Simultaneous Tracking Research Articles

Monitoring spring leaf phenology of individual trees in a temperate forest fragment with multi-scale satellite time series

Forest fragmentation has been increasingly exacerbated by deforestation, urbanization, and agricultural expansion. Monitoring the forest fragments via the lens of tree-crown scale leaf phenology is critical to understand tree species phenological responses to climate change and identify the fragment species vulnerable to environmental disturbance. Despite advances in remote sensing for phenology monitoring, detecting tree-crown scale leaf phenology in fragmented forests remains challenging. Simultaneous tracking of key spring phenological events that are crucial to ecosystem functions and climate change responses is also neglected. To address these challenges, we develop a novel tree-crown scale remote sensing phenological monitoring framework to characterize all the critical spring phenological events of individual trees of deciduous forest fragments, with Trelease Woods in Champaign, Illinois as a case study. The novel framework comprises four components: 1) generate high spatiotemporal resolution fusion imagery from multi-scale satellite time series with a hybrid deep learning fusion model; 2) calibrate PlanetScope imagery time series with fusion data using histogram matching; 3) model tree-crown scale phenology trajectory with a Beck logistic-based method; 4) detect a diversity of tree-crown scale phenological events using several phenological metric extraction methods (i.e., threshold- and curve feature-based methods). Combined with weekly in-situ phenological observations of 123 individual trees across 12 broadleaf species from 2017 to 2020, the framework effectively bridges the satellite- and field-based phenological measures for the key spring phenological events (i.e., budswell, budburst, leaf expansion, and leaf maturity events) at the tree-crown scale, particularly for large individuals (RMSE <1 week for most events). Calibration of PlanetScope imagery using multi-scale satellite fusion data in consideration of landscape fragmentation is critical for monitoring tree phenology of forest fragments. Compared to curve feature-based methods, threshold-based phenometric extraction methods demonstrate enhanced capability in detecting spring leaf phenological dynamics of individual trees. Among the phenological events, full leaf out and early leaf expansion events are retrieved with high accuracy using calibrated PlanetScope time series (RMSE from 3 to 5 days and R-squared higher than 0.8). With both intensive satellite and field phenological efforts, this novel framework is at the forefront of interpreting tree-crown scale remotely sensed phenological metrics in the context of biologically meaningful field phenological events in fragmented forest setting.

A Bayesian track management scheme for improved multi‐target tracking and classification in drone surveillance radar

AbstractIn this article, a simple, yet effective, Bayesian scheme for tracks maintenance, promotion, and deletion in drone surveillance radar is presented. It enables the simultaneous tracking of the target body and micro‐Doppler components that originate from the motion of rotors (if any) onboard an unmanned air system. This not only delivers more accurate multi‐target tracking, but also substantially improves the radar automatic target classification capability (e.g. discriminating between drone and non‐drone targets). Challenging and diverse real staring radar datasets are used here to demonstrate the efficacy and benefits of the proposed track management approach.

Gyroscopic Balancer-Enhanced Motion Control of an Autonomous Bikebot

Abstract Bikebot (i.e., bicycle-based robot) is a class of underactuated balance robotic systems that require simultaneous trajectory tracking and balance control tasks. We present a tracking and balance control design of an autonomous bikebot. The external-internal convertible structure of the bikebot dynamics is used to design a causal feedback control to achieve both the tracking and balance tasks. A balance equilibrium manifold is used to define and capture the platform balance profiles and coupled interaction with the trajectory tracking performance. To achieve fully autonomous navigation, a gyrobalancer actuation is integrated with the steering and velocity control for stationary platform balance and stationary-moving switching. Stability and convergence analyses are presented to guarantee the control performance. Extensive experiments are presented to validate and demonstrate the autonomous control design. We also compare the autonomous control performance with human riding experiments and similar action strategies are found between them.

