There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

Information Theory and Reliable Communication

Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in the UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate more than $45 Billion market value of UAV usage. In this paper, we present UAV civil applications and their challenges. We also discuss the current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including charging challenges, collision avoidance and swarming challenges, and networking and security-related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges

Spectrum sensing is a key technology of cognitive radio (CR). In CR systems, the main requirement of spectrum sensing is the ability of rapid and accurate detection of the presence of the primary user. In this paper, we propose a spectrum sensing method using artiﬁcial neural network (ANN). Detailed analysis shows that the proposed scheme is appropriate to detect the signals under considerably low signal-to-noise ratio (SNR) environment and has reliable performance. Simulations also illustrate that the discussed method outperforms over previous approaches in noisy environment when sensing the spectrum for CR systems.

Artificial Neural Network Based Spectrum Sensing Method for Cognitive Radio

In this paper, we study the orthogonal least squares (OLS) algorithm for sparse recovery. On one hand, we show that if the sampling matrix $\mathbf {A}$ satisfies the restricted isometry property of order $K + 1$ with isometry constant $\delta _{K+ 1} then OLS exactly recovers the support of any $K$ -sparse vector $\mathbf {x}$ from its samples $\mathbf {y} = \mathbf {A} \mathbf {x}$ in $K$ iterations. On the other hand, we show that OLS may not be able to recover the support of a $K$ -sparse vector $\mathbf {x}$ in $K$ iterations for some $K$ if $\delta _{K+ 1} \geq \frac{1}{\sqrt{K+\frac{1}{4}}}$ .

Nearly Optimal Bounds for Orthogonal Least Squares

Cooperative localization can enhance the accuracy of wireless network localization by incorporating range information among agent nodes in addition to those between agents and anchors. In this paper, we investigate the optimal allocation of the restricted resources, namely, power and bandwidth, to different nodes. We formulate the optimization problems for both synchronous networks and asynchronous networks, where one way and round trip measurements are applied for range estimation, respectively. Since the optimization problems are nonconvex, we develop an iterative linearization-based technique, and show by comparison with brute-force search that it provides near-optimal performance in the investigated cases. We also show that especially in the case of inefficient anchor placement and/or severe shadowing, cooperation among agents is important and more resources should be allocated to the agents correspondingly.

Joint Power and Bandwidth Allocation in Wireless Cooperative Localization Networks

High-throughput satellite (HTS) is viewed as a promising solution for the next generation of satellite-based Internet of Things (S-IoT). Considering that the onboard communication resources, such as power and storage, are limited, we formulate a joint network stability and resource allocation optimization problem to maximize the long-term network utility of a nonorthogonal multiple access (NOMA) S-IoT downlink system. First, we establish two virtual queues for both the data queueing and power expenditure. Then, a joint optimal problem can be formulated as a problem that optimizes the time average of network utility, which perfectly matches the Lyapunov optimization framework. Therefore, by taking into account the condition of successive interference cancellation decoding, we propose a practical solution under the Karush–Kuhn–Tucker (KKT) conditions, and further introduce an optimal solution by using the particle swarm optimization (PSO) algorithm for the joint resource allocation problem. The simulation results demonstrate that our joint optimization allocation schemes outperform the existing benchmark schemes.

Network Utility Maximization Resource Allocation for NOMA in Satellite-Based Internet of Things

In order to better explore the ocean, autonomous underwater vehicles (AUVs) have been widely applied to facilitate the information collection. However, considering the extremely large-scale deployment of sensor nodes in the Internet of Underwater Things (IoUT), a homogeneous AUV-enabled information collection system cannot support timely and reliable information collection considering the time-varying underwater environment as well as AUV’s energy and mobility constraints. In this article, we propose a multi-AUV-assisted heterogeneous underwater information collection scheme for the sake of optimizing the peak Age of Information (AoI). Moreover, the limited service M/G/1 vacation queueing model is utilized to model the process of information exchange, where the optimal upper limit of the number of AUVs served in the queueing system as well the steady-state distribution of the queue length are derived. A low-complexity adaptive algorithm for adjusting the upper limit of the queuing length is also proposed. Finally, simulation results validate the effectiveness of our proposed scheme and algorithm, which outperform traditional methods in terms of the peak AoI.

Qinyu Zhang

Papers

Artificial Neural Network Based Spectrum Sensing Method for Cognitive Radio

Nearly Optimal Bounds for Orthogonal Least Squares

Joint Power and Bandwidth Allocation in Wireless Cooperative Localization Networks

Network Utility Maximization Resource Allocation for NOMA in Satellite-Based Internet of Things

AoI-Inspired Collaborative Information Collection for AUV-Assisted Internet of Underwater Things