Reliable Tracking Research Articles

Modal Parameter Tracking in a Carbon Fiber-Reinforced Structure over Different Carbon Fiber Angles

The dynamics of carbon fiber-reinforced plastic (CFRP) change according to the carbon fiber angle, and a mode order shift may occur in CFRP specimens. The variation trends in modal parameters differ in each mode; thus, an efficient mode-tracking method is needed to identify the reliable dynamic behavior of the CFRP structure. The mode-tracking method was assumed to be applicable for the same configuration of the tested specimen except for the differences in carbon fiber angle of the CFRP specimen. Simple rectangular specimens were prepared for one isotropic material, SS275, and five anisotropic CFRP specimens with five carbon fiber angles ranging from 0° to 90°. An experimental impact test was conducted to obtain all the modal parameters. The proposed mode-tracking method was applied using three indicators: the modal assurance criterion (MAC) and two modal parameters (resonance frequency and modal damping ratio). The MAC value was valid for the three bending modes at 0°, 30°, and 90°, but not for the two torsional modes. However, the variation in the resonance frequencies was a more efficient indicator with which to track all the modes of interest, except for the second torsional mode. The variation in the modal damping ratio was also a valid indicator for the two torsional modes. Therefore, the proposed three indicators were all required to derive reliable mode tracking for the CFRP specimens considering the mode order shift.

Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment.

In order to improve behavioral analysis systems in urban environments, this paper proposes, using data extracted from video surveillance cameras, a tracking method through two approaches. The first approach consists in comparing the position of people between two images of a video and to perform tracking by proximity. The second method using Kalman filters is based on the anticipation of the position of an individual in the upcoming image. The use of this method proves to be more efficient as it allows continuing a detection even when people cross each other or when they pass behind obstacles. The use of Kalman filters in this domain provides a new approach to obtain reliable tracking and information on speed and trajectory variations. The proposed method is innovative in the way the tracking is performed and the results are exploited. Experiments were conducted in a real situation and showed that the use of some elements of the first method could be reused to integrate a notion of distance in the method based on the Kalman filter and thus improve the latter both in tracking and in detecting of abnormal behavior. This article deals with the functioning of the two methods as well as the results obtained with the same scenarios. The experimentation concludes through concrete results that the Kalman filter method is more efficient than the proximity method alone. A sample result is available online for two of the seven videos used in this article (accessed on 19 July 2021).

Observation and Maneuver Detection for Cislunar Vehicles

International interest in the sustained development of cislunar space will generate traffic and debris in the region which requires monitoring; similar to how current space situation awareness is necessary for the traffic and debris near Earth. There are many challenges associated with developing a cislunar situation awareness program, but 2 primary issues addressed by this paper are observational strategies and maneuver detection methods. This work proposes an observational strategy that utilizes the ballistic Optimal Control Based Estimator (OCBE) to filter measurements from cislunar optical observers. To reduce numerical issues associated with filtering, new modifications to the ballistic Optimal Control Based Estimator (OCBE) are introduced that preserve the OCBE update equations in Square Root Information (SRI) space. This new derivation produces a more stable version of the ballistic OCBE which is beneficial for filtering larger data sets with non-linear motion and measurements. Applying the SRI OCBE to the estimation problem it was found that only a single L2 observer with angle and angle-rate measurements provided sufficient information for consistent estimation. Then a newly developed maneuver detection method is presented to statistically identify maneuvers. The method applies a binary hypothesis test to the optimal control policy of the ballistic OCBE to quantify mismodeling. This method was tested given a impulsive maneuver policy with a mean of 50 mm/s and standard deviation of 15 mm/s, and 194 out of 200 tests correctly identified if a maneuver occurred. The OCBE control policy also provided appropriate impulse estimates of mismodeling, which may be used to reconstruct maneuvers in future work. Together, the proposed observation and maneuver detection methodology yields reliable tracking and provides a statistical framework to detect maneuvers.

