다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

The unprecedented outbreak of the 2019 novel coronavirus, termed as COVID-19 by the World Health Organization (WHO), has placed numerous governments around the world in a precarious position. The impact of the COVID-19 outbreak, earlier witnessed by the citizens of China alone, has now become a matter of grave concern for virtually every country in the world. The scarcity of resources to endure the COVID-19 outbreak combined with the fear of overburdened healthcare systems has forced a majority of these countries into a state of partial or complete lockdown. The number of laboratory-confirmed coronavirus cases has been increasing at an alarming rate throughout the world, with reportedly more than 3 million confirmed cases as of 30 April 2020. Adding to these woes, numerous false reports, misinformation, and unsolicited fears in regards to coronavirus, are being circulated regularly since the outbreak of the COVID-19. In response to such acts, we draw on various reliable sources to present a detailed review of all the major aspects associated with the COVID-19 pandemic. In addition to the direct health implications associated with the outbreak of COVID-19, this study highlights its impact on the global economy. In drawing things to a close, we explore the use of technologies such as the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), blockchain, Artificial Intelligence (AI), and 5G, among others, to help mitigate the impact of COVID-19 outbreak.

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A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact

As a fundamental and critical task in various visual applications, image matching can identify then correspond the same or similar structure/content from two or more images. Over the past decades, growing amount and diversity of methods have been proposed for image matching, particularly with the development of deep learning techniques over the recent years. However, it may leave several open questions about which method would be a suitable choice for specific applications with respect to different scenarios and task requirements and how to design better image matching methods with superior performance in accuracy, robustness and efficiency. This encourages us to conduct a comprehensive and systematic review and analysis for those classical and latest techniques. Following the feature-based image matching pipeline, we first introduce feature detection, description, and matching techniques from handcrafted methods to trainable ones and provide an analysis of the development of these methods in theory and practice. Secondly, we briefly introduce several typical image matching-based applications for a comprehensive understanding of the significance of image matching. In addition, we also provide a comprehensive and objective comparison of these classical and latest techniques through extensive experiments on representative datasets. Finally, we conclude with the current status of image matching technologies and deliver insightful discussions and prospects for future works. This survey can serve as a reference for (but not limited to) researchers and engineers in image matching and related fields.

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Image Matching from Handcrafted to Deep Features: A Survey

Feature matching, which refers to establishing reliable correspondence between two sets of features (particularly point features), is a critical prerequisite in feature-based registration. In this paper, we propose a flexible and general algorithm, which is called locally linear transforming (LLT), for both rigid and nonrigid feature matching of remote sensing images. We start by creating a set of putative correspondences based on the feature similarity and then focus on removing outliers from the putative set and estimating the transformation as well. We formulate this as a maximum-likelihood estimation of a Bayesian model with hidden/latent variables indicating whether matches in the putative set are outliers or inliers. To ensure the well-posedness of the problem, we develop a local geometrical constraint that can preserve local structures among neighboring feature points, and it is also robust to a large number of outliers. The problem is solved by using the expectation–maximization algorithm (EM), and the closed-form solutions of both rigid and nonrigid transformations are derived in the maximization step. In the nonrigid case, we model the transformation between images in a reproducing kernel Hilbert space (RKHS), and a sparse approximation is applied to the transformation that reduces the method computation complexity to linearithmic. Extensive experiments on real remote sensing images demonstrate accurate results of LLT, which outperforms current state-of-the-art methods, particularly in the case of severe outliers (even up to 80%).

Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming

In the last 10 years, development in robotics, computer vision, and sensor technology has provided new spectral remote sensing tools to capture unprecedented ultra-high spatial and high spectral resolution with unmanned aerial vehicles (UAVs). This development has led to a revolution in geospatial data collection in which not only few specialist data providers collect and deliver remotely sensed data, but a whole diverse community is potentially able to gather geospatial data that fit their needs. However, the diversification of sensing systems and user applications challenges the common application of good practice procedures that ensure the quality of the data. This challenge can only be met by establishing and communicating common procedures that have had demonstrated success in scientific experiments and operational demonstrations. In this review, we evaluate the state-of-the-art methods in UAV spectral remote sensing and discuss sensor technology, measurement procedures, geometric processing, and radiometric calibration based on the literature and more than a decade of experimentation. We follow the 'journey' of the reflected energy from the particle in the environment to its representation as a pixel in a 2D or 2.5D map, or 3D spectral point cloud. Additionally, we reflect on the current revolution in remote sensing, and identify trends, potential opportunities, and limitations.

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Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever-growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.

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A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

Image Registration is the first step towards using remote sensed images for any purpose Despite numerous techniques being developed for image registration, only a handful has proved to be useful for registration of remote sensing images due to their characteristic of being computationally heavy Recent flux in technology has prompted a legion of approaches that may suit divergent remote sensing applications This paper presents a comprehensive survey of such literatures including recently developed techniques

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Remote sensing image registration techniques: a survey

knowledge delivery methods in education should move away from memory based learning to more motivated and creative education. This paper will emphasize on the advantages tangible interaction can bring to education. Augmented Chemistry provides an efficient way for designing and interacting with the molecules to understand the spatial relations between molecules. For Students it is very informative to see actual molecules representation 3D environment, inspect molecules from multiple viewpoints and control the interaction of molecules.We present in this paper an Augmented Reality system for teaching spatial relationships and chemical-reaction problem-solving skills to school-level students based on the VSEPR theory. Our system is based on inexpensive webcams and open-source software. We hope this willgenerate more ideas for educators and researcher to explore Augmented Reality technology in the field of interactive education.

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Augmented Chemistry: Interactive Education System

Scheduling drone charging for multi-drone network based on consensus time-stamp and game theory

Parking lot allocation problem has received much attention in recent years. There have been various works in the literature that target the parking slot allocation problem. However, most of these works use algorithms that run on centralized servers and are based on some predictions on historical data. Due to the dynamic nature of vehicular networks, the accuracy of such prediction models is not high which ends up in a chaotic situation for the parking lot owners as well as the vehicle owners. Therefore, a distributed Parking slot Allocation Framework based on Adaptive Pricing Algorithm and Virtual Voting is proposed in this paper. The proposed model is based on virtual voting and hashgraph consensus algorithm. Using the model, all users and parking lot owners can easily come to consensus finality about the allocation of a parking slot with the use of minimal bandwidth. The proposed model provides a fair, fast and cost-optimal parking slot allocation method. The perfect ordering of allocation requests is also maintained based on consensus timestamp. Further, an adaptive pricing model is proposed to enhance the overall revenue of the parking lot owners and comfort of the users. The proposed model is deterministic and can reduce the average parking cost and time. Performance evaluations reveal that the proposed model outperforms its counterparts in terms of accurate parking slot allocation, reduced cost and parking lot resource utilization.

Vikas Saxena

Papers

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

Remote sensing image registration techniques: a survey

Augmented Chemistry: Interactive Education System

Scheduling drone charging for multi-drone network based on consensus time-stamp and game theory

A Parking Slot Allocation Framework Based on Virtual Voting and Adaptive Pricing Algorithm