基于卓越工程师教育的频率特性分析课堂教学研究

频率特性分析在工程应用中具有重要的作用,在电路分析、模拟电子技术、信号与系统、自动控制理论等相关课程中都涉及到相关章节内容,在不同课程中如何根据工程应用和学生学习阶段把握具体的讲解内容和讲解方式非常重要。本文从频率特性的求解方法和具体物理意义角度开展在不同课程中讲解的方法,从时域和频域角度分别讨论其物理意义和具体的分析方法。通过渐进深化的教学过程和教学方法不断提高学生的知识掌握和应用技能,提高学生的工程意识、工程素质和工程创新能力,强化学生对于频率特性课程知识的综合应用能力。     

Synthesis,characterization and charge transport properties of Pr–Ni Co-doped SrFe2O4 spinel for high frequency devices applications

Ferrites are materials of interest due to their broad applications in high technological devices and a lot of research has been focused to synthesize new ferrites. In this regard, an effort has been devoted to synthesize spinel Pr–Ni co-substituted strontium ferrites with a nominal formula of Sr_1-xPr_xFe_2-yNi_yO₄ (0.0 ≤ x ≤ 0.1, 0.0 ≤ y ≤ 1.0). The cubic structure of pure and Pr–Ni co-substituted strontium ferrite samples calcinated at 1073 K for 3 h has been confirmed through X-ray diffraction (XRD). Average sizes of crystallites (18–25 nm) have been estimated from XRD analysis and nanometer particle sizes of synthesized ferrites have been further verified by scanning electron microscopy (SEM). SEM results have also shown that particles are mostly agglomerated and all the samples possess porosity. It has been observed that at 298 K, the values of resistivity (ρ) increase, while that of AC conductivity, dielectric loss, and dielectric constants decrease with increasing amounts of Pr³⁺ and Ni²⁺ ions. The values of dielectric parameters initially decrease with frequency and later become constant and can be explained on the basis of dielectric polarization. Electrochemical impedance spectroscopy (EIS) studies show that the charge transport phenomenon in ferrite materials is mainly controlled via grain boundaries. Overall, synthesized ferrite materials own enhanced resistivity values in the range of 1.38 × 10⁹–1.94 × 10⁹ Ω cm and minimum dielectric losses, which makes them suitable candidates for high frequency devices applications.     

激光在线测厚振动分析与精度优化

激光测厚具有安全可靠、测量精度高、测量范围大等优点,广泛应用于纸张、电池极片等薄膜类材料厚度的在线测量。带材宽幅方向扫描测厚时由于扫描架往复运动会产生机械振动,影响在线测厚精度。针对该问题,以锂离子电池极片厚度测量为例,使用双激光差动式测厚平台对电池极片和铜箔分别进行厚度测量,然后对测厚数据进行频谱分析,探究其振动规律的相似性,并基于频谱分析结果采用滑动带阻滤波方式对测厚数据进行处理,滤波后极片和铜箔的厚度极差分别降低了33.4%和73.8%,有效过滤了机械振动导致的测量误差,可满足极片和铜箔厚度测量的精度要求。     

Feedback-based object detection for multi-person pose estimation

In this paper, a novel method is proposed for increasing the performance through coupling of top-down models adjusting the object detector based on a new loss function. Generally, object detectors and keypoint estimators are sequentially used in real-time multi-person pose estimations; however, these two models are separately trained. Therefore, the results of the object detector are not optimized for the keypoint estimator. To solve this problem, we analyze the relationship between the two models and propose a feedback-based loss optimization in the object detector, based on the estimation results of the keypoint estimator. In addition, the resulting bounding box of the object detector is readjusted to improve the accuracy of the keypoint estimation model. The experimental results demonstrate that the proposed approach can perform real-time operations with a high frame rate similar to that of the baseline model. Moreover, it achieved an accuracy of 74.2 average precision (AP), which is higher than the state-of-the-arts model including the human detector used in the experiment.     

