Synergistic effects of micro-/nano-fillers on conductive and electromagnetic shielding properties of polypropylene nanocomposites

This study presents the synergistic effects of graphene nanosheets (GNSs) and carbon fibers (CFs) additions on the electrical and electromagnetic shielding properties of GNS/CF/polypropylene (PP) composites. These composites were fabricated by the melt blending of different ratios of GNSs and CFs (20:0, 15:5, 10:10, 5:15 and 0:20 wt/wt%) into a PP polymer matrix using a Brabender mixer. Besides, the chemical and crystalline structures and the thermal stability of the resultant GNS/CF/PP composites were characterized by Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD) and thermogravimetric analysis (TGA). FT-IR and XRD showed that with the addition of GNSs content, transmittances at 1373.4?cm^?1 and 1454.4?cm^?1 became smaller and the characteristic peak at 26.82° became stronger. TGA showed that the GNS/CF/PP composite can be used at high temperature below 456°C. Blending 10?wt% CFs and 10?wt% GNSs into the PP polymer resulted in excellent conductivity (0.397 S/cm), which indicated the occurrence of the critical percolation threshold phenomenon, and also reached the maximum electromagnetic shielding effectiveness (EMSE) of 20?dB at 1.28–2.00?GHz. Laminated with five layers of composites, its EMSE achieved 25–38?dB at 0.3–3.0?GHz, corresponding to blocking of 94.38–98.74% electromagnetic waves.     

Piezoresistive Pressure Sensor Based on Synergistical Innerconnect Polyvinyl Alcohol Nanowires/Wrinkled Graphene Film

Piezoresistive sensor is a promising pressure sensor due to its attractive advantages including uncomplicated signal collection, simple manufacture, economical and practical characteristics. Here, a flexible and highly sensitive pressure sensor based on wrinkled graphene film (WGF)/innerconnected polyvinyl alcohol (PVA) nanowires/interdigital electrodes is fabricated. Due to the synergistic effect between WGF and innerconnected PVA nanowires, the as‐prepared pressure sensor realizes a high sensitivity of 28.34 kPa^?1. In addition, the device is able to discern lightweight rice about 22.4 mg (≈2.24 Pa) and shows excellent durability and reliability after 6000 repeated loading and unloading cycles. What is more, the device can detect subtle pulse beat and monitor various human movement behaviors in real‐time.     

Optimal classification methods for diagnosing latent skills and misconceptions for option-scored multiple-choice item quizzes

Behaviormetrika - The paper’s extended Diagnostic Classification Modeling setting assumes (a) nominal item (question) coding, thus including multiple-choice (MC) items, and (b)...     

关于Markov过程的强大数定律

本文研究一般马氏过程的强大数定律．     

基于FPGA的某雷达多功能数字替换板设计

为了提高高技术战争条件下电子装备战场抢修的快捷性、时效性,减少备件携带数量,以某雷达火控计算机中4块数字电路板为背景设计了一种多功能替换版;该替换版以一块FPGA芯片为控制核心,并配以海绵电路区和跳线插座的设计,使得在战场紧急情况下只需更换EPROM存储芯片,并在海绵电路区上焊接相应的集成电路器件以及调整跳线插座来控制信号走向,就可以在短时间内方便快捷地对损坏电路板实施替换;结果证实,该技术可作为一种备用的维修手段,可以在很大程度上提高部队装备战场维修率.     

A novel adaptive steganography based on local complexity and human vision sensitivity

This paper presents a novel adaptive steganographic scheme that is capable of both preventing visual degradation and providing a large embedding capacity. The embedding capacity of each pixel is dynamically determined by the local complexity of the cover image, allowing us to maintain good visual quality as well as embedding a large amount of secret messages. We classify pixels into three levels based on the variance of the local complexity of the cover image. When determining which level of local complexity a pixel should belong to, we take human vision sensitivity into consideration. This ensures that the visual artifacts appeared in the stego image are imperceptible, and the difference between the original and stego image is indistinguishable by the human visual system. The pixel classification assures that the embedding capacity offered by a cover image is bounded by the embedding capacity imposed on three levels that are distinguished by two boundary thresholds values. This allows us to derive a combination ratio of the maximal embedding capacity encountered with at each level. Consequently, our scheme is capable of determining two threshold values according to the desired demand of the embedding capacity requested by the user. Experimental results demonstrated that our adaptive steganographic algorithm produces insignificant visual distortion due to the hidden message. It provides high embedding capacity superior to that offered by a number of existing schemes. Our algorithm can resist the RS steganalysis attack, and it is statistically invisible for the attack of histogram comparison. The proposed scheme is simple, efficient and feasible for adaptive steganographic applications.     

Passive control of uncertain multiple input-delayed systems using reduction method

In order to conveniently analyze stability of uncertain multiple input-delayed closed-loop systems and successfully synthesize passive controllers for a general class of uncertain multiple input-delayed systems with disturbance, the so-called reduction method is applied to the multiple input-delayed systems with parametric uncertainties. A feedback controller associated with the system state and the past control action is designed to guarantee the passivity of the closed-loop systems for all admissible uncertainties. The theoretical findings are illustrated and verified with one numerical example.     

Robust nonlinear power iteration algorithm for adaptive blind separation of independent signals

In this paper, we elaborate an extension of classical power iteration method to nonlinear power iteration for blind separation of multiple independent sources from observed array output signals. The present algorithm, referred to as NPI, considers the estimating of the separating matrix as a nonlinear power iteration problem. By naturally choosing the positive definite normalizer for nonlinear power term, the resulting algorithm not only yields robust convergence behavior but also guarantees the orthonormality of the separating matrix at each iteration. To circumvent the difficulty of solving the inverse square root for the normalizer, an efficient adaptive singular value decomposition (SVD) technique is also adopted to obtain a fast implementation of NPI. The estimation accuracy and convergence speed of the present algorithm are illustrated through simulation results and compared with the existing adaptive algorithms.