Design of Track/Hold (T/H) Circuit of Broad Bandwidth and High Speed Pipeline Structure ADC Based on the Super Frequency Application

A track/hold （T/H） circuit of broad bandwidth high speed pipeline structure ADC based on the super frequency application is designed in the paper. Some main factors affecting SNR of high speed ADC, such as aperture uncertainty, switch capacitor, and MOS switch, are analyzed. In the circuit, the full-differential structure and the bottom plate sampling technique are adopted to optimize the switch capacitors and MOS switches. The result based on the Spectre simulation on 0.35pm Bi- CMOS technology indicate that the aperture uncertainty, charge-injection, and non-linearity of clock feed-through are considerably restrained and the performance of T/H circuit is enhanced

Maneuvering target tracking using threshold interacting multiple model algorithm

To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model （TIMM） is proposed. This algorithm is based on the interacting multiple model （IMM） method and applies a threshold controller to improve tracking accuracy. It is also applicable to other advanced algorithms of IMM. In this research, we also compare the position and velocity root mean square （RMS） errors of TIMM and IMM algorithms with two different examples. Simulation results show that the TIMM algorithm is superior to the traditional IMM alzorithm in estimation accuracy.

Track-MT3:一种基于Transformer的新型多目标跟踪算法

针对复杂环境中多目标跟踪数据关联难度大、难以实现目标长时间稳定跟踪的问题,该文创新性地提出了一种基于Transformer网络的端到端多目标跟踪模型Track-MT3。首先,引入了检测查询和跟踪查询机制,隐式地执行量测-目标的数据关联并且实现了目标的状态估计任务。然后,采用跨帧目标对齐策略增强跟踪轨迹的时间连续性。同时,设计了查询变换与时间特征编码模块强化目标运动建模能力。最后,在模型训练中采用了集体平均损失函数,实现了模型性能的全局优化。通过构造多种复杂的多目标跟踪场景,并利用多重性能指标进行评估,Track-MT3展现了优于MT3等基线方法的长时跟踪性能,与JPDA和MHT方法相比整体性能分别提高了6%和20%,能够有效挖掘时序信息,在复杂动态环境下实现稳定、鲁棒的多目标跟踪。

基于改进Tracktor的行人多目标跟踪算法

在多目标视频跟踪中,针对受交互遮挡等影响导致检测偏差从而致使目标身份丢失的问题,提出一种基于改进Tracktor的行人多目标跟踪算法DUTracktor。在检测框回归中设计一个动态更新模块,利用孪生网络对建议框进一步检测定位;利用时序信息增强模块更新当前帧更适合的模板,建立全局上下文关系;并通过像素相关进行特征融合,从而增强目标边缘信息和尺度信息;利用相机运动补偿和融合相似矩阵构建二级关联跟踪机制,建立检测框和轨迹更强大的关联性,提高目标跟踪的鲁棒性。在公开的MOT16数据集上进行实验测试,并与当前主流算法相比,该算法跟踪精度表现较优,具有良好的鲁棒性,FPS稳定在24帧。

BEVTrack:基于难例挖掘训练的端到端三维多目标跟踪方法

多目标跟踪已经成为自动驾驶系统中的一个关键组成部分,其目的是在连续的视频流与点云流中识别、定位并标识所有感兴趣的目标。目前三维多目标跟踪方法多依赖人工多阶段调参以保证整体跟踪性能,难以对复杂遮挡或运动进行有效建模。而现有的三维端到端多目标跟踪方法,如MUTR等,精度普遍较低。其核心原因为三维空间中的特征聚合和感知相对于二维图像更具挑战性,简单的网络难以实现复杂的三维特征聚合,并大量的噪声信息与难例信息干扰严重,影响模型的特征提取能力。针对以上问题,本文提出了一种基于难例挖掘训练的端到端多目标跟踪框架BEVTrack。针对三维特征关联问题,本文设计了基于鸟瞰图(BEV)位置编码的三维跟踪查询。通过基于BEV特征的三维跟踪查询,本文方法能够更好地将跟踪查询与实际三维特征进行有效关联,从而大幅度提升了跟踪精度。同时,模型依靠BEV数据进行特征关联,仅需轻量化的网络便可以实现快速有效的跟踪。针对数据噪声问题,本文提出了面向多目标跟踪的难例挖掘训练,通过针对检测难例与跟踪难例分别处理,训练模型去除检测错误噪声与跟踪匹配的能力,从而提升在真实场景下模型处理噪声信息与难例干扰的能力。在实验结果方面,基于Nuscenes数据集,我们进行了大量的对比实验与模型消融实验,实验结果证明本文的方法在该数据集上取得了领先的性能。