采用温控和碘吸收池技术的发射激光稳频技术

多普勒测风激光雷达通过分析系统回波信号的多普勒频移反演出风速，为提高风场探测精度，从稳频技术方面展开研究。在稳频过程中，分别采取措施消除激光频率的长期漂移和短期抖动。针对激光频率的长期漂移，设计并研制了种子激光器温控箱，通过水浴的控温方式大大减小了激光频率的长期漂移，将激光频率稳定在±50 MHz以内；针对激光频率的短期抖动，采用以碘分子吸收池为核心器件的稳频系统，通过半导体控温方式对碘分子吸收池精确控温，控温精度达0.03 ℃，提高了稳频精度，将激光频率进一步稳定在±8 MHz以内，满足±10 MHz以内的设计精度要求。通过搭建多普勒测风激光雷达系统，对发射激光稳频装置进行系统验证，连续4组风场观测结果表明:系统探测高度为17 km，绝大部分方差在4 m/s以下，满足测风激光雷达测量指标的要求。     

Secure image encryption scheme using 4D-Hyperchaotic systems based reconfigurable pseudo-random number generator and S-Box

This paper introduces the design of a hardware efficient reconfigurable pseudorandom number generator (PRNG) using two different feedback controllers based four-dimensional (4D) hyperchaotic systems i.e. Hyperchaotic-1 and -2 to provide confidentiality for digital images. The parameter's value of these two hyperchaotic systems is set to be a specific value to get the benefits i.e. all the multiplications (except a few multiplications) are performed using hardwired shifting operations rather than the binary multiplications, which doesn't utilize any hardware resource. The ordinary differential equations (ODEs) of these two systems have been exploited to build a generic architecture that fits in a single architecture. The proposed architecture provides an opportunity to switch between two different 4D hyperchaotic systems depending on the required behavior. To ensure the security strength, that can be also used in the encryption process in which encrypt the input data up to two times successively, each time using a different PRNG configuration. The proposed reconfigurable PRNG has been designed using Verilog HDL, synthesized on the Xilinx tool using the Virtex-5 (XC5VLX50T) and Zynq (XC7Z045) FPGA, its analysis has been done using Matlab tool. It has been found that the proposed architecture of PRNG has the best hardware performance and good statistical properties as it passes all fifteen NIST statistical benchmark tests while it can operate at 79.101-MHz or 1898.424-Mbps and utilize only 0.036 %, 0.23 %, and 1.77 % from the Zynq (XC7Z045) FPGA's slice registers, slice LUTs, and DSP blocks respectively. Utilizing these PRNGs, we design two 16 × 16 substitution boxes (S-boxes). The proposed S-boxes fulfill the following criteria: Bijective, Balanced, Non-linearity, Dynamic Distance, Strict Avalanche Criterion (SAC) and BIC non-linearity criterion. To demonstrate these PRNGs and S-boxes, a new three different scheme of image encryption algorithms have been developed: a) Encryption using S-box-1, b) Encryption using S-box-2 and, c) Two times encryption using S-box-1 and S-box-2. To demonstrate that the proposed cryptosystem is highly secure, we perform the security analysis (in terms of the correlation coefficient, key space, NPCR, UACI, information entropy and image encryption quantitatively in terms of (MSE, PSNR and SSIM)).     

Deconvoluting Energy Transport Mechanisms in Metal Halide Perovskites Using CsPbBr3 Nanowires as a Model System

Understanding energy transport in metal halide perovskites is essential to effectively guide further optimization of materials and device designs. However, difficulties to disentangle charge carrier diffusion, photon recycling, and photon transport have led to contradicting reports and uncertainty regarding which mechanism dominates. In this study, monocrystalline CsPbBr₃ nanowires serve as 1D model systems to help unravel the respective contribution of energy transport processes in metal-halide perovskites. Spatially, temporally, and spectrally resolved photoluminescence (PL) microscopy reveals characteristic signatures of each transport mechanism from which a robust model describing the PL signal accounting for carrier diffusion, photon propagation, and photon recycling is developed. For the investigated CsPbBr₃ nanowires, an ambipolar carrier mobility of μ = 35 cm² V⁻¹ s⁻¹ is determined, and is found that charge carrier diffusion dominates the energy transport process over photon recycling. Moreover, the general applicability of the developed model is demonstrated on different perovskite compounds by applying it to data provided in previous related reports, from which clarity is gained as to why conflicting reports exist. These findings, therefore, serve as a useful tool to assist future studies aimed at characterizing energy transport mechanisms in semiconductor nanowires using PL.     

基于音频特征的工程车辆工况识别研究

高效率地使用工程车辆是工程项目管理中节约成本的有效方法，无人监管环境下工程车辆的工况识别，是实现工程车辆高效率使用的有效手段。目前以GPS等技术为核心的车辆智能管理系统未对工程车辆进行工况识别，提出一种基于GRU循环神经网络的工程车辆工况识别方法，通过对工程车辆在不同工况下产生的音频信号进行分析，从中提取Mel倒谱系数作为主要特征，构建GRU循环神经网络模型进行训练和识别。实验结果表明，该方法可以实现对工程车辆工况的有效识别。     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).