Explore the vast range of titles available.

C-V2X (Cellular Vehicle-to-Everything) is a state-of-the-art wireless technology used in autonomous driving and intelligent transportation systems (ITS). This technology has extended the coverage and blind-spot detection of autonomous driving vehicles. Economically, C-V2X is much more cost-effective than the traditional sensors that are commonly used by autonomous driving vehicles. This cost-benefit makes it more practical in a large scale deployment. PC5-based C-V2X uses an RF (Radio Frequency) sidelink direct communication for low latency mission-critical vehicle sensor connectivity. Over the C-V2X radio communications, the autonomous driving vehicle’s sensor ability can now be largely enhanced to the distances as far as the network covers. In 2020, 5G is commercialized worldwide, and Taiwan is at the forefront. Operators and governments are keen to see its implications in people’s daily life brought by its low latency, high reliability, and high throughput. Autonomous driving class L3 (Conditional Automation) or L4 (Highly Automation) are good examples of 5G’s advanced applications. In these applications, the mobile networks with URLLC (Ultra-Reliable Low-Latency Communication) are perfectly demonstrated. Therefore, C-V2X evolution and 5G NR (New Radio) deployment coincide and form a new ecosystem. This ecosystem will change how people will drive and how transportation will be managed in the future. In this paper, the following topics are covered. Firstly, the benefits of C-V2X communication technology. Secondly, the standards of C-V2X and C-V2X applications for automotive road safety system which includes V2P/V2I/V2V/V2N, and artificial intelligence in VRU (Vulnerable Road User) detection, object recognition and movement prediction for collision warning and prevention. Thirdly, PC5-based C-V2X deployment status in global, especially in Taiwan. Lastly, current challenges and conclusions of C-V2X development.

Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD) scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain.

Accidents are continuously reported for autonomous driving vehicles including those with advanced sensors installed. Some of accidents are usually caused by bad weather, poor lighting conditions and non-line-of-sight obstacles. Cellular Vehicle-to-Everything (C-V2X) radio technology can significantly improve those weak spots for autonomous driving. This paper describes one of the C-V2X system solutions: Vulnerable Road User Collision Warning (VRUCW) for autonomous driving. The paper provides the system architecture, design logic, network topology, message flow, artificial intelligence (AI) and network security feature. As a reference it also includes a commercial project with its test results.

Urban swarming transportation (UST) is a type of road transportation where multiple types of vehicles such as cars, buses, trucks, motorcycles, and bicycles, as well as pedestrians are allowed and mixed together on the roads. Predicting the traffic jam speed under UST is very different and difficult from the single road network traffic prediction which has been commonly studied in the intelligent traffic system (ITS) research. In this research, the road network wide (RNW) traffic prediction which predicts traffic jam speeds of multiple roads at once by utilizing citizens’ mobile GPS sensor records is proposed to better predict traffic jam under UST. In order to conduct the RNW traffic prediction, a specific data preprocessing is needed to convert traffic data into an image representing spatial-temporal relationships among RNW. In addition, a revised capsule network (CapsNet), named OCapsNet, which utilizes nonlinearity functions in the first two convolution layers and the modified dynamic routing to optimize the performance of CapsNet, is proposed. The experiments were conducted using real-world urban road traffic data of Jakarta to evaluate the performance. The results show that OCapsNet has better performance than Convolution Neural Network (CNN) and original CapsNet with better accuracy and precision.

Forest fires are among the most critical natural tragedies threatening forest lands and resources. The accurate and early detection of forest fires is essential to reduce losses and improve firefighting. Conventional firefighting techniques, based on ground inspection and limited by the field-of-view, lead to insufficient monitoring capabilities for large areas. Recently, due to their excellent flexibility and ability to cover large regions, unmanned aerial vehicles (UAVs) have been used to combat forest fire incidents. An essential step for an autonomous system that monitors fire situations is first to locate the fire in a video. State-of-the-art forest-fire segmentation methods based on vision transformers (ViTs) and convolutional neural networks (CNNs) use a single aerial image. Nevertheless, fire has an inconsistent scale and form, and small fires from long-distance cameras lack salient features, so accurate fire segmentation from a single image has been challenging. In addition, the techniques based on CNNs treat all image pixels equally and overlook global information, limiting their performance, while ViT-based methods suffer from high computational overhead. To address these issues, we proposed a spatiotemporal architecture called FFS-UNet, which exploited temporal information for forest-fire segmentation by combining a transformer into a modified lightweight UNet model. First, we extracted a keyframe and two reference frames using three different encoder paths in parallel to obtain shallow features and perform feature fusion. Then, we used a transformer to perform deep temporal-feature extraction, which enhanced the feature learning of the fire pixels and made the feature extraction more robust. Finally, we combined the shallow features of the keyframe for de-convolution in the decoder path via skip-connections to segment the fire. We evaluated empirical outcomes on the UAV-collected video and Corsican Fire datasets. The proposed FFS-UNet demonstrated enhanced performance with fewer parameters by achieving an F1-score of 95.1% and an IoU of 86.8% on the UAV-collected video, and an F1-score of 91.4% and an IoU of 84.8% on the Corsican Fire dataset, which were higher than previous forest fire techniques. Therefore, the suggested FFS-UNet model effectively resolved fire-monitoring issues with UAVs.

