Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G network

The high bandwidth and low latency of 6G network technology enable the successful application of monocular 3D object detection on vehicle platforms.Monocular 3D-object-detection-based Pseudo-LiDAR is a low-cost,lowpower solution compared to LiDAR solutions in the field of autonomous driving.However,this technique has some problems,i.e.,(1)the poor quality of generated Pseudo-LiDAR point clouds resulting from the nonlinear error distribution of monocular depth estimation and(2)the weak representation capability of point cloud features due to the neglected global geometric structure features of point clouds existing in LiDAR-based 3D detection networks.Therefore,we proposed a Pseudo-LiDAR confidence sampling strategy and a hierarchical geometric feature extraction module for monocular 3D object detection.We first designed a point cloud confidence sampling strategy based on a 3D Gaussian distribution to assign small confidence to the points with great error in depth estimation and filter them out according to the confidence.Then,we present a hierarchical geometric feature extraction module by aggregating the local neighborhood features and a dual transformer to capture the global geometric features in the point cloud.Finally,our detection framework is based on Point-Voxel-RCNN(PV-RCNN)with high-quality Pseudo-LiDAR and enriched geometric features as input.From the experimental results,our method achieves satisfactory results in monocular 3D object detection.

基于CD-BSMOTE的D-S证据融合变压器故障诊断

针对变压器油中溶解气体数据集不均衡特性对故障诊断结果的影响,提出一种基于清除临界点改进的边界合成少数类过采样算法均衡数据集和Pearson冲突距离改进D-S证据融合的变压器故障诊断模型。首先,对少数类样本进行均衡化处理,根据K-means聚类结果清除处于临界位置的样本;其次,搭建梯度提升树、随机森林、BP神经网络的故障诊断模型,实现变压器故障初步诊断;接着引入Pearson冲突距离改进D-S证据融合模型,实现诊断结果的融合决策;最后,经实际算例分析,诊断精确率达到92.65%。结果表明,所建模型能有效解决数据不平衡对诊断结果的影响,提高故障诊断精度。

基于d-q变换及WOA-LSTM的异步电机定子匝间短路故障诊断方法

为了实现对异步电机定子绕组匝间短路故障的可靠在线诊断,提出一种基于d-q变换及鲸鱼优化算法(WOA)优化的长短期记忆网络(LSTM)的故障诊断方法。通过理论推导可知,d-q变换可有效提取定子电流中的特征频谱数据。采用鲸鱼优化算法对长短期记忆网络中的3个关键参数进行优化,建立WOA-LSTM故障分类模型。为了验证基于d-q变换和WOA-LSTM故障诊断方法的有效性,分别以小波变换、快速傅里叶变换及d-q变换提取电流频谱数据作为输入数据集,以一台YE2-100L1-4型异步电机为实验对象进行实验验证。研究结果表明:相比于小波变换及快速傅里叶变换,采用d-q变换能更准确的提取出定子电流中的故障特征,更精确地反映电机故障状态,有助于提高故障分类准确率;相比于传统的LSTM算法,经WOA优化后的LSTM算法分类准确率可达98.3%,能可靠地实现不同程度匝间短路故障的诊断。

Fault Current Identification of DC Traction Feeder Based on Optimized VMD and Sample Entropy

A current identification method based on optimized variational mode decomposition(VMD)and sample entropy(SampEn)is proposed in order to solve the problem that the main protection of the urban rail transit DC feeder cannot distinguish between train charging current and remote short circuit current.This method uses the principle of energy difference to optimize the optimal mode decomposition number k of VMD;the optimal VMD for DC feeder current is decomposed into the intrinsic modal function(IMF)of different frequency bands.The sample entropy algorithm is used to perform feature extraction of each IMF,and then the eigenvalues of the intrinsic modal function of each frequency band of the current signal can be obtained.The recognition feature vector is input into the support vector machine model based on Bayesian hyperparameter optimization for training.After a large number of experimental data are verified,it is found that the optimal VMD_SampEn algorithm to identify the train charging current and remote short circuit current is more accurate than other algorithms.Thus,the algorithm based on optimized VMD_SampEn has certain engineering application value in the fault current identification of the DC traction feeder.

