Abstract. The nature of the transition from axial highs to axial valleys at mid-ocean ridges and the physical processes involved in the transition are important for understanding how axial morphology changes with spreading rate, mantle temperature, and lithospheric strength at midocean ridges. In order to provide observational constraints on the nature of the changes in axial morphology, we examined the regional-and segment-scale variations in axial and flank morphology at the intermediate spreading Southeast Indian Ridge (SEIR) using newly collected geophysical data. An empirical orthogonal function analysis was used to separate regional and local components of the topography field and to estimate bathymetric roughness. Three distinct types of axial morphology were identified from the regional component of ridge topography in our area: axial highs, shallow axial valleys, and "Mid-Atlantic Ridge-type" deep axial valleys. Axial depth increases by -2100 m from 88øE and 118øE, while off-axis depth only increases by -500 m. In addition, except for one segment with a deep axial valley, there is little change in off-axis depth within segments, in contrast to the large intrasegment variations in axial depth. These observations indicate that the overall and intrasegment variations in crustal thickness are much smaller than would be predicted from the variations in axial depth and that the major portion of the variations in ridge axis depth are dynamically supported. There are step-like increases in bathymetric roughness as axial morphology changes from an axial high to a shallow axial valley and from a shallow axial valley to a deep axial valley. The step changes in roughness imply that the change from one mode of axial morphology to another is accompanied by an abrupt change in the strength of the lithosphere. The abrupt changes in lithospheric strength may be due to the existence of a "threshold" mantle temperature or crustal thickness about which the lithospheric strength is very sensitive to small fluctuations. Systematic intrasegment variations in roughness are also observed. Roughness shows V-shaped patterns within segments with axial highs but no clear pattern within segments with axial valleys. The different patterns in roughness at axial highs and axial valleys on the SEIR may result from the presence or absence of a magma chamber. The presence of a magma chamber at a ridge segment with an axial high implies weaker axial lithosphere and hence lower roughness near the center of segments relative to the segment ends.

This paper aims to propose a novel idea for the modeling and trajectory tracking control of a lower limb rehabilitation robot. A polynomial nonlinear uncertain model is established to deal with the difficulty of accurate modeling for the lower limb rehabilitation robot with complex dynamic characteristics. A high‐order nonlinear disturbance observer (HONDO) is utilized to estimate the lumped disturbance with fast time‐varying and the corresponding observer gain matrix is obtained by H∞ performance. Then an HONDO‐based composite tracking controller is designed to achieve good tracking performance of the lower limb rehabilitation robot. In addition, all solvability conditions can be directly handled by sum of squares programming, effectively avoiding the calculation problem. Finally, numerical simulations are conducted to verify the validity of the proposed method.

The identification of orbital angular momentum (OAM) modes with high-accuracy and-speed is always a difficult issue in practically applying optical vortex beams (OVs). In this work, we propose and experimentally investigate a convolutional neural network (CNN) method for optical OAM mode identification and shift-keying (SK) communications. The CNN model, including convolution and pooling layers, was designed to extract mode information from the diffraction patterns produced by diffracting the OVs with a cylindrical lens. After trained with loads of studying samples, the CNN model has a good generation ability in recognizing the OAM modes of OVs ranging from −15 to 15. The recognition accuracy reaches 99% with the turbulence intensity of C 2 n = 1 × 10 −13 m −2/3 , z = 50 m. Even under the turbulence of C 2 n = 1 × 10 −12 m −2/3 , z = 50 m, the accuracy still exceeds 89%. By mapping and encoding a Lena gray image with the size of 100 × 100 pixels to two OAM channels, the OAM-SK signals with 900 modulation orders were successfully demodulated by the CNN model, and the image was well recovered after transmission. With an I5-8500 Central Processing Unit, this recognition process only takes 1 × 10 −3 s per mode. It is anticipated that the CNN methods might provide an effective way for identifying OAM modes with high-accuracy and-speed, which may have great potentials in OAM communication, quantum information processing, and astronomical application, etc. INDEX TERMS Neural networks, optical vortices, optical signal detection.

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