Siamese-Based Deep Learning for Markerless Lung Tumor Tracking During Stereotactic Radiotherapy

<h3>Purpose/Objective(s)</h3> During stereotactic lung radiotherapy we want to monitor the target position to ensure it remains inside the planning target volume (PTV). This ensures accurate treatment delivery and may allow for margin reduction. Many LINAC systems have a kilo-voltage (kV) imaging system mounted orthogonally to the MV beam. This can acquire images during volumetric modulated arc therapy delivery. The 2D-kV images can be used to identify the tumor position, e.g., using template matching and triangulation (TMT). However, small lung targets are difficult to track, due to e.g., low tumor contrast, occlusion (e.g., by spine), or noisy images. We investigated deep learning (DL) as an alternative method for lung tumor tracking. <h3>Materials/Methods</h3> Clinical data from 6 patients (P1-P6) with small lung tumors (0.5-3 cm diameter) and a 3D-printed anthropomorphic phantom (P0), including a low-density lung tumor, was used. For each patient, data included 4D-CT scans, fluoroscopic kV images and breathing-related surface motion (Real-Time Position Management, RPM). For the phantom, a 3D-CT scan and kV images taken with a static and moving couch (regular and irregular motion) were available. Due to lack of ground truth tumor locations for the kV images, a patient-specific DL model is trained on Digitally Reconstructed Radiographs (DRRs), generated from the planning CT scans. To enhance the tumor's visual diversity in DRRs, we augmented the training data with locally deformed CT scans. A Siamese-based tracking by similarity method is hypothesized to be better suited for the tumor tracking task than other DL methods, due to its more localized search space and the use of previous frames (temporal information). <h3>Results</h3> Table 1 shows the superior-inferior (SI) direction results obtained by the Siamese model and TMT, on all kV images. For the patient data, the <b>Correlation coefficient</b> between estimated tumor trajectory and RPM was computed, due to lack of ground truth locations. For the phantom, the <b>Mean Absolute Distance</b> (MAD) between detected and ground truth center coordinates was computed. For all 6 patients and the phantom, the Siamese model outperformed TMT. <h3>Conclusion</h3> A novel approach for real-time markerless lung tumor tracking is presented, based on deep Siamese networks. The model compares favorably to the template matching/triangulation-based technique and merits further investigation. The current analysis presents only SI tracking. Reliable 3D tracking, with sub-mm accuracy, is the ultimate goal. Further work is needed to achieve this.

Review on Lane Detection and Tracking Algorithms of Advanced Driver Assistance System

Autonomous vehicles and advanced driver assistance systems are predicted to provide higher safety and reduce fuel and energy consumption and road traffic emissions. Lane detection and tracking are the advanced key features of the advanced driver assistance system. Lane detection is the process of detecting white lines on the roads. Lane tracking is the process of assisting the vehicle to remain in the desired path, and it controls the motion model by using previously detected lane markers. There are limited studies in the literature that provide state-of-art findings in this area. This study reviews previous studies on lane detection and tracking algorithms by performing a comparative qualitative analysis of algorithms to identify gaps in knowledge. It also summarizes some of the key data sets used for testing algorithms and metrics used to evaluate the algorithms. It is found that complex road geometries such as clothoid roads are less investigated, with many studies focused on straight roads. The complexity of lane detection and tracking is compounded by the challenging weather conditions, vision (camera) quality, unclear line-markings and unpaved roads. Further, occlusion due to overtaking vehicles, high-speed and high illumination effects also pose a challenge. The majority of the studies have used custom based data sets for model testing. As this field continues to grow, especially with the development of fully autonomous vehicles in the near future, it is expected that in future, more reliable and robust lane detection and tracking algorithms will be developed and tested with real-time data sets.

In Vivo PET Imaging of Monocytes Labeled with [89Zr]Zr-PLGA-NH2 Nanoparticles in Tumor and Staphylococcus aureus Infection Models.

