Adaption of Autonomous Vehicles (via APIs) in Society

by Manish Rathore, Inderjit Rai, and Salah Sharieh, Ph.D

Autonomous Vehicles (AVs) were considered an interesting topic in science fiction movies such as Knight Rider, however, AVs are becoming a reality and will change the way we get around. Not only the major automakers but software companies such as Google, Uber, and Apple are all developing autonomous vehicle capabilities. Experts have categorized AVs into five different levels. In this work we will primarily focus on Level 5, which is defined as vehicles that “can do all the driving in all circumstances”.  The human occupants are just passengers and need never be involved in driving. There has been quite a bit of discussion around the benefits of Level 5 autonomy; we would like to cover whether people are ready to embrace the autonomous vehicle future, or are all these companies wasting their time and won’t see returns for the foreseeable future. 

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Clinical Decision Support Systems Leverage Machine Learning for Predictive Analytics – Part 1

By Tahir Hameed

Medical practice has always remained at the forefront of data-driven decision-making. For instance, primary care physicians have commonly used several types of risk scores and diagnostic data to predict morbidity and mortality in their patients. However, with Terabyte (TB) size structured and unstructured datasets abound, a massive shift is underway in the clinical decision support scene. The costs, efficiency, and effectiveness of decision-making for care planning, diagnosis, treatment, adherence monitoring and management of patient health outcomes have been improving at unprecedented rates in the last two decades. 

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Offload Computation in Mobile Edge Computing with Wide-Band Support of Channel Bonding

By Ali Raza, Syed Hashim Raza Bukhari, and Farhan Aadil

Extensive enhancement in technology enables the use of mobile and Internet of Things (IoT) devices in the high demanding applications and services. The users’ demands for quality of service (QoS) and computation intensive applications such as augmented reality, online gaming, e-health, smart home and environmental monitoring etc. are also increasing exponentially. Although, mobile and IoT devices have become much more powerful in the last decades and can perform a variety of tasks in an efficient manner, these are still incapable for computation intensive and time critical applications. With the low processing capacity, IoT devices cannot produce results within a given time constraint. Even with high CPU power, executing a computational expensive task depletes their battery energy and shortens the operational lifetime. Mobile cloud computing (MCC) is one of the most promising solutions in the last decade. It empowers the resource constrained mobile and IoT devices with elastic computing power and large storage capacity. MCC enables the realization of centralized computing and allows offload computation for CPU hungry and time sensitive tasks at the cloud server. However, intrinsically the MCC has certain serious concerns such as long transmission latency, privacy/security of user data, immense increase in Internet traffic due to billions of IoT devices in the near future. These issues motivate the emerging a new paradigm known as mobile edge computing (MEC). MEC decouples the cloud resources into edge servers which are located near the end user, usually alongside access point (AP).

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