Design and Implementation of Intelligent Truck Based on Azure Kinect

In recent years, due to the impact of COVID-19, the market prospect of non-contact handling has improved and the development potential is huge. This paper designs an intelligent truck based on Azure Kinect, which can save manpower and improve efficiency, and greatly reduce the infection risk of medical staff and community workers. The target object is visually recognized by Azure Kinect to obtain the center of mass of the target, and the GPS and Kalman filter are used to achieve accurate positioning. The 4-DOF robot arm is selected to grasp and transport the target object, so as to complete the non-contact handling work. In this paper, different shapes of objects are tested. The experiment shows that the system can accurately complete the positioning function, and the accuracy rate is 95.56%. The target object recognition is combined with the depth information to determine the distance, and the spatial coordinates of the object centroid are obtained in real time. The accuracy rate can reach 94.48%, and the target objects of different shapes can be recognized. When the target object is grasped by the robot arm, it can be grasped accurately according to the depth information, and the grasping rate reaches 92.67%. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

3D Convolutional Neural Network for Covid Assessment on CT Scans

Due to the rapid spread of the COVID-19 respiratory pathology, an effective diagnosis of positive cases is necessary to stop the contamination. CT scans offer a 3D view of the patient’s thorax and COVID-19 appears as ground glass opacities on these images. This paper describes a deep learning based approach to automatically classify CT scan images as COVID-19 or not COVID-19. We first build a dataset and preprocess this data. Preprocessing includes normalization, resizing and data augmentation. Then, the training step is based on a neural network used for tuberculosis pathology. Training of the dataset is performed using a 3D convolutional neural network. The results of the neural network model on the test set returns an accuracy of 80%. A prototype of the approach is implemented in a form of a web application to assist doctors and speed up the COVID-19 diagnosis. Codes of both the training and the web application are available online for further research. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Coronavirus disease situation analysis and prediction using machine learning: a study on Bangladeshi population

During a pandemic, early prognostication of patient infected rates can reduce the death by ensuring treatment facility and proper resource allocation. In recent months, the number of death and infected rates has increased more distinguished than before in Bangladesh. The country is struggling to provide moderate medical treatment to many patients. This study distinguishes machine learning models and creates a prediction system to anticipate the infected and death rate for the coming days. Equipping a dataset with data from March 1, 2020, to August 10, 2021, a multi-layer perceptron (MLP) model was trained. The data was managed from a trusted government website and concocted manually for training purposes. Several test cases determine the model's accuracy and prediction capability. The comparison between specific models assumes that the MLP model has more reliable prediction capability than the support vector regression (SVR) and linear regression model. The model presents a report about the risky situation and impending coronavirus disease (COVID-19) attack. According to the prediction produced by the model, Bangladesh may suffer another COVID-19 attack, where the number of infected cases can be between 929 to 2443 and death cases between 19 to 57. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

Epidemic Prevention System Based on Voice Recognition Combined with Intelligent Recognition of Mask and Helmet

At present, COVID-19 cross-infection is easy to occur in dense places such as elevators. There are no epidemic prevention measures for construction site elevators on the market, and most of them require manual temperature measurement and reminders to wear masks and helmets to avoid the spread of the epidemic. This paper designs an intelligent epidemic prevention system for the elevator ride process in a modern construction site environment, which can achieve non-contact human temperature measurement, mask and helmet recognition and voice call elevator function. The system uses Arduino UNO as the control core, Kendryte K210 as machine vision processing module, non-contact infrared temperature sensor MLX90614, and voice recognition sensor LD3320. The system has the functions of non-contact temperature detection, mask/helmet recognition(YOLOv3) and voice call elevator. Experimental results showed that the recognition accuracy rate of helmet, mask, voice call elevator is 91.5%, 92.0% and 93.0% respectively. The temperature measurement accuracy rate is 0.2ĝ., which can effectively prevent the spread of the epidemic caused by contact and breathing, and has the advantages of stable, intelligent, and safe work. © 2021 ACM.