AWGN信道下Turbo码误比特率模型

本文研究信道编解码及信道特征对误比特率性能的影响。在实验基础上,通过曲线拟舍等数学工具,得到加性高斯白噪声信道条件下,信道编码采用Turbo码、通信系统端到端的误比特率经验模型。通过该模型,可以在发送端预测出在一定信道条件及系统参数设置下接收端的误比特率。实验表明,该模型具有极高的精度,平均误差在3％左右。该模型可以应用在信源信道联合编码及端到端的率失真优化中。     

Students’ perceptions of collaboration, self-regulated learning, and information seeking in the context of Internet-based learning and traditional learning

This study aims to investigate students’ perceptions of three aspects of learning - collaboration, self-regulated learning (SRL), and information seeking (IS) in both Internet-based and traditional face-to-face learning contexts. A multi-dimensional questionnaire was designed to evaluate each aspect in terms of perceived capability, experience, and interest. The analyses explore (1) potential differences of students’ perceptions between Internet-based and face-to-face learning environments and (2) potential differences in the three aspects in relation to learners’ attributes and the use of the Internet and enrollment in online courses. This study surveyed students in a higher education institute who had had experiences with Internet-based and face-to-face learning. The results showed that students perceived higher levels of collaboration (capability only), SRL (capability and experience) and IS (capability, interest, and experience) in Internet-based learning than in traditional learning environments. In terms of students’ education level, graduate students perceived higher levels of capabilities and interests in some of the aspects, than undergraduate students. In addition, for Internet-based learning, significant differences in collaboration and SRL were found derived from time spent on the Internet related to learning; and students’ perceptions of collaboration, SRL, and IS were all positively correlated to students’ online course-taking experience. Implications for online learning practices and instructor’s facilitation are discussed.     

The feedrate scheduling of NURBS interpolator for CNC machine tools

This paper proposes an off-line feedrate scheduling method of CNC machines constrained by chord tolerance, acceleration and jerk limitations. The off-line process for curve scanning and feedrate scheduling is realized as a pre-processor, which releases the computational burden in real-time task. The proposed method first scans a non-uniform rational B-spline (NURBS) curve and finds out the crucial points with large curvature (named as critical point) or G⁰ continuity (named as breakpoint). Then, the NURBS curve is divided into several NURBS sub-curves using curve splitting method which guarantees the convergence of predictor–corrector interpolation (PCI) algorithm. The suitable feedrate at critical point is adjusted according to the limits of chord error, centripetal acceleration and jerk, and at breakpoint is adjusted based on the formulation of velocity variation. The feedrate profile corresponding to each NURBS block is constructed according to the block length and the given limits of acceleration and jerk. In addition, feedrate compensation method for short NURBS blocks is performed to make the jerk-limited feedrate profile more continuous and precise. Because the feedrate profile is established in off-line, the calculation of NURBS interpolation is extremely efficient for CNC high-speed machining. Finally, simulations and experiments with two free-form NURBS curves are conducted to verify the feasibility and applicability of the proposed method.     

二价铁过量M型钡铁氧体的放电等离子体烧结合成

在氩气保护下用化学共沉淀工艺制备了名义组成为BaFe³⁺_11.8Fe²⁺_0.3O₁₉的Fe²⁺过量M型钡铁氧体（BaM）前驱体,采用XRD、SEM和Archimedes法研究了前驱体在放电等离子体烧结（SPS）条件下的物相组成、显微结构和致密度. 结果表明,合成高致密度的单相Fe²⁺过量BaM材料的优化SPS工艺为850℃、保温30min和压力10MPa,其结晶机制为低温下前驱体分解形成α-Fe₂O₃和Fe₃O₄或γ-Fe₂O₃,高温下Fe²⁺过量BaM结晶,整个结晶过程中没有BaFe₂O₄中间相形成. 单相Fe²⁺过量BaM材料的饱和磁化强度为58.2A·m²·kg^-1,矫顽力为255kA·m^-1, 说明Fe²⁺占据自旋向上的2a磁晶位.     

Preparation and Enhanced Thermoelectric Properties of p-Type BaFe12O19/CeFe3CoSb12 Magnetic Nanocomposite Materials

A series of p-type xBaFe₁₂O₁₉/CeFe₃CoSb₁₂ (x = 0, 0.05%, 0.10%, 0.20%, 0.40%) magnetic nanocomposite thermoelectric (TE) materials have been prepared by the combination of ultrasonic dispersion and spark plasma sintering (SPS). The effects of BaFe₁₂O₁₉ magnetic nanoparticles on the phase composition, microstructure, and TE properties of the nanocomposite materials were investigated in this work. x-Ray diffraction analysis shows that all the SPSed bulk samples are composed of main phase skutterudite besides a small amount of FeSb₂ and Sb. The TE transport measurements demonstrated that remarkable enhancements in electrical conductivity and Seebeck coefficient can be simultaneously realized by optimizing the doping content of BaFe₁₂O₁₉ magnetic nanoparticles. The lattice thermal conductivity was significantly reduced because of enhanced phonon scattering induced by BaFe₁₂O₁₉ nanoparticles. The highest ZT value reached 0.75 at 800 K for the sample with x = 0.05%, increased by 41.5% as compared with that of p-type CeFe₃CoSb₁₂ bulk material without BaFe₁₂O₁₉ magnetic nanoparticles. This work confirms that doping a small amount of BaFe₁₂O₁₉ magnetic nanoparticles can significantly improve the ZT value of p-type xBaFe₁₂O₁₉/CeFe₃CoSb₁₂ magnetic nanocomposite TE materials.     

闪存数据库:现状、技术与展望

随着闪存存储技术的发展,闪存已经广泛应用于各种移动设备、PC机和服务器中.作为一种完全不同于磁盘的新型存储介质,闪存具有非易失、高速读写、抗震、低功耗、高存储密度等物理特性,这使得基于闪存的数据管理问题成为新的挑战.数据库系统是数据管理的重要技术,将现有的数据库系统直接移植到闪存上并不能充分发挥其硬件特性,设计实现基于闪存的数据库系统是当前的一个研究热点.文中介绍了闪存的特性和闪存转换层;总结了缓冲区、索引、查询和事务等数据库关键技术;讨论了基于闪存的混合存储数据管理.最后,基于该领域亟待解决的诸多问题,指出了未来的研究方向.     

Robot Control Optimization Using Reinforcement Learning

Conventional robot control schemes are basically model-based methods. However, exact modeling of robot dynamics poses considerable problems and faces various uncertainties in task execution. This paper proposes a reinforcement learning control approach for overcoming such drawbacks. An artificial neural network (ANN) serves as the learning structure, and an applied stochastic real-valued (SRV) unit as the learning method. Initially, force tracking control of a two-link robot arm is simulated to verify the control design. The simulation results confirm that even without information related to the robot dynamic model and environment states, operation rules for simultaneous controlling force and velocity are achievable by repetitive exploration. Hitherto, however, an acceptable performance has demanded many learning iterations and the learning speed proved too slow for practical applications. The approach herein, therefore, improves the tracking performance by combining a conventional controller with a reinforcement learning strategy. Experimental results demonstrate improved trajectory tracking performance of a two-link direct-drive robot manipulator using the proposed method.     

Antireflective polymer films via roll to roll UV nanoimprint lithography using an AAO mold

Microsystem Technologies - Anti-reflection based on the mimicking of moth eye subwavelength structures (SWSs) has several advantages over conventionally used multilayer thin film antireflective...