신지태 교수

This paper presents a futuristic framework for quality-of-service (QoS) mapping between practically categorized packet video and relative differentiated service (DiffServ or DS) network employing unified priority index and adaptive packet forwarding mechanism under a given pricing model (e.g., DiffServ level differentiated price/packet). Video categorization is based on the relative priority index (RPI), which represents the relative preference per each packet in terms of loss and delay. We prop

Abstract In the autonomous driving environment, object instances in an image can be affected by various factors such as camera, driving state, weather, and system component. However, the deep learning‐based vision systems are vulnerable to perturbation, which contains noise. Thus, robust object detection under harsh autonomous‐driving environments is a more difficult than the generic situation. In this paper, it is found that not only the accuracy, but also the speed of the non‐maximum suppressi

Abstract Rapid penetration of smart wireless devices and enormous growth of wireless communication technologies has already set the stage for deployment of wireless sensor networks (WSNs). While these small sensor nodes are often considered as the future of wireless communications, they also suffer from energy constraints. On the other hand, with increasing demand for real‐time services in next generation wireless networks, quality‐of‐service (QoS)‐based routing has emerged as an interesting res

Abstract A dynamic quality of service (QoS) mapping control scheme, which includes feedforward and feedback QoS control, is proposed for the differentiated services (DiffServ) networks in this work. To achieve reliable and consistent end—to—end video streaming with relative service differentiation, the proposed solution consists of two parts: (1) relative priority—based indexing and categorization of streaming video content at the sending end—system and (2) dynamic and aggregate QoS mapping cont

Continuous media applications have exceptionally stringent QoS requirements, and QoS for multimedia will remain a challenge well into the future. The solution begins with service-differentiated networks capable of providing appropriate grades of service to each application. This book takes the next step, showing how continuous media applications and QoS-enabled networks can interact, and offering a leading-edge framework in which applications and the network can cooperatively optimize end-to-end

The objective of this study was to improve user experience when appreciating visual artworks with soundscape music chosen by a deep neural network based on weakly supervised learning. We also propose a multi-faceted approach to measuring ambiguous concepts, such as the subjective fitness, implicit senses, immersion, and availability. We showed improvements in appreciation experience, such as the metaphorical and psychological transferability, time distortion, and cognitive absorption, with in-de

In this research, we investigate quality of service (QoS) interaction provisioning between video applications and components of the DiffServ network. QoS interaction is performed through the mapping of video packets based on the relative quality index (RQI), which represents the relative preference in terms of loss and delay, onto the adaptive packet forwarding mechanism in a DiffServ network. To verify the efficiency of the proposed strategy, the end-to-end performance is evaluated through erro

Diabetic Retinopathy (DR) is one of the major causes of blindness. If the lesions observed in DR occur in the central part of the fundus, it can cause severe vision loss, and we call this symptom Diabetic Macular Edema (DME). All patients with DR potentially have DME since DME can occur in every stage of DR. While synthesizing future fundus images, the task of predicting the progression of the disease state is very challenging since we need a lot of longitudinal data over a long period of time.

The performance of existing face age progression or regression methods is often limited by the lack of sufficient data to train the model. To deal with this problem, we introduce a novel framework that exploits synthesized images to improve the performance. A conditional generative adversarial network (GAN) is first developed to generate facial images with targeted ages. The semi-supervised GAN, called SS-FaceGAN, is proposed. This approach considers synthesized images with a target age and the

Despite tremendous developments in continuous blood glucose measurement (CBGM) sensors, they are still not accurate for all patients with diabetes. As glucose concentration in the blood is <1% of the total blood volume, it is challenging to accurately measure glucose levels in the interstitial fluid using CBGM sensors due to within-patient and between-patient variations. To address this issue, we developed a novel data-driven approach to accurately predict CBGM values using personalized calibrat