This paper presents the generic concept of using cloud-based intelligent car parking services in smart cities as an important application of the Internet of Things (IoT) paradigm. This type of services will become an integral part of a generic IoT operational platform for smart cities due to its pure business-oriented features. A high-level view of the proposed middleware is outlined and the corresponding operational platform is illustrated. To demonstrate the provision of car parking services, based on the proposed middleware, a cloud-based intelligent car parking system for use within a university campus is described along with details of its design, implementation, and operation. A number of software solutions, including Kafka/Storm/Hbase clusters, OSGi web applications with distributed NoSQL, a rule engine, and mobile applications, are proposed to provide ‘best’ car parking service experience to mobile users, following the Always Best Connected and best Served (ABC&S) paradigm.

Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique residual structure of the model, the utilized CNN layered structure can be deepened in order to achieve better classification performance. The results of applying the proposed model to the MIT-BIH arrhythmia database demonstrate that the model achieves higher accuracy (96.50%) compared to other state-of-the-art classification models, while specifically for the ventricular ectopic heartbeat class, its sensitivity is 93.83% and the precision is 97.44%.

Rapid and precise detection and classification of vehicles are vital for the intelligent transportation systems (ITSs). However, due to small gaps between vehicles on the road and interference features of photos, or video frames containing vehicle images, it is difficult to detect and identify vehicle types quickly and precisely. For solving this problem, a new vehicle detection and classification model, named YOLOv4_AF, is proposed in this paper, based on an optimization of the YOLOv4 model. In the proposed model, an attention mechanism is utilized to suppress the interference features of images through both channel dimension and spatial dimension. In addition, a modification of the Feature Pyramid Network (FPN) part of the Path Aggregation Network (PAN), utilized by YOLOv4, is applied in order to enhance further the effective features through down-sampling. This way, the objects can be steadily positioned in the 3D space and the object detection and classification performance of the model can be improved. The results, obtained through experiments conducted on two public data sets, demonstrate that the proposed YOLOv4_AF model outperforms, in this regard, both the original YOLOv4 model and two other state-of-the-art models, Faster R-CNN and EfficientDet, in terms of the mean average precision ( mAP ) and F1 score , by achieving respective values of 83.45% and 0.816 on the BIT-Vehicle data set, and 77.08% and 0.808 on the UA-DETRAC data set.

Lung cancer has become one of the malignant tumors with the highest morbidity and mortality rate worldwide. The early images of lung cancer are pulmonary nodules, and early detection and diagnosis can help reduce the incidence of lung cancer. However, due to the large differences in shape, size and location of pulmonary nodules in medical imaging, the detection of small nodules is very challenging. This paper proposes a new model, based on improved YOLOv5s, called YOLOv5-CASP. The proposed YOLOv5s improvements include: (i) incorporating Convolutional Block Attention Modules (CBAM) to suppress the interference features of the medical images through a channel dimension and spatial dimension, and to improve the detection accuracy of the model; (ii) optimizing the Spatial Pyramid Pooling - Fast (SPPF) module of YOLOv5s to Atrous Spatial Pyramid Pooling (ASPP) as to increase the model’s receptive field of images of different sizes and extract multi-scale contextual information for improving the detection performance on small lung nodules; and (iii) introducing a Contextual Transformer (CoT) module to optimize part of the CSPDarknet53 module of YOLOv5s in order to enhance the characteristics of the model while removing redundant operations extraction capacity. Experimental results conducted on two public datasets confirm that the proposed YOLOv5-CASP model outperforms the original YOLOv5s model and other five state-of-the-art models (Faster R-CNN, SSD, YOLOv4-Tiny, DETR-R50, Deformable DETR-R50), in terms of the mean average precision ( mAP ) and F1 score , by achieving corresponding values of 72% and 0.740 on the LUNA16 dataset, and 79.44% and 0.766 on the X-Nodule dataset.