Recently, cloud computing has become a vital part that supports people’s normal lives and production. However, accompanied by the increasing complexity of the cloud network, failures constantly keep coming up and cause huge economic losses. Thus, to guarantee the cloud network performance and prevent execrable effects caused by failures, cloud network diagnostics has become of great interest for cloud service providers. Due to the characteristics of cloud network (e.g., virtualization and multi-tenancy), transplanting traditional network diagnostic tools to the cloud network face several difficulties. Additionally, many existing tools cannot solve problems in the cloud network. In this paper, we summarize and classify the state-of-the-art technologies of cloud diagnostics which can be used in the production cloud network according to their features. Moreover, we analyze the differences between cloud network diagnostics and traditional network diagnostics based on the characteristics of the cloud network. Considering the operation requirements of the cloud network, we propose the points that should be cared about when designing a cloud network diagnostic tool. Also, we discuss the challenges that cloud network diagnostics will face in future development.
Over-the-air (OTA) testing is an industry standard practice for evaluating transceiver performance in wireless devices. For the fifth generation (5G) and beyond wireless systems with high integration, OTA testing is probably the only reliable method to accurately measure the transceiver performance, suitable for certification as well as for providing feedback for design verification and optimization. Further, multiple-input multiple-output (MIMO) technology is extensively applied for stable connection, high throughput rate, and low latency. In this paper, we provide an overview of the three main methods for evaluating the MIMO OTA performance, namely, the multiprobe anechoic chamber (MPAC) method, the reverberation chamber plus channel emulator (RC+CE) method, and the radiated two-stage (RTS) method, with the aim of providing a useful guideline for developing effective wireless performance testing in future 5G-and-beyond wireless systems.
This paper studies the sampled data based containment control problem of second-order multi-agent systems with intermittent communications, where velocity measurements for each agent are unavailable. A novel controller for second-order containment is put forward via intermittent sampled position data measurement. Several necessary and sufficient conditions are derived to achieve intermittent sampled containment control by means of analyzing the relationship among control gains, eigenvalues of the Laplacian matrix, the sampling period, and the communication width. Finally, several simulation examples are used to testify the correctness and effectiveness of the theoretical results.
This study investigates the consensus problem of a nonlinear discrete-time multi-agent system (MAS) under bounded additive disturbances. We propose a self-triggered robust distributed model predictive control consensus algorithm. A new cost function is constructed and MAS is coupled through this function. Based on the proposed cost function, a self-triggered mecha-nism is adopted to reduce the communication load. Furthermore, to overcome additive disturbances, a local minimum– maximum optimization problem under the worst-case scenario is solved iteratively by the model predictive controller of each agent. Sufficient conditions are provided to guarantee the iterative feasibility of the algorithm and the consensus of the closed-loop MAS. For each agent, we provide a concrete form of compatibility constraint and a consensus error terminal region. Numerical examples are provided to illustrate the effectiveness and correctness of the proposed algorithm.
The artificial bee colony (ABC) algorithm is an evolutionary optimization algorithm based on swarm intelligence and inspired by the honey bees’ food search behavior. Since the ABC algorithm has been developed to achieve optimal solutions by searching in the continuous search space, modification is required to apply it to binary optimization problems. In this study, we modify the ABC algorithm to solve binary optimization problems and name it the improved binary ABC (IbinABC). The proposed method consists of an update mechanism based on fitness values and the selection of different decision variables. Therefore, we aim to prevent the ABC algorithm from getting stuck in a local minimum by increasing its exploration ability. We compare the IbinABC algorithm with three variants of the ABC and other meta-heuristic algorithms in the literature. For comparison, we use the well-known OR-Library dataset containing 15 problem instances prepared for the uncapacitated facility location problem. Computational results show that the proposed algorithm is superior to the others in terms of convergence speed and robustness. The source code of the algorithm is available at https://github.com/rafetdurgut/ibinABC.