Photographic reproduction and enhancement is challenging because it requires the preservation of all the visual information during the compression of the dynamic range of the input image. This paper presents a cascaded-architecture-type reproduction method that can simultaneously enhance local details and retain the naturalness of original global contrast. In the pre-processing stage, in addition to using a multiscale detail injection scheme to enhance the local details, the Stevens effect is considered for adapting different luminance levels and normally compressing the global feature. We propose a modified histogram equalization method in the reproduction stage, where individual histogram bin widths are first adjusted according to the property of overall image content. In addition, the human visual system (HVS) is considered so that a luminance-aware threshold can be used to control the maximum permissible width of each bin. Then, the global tone is modified by performing histogram equalization on the output modified histogram. Experimental results indicate that the proposed method can outperform the five state-of-the-art methods in terms of visual comparisons and several objective image quality evaluations.

Automatic segmentation of the organ’s tumor and lesion on biomedical imaging is an essential initiative towards clinical study, treatment planning and digital biomedical research. However, precise tumor segmentation on medical imaging is still an open challenge due to the presence of noise in the imaging sequence, the similar tumor pixel intensity with its neighboring tissues, and heterogeneity among human anatomy. Although most of the state-of-the-art algorithms are architecturally dependent on deep convolution networks (DCNs), like 2D and 3D U-Net, they act as a foundation for many biomedical image segmentation. However, 2D DCNs are incompetent to leverage context information from inter-slice completely. At the same time, 3D DCNs can accumulate inter-slice contextual information over the sizeable receptive texture in the organ, but it consumes a considerable amount of GPU memory and burdens with the high execution cost. In order to achieve a promising solution, we proposed a segmentation network called Cascaded Atrous Dual-Attention U-Net. First, our network structure concatenates features from 3D liver segmentation to 2D tumor segmentation for preserving volumetric information as well as enlarging resolution with segmentation accuracy. Second, we embedded dual attention gate in each skip connection layer of the 2D segmentation model, which determines to concentrate on certain discriminative features in order to find tumor segmentation in different organs. Finally, we adopted atrous encoder which extracts wider context features from computed tomography as compared to normal encoder. Furthermore, we tested the proposed method on four different datasets, including liver tumor segmentation benchmark (LiTS), MSD liver, pancreas tumor segmentation and Kidney tumor segmentation (KiTS). Experimental results were compared with the other state-of-the-art segmentation methods. Our proposed approach performs remarkably better than existing methods with around 4 ∼ 6 % improvement on each benchmark.

Code generation from graphical user interface images is a promising area of research. Recent progress on machine learning methods made it possible to transform user interface into the code using several methods. The encoder–decoder framework represents one of the possible ways to tackle code generation tasks. Our model implements the encoder–decoder framework with an attention mechanism that helps the decoder to focus on a subset of salient image features when needed. Our attention mechanism also helps the decoder to generate token sequences with higher accuracy. Experimental results show that our model outperforms previously proposed models on the pix2code benchmark dataset.

Every year, fire accidents cause substantial economic losses and casualties. Being able to detect a fire at the early stage is the only way to avoid notable disasters. Although conventional fire alarm systems (CFAs) that depend on heat and flame sensors are used for a fire safety-catch in our society, they cannot be used effectively for large and open spaces due to performance parameters of the sensors. Recently, most of the state-of-the-art methods for fire detection are evolving based on deep learning (DL) technique. However, it is a difficult task to detect fire from visual scenes due to significant irregularities in the color, size, form, texture and flickering frequency of fire. In the present work, we proposed a two-stage cascaded architecture to improve accuracy. In the first stage, we introduced the Spatio-Temporal network, which efficiently and effectively combines both shape and motion flicker based characteristics to obtain heatmaps of fire regions in the input images. By analyzing the heatmaps with a threshold segmentation method, the candidate of the fire region in the input image can be automatically located. Besides, to minimize false-positive due to some object similar to flame, in the second stage, original image and heatmaps of candidate region are fused for improving abilities of classifier to distinguish whether it is a fire or not. Also, the center loss function is adopted to backpropagate fused features to overcome the impact of intraclass heterogeneity on the representation of features. Furthermore, we tested the proposed method on three different datasets, and the results of our experiments reveal that the proposed method has achieved better performance than the other existing state-of-the-art methods.

Depth has been a valuable piece of information for perception tasks such as robot grasping, obstacle avoidance, and navigation, which are essential tasks for developing smart homes and smart cities. However, not all applications have the luxury of using depth sensors or multiple cameras to obtain depth information. In this paper, we tackle the problem of estimating the per-pixel depths from a single image. Inspired by the recent works on generative neural network models, we formulate the task of depth estimation as a generative task where we synthesize an image of the depth map from a single Red, Green, and Blue (RGB) input image. We propose a novel generative adversarial network that has an encoder-decoder type generator with residual transposed convolution blocks trained with an adversarial loss. Quantitative and qualitative experimental results demonstrate the effectiveness of our approach over several depth estimation works.