Cloud computing is a new commercial model that enables customers to acquire large amounts of virtual resources on demand. Resources including hardware and software can be delivered as services and measured by specific usage of storage, processing, bandwidth, etc. In Cloud computing, task scheduling is a process of mapping cloud tasks to Virtual Machines (VMs). When binding the tasks to VMs, the scheduling strategy has an important influence on the efficiency of datacenter and related energy consumption. Although many traditional scheduling algorithms have been applied in various platforms, they may not work efficiently due to the large number of user requests, the variety of computation resources and complexity of Cloud environment. In this paper, we tackle the task scheduling problem which aims to minimize makespan by Genetic Algorithm (GA). We propose an incremental GA which has adaptive probabilities of crossover and mutation. The mutation and crossover rates change according to generations and also vary between individuals. Large numbers of tasks are randomly generated to simulate various scales of task scheduling problem in Cloud environment. Based on the instance types of Amazon EC2, we implemented virtual machines with different computing capacity on CloudSim. We compared the performance of the adaptive incremental GA with that of Standard GA, Min-Min, Max-Min , Simulated Annealing and Artificial Bee Colony Algorithm in finding the optimal scheme. Experimental results show that the proposed algorithm can achieve feasible solutions which have acceptable makespan with less computation time.

With the wide application of Light Detection and Ranging (LiDAR) in the collection of high-precision environmental point cloud information, three-dimensional (3D) object classification from point clouds has become an important research topic. However, the characteristics of LiDAR point clouds, such as unstructured distribution, disordered arrangement, and large amounts of data, typically result in high computational complexity and make it very difficult to classify 3D objects. Thus, this paper proposes a Convolutional Neural Network (CNN)-based 3D object classification method using the Hough space of LiDAR point clouds to overcome these problems. First, object point clouds are transformed into Hough space using a Hough transform algorithm, and then the Hough space is rasterized into a series of uniformly sized grids. The accumulator count in each grid is then computed and input to a CNN model to classify 3D objects. In addition, a semi-automatic 3D object labeling tool is developed to build a LiDAR point clouds object labeling library for four types of objects (wall, bush, pedestrian, and tree). After initializing the CNN model, we apply a dataset from the above object labeling library to train the neural network model offline through a large number of iterations. Experimental results demonstrate that the proposed method achieves object classification accuracy of up to 93.3% on average.

Scientists have explored the human body for hundreds of years, and yet more relationships between the behaviors and health are still to be discovered. With the development of data mining, artificial intelligence technology, and human posture detection, it is much more possible to figure out how behaviors and movements influence people’s health and life and how to adjust the relationship between work and rest, which is needed urgently for modern people against this high-speed lifestyle. Using smart technology and daily behaviors to supervise or predict people’s health is a key part of a smart city. In a smart city, these applications involve large groups and high-frequency use, so the system must have low energy consumption, a portable system, and a low cost for long-term detection. To meet these requirements, this paper proposes a posture recognition method based on multisensor and using LoRa technology to build a long-term posture detection system. LoRa WAN technology has the advantages of low cost and long transmission distances. Combining the LoRa transmitting module and sensors, this paper designs wearable clothing to make people comfortable in any given posture. Aiming at LoRa’s low transmitting frequency and small size of data transmission, this paper proposes a multiprocessing method, including data denoising, data enlarging based on sliding windows, feature extraction, and feature selection using Random Forest, to make 4 values retain the most information about 125 data from 9 axes of sensors. The result shows an accuracy of 99.38% of extracted features and 95.06% of selected features with the training of 3239 groups of datasets. To verify the performance of the proposed algorithm, three testers created 500 groups of datasets and the results showed good performance. Hence, due to the energy sustainability of LoRa and the accuracy of recognition, this proposed posture recognition using multisensor and LoRa can work well when facing long-term detection and LoRa fits smart city well when facing long-distance transmission.

Point clouds have been widely used in three-dimensional (3D) object classification tasks, i.e., people recognition in unmanned ground vehicles. However, the irregular data format of point clouds and the large number of parameters in deep learning networks affect the performance of object classification. This paper develops a 3D object classification system using a broad learning system (BLS) with a feature extractor called VB-Net. First, raw point clouds are voxelized into voxels. Through this step, irregular point clouds are converted into regular voxels which are easily processed by the feature extractor. Then, a pre-trained VoxNet is employed as a feature extractor to extract features from voxels. Finally, those features are used for object classification by the applied BLS. The proposed system is tested on the ModelNet40 dataset and ModelNet10 dataset. The average recognition accuracy was 83.99% and 90.08%, respectively. Compared to deep learning networks, the time consumption of the proposed system is significantly decreased.

Accurate, reliable, and fast intelligent detection of leather surface defects has become an important subject in industrial inspection, which aims at improving production efficiency and increasing automation levels. This work focuses on the rapid defect recognition and localization of leather surface defects for industrious applications, which is based on the state-of-the-art real-time detection model YOLO. Three experimental Schemes with different challenges were designed to find the optimal YOLO-based leather surface defect detection scheme. Typical tanned leather surface defect images from the factory were collected, which are comprised of eight types of defects, namely rotten surface, hole, scratch, crease, healed injury, bacterial injury, growth line, and pinhole, which exhibit variations in shapes, sizes, and colors, reflecting the various characteristics found in tanned leather defects. A comprehensive and in-depth review of the YOLO series of models is presented, including YOLOv1 to YOLOv8. The systematic and extensive experiments indicate that the YOLO models can simultaneously detect multiple types of defects present in each leather image. The multi-defect detection task achieved a maximum of 52.3% mean average precision (mAP), 58.2% precision, and 68.7% recall. For single-class detection tasks, the highest performance reached 85.1% mAP, 90.9% precision, and 81.8% recall. These works provide feasible intelligent solutions for surface defects in the leather industry, laying a solid foundation for the design and development of new solutions for leather defect detection.