Effect of global warming on monsoon:With reference to Chhattisgarh State

Global warming is increasing the average temperatures of the earth. A rise in earth’s temperatures can in turn root to other alternation of ecology including sea level and modifying the quantities and patterns of rain fall. This modification may boost the occurrence and concentration of severe climate events such as floods famines, heat waves tornados and twisters. Scientists all over the world are making prediction about the ill effect of global warming and connecting some of the events that have taken place in the Past few decades as an alarm of global warming. The Intergovernmental panel on climate change (IPCC) conclusion that increasing greenhouse gases concentration resulting from human activities such as fossils fuel burning and deforestation are responsible for most of the observed temperature increase since the middle of 20th century. As of June 2012, only the United State historically the world’s largest emitter of green house gases which is main cause of global warming has refused to rectify the KYOTO protocol treaty. In Chhattisgarh state gradual decrease in rain fall has been seen since last 50 years due to global warming. During the last 5 decades the average rainfall in Chhattisgarh state has fallen down from 1301 millimeter to 1150 millimeter .In Chhattisgarh state the arrival of monsoon every year has been noted the 2nd week of June and this year monsoon arrived on 5th June, 2012, this monsoon word is derived from Arabic word mousim. Recently the scientists have prepared a Visual ALTAS to know where the rain falls have occurred. This visual atlas has been prepared by international team led by National University of Australia

Single-stage unity power factor based electronic ballast

This paper deals with the design, modeling, analysis and implementation of unity power factor (UPF) based electronic ballast for a fluorescent lamp (FL). The proposed electronic ballast uses a boost AC–DC converter as a power factor corrector (PFC) to improve the power quality at the input ac mains. In this single-stage UPF based electronic ballast, boost PFC converter and a half bridge series resonant inverter (HBSRI) share a common power switch. Thus one power switch is reduced as compared to the conventional two-stage approach. The design, modeling, analysis and implementation of this topology were carried out in MATLAB-Simulink environment for a T8 36 W, 220 V, 50 Hz fluorescent lamp. The switching frequency was kept more than the resonant frequency of the inverter, to ensure the zero voltage switching (ZVS) operation of both power switches. This resulted in reduction of high frequency switching losses. The power quality parameters such as displacement power factor (DPF), distortion factor (DF), power factor (PF), crest factor (CF) and total harmonic distortion of ac mains current (THDi) were evaluated to analyze the performance of proposed electronic ballast. Test results on a developed prototype of PFC electronic ballast were included to validate the design and simulated results

SPARSE REPRESENTATION, DISCRIMINATIVE DICTIONARIES AND PROJECTIONS FOR VISUAL CLASSIFICATION

Developments in sensing and communication technologies have led to an explosion in the availability of visual data from multiple sources and modalities. Millions of cameras have been installed in buildings, streets, and airports around the world that are capable of capturing multimodal information such as light, depth, heat etc. These data are potentially a tremendous resource for building robust visual detectors and classifiers. However, the data are often large, mostly unlabeled and increasingly of mixed modality. To extract useful information from these heterogeneous data, one needs to exploit the underlying physical, geometrical or statistical structure across data modalities. For instance, in computer vision, the number of pixels in an image can be rather large, but most inference or representation models use only a few parameters to describe the appearance, geometry, and dynamics of a scene. This has motivated researchers to develop a number of techniques for finding a low-dimensional representation of a high-dimensional dataset. The dominant methodology for modeling and exploiting the low-dimensional structure in high dimensional data is sparse dictionary-based modeling. While discriminative dictionary learning have demonstrated tremendous success in computer vision applications, their performance is often limited by the amount and type of labeled data available for training. In this dissertation, we extend the sparse dictionary learning framework for weakly supervised learning problems such as semi-supervised learning, ambiguously labeled learning and Multiple Instance Learning (MIL). Furthermore, we present nonlinear extensions of these methods using the kernel trick. We also address the problem of choosing the optimal kernel for sparse representation-based classification using Multiple Kernel Learning (MKL) methods. Finally, in order to deal with heterogeneous multimodal data, we present a feature level fusion method based on quadratic programing. The dissertation has been divided into following four parts: 1) In the first part, we develop a discriminative non-linear dictionary learning technique which utilizes both labeled and unlabeled data for learning dictionaries. We compute a probability distribution over class labels for all the unlabeled samples which is updated together with dictionary and sparse coefficients. The algorithm is also extended for ambiguously labeled data when part of the data contains multiple labels for a training sample. 2) Using non-linear dictionaries, we present a multi-class Multiple Instance Learning (MIL) algorithm where the data is given in the form of bags. Each bag contains multiple samples, called instances, out of which at least one belongs to the class of the bag. We propose a noisy-OR model and a generalized mean-based optimization framework for learning the dictionaries in the feature space. The proposed method can be viewed as a generalized dictionary learning algorithm since it reduces to a novel discriminative dictionary learning framework when there is only one instance in each bag. 3) We propose a Multiple Kernel Learning (MKL) algorithm that is based on the Sparse Representation-based Classification (SRC) method. Taking advantage of the non-linear kernel SRC in efficiently representing the non-linearities in the high-dimensional feature space, we propose an MKL method based on the kernel alignment criteria. Our method uses a two step training method to learn the kernel weights and the sparse codes. At each iteration, the sparse codes are updated first while fixing the kernel mixing coefficients, and then the kernel mixing coefficients are updated while fixing the sparse codes. These two steps are repeated until a stopping criteria is met. 4) Finally, using a linear classification model, we study the problem of fusing information from multiple modalities. Many current recognition algorithms combine different modalities based on training accuracy but do not consider the possibility of noise at test time. We describe an algorithm that perturbs test features so that all modalities predict the same class. We enforce this perturbation to be as small as possible via a quadratic program (QP) for continuous features, and a mixed integer program (MIP) for binary features. To efficiently solve the MIP, we provide a greedy algorithm and empirically show that its solution is very close to that of a state-of-the-art MIP solver

