다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

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Convex Analysisの二,三の進展について

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, DRL, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of DRL from fundamental concepts to advanced models. Then, we review DRL approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks, such as 5G and beyond. Furthermore, we present applications of DRL for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying DRL.

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

The Internet of Things is a novel paradigm with access to wireless communication systems and artificial intelligence technologies, which is considered to be applicable to a variety of promising fields and applications. Meanwhile, the development of the fifth-generation cellular network technologies creates the possibility to deploy enormous sensors in the framework of the IoT and to process massive data, challenging the technologies of communications and data mining. In this article, we propose a novel paradigm, 5G Intelligent Internet of Things (5G I-IoT), to process big data intelligently and optimize communication channels. First, we articulate the concept of the 5G I-IoT and introduce three major components of the 5G I-IoT. Then we expound the interaction among these components and introduce the key methods and techniques based on our proposed paradigm, including big data mining, deep learning, and reinforcement learning. In addition, an experimental result evaluates the performance of 5G I-IoT, and the effective utilization of channels and QoS have been greatly improved. Finally, several application fields and open issues are discussed.

From IoT to 5G I-IoT: The Next Generation IoT-Based Intelligent Algorithms and 5G Technologies

With the increasing popularity of multimedia technology and the prevalence of various smart electronic devices, severe security problems have recently arisen in information and communication systems. The image maintenance can be treated as a typical example of cloud storage outsourcing as images require much more storage space than text documents. An image encryption method with high efficiency is crucial to preserve the privacy of sensitive and critical images in cloud‐edge communications. Compressive sensing (CS) is capable of reconstructing compressible or sparse signals using fewer measurements than traditional methods when the support of the signals is unobtainable in advance. Encryption methods based on CS decrease resources demands during signal acquisition and protect the image data to avoid theft of confidential information by malicious users. Moreover, the CS‐based encryption method can achieve the encryption and the compression of an image simultaneously. Recently, a considerable amount of work using CS theory has been reported. However, most of the existing methods result in recovered images with unsatisfactory quality and employ a massive measurement matrix as the encryption key. This paper proposes an encryption scheme for images on cloud based on a single‐round dictionary and chaotic sequences. Both the experimental and analytical results verify the proposed scheme's effectiveness, high level of security, and substantial image quality improvement after decryption.

Image encryption based on a single-round dictionary and chaotic sequences in cloud computing

Vehicular infotainment systems have attracted great interests from both academia and industry. A vehicular infotainment system includes two parts: 1) the information part and 2) the entertainment part, which are integrated together to provide a unified platform to drivers and passengers. Although some excellent works have been done on vehicular infotainment systems, there are still some significant challenges that need to be addressed. Recently, compressed sensing (CS) has emerged as a new technique, which could be used to tackle the challenges in vehicular infotainment systems. In this paper, we present a comprehensive survey and research challenges on the applications of CS in vehicular infotainment systems, including object tracking for driving safety, privacy protection and security, vehicular crowdsensing, vehicular communications, road traffic estimation, video streaming, and object recognition. In addition, we compare the traditional methods and CS methods in solving the main problems in vehicular infotainment systems. Moreover, research challenges of applying CS in vehicular infotainment systems are discussed.

A Survey on Compressed Sensing in Vehicular Infotainment Systems

Background: Pancreatic ductal adenocarcinoma (PDAC) is an almost universally lethal disease Deregulation or dysfunction of miRNAs contribute to cancer developmen

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MiR-940 inhibited pancreatic ductal adenocarcinoma growth by targeting MyD88.

Faced with constant increasingly complicated communication network architecture issues and blossoming traffic demand over wireless systems, it is still insufficient for dynamic spectrum resource allocation in the fifth generation (5G) networks if cognitive radio (CR) technology lacks intelligence. For an efficient real-time process, a distributed cognitive cellular network is proposed in this article, which integrates artificial intelligence and CR technology into a sophisticated multi-agent system (MAS). It is a novel paradigm for 5G cellular communication networks. Balancing the resource allocation among primary users, secondary users, and base stations is of fundamental importance for dynamic time-frequency-space resource allocation to improve the utilization of the spectrum resources in the cognitive cellular network. We introduce a four-layer distributed networking framework and establish a hierarchical MAS model. Furthermore, we expound the key methods and technologies and evaluate the effectiveness through numerical simulations. Finally, challenges and open issues are discussed.

Bin Song

Papers

From IoT to 5G I-IoT: The Next Generation IoT-Based Intelligent Algorithms and 5G Technologies

Image encryption based on a single-round dictionary and chaotic sequences in cloud computing

A Survey on Compressed Sensing in Vehicular Infotainment Systems

MiR-940 inhibited pancreatic ductal adenocarcinoma growth by targeting MyD88.

Intelligent Cognitive Radio in 5G: AI-Based Hierarchical Cognitive Cellular Networks