Abstract-Electric, plug in electric and plug in hybrid electric vehicles (xEVs) are receiving an utmost attention from automobile industries, policymakers, R&D agencies and vehicle vendors in the contemporary smart transportation era. Penetration of electric vehicle fleet into the existing charging infrastructure multiplies the load on the underlying grid system. Smart grid technologies in collaboration with smart charging management strategies may circumvent the power load, thus enabling a reliable, efficient, consistent and flexible operation of the underlying power grid system. This work demonstrates commercially viable multi-tier cloud enabled vehicle to cloud (V2C) smart fleet charging model for coordinating the charging of xEVs fleet that can support vehicle mobility satisfying the triangle equivalence namely minimum charging tariff, shortest travelling distance and minimum waiting time at the charging station. Following a multi-tier cloud infrastructure for interactive and real-time control and monitoring of the massive vehicle fleet, this work also highlights the Big Data research thrusts, opportunities and challenges that are being evolved due to integration of a distributed cloud framework with the intelligent entities like smart meters, smart charging stations, xEVs etc. for commercial viability, implementation and deployment of emerging transport oriented cities (TOCs).Keyword-Big-Data, Range anxiety, Vehicle to Cloud, xEVs Charging Management I. INTRODUCTION The development agenda in smart cities primarily demand green technology transportation. Proactive measures are needed to nullify the gaseous emissions from vehicles run by non-conventional fuels like gasoline [1]. Due to the substantial price-hike in the crude oil and prolonged dependence on foreign oil over the past decades, power GENCOs as well as the consumer, are directly or indirectly forced to opt for alternative energy sources [1]. The hybrid cars introduced by automotive industries like Nissan, Honda and Toyota are a step towards this innovation attempting to replace the Internal Combustion Engines (ICEs) with rechargeable batteries and electric motors. The bulk of the research is going on to ensure a zero-emission mode of transport. Fortunately positive results have been shown by such inventions as Electric/Plug-in electric/ Plug-in Hybrid Electric Vehicles (xEVs) which can serve as the best recourse in this storyline. Conceptually, xEVs integrate the electrical networks with so called data and communication infrastructures through smart metering and sensing utilities [2]. The heterogeneous data generated from these devices roots the use of efficient data management techniques to deliver a consistent, reliable and real time operation of the massive vehicle fleet [3]. Cloud computing is an emerging approach, that is envisaged to provide a flexible, secure and cost effective platform for storage, as well as execution of the computing resources thereby offering a robust architecture destined to have a reliable and real time operation of e...

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These days, it's becoming harder to feel safer when we go out at night. So, to tackle this security problem, the authors propose a night patrolling mechanism to detect objects in low light conditions. Images taken during the nighttime have difficulties with less contrast, brightness, and noise owing to inadequate light or insufficient exposure. Deep learning-based methods accomplish end-to-end, unsupervised object recognition using convolutional neural networks, which abolishes the requirement to describe and draw out attributes separately. Despite the fact that deep learning has led to the invention of many successful object detection algorithms; many state-of-the-art object detectors, like Faster-RCNN and others, can't carry out at their best under low-light situations. Even with an extra light source, it is hard to detect the features of an item due to the uneven division of brightness. This chapter proposes a deep learning algorithm called single shot detector, with Mobilenet v2 as the backbone to tackle the issues of object detection under low-light situations.