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Iteration rules product development, but it isn't enough to produce dramatic results. This book champions Radical Product Thinking, a systematic methodology for building visionary, game-changing products.Methodologies such as Lean and Agile have democratized innovation by teaching us to harness the power of iteration to innovate faster, but our ability to set a clear destination hasn't kept up with the pace. When we iterate without a clear vision or strategy, our products become bloated, fragmented, and driven by irrelevant metrics. They catch “product diseases” that are often fatal to true innovation. In Radical Product Thinking (RPT), product development is led by the vision for the change it's intended to create. This methodology helps leaders reimagine the problems they face and align their team to find creative solutions using five elements: Vision, Strategy, Prioritization, Execution, and Culture. R. Dutt guides readers through these elements so they develop a clear process for achieving their desired change, incorporate it into daily activities, and turn RPT skills into muscle memory. This book gives organizations a repeatable model for building vision-driven products by helping us systematically translate vision and strategy into everyday actions so our product becomes a vehicle for creating the change we want to see in the world. Dutt shows us that you don't have to be a natural-born visionary to produce extraordinary results.

With an increased focus on green transportation, hybrid energy storage systems (HESS) for Electric Vehicles (EV) are gaining importance in recent years. In this paper, we propose a control strategy for optimal distribution of power demand between battery and supercapacitor (SC) in a HESS, as well as efficient utilization of the regenerative braking power. A DC/DC boost converter based power splitting strategy has been proposed. Two control blocks are proposed for charging and discharging of energy storage, which reduces the rate of discharge current, thereby improving the health and life of the battery. Simulation results are presented for the proposed circuit and compared with the state-of-the-art topology. The results show that the proposed control strategy improves State-of-Charge (SOC) of the battery from 84% to 94.4% in comparison with only battery held systems. The depth of discharge (DOD) also improved by 10.4%, which helps to enhance the range of the HESS based EV from a single charge.

The recent progressions in semiconductor and computing technology have empowered the PPG utilization in medical diagnosis. This paper presents a reconfigurable deep learning framework 'CardioNet' for early diagnosis of cardiovascular risk factors or most common diseases (such as diabetes, hypertension, cerebrovascular, cerebra-infraction) using the PPG data. The proposed model has a light-weight architecture, designed by exploiting the deep learning framework of convolutional neural network, exhibiting inherent capability of feature extraction, thereby, eliminating the cost effective steps of feature selection and extraction. The performance demonstration of the proposed model is done on a healthy dataset comprising 657 data segments of 219 subjects holding records of common CVD risk factors (diabetes, hypertension, cerebrovascular, cerebra-infraction). The obtained results of an overall accuracy of 97% for diagnosis of CVD risk factors, show the efficiency of the proposed model for real-time usability. The clinical significance of this work to provide an accurate and non-invasive method for early diagnosis and monitoring of cardio-risk factors. © 2020 IEEE

Real-time and accurate estimation of battery states has gained immense importance in recent years due to emerging applications of Battery Energy Storage Systems (BESS) in smart grid and Electric Vehicles. The behaviour of BESS modelled as a 2-RC Circuit and State-of-Charge (SOC) and RC parameter estimation using Unscented Kalman Filter (UKF) has emerged as an optimal model for online Battery Management Systems (BMS). However, the stability of UKF degrades due to error covariance matrix becoming ill-conditioned. This paper presents a dual Square Root Unscented Kalman Filter (SRUKF) based SOC and parameter estimation algorithms for BMS. The proposed SRUKF methodology improves the stability of the system as the square root form of the error covariance matrix always remains positive semi-definite. The methodology has been designed and implemented in MATLAB/Simulink and compared with dual EKF and state-of-the-art dual UKF algorithms. The results show that dual SRUKF is 74% more accurate than the state-of-the-art and remains stable once it converges to true SOC value.

Sand migration in the vast Taklamakan desert within the Tarim Basin (Xinjiang Uyghur Autonomous region, PR China) is governed by two competing transport agents: wind and water, which work in diametrically opposed directions. Net aeolian transport is from northeast to south, while fluvial transport occurs from the south to the north and then west to east at the northern rim, due to a gradual northward slope of the underlying topography. We here present the first comprehensive provenance study of Taklamakan desert sand with the aim to characterise the interplay of these two transport mechanisms and their roles in the formation of the sand sea, and to consider the potential of the Tarim Basin as a contributing source to the Chinese Loess Plateau (CLP). Our dataset comprises 39 aeolian and fluvial samples, which were characterised by detrital-zircon U-Pb geochronology, heavy-mineral, and bulk-petrography analyses. Although the inter-sample differences of all three datasets are subtle, a multivariate statistical analysis using multidimensional scaling (MDS) clearly shows that Tarim desert sand is most similar in composition to rivers draining the Kunlun Shan (south) and the Pamirs (west), and is distinctly different from sediment sources in the Tian Shan (north). A small set of samples from the Junggar Basin (north of the Tian Shan) yields different detrital compositions and age spectra than anywhere in the Tarim Basin, indicating that aeolian sediment exchange between the two basins is minimal. Although river transport dominates delivery of sand into the Tarim Basin, wind remobilises and reworks the sediment in the central sand sea. Characteristic signatures of main rivers can be traced from entrance into the basin to the terminus of the Tarim River, and those crossing the desert from the south to north can seasonally bypass sediment through the sand sea. Smaller ephemeral rivers from the Kunlun Shan end in the desert and discharge their sediment there. Both river run-o