COVID-19 pandemic has led to serious economic and life losses. Face Masks serve as first infection barrier when used in public spaces. In this paper, we propose a new near-realtime method to automatically recognize face mask wearing that combines human posture recognition with convolutional neural network (CNN). We use the power of human posture recognition to perform background filtering and spatial reduction in the original images. The outcome is then used by a trained CNN model to identify if the subject is wearing a mask. We exploit Openpose to identify the skeleton of human body and locate the facial region thus spatially reducing the area to be processed by the CNN framework. We then adopt supervised learning approach to detect if a face mask is present. The CNN is trained using images, cropped to the supposed face mask covered region. This approach led to a substantial reduction in neural network complexity yet improving the recognition accuracy. The system has been evaluated in a multitude of scenarios using images taken in public places at different time of day and with different angles. Overall, our system achieves a recognition accuracy of 95.8% and 94.6% in daytime and nighttime respectively.
Recently, with the rapid development and use of technology in network infrastructures, Macao is being transformed into a smart city progressively, even though it is recognized as a World heritage city for its architectural wonders and rich cultural traditions. The benefits of machine learning technology open up new avenues for environmental monitoring, such as weather forecasting, early extreme or severe weather alerts and safeguards, disaster mitigation enhancement, and technological revolution as part of overall smart city planning in Macao. This study focuses on the evolution of artificial intelligence, the important use of machine learning technology in the field of meteorology, and suggestions on the major future development trends in Macao to the migration of a smart city.We discussed solutions tackling the problems in weather forecasting in Macao while creating advances in its transformation to be a smarter city, starting with the essence of machine learning and turning challenges into opportunities.
Triangle routing is one of the serious attacks to the Internet infrastructure. It can be caused by malicious routers which misroute packets to wrong directions. This kind of attacks creates network problems such as network congestion, denial of service and network partition and results in degrade of network performance. This paper gives a comprehensive study on how the path analysis combats the triangle routing attacks. We discuss the method, implementation and limitation of path analysis to detect triangle routing in IPv4 network. We also discuss the implementation of path analysis in IPv6 by proposing a new extension header.
In this article, we consider in improving the throughput of existing multicast routing protocols by using multiple multicast trees in ad hoc wireless networks. To achieve our goal, we propose virtual layer, a multi-layer container for multicast trees, that provides an scalable routing structure with transmission gain in multicast routing. Without modification of multicast routing protocols, we enable a layer concept into the protocols so that multiple multicast trees can be constructed in a multicast group. Although virtual layer improves throughput, excessive data traffic will be introduced into a network that may cause network congestion. To resolve this situation, we consider a random coding scheme, a practical network coding implementation that encodes data packets for routing in disjoint-path tree while avoiding congestion. We observed in our simulations that a transmission gain trades off the excessive traffic that causes network congestion.
In this paper, we propose a Multiple Disjoint Path (MDP) routing protocol to maximize network throughput and minimize the protocol overheads in wireless ad hoc networks. MDP consists of two components: virtual source routing and core heuristic. Virtual source routing constructs virtual paths that do not suffer from scalability, privacy and efficiency problems caused by long paths for DSR in large networks. Core heuristic is to ensure the efficiency of request packet propagation in route discovery operation. Our simulation results reveal that MDP performs at a satisfactory level in dense networks in terms of connectivity and transmission efficiency. Network resources can be utilized so that network throughput can be significantly increased at a cost of minimal protocol overheads comparatively.
Digital Library
The rapid digitization of the real estate and architectural design industries has created a high demand for automated tools capable of parsing 2D raster floor plans. Traditional manual measurement and visual inspection are not only time-consuming but also highly susceptible to human error. In this paper, we propose a comprehensive, end-to-end deep learning framework designed to automatically extract rich semantic information from unstructured 2D floor plan images and provide professional design guidance via Large Language Models (LLMs). Our integrated pipeline employs the state-of-the-art YOLOv8 object detection model to accurately localize and classify 18 distinct architectural symbols and furniture items (e.g., doors, windows, beds, cupboards). Simultaneously, a U-Net architecture with a ResNet34 encoder is utilized for the precise semantic segmentation of structural elements, specifically walls and interior room spaces. To translate pixel-level predictions into actionable real-world metrics, we introduce a robust area calculation algorithm based on user-defined reference scale calibration. Furthermore, to bridge the gap between raw geometric data and actionable architectural intelligence, we introduce an LLM-driven evaluation module utilizing a local Ollama deployment and a Retrieval-Augmented Generation (RAG) pipeline to assess design compliance and quality. To overcome the scarcity of annotated architectural datasets, we implement a systematic data augmentation strategy, expanding a core dataset of 101 manually annotated floor plans to 303 varied instances, thereby significantly enhancing model generalization. Experimental results indicate that our YOLOv8-based detection module achieves a mean Average Precision (mAP50) of 92.3%, while the U-Net segmentation module achieves a mean Intersection over Union (mIoU) of 95.71%. Furthermore, the integrated system is deployed as a user-friendly, interactive web application, acting as an intelligent architectural assistant and demonstrating its practical viability and high efficiency for real-world engineering and architectural applications.