Simple SummaryImmune cells are increasingly used for therapy in cancer and other diseases. To better understand immune-cell kinetics, cell-tracking with highly sensitive imaging modalities is required. The aim of this study was to develop a new strategy for the in vivo tracking of a small number of cells, using positron emission tomography (PET). We labeled poly(lactic-co-glycolic acid) nanoparticles containing a primary endcap (PLGA-NH2) with the radionuclide zirconium-89. The nanoparticles were characterized for size, polydispersity index, zetapotential and radiolabel retention. Subsequently, they were used for the ex vivo radiolabeling of a monocyte cell line (THP-1). We demonstrated that these radiolabeled monocyte cells can be traced in vivo in mouse tumor and infection models.The exponential growth of research on cell-based therapy is in major need of reliable and sensitive tracking of a small number of therapeutic cells to improve our understanding of the in vivo cell-targeting properties. 111In-labeled poly(lactic-co-glycolic acid) with a primary amine endcap nanoparticles ([111In]In-PLGA-NH2 NPs) were previously used for cell labeling and in vivo tracking, using SPECT/CT imaging. However, to detect a low number of cells, a higher sensitivity of PET is preferred. Therefore, we developed 89Zr-labeled NPs for ex vivo cell labeling and in vivo cell tracking, using PET/MRI. We intrinsically and efficiently labeled PLGA-NH2 NPs with [89Zr]ZrCl4. In vitro, [89Zr]Zr-PLGA-NH2 NPs retained the radionuclide over a period of 2 weeks in PBS and human serum. THP-1 (human monocyte cell line) cells could be labeled with the NPs and retained the radionuclide over a period of 2 days, with no negative effect on cell viability (specific activity 279 ± 10 kBq/106 cells). PET/MRI imaging could detect low numbers of [89Zr]Zr-THP-1 cells (10,000 and 100,000 cells) injected subcutaneously in Matrigel. Last, in vivo tracking of the [89Zr]Zr-THP-1 cells upon intravenous injection showed specific accumulation in local intramuscular Staphylococcus aureus infection and infiltration into MDA-MB-231 tumors. In conclusion, we showed that [89Zr]Zr-PLGA-NH2 NPs can be used for immune-cell labeling and subsequent in vivo tracking of a small number of cells in different disease models.

Introducing Fog Computing (FC) Technology to Internet of Things (IoT) Cloud-Based Anti-Theft Vehicles Solutions

Securing vehicles, especially against theft, has become a significant concern. Smart antitheft solutions have emerged to provide better protection. However, most existing smart vehicle antitheft solutions use (GSM) and (GPS) technologies to track stolen vehicles and these technologies are not sufficiently efficient in tracking vehicles in real-time. Hence, there is a need to optimise solutions to incorporate new technologies such as Internet of Things (IoT), Fog Computing (FC), and Face Recognition (FR) technologies. This paper introduces the new concept of Fog Computing to existing tracking systems and presents the design and the development of the Internet of Things (IoT) Cloud-based vehicle anti-theft system to pinpoint the exact location of the stolen vehicle in real-time. The proposed system extends the existing tracking systems to include advanced features influenced by advanced computing technologies such as Fog, Cloud, IoT and FR. Furthermore, it sheds light on the benefits of using FC combined with Cloud Computing (CC) to provide a more accurate and reliable tracking system.

Visualization of Vaccine Dynamics with Quantum Dots for Immunotherapy.

The direct visualization of vaccine fate is important to investigate its immunoactivation process to elucidate the detailed molecular reaction process at single‐molecular level. Yet, visualization of the spatiotemporal trafficking of vaccines remains poorly explored. Here, we show that quantum dot (QD) nanomaterials allow for monitoring vaccine dynamics and for amplified immune response. Synthetic QDs enable efficient conjugation of antigen and adjuvants to target tissues and cells, and non‐invasive imaging the trafficking dynamics to lymph nodes and cellular compartments. The nanoparticle vaccine elicits potent immune responses and anti‐tumor efficacy alone or in combination with programmed cell death protein 1 blockade. The synthetic QDs showed high fluorescence quantum yield and superior photostability, and the reliable and long‐term spatiotemporal tracking of vaccine dynamics was realized for the first time by using the synthetic QDs, providing a powerful strategy for studying immune response and evaluating vaccine efficacy.

