As an off-line simulation tool, the modular modelling method of Matlab/Simulik has the features of high efficiency and visualization. In order to realize the fast design and the simulation of prototype systems, the new method of SignalWAVe/Simulink mix modelling is presented, and the Reed-Solomon codec encoder-decoder model is built. Reed-Solomon codec encoder-decoder model is simulated by Simulink. Farther, the C language program and model the. out executable file are created by SignalWAVe RTW Options module, which completes the hard ware co-simulation. The simulation result conforms to the theoretical analysis, thus it has proven the validity and the feasibility of this method.
To improve the efficiency of surgical trajectory segmentation for surgical assessment and robot learning in robot-assisted minimally invasive surgery, this paper presents a fast unsupervised method using video and kinematic data, followed by a promoting procedure to address the over-segmentation issue. An unsupervised deep learning network called dense convolutional encoder-decoder network (DCED-Net) is first proposed to extract more discriminative features from videos in an effective way. DCED-Net has several advantages. It compresses the encoding-decoding structure, strengthens the feature propagation, and avoids the manual annotation. To further improve the accuracy of segmentation, on one hand, a modified transition state clustering model is employed with a strategy of reducing the redundancy of transition points. On the other hand, the segmentation results are promoted by identifying the over-segmented trajectories based on predefined similarity measurements. Extensive experiments on the public data set JIGSAWS show that with our method, the percentage increase in accuracy is 20.3% and the percentage decrease in time cost is 92.6%.
At present, the dynamic encoding–decoding scheme is utilized in the impulsive consensus control of multiagent systems (MASs) to solve the limited bandwidth problem. However, the unknown nonlinear dynamics and deception attacks will generate some uncertainties inevitably in the encoding–decoding, which may cause the quantizer saturation and then influence the consensus performance. Therefore, the impulsive consensus control problem of uncertain nonlinear MASs based on encoding–decoding under deception attacks is investigated in this article. To address the system uncertainty, an adaptive algorithm with neural networks is designed. Under the proposed estimator for each follower, the designed adaptive law for every follower only needs the information from its own sensor and estimator instead of the quantized information from its neighbors. Then a more general scenario of uncertain deception attack is considered, in which the uncertain deception attacks can occur in the both parts of hybrid impulsive control protocol. An attack observer is introduced to handle this more complex attack by compensating impacts of deception attacks. Next the sufficient conditions for secure consensus of MASs with limited bandwidth are derived. Moreover, although some uncertainties exist in the encoding–decoding, the quantizer saturation can be eliminated by adjusting the parameters of controller. Finally, the validity of given theorems is demonstrated by the simulation experiments.
USB is more used than some traditional inter-PC Bus such as PCI and ISA due to the high speed and agility and also provides properly convenient communication interface for some A/D conversion, FPGA of the external logic device. This passage uses the asynchronies work mode of slave FIFO and adopts HDL to compose state machine for FPGA to produce some control signals which implements data acquisition for the solution of high speed and reliability communication between SoC and PC. The results of the final experiment show that this scheme is very fast and efficient for the data acquisition and it can also apply other cases such as video acquisition for the embedded system through USB.
Mobile virtualization introduces extra layers in software stacks, which leads to performance degradation. Especially, each I/O operation has to pass through several software layers to reach the NAND-flash-based storage systems. This paper targets at optimizing I/O for mobile virtualization, since I/O becomes one of major performance bottlenecks that seriously affects the performance of mobile devices. Among all the I/O operations, a large percentage is updating metadata. Frequent updating metadata not only degrades overall I/O performance but also severely reduces flash memory lifetime. In this paper, we propose a novel I/O optimization techniqueto identify the metadata of a guest file system which is storedin a VM (Virtual Machine) image file and frequently updated. Then, these metadata are stored in a small additional NVM(Non-Volatile Memory) which is faster and more endurableto greatly improve flash memory's performance and lifetime. To the best of our knowledge, this is the first work to identifythe file system metadata from regular data in a guest OS VMimage file under mobile virtualization. The proposed schemeis evaluated on a real hardware embedded platform. Theexperimental results show that the proposed techniques canimprove write performance to 45.21% in mobile devices withvirtualization.
A fire-fighting control system, which is usually implemented through programmable logic controllers, is a typical type of safety-critical cycle physical system. It has been widely used in currently complex industrial applications. So it is significant for a fire-fighting control system to conduct safety checking. There have been many methods to check safety of a fire-fighting control system so far, but they all ignore the effect of communication networks which are important for data transmission. In this paper, considering communication networks, we propose to model a fire-fighting control system with timed automata and describe system requirements with computation tree logic (CTL) formulas. A real dock fire fighting control system illustrates the method. And some safety properties are verified in the model checking tool Uppaal, and verified results show the effectiveness of the method.
ABSTRACT The Longmen Shan fault zone that was shocked by the 12 May 2008 M 8.0 Wenchuan earthquake acts as the boundary between the western edge of the Sichuan basin and the steep eastern margin of the Songpan-Ganze block. In this study, continuous seismic data recorded by 176 temporary short-period seismic stations between 22 October and 20 November 2017 are used to study the shallow crustal structure of the Longmen Shan fault zone by applying ambient-noise tomography and horizontal-to-vertical spectral ratio (HVSR) analysis. From ambient-noise analysis, fundamental-mode Rayleigh-wave dispersion curves between 0.25 and 1 Hz are extracted. Then, the direct surface-wave tomographic method is used to invert surface-wave dispersion data for the 3D shallow shear-wave velocity structure. Our results show that low shear-wave velocities are mainly distributed around the surface rupture trace of the Wenchuan earthquake at least down to 2 km. From the HVSR method, the sites are sorted into two types according to the pattern of HVSR curves with single peak or double peak. By converting frequency to depth, the results show that the sediments are thicker near the surface rupture. The low-velocity zone based on ambient-noise tomography agrees well with the distribution of sedimentary cover estimated from HVSR, which are generally consistent with geological information. Our results provide high-resolution shallow crustal velocity structure for future detailed studies of the Longmen Shan fault.
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