Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS.

In this paper, by taking full consideration of demographics, transfer from infectious to sus-ceptible and contact heterogeneity of the individuals, we construct an improved Susceptible-Infected-Removed-Susceptible (SIRS) epidemic model on complex heterogeneous networks. Using the next generation matrix method, we obtain the basic reproduction number $\mathcal{R}_0$ which is a critical value and used to measure the dynamics of epidemic diseases. More specifically, if $\mathcal{R}_0$ 1, then there exists a unique endemic equilib-rium and the permanence of the disease is shown in detail. By constructing an appropriate Lyapunov function, the global stability of the endemic equilibrium is proved as well under some conditions. Moreover, the effects of three major immunization strategies are investigated. Finally, some numerical simulations are carried out to demonstrate the correctness and validness of the theoretical results.

Global dynamics of an SIRS model with demographics and transfer from infectious to susceptible on heterogeneous networks

Due to its widespread application in the robotics field, the Kalman filter has received increased attention from researchers. This work reviews some of the modifications conducted on to this algorithm over the last years. Problems such as the consistency, convergence, and accuracy of the filter are also dealt with. Sixty years after its creation, the Kalman filter is still used in autonomous navigation processes, robot control, and trajectory tracking, among other activities. The filter is not only restricted to robotics but is also present in different fields, such as economics and medicine. In addition, the characteristics of each modification on this filter are analyzed and compared.

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Kalman Filter: Historical Overview and Review of Its Use in Robotics 60 Years after Its Creation

Object detection plays a vital role in the fields of computer vision, machine learning, and artificial intelligence applications (such as FUSE-AI (E-healthcare MRI scan), face detection, people counting, and vehicle detection) to identify good and defective food products In the field of artificial intelligence, target detection has been at its peak, but when it comes to detecting multiple targets in a single image or video file, there are indeed challenges This article focuses on the improved K-nearest neighbor (MK-NN) algorithm for electronic medical care to realize intelligent medical services and applications We introduced modifications to improve the efficiency of MK-NN, and a comparative analysis was performed to determine the best fuse target detection algorithm based on robustness, accuracy, and computational time The comparative analysis is performed using four algorithms, namely, MK-NN, traditional K-NN, convolutional neural network, and backpropagation Experimental results show that the improved K-NN algorithm is the best model in terms of robustness, accuracy, and computational time

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Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

• A comprehensive review for mobile robots Visual Slam application. • Including the state-of-the-art deep learning methods for Visual Slam. • Simulation results for each section based on feature extraction techniques. This paper is an overview to Visual Simultaneous Localization and Mapping (V-SLAM). We discuss the basic definitions in the SLAM and vision system fields and provide a review of the state-of-the-art methods utilized for mobile robot’s vision and SLAM. This paper covers topics from the basic SLAM methods, vision sensors, machine vision algorithms for feature extraction and matching, Deep Learning (DL) methods and datasets for Visual Odometry (VO) and Loop Closure (LC) in V-SLAM applications. Several feature extraction and matching algorithms are simulated to show a better vision of feature-based techniques. 

A survey of state-of-the-art on visual SLAM

For more than two decades, the issue of simultaneous localization and mapping (SLAM) has gained more attention from researchers and remains an influential topic in robotics. Currently, various algorithms of the mobile robot SLAM have been investigated. However, the probability-based mobile robot SLAM algorithm is often used in the unknown environment. In this paper, the authors proposed two main algorithms of localization. First is the linear Kalman Filter (KF) SLAM, which consists of five phases, such as (a) motionless robot with absolute measurement, (b) moving vehicle with absolute measurement, (c) motionless robot with relative measurement, (d) moving vehicle with relative measurement, and (e) moving vehicle with relative measurement while the robot location is not detected. The second localization algorithm is the SLAM with the Extended Kalman Filter (EKF). Finally, the proposed SLAM algorithms are tested by simulations to be efficient and viable. The simulation results show that the presented SLAM approaches can accurately locate the landmark and mobile robot.

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Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

Mobile robot localization has attracted substantial consideration from the scientists during the last two decades. Mobile robot localization is the basics of successful navigation in a mobile network. Localization plays a key role to attain a high accuracy in mobile robot localization and robustness in vehicular localization. For this purpose, a mobile robot localization technique is evaluated to accomplish a high accuracy. This paper provides the performance evaluation of three localization techniques named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). In this work, three localization techniques are proposed. The performance of these three localization techniques is evaluated and analyzed while considering various aspects of localization. These aspects include localization coverage, time consumption, and velocity. The abovementioned localization techniques present a good accuracy and sound performance compared to other techniques.

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Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

This paper proposes a new virus spreading model, susceptible–infected–susceptible–recovered–susceptible, which is based on partial immunization and immune invalidity in a complex network. On the basis of mean-field theory, the epidemic dynamics behavior of this model in a uniform network and a scale-free network is studied. After modifying the formula for the effective spread rate, we obtain the theoretical result that the coexistence of partial immunization and immune failure does not affect the network spread threshold. At the same time, the experimental results show that the existence of the above two conditions greatly increases the spread of the virus and extends the diffusion range. The study also found that in the scale-free network, peak viral infection occurs at the beginning of the spreading process, and after the immunization strategy is applied, the early infection peak is effectively curbed.

Epidemic spreading on a complex network with partial immunization

The invention discloses a video coding and decoding method based on multiple description CS measurement value. The coding method comprises steps: a video image sequence is divided into a key frame and a CS frame; block-based measurement, quantization and entropy coding are adopted for the key frame; a multiple description idea is adopted for the CS frame to describe a block-based measurement value in two ways; and quantization and entropy coding are separately carried out. The decoding method comprises steps: after key frame data are received, inverse quantization is firstly carried out on entropy decoding to obtain a measurement value, the key frame block is initially reconstructed, and multiple hypothesis prediction and reconstruction are carried out inside the frame; after CS frame multiple description code stream is received, inverse quantization is carried out on entropy decoding; a measurement value based on multiple description is generated; and CS frame multiple description adaptive inter-frame multiple hypothesis reconstruction is carried out. In the condition of not increasing complexity of the coding end, the reconstruction quality and robustness of a video image are improved.

Video coding and decoding method based on multiple description CS measurement value

Image dehazing technology has attracted much interest in the field of image processing. Most existing dehazing methods based on neural networks are inflexible and do not consider the loss in haze‐related feature space. They sacrificed texture details and perceptual characteristics in images. To overcome these weaknesses, we propose an image‐to‐image dehazing model based on generative adversarial networks (DHGAN) with dark channel prior. The DHGAN takes a hazy image as input and directly outputs a haze‐free image by applying a U‐net‐based generator. In addition to pixelwise loss and perceptual loss, we introduce dark‐channel‐minimizing loss to constrain the generated images to the manifold of natural images, thus leading to better texture details and perceptual properties. Comparative experiments on benchmark images with several state‐of‐the‐art dehazing methods demonstrate the effectiveness of the proposed DHGAN.

Xuewu Zhang

Papers

Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

Epidemic spreading on a complex network with partial immunization

Video coding and decoding method based on multiple description CS measurement value

DHGAN: Generative adversarial network with dark channel prior for single‐image dehazing