Energy efficient contact tracing and social interaction based patient prediction system for COVID-19 pandemic

Due to the spread of Coronavirus disease 2019 (COVID-19), the world has encountered an ongoing pandemic to date. It is a highly contagious disease. In addition to the vaccination, social distancing and isolation of patients are proven to be one of the commonly used strategies to reduce the spread of disease. For efficient social distancing, contact tracing is a critical requirement in the incubation period of 14-days of the disease to contain any further spread. However, we identify that there is a lack of reliable and practical social interaction tracking methods and prediction methods for the probability of getting the disease. This paper focuses on user tracking and predicting the infection probability based on these social interactions. We first developed an energy-efficient BLE (Bluetooth Low Energy) based social interaction tracking system to achieve this. Then, based on the collected data, we propose an algorithm to predict the possibility of getting the COVID-19. Finally, to show the practicality of our solution, we implemented a prototype with a mobile app and a web monitoring tool for healthcare authorities. In addition to that, to analyze the proposed algorithm's behaviour, we performed a simulation of the system using a graph-based model.

A computational intelligence based maximum power point tracking for photovoltaic power generation system with small‐signal analysis

AbstractThere are multiple peak functions in its output power characteristic curve of a photovoltaic (PV) array under partial shading conditions (PSCs), the perturb and observe (P&amp;O) may fail to track the global maximum power point (GMPP). Therefore, a reliable maximum power point tracking (MPPT) technique is essential to track the GMPP within an appropriate time. This article proposes a hybrid technique by combining an evolutionary optimization technique, namely the modified invasive weed optimization (MIWO) with the conventional P&amp;O algorithm to enhance the search performance for the maximum power output of the PV system. MIWO executes in the initial stages of the tracking followed by the P&amp;O at the final stages in the MPPT search process. The combined approach ensures faster convergence and better search to the GMPP under rapid climate change and PSCs. The search performance of the hybrid MIWO+P&amp;O technique is examined on a standalone PV system through both MATLAB/Simulink environment and experimentally using dSPACE (DS1103)‐based real‐time microcontroller hardware setup. The performance of the proposed hybrid MPPT scheme is compared with the recent state‐of‐the‐art MPPPT techniques. In addition, the small‐signal analysis of the PV system is carried out to evaluate the loop robustness of the controller design. For a given set of system parameters, simulations for the small‐signal model and robustness studies are analyzed to verify the results. The overall results justify the efficacy of the proposed hybrid MPPT algorithm.

MouseView.js: Reliable and valid attention tracking in web-based experiments using a cursor-directed aperture

Psychological research is increasingly moving online, where web-based studies allow for data collection at scale. Behavioural researchers are well supported by existing tools for participant recruitment, and for building and running experiments with decent timing. However, not all techniques are portable to the Internet: While eye tracking works in tightly controlled lab conditions, webcam-based eye tracking suffers from high attrition and poorer quality due to basic limitations like webcam availability, poor image quality, and reflections on glasses and the cornea. Here we present MouseView.js, an alternative to eye tracking that can be employed in web-based research. Inspired by the visual system, MouseView.js blurs the display to mimic peripheral vision, but allows participants to move a sharp aperture that is roughly the size of the fovea. Like eye gaze, the aperture can be directed to fixate on stimuli of interest. We validated MouseView.js in an online replication (N = 165) of an established free viewing task (N = 83 existing eye-tracking datasets), and in an in-lab direct comparison with eye tracking in the same participants (N = 50). Mouseview.js proved as reliable as gaze, and produced the same pattern of dwell time results. In addition, dwell time differences from MouseView.js and from eye tracking correlated highly, and related to self-report measures in similar ways. The tool is open-source, implemented in JavaScript, and usable as a standalone library, or within Gorilla, jsPsych, and PsychoJS. In sum, MouseView.js is a freely available instrument for attention-tracking that is both reliable and valid, and that can replace eye tracking in certain web-based psychological experiments.

