Advance Vehicle and Driver Profile Management Using Cloud Frameworks

Advancements in semiconductor technology, embedded automotive computing, AI/ML computing and cloud computing has recently helped automotive industry to reach to provide the next generation vehicular experience such as self-driving cars, advanced safety features, cloud-based fleet management, highly efficient automotive manufacturing, connected cars, telematics and many more. Automotive or vehicular industry also started focus on providing better driving experience, in vehicle connectivity, entertainment, remote vehicle diagnostics and driver assistance, etc. Automotive cloud computing is one such domain which helped automotive industry to scale itself to connect the vehicles to cloud and remotely manage and control the vehicles, provide emergency assistance, data science and analytics services to dealers, insurance companies, car manufacturers, fleet management, etc. In this paper we present the research of recent advancements of automotive industry especially using cloud computing and how the cloud computing frameworks are making huge impact on auto industry such as advance driver’s profiles management using cloud framework. This paper also discusses the implementation approach for electronically managing the vehicle and driver’s preferences for their next generation electric and hybrid vehicles. And the paper proposes the smartphone’s NFC or BLE based driver’s profiles management approach

Lie Group Analysis for Boundary Layer Flow of Nanofluids near the Stagnation-Point over a Permeable Stretching Surface Embedded in a Porous Medium in the Presence of Radiation and Heat Generation/Absorption

This study investigates the influence of thermal radiation and heat generation/absorption on a two dimensional steady boundary layer flow near the stagnation-point on a permeable stretching sheet in a porous medium saturated with nanofluids. The governing partial differential equations with the appropriate boundary conditions are reduced to a set of ordinary differential equations via Lie-group analysis. The resultant equations are then solved numerically using Runge - Kutta fourth order method along with shooting technique. Two types of nanofluids, namely, copper-water and alumina-water are considered. The velocity and temperature as well as the shear stress and heat transfer rates are computed. The influence of pertinent parameters such as radiation parameter Nr, nanofluid volume fraction parameter , the ratio of free stream velocity and stretching velocity parameter a/c , the permeability parameter K1, suction/blowing parameter S, and heat source/sink parameter on the flow and heat transfer characteristics is discussed. The present study helps to understand the efficiency of heat transfer transport in nanofluids which are likely to be the smart coolants of the next generation

Simulation of VSC Based HVDC Transmission System under Fault Conditions

Voltage-source-converter high-voltage dc (VSC-HVDC) transmission systems have evolved from simple two-level converters to neutral-point clamped converters and then to true multilevel converters such as modular converters. Present VSC-HVDC transmission systems rely on their converter station control systems and effective impedance between the point-of-common-coupling (PCC) and the converter terminals to ride-through dc side faults. A VSC-HVDC transmission system is a candidate to meet these challenges due to its operational flexibility, such as provision of voltage support to ac networks, its ability to operate independent of ac network strength therefore makes it suitable for connection of weak ac networks such as offshore wind farms, suitability for multi terminal HVDC network realization as active power reversal is achieved without dc link voltage polarity change, and resiliency to ac side faults. This paper proposes a new breed of high-voltage dc (HVDC) transmission systems based on a hybrid multilevel voltage source converter (VSC) with ac-side cascaded H-bridge cells. This paper assesses its dynamic performance during steady-state and network alterations, including its response to ac and dc side faults by using MAT Lab/Simulink

Sulfonyl pyrazoles and isoxazoles: Cycloaddition of nitrile imines and nitrile oxides to aryl styryl sulfones

1286-128

Prediction of Cardiovascular Diseases by Integrating Electrocardiogram (ECG) and Phonocardiogram (PCG) Multi-Modal Features using Hidden Semi Morkov Model

Because the health care field generates a large amount of data, we must employ modern ways to handle this data in order to give effective outcomes and make successful decisions based on data. Heart diseases are the major cause of mortality worldwide, accounting for 1/3th of all fatalities. Cardiovascular disease detection can be accomplished by the detection of disturbance in cardiac signals, one of which is known as phonocardiography. The aim of this project is for using machine learning to categorize cardiac illness based on electrocardiogram (ECG) and phonocardiogram (PCG) readings. The investigation began with signal preprocessing, which included cutting and normalizing the signal, and was accompanied by a continuous wavelet transformation utilizing a mother wavelet analytic morlet. The results of the decomposition are shown using a scalogram, and the outcomes are predicted using the Hidden semi morkov model (HSMM). In the first phase, we submit the dataset file and choose an algorithm to run on the chosen dataset. The accuracy of each selected method is then predicted, along with a graph, and a modal is built for the one with the max frequency by training the dataset to it. In the following step, input for each cardiac parameter is provided, and the sick stage of the heart is predicted based on the modal created. We then take measures based on the patient's condition. When compared to current approaches, the suggested HSMM has 0.952 sensitivity, 0.92 specificity, 0.94 F-score, 0.91 ACC, and 0.96 AUC

Chemical Reaction Effects on an Unsteady MHD Mixed Convective and Radiative Boundary Layer Flow over a Circular Cylinder