Volume rendering plays a significant role in medical imaging and engineering applications. To obtain an improved three-dimensional shape perception of volumetric datasets, realistic volume illumination has been considerably studied in recent years. However, the calculation overhead associated with interactive volume rendering is unusually high, and the solvability of the problem is adversely affected when the data size and algorithm complexity are increased. In this study, a scalable and GPU-based multi-slice per pass (MSPP) volume rendering algorithm is proposed which can quickly generate global volume shadow and achieve a translucent effect based on the transfer function, so as to improve perception of the shape and depth of volumetric datasets. In our real-world data tests, MSPP significantly outperforms some complex volume shadow algorithms without losing the illumination effects, for example, half-angle slicing. Furthermore, the MSPP can be easily integrated into the parallel rendering frameworks based on sort-first or sort-last algorithms to accelerate volume rendering. In addition, its scalable slice-based volume rendering framework can be combined with several traditional volume rendering frameworks.
This paper presents a mechanical reliability study of 3-dB waveguide hybrid couplers in submillimeter and terahertz bands. To show the necessity of improving the mechanical properties of the coupler’s branch in submillimeter and terahertz bands, a comprehensive study regarding the displacement of hybrid branch variation with varying width-length ratio and height-length ratio has been completed. In addition, a modified 3-dB waveguide hybrid coupler is designed and presented. Compared with the traditional branch structure, the proposed hybrid consists of a modified middle branch with circular cutouts at the top and bottom on both sides instead of the traditional rectangle branch, which increases the branch size and improves its mechanical reliability while achieving the same performance. Simulation results show that the deformation of the modified hybrid branch is 22% less than those of other traditional structure designs under the same stress. In practice, a vibration experiment is set up to verify the mechanical reliability of hybrid couplers. Measurement results show that the experiment deteriorates the coupling performance. Experimental results verify that the performance of the novel structure coupler is better than that of a traditional structure branch hybrid coupler under the same electrical properties.
We demonstrate a heuristic approach for optimizing the posterior density of the data association tracking algorithm via the random finite set (RFS) theory. Specifically, we propose an adjusted version of the joint probabilistic data association (JPDA) filter, known as the nearest-neighbor set JPDA (NNSJPDA). The target labels in all possible data association events are switched using a novel nearest-neighbor method based on the Kullback–Leibler divergence, with the goal of improving the accuracy of the marginalization. Next, the distribution of the target-label vector is considered. The transition matrix of the target-label vector can be obtained after the switching of the posterior density. This transition matrix varies with time, causing the propagation of the distribution of the target-label vector to follow a non-homogeneous Markov chain. We show that the chain is inherently doubly stochastic and deduce corresponding theorems. Through examples and simulations, the effectiveness of NNSJPDA is verified. The results can be easily generalized to other data association approaches under the same RFS framework.
Given the potential for bit flipping of data on a memory medium, a high-speed parallel Bose–Chaudhuri–Hocquenghem (BCH) error correction scheme with modular characteristics, combining logic implementation and a look-up table, is proposed. It is suitable for data error correction on a modern field programmable gate array full with on-chip embedded memories. We elaborate on the optimization method for each part of the system and analyze the realization process of this scheme in the case of the BCH code with an information bit length of 1024 bits and a code length of 1068 bits that corrects the 4-bit error.
In this study, we provide a detailed analysis of the frequency division duplex long term evolution downlink (FDD LTEDL) signal for passive bistatic radars that use the signal as an illuminator of opportunity. In particular, we analyze the crossambiguity function and illustrate its undesired deterministic peaks in the Doppler dimension due to the specific structure of the FDD LTE DL signal. A new adaptive mismatched filtering method is proposed for pre-processing the original reference signal to suppress these undesired deterministic peaks in the range-Doppler processing. The effectiveness of our proposed method is demonstrated via simulations following robustness analysis, showing that all undesired peaks are suppressed below −40 dB, with only 1.7 dB reduction in the main peak.