Active control for vehicle suspension using a self-powered dual-function active electromagnetic damper

Concerning vehicle suspension techniques, active suspension tends to offer the most precise control performance compared to passive and semi-active alternatives. However, due to its high-power consumption, its practical use is often limited. This study aims to develop an innovative self-powered active suspension by assessing the performance and feasibility of a dual-function active electromagnetic damper (DF-AEMD), which is capable of providing simultaneous force tracking and energy harvesting functions. In addition to outlining its working mechanism, the DF-AEMD is utilized to track the full-loop target control force calculated from the classic linear quadratic regulator in a self-powered manner. From the force tracking standpoint, the DF-AEMD can not only provide damping forces, but also generate actuation forces, and thus achieve better tracking accuracies of target control force and control performances for vehicle suspension than the traditional semi-active damper; while from the energy harvesting aspect, sufficient energy can be harvested simultaneously to realize the self-powered active control. Systematic simulations are conducted to discuss the relationship between control performance and energy harvesting efficiency of the DF-AEMD.

Simultaneous Tracking and Recognizing Drone Targets with Millimeter-Wave Radar and Convolutional Neural Network

In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view, including drone and non-drone targets. The analysis of the received signals allows multiple targets to be distinguished because of their different reflection patterns. The proposed framework consists of four processes: signal processing, cloud point clustering, target tracking, and target recognition. Signal processing translates the raw collected signals into spare cloud points. These points are merged into several clusters, each representing a single target in three-dimensional space. Target tracking estimates the new location of each detected target. A novel convolutional neural network model was designed to extract and recognize the features of drone and non-drone targets. For the performance evaluation, a dataset collected with an IWR6843ISK mmWave sensor by Texas Instruments was used for training and testing the convolutional neural network. The proposed recognition model achieved accuracies of 98.4% and 98.1% for one and two targets, respectively.

Simultaneous flow measurement and deformation tracking for passive flow control experiments involving fluid–structure interactions

Fluid–structure interactions (FSI) on highly flexible structures involve large deformations and require specific techniques for a thorough investigation of the flow field and structural deformation. To this purpose, a physics-informed method is introduced that allows for simultaneous determination of the flow fields and the structural deformation by using Particle Image Velocimetry (PIV) raw images. The method combines apriori knowledge of the mechanical characteristics of the flexible structure with classical image processing techniques for segmentation. PIV recordings of an actively pitched, highly deformable hydrodynamic profile experiment in a closed water tunnel serve as an example case. To achieve accurate results, the contour obtained from image segmentation is further defined under the assumption that its flexure can be described with the Euler–Bernoulli beam theory model. This makes it possible to determine the neutral fiber of the structure and the final reconstruction becomes possible from knowledge of the original geometry. The resulting procedure allows for a recognition of the structure itself and is suitable for cross-section deformation measurements and for masking of the structure in the raw images to improve the PIV processing. A test case comprising synthetic data similar to the application with a modeled profile geometry of known shape is used to investigate the accuracy of the method and its validity for deformation measurements. Under conditions of cyclic dynamic stall, a mean absolute error of 0.84° could be reached, with a deterioration up to 2° mean absolute error under static stall. The method has a major advantage compared to other technically more sophisticated and complex methods, such as the combination of Laser interferometers combined with Laser-Doppler Anemometry: the method allows for the usage of a single data source for both, fluid and solid in a unified measurement method. Therefore a direct comparison of instantaneous flow field and deformation is possible. In consequence it is in particular useful for highly dynamic multi-physical processes involving extreme deformations, such as passive flow control and soft actuated or flexible under water robotics.

High-frequency measurement of concentration in an isothermal methane–air gas mixture using spontaneous Raman spectroscopy

A high-frequency (1.5 kHz) spontaneous Raman spectroscopy measurement technique is developed and applied to measure external fluctuations generated in the local concentration of an isothermal binary gas mixture of methane and air. Raman excitation is provided by a high-frequency laser at 527 nm in dual-pulsed mode. The Stokes Raman signal is collected using an EMCCD camera coupled to a high-frequency intensifier as a shutter. The emitted signal is collected over the 596–627 nm wavelength range, which allows for the simultaneous tracking of methane and nitrogen Stokes Q-branch mode signals. Calibration curves are initially obtained for each species ({text{CH}}_{4} and {mathrm{N}}_{2}) based on steady-state concentrations, and further corrected during use to detect local unsteady mixture fluctuations at gas pulsation frequencies up to 250 Hz. The main novelty is the demonstration of Raman spectroscopy for the simultaneous multispecies measurement of unsteady concentrations of gas-phase methane and air mixtures using a laser beam with a high-repetition rate, low energy per pulse, combined with a high-frequency intensifier and a single camera.

