The various defects that occur on asphalt pavement are a direct cause car accidents, and countermeasures are required because they cause significantly dangerous situations. In this paper, we propose fully convolutional neural networks (CNN)-based road surface damage detection with semi-supervised learning. First, the training DB is collected through the camera installed in the vehicle while driving on the road. Moreover, the CNN model is trained in the form of a semantic segmentation using the deep convolutional autoencoder. Here, we augmented the training dataset depending on brightness, and finally generated a total of 40,536 training images. Furthermore, the CNN model is updated by using the pseudo-labeled images from the semi-supervised learning methods for improving the performance of road surface damage detection technique. To demonstrate the effectiveness of the proposed method, 450 evaluation datasets were created to verify the performance of the proposed road surface damage detection, and four experts evaluated each image. As a result, it is confirmed that the proposed method can properly segment the road surface damages.

Road surfaces should be maintained in excellent condition to ensure the safety of motorists. To this end, there exist various road-surface monitoring systems, each of which is known to have specific advantages and disadvantages. In this study, a smartphone-based dual-acquisition method system capable of acquiring images of road-surface anomalies and measuring the acceleration of the vehicle upon their detection was developed to explore the complementarity benefits of the two different methods. A road test was conducted in which 1896 road-surface images and corresponding three-axis acceleration data were acquired. All images were classified based on the presence and type of anomalies, and histograms of the maximum variations in the acceleration in the gravitational direction were comparatively analyzed. When the types of anomalies were not considered, it was difficult to identify their effects using the histograms. The differences among histograms became evident upon consideration of whether the vehicle wheels passed over the anomalies, and when excluding longitudinal anomalies that caused minor changes in acceleration. Although the image-based monitoring system used in this research provided poor performance on its own, the severity of road-surface anomalies was accurately inferred using the specific range of the maximum variation of acceleration in the gravitational direction.

Poor road-surface conditions pose a significant safety risk to vehicle operation, especially in the case of autonomous vehicles. Hence, maintenance of road surfaces will become even more important in the future. With the development of deep learning-based computer image processing technology, artificial intelligence models that evaluate road conditions are being actively researched. However, as the lighting conditions of the road surface vary depending on the weather, the model performance may degrade for an image whose brightness falls outside the range of the learned image, even for the same road. In this study, a semantic segmentation model with an autoencoder structure was developed for detecting road surface along with a CNN-based image preprocessing model. This setup ensures better road-surface crack detection by adjusting the image brightness before it is input into the road-crack detection model. When the preprocessing model was applied, the road-crack segmentation model exhibited consistent performance even under varying brightness values.

The next generation of mobile networks is predicted to deliver high data speeds, lower latency, and increase the spectral and energy efficiency of wireless communication systems. Several technologies are being investigated for usage in 5G networks. Massive multiple-input multiple-output (mMIMO) systems are one of the most promising technologies for enabling 5G. Even after the recent advancements and research, numerous challenges still exist for channel estimation for mMIMO systems. In the context of pilot contamination and feedback overhead, this study tracks the most recent developments in research on mMIMO system difficulties. The primary goals of this study are to identify the problems with channel estimation, provide a summary of the cutting-edge solutions suggested in the literature, and then discuss newly emerging open research issues that must be taken into account for the implementation of beyond-5G networks.

The non-orthogonal multiple access (NOMA) technique for addressing fifth-generation (5G) new radio services is emerging as a promising technology. In contrast to the traditional orthogonal multiple access (OMA) techniques, NOMA can transmit a signal to various devices from the same resource block by exploiting the power domain and decoding the desired signal using the successive interference cancellation (SIC) technique. However, in large-scale 5G macro cell scenarios, NOMA can need help to efficiently serve far-end devices due to long-distance transmission links, multi-path fading, and non-line-of-sight (NLOS) problems. We explore flying small base stations (BSs), which can act as relays to mitigate link quality-related issues. Using the NOMA technique, multiple small-scale flying BSs can fly over the affected devices simultaneously and serve them without utilizing fewer resources compared to the conventional OMA method. We develop a novel intelligent unmanned aerial vehicle (UAV)-based navigation solution using federated deep reinforcement learning whereby cost-efficient multiple UAV base stations (UAV-BSs) fly over certain areas autonomously and serve ground devices using 5G NOMA systems without any interruption. We consider a greedy policy as a benchmark, where the UAV-BSs fly over high-priority devices to serve them. Additionally, we consider the traveling salesman problem (TSP)-based navigation solution as a benchmark. We perform extensive simulation analysis for different system parameters, i.e., coverage time, coverage score (CS), and channel noise ratio (CNR), and conclude that the proposed scheme outperforms other state-of-the-art methods.

Speech synthesis is a technology that converts text into speech waveforms. With the development of deep learning, neural network-based speech synthesis technology is being researched in various fields, and the quality of synthesized speech has significantly improved. In particular, Grad-TTS, a speech synthesis model based on the denoising diffusion probabilistic model (DDPM), exhibits high performance in various domains, generates high-quality speech, and supports multi-speaker speech synthesis. However, speech synthesis for an unseen speaker is not possible. Therefore, this study proposes an effective zero-shot multi-speaker speech synthesis model that improves the Grad-TTS structure. The proposed method enables the reception of speaker information from speech references using a pre-trained speaker recognition model. In addition, by converting speaker information via information perturbation, the model can learn various types of speaker information, excluding those in the dataset. To evaluate the performance of the proposed method, we measured objective performance indicators, namely speaker encoder cosine similarity (SECS) and mean opinion score (MOS). To evaluate the synthesis performance for both the seen speaker and unseen speaker scenarios, Grad-TTS, SC-GlowTTS, and YourTTS were compared. The results demonstrated excellent speech synthesis performance for seen speakers and a performance similar to that of the zero-shot multi-speaker speech synthesis model.