In this work, we demonstrate a polymer poly(3-hexylthiophene-2,5-diyl) (P3HT) based organic thin-film transistor nonvolatile memory, in which the four-layer stacked core architecture is sequentially processed by a method of fully solution spin-coating. The floating-gate layer consists of separated molecular semiconductor 6,13-bis(triisopropylsilylethynyl)pentacene (TIPS-Pen) microdomains distributing in the matrix of polymer poly(styrene) (PS), which is formed by phase-separation during the spin-coating from their blend solution. At the writing/ erasing operation, the holes transfer between P3HT active layer and TIPS-Pen microdomains, which results in an prominent memory property, with a memory window of 14 V, memory on/ off ratio larger than 350, reliable memory endurance over 500 cycles and a good retention capability over 5000 s with obvious distinguishing reading current at binary 0 and 1 states.

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Research shows that it is practical for the normal human movement mechanism to assist the patients with stroke in robot-assisted gait rehabilitation. In passive training, the effect of rehabilitation training for patients can be improved by imitating normal human walking. To make the lower limb exoskeleton robot (LLER) move like a normal human, a tracking control scheme based on human gait data is proposed in this paper. The real human gait data is obtained from healthy subjects using a three-dimensional motion capture platform (3DMCP). Furthermore, the normal human motion characteristics are adopted to enhance the scientificity and effectiveness of assistant rehabilitation training using LLER. An adaptive radial basis function network (ARBFN) controller based on feed-forward control is presented to improve the trajectory tracking accuracy and tracking performance of the control system, where the ARBFN controller is deployed to predict the uncertain model parameters. The feed-forward controller based on the tracking errors is used to compensate for the input torque of LLER. The effectiveness of the presented control scheme is confirmed by simulation results based on experimental data.

Foreign fibers accounted for a small proportion in cotton, but there is serious impact on the quality of textile. Foreign fibers are removed by hand, which is low efficiency. Generally, the methods of fixed threshold are used to identify foreign fibers in cotton, but high speed flow of cotton is easy to result in fluctuations on light, the color of captured images will be changed accordingly, then misidentification possibility will be increased. But the suitable amount of sample libraries are used in the identification algorithm of supervised classification, which eliminate this defect to meet the requirements of accuracy and real-time. In this paper, according to the character of image gray of foreign fibers in cotton, and mathematical model is established. Further, important image features are enhanced by image processing, foreign fibers' characters are drawn. At last, Euclidean distance and k-nearest neighbor classification are adopted in identification algorithm, and finally foreign fibers are identified.