Inorganic nanoparticle-integrated mesenchymal stem cells: A potential biological agent for multifaceted applications.

Mesenchymal stem cell (MSC)-based therapies are flourishing. MSCs could be used as potential therapeutic agents for regenerative medicine due to their own repair function. Meanwhile, the natural predisposition toward inflammation or injury sites makes them promising carriers for targeted drug delivery. Inorganic nanoparticles (INPs) are greatly favored for their unique properties and potential applications in biomedical fields. Current research has integrated INPs with MSCs to enhance their regenerative or antitumor functions. This model also allows the in vivo fate tracking of MSCs in multiple imaging modalities, as many INPs are also excellent contrast agents. Thus, INP-integrated MSCs would be a multifunctional biologic agent with great potential. In this review, the current roles performed by the integration of INPs with MSCs, including (i) enhancing their repair and regeneration capacity via the improvement of migration, survival, paracrine, or differentiation properties, (ii) empowering tumor-killing ability through agent loaded or hyperthermia, and (iii) conferring traceability are summarized. An introduction of INP-integrated MSCs for simultaneous treatment and tracking is also included. The promising applications of INP-integrated MSCs in future treatments are emphasized and the challenges to their clinical translation are discussed.

Macropinoscope: Real-Time Simultaneous Tracking of pH and Cathepsin B Activity in Individual Macropinosomes.

A fluorescent sensor that allows simultaneous analysis of environmental factors in a limited cellular space is useful for understanding precise molecular interactions in live cells and their biological responses. Macropinocytosis is a ubiquitous endocytic pathway for massive uptake of extracellular fluids, resulting in the formation of macropinosomes. Although macropinocytosis may impact intracellular delivery and cancer proliferation, information on the intracellular behaviors of macropinosomes is limited. Here, we aimed to develop a macropinoscope, a sensor that simultaneously detects pH and cathepsin B activity in individual macropinosomes. A macropinosome-specific marker, dextran (70 kDa), was employed as a platform, onto which fluorescein, Oregon Green, and tetramethylrhodamine were loaded for ratiometric pH sensing and imaging. A cathepsin-B-cleavable peptide sequence bearing sulfo-Cy5 and the quencher BHQ-3 was also mounted; cleavage of the sequence was detected as an increase in sulfo-Cy5 fluorescence. A steep decrease in pH was observed 5-10 min after macropinosome formation, which was accompanied by an immediate increase in cathepsin B activity. Our design concept will lead to the development of other macropinoscopes for the simultaneous detection of other parameters in individual macropinosomes.

Development of an Automatic Solar Tracker Control System for a Tandem-Winged UAV and Its Implementation Strategies

Solar power is a popular option for powering Unmanned Aerial Vehicles (UAVs) due to its ability to provide power for long-endurance flight. However, solar-powered UAVs face challenges, including operational reliability problems. To address their operational reliability issues, this paper proposes an automatic solar tracker system and tests two implementation strategies for controlling a tandem-winged and solar-powered UAV, developed by the Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung. Due to its unconventional configuration, a mathematical model for the UAV is developed to evaluate the vehicle’s dynamic characteristics. Based on these, three feedback control systems are designed, i.e., roll attitude control, a heading tracker, and a solar tracker. Two implementation strategies are proposed to combine the control systems, i.e., the mode-switching and the simultaneous tracking strategies. A series of simulations are then conducted to check the vehicle’s overall flight performance, as well as the gathering of solar energy. The mode-switching strategy was able to gather up to 13% more solar energy than the simultaneous strategy, which could only reach slightly above 2%. Mode-switching, however, resulted in a shorter range compared to the latter, due to the time spent in the charging mode, flying in a circular pattern.

