Driving pattern analysis to determine driver behaviors for local authority based on cloud using OBD II

Aggressive driving is the main cause of road accidents and it is affected by driving behavior which endanger not only the driver himself but also the people around. It is very significant step to identify such behaviors of the drivers by the local authorities which would help in correcting the behaviors or to understand the root cause of the accidents by analyzing the data recorded by the On Board Diagnostic( OBD ) II device. An aggressive driving behavior is characterized by sudden change inmaneuverings of vehicle which eventually yields non uniform parameters values returned by the ECU (Engine Control Unit) system without any specific reason. In this research work, the real time data is recorded from ECU using OBD II and the accelerometer. The Artificial Intelligenceis used in grouping the different types of data toidentifythe behaviors data on the basis of similarity of datapoints.The purpose of this research work is to identify such drivers and reduce the risk of further accidents.The work identifies the behaviors as bad, normal and aggressive behavior. As the clustering is made on basis crowded data which signifies the similar driving patterns for most of the time in the course of recording, therefore, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) unsupervised learning algorithm was used. The data will be sent to the cloud so that it can be accessed by the authority from any place for further action.ANOVA test is conducted usingIBMSPSS(Statistical Package for the Social Sciences) package to compare and determine the best method to collect data by comparing the means between groups

Price and Volatility Spillovers across North American, European and Asian Stock Markets: With Special Focus on Indian Stock Market

<div align=justify>This paper investigates interdependence of fifteen world indices including an Indian market index in terms of return and volatility spillover effect. Interdependence of Indian stock market with other fourteen world markets in terms of long run integration, short run dependence (return spillover) and volatility spillover are investigated. These markets are that of are Canada, China, France, Germany, Hong-Kong, Indonesia, Japan, Korea, Malaysia, Pakistan, Singapore, Taiwan, United Kingdom and United States. Long run and short run integration is examined through Johansen cointegration techniques and Granger causality test respectively. Vector autoregressive model (VAR 15) is used to estimate the conditional return spillover among these indices in which all fifteen indices are considered together. The effect of same day return in explaining the return spillover is also modeled using univariate models. Volatility spillover is estimated through AR-GARCH in which residuals from the index return is used as explanatory variable in GARCH equation. Return and volatility spillover between Indian and other markets are modeled through bivariate VAR and multivariate GARCH (BEKK) model respectively. It is found that there is greater regional influence among Asian markets in return and volatility than with European and US. Japanese market, which is first to open, is affected by US and European markets only and affects most of the Asian Markets. Also, high degree of correlation among European indices namely FTSE, CAC and DAX is observed. US market is influenced by both Asian and European markets. Specific to Indian context, it is found that Indian market is not cointegrated with rest of the world except Indonesia. This may provide diversification benefits for potential investors. However, strong short run interdependence is found between Indian markets and most of the other markets. Indian and other markets like US, Japan, Korea, and Canada positively affect each others conditional returns significantly. Indian market also has significant effect on Malaysia, Pakistan, and Singapore return. This study found that there is significant positive volatility spillover from other markets to Indian market, mainly from Hong Kong, Korea, Japan, and Singapore and US market. Indian market affects negatively the volatility of US and Pakistan. It is interesting to note that Chinese and Pakistan markets are less integrated with other Asian, European and US markets.</div>

Cost Benefit Analysis on Pond Ecosystem Services in Peri -Urban Agriculture

The fast pace of urbanization is upsetting the natural ecosystems in peri-urban areas that further impact the livelihood of people and other environmental services. Some of the first casualties of this phenomenon are the common resources like ponds or water-bodies. Water for agriculture, storm-and waste-water regulation, along with protection from natural disasters and soil erosion, are some of the services that affect small and marginal farmers. This research is based upon an actual case study from a village in peri-urban area of Gorakhpur, Uttar Pradesh, where a pond with an area of 0.55 acre was constructed under MNREGA in the year 2004.The paper focuses on the cost benefit analysis of natural and manmade ponds or wetlands located in the peri-urban areas in providing the ecosystem and livelihood services to small and marginal farmers around Gorakhpur city. Community consultations were undertaken in these areas to ascertain the vulnerabilities and resilience options. Based on these community consultations a qualitative benefit-cost ratio was derived which was followed by a more rigorous quantitative CBA using data collected from secondary sources as well as from the community. It was found that the people value the services provided by the ponds to be significant as shown by the highly positive benefit to cost ratio in both qualitative as well as quantitative analyses. Keywords: Cost Benefit Analysis, Livelihood, Peri-Urban areas, ponds, Qualitative CBA

The Dynamic Relationship between Price and Trading Volume:Evidence from Indian Stock Market

This study investigates the nature of relationship between price and trading volume for 50 Indian stocks. Firstly the contemporaneous and asymmetric relation between price and volume are examined. Then we examine the dynamic relation between returns and volume using VAR, Granger causality, variance decomposition (VD) and impulse response function (IRF). Mixture of Distributions Hypothesis (MDH), which tests the GARCH vs. Volume effect, is also studied between the conditional volatility and volume. The results show that there is positive and asymmetric relation between volume and price changes. Further the results of VAR and Granger causality show that there is a bi-directional relation between volume and returns. However, the results of VD imply weak dynamic relation between returns and volume which becomes more evident from the plots of IRF. On MDH, our results are mixed, neither entirely rejecting the MDH nor giving it an unconditional support.

Power Efficient Data-Aware SRAM Cell for SRAM-Based FPGA Architecture

The design of low-power SRAM cell becomes a necessity in today\u27s FPGAs, because SRAM is a critical component in FPGA design and consumes a large fraction of the total power. The present chapter provides an overview of various factors responsible for power consumption in FPGA and discusses the design techniques of low-power SRAM-based FPGA at system level, device level, and architecture levels. Finally, the chapter proposes a data-aware dynamic SRAM cell to control the power consumption in the cell. Stack effect has been adopted in the design to reduce the leakage current. The various peripheral circuits like address decoder circuit, write/read enable circuits, and sense amplifier have been modified to implement a power-efficient SRAM-based FPGA