Simulative design and performance analysis of hybrid optimization technique for PEM fuel cell stack based EV application

The fuel cell power generation systems are using in most of the present hybrid Electric Vehicle (EV) technology. The output characteristics of fuel cell arenonlinear. Also, the fuel operating point fluctuate continuously based on its operating temperature condition. In order to track and stabilize the operating point fuel cell, an Artificial Neuro Fuzzy Inference System-Genetic Algorithm Optimization (ANFIS-GAO) method is used. The merits of this proposed Maximum Power Point Tracking Technique (MPPT) are high extraction power capacity, high reliability, less oscillations, and fast tracking speed. The fuel cell is having a drawback of high output current and its less output voltage generation capability. To overcome this issue, in this work, an interleaved dc-dc converter circuit is used to enhance the fuel cell energy. The advantages of the proposed dc-dc converter are low voltage stress across the switches, less input current ripples, faster response, reduced electromagnetic emission, and high reliability. The proposed hybrid power point tracking technique performance is evaluated against with the Variable Step-Perturb & Observe (VS-P&O) algorithm. The MATLAB/Simulink environment is used for the performance analysis of proposed system.

Lagrangian coherent track initialization

Advances in time-resolved three-dimensional Particle Tracking Velocimetry (4D-PTV) techniques have consistently revealed more accurate Lagrangian particle motions. A novel track initialization technique as a complementary part of 4D-PTV, based on local temporal and spatial coherency of neighbor trajectories, is proposed. The proposed Lagrangian Coherent Track Initialization (LCTI) applies physics-based Finite Time Lyapunov Exponent (FTLE) to build four frame coherent tracks. We locally determine Lagrangian coherent structures among neighbor trajectories by using the FTLE boundaries (i.e., ridges) to distinguish the clusters of coherent motions. To evaluate the proposed technique, we created an open-access synthetic Lagrangian and Eulerian dataset of the wake downstream of a smooth cylinder at a Reynolds number equal to 3900 obtained from three-dimensional direct numerical simulation. Performance of the proposed method based on three characteristic parameters, temporal scale, particle concentration (i.e., density), and noise ratio, showed robust behavior in finding true tracks compared to the recent initialization algorithms. Sensitivity of LCTI to the number of untracked and wrong tracks is also discussed. We address the capability of using the proposed method as a function of a 4D-PTV scheme in the Lagrangian particle tracking challenge. We showed that LCTI prevents 4D-PTV divergence in flows with high particle concentrations. Finally, the LCTI behavior was demonstrated in a jet impingement experiment. LCTI was found to be a reliable tracking tool in complex flow motions, with a strength revealed for flows with high velocity and acceleration gradients.

Environmental Life Cycle Assessment of Refrigerator Modelled with Application of Various Electricity Mixes and Technologies

Improving national electricity mixes and increasing a share of renewable energy covered by credible and reliable tracking systems are vital topics, also in a context of life cycle assessment. There are many publications devoted to the relevance of energy in the life cycle of products, but only few LCA examples applying residual mixes have been found in the literature. The paper presents the results of an LCA study for a refrigerator calculated with using different electricity mixes and technologies. The life cycle was divided into eight stages and the electricity consumption was modelled as renewable energy, national residual mix, or national supplier mix. Electricity mixes for three different countries were selected and used. The study aimed to answer the following questions: “what are the most relevant elements in the life cycle of the analysed refrigerator?”, “do the elements change if various electricity mixes are applied?”, and “what differences are there in the environmental impact of electricity generation modelled as residual and supplier mixes?”. From the life cycle perspective, not only may differences in national electricity systems between countries turn out to be important, but equally significant may be the choice between different types of mixes for a certain country.

