This brief proposes an augmented complex-valued total least mean phase (ACTLMP) algorithm for the estimation of frequency in unbalanced three-phase power systems subject to noisy measurements. This is enabled by explicitly addressing the errors-in-variables nature residing in the widely linear frequency estimation model and targeting minimization of the normalized squared phase error residuals, rather than focusing on the amplitude information. The proposed ACTLMP algorithm shows high sensitivity to angle deviation in a noisy environment, allowing for rapid and precise frequency estimation. Extensive simulations across various synthetic scenarios and case studies on real-world measurement demonstrate the superior frequency estimation accuracy of the proposed ACTLMP algorithm over existing methods.

In this paper, we propose a novel augmented complex-valued gradient-descent total least-squares (ACGDTLS) adaptive filter for processing noisy input and output noncircular complex-valued signals. First, a Rayleigh quotient cost function is formulated by incorporating augmented complex-valued statistics and the output-to-input-noise-ratio within the widely linear error-in-variable model, whereby the ACGDTLS is developed using the gradient-descent approach. Next, rigorous analysis is conducted to establish a conservative step-size bound guaranteeing mean convergence, a closed-form expression for the steady-state mean-squared deviation, and the algorithm’s computational complexity. Finally, through simulations conducted in system identification, wind/speech prediction, and stereophonic acoustic echo cancellation, the analytical findings are validated, and the proposed ACGDTLS filter demonstrates superior estimation accuracy compared to the augmented complex-valued least-mean-square algorithm and two state-of-the-art bias-compensated methods. Remarkably, this performance advantage persists across a wide range of step-sizes, input noise variances, and output noise variances.

This paper presents a novel self-calibrating paper counting device that integrates an embedded processor with a capacitance sensor chip. The device operates on the principle of capacitance measurement, detecting variations between two copper-coated plates as paper sheets are inserted. The capacitance data, acquired via an I2C interface, undergoes filtering and averaging to enhance stability and accuracy. Further processing involves scaling and transformation to refine the data, which is then utilized in curve fitting to determine the precise sheet count. The final result is displayed on an LCD screen, accompanied by an audible signal upon measurement completion. Experimental results demonstrate that the incorporation of data fusion and curve fitting significantly improves measurement accuracy, maintaining it consistently above 97%. This highlights the proposed system’s high precision and robustness, making it a promising solution for paper counting applications.

The Ackermann kinematic structure has a wide range of applications in various fields. This system has been utilized to design an obstacle avoidance navigation system for Ackermann robots using a Multi-sensor fusion SLAM approach, enabling the fusion of data obtained from an inertial measurement unit and laser odometry. Additionally, the traditional A* algorithm is improved including the heuristic function and point selection strategy of the algorithm, utilizing a combination of improved A* algorithm and local planning algorithm TEB as the navigation strategy to achieve motion control, mapping, positioning, navigation, based on the characteristics of the Ackerman steering structure. To verify the system's performance, an experimental test was conducted in a laboratory environment, using the Ackermann robot hardware platform. The experimental results demonstrate that the system can achieve high map accuracy, reaching the level of cm, and the improved A* algorithm is superior to the traditional A* algorithm, indicating excellent navigation accuracy.

针对电子信息类专业四年级学生开设的"电子系统设计",开展了逆向设计与正向实施.首先明确层次化的学习目标,设计了情境化的学习活动,模拟解决工程问题的全环节,最后强化形成性评价,完成学习成果合理科学地反馈,并持续改进学习活动和学习目标,充分体现产出导向的教育(OBE)理念,帮助学生更好地达成预期的学习目标,锻炼学生的实践动手能力和创新能力.

Triboelectric nanogenerators (TENGs) are at the forefront of energy harvesting and self-powered sensors, while further improvements in TENGs' development and utilization essentially depend on the theoretical models. To understand the intrinsic mechanism of TENGs, a formal physical framework of TENGs was introduced based on Maxwell's equations. Triboelectric charges would be produced on the dielectric layer surface after contact triboelectrification. As concerned about any transient, the electric field originating from the arbitrary fixed position over the finite charged plane would not change, constituting the electrostatic field. Since TENGs maintain low-frequency movement, they enter a quasi-electrostatic state. Under this state, the electric field would change along with the distance from the charged plane. Herein, a quasi-electrostatic three-dimensional (QETD) charge model was constructed to refine the theoretical modeling of TENGs. Finite element modeling (FEM) simulations were first provided to reveal the distribution of polarization vector (Pz), electric potential (φ), electric field (Ez) and electric displacement (Dz). Furthermore, an optimized theoretical framework for contact-separation TENGs was established, and it was validated by different driving and structural variables. Besides, the intrinsic displacement current of TENGs was linked with the conduction current to deduce the output capability. Compared with previous works, this QETD model shows the most consistent trend with experimental results, providing accurate predicts for distinct TENGs' performance.

