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Recently, IP over elastic optical network (IP-over-EON) has become a promising architecture for metro and core networks. This work studies how to visualize both layers of an IP-over-EON in real time, at different granularities (e.g., at flow-level, lightpath-level, and link-level), and with self-adaptivity. Specifically, we consider the multilayer application of in-band telemetry (INT) and propose entropy-driven adaptive INT (namely, EntropyINT). We introduce stateful processing to programmable data plane (PDP) switches for EntropyINT, such that they can make local decisions to determine whether and what type of telemetry data about the IP and EON layers should be encoded in each packet. The local decisions are designed to be based on the amount of information that can be conveyed by telemetry data to the network automation system. Meanwhile, we make EntropyINT cooperate with out-of-band monitoring, to detect and locate exceptions in the EON layer. Our proposal is implemented in a real-world testbed of IP-over-EON, to evaluate its assistance to network automation. Experimental results verify the effectiveness of our proposal, and indicate that the telemetry data collected by EntropyINT and out-of-band monitoring can better assist the machine learning in network automation, for status prediction and anomaly detection. [ABSTRACT FROM AUTHOR]

With the evolution of Internet infrastructure and network services, multilayer in-band network telemetry (ML-INT) and data analytics (DA) have been considered as key enabling techniques to realize real-time and fine-grained network monitoring, especially for backbone IP-over-Optical networks. However, the existing ML-INT&DA systems have privacy and security issues, because plaintext ML-INT data is reported from the data plane and gets analyzed in the control plane. In this work, we address these issues by designing a privacy-preserving ML-INT&DA system for IP-over-Optical networks. We first leverage vector homomorphic encryption (VHE) to design a lightweight encryption scheme, which overcomes the security breaches due to eavesdropping and preserves the delicate correlations buried in multi-dimensional ML-INT data. Then, we develop an effective data compression scheme to further encode the encrypted ML-INT data and make the results suitable for hash-based signature. The signature is for data certification and enables the DA in the control plane to verify the integrity of received ML-INT data. Hence, the threats from data tampering are removed. Next, we architect a deep learning (DL) model that can directly operate on encrypted ML-INT data for anomaly detection. Finally, we implement the proposed ML-INT&DA system, and experimentally demonstrate its effectiveness in a real IP over elastic optical network (IP-over-EON) testbed, whose key elements, i.e., optical line system (OLS), bandwidth-variable wavelength-selective switches (BV-WSS’) and programmable data plane (PDP) switches, are all commercial products. [ABSTRACT FROM AUTHOR]

It is known that by leveraging programmable data plane, in-band network telemetry (INT) can be realized to provide a powerful and promising method to collect realtime network statistics for monitoring and troubleshooting. However, existing INT implementations still exhibit a few drawbacks such as lack of runtime-programmability and relatively high overheads due to per-packet operation. In this work, we propose and design a runtime-programmable selective INT system, namely, Sel-INT, to resolve these issues. Specifically, we first design a runtime-programmable selective INT scheme based on protocol oblivious forwarding (POF), and then prototype our design by extending the famous OpenvSwitch (OVS) platform to obtain a software switch that supports Sel-INT and implementing a Data Analyzer to parse, extract and analyze the INT data. Our implementation of Sel-INT is verified and evaluated in a real network testbed that consists of a few stand-alone software switches. The experimental results demonstrate that Sel-INT can not only adjust the sampling rate of INT in runtime but also program the corresponding data types dynamically, and they also confirm that our proposal can ensure proper accuracy and timeliness for network monitoring while greatly reducing the overheads of INT. [ABSTRACT FROM AUTHOR]

Recently, the fast development of backbone networks has made the traffic, services, and infrastructure of packet-over-optical networks increasingly complicated. This stimulates research and development on fine-grained and real-time performance monitoring and troubleshooting. In this article, we propose a ProML-INT system to oversee packet-over-optical networks in real time and enable customized performance monitoring and troubleshooting. We introduce the system design in detail, and explain how to control the overhead of multilayer INT ML-INT by inserting INT fields in packets selectively. Experiments demonstrate the ProML-INT system, a small-scale packet-over-optical network testbed. The experimental results confirm that our proposal can monitor packet and optical layers jointly in real time, and the homemade data analyzer in it can leverage artificial intelligence to identify the root causes of exceptions in packet-over-optical networks correctly and promptly. [ABSTRACT FROM AUTHOR]

Recently, with the development of IP and elastic optical networks (EONs), the network control and management (NC&M) scheme for IP-over-EONs, which can facilitate effective cross-layer orchestration (XLyr-O), has become an interesting but challenging research topic. In this paper, we consider a software-defined IP-over-EON (SD-IPoEON), leverage deep learning (DL) to analyze and predict the traffic fluctuation on established lightpaths in it, and design a proactive DL-assisted XLyr-O scheme. Specifically, we study the DL-assisted XLyr-O scheme from algorithm design to system prototype. A DL module based on the long/short-term memory based neural network (LSTM-NN) is first designed and optimized for precise IP traffic prediction. Then, we develop algorithms to explore the traffic prediction for realizing proactive XLyr-O to deal with hard/soft failures constantly, i.e., making intelligent online decisions to re-groom and reroute IP flows and to reconfigure lightpaths such that the performance tradeoff among lightpath utilization, congestion probability, and reconfiguration frequency is balanced well. Finally, we implement our proposed algorithm in a small-scale but real SD-IPoEON testbed to prototype the DL-assisted XLyr-O, and conduct experiments with it. Experimental results demonstrate that compared with the reactive benchmark without DL-assistance, our proposal not only invokes less network reconfigurations but also reduces packet losses significantly. [ABSTRACT FROM AUTHOR]