Real-time MR tracking of AAV gene therapy with βgal-responsive MR probe in a murine model of GM1-gangliosidosis

Transformative results of adeno-associated virus (AAV) gene therapy in patients with spinal muscular atrophy and Leber’s congenital amaurosis led to approval of the first two AAV products in the United States to treat these diseases. These extraordinary results led to a dramatic increase in the number and type of AAV gene-therapy programs. However, the field lacks non-invasive means to assess levels and duration of therapeutic protein function in patients. Here, we describe a new magnetic resonance imaging (MRI) technology for real-time reporting of gene-therapy products in the living animal in the form of an MRI probe that is activated in the presence of therapeutic protein expression. For the first time, we show reliable tracking of enzyme expression after a now in-human clinical trial AAV gene therapy (ClinicalTrials.gov: NTC03952637) encoding lysosomal acid beta-galactosidase (βgal) using a self-immolative βgal-responsive MRI probe. MRI enhancement in AAV-treated enzyme-deficient mice (GLB-1−/−) correlates with βgal activity in central nervous system and peripheral organs after intracranial or intravenous AAV gene therapy, respectively. With >1,800 gene therapies in phase I/II clinical trials (ClinicalTrials.gov), development of a non-invasive method to track gene expression over time in patients is crucial to the future of the gene-therapy field.

Optimal design and analysis of DC–DC converter with maximum power controller for stand-alone PV system

This paper proposes a method for selecting the parameters of the main components of an autonomous photovoltaic (PV) stations to ensure the most efficient conversion and use of solar energy. The main energy characteristics are analyzed and the mathematical models of the components of an autonomous PV system are developed to study the modes of tracking the maximum power point (MPP). The necessary conditions for matching the parameters of the PV and the DC–DC buck converter are evaluated to track the MPP. This paper aims to create an original method and algorithm for calculating and selecting parameters of the main elements of a PV stations. The optimal value of the sampling time of the MPP controller is obtained for different schemes of the PV system . A practical example is considered for selecting the parameters of a DC–DC buck converter and a digital MPP of an autonomous PV station. The simulation of dynamic modes of an autonomous photovoltaic (PV) station is performed in the MATLAB/SIMULINK software package. The simulation results of dynamic operations of the PV system show that the voltage converter and the MPP controller with the selected parameters according to the proposed method provide reliable and effective tracking of the MPP in all different connections of the PV.

Robust adaptation of PKC ζ-IRS1 insulin signaling pathways through integral feedback control

Insulin signaling pathways in muscle tissue play a major role in maintaining glucose homeostasis. Dysregulation in these pathways results in the onset of serious metabolic disorders like type 2 diabetes. Robustness is an essential characteristic of insulin signaling pathways that ensures reliable signal transduction in the presence of perturbations as a result of several feedback mechanisms. Integral control, according to control engineering, provides reliable setpoint tracking and disturbance rejection. The presence of negative feedback and integrating process is crucial for biological processes to achieve integral control. The existence of an integral controller leads to the rejection of perturbations which resulted in the robust regulation of biochemical entities within acceptable levels. In the present in silico research work, the presence of integral control in the protein kinase C ζ - insulin receptor substrate-1 (PKC ζ-IRS1) pathway is identified, verified mathematically and model is simulated in Cell Designer. The data is exported to Minitab software and robustness analysis is carried out statistically using the Mann-Whitney test. The p-value of the results obtained with given parameters perturbed by ±1% is greater than the significance level of 0.05 (0.2132 for 1% error in k7(rate constant of IRS1 phosphorylation), 0.2096 for −1% error in k7, 0.9037 for both ±1% error in insulin and 0.9037 for ±1% error in k1(association rate constant of the first molecule of insulin to bind the insulin receptor), indicated that our hypothesis is proved The results satisfactorily indicate that even when perturbations are present, glucose homeostasis in muscle tissue is robust due to the presence of integral regulation in the PKC ζ-IRS1 insulin signaling pathways. In this paper, we have analysed the findings from the framework of robust control theory, which has allowed us to examine that how PKC ζ-IRS1 insulin signaling pathways produces desired output in presence of perturbations.

