Modeling of objects using planar facets in noisy range images

Products designed and manufactured before the advent of Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) technology have not been documented electronically. To avoid the laborious procedure of redesigning the parts, a reverse engineering approach can be adopted. This approach involves, taking a picture of the object and constructing a solid model from the image data. Range image is a three dimensional image of an object or a scene. This image can be obtained from special cameras, called range image cameras, or can be constructed from the Coordinate Measuring Machine\u27s (CMM) output data. Adaptive Fuzzy c-Elliptotype (AFC) clustering algorithm is used to identify the planar facets in a range image. A modified version of AFC algorithm can handle noisy range images. Unknown number of planar facets can be identified using the Agglomerative clustering approach. The object is reconstructed using segmented image data. The equations of the edge are obtained from the plane intersections. An edge validity criterion is developed to validate the existence of an edge. Vertices are the two extreme points on the edge. A Boundary representation of the object is developed. The information about this object is then passed to a CAD software using Initial Graphics Exchange Specification (IGES)

Measuring program similarity for efficient benchmarking and performance analysis of computer systems

textComputer benchmarking involves running a set of benchmark programs to measure performance of a computer system. Modern benchmarks are developed from real applications. Applications are becoming complex and hence modern benchmarks run for a very long time. These benchmarks are also used for performance evaluation in the early design phase of microprocessors. Due to the size of benchmarks and increase in complexity of microprocessor design, the effort required for performance evaluation has increased significantly. This dissertation proposes methodologies to reduce the effort of benchmarking and performance evaluation of computer systems. Identifying a set of programs that can be used in the process of benchmarking can be very challenging. A solution to this problem can start by identifying similarity between programs to capture the diversity in their behavior before they can be considered for benchmarking. The aim of this methodology is to identify redundancy in the set of benchmarks and find a subset of representative benchmarks with the least possible loss of information. This dissertation proposes the use of program characteristics which capture the performance behavior of programs and identifies representative benchmarks applicable over a wide range of system configurations. The use of benchmark subsetting has not been restricted to academic research. Recently, the SPEC CPU subcommittee used the information derived from measuring similarity based on program behavior characteristics between different benchmark candidates as one of the criteria for selecting the SPEC CPU2006 benchmarks. The information of similarity between programs can also be used to predict performance of an application when it is difficult to port the application on different platforms. This is a common problem when a customer wants to buy the best computer system for his application. Performance of a customer's application on a particular system can be predicted using the performance scores of the standard benchmarks on that system and the similarity information between the application and the benchmarks. Similarity between programs is quantified by the distance between them in the space of the measured characteristics, and is appropriately used to predict performance of a new application using the performance scores of its neighbors in the workload space.Electrical and Computer Engineerin

A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems.

The objective of this review is to describe the implementation of human factors principles for the design of alerts in clinical information systems. First, we conduct a review of alarm systems to identify human factors principles that are employed in the design and implementation of alerts. Second, we review the medical informatics literature to provide examples of the implementation of human factors principles in current clinical information systems using alerts to provide medication decision support. Last, we suggest actionable recommendations for delivering effective clinical decision support using alerts. A review of studies from the medical informatics literature suggests that many basic human factors principles are not followed, possibly contributing to the lack of acceptance of alerts in clinical information systems. We evaluate the limitations of current alerting philosophies and provide recommendations for improving acceptance of alerts by incorporating human factors principles in their design

A Systematic Literature Review With Bibliometric Meta-Analysis Of Deep Learning And 3D Reconstruction Methods In Image Based Food Volume Estimation Using Scopus, Web Of Science And IEEE Database