高光谱应用的带可调复位时间CDS的低噪声红外焦平面读出电路

低辐射量的高光谱应用对红外焦平面读出电路(ROIC)提出了低噪声的设计要求。相关双采样(CDS)是常用的减少噪声的结构。本文通过调节钳位和采样保持之间的时间间隔来改进CDS,可灵活消除低频噪声。采用180 nm CMOS工艺设计和制造了640×512规模、15μm像元中心距的读出电路。输入级集成了低噪声CTIA与本文提出的可调复位时间CDS(AICDS),所设计的时序产生器使CDS复位时间可以延长0~270个时钟周期。通过延长复位时间减少这个时间间隔,噪声电子数可以由39 e^(-)减少到18.3 e^(-)。SPECTRE仿真结果和实验测试结果证实了提出的AICDS结构可以提升高光谱应用读出电路的噪声性能,因此可以广泛应用。

一种基于MIPI D-PHY物理层的高速比较器

基于MIPI D-PHY物理层传输协议,文章设计一种高速低功耗的自偏置比较器电路,并对电路进行理论分析和仿真验证。该高速比较器总体结构由二级运放构成:共栅极和共源极以及工作在线性区的NMOS管组成第1级放大结构;电流源作负载的四管运放组成第2级放大结构。差分信号通过NMOS源极进行输入,提升信号的共模电压接收范围。电路结构中无额外电流源偏置,提高数据传输速率的同时减小了功耗。基于SMIC 0.18μm CMOS工艺设计,采用1.8 V电压供电,仿真结果表明:高速比较器能准确接收低共模电平的差分信号,直流增益为37.4 dB,传输速率达到2.5 Gb/s,功耗达到326μW/(Gb/s),可以接收到差分信号的共模电平范围为30~330 mV。

基于d-q理论的双馈异步电机定子匝间短路故障诊断方法

为实现双馈异步电机定子绕组匝间短路的早期故障诊断,提出一种基于转子电流特征频率分量的故障诊断方法 .首先,通过分析故障后电机的电磁特性,得到故障后转子电流中会产生特征频率分量的结论 .然后,通过对转子单相电流进行特殊的相移构造出三相电流,并对该组电流进行d-q变换,利用理论推导证明d-q变换后的直流分量幅值能够反映特征谐波的大小,将直流分量作为故障特征量进行双馈异步电机匝间短路故障的诊断.最后,依据有限元法建立故障前后电机的仿真模型,对不同工况下故障特征量的变化进行研究.结果表明:基于d-q理论提取的故障特征量与定子绕组的短路匝数呈正相关,且在转速变化和电压不平衡时也可有效诊断出匝间短路故障;通过设置合理的故障阈值可实现对双馈异步电机定子绕组匝间短路故障的精确辨识.研究结果可以为电机绕组匝间短路故障诊断提供参考.

3D_Multi Resistor Electric Circuit

This report addresses the issues concerning the analysis of an electric circuit composed of multiple resistors configured in a 3-Dimension structure. Noting, all the standard textbooks of physics and engineering irrespective of the used components are circuits assembled in two dimensions. Here, by deviating from the “norm” we consider a case where the resistors are arranged in a 3D structure;e.g., a cube. Although, independent of the dimension of the design the same physics principles apply, transitioning from a 2D to a 3D makes the corresponding analysis considerably challenging. In general, with no exception, depending on the used components the analysis faces with solving a set of either algebraic or differential-algebraic equations. Practically, this interfaces with a Computer Algebra System (CAS). The main objective is symbolically to identify the current distributions and the equivalent resistor (s) of cubically assembled resistors.

Three Dimensional Electric Circuits with Multiple Capacitors and Resistors

Two cubical 3D electric circuits with single and double capacitors and twelve ohmic resistors are considered. The resistors are the sides of the cube. The circuit is fed with a single internal emf. The charge on the capacitor(s) and the current distributions of all twelve sides of the circuit(s) vs. time are evaluated. The analysis requires solving twelve differential-algebraic intertwined symbolic equations. This is accomplished by applying a Computer Algebra System (CAS), specifically Mathematica. The needed codes are included. For a set of values assigned to the elements, the numeric results are depicted.