An adaptive waveform-detection threshold joint optimization method for target tracking

The joint optimization of detection threshold and waveform parameters for target tracking which comes from the idea of cognitive radar is investigated for the modified probabilistic data association(MPDA)filter.The transmitted waveforms and detection threshold are adaptively selected to enhance the tracking performance.The modified Riccati equation is adopted to predict the error covariance which is used as the criterion function,while the optimization problem is solved through the genetic algorithm(GA).The detection probability,false alarm probability and measurement noise covariance are all considered together,which significantly improves the tracking performance of the joint detection and tracking system.Simulation results show that the proposed adaptive waveform-detection threshold joint optimization method outperforms the adaptive threshold method and the fixed parameters method,which will reduce the tracking error.The average reduction of range error between the adaptive joint method and the fixed parameters method is about 0.6 m,while that between the adaptive joint method and the adaptive threshold only method is about 0.3 m.Similar error reduction occurs for the velocity error and acceleration error.

A High Performance Track and Hold Circuit for High-Resolution High-Speed ADC

Design of a high performance track and hold （T/H） circuit for high-resolution high-speed analog-to-digital converter （ADC） is presented, which has been implemented in 0.18 μm CMOS process. An improved bootstrapped and bulk-switching technique is introduced to greatly minimize the nonlinearity of sampling network over a wide bandwidth, and the addition of a modified pre-charge circuit helps reducing the total power consumption. The experimental results show that the proposed T/H circuit achieves over 77 dB SFDR （spurious-free dynamic range） and 70 dB THD （total harmonic distortion） at 100 MHz sampling rate and maintains the performance with input frequency up to 305 MHz while consuming 47 mW power.

基于改进CStrack关联策略的多目标跟踪算法

针对复杂场景下目标外观变化明显、运动不规律易导致轨迹中断和身份切换频繁等问题,从重识别(Re-Identification,Re-ID)特征、数据关联和插值等方面对跟踪器进行改进,提出基于改进CStrack关联策略的多目标跟踪算法。使用外观特征更新模块,减小因视角改变、目标移动导致特征剧烈变化而产生的影响,增强特征间的关联。提出二次关联方法,根据高低置信度检测结果的特点,使用不同的度量方式进行二次关联:第一次关联使用IoU距离融合外观特征作为关联的代价矩阵,第二次使用扩展IoU关联,缓解运动估计偏差、外观不可区分导致度量失效的问题;采用高斯回归算法,考虑运动信息,通过插值补偿漏检。在MOT17、MOT20数据集上进行测试,跟踪精度分别达到73.9%、64.2%。实验结果表明,该方法在跟踪精度上有明显优势,能够较好地适应复杂场景。

Computer Program Calculation for Distortion of Wide-Band Track and Hold Amplifier

Tow different computer calculation methods for distortion of the wide-band diode bridge track and hold amplifier (THA) are presented based on a high frequency Schottky diode model. One of the computer programs calculates the distortion of weekly nonlinear THA based on the KCL and the nonlinear-current method. The other calculates the weekly nonlinear distortion by using a Volterra series method and a nodal formulation. Comparative calculation results for the diode bridge THA have shown good agreement with these two computer program calculation methods, whereas the overall computational efficiency of the nonlinear-current method is better than that of the nodal formulation method in a special evaluation.

基于Bytetrack的多目标跟踪算法在斑马鱼毒性行为识别中的应用

利用计算机视觉技术识别斑马鱼(Danio rerio)在不同污染物暴露下的行为变化是水质毒性评价的常用方法之一,但传统方法存在效率低、面对遮挡和复杂环境时性能差等缺陷。针对这些问题,本研究使用基于Bytetrack的多目标跟踪算法追踪斑马鱼在4种污染物(Zn、Pb、Cr和苯酚)暴露2 h后的行为变化,对斑马鱼在4种浓度梯度中的平均速度、最大速度、最低速度、平均碰撞次数和行为轨迹等指标进行分析。结果显示,算法的追踪精度、漏检率和检测时间(每300帧)分别能达到90.26%、16.33%和0.19 min,检测时间和精度相比于传统目标检测方法有较大提升。同时,根据污染物不同,该方法能准确识别特定污染物环境中斑马鱼相应的运动状态及轨迹变化,可实现精确识别和实时响应,在鱼类毒性行为识别领域具有重要参考意义。