Simultaneous 3D tracking and temperature measurements of L-PBF spatter particles using a single camera

Simultaneous 3D tracking and temperature measurements of L-PBF spatter particles using a single camera

Efficient and Accurate Object Detection With Simultaneous Classification and Tracking Under Limited Computing Power

Interacting with the environment, such as object detection and tracking, is a crucial ability of mobile robots. Besides high accuracy, efficiency in terms of processing effort and energy consumption are also desirable. To satisfy both requirements, we propose a detection framework based on simultaneous classification and tracking in the point stream. In this framework, a tracker performs data association in sequences of the point cloud, guiding the detector to avoid redundant processing (i.e. classifying already-known objects). For objects whose classification is not sufficiently certain, a fusion model is designed to fuse selected key observations that provide different perspectives across the tracking span. Therefore, performance (accuracy and efficiency of detection) can be enhanced. This method is particularly suitable for detecting and tracking moving objects, a process which would require expensive computations if solved using conventional procedures. Experiments were conducted on the benchmark dataset, and the results showed that the proposed method outperforms original tracking-by-detection approaches in both efficiency and accuracy.

Multimode Control of a Parallel-Mobile-Coil System for Adaptable Large-Workspace Microrobotic Actuation

Recent studies on magnetic microrobotics for biomedical applications have resulted in varieties of magnetic actuation systems. However, the simultaneous actuation and tracking of magnetic microrobots in large workspace (e.g., the human-body size) remains challenging due to the fast decay of magnetic fields and the contradiction between resolution and field of view of a feedback instrument. Our previous work reported the concept of the parallel-mobile-coil system (PMCS), which comprises three electromagnetic coils that can move in large 3-D space for microrobotic actuation. Here, to make the PMCS have adaptability to different actuation scenarios or microrobot types, we propose three different control modes by utilizing the mobility of the coils. The torque control, force-assisted torque control, and force control modes are designed for pure torque actuation with energy conservation, torque actuation with force assistance, and pure force actuation, respectively. Comparative simulations and experiments validate the effectiveness of the three control modes. Moreover, to endow the system with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-vivo</i> feedback capability, we implement Hall-effect sensors and demonstrate the 3-D large-workspace actuation based on magnetic localization.

Biological condensates form percolated networks with molecular motion properties distinctly different from dilute solutions.

Formation of membraneless organelles or biological condensates via phase separation and related processes hugely expands the cellular organelle repertoire. Biological condensates are dense and viscoelastic soft matters instead of canonical dilute solutions. To date, numerous different biological condensates have been discovered, but mechanistic understanding of biological condensates remains scarce. In this study, we developed an adaptive single-molecule imaging method that allows simultaneous tracking of individual molecules and their motion trajectories in both condensed and dilute phases of various biological condensates. The method enables quantitative measurements of concentrations, phase boundary, motion behavior, and speed of molecules in both condensed and dilute phases, as well as the scale and speed of molecular exchanges between the two phases. Notably, molecules in the condensed phase do not undergo uniform Brownian motion, but instead constantly switch between a (class of) confined state(s) and a random diffusion-like motion state. Transient confinement is consistent with strong interactions associated with large molecular networks (i.e., percolation) in the condensed phase. In this way, molecules in biological condensates behave distinctly different from those in dilute solutions. The methods and findings described herein should be generally applicable for deciphering the molecular mechanisms underlying the assembly, dynamics, and consequently functional implications of biological condensates.

Information Segregating Towards Simultaneous Tracking and Imaging Based on Ghost Imaging

Simultaneously tracking and imaging moving objects has been an open problem, due to the requirement on a large amount of information and the low photon flux during the very moment that the object can be taken as stationary. Information of the moving object can be decomposed into motion information of the centroid and image of the object. The amount of motion information can be far less than that for imaging. Therefore, we propose to solve the problem by information segregating. That is, to obtain the motion trajectory and gradually achieve the image during the tracking process. As an example, we unify tracking and imaging within a single system, based on ghost imaging with a four-quadrant detector. The differential signals from the quadrant detector are used for tracking, and the sum signals are for imaging. Experimental results show that our method provides high tracking accuracy and high-quality imaging. For objects of angular velocity up to 165 mrad/s, the tracking accuracy reaches 1/7 of the imaging resolution. Possible influences from different practical factors are also discussed.