A mathematical model is presented for an optically dense fluid past an isothermal circular cylinder with chemical reaction taking place in it. A constant, static, magnetic field is applied transverse to the cylinder surface. The cylinder surface is maintained at a constant temperature. New variables are introduced to transform the complex geometry into a simple shape and the boundary layer conservation equations, which are parabolic in nature, are normalized into non-similar form and then solved numerically with the well-tested, efficient, implicit, Crank-Nicolson finite difference scheme. Numerical computations are made and the effects of the various material parameters on the velocity, temperature and concentration as well as the surface skin friction and surface heat and mass transfer rates are illustrated graphs and tables. Increasing magnetohydrodynamic body force parameter (M) is found to decelerate the flow but enhance temperatures. Thermal radiation is seen to reduce both velocity and temperature in the boundary layer. Local Nusselt number is also found to be enhanced with increasing radiation parameter

Intrusion Detection Recording System with Biometric Lock

The spread of COVID-19 in the entire world has put humankind in danger. The assets of probably the biggest economies are worried because of the enormous infectivity and contagiousness of this illness. The ability of machine learning algorithms to predict the number of possible COVID-19 patients is generally seen as a potential challenge to mankind. The undermining components of COVID-19 were determined using four normal estimating models: Support Vector Machine (SVM), least total shrinkage, and determination administrator (LASSO), linear regression (LR). Any one of the models makes three types of predictions, such as the number of newly infected occurrences, the number of passings, and the rate of recoveries, but they cannot predict the exact result for the patients. To defeat the issue, the Proposed strategy utilizing exponential smoothing (ES) The number of cases of COVID-19 and the impact of COVID-19 preventive steps including certain social insulation and latch on infectious diseases was expected in the next 30 days to come

The effects of scale on the costs of targeted HIV prevention interventions among female and male sex workers, men who have sex with men and transgenders in India

BACKGROUND: The India AIDS Initiative (Avahan) project is involved in rapid scale-up of HIV-prevention interventions in high-risk populations. This study examines the cost variation of 107 non-governmental organisations (NGOs) implementing targeted interventions, over the start up (defined as period from project inception until services to the key population commenced) and first 2 years of intervention. METHODS: The Avahan interventions for female and male sex workers and their clients, in 62 districts of four southern states were costed for the financial years 2004/2005 and 2005/2006 using standard costing techniques. Data sources include financial and economic costs from the lead implementing partners (LPs) and subcontracted local implementing NGOs retrospectively and prospectively collected from a provider perspective. Ingredients and step-down allocation processes were used. Outcomes were measured using routinely collected project data. The average costs were estimated and a regression analysis carried out to explore causes of cost variation. Costs were calculated in US$2006. RESULTS: The total number of registered people was 134,391 at the end of 2 years, and 124,669 had used STI services during that period. The median average cost of Avahan programme for this period was$76 per person registered with the project. Sixty-one per cent of the cost variation could be explained by scale (positive association), number of NGOs per district (negative), number of LPs in the state (negative) and project maturity (positive) (p<0.0001). CONCLUSIONS: During rapid scale-up in the initial phase of the Avahan programme, a significant reduction in average costs was observed. As full scale-up had not yet been achieved, the average cost at scale is yet to be realised and the extent of the impact of scale on costs yet to be captured. Scale effects are important to quantify for planning resource requirements of large-scale interventions. The average cost after 2 years is within the range of global scale-up costs estimates and other studies in India

Intrusion Detection Recording System with Biometric Lock

We are coming across cases, where the education system is being distorted and perverted. As the present security of our education system has several loopholes that can be exploited to obtain access to the locations were question papers are being kept, posing a significant threat to society, to address this problem we engineered an intrusion detection recording system with a biometric lock. Our project is a result of combination of two already existing methodologies – Wireless Biometric Lock and Noise detector with automatic recording system. This combination provides us with a more secure system than the existing individual implementation. Thus, our project is a noise sensor-based device&nbsp;with an automatic recording system that can also lock the locker or door using a fingerprint-based biometric interface which not only detects intrusion but also traps the person who tries to intrude

A Study on Process Parameters in Milling of Al-MMC

Metal Matrix Composites have become a leading material among composite materials. In particular, particle reinforced Aluminum Metal Matrix Composite (AMMC) have received considerable attention due to their excellent engineering properties. Aluminum metal matrix refers to the class of light weight high performance aluminum centric material systems. The reinforcement in AMC’s could be in the form of   continuous/  discontinuous  fibers,  whiskers  or Particulates in volume fractions ranging in percentages. Properties of AMC’s can be tailored to the demands of different industrial applications by suitable combinations of matrix reinforcement and processing routes. In this study an attempt has been made to manufacture the AMC through a liquid metallurgical route. The matrix used in the study is Aluminum alloy and reinforcement particles selected are Silicon Carbide (SiC) particles ranging its size from 50-70 microns. The study also focuses to establish a mathematical relationship between process parameters like Cutting speed, Feed Rate, Depth of cut and surface roughness (Ra) in End Milling operation. A CNC machining center with 8 mm diameter HSS end milling cutter with 30° helix angle is used for machining by employing a L16 (44) Taguchi’s orthogonal array for the experiment plan. A linear and multiple regression analysis using analysis of variance is  conducted to determine the performance of experimental measurements and to show the effect of four cutting parameters on the surface roughness.RSM methodology  was selected to optimize the surface roughness resulting minimum values of surface roughness and their  respective optimal condition.