An ABA triblock copolymer strategy for intrinsically stretchable semiconductors

A novel semiconductor-rubber-semiconductor (P3HT-PMA-P3HT) triblock copolymer has been designed and prepared according to the principle of thermoplastic elastomers. It behaves as a thermoplastic elastomer with a Young's modulus (E) of 6 MPa for an elongation at break of 140% and exhibits good electrical properties with a carrier mobility of 9 x 10(-4) cm(2) V-1 s(-1). This novel semiconductor may play an important role in low-cost and large-area stretchable electronics.open112223sciescopu

Broadband gradient impedance matching using an acoustic metamaterial for ultrasonic transducers

2016-2017 > Academic research: refereed > Publication in refereed journal201804_a bcmaVersion of RecordPublishe

The Domestication Process and Domestication Rate in Rice: Spikelet Bases from the Lower Yangtze

The process of rice domestication occurred in the Lower Yangtze region of Zhejiang, China, between 6900 and 6600 years ago. Archaeobotanical evidence from the site of Tianluoshan shows that the proportion of nonshattering domesticated rice (Oryza sativa) spikelet bases increased over this period from 27% to 39%. Over the same period, rice remains increased from 8% to 24% of all plant remains, which suggests an increased consumption relative to wild gathered foods. In addition, an assemblage of annual grasses, sedges, and other herbaceous plants indicates the presence of arable weeds, typical of cultivated rice, that also increased over this period

End-to-end 6-DoF Object Pose Estimation through Differentiable Rasterization

Here we introduce an approximated differentiable renderer to refine a 6-DoF pose prediction using only 2D alignment information. To this end, a two-branched convolutional encoder network is employed to jointly estimate the object class and its 6-DoF pose in the scene. We then propose a new formulation of an approximated differentiable renderer to re-project the 3D object on the image according to its predicted pose; in this way the alignment error between the observed and the re-projected object silhouette can be measured. Since the renderer is differentiable, it is possible to back-propagate through it to correct the estimated pose at test time in an online learning fashion. Eventually we show how to leverage the classification branch to profitably re-project a representative model of the predicted class (i.e. a medoid) instead. Each object in the scene is processed independently and novel viewpoints in which both objects arrangement and mutual pose are preserved can be rendered. Differentiable renderer code is available at:https://github.com/ndrplz/tensorflow-mesh-renderer

Improved gradient descent algorithms for time-delay rational state-space systems: Intelligent search method and momentum method

This study proposes two improved gradient descent parameter estimation algorithms for rational state-space models with time-delay. These two algorithms, based on intelligent search method and momentum method, can simultaneously estimate the time-delay and parameters without the matrix eigenvalue calculation in each iteration. Compared with the traditional gradient descent algorithm, the improved algorithms come with two advantages: having quicker convergence rates and less computational efforts, particularly meaningful for those large scale systems. A simulated example is selected to illustrate the efficiency of the proposed algorithms