Maternal anthropometry in relation to birth weight of newborn: A prospective hospital based study

Background: Birth weight of neonate is probably the most important factor that affects the future survival and quality of life ofthe neonates. Objective: To study the maternal anthropometric parameters in relation to birth weight of neonate. Methods: Thisobservational case-control study was conducted in a tertiary care teaching hospital in central India from March 2013 to September 2014.Low birth weight (LBW) neonates (<2.5 kg) and their mothers were taken as cases, and normal birth weight (NBW) neonates (≥2.5 kg)and their mothers were taken as controls. Data regarding demographic profile, maternal and neonatal anthropometry, antenatal, and natalevents were recorded. Results: Of total 600 subjects, 300 neonates were LBW (cases) and 300 were NBW (controls). Mean birth weightwas 2.05±0.252 kg (in cases) and 2.9±0.295 kg (in control). The weight of 59.3% mothers in the case group was below 50 kg (odds ratio[OR] - 3.4) and height was <150 cm (OR - 1.22). Mean maternal body mass index (BMI) in study and control group was 21.73±0.25and 22.06±0.87, respectively (p<0.0677). BMI ranged from 21.5 to 22 in 85% of NBW and 19% of LBW neonates, and from 22.1 to22.9 in 81% of LBW and 15% of NBW babies. Mean maternal mid-upper arm circumference (MUAC) in this study and control groupwere 20±2.13 cm and 25.5±1.36 cm, respectively (p<0.0001). Maternal MUAC was 19-23 cm in 81% of LBW babies and 23-27 cm inmothers of NBW babies (OR - 24). Conclusion: MUAC can be used as an easy cost-effective tool to identify mother at risk of deliveringLBW babies so that they can be subjected appropriate care intervention at hospital and home to improve the perinatal outcome

OPTIMIZATION OF CYCLE TIME IN HYUNDAI MOTORS INDIA LTD

This paper sheds light on the application of quality control tools to reduce Cycle time (including Weld time and Squeeze time). Successive use of these tools identifies and addresses the weakness and leads to improved processes. These tools address the problems in logical and sequential manners which are easy to observe and interpret the result. The main gist of this paper is to achieve the target cycle time of body build line. Cycle time includes process time, during which a unit is acted upon to bring it closer to an output. Every production line always has a target cycle time. With the collected data it was found that cycle time was higher than the target cycle time, so it needed to be optimized. There are lots of factors which are to be eliminated in order to achieve the target cycle time. The root cause of the factors affecting the target cycle time was observed using quality improvement tools known as the Ishikawa diagram or the ‘Fish Bone Diagram’

Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings

Conventional feature-based and model-based gaze estimation methods have proven to perform well in settings with controlled illumination and specialized cameras. In unconstrained real-world settings, however, such methods are surpassed by recent appearance-based methods due to difficulties in modeling factors such as illumination changes and other visual artifacts. We present a novel learning-based method for eye region landmark localization that enables conventional methods to be competitive to latest appearance-based methods. Despite having been trained exclusively on synthetic data, our method exceeds the state of the art for iris localization and eye shape registration on real-world imagery. We then use the detected landmarks as input to iterative model-fitting and lightweight learning-based gaze estimation methods. Our approach outperforms existing model-fitting and appearance-based methods in the context of person-independent and personalized gaze estimation