An Ultra-low Power IoT System for Indoor Air Quality Monitoring

Air pollutions induce most deaths from heart-diseases, lung-cancer, and strokes. Therefore, a mechanism to create cognizance of air pollution amongst the habitats of any locality is indispensable. This work presents an Ultra-low-power IoT System (ULP-IoTS) for Indoor Air-Quality Monitoring. The developed ULP-IoTS provides instantaneous indoor atmospheric air quality readings on Carbone monoxide (CO), Carbon dioxide (CO2), Fine-particle (PM2.5), Temperature, and Humidity. The IoT-node integrates MICS-5524 and MQ-135 DHT-11 multi-sensors with ESP32 (ultra-low-power) microchip. The node architecture exploits ESP32 Deep-sleep, and Modem-sleep configurable modes to complement the Active-mode and aid optimal power-saving. The real-time readings from the ULP-IoT node is dispatched to an IoT-gateway that eventually post the pollutant-reading to an external IoT-server for user-retrieval on android-applications. The test-bed experimental results validate the proposed ULP-IoTS to guarantee long-term minimal power consumption and reliable indoor air-quality tracking.

21.2: The Design and Optimization of Dual‐Camera based Eye Tracking System for Dynamic Autostereoscopic 3D Display

Eye‐tracking based autostereoscopic 3D display is a promising technology to provide high resolution and low crosstalk stereoscopic images to the user without wearing any glasses. However, current systems fail to provide satisfactory viewing experience due to inherent latency caused by the tracking camera system and algorithm computation time and the low reliability of tracking algorithms, especially when users are in fast motion. High instantaneous crosstalk and intermittent interference patterns will be observed, which significantly degrades the immersive level of the viewing experience. This becomes even worse when the autostereoscopic 3D display is used to provide a simulated reality (SR) experience, in which the user constantly moves the head to explore the virtual reality space from different viewing directions. To solve the problem, this paper proposes a dual‐camera tracking system, focusing on the key design factors to improve its tracking reliability and reduce the user perceived latency. For the latter, it leverages motion prediction and compensate the latency with predictive rendering technique. To measure the performance, this paper defines the Perceived Autostereoscopic 3D Display Motion to Picture (PAMTP) latency to represent the impact of the system latency on the viewing experience. PAMTP latency and the instantaneous crosstalk are evaluated via simulations and experiments, which show our method can reduce the instantaneous crosstalk by nearly 70% and provide highly reliable tracking.

COVID-19 Preparedness and Response Plans from 106 countries: a review from a health systems resilience perspective.

Coronavirus disease (COVID-19) has exposed long-standing fragmentation in health systems strengthening efforts for health security and universal health coverage while these objectives are largely interdependent and complementary. In this prevailing background, we reviewed countries’ COVID-19 Preparedness and Response Plans (CPRPs) to assess the extent of integration of non-COVID-19 essential health service continuity considerations alongside emergency response activities. We searched for COVID-19 planning documents from governments and ministries of health, World Health Organization (WHO) country offices and United Nations (UN) country teams. We developed document review protocols using global guidance from the WHO and UN and the health systems resilience literature. After screening, we analysed 154 CPRPs from 106 countries. The majority of plans had a high degree of alignment with pillars of emergency response such as surveillance (99%), laboratory systems (96%) and COVID-19-specific case management (97%). Less than half considered maintaining essential health services (47%); 41% designated a mechanism for health system–wide participation in emergency planning; 34% considered subnational service delivery; 95% contained infection prevention and control (IPC) activities and 29% considered quality of care; and 24% were budgeted for and 7% contained monitoring and evaluation of essential health services. To improve, ongoing and future emergency planning should proactively include proportionate activities, resources and monitoring for essential health services to reduce excess mortality and morbidity. Specifically, this entails strengthening subnational health services with local stakeholder engagement in planning; ensuring a dedicated focus in emergency operations structures to maintain health systems resilience for non-emergency health services; considering all domains of quality in health services along with IPC; and building resilient monitoring capacity for timely and reliable tracking of health systems functionality including service utilization and health outcomes. An integrated approach to planning should be pursued as health systems recover from COVID-19 disruptions and take actions to build back better.