Human-Computer Interaction (HCI) nowadays mostly uses physical contact, such as people using the mouse to choose something in an application. However, there are certain problems that people face in using conventional HCI. The research tries to overcome some problems when people use conventional HCI using the computer vision method. The research focuses on creating and evaluating the object detection model for classifying hand symbols. The research applies the fifth version of YOLO with the architecture of YOLOv5m to classify hand symbols in real time. The methods are divided into three steps. Those steps are dataset creation consisting of 100 images in each class, training phase, and performance evaluation of the model. The hand gesture classes made in the research are ‘ok’, ‘cancel’, ‘previous’, ‘next’, and ‘confirm’, the dataset is made by the researchers custom. After the training phase, the validation results show 93% for accuracy, 99% for precision, 100% for recall, and 99% for F1 score. Meanwhile, in real-time detection, the performance of the model for classifying hand symbols is 80% for accuracy, 95% for precision, 84% for recall, and 89% for F1 score. Although there are differences, it still acceptable for the research and can be improved in future research.

Hand Symbol Classification for Human-Computer Interaction Using the Fifth Version of YOLO Object Detection

This paper’s objective is to provide an autonomous bot that serves as an equipment handling vehicle and can be implemented in warehouses. The study explores the possibilities for further development and implementation in real-time applications while outlining the necessity for such a design. Apart from autonomous motion which uses LiDAR it also deals with equipment handling system that utilizes DCNN (deep convolutional neural network) which identifies the object, and it is picked by forklift setup. An NVDIA Jetson Nano single-board computer is used as the control unit computational of the system. In the current scenario, Equipment handling vehicle is manually controlled by humans which utilizes more energy and time of the workers so autonomous operation helps in reducing it and therefore their time and energy can be spent on other operations in warehouse. This invention is cost effective in both manufacturing and implementing in modern autonomous world. 

Autonomous Equipment Handling Vehicle for Warehouse

Enhancing Passenger Safety in an Autonomous Bus: A Multimodal Fall Detection Approach for Effective Remote Monitoring

In recent years, LIDARs (Light Detection and Ranging) have gained a lot of insight into various fields such as agriculture, astronomy, robotics, autonomous driving, forestry, etc. Point Clouds obtained through LIDAR have gained attention in the field of computer vision, robotics, and autonomous driving. In computer vision, there are several high level tasks namely classification, detection, tracking, segmentation and registration. Object detection in 3D Point Clouds is one of the challenges faced in the field of computer vision, which is one of the high level tasks. Due to the unordered nature of point cloud data, detecting objects by designing deep neural networks is not a straightforward task. This paper aims to provide a solution to detect the objects in a 3D point cloud data by transforming into 2D range image obtained from LIDAR using the improved YOLOV5 (You Only Look Once- Version5) approach by embedding the coordinate attention module. The improved YOLOV5 is used to detect objects in an image and conducted experiments on different sizes of custom 3D point cloud datasets with and without augmentation. The experimental results showed how the precision, recall and mean average precision (mAP) metrics improve as the dataset size increases. Also, the comparative results with YOLOV5 yielded better results on our data.

Multi-Object Detection in 3D Point Cloud's Range Image Using Deep-Learning Technique

Abstract: The purpose of this study is to discuss Programmable Logic Controller (PLC). PLC is a type of digital computer that automates electromechanical activities like controlling machinery or manufacturing lines, among others. PLC’s are employed in numerous equipment and industries. The primary purpose of implementing a PLC is to eliminate concerns with relay irregularity and logic control. HMI (Human Machine Interface) is used in conjunction with a PLC to control and monitor the performance of industrial devices The aim of this project is to illustrate the usage of PLC in automated CNG tank for chuter assembly line and the utilization of its high capabilities and efficiency to process input and output signals from several sensors. With the help of software validation and hardware implementation used for demonstrating the ease of operation with the control system.

/pdf/implementation-of-plc-based-automated-cng-tank-for-chuter-2lfwej8m.pdf

Implementation of PLC Based Automated CNG Tank for Chuter Assembly Line

Image-based fall detection in bus compartment scene

With the development of science and technology and the expansion of intelligent manufacturing technology, there are more and more automatic production lines in China, but the overall structure of the automatic production line is complex and the R & D investment cycle is long, resulting in low production efficiency. From the point of view of automatic production line structure and data virtual simulation, this paper studies the simulation control debugging system of automatic production line driven by PLC data. According to PLC data, the developer can carry out virtual commissioning of automatic production line and verify the timing of production line.

On Simulation Control System of Automatic Production Line Based on PLC Data Drive

Simulating biological intelligence has been proved to be an effective way to design intelligent robots, and simultaneously can solve the problems existing in machine learning methods. For creatures, their motor skills achieving is the first stage of learning. By combining two important cognitive elements: orientation and curiosity, this article proposes a new neurobiologically-inspired sensorimotor developmental learning method for the mobile robot. In this method, curiosity promotes robot’s exploration of the environment, while orientation enhances robot’s exploitation knowledge of the environment. The orientation cognitive algorithm is designed based on Skinner’s operant conditioning theory, and its rationality is proved. The balance of exploration and exploitation, which is a key problem for all the cognitive learning method, is solved in this method. The developmental learning process can avoid fixed sensorimotor mapping space problem, and help reduce learning waste as well as computing waste. All of the developmental learning method’s characters are finally verified via simulations on a virtual mobile robot.

/pdf/mobile-robot-s-sensorimotor-developmental-learning-from-28zm6yo1bo.pdf

Mobile Robot’s Sensorimotor Developmental Learning From Orientation and Curiosity

In this work, a novel abnormal objects analyzing and detecting (AOAD) algorithm inside the bus is proposed, and the AOAD algorithm is further applied to the practical bus by designing an embedded video analysis system. The proposed algorithm is based on the deep learning YOLOv5 (You Only Look Once) algorithm, which has better timeliness and accuracy in detecting anomalous objects. The anomalous objects with larger boxes are detected by building an anomalous object dataset for training. The effectiveness and applicability of the proposed algorithm is verified by extensive experiments on video data based on real buses.

A Novel YOLOv5-based Anomalous Object Detection Algorithm in Buses

A Face Quality Assessment System for Unattended Face Recognition: Design and Implementation

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