Optimal Energy Management Policies for Energy Harvesting Sensor Nodes

We study a sensor node with an energy harvesting source. The generated energy can be stored in a buffer. The sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time. We obtain energy management policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue.We also compare performance of several easily implementable sub-optimal energy management policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay.Comment: Submitted to the IEEE Transactions on Wireless Communications; 22 pages with 10 figure

Accident Detection in Live Surveillance

With the increase in number of vehicles in the country vehicle detection is an important in road traffic management system. Different traffic accident causes such as vehicle overspeeding, wrong way driving, collision and accident can be detected by CCTV installed on roads. The results obtained from traffic parameters can be applied for vehicle tracking, vehicle classification, parking area monitoring, road traffic monitoring and management etc. The main objective of this project is to decrease the deaths caused by accident occurring because over speeding, wrong war driving by ensuring public safety and also a building a better system for managing the traffic on the roads. The aim of this paper is to develop a system that can detect the vehicle accident which are caused by overspeeding, wrong way driving and collision detection on city roads. A prototype system is developed and tested

Emerging Pharma Markets

This report is an individual extension of the group report done by our team for Parexel, covering a scenario planning exercise for the next 20 years’ time period as part of management project. One of the key highlights from the group report was the importance and opportunities in the emerging markets with respect to clinical trials. The purpose of this report is to investigate the opportunities and financial implications of growth in emerging markets for pharmaceutical contract research organizations, such as PARAXEL. This report will focus on both the strategic logics and financial effects of the growth opportunities in emerging markets. In developed markets, pharmaceutical companies experience a hostile environment to achieve sustainable growth rate due to expiring patents and high pricing pressures. This has led to an increase in the focus on developing and emerging markets as they are seen with a more optimistic outlook for medium to long-term prospects. Countries like Brazil, Russia, India and China, known as the BRIC countries, are some of the largest economies of the world and still offer growth prospects. Rapid growth can also be expected from some of the smaller economies in the Middle East, Latin America and Southeast Asia

Design study of MovementSlicer : an interactive visualization of patterns and group meetings in 2D movement data

Movement data collected through GPS or other technologies is increasingly common, but is difficult to visualize due to overplotting and occlusion of movements when displayed on 2D maps. An additional challenge is the extraction of useful higher-level information (such as meetings) derived from the raw movement data. We present a design study of MovementSlicer, a tool for visualizing the places visited, and behaviors of, individual actors, and also the meetings between multiple actors. We first present a taxonomy of visualizations of movement data, and then consider tasks to support when analyzing movement data and especially meetings of multiple actors. We argue that Gantt charts have many advantages for understanding the movements and meetings of small groups of moving entities, and present the design of a Gantt chart that can nest people within locations or locations within people along the vertical axis, and show time along the horizontal axis. The rows of our Gantt chart are sorted by activity level and can be filtered using a weighted adjacency matrix showing meetings between people. Empty time intervals in the Gantt chart can be automatically folded, with smoothly animated transitions, yielding a multi-focal view. Case studies demonstrate the utility of our prototype

Trends in COVID-19 Publications: Streamlining Research Using NLP and LDA.

Background: Research publications related to the novel coronavirus disease COVID-19 are rapidly increasing. However, current online literature hubs, even with artificial intelligence, are limited in identifying the complexity of COVID-19 research topics. We developed a comprehensive Latent Dirichlet Allocation (LDA) model with 25 topics using natural language processing (NLP) techniques on PubMed® research articles about "COVID." We propose a novel methodology to develop and visualise temporal trends, and improve existing online literature hubs. Our results for temporal evolution demonstrate interesting trends, for example, the prominence of "Mental Health" and "Socioeconomic Impact" increased, "Genome Sequence" decreased, and "Epidemiology" remained relatively constant. Applying our methodology to LitCovid, a literature hub from the National Center for Biotechnology Information, we improved the breadth and depth of research topics by subdividing their pre-existing categories. Our topic model demonstrates that research on "masks" and "Personal Protective Equipment (PPE)" is skewed toward clinical applications with a lack of population-based epidemiological research

To study the incidence of retinopathy of prematurity in high-risk neonates and the risk factors associated with the disease

Background: Retinopathy of prematurity (ROP) is a complex disease of the developing retinal vasculature in premature infant. The challenge in India is that a large number of neonatal intensive care units (NICUs) don’t have an effective ROP screening strategy. Objective: To measure the incidence of ROP in neonates with gestational age (≤32 weeks) or weighing <2000 g with risk factors and evaluation of risk factor associated with ROP. Materials and Methods: This prospective study was conducted in the Department of Pediatrics and Ophthalmology from January to July 2016. Neonates with gestational age ≤32 weeks, birth weight (BW) ≤1500 g, and selected preterm infants with a BW between 1501 and 2000 g with risk factors admitted in NICU/special new-born care unit were included in the study and screened for ROP by trained ophthalmologist under supervision of the pediatrician. All data were analyzed using SPSS or MedCalc. Univariate and multivariate logistic regression was done to determine the risk factors for the development of any ROP. Result: The incidence of ROP in our study was 19%. 4% of the neonates have severe (early treatment for ROP [ETROP] Type 1) ROP while 15% have non-severe (ETROP Type 2) ROP. 8 neonates developed Stage 1 ROP (42.11%), 7 developed Stage 2 (36.84%), and 4 neonates developed Stage 3 ROP (21.05%). Conclusion: The current study revealed that the incidence of ROP in sick neonates was 19%. Significant risk factors were found to be low BW, low gestational age, supplemental oxygen, and mechanical ventilation, culture proven sepsis, anemia, apnea, and respiratory distress syndrome (RDS)