We aim at examining the current status of advanced control methods in spacecrafts from an engineer’s perspective. Instead of reviewing all the fancy theoretical results in advanced control for aerospace vehicles, the focus is on the advanced control methods that have been practically applied to spacecrafts during flight tests, or have been tested in real time on ground facilities and general testbeds/simulators built with actual flight data. The aim is to provide engineers with all the possible control laws that are readily available rather than those that are tested only in the laboratory at the moment. It turns out that despite the blooming developments of modern control theories, most of them have various limitations, which stop them from being practically applied to spacecrafts. There are a limited number of spacecrafts that are controlled by advanced control methods, among which H₂/H_∞ robust control is the most popular method to deal with flexible structures, adaptive control is commonly used to deal with model/parameter uncertainty, and the linear quadratic regulator (LQR) is the most frequently used method in case of optimal control. It is hoped that this review paper will enlighten aerospace engineers who hold an open mind about advanced control methods, as well as scholars who are enthusiastic about engineering-oriented problems.

Our long-term objective is to develop a software toolbox for pre-embodiment design of complex and heterogeneous systems, such as cyber-physical systems. The novelty of this toolbox is that it uses system manifestation features (SMFs) for transdisciplinary modeling of these systems. The main challenges of implementation of the toolbox are functional design- and language-independent computational realization of the warehouses, and systematic development and management of the various evolving implements of SMFs (genotypes, phenotypes, and instances). Therefore, an information schema construct (ISC) based approach is proposed to create the schemata of the associated warehouse databases and the above-mentioned SMF implements. ISCs logically arrange the data contents of SMFs in a set of relational tables of varying semantics. In this article we present the ISCs necessary for creation of genotypes and phenotypes. They increase the efficiency of the database development process and make the data relationships transparent. Our follow-up research focuses on the elaboration of the SMF instances based system modeling methodology.

In this paper, we address fault-diagnosis agreement (FDA) problems in distributed wireless networks (DWNs) with arbitrary fallible nodes and healthy access points. We propose a new algorithm to reach an agreement among fault-free members about the faulty ones. The algorithm is designed for fully connected DWN and can also be easily adapted to partially connected networks. Our contribution is to reduce the bit complexity of the Byzantine agreement process by detecting the same list of faulty units in all fault-free members. Therefore, the malicious units can be removed from other consensus processes. Also, each healthy unit detects a local list of malicious units, which results in lower packet transmissions in the network. Our proposed algorithm solves FDA problems in 2t+1 rounds of packet transmissions, and the bit complexity in each wireless node is O(n^t+1).

In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific features are required so that the classifier can improve the classification performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network (CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fine-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specific feature extracted from highly similar categories are fused into a feature vector. Then the final feature representation is fed into a linear classifier. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count (CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classification accuracy in comparison with flat multiple classification methods.

We propose a novel dynamic traffic signal coordination method that takes account of the special traffic flow characteristics of urban arterial roads. The core of this method includes a control area division module and a signal coordination control module. Firstly, we analyze and model the influences of segment distance, traffic flow density, and signal cycle time on the correlation degree between two neighboring intersections. Then, we propose a fuzzy computing method to estimate the correlation degree based on a hierarchical structure and a method to divide the control area of urban arterial roads into subareas based on correlation degrees. Subarea coordination control arithmetic is used to calculate the public cycle time of the control subarea, up-run offset and down-run offset of the section, and the split of each intersection. An application of the method in Shaoxing City, Zhejiang Province, China shows that the method can reduce the average travel time and the average stop rate effectively.

Interval-valued data appear as a way to represent the uncertainty affecting the observed values. Dealing with interval-valued information systems is helpful to generalize the applications of rough set theory. Attribute reduction is a key issue in analysis of interval-valued data. Existing attribute reduction methods for single-valued data are unsuitable for interval-valued data. So far, there have been few studies on attribute reduction methods for interval-valued data. In this paper, we propose a framework for attribute reduction in interval-valued data from the viewpoint of information theory. Some information theory concepts, including entropy, conditional entropy, and joint entropy, are given in interval-valued information systems. Based on these concepts, we provide an information theory view for attribute reduction in interval-valued information systems. Consequently, attribute reduction algorithms are proposed. Experiments show that the proposed framework is effective for attribute reduction in interval-valued information systems.

We investigate the secrecy outage performance of maximal ratio combining (MRC) in cognitive radio networks over Rayleigh fading channels. In a single-input multiple-output wiretap system, we consider a secondary user (SU-TX) that transmits confidential messages to another secondary user (SU-RX) equipped with M (M≥1) antennas where the MRC technique is adopted to improve its received signal-to-noise ratio. Meanwhile, an eavesdropper equipped with N (N≥1) antennas adopts the MRC scheme to overhear the information between SU-TX and SU-RX. SU-TX adopts the underlay strategy to guarantee the service quality of the primary user without spectrum sensing. We derive the closed-form expressions for an exact and asymptotic secrecy outage probability.

In this paper, spatial channel pairing (SCP) is introduced to coherent combining at the relay in relay networks. Closed-form solution to optimal coherent combining is derived. Given coherent combining, the approximate SCP solution is presented. Finally, an alternating iterative structure is developed. Simulation results and analysis show that, given the symbol error rate and data rate, the proposed alternating iterative structure achieves signal-to-noise ratio gains over existing schemes in maximum ratio combining (MRC) plus matched filter, MRC plus antenna selection, and distributed space-time block coding due to the use of SCP and iterative structure.

In this study, a displacement measurement method based on digital moiré fringe is described and experimentally demonstrated. The method is formed by only one grating with a constant pitch. First, the magnified grating image is received by an imaging array and is sent to a computer. Then, the digital moiré fringes are generated by overlaying the grating image with its mirrored one. Finally, a specifically designed algorithm is used to obtain the fringes’ phase difference before and after movement and calculate the displacement. This method has the effects of amplifying displacement and averaging the grating lines error, the same as the traditional moiré technique using two pieces of gratings. At the same time, the proposed system is much easier to assemble and the measurement resolution can be set more flexibly. One displacement measuring system based on this method was built up. Experiment results show that its measurement errors are less than 0.3 µm and less than 0.12 µm at the resolutions of 0.1 µm and 0.03 µm, respectively.

We propose a pipelined Reed-Solomon (RS) decoder for an ultra-wideband system using a modified step-by-step algorithm. To reduce the complexity, the modified step-by-step algorithm merges two cases of the original algorithm. The pipelined structure allows the decoder to work at high rates with minimum delay. Consequently, for RS(23,17) codes, the proposed architecture requires 42.5% and 24.4% less area compared with a modified Euclidean architecture and a pipelined degree-computationless modified Euclidean architecture, respectively. The area of the proposed decoder is 11.3% less than that of the previous step-by-step decoder with a lower critical path delay.

A novel dual-edge implicit pulse-triggered flip-flop with an embedded clock-gating scheme (DIFF-CGS) is proposed, which employs a transmission-gate-logic (TGL) based clock-gating scheme in the pulse generation stage. This scheme conditionally disables the inverter chain when the input data are kept unchanged, so redundant transitions of delayed clock signals and internal nodes of the latch are all eliminated, leading to low power efficiency. Based on SMIC 65 nm technology, extensive post-layout simulation results show that the proposed DIFF-CGS gains an improvement of 41.39% to 56.21% in terms of power consumption, compared with its counterparts at 10% data-switching activity. Also, full-swing operations in both implicit pulse generation and the static latch improve the robustness of the design. Thus, DIFF-CGS is suitable for low-power applications in very-large-scale integration (VLSI) designs with low data-switching activities.