Purpose- Estimation of food portions is necessary in image based dietary monitoring techniques. The purpose of this systematic survey is to identify peer reviewed literature in image-based food volume estimation methods in Scopus, Web of Science and IEEE database. It further analyzes bibliometric survey of image-based food volume estimation methods with 3D reconstruction and deep learning techniques. Design/methodology/approach- Scopus, Web of Science and IEEE citation databases are used to gather the data. Using advanced keyword search and PRISMA approach, relevant papers were extracted, selected and analyzed. The bibliographic data of the articles published in the journals over the past twenty years were extracted. A deeper analysis was performed using bibliometric indicators and applications with Microsoft Excel and VOS viewer. A comparative analysis of the most cited works in deep learning and 3D reconstruction methods is performed. Findings: This review summarizes the results from the extracted literature. It traces research directions in the food volume estimation methods. Bibliometric analysis and PRISMA search results suggest a broader taxonomy of the image-based methods to estimate food volume in dietary management systems and projects. Deep learning and 3D reconstruction methods show better accuracy in the estimations over other approaches. The work also discusses importance of diverse and robust image datasets for training accurate learning models in food volume estimation. Practical implications- Bibliometric analysis and systematic review gives insights to researchers, dieticians and practitioners with the research trends in estimation of food portions and their accuracy. It also discusses the challenges in building food volume estimator model using deep learning and opens new research directions. Originality/value- This study represents an overview of the research in the food volume estimation methods using deep learning and 3D reconstruction methods using works from 1995 to 2020. The findings present the five different popular methods which have been used in the image based food volume estimation and also shows the research trends with the emerging 3D reconstruction and deep learning methodologies. Additionally, the work emphasizes the challenges in the use of these approaches and need of developing more diverse, benchmark image data sets for food volume estimation including raw food, cooked food in all states and served with different containers

A Bibliometric Survey of Smart Wearable in the Health Insurance Industry

Smart wearables help real-time and remote monitoring of health data for effective diagnostic and preventive health care services. Wearable devices have the ability to track and monitor healthcare vitals such as heart rate, physical activities, BMI (Body Mass Index), blood pressure, and keeps an individual notified about the health status. Artificial Intelligence-enabled wearables show an ability to transform the health insurance sector. This would not only enable self-management of individual health but also help them focus from treatments to the preventions of health hazards. With this customer-centric approach to health care, it will enable the insurance companies to track the health behaviour of the individuals. This can perhaps lead to better incentivization models with a lower premium to the health-centric customers. Health insurance companies can have better outreach with these customer-centric products. The area is exceptionally novel and shows potential for the research opportunities. Although the literature shows the presence of few works incepting the application of smart wearables in health insurance, it was found that the works are across sections of the society and extremely limited to regions and boundaries. Thus, a need for Bibliometric survey in the area of Smart Wearables in Health insurance is necessary to track the research trends, progress and scope of the future research. This paper conducts Bibliometric study for “Smart Wearables in Health Insurance Industry” by extracting documents of total 287 from Scopus database using keywords like wearables, health insurance, health care, machine learning and health risk prediction. The study is conducted since the last decade that is 2011-2020 for the research analysis. From the study, it is observed that application of wearables in health insurance are in a nascent stage and there is a scope for researchers, insurance, health care stakeholders to explore the used cases for a better user experience

Big Philanthropy in India: Perils and Opportunities

Philanthropy in this report refers only to personal philanthropy. It does not include corporate giving or Corporate Social Responsibility (CSR) spend. The term 'big philanthropy' is used for the philanthropy of Ultra High Net Worth Individuals (UHNWIs). Throughout the report, 'philanthropist' refers to Ultra High Net Worth Individual philanthropist. This usage is only for brevity, as the scope of this study is focused on philanthropy of the very wealthy, rather than the entire universe of remarkable philanthropists who come from all walks of life, and are equally, if not more generous than the subjects of this study. This should not give an impression that the authors and editors believe that only Ultra High Net Worth Individuals can be philanthropists.The study recognizes the right of the philanthropist to deploy her own wealth. The intent is to aid her thinking and decision making so that assessing potential risks and pitfalls of proposed philanthropic interventions becomes integral to the act of philanthropy. Suggestions made here should be considered by philanthropists in the context of their work. This study emphasizes the need to detail the social risks and pitfalls of big philanthropy before funding or implementing large interventions. Many experts speak of the need for wealthy philanthropists to take more financial risk and use their philanthropy as risk capital towards ambitious social goals. While highlighting the need to minimize social risks, this study, in no way implies that philanthropists should not take financial risks. While the study touches upon a third kind of risk – personal or reputational risk to philanthropists, it does not examine it rigorously

A Bibliometric Analysis of Online Extremism Detection

The Internet has become an essential part of modern communication. People are sharing ideas, thoughts, and beliefs easily, using social media. This sharing of ideas has raised a big problem like the spread of the radicalized extremist ideas. The various extremist organizations use the social media as a propaganda tool. The extremist organizations actively radicalize and recruit youths by sharing inciting material on social media. Extremist organizations use social media to influence people to carry out lone-wolf attacks. Social media platforms employ various strategies to identify and remove the extremist content. But due to the sheer amount of data and loopholes in detection strategies, extremism remain undetected for a significant time. Thus, there is a need of accurate detection of extremism on social media. This study provides Bibliometric analysis and systematic mappings of existing literature for radicalisation or extremism detection. Bibliometric analysis of Machine Learning and Deep Learning articles in extremism detection are considered. This is performed using SCOPUS database, with the tools like Sciencescape and VOS Viewer. It is observed that the current literature on extremist detection is focused on a particular ideology. Though it is noted that few researchers are working in the extremism detection area, it is preferred among researchers in the recent years