A key challenge of visible-infrared person re-identification (VI-ReID) comes from the modality difference between visible and infrared images, which further causes large intra-person and small inter-person distances. Most existing methods design feature extractors and loss functions to bridge the modality gap. However, the unpaired-images constrain the VI-ReID model's ability to learn instance-level alignment features. Different from these methods, in this paper, we propose a pose attention-guided paired-images generation network (PAPG) from the standpoint of data augmentation. PAPG can generate cross-modality paired-images with shape and appearance consistency with the real image to perform instance-level feature alignment by minimizing the distances of every pair of images. Furthermore, our method alleviates data insufficient and reduces the risk of VI-ReID model overfitting. Comprehensive experiments conducted on two publicly available datasets validate the effectiveness and generalizability of PAPG. Especially, on the SYSU-MM01 dataset, our method accomplishes 7.76% and 5.87% gains in Rank-1 and mAP. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/qyhsxdx/PAPG.</uri>
Large models and deep learning models for real-time services are trained from time to time to refresh the new features and achieve better accuracy. Multivariate time-series decomposition and recombination (MTS-DR) models effectively improve training efficiency. In this research, an MTS method that is more stable and can handle finer decomposed components helps the training process of deep learning and Large Language Model Meta AI (LLaMA), and is crucial for our subsequent study on the capabilities of adaptive features engineering. Thus, this study introduces an innovative method developed using the Multiple Seasonal-Trend Decomposition using LOESS (MSTL) algorithm, termed “Multiseasonal Scalable Sub-model MSTL decomposition and recombining” (Multiseasonal SS-MSTL-DR) model. This method is designed to upgrade the decomposition and recombining of multiseasonal trends within a scalable sub-model framework. Its architecture is specifically tailored for seamless integration into deep learning systems to enhance the efficiency of model training. The objective is to establish a robust foundation for analyzing time-series data exhibiting distinctive and intricate seasonal patterns. Research methods include conducting a State-of-the-Art (SOTA) literature review, data preparation, model building, feasibility tests, comprehensive evaluation, and summary. Through a case of subtropical urban air quality prediction, it is found that the “Multiseasonal SS-MSTL-DR” model can further reduce the complexity of time-series and highlight its inherent characteristics. The multiple seasonality design and the SS training make the characteristics easier to mine in MTS models. Those experimental results of the cold/warm seasons show that the learning speed and accuracy of deep learning and LLaMA models have been improved, especially in the MTS deep learning model. Since we have extended Multiseasonal SS-MSTL-DR to six different air pollutant concentration data sets, the six of them have completely different spatial characteristics of physical and chemical, which is equivalent to proving the commonality of application, i.e. the proposed method is suitable for other multiseasonal time-series data.
In recent years, the number of vehicles on the road is increasing rapidly. The traffic loading of the roads increases and the traffic slows down. If a car accident occurs or a car is illegally parked on the road, the traffic would become congested. To ease the problem, it is crucial to understand the traffic on roads in real-time before resolving the solution. This project will show a new method to visualize the traffic on the road, which is called Differential Timing Method (DTM). By checking the passing time when vehicles enter and exit a road segment, we compare the average passing time with the standard passing time. The traffic condition on the road can be determined. To check the time when a vehicle is seen, the Bluetooth technology is used. When the vehicle passes by a Bluetooth reader, it will be discovered and the discovery time will be recorded down. Thus, a traffic dataset can be built. To visualize the traffic dataset, the data will be aggregated and illustrated by bar chart. From the bar chart, the traffic condition of the road will be disclosed and indicated as congestion, slow moving, or free flow.
