Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societies' interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesised to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this study, we aim to explore how three emerging paradigms (Blockchain, IoT and Artificial Intelligence) will influence future cloud computing systems. Further, we identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of cloud computing. Finally, we proposed a conceptual model for cloud futurology to explore the influence of emerging paradigms and technologies on evolution of cloud computing.
Keywords: PRT personal rapid transit travel demand binary logit model Dwarka Personal Rapid Transit (PRT) is an efficient rapid transit system which provides the last mile connectivity to the users with a high level of reliability and comfort. This paper is focused on the estimation of travel demand for a PRT system in an area using stated preference technique and binary logit models. Dwarka is a township in south-western region of New Delhi, India, and it has been selected as the case study area for this study. Primary data has been collected during household and establishment surveys in the area. The surveys were conducted using stated preference technique and coupled with willingness to pay survey. Further, binary logit models have been developed to estimate a 36 percent (222,456 trips per day) shift to PRT from the existing modes in the area. Travel demand estimation is one of the critical aspects of planning a PRT system in an area. Using stated preference technique and binary logit models, the travel demand can be estimated very precisely for any area-wide or a larger city-wide PRT system.
We present a probabilistic description of the Harmonic plus Noise Model (HNM) for speech signals. This probabilistic formulation permits Maximum Likelihood (ML) parameter estimation and speech synthesis becomes a straightforward sampling from a distribution. It also permits development of a Kalman filter that tracks model parameters such as pitch, harmonic amplitudes, and autoregressive coefficients. We focus here on pitch tracking for which the estimator is highly non-linear. As a result it is necessary to develop an approximate Kalman filter that goes beyond extended Kalman filtering.
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