SGT-Net: A Transformer-Based Stratified Graph Convolutional Network for 3D Point Cloud Semantic Segmentation

In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently sparse.Therefore,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space.Most current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks.In this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range dependency.Specifically,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive field.In addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is effective.We demonstrate the validity and superiority of our method on the S3DIS and ShapeNet datasets.Through ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.

Three-dimensional(3D)parametric measurements of individual gravels in the Gobi region using point cloud technique

Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materials constituting the Gobi result in notable differences in saltation processes across various Gobi surfaces.It is challenging to describe these processes according to a uniform morphology.Therefore,it becomes imperative to articulate surface characteristics through parameters such as the three-dimensional(3D)size and shape of gravel.Collecting morphology information for Gobi gravels is essential for studying its genesis and sand saltation.To enhance the efficiency and information yield of gravel parameter measurements,this study conducted field experiments in the Gobi region across Dunhuang City,Guazhou County,and Yumen City(administrated by Jiuquan City),Gansu Province,China in March 2023.A research framework and methodology for measuring 3D parameters of gravel using point cloud were developed,alongside improved calculation formulas for 3D parameters including gravel grain size,volume,flatness,roundness,sphericity,and equivalent grain size.Leveraging multi-view geometry technology for 3D reconstruction allowed for establishing an optimal data acquisition scheme characterized by high point cloud reconstruction efficiency and clear quality.Additionally,the proposed methodology incorporated point cloud clustering,segmentation,and filtering techniques to isolate individual gravel point clouds.Advanced point cloud algorithms,including the Oriented Bounding Box(OBB),point cloud slicing method,and point cloud triangulation,were then deployed to calculate the 3D parameters of individual gravels.These systematic processes allow precise and detailed characterization of individual gravels.For gravel grain size and volume,the correlation coefficients between point cloud and manual measurements all exceeded 0.9000,confirming the feasibility of the proposed methodology for measuring 3D parameters of individual gravels.The proposed workflow yields accurate calculations of relevant parameters for Gobi gravels,providing essential data support for subsequent studies on Gobi environments.

Determination of the full-field stress and displacement using photoelasticity and sampling moirémethod in a 3D-printed model

The quantitative characterization of the full-field stress and displacement is significant for analyzing the failure and instability of engineering materials.Various optical measurement techniques such as photoelasticity,moiréand digital image correlation methods have been developed to achieve this goal.However,these methods are difficult to incorporate to determine the stress and displacement fields simultaneously because the tested models must contain particles and grating for displacement measurement;however,these elements will disturb the light passing through the tested models using photoelasticity.In this study,by combining photoelasticity and the sampling moirémethod,we developed a method to determine the stress and displacement fields simultaneously in a three-dimensional(3D)-printed photoelastic model with orthogonal grating.Then,the full-field stress was determined by analyzing 10 photoelastic patterns,and the displacement fields were calculated using the sampling moirémethod.The results indicate that the developed method can simultaneously determine the stress and displacement fields.

Influence of sampling on face measuring system based on composite structured light

Human face can be rebuilt to a three-dimensional (3 D) digital profile based on an optical 3D sensing system named Composite Fourier-Transform Profilometry (CFTP) where a composite structured light will be used. To study the sampling effect during the digitization process in practical CFTP, the pectinate function and convolution theorem were introduced to discuss the potential phase errors caused by sampling the composite pattern along two orthogonal directions. The selecting criterions of sampling frequencies are derived and the results indicate that to avoid spectral aliasing, the sampling frequency along the phrase variation direction must be at least four times as the baseband and along the orthogonal direction it must be at least three times as the larger frequency of the two carrier frequencies. The practical experiment of a model face reconstruction verified the theories.

20 MHz Switched-Current Sample-and-Hold Circuit with Low Charge Injection

A switched-current sample-and-hold circuit with low charge injection was proposed. To obtain low noise and charge injection, the zero-voltage switching was used to remove the signal-dependent charge injection, and the signal-independent charge injection was reduced by removing the feed-through voltage from the input port of the memory transistor directly. This current sample-and-hold circuit was implemented using CMOS 180 nm 1.8 V technology. For a 0.8 MHz sinusoidal signal input, the simulated signal-to-noise and distortion ratio and total harmonic distortion were improved from 53.74 dB and -51.24 dB to 56.53 dB and -54.36 dB at the sampling rate of 20 MHz respectively, with accuracy of 9.01 bit and power consumption of 0.44 mW.