Sound zone control with fixed acoustic contrast and simultaneous tracking of acoustic transfer functions

As environmental changes are inevitable, online algorithms that can track the variation of acoustic transfer functions are preferred in sound zone control (SZC) systems. In this paper, it is found that for the time domain SZC, the nonuniqueness problem caused by the coupling in multichannel transfer function modeling can be solved with time-varying control filters. To keep a stable acoustic contrast (AC) performance irrespective of environmental changes and also avoid the extreme distortion introduced by maximizing the AC in the time domain, the objective of maintaining a desired level of AC is used and an adaptive control algorithm is developed. The simulations validate that the proposed method tracks the environmental changes and adapts the control filters to give a stable AC performance.

Design of an optical system for a Multi-CubeSats debris surveillance mission

The detection and observation of space debris in Low Earth Orbit is generally carried out through the use of ground based radars and telescopes. These instruments allow for a precise reconstruction of the space debris trajectories, and therefore represent a key asset for planning avoidance maneuvers when threats of collisions are predicted.The recent deployment of mega-constellations, with the consequent increase of the number of satellites, imposes new challenges in terms of simultaneous tracking capability and readiness of the current space situational awareness systems. This adds to the current need to track small and dull objects to further mitigate the probability of triggering cascade collisions. However, ground based observations are limited due to their intrinsic sensibility to atmospheric refraction, their diurnal inoperability and their dependence on meteorological hazards.This paper proposes to study the feasibility and the benefits of a potential deployment of a constellation of CubeSats in Low Earth Orbit, to acquire optical observations of space debris with enhanced accuracy, as part of the ORCA mission: Orbit Refinement for Collision Avoidance. Here, the focus is on the optical design of the payload instrument to be integrated onboard of the orbiting platforms.The study trades-off the current state-of-art of optical detection technologies, by assessing their performance against a set of specific requirements: (a) the minimization of the uncertainty associated to the image resolution; (b) a field of view that maximizes the extent of the monitored area; (c) an optimal exposure time to avoid under or overexposure of the image; (d) minimization of the effects of light diffraction and above all, (e) the maximization of the signal to noise ratio to detect the smallest and dullest objects possible.Several configurations of optical systems are then chosen as suitable for the ORCA constellation, also considering the system design implications of its integration into a CubeSat, such as size requirements. Commercial Off-The-Shelf hardware are explored and performances of the optical system are evaluated through numerical simulations in order to estimate the detectable sizes of space debris while taking into account their potential distances from the sensor. This paper concludes with estimates of the impact of the ORCA mission on space situational awareness for decades to come.

Optimal Clustering of Point Cloud by 2D-LiDAR Using Kalman Filter

Light detection and ranging (LiDAR) has been the primary sensor for autonomous mobility and navigation system owing to its stability. Although multiple-channel LiDAR (3D-LiDAR) can obtain dense point clouds that provide optimal performance for several tasks, the application scope is limited by its high-cost. When employing single channel LiDAR (2D-LiDAR) as a low-cost alternative, the quantity and quality of the point cloud cause conventional methods to perform poorly in clustering and tracking tasks. In particular, when handling multiple pedestrian scenarios, the point cloud is not distinguished and clustering is unable to succeed. Hence, we propose an optimized clustering method combined with a Kalman filter (KF) for simultaneous clustering and tracking applicable to 2D-LiDAR.

Smoothing filter design: A general framework

Signal filtering/smoothing is a challenging problem arising in many applications ranging from image, speech, radar and biological signal processing. In this paper, we present a general framework to signal smoothing. The key idea is to use a suitable linear (time-variant or time-invariant) differential equation model in the regularization of an optimization problem. The presented approach has many applications in signal processing. Specifically, the quadratic variation (QV) regularization and smoothness priors are special cases of the proposed framework. These two methods are particularly suited for polynomial signal smoothing, as they use the derivatives of the signal in the regularization term of the optimization problem. Therefore, their performance is significantly decreased for signals that cannot be well modeled with a low order polynomial function. The approach presented in this paper was used to overcome this limitation. As proof of other applications, it was employed for designing an extension of QV regularization to remove the baseline wander in electrocardiogram (ECG), simultaneous tracking of powerline interference and baseline wander, and T-wave amplitude computation.

Simultaneous Collision Avoiding and Target Tracking for Unmanned Ground Vehicle with Velocity and Heading Rate Constraints

Simultaneous Collision Avoiding and Target Tracking for Unmanned Ground Vehicle with Velocity and Heading Rate Constraints