基于改进YOLOv7-ByteTrack的干制哈密大枣缺陷检测与计数系统

针对目前无法同时对随机多列排布干制哈密大枣进行快速缺陷检测和统计计数问题,该研究设计了一款干制哈密大枣在线检测与计数系统。以干制哈密大枣为研究对象,利用工业相机拍摄传送带上随机排列的多类别缺陷干制哈密大枣视频为数据源,采用改进的YOLOv7模型进行干制哈密大枣多类别缺陷检测并将检测结果作为后续多目标跟踪算法的输入;考虑到传送带上干制哈密大枣的外观相似性高以及排列密集等特点,该研究结合ByteTrack多目标跟踪算法的思想,设计了一种多类别干制哈密大枣的画线计数方法,实现了随机排布多类别干制哈密大枣的缺陷检测、准确定位及计数。试验结果表明:1)改进的YOLOv7模型浮点计算量为64.6 G,在干制哈密大枣目标检测数据的测试集上的平均检测精度、召回率、F_(1)平衡分数分别达到了98.03%、93.43%和95.00%,相比YOLOv7模型分别提高了4.40、6.88和7.00个百分点,浮点计算量下降了38.6%;2)基于改进YOLOv7为目标检测器开发的ByteTrack算法计数模型对干制哈密大枣计数的准确率为90.12%。该研究可为干制哈密大枣检测计数和分选分级提供技术支持。

Experimental and numerical settlement analysis of railway track over geogrid reinforced ballast

Purpose-The main objective of the present research is to investigate the benefits of using geogrid reinforcement in minimizing the rate of deterioration of ballasted rail track geometry resting on soft clay and to explore the effect of load amplitude,load frequency,presence of geogrid layer in ballast layer and ballast layer thickness on the behavior of track system.These variables are studied both experimentally and numerically.This paper examines the effect of geogrid reinforced ballast laying on a layer of clayey soil as a subgrade layer,where a half full scale railway tests are conducted as well as a theoretical analysis is performed.Design/methodologylapproach-The experimental tests work consists of laboratory model tests to investigate the reduction in the compressibility and stress distribution induced in soft clay under a ballast railway reinforced by geogrid reinforcement subjected to dynamic load.Experimental model based on an approximate half scale for general rail track engineering practice is adopted in this study which is used in Iraqi railways.The investigated parameters are load amplitude,load frequency and presence of geogrid reinforcement layer.A half fuli-scale railway was constructed for carrying out the tests,which consists of two rails 800 mm in iength with three w00den sleepers(900 mm×90 mm×90 mm).The ballast was overlying 500 mm thick clay layer.The tests were carried out with and without geogrid reinforcement,the tests were carried out in a well tied steel box of 1.5 m length X 1 m width X 1 m height.A series of laboratory tests were conducted to investigate the response of the ballast and the clay layers where the ballast was reinforced by a geogrid.Settlement in ballast and clay,was measured in reinforced and unreinforced ballast cases.In addition to the laboratory tests,the application of numerical analysis was made by using the finite element program PLAXIS 3D 2013.Findings-It was concluded that the settlement increased with increasing the simulated train load amplitude,there is a sharp increase in settlement up to the cycle 500 and after that,there is a gradual increase to level out between,2,500 and 4,500 cycles depending on the load frequency.There is a little increase in the induced settlement when the load amplitude increased from 0.5 to i ton,but it is higher when the load amplitude increased to 2 ton,the increase in settlement depends on the geogrid existence and the other studied parameters.Both experimental and numerical results showed the same behavior.The effect of load frequency on the settlement ratio is almost constant after 500 cycles.In general,for reinforced cases,the effect of load frequency on the settlement ratio is very small ranging between 0.5 and 2%compared with the unreinforcedcase.Originalitylvalue-Increasing the ballast layer thickness from 20 cm to 30 cm leads to decrease the settlement by about 50%.This ascertains the efficiency of ballast in spreading the waves induced by the track.

Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints

The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this paper.Herein, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy,without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.

Label-free in-vivo classi-cation and tracking of red blood cells and platelets using Dynamic-YOLOv4 network

In-vivo flow cytometry is a noninvasive real-time diagnostic technique that facilitates continuous monitoring of cells without perturbing their natural biological environment,which renders it a valuable tool for both scientific research and clinical applications.However,the conventional approach for improving classification accuracy often involves labeling cells with fluorescence,which can lead to potential phototoxicity.This study proposes a label-free in-vivo flow cytometry technique,called dynamic YOLOv4(D-YOLOv4),which improves classification accuracy by integrating absorption intensity fluctuation modulation(AIFM)into YOLOv4 to demodulate the temporal features of moving red blood cells(RBCs)and platelets.Using zebrafish as an experimental model,the D-YOLOv4 method achieved average precisions(APs)of 0.90 for RBCs and 0.64 for thrombocytes(similar to platelets in mammals),resulting in an overall AP of 0.77.These scores notably surpass those attained by alternative network models,thereby demonstrating that the combination of physical models with neural networks provides an innovative approach toward developing label-free in-vivoflow cytometry,which holds promise for diverse in-vivo cell classification applications.