A study on sampling strategies in the figure cognitive process

This study was aimed at investigating the sampling strategies for 2 types of figures: 3-D cubes and human faces. The research was focused on: (a) from where the sampling process started; (b) in what order the figures' features were sampled. The study consisted of 2 experiments: (a) sampling strategies for 3-D cubes; (b) sampling strategies for human faces. The results showed that: (a), for 3-D cubes, the first sampling was mostly located at the outline parts, rarely at the center part; while for human faces, the first sampling was mostly located at the hair and outline parts, rarely at the mouth or cheek parts, in most cases, the first sampling-position had no significant effects on cognitive performance and that (b), the sampling order, both for 3-D cubes and for human faces, was determined by the degree of difference among the sampled-features.

Increasing the Resolution and SNR of an ADC′s Measurement with a Method of Over- Sampling and Averaging

By analyzing the theory of over-sampling and averaging, the conclusion is educed that white noise accompanies the signal and the addition of each bit of resolution can be achieved via a fourfold sampling frequency. The addition of each bit will approximately increase the SNR （signal to noise ratio） to 6dB.

8位高速低功耗流水线型ADC优化设计研究

采用每级为1.5位精度的7级流水线结构,即7级子ADC设计一个8位80 MS/s的低功耗模数转换电路。通过设计精简且高效的数字校准和输出寄存模块,消除ADC实现过程中各种因素的影响,提高ADC的精度和信噪比。采用0.18μm CMOS工艺完成加工后,测得该ADC在输入信号为36.25 MHz,采样速率为80 MHz下的信噪比(SNR)为49.6 dB,有效位数(ENOB)接近8位,典型的功耗电流只有18 mA,整个ADC的芯片面积为0.5 mm^(2)。

ON EQUIVALENCE BETWEEN THE SEQUENTIAL CIRCUITS IN SERIES AND IN PARALLEL

Propose the sequential circuits with the ternary D-ffs in series^1. Discuss the equivalence between the sequential circuits with the p-valued flip-flops in series and in parallel as a part of studying the multiple valued logic circuits.

多光谱TDI-CMOS图像传感器读出电路设计

针对3D集成式多光谱TDI-CMOS图像传感器的数字化处理和高速读出需求,为了解决与TDICCD探测器的整体布局、物理尺寸和接口的匹配性和一致性问题,研制了适用于五谱段TDICCD的CMOS读出电路芯片。该读出电路芯片创新地设计了一种使用多相位ADC时钟、支持相关多次采样的新型列级单斜ADC电路结构,实现了TDICCD信号的数字化和高速输出,有效提升了探测器的动态范围和噪声指标。流片测试结果表明:读出电路芯片的功能正常,集成式TDICCD的成像效果良好,新型列级ADC工作正常,读出电路以最小9.5μs的行周期输出14 bit数据,相关多次采样具备降低输出信号噪声的作用,实现了TDICCD信号的高精度数字化处理和高速输出,满足3D集成式TDI-CMOS图像传感器的研制要求。

一种用于Pipeline ADC的高线性度栅压自举开关

在流水线模数转换器(Pipeline ADC)电路中,栅压自举开关中的非线性电容会对开关管的导通电阻产生直接的影响,导致采样非线性。设计了一种三路径的高线性度栅压自举开关,采用三个自举电容,分别构成两条主路径和一条辅助路径,使得输入信号在通过两条主路径传输到开关管栅端时加快栅端电压的建立,同时利用辅助路径驱动非线性电容,减少电路中非线性电容对采样电路线性度的影响,从而增强信号驱动能力,提高整体电路的精度。本文设计的栅压自举开关应用于14 bit 500 MHz流水线ADC的采样保持电路中。采用TSMC 28 nm CMOS工艺进行电路设计。仿真结果表明,在输入频率为249 MHz,采样频率为500 MHz的条件下,该栅压自举开关的信噪比(SNDR)达到92.85 dB,无杂散动态范围(SFDR)达到110.98 dB。