Graphene-based spintronic components

A major challenge of spintronics is in generating, controlling and detecting spin-polarized current. Manipulation of spin-polarized current, in particular, is difficult. We demonstrate here, based on calculated transport properties of graphene nanoribbons, that nearly +-100% spin-polarized current can be generated in zigzag graphene nanoribbons (ZGNRs) and tuned by a source-drain voltage in the bipolar spin diode, in addition to magnetic configurations of the electrodes. This unusual transport property is attributed to the intrinsic transmission selection rule of the spin subbands near the Fermi level in ZGNRs. The simultaneous control of spin current by the bias voltage and the magnetic configurations of the electrodes provides an opportunity to implement a whole range of spintronics devices. We propose theoretical designs for a complete set of basic spintronic devices, including bipolar spin diode, transistor and logic gates, based on ZGNRs.Comment: 14 pages, 4 figure

Adaptive Finite-Time Model Estimation and Control for Manipulator Visual Servoing using Sliding Mode Control and Neural Networks

The image-based visual servoing without models of system is challenging since it is hard to fetch an accurate estimation of hand-eye relationship via merely visual measurement. Whereas, the accuracy of estimated hand-eye relationship expressed in local linear format with Jacobian matrix is important to whole system's performance. In this article, we proposed a finite-time controller as well as a Jacobian matrix estimator in a combination of online and offline way. The local linear formulation is formulated first. Then, we use a combination of online and offline method to boost the estimation of the highly coupled and nonlinear hand-eye relationship with data collected via depth camera. A neural network (NN) is pre-trained to give a relative reasonable initial estimation of Jacobian matrix. Then, an online updating method is carried out to modify the offline trained NN for a more accurate estimation. Moreover, sliding mode control algorithm is introduced to realize a finite-time controller. Compared with previous methods, our algorithm possesses better convergence speed. The proposed estimator possesses excellent performance in the accuracy of initial estimation and powerful tracking capabilities for time-varying estimation for Jacobian matrix compared with other data-driven estimators. The proposed scheme acquires the combination of neural network and finite-time control effect which drives a faster convergence speed compared with the exponentially converge ones. Another main feature of our algorithm is that the state signals in system is proved to be semi-global practical finite-time stable. Several experiments are carried out to validate proposed algorithm's performance.Comment: 24 pages, 10 figure

A Novel Uncalibrated Visual Servoing Controller Baesd on Model-Free Adaptive Control Method with Neural Network

Nowadays, with the continuous expansion of application scenarios of robotic arms, there are more and more scenarios where nonspecialist come into contact with robotic arms. However, in terms of robotic arm visual servoing, traditional Position-based Visual Servoing (PBVS) requires a lot of calibration work, which is challenging for the nonspecialist to cope with. To cope with this situation, Uncalibrated Image-Based Visual Servoing (UIBVS) frees people from tedious calibration work. This work applied a model-free adaptive control (MFAC) method which means that the parameters of controller are updated in real time, bringing better ability of suppression changes of system and environment. An artificial intelligent neural network is applied in designs of controller and estimator for hand-eye relationship. The neural network is updated with the knowledge of the system input and output information in MFAC method. Inspired by "predictive model" and "receding-horizon" in Model Predictive Control (MPC) method and introducing similar structures into our algorithm, we realizes the uncalibrated visual servoing for both stationary targets and moving trajectories. Simulated experiments with a robotic manipulator will be carried out to validate the proposed algorithm.Comment: 16 pages, 8 figure

On air: Evaluating streaming MPEG4 video performance over wireless networks [abstract]

Abstract only availableThere is not a clear consensus on how open-standard video streaming technologies perform across wireless computer networks. Wireless networking technologies have become nearly ubiquitous, particularly in residential networks, but consumers may not realize that the performance of wireless networks may differ significantly from that of wired networks. Advances in video compression and wireless network bandwidth may allow for the ability to stream higher-quality video content than previously possible. We seek to evaluate how video content, encoded by the MPEG4 codec, performs when streamed across simulated residential wired and wireless networks. We are interested in how the transmission of the video across the network link affects the subjective and objective appearance of the video on the client computer. Our network testbed comprises nine typical desktop computers equipped with a modified version of Videolan Client to playback a network video stream, provided by a server running the Darwin Streaming Server from Apple Inc., connected using wired Ethernet connections and 802.11b, 802.11g, and draft 802.11n version 1.0 wireless connections. Each client was monitored while receiving a sample of raw video data encoded at one of several common bitrates to note any lost content. Each client saved a copy of the video locally for later comparison with the original using the PSNR (Peak Signal to Noise Ratio) and SSIM (Structural SIMilarity) metrics. Looking strictly at established wireless standards (802.11b and g), we found that they are not capable of streaming multiple ITU-R BT.709 high definition video streams across a wireless network link. Wired and draft 802.11n wireless connections did prove robust enough to handle multiple high definition video streams concurrently. Hopefully, our work will lead to a better understanding of the technical issues, performance, and trade-offs in home networking, thus facilitating the rapid deployment of advanced home networking services and applications

Single-cell multi-omics analysis reveals dysfunctional Wnt signaling of spermatogonia in non-obstructive azoospermia

BackgroundNon-obstructive azoospermia (NOA) is the most severe type that leads to 1% of male infertility. Wnt signaling governs normal sperm maturation. However, the role of Wnt signaling in spermatogonia in NOA has incompletely been uncovered, and upstream molecules regulating Wnt signaling remain unclear.MethodsBulk RNA sequencing (RNA-seq) of NOA was used to identify the hub gene module in NOA utilizing weighted gene co-expression network analyses (WGCNAs). Single-cell RNA sequencing (scRNA-seq) of NOA was employed to explore dysfunctional signaling pathways in the specific cell type with gene sets of signaling pathways. Single-cell regulatory network inference and clustering (pySCENIC) for Python analysis was applied to speculate putative transcription factors in spermatogonia. Moreover, single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) determined the regulated genes of these transcription factors. Finally, spatial transcriptomic data were used to analyze cell type and Wnt signaling spatial distribution.ResultsThe Wnt signaling pathway was demonstrated to be enriched in the hub gene module of NOA by bulk RNA-seq. Then, scRNA-seq data revealed the downregulated activity and dysfunction of Wnt signaling of spermatogonia in NOA samples. Conjoint analyses of the pySCENIC algorithm and scATAC-seq data indicated that three transcription factors (CTCF, AR, and ARNTL) were related to the activities of Wnt signaling in NOA. Eventually, spatial expression localization of Wnt signaling was identified to be in accordance with the distribution patterns of spermatogonia, Sertoli cells, and Leydig cells.ConclusionIn conclusion, we identified that downregulated Wnt signaling of spermatogonia in NOA and three transcription factors (CTCF, AR, and ARNTL) may be involved in this dysfunctional Wnt signaling. These findings provide new mechanisms for NOA and new therapeutic targets for NOA patients