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Rfid Handbook Fundamentals And Applications In Contactless Smart Cards And Identification

In the last decades, 3D city models appear to have been predominantly used for visualisation; however, today they are being increasingly employed in a number of domains and for a large range of tasks beyond visualisation. In this paper, we seek to understand and document the state of the art regarding the utilisation of 3D city models across multiple domains based on a comprehensive literature study including hundreds of research papers, technical reports and online resources. A challenge in a study such as ours is that the ways in which 3D city models are used cannot be readily listed due to fuzziness, terminological ambiguity, unclear added-value of 3D geoinformation in some instances, and absence of technical information. To address this challenge, we delineate a hierarchical terminology (spatial operations, use cases, applications), and develop a theoretical reasoning to segment and categorise the diverse uses of 3D city models. Following this framework, we provide a list of identified use cases of 3D city models (with a description of each), and their applications. Our study demonstrates that 3D city models are employed in at least 29 use cases that are a part of more than 100 applications. The classified inventory could be useful for scientists as well as stakeholders in the geospatial industry, such as companies and national mapping agencies, as it may serve as a reference document to better position their operations, design product portfolios, and to better understand the market.

/pdf/applications-of-3d-city-models-state-of-the-art-review-3yewcl5e4i.pdf

Applications of 3D City Models: State of the Art Review

Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.

/pdf/plant-diseases-and-pests-detection-based-on-deep-learning-a-518qywcd1i.pdf

Plant diseases and pests detection based on deep learning: a review.

This text is a survey on cross-validation We define all classical cross-validation procedures, and we study their properties for two different goals: estimating the risk of a given estimator, and selecting the best estimator among a given family For the risk estimation problem, we compute the bias (which can also be corrected) and the variance of cross-validation methods For estimator selection, we first provide a first-order analysis (based on expectations) Then, we explain how to take into account second-order terms (from variance computations, and by taking into account the usefulness of overpenalization) This allows, in the end, to provide some guidelines for choosing the best cross-validation method for a given learning problem

Cross-validation

A new 3D indoor/outdoor spatial model for indoor emergency response facilitation

Plant leaf disease classification using EfficientNet deep learning model

Classification of white blood cells using capsule networks.

The concept of clustering is to separate clusters based on the similarity which is greater within cluster than among clusters. The similarity consists of two principles, namely, connectivity and cohesion. However, in partitional clustering, while some algorithms such as K-means and K-medians divides the dataset points according to the first principle (connectivity) based on centroid clusters without any regard to the second principle (cohesion), some others like K-medoids partially consider cohesion in addition to connectivity. This prevents to discover clusters with convex shape and results are affected negatively by outliers. In this paper a new Gravity Center Clustering (GCC) algorithm is proposed which depends on critical distance (λ) to define threshold among clusters. The algorithm falls under partition clustering and is based on gravity center which is a point within cluster that verifies both the connectivity and cohesion in determining the similarity of each point in the dataset. Therefore, the proposed algorithm deals with any shape of data better than K-means, K-medians and K-medoids. Furthermore, GCC algorithm does not need any parameters beforehand to perform clustering but can help user improving the control over clustering results and deal with overlapping and outliers providing two coefficients and an indicator. In this study, 22 experiments are conducted using different types of synthetic, and real healthcare datasets. The results show that the proposed algorithm satisfies the concept of clustering and provides great flexibility to get the optimal solution especially since clustering is considered as an optimization problem.

https://pure.hud.ac.uk/ws/files/20976265/Farag_GCC_rev_2_v7.pdf

A novel data clustering algorithm based on gravity center methodology

The need for 3D visualization and navigation within 3D-GIS environment is increasingly growing and spreading to various fields. When we consider current navigation systems, most of them are still in 2D environment that is insufficient to realize 3D objects and obtain satisfactory solutions for 3D environment. One of the most important research areas is evacuating the buildings with safety as more complex building infrastructures are increasing in today’s world. The end user side of such evacuation system needs to run in mobile environment with an accurate indoor positioning while the system assist people to the destination with support of visual landscapes and voice commands. For realizing such navigation system we need to solve complex 3D network analysis. The objective of this paper is to investigate and implement 3D visualization and navigation techniques and solutions for indoor spaces within 3D-GIS. As an initial step and as for implementation a GUI provides 3D visualization of Corporation Complex in Putrajaya based on CityGML data, stores spatial data in a Geo-Database and then performs complex network analysis under some different kind of constraints. The GUI also provides a routing simulation on a calculated shortest path with voice commands and visualized instructions which are intended to be the infrastructure of a voice enabled mobile navigation system in our future work.

A 3D-GIS implementation for realizing 3D network analysis and routing simulation for evacuation purpose

We propose SmartEscape, a real-time, dynamic, intelligent and user-specific evacuation system with a mobile interface for emergency cases such as fire. Unlike past work, we explore dynamically changing conditions and calculate a personal route for an evacuee by considering his/her individual features. SmartEscape, which is fast, low-cost, low resource-consuming and mobile supported, collects various environmental sensory data and takes evacuees’ individual features into account, uses an artificial neural network (ANN) to calculate personal usage risk of each link in the building, eliminates the risky ones, and calculates an optimum escape route under existing circumstances. Then, our system guides the evacuee to the exit through the calculated route with vocal and visual instructions on the smartphone. While the position of the evacuee is detected by RFID (Radio-Frequency Identification) technology, the changing environmental conditions are measured by the various sensors in the building. Our ANN (Artificial Neural Network) predicts dynamically changing risk states of all links according to changing environmental conditions. Results show that SmartEscape, with its 98.1% accuracy for predicting risk levels of links for each individual evacuee in a building, is capable of evacuating a great number of people simultaneously, through the shortest and the safest route.

https://www.mdpi.com/2220-9964/7/6/223/pdf

Ümit Atila

Papers

Plant leaf disease classification using EfficientNet deep learning model

Classification of white blood cells using capsule networks.

A novel data clustering algorithm based on gravity center methodology

A 3D-GIS implementation for realizing 3D network analysis and routing simulation for evacuation purpose

SmartEscape: A Mobile Smart Individual Fire Evacuation System Based on 3D Spatial Model