Masked Autoencoders as Single Object Tracking Learners

Significant advancements have beenwitnessed in visual tracking applications leveragingViT in recent years,mainly due to the formidablemodeling capabilities of Vision Transformer(ViT).However,the strong performance of such trackers heavily relies on ViT models pretrained for long periods,limitingmore flexible model designs for tracking tasks.To address this issue,we propose an efficient unsupervised ViT pretraining method for the tracking task based on masked autoencoders,called TrackMAE.During pretraining,we employ two shared-parameter ViTs,serving as the appearance encoder and motion encoder,respectively.The appearance encoder encodes randomly masked image data,while the motion encoder encodes randomly masked pairs of video frames.Subsequently,an appearance decoder and a motion decoder separately reconstruct the original image data and video frame data at the pixel level.In this way,ViT learns to understand both the appearance of images and the motion between video frames simultaneously.Experimental results demonstrate that ViT-Base and ViT-Large models,pretrained with TrackMAE and combined with a simple tracking head,achieve state-of-the-art(SOTA)performance without additional design.Moreover,compared to the currently popular MAE pretraining methods,TrackMAE consumes only 1/5 of the training time,which will facilitate the customization of diverse models for tracking.For instance,we additionally customize a lightweight ViT-XS,which achieves SOTA efficient tracking performance.

Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

Practical prescribed-time fuzzy tracking control for uncertain nonlinear systems with time-varying actuators faults

The paper investigates the practical prescribed-time fuzzy tracking control problem for a category of nonlinear system subject to time-varying actuator faults.The presence of unknown nonlinear dynamics and actuator faults makes achieving tracking control within a prescribed-time challenging.To tackle this issue,we propose a novel practical prescribed-time fuzzy tracking control strategy,which is independent of the initial state of the system and does not rely on precise modeling of the system and actuators.We apply the approximation capabilities of fuzzy logic systems to handle the unknown nonlinear functions and unidentified actuator faults in the system.The piecewise controller and adaptive law constructed based on piecewise prescribed time-varying function and backstepping technique method establish the theoretical framework of practical prescribed-time tracking control,and extend the range of prescribed-time tracking control to infinity.Regardless of the initial conditions,the proposed control strategy can guarantee that all signals remain uniformly bounded within the practical prescribed time in the presence of unknown nonlinear item and time-varying actuator faults.Simulation example is presented to demonstrate the effectiveness of the proposed control strategy.

A vertical track nonlinear energy sink

Eliminating the effects of gravity and designing nonlinear energy sinks(NESs)that suppress vibration in the vertical direction is a challenging task with numerous damping requirements.In this paper,the dynamic design of a vertical track nonlinear energy sink(VTNES)with zero linear stiffness in the vertical direction is proposed and realized for the first time.The motion differential equations of the VTNES coupled with a linear oscillator(LO)are established.With the strong nonlinearity considered of the VTNES,the steady-state response of the system is analyzed with the harmonic balance method(HBM),and the accuracy of the HBM is verified numerically.On this basis,the VTNES prototype is manufactured,and its nonlinear stiffness is identified.The damping effect and dynamic characteristics of the VTNES are studied theoretically and experimentally.The results show that the VTNES has better damping effects when strong modulation responses(SMRs)occur.Moreover,even for small-amplitude vibration,the VTNES also has a good vibration suppression effect.To sum up,in order to suppress the vertical vibration,an NES is designed and developed,which can suppress the vertical vibration within certain ranges of the resonance frequency and the vibration intensity.

改进YOLOv7+Bytetrack的小目标检测与追踪

近年来,目标检测技术已经相当成熟,但小目标检测一直是目标检测领域的一大挑战。为了解决这一问题,设计一种名为MFF-YOLOv7的小目标检测算法,该算法旨在提高小目标检测的准确率。设计级联双向特征金字塔KBiFPN,以及联合提出的多级感受野特征聚合模块MFA,来聚合浅层特征并增强特征的信息表达能力。为了解决小目标漏检问题,设计了新的解耦头和新的注意力机制。新的解耦头对小目标的检测能力更强,新的注意力机制可以重点关注感兴趣的小目标区域。引入了一种新的损失函数ECIOU,旨在加快模型的收敛速度。为了验证模型的性能,分别在三个小目标数据集上进行了实验。实验结果表明,MFF-YOLOv7算法提高了检测精度。同时,使用多目标追踪Bytetrack算法在MOT17和VisDrone2019-MOT两个多目标追踪数据集上对新模型进行了验证,进一步证明了其有效性。此外,MFF-YOLOv7算法在动态视频追踪中表现出了良好的性能。

Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer

This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.