to past. Also sophisticated models are being used for prediction of weather and climate variability, generating vast amount of multidimensional digital data. As such climate data is considered to be big data which is multi-dimensional, multi-approach and multisource. So the demand of high performance computing and cloud computing is being increased to do the climate research. This paper gives an outline of a few strategies in supporting big data administration and investigation in geoscience domain for climate studies. By analyzing contemporary information technologies and approaches, it can confirm what operational program framework and approaches are at hand and pertinent in developing big data-driven climate research. A transitory overview of HBase for storing and managing notable geoscience data across distributed machinery is highlighted. Withal MapReduce-predicated techniques to fortify parallel access of massive NetCDF data are considered. The outcomes can recognize basic issues and enhances the proficiency of dissecting huge geoscience information by lessening information preparing time.

Typical Battery charger mainly consists of two stages: AC to DC stage employing boost or interleaved boost converter where power factor correction is taken care by employing suitable control technique and boosting voltage level to an intermediate dc bus level. The second stage is DC/DC stage where the voltage is regulated according to the battery requirement and provides the galvanic isolation for onboard battery chargers. This paper discusses about battery charger consisting of interleaved ac/dc boost converter with average current mode control technique combined with an isolated DC/DC converter. The first stage involves in making the input power factor acceptable to the standards and regulating dc link bus voltage and the second stage provides galvanic isolation. In addition the dc-dc converter achieves ZVS turn-on, ZCS turn-off for the inverter switches and ZCS turn-on, ZCS turn-off for the rectifier diodes, thus improving the efficiency of the circuit. The prototype is simulated for 200 watts using PSIM 9.1. From the results, the overall efficiency of the battery charger obtained is around 94.7% with improved input power factor of 0.95.

This paper discusses about a resonant topology of the Isolated DC-DC converter. The main aim is to obtain the zero voltage switching (ZVS) for the lower leg switches (M3 and M4) of the converter with the help of resonant circuit i.e. resonant inductor provided in the circuit minimizes the power losses In addition, the leakage inductance of transformer helps in achieving zero current switching (ZCS). Thus by achieving soft switching and eliminating the conventional RCD voltage clamping circuit for the proposed converter, the losses are minimized and the efficiency is improved accordingly. The converter has applications in on-board charger for plugin hybrid electric vehicles.

This paper discusses about a nonlinear control technique employed for AC/DC PFC converter to charge the battery of PHEV. It consists of two stages, first stage is conversion from ac to dc where the power factor improvement is achieved by reducing the harmonics at the input side. In second stage, output dc voltage is boosted according to the requirement. The control law implemented in the first stage is based on discrete energy control which helps in recovering the output at a faster rate during transient period. This also helps in maintaining unity power factor at the input side. The discrete optimal control is compared with the average current mode control and the significant simulation results are presented. From the results it is depicted that the discrete energy function control has a good dynamic response for load transients compared to average current mode control.

Cloud resources like Amazon EC2 and Google Compute Engine have become an integral part of our life with their pay-per-use pricing models. On the other hand, attempts have also been made to create virtual clouds on the fly using available devices in the locality like smartphones and tablets. Such devices being equipped with powerful processors, this is a novel method of utilizing their normally unused computational capabilities. We attempt to design and develop a pricing model for such resources created for opportunistic computing. Since the values of the resources in these virtual clouds cannot be calculated, the pay-per-resource model has to give way to a pay-per-effort model. We model the scenario as a principal-agent problem with moral hazards and propose an incentive-driven Quality of Service (QoS) to ensure that the agents act appropriately. In view of the short contractual duration, we consider only deterministic contracts which are easy to understand and implement. We show that the mobile devices can be modeled as economic actors in a contractual arrangement and discuss how the optimal decisions are identified.

Changes in the vegetation of any geographic area is seen constantly. In order to ensure the preservation of these agricultural characteristics, the changes are detected to build knowledge base. This research aims at calculating the agricultural changes near Vellore district using LANDSAT and images taken from 2003–2009. SVM classification is the method used for obtaining the results.

The human hand is a complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the efforts required to the user to carry out the different movements are quite small. On the contrary, prosthetic hands are just a pale replication of the natural hand, with significantly reduced grasping capabilities and no sensory information delivered back to the user. Several attempts have been carried out to develop multifunctional Prosthetic devices controlled by electromyography (EMG) signals (myoelectric hands), harness (kinematic hands), dimensional changes in residual muscles, and so forth, but none of these methods permits the “natural” control of more than two DoFs. This paper presents a review of the traditional methods used to control artificial hands by means of EMG signal, in both the clinical and research contexts, and introduces what could be the future developments in the control strategy of these devices. This paper describes a novel approach to the control of a multifunctional prosthesis based on the classification of myoelectric patterns. It is shown that the myoelectric signal exhibits a deterministic structure during the initial phase of a muscle contraction. Features are extracted from several time segments of the myoelectric signal to preserve pattern structure. These features are then classified using an artificial neural network. The control signals are derived from natural contraction patterns which can be produced reliably with little subject training. The new control scheme increases the number of functions which can be controlled by a single channel of myoelectric signal but does so in a way which does not increase the effort required by the amputee. Results are presented to support this approach.

Image segmentation is an important process to extract information from complex medical images. Segmentation has wide application in medical field. The main objective of image segmentation is to partition an image into mutually exclusive and exhausted regions such that each region of interest is spatially contiguous and the pixels within the region are homogeneous with respect to a predefined criterion. Widely used homogeneity criteria include values of intensity, texture, color, range, surface normal and surface curvatures. During the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of image segmentation. This paper aims to develop an improved method of segmentation using Fuzzy- Neuro logic to detect various tissues like white matter, gray matter; cerebral spinal fluid and tumor for a given magnetic resonance image data set. Generally magnetic resonance images always contain a significant amount of noise caused by operator performance, equipment, and the environment, which can lead to serious inaccuracies. So segmentation of such medical images is a challenging problem in the field of image analysis. Several diagnostics are based on proper segmentation of the digitized image. Segmentation of medical images is needed for applications involving estimation of the boundary of an object, classification of tissue abnormalities, shape analysis, contour detection. In particular Fuzzy-Neuro logic segmentation algorithm is used to provide satisfactory results compared to K-means, Fuzzy C-Means, Neural Network and Fuzzy logic.