Land-cover classification provides the crucial data component for related Earth-science research. Currently, although multiscale remote-sensing images are the main source of data for classifying land-cover, the response of multi-resolution images to land-cover classification remains highly uncertain. In addition, because of the scarcity of time-synchronous multiscale satellite images of certain regions, up-scaling algorithms are generally used to generate and apply multiscale images. However, when using resolution-resampling images, it remains uncertain to what extent spectral loss or information distortion is responsible for the underlying differences in the accuracy of land-cover classification of various landscapes. To clarify this situation, we study the Hetian basin of Changting County in Fujian Province, south-east China by using quasi-synchronous multiple-resolution satellite observations (seven spatial resolution levels: 1 m, 2 m, 4 m, 8 m, 16 m, 30 m, and 50 m) to investigate possible correlations between spatial resolution and the land-cover classification. The classification is obtained by applying a support vector machine spectral classifier to random recordings made in 1875 sample plots. We also explore the effect of using lower-resolution images by comparing the classification results obtained by using several common up-scaling algorithms, such as nearest neighbour (NN), bilinear (BI), cubic convolution (CC), and pixel aggregation (PA). The results indicate that classification accuracy is significantly influenced by the spatial resolution of images (p < 0.05), with the accuracy increasing as the spatial resolution goes from 1 m to 4 m, then decreasing as the spatial resolution decreases beyond 4 m. In addition, for a resolution of 1 m to 30 m, almost all the up-scaled images provide a classification accuracy that differs from that obtained by using the native remote-sensing images of each resolution (p < 0.05), and the difference increases as the spatial-resolution ratio or up-scaling amplification factor increases. According to an analysis of the spatial scale of images using, e.g., multiband spectral reflectance and vegetation index, the up-scaling algorithms are less sensitive to spatial resolution and represent poorly the actual image characteristics. This result is attributed to the strong dependence of the spectral information in up-scaled images on the original images, which leads to discrepancies with respect to actual observations at the given scale. These results indicate that the effects of resolution cannot be ignored and that resampling data may not be adequate for multi-spatial-scale classification compared with the native satellite images. It is thus urgent to obtain an effective up-scaling algorithm that sharply reduces the problems caused by spatial heterogeneity. [ABSTRACT FROM AUTHOR]

In this paper, a new type 12/14 bearingless switched reluctance motor with double windings is presented. The stator includes two types of poles such as suspending pole and torque pole. Different from the previous 8/10 structure, double windings including bias winding and control winding are installed on the same suspending pole. In addition, in order to reduce the coupling between torque and levitation force, the stator torque poles are shifted with one angle. Windings of two adjacent torque poles are connected in series. Therefore in order to explain advantages of proposed motor, structure and basic operating principle are introduced. In the meanwhile, levitation force is derived by means of equivalent magnetic circuit. The force characteristics with respect to current and displacement are analysed. In addition, by means of finite element analysis, coupling characteristic and self‐starting characteristic are investigated. The validity of the structure is verified by the analysis and experimental results. [ABSTRACT FROM AUTHOR]

Shadows exist universally in sunlight-source remotely sensed images, and can interfere with the spectral morphological features of green vegetations, resulting in imprecise mathematical algorithms for vegetation monitoring and physiological diagnoses; therefore, research on shadows resulting from forest canopy internal composition is very important. Red edge is an ideal indicator for green vegetation's photosynthesis and biomass because of its strong connection with physicochemical parameters. In this study, red edge parameters (curve slope and reflectance) and the normalized difference vegetation index (NDVI) of two species of coniferous trees in Inner Mongolia, China, were studied using an unmanned aerial vehicle's hyperspectral visible-to-near-infrared images. Positive correlations between vegetation red edge slope and reflectance with different illuminated/shaded canopy proportions were obtained, with all R2s beyond 0.850 (p < 0.01). NDVI values performed steadily under changes of canopy shadow proportions. Therefore, we devised a new vegetation index named normalized difference canopy shadow index (NDCSI) using red edge's reflectance and the NDVI. Positive correlations (R2 = 0.886, p < 0.01) between measured brightness values and NDCSI of validation samples indicated that NDCSI could differentiate illumination/shadow circumstances of a vegetation canopy quantitatively. Combined with the bare soil index (BSI), NDCSI was applied for linear spectral mixture analysis (LSMA) using Sentinel-2 multispectral imaging. Positive correlations (R2 = 0.827, p < 0.01) between measured brightness values and fractional illuminated vegetation cover (FIVC) demonstrate the capacity of NDCSI to accurately calculate the fractional cover of illuminated/shaded vegetation, which can be utilized to calculate and extract the illuminated vegetation canopy from satellite images. [ABSTRACT FROM AUTHOR]