Triboelectric nanogenerators (TENGs) have exhibited great potential as the promising energy harvesters in recent decades. To obtain superior output performance, advances in materials science and engineering technology have been applied to promote the surface charge density and energy output. In this work, the mathematical deriva-tions have been developed to derive the theoretical boundary under the air breakdown restriction for contact -separation mode TENGs. Through the optimization of limitation equations, mathematical relations were deduced between the output evaluation metrics and TENGs' design including the structural and material factors. In addition, methodologies for output performance enhancement were proposed, from which an optimized model possessing 10 mu m silicone rubber as the dielectric layer exhibits 2.85 mC/m2 in surface charge density and 1.12 mJ in energy output under 2 cm separation distance. This work provides systematical analysis and compre-hensive predicts for the theoretical boundary of TENGs' capability, which gives guidance on the design and optimization for high-performance TENGs.

该文设计了基于高斯混合模型的说话人识别系统实验,通过录制小型语音库、提取表征说话人个性的特征参数、训练说话人模型和似然度判决,实现文本无关的说话人识别.实验以小组形式协作完成,并鼓励学生展开进阶研究,帮助学生提升团队协作精神和创新精神,以及解决复杂工程问题的能力.所建立的全过程多维度立体化的实验考核体系,着眼于对学生的方案设计、实验操作、报告撰写、演示答辩等全过程评价,有助于激发学生的创造力和学习热情.

本文将YOLOv3[1]目标检测算法与双目测距方法融合并应用在无人机上,不仅能够检测出无人机前方是否有物体,还能够对物体进行识别和分类,同时测量与目标物体的距离.此算法首先通过训练好的YOLOv3-tiny模型对前方的物体进行检测,再用标定过的双目相机获取视角内的深度图,对识别到的目标物体进行测距.为验证检测结果的有效性,本文选用不同目标物体进行验证实验.实验结果表明,该算法能够较为准确地识别目标物的类别,从深度图中获取到的目标距离也较为精确.

As the existence of interfacial electric field, the surface charges of triboelectric nanogenerators (TENGs) would diffuse into air or tribo-material interior, resulting in the charge decay. Therefore, charge regulation from charge storage is crucial in addition to charge generation. In this work, a MXene/TiO 2 hybrid film has been prepared through partial oxidation of MXene in a contact-separate mode TENG (MT-TENG) as an intermediate layer between negative tribo-material and bottom electrode. It demonstrates the electric outputs of 128 μC/m 2 in charge density, 73.78 μW/cm 3 and 63.78 μW/g in average power density with an energy conversion efficiency of 34.81%. The surface terminals in MXene nanosheets and the oxygen vacancies in TiO 2 nanoparticles provide abundant electron trap sites. Moreover, the energy band bending resulting from the polarization effect of the hybrid film could further hinder the free electrons drift to the bottom electrode, and consequently prevent the charge recombination. With the synergetic effect of electron storage and polarization, the MT-TENG with an intermediate layer rapidly reaches a charge dynamic equilibrium with a charge density of 80 μC/m 2 . It is expected to provide new insights for material science and structure design from the perspective of charge regulation to improve the output performance of TENGs.

本文主要介绍了基于ARM Cortex-A8的车牌识别系统.该系统采用BeagleBone Black开发板作为主控制器,罗技C170 USB网络摄像头作为图像采集设备,微雪7寸HDMI LCD触摸屏作为显示设备.开发板运行Debian操作系统,使用Qt作为集成开发环境,调用OpenCV以及Tesseract算法库.本文设计的软件进行基于灰度化、高斯滤波、So-bel边缘检测以及OTSU二值化的预处理,采用形态学方法实现车牌定位,利用水平投影和垂直投影方法,结合中国车牌先验知识完成字符分割,最后通过Tesseract OCR引擎完成字符识别.整个系统性能稳定,识别准确率高,适用于大多数车牌识别的场景.

As accurate step counting is a critical indicator for exercise evaluation in daily life, pedometers give a quantitative prediction of steps and analyze the amount of exercise to regulate the exercise plan. However, the merchandized pedometers still suffer from limited battery life and low accuracy. In this work, an integrated self-powered real-time pedometer system has been demonstrated. The highly integrated system contains a porous triboelectric nanogenerator (P-TENG), a data acquisition and processing (DAQP) module, and a mobile phone APP. The P-TENG works as a pressure sensor that generates electrical signals synchronized with users' footsteps, and combining it with the analogue front-end (AFE) circuit yields an ultrafast response time of 8 ms. Moreover, the combination of a mini press-to-spin-type electromagnetic generator (EMG) and a supercapacitor enables a self-powered and self-sustained operation of the entire pedometer system. This work implements the regulation of TENG signals by electronic circuit design and proposes a highly integrated system. The improved reliability and practicality provide more possibilities for wearable self-powered electronic devices.

Triboelectric nanogenerator (TENG) has shown great advances in converting low-frequency discrete mechanical energy into electricity and multifunctional real-time self-powered sensing systems. It has confirmed that the air breakdown effect is the main factor limiting the maximum effective power output of the TENG. Charges generated on the surface of TENGs diffuse into the atmosphere and internal triboelectric layer, resulting in charge loss and decrease of surface charge density. Breaking through the limitation of air breakdown and prolonging charge decay time are the two priorities for boosting TENG output. By embedding superior intermediate layer into TENG induced output enhancement provides an effective strategy to improve the output performance. Here, the working mechanisms of different materials belonging to the classifications of metals, inorganic nonmetal materials, and organic polymers as the intermediate layer are reviewed elaborately. Moreover, the influences of structure parameters, such as thickness of dielectric layer, dielectric layer number, and ground connection design are discussed accordingly. Future challenges and optimizations for improvement of the intermediate layer are finally presented in the review.

