Edge Computing-Based Vehicle Detection in Intelligent Transportation Systems

Vehicle detection in intelligent transportation systems usually adopts cloud computing mode. The increasing amount of traffic surveillance video has brought challenges to the storage, communication, and processing of intelligent transportation systems based on cloud computing models. In this paper, we propose a vehicle detection scheme based on edge computing. First, the traffic surveillance video is preprocessed at the edge device. Using the frame difference algorithm based on structural similarity (SSIM) to remove video redundant frames, and avoid repeated frames in the subsequent extracted key frame sequence. Then, a frame difference algorithm based on local maxima is used to extract key frames as the basis for subsequent vehicle detection. Finally, the YOLOv5s is improved and used for vehicle detection. The efficient channel attention mechanism (ECA) is introduced to enhance the important features of the vehicle and suppress the general features to strengthen the detection network's ability to identify vehicle targets. At the same time, the Focal loss function is introduced to solve the positive and negative sample imbalance problem and improve the detection speed. The experimental results show that the scheme has more advantages than the original YOLOv5s in terms of precision, recall, and mean average precision

HS-CGK: A Hybrid Sampling Method for Imbalance Data Based on Conditional Tabular Generative Adversarial Network and K-Nearest Neighbor Algorithm

Class imbalance problem in datasets can lead to biased classification decisions in favor of majority class samples. Additionally, class overlap can cause fuzzy classification boundaries, affecting the performance of classification algorithms. To address these issues, we propose a hybrid sampling method based on conditional tabular generative adversarial network (CTGAN) and K-nearest neighbor (KNN) algorithm. Firstly, we introduce an oversampling algorithm, named DB-CTGAN, based on CTGAN. This algorithm filters noisy and boundary samples using the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm and generates synthetic samples that conform to the real data distribution using CTGAN. Finally, we combine the expanded fraudulent samples generated by DB-CTGAN with the normal samples and use the KNN overlap undersampling algorithm to remove the samples in the overlap region, solving the class overlap problem. Experimental results show that compared with eight sampling methods using four standard classification models (Random Forest, Decision Tree, Support Vector Classification, and XGBoost), the proposed method significantly improves the F1, AUC, and G-mean metrics on five real datasets

Privacy-Preserving Health Data Collection for Preschool Children

With the development of network technology, more and more data are transmitted over the network and privacy issues have become a research focus. In this paper, we study the privacy in health data collection of preschool children and present a new identity-based encryption protocol for privacy protection. The background of the protocol is as follows. A physical examination for preschool children is needed every year out of consideration for the children's health. After the examination, data are transmitted through the Internet to the education authorities for analysis. In the process of data collection, it is unnecessary for the education authorities to know the identities of the children. Based on this, we designed a privacy-preserving protocol, which delinks the children’s identities from the examination data. Thus, the privacy of the children is preserved during data collection. We present the protocol in detail and prove the correctness of the protocol

A GRU-based traffic situation prediction method in multi-domain software defined network

With the continuous development and improvement of Software-Defined Networking (SDN), large-scale networks are divided into multiple domains. Each domain, which is managed by a controller, forms multi-domain SDN architecture. In multi-domain SDN, the dynamics and complexity are more significant, bringing great challenges to network management. Comprehensively and accurately predicting traffic situation in multi-domain SDN can better maintain network stability. In this article, we propose a traffic situation prediction method based on the gated recurrent unit (GRU) network in multi-domain SDN. We first analyzed the relevant factors that affect data traffic and control traffic and transformed them into a time series of actual situation values. Then, to enhance the prediction performance of GRU, we used the salp swarm algorithm to optimize the hyperparameters of GRU automatically. Finally, we adopted hyperparameter optimized GRU to achieve traffic situation prediction in multi-domain SDN. The experimental results indicate that the proposed method outperforms other traditional machine learning algorithms in terms of prediction accuracy.</jats:p

A Field Performance Evaluation Scheme for Microwave-Absorbing Material Coatings

Performance evaluation is an important aspect in the study of microwave-absorbing material coatings. The reflectivity of the incident wave is usually taken as the performance indicator. There have been various methods to directly or indirectly measure the reflectivity, but existing methods are mostly cumbersome and require a strict testing environment. What is more, they cannot be applied to field measurement. In this paper, we propose a scheme to achieve field performance evaluation of microwave-absorbing materials, which adopts a small H-plane sectoral horn antenna as the testing probe and a small microwave reflectometer as the indicator. When the size of the H-plane sectoral horn antenna is specially designed, the field distribution at the antenna aperture can be approximated as a plane wave similar to the far field of the microwave emitted by a radar unit. Therefore, the reflectivity can be obtained by a near-field measurement. We conducted experiments on a kind of ferrite-based microwave-absorbing material at X band (8.2–12.4 GHz) to validate the scheme. The experimental results show that the reflectivity is in agreement with the reference data measured by the conventional method as a whole