This paper presents an effective algorithm for inserting an MPEG-2 transport stream (TS) into a Digital Audio Broadcasting (DAB) ensemble without any bandwidth waste in a Digital Multimedia Broadcasting (DMB) transmission system. The key technologies of this algorithm include packet rate control and program clock reference correction, which are important for TS processing. The proposed algorithms are applied to the various DMB transmission systems based on Eureka-147, and the performance of the proposed algorithm is confirmed through the experimental DMB broadcasting.

Despite advanced machine learning methods, the implementation of emotion recognition systems based on real-world video content remains challenging. Videos may contain data such as images, audio, and text. However, the application of multimodal models using two or more types of data to real-world video media (CCTV, illegally filmed content, etc.) lacking sound or subtitles is difficult. Although facial expressions in image sequences can be utilized in emotion recognition, the diverse identities of individuals in real-world content limits computational models of relationships between facial expressions. This study proposed a transformation model which employed a video vision transformer to focus on facial expression sequences in videos. It effectively understood and extracted facial expression information from the identities of individuals, instead of fusing multimodal models. The design entailed capture of higher-quality facial expression information through mixed-token embedding facial expression sequences augmented via various methods into a single data representation, and comprised two modules: spatial and temporal encoders. Further, temporal position embedding, focusing on relationships between video frames, was proposed and subsequently applied to the temporal encoder module. The performance of the proposed algorithm was compared with that of conventional methods on two emotion recognition datasets of video content, with results demonstrating its superiority.

In this letter, we present a layered structure of the Korean terrestrial Digital Multimedia Broadcasting (T-DMB) transmission system for multimedia broadcasting service and introduce a device called the Ensemble Remultiplexer which is designed to remultiplex the Ensemble Transport Interface (ETI) for T-DMB service. This letter describes the remultiplexing process of the Ensemble Remultiplexer.

With the increasing frequency and severity of disasters and accidents, there is a growing need for efficient emergency alert systems. The ultra-high definition (UHD) broadcasting service based on Advanced Television Systems Committee (ATSC) 3.0, a leading terrestrial digital broadcasting system, offers such capabilities, including a wake-up function for minimizing damage through early alerts. In case of a disaster situation, the emergency alert wake-up signal is transmitted, allowing UHD TVs to be activated, enabling individuals to receive emergency alerts and access emergency broadcasting content. However, conventional methods for detecting the bootstrap signal, essential for this function, typically require an ATSC 3.0 demodulator. In this paper, we propose a novel deep learning-based method capable of detecting an emergency wake-up signal without the need for an ATSC 3.0. The proposed method leverages deep learning techniques, specifically a deep neural network (DNN) structure for bootstrap detection and a convolutional neural network (CNN) structure for wake-up signal demodulation and to detect the bootstrap and 2 bit emergency alert wake-up signal. Specifically, our method eliminates the need for Fast Fourier Transform (FFT), frequency synchronization, and interleaving processes typically required by a demodulator. By applying a deep learning in the time domain, we simplify the detection process, allowing for the detection of an emergency alert signal without the full suite of demodulator components required for ATSC 3.0. Furthermore, we have verified the performance of the deep learning-based method using ATSC 3.0-based RF signals and a commercial Software-Defined Radio (SDR) platform in a real environment.

Image captioning has been widely studied using various deep learning models on extensive and well-prepared datasets. Accurately captioning images of serious and sudden disasters is important; however, the study of disaster image captioning is yet to be thoroughly investigated compared to natural image captioning. Furthermore, existing image captioning models may need to perform better in generating captions for disaster images because there are fewer disaster images in popular datasets than non-disaster images. To address these problems, we refine and propose a cleaned disaster dataset and an image captioning model optimized for the dataset. Experimental results showed that our proposed model outperformed the existing model in terms of generating accurate captions for disaster images.