针对目前永磁同步电机存在的PI参数整定困难复杂,基于永磁同步电机数学模型,分析矢量控制原理,由数学模型推导永磁同步电机参数测量方法,给出传统的控制算法的PI参数整定方法,结合传统内模控制(IMC)策略的PI参数整定方法,引入新的参数优化控制,采用实际电机参数测量方法,简化永磁同步电机闭环参数设计过程,推导出新的参数整定公式.同时通过Matlab Simulink搭建永磁同步电机矢量控制系统的仿真模型,并搭建STM32硬件平台,实验结果表明电机测量参数与PI参数整定的正确性,设计的PI调节器的参数具有较好的动态性能和抗扰动能力,对实际电机参数调节具有指导意义.

The treatment, diagnosis, and monitoring of diseases have attracted more and more attention in recent years. Healthcare electronics help effectively treat and real‐time monitor diseases. Triboelectric nanogenerators (TENGs) show great potential for healthcare applications because of their superiority including low cost, flexible structures, and self‐powered property. Herein, the recent key advancements in TENG‐based healthcare applications are comprehensively reviewed. TENGs could not only harvest the mechanical energy from the body to make electrical stimulation but also generate different electrical signals in response to external stimuli. Integrated systems combined with TENGs and other sensors could also promote sensing stability. The materials, structures, working mechanisms, and performance of each application are discussed. The existing limitations and prospects for further TENG‐based healthcare are finally put forward.

本文设计了一种光伏并网发电模拟系统,研究符合国家电网标准的输出电流,减少并网发电系统中的高次谐波分量.本系统以STM32F103ZET6为主控器,采用降压变压器及电压比较器提取电网中高压交流电的零相位时刻作为参考相位,对发电模拟系统的输出与参考相位之间存在的相位差进行比例调节,提高系统跟踪相位的速度和系统的稳定性.测试结果表明,本文设计的光伏并网发电模拟系统可输出50Hz且频率偏差小于0.1Hz的正弦波,符合国家电网要求,验证了方案的可行性与有效性.

针对目前网络生活应用中越来越多的图片验证码,提出了一种基于卷积神经网络的验证码识别方案,使用4 500张四位数字验证码作为训练集,利用TensorFlow深度学习框架训练网络模型,经过测试集验证后正确率为98.55％.网络验证通过后将该含参数的网络模型移植至Android平台,采用OpenCV对验证码图片进行预处理,并利用该网络模型实现验证码识别.

设计了一种基于ARM9的视频显示系统.采用ARM9内核的N32926微处理器作为主控制器,Linux系统作为嵌入式操作系统,移植了Mplayer视频播放器实现视频播放,CAN总线功能的实现采用了基于SPI接口的CAN控制器MCP2515.在软件方面,基于QT图形界面架构设计了用户界面.该系统成本低,性能稳定,可以满足视频的播放和CAN通信的要求.

本文针对宽带信号频谱谐波测量设备,如何提高测量精度及设备的性价比,设计了基于51单片机的非侵入式宽带信号谐波测量系统,提出了一种将频谱分段并细化的高精度频谱谐波测量的方法.本系统以STC15W4K48S4为主控器,采用电流互感器采集被测信号的电流信息,在LCD12864显示屏实现被测信号频谱谐波的电流幅度、频率等参数的实时显示.为了提高谐波测量的精度,本文采用分段频谱细化的方法,基于FFT,精确地从存在频谱泄漏的频谱中提取出各次谐波参数.测试结果表明,本系统可以实时实现50Hz～1KHz任意周期信号的谐波测量,测量误差小于2％,分段频谱细化的方法可行且系统具有很高的性价比.

Abstract Continuous deforming always leads to the performance degradation of a flexible triboelectric nanogenerator due to the Young’s modulus mismatch of different functional layers. In this work, we fabricated a fiber-shaped stretchable and tailorable triboelectric nanogenerator (FST–TENG) based on the geometric construction of a steel wire as electrode and ingenious selection of silicone rubber as triboelectric layer. Owing to the great robustness and continuous conductivity, the FST–TENGs demonstrate high stability, stretchability, and even tailorability. For a single device with ~ 6 cm in length and ~ 3 mm in diameter, the open-circuit voltage of ~ 59.7 V, transferred charge of ~ 23.7 nC, short-circuit current of ~ 2.67 μA and average power of ~ 2.13 μW can be obtained at 2.5 Hz. By knitting several FST–TENGs to be a fabric or a bracelet, it enables to harvest human motion energy and then to drive a wearable electronic device. Finally, it can also be woven on dorsum of glove to monitor the movements of gesture, which can recognize every single finger, different bending angle, and numbers of bent finger by analyzing voltage signals.