Combining Field-Based Behavioral Experiments With Genomics to Understand Rapid Avian Speciation

Behavioral and phenotypic traits play an important role early in speciation by influencing 1) where and when individuals come into contact and 2) whether closely related organisms are recognized as potential mates. Yet, despite the key role of pre-mating isolation in the evolution of biodiversity, our understanding of the specific mechanisms by which phenotypes and behavioral processes contribute to the generation of reproductive isolation during incipient speciation remains limited. My dissertation research examines the ways in which migratory behavior and sexual signals influence gene flow in two avian radiations in the early stages of divergence: southern capuchino seedeaters (Sporophila) and the barn swallow species complex (Hirundo rustica). First, I present a conceptual chapter that synthesizes current literature and organizes hypothesis testing about the ways in which behavioral and phenotypic traits, specifically migratory strategy, may mediate patterns of gene flow early in the speciation process. Then, I examine the importance of divergent migratory behavior in the evolution of two subspecies of barn swallow (H. r. rustica and H. r. gutturalis) that form a hybrid zone in Gansu Province, China. The subspecies exhibit a striking migratory divide that spans two continents and is closely associated with genomic differentiation across the hybrid zone, suggesting that assortative mating by timing of arrival and/or selection against hybrids that inherit intermediate migratory traits may limit interbreeding between the subspecies. My fourth chapter analyzes the genomic and behavioral bases of pre-mating isolation between two species of capuchino seedeaters (S. hypoxantha and S. iberaensis) that co-occur during the breeding season in Iber&aacute; National Park, Argentina. Though the species lack obvious ecological barriers to reproduction, I document behaviorally-mediated species recognition and strong assortative mating associated with genomic regions underlying male plumage patterning. Finally, I generate fine-scale recombination maps for capuchino seedeaters to examine the role that variation in recombination rate has played in generating phenotypic diversity and peaks of genomic differentiation early in the speciation process. By combining fine-scale behavioral analyses with phenotype data and high-throughput genomic sequencing, these chapters investigate the traits underlying reproductive isolation and their implication for speciation in recent avian radiations.</p

Environmental impact assessment of online advertising

There are no commonly agreed ways to assess the total energy consumption of the Internet. Estimating the Internet's energy footprint is challenging because of the interconnectedness associated with even seemingly simple aspects of energy consumption. The first contribution of this paper is a common modular and layered framework, which allows researchers to assess both energy consumption and CO2e emissions of any Internet service. The framework allows assessing the energy consumption depending on the research scope and specific system boundaries. Further, the proposed framework allows researchers without domain expertise to make such an assessment by using intermediate results as data sources, while analyzing the related uncertainties. The second contribution is an estimate of the energy consumption and CO2e emissions of online advertising by utilizing our proposed framework. The third contribution is an assessment of the energy consumption of invalid traffic associated with online advertising. The second and third contributions are used to validate the first. The online advertising ecosystem resides in the core of the Internet, and it is the sole source of funding for many online services. Therefore, it is an essential factor in the analysis of the Internet's energy footprint. As a result, in 2016, online advertising consumed 20–282 TWh of energy. In the same year, the total infrastructure consumption ranged from 791 to 1334 TWh. With extrapolated 2016 input factor values without uncertainties, online advertising consumed 106 TWh of energy and the infrastructure 1059 TWh. With the emission factor of 0.5656 kg CO2e/kWh, we calculated the carbon emissions of online advertising, and found it produces 60 Mt CO2e (between 12 and 159 Mt of CO2e when considering uncertainty). The share of fraudulent online advertising traffic was 13.87 Mt of CO2e emissions (between 2.65 and 36.78 Mt of CO2e when considering uncertainty). The global impact of online advertising is multidimensional. Online advertising affects the environment by consuming significant amounts of energy, leading to the production CO2e emissions. Hundreds of billions of ad dollars are exchanged yearly, placing online advertising in a significant role economically. It has become an important and acknowledged component of the online-bound society, largely due to its integration with the Internet and the amount of revenue generated through it