High Update Research Articles

Application of Instance Segmentation to Identifying Insect Concentrations in Data from an Entomological Radar

Entomological radar is one of the most effective tools for monitoring insect migration, capable of detecting migratory insects concentrated in layers and facilitating the analysis of insect migration behavior. However, traditional entomological radar, with its low resolution, can only provide a rough observation of layer concentrations. The advent of High-Resolution Phased Array Radar (HPAR) has transformed this situation. With its high range resolution and high data update rate, HPAR can generate detailed concentration spatiotemporal distribution heatmaps. This technology facilitates the detection of changes in insect concentrations across different time periods and altitudes, thereby enabling the observation of large-scale take-off, landing, and layering phenomena. However, the lack of effective techniques for extracting insect concentration data of different phenomena from these heatmaps significantly limits detailed analyses of insect migration patterns. This paper is the first to apply instance segmentation technology to the extraction of insect data, proposing a method for segmenting and extracting insect concentration data from spatiotemporal distribution heatmaps at different phenomena. To address the characteristics of concentrations in spatiotemporal distributions, we developed the Heatmap Feature Fusion Network (HFF-Net). In HFF-Net, we incorporate the Global Context (GC) module to enhance feature extraction of concentration distributions, utilize the Atrous Spatial Pyramid Pooling with Depthwise Separable Convolution (SASPP) module to extend the receptive field for understanding various spatiotemporal distributions of concentrations, and refine segmentation masks with the Deformable Convolution Mask Fusion (DCMF) module to enhance segmentation detail. Experimental results show that our proposed network can effectively segment concentrations of different phenomena from heatmaps, providing technical support for detailed and systematic studies of insect migration behavior.

S-LDM: Server local dynamic map for 5G-based centralized enhanced collective perception

The automotive field is undergoing significant technological advances, which includes making the next generation of autonomous vehicles smarter, greener and safer through vehicular networks, which are often referred to as Vehicle-to-Everything (V2X) communications. Together with V2X, centralized maneuver management services for autonomous vehicles are increasingly gaining importance, as, thanks to their complete view over the road, they can optimally manage even the most complex maneuvers targeting L4 driving and beyond. These services face the challenge of strictly requiring a high reliability and low latency, which are tackled with the deployment at orchestrated Multi-Access Edge Computing (MEC) platforms. In order to properly manage safety-critical maneuvers, these services need to receive a large amount of data from vehicles, even though the useful subset of data is often related to a specific context on the road (e.g., to specific road users or geographical areas). Decoding and post-processing a large amount of raw messages, which are then for the most part filtered, increases the load on safety-critical services, which should instead focus on meeting the deadlines for the actual control and management operations. On this basis, we present an innovative open-source, 5G & MEC enabled service, called Server Local Dynamic Map (S-LDM). The S-LDM is a service that collects information about vehicles and other non-connected road objects using standard-compliant messages. Its primary purpose is to create a centralized dynamic map of the road that can be shared efficiently with other services managing L4 automation, when needed. By doing so, the S-LDM enables these services to widely and precisely understand the current situation of sections of the road, offloading them from the need of quickly processing a large number of messages. After a detailed description of the service architecture, we validate it through extensive laboratory and pilot trials, involving the MEC platforms and production 5G networks of three major European network operations and two Stellantis vehicles equipped with V2X On-Board Units (OBUs). We show how it can efficiently handle high update rates and process each messages in less than few tenths of microseconds. We also provide a complete scalability analysis with details on deployment options, providing insights on where new instances should be created in practical 5G-based V2X scenarios.

Impact of Cyclic Error on Absolute Distance Measurement Based on Optical Frequency Combs.

Absolute distance measurements based on optical frequency combs (OFCs) have greatly promoted advances in both science and technology, owing to the high precision, large non-ambiguity range (NAR), and a high update rate. However, cyclic error, which is extremely difficult to eliminate, reduces the linearity of measurement results. In this study, we quantitatively investigated the impact of cyclic error on absolute distance measurement using OFCs based on two types of interferometry: synthetic wavelength interferometry and single-wavelength interferometry. The numerical calculations indicate that selecting a suitable reference path length can minimize the impact of cyclic error when combining the two types of interferometry. Recommendations for selecting an appropriate synthetic wavelength to address the tradeoff between achieving a large NAR and minimizing the risk of failure when combining the two methods are provided. The results of this study are applicable not only in absolute distance measurements but also in other applications based on OFCs, such as surface profile, vibration analysis, etc.

Long-distance and high-precision ranging with dual-comb nonlinear asynchronous optical sampling

Precise distance metrology and measurements play an important role in many fields of scientific research and industrial manufacture. Dual-comb laser ranging combines sub-wavelength ranging precision, large non-ambiguity range, and high update rate, making it the most promising candidate in precise distance metrology and measurements. However, previous demonstrations of dual-comb ranging suffer from short working distances, limited by the decoherence of lasers in interferometric schemes or by the low sensitivity of the photodetectors in response to the sparse echo photons. Here, we propose and demonstrate time-of-flight laser ranging with dual-comb nonlinear asynchronous optical sampling and photon counting by a fractal superconducting nanowire single-photon detector, achieving ranging precision of 6.2 micrometers with an acquisition time of 100 ms and 0.9 micrometers with an acquisition time of 1 s in measuring the distance of an outdoor target approximately 298 m away.

Fiber-optic spectrum monitoring of wavelength-division-multiplexed telecommunication signals with MHz update rates.

We propose a novel (to our knowledge) and simple real-time optical monitoring (RTOM) system for dynamic spectral analysis of telecommunication signals, involving electro-optic (EO) temporal sampling followed by dispersion-induced frequency-to-time mapping and high-speed photodetection. This system enables tracking of the presence and relative intensity of multiple wavelength-division-multiplexed (WDM) data streams that span over a broad frequency band with high resolution, accuracy, and fast measurement update rates. We derive the design conditions and trade-offs of the proposed scheme and report proof-of-concept experiments and a numerical result that demonstrate successful spectral monitoring of dense-WDM signals with different modulation formats and bit rates, over the full C-band, with the needed resolution to discern channels separated by a few tens of GHz, and with an unprecedented fast measurement update rate in the MHz range.

A water track laser Doppler velocimeter for use in underwater navigation

Doppler velocity log (DVL) is usually employed to suppress the divergency of the Strapdown Inertial Navigation System (SINS) in underwater navigation, which is not concealable due to high transmittance for acoustic wave in the water. To conduct underwater navigation task with high concealment, a differential laser Doppler velocimeter (LDV) working at water track mode is integrated with SINS in this paper. The developed LDV measures the advance velocity of the underwater carrier with respect to the surrounding water in underwater navigation scenario with advantages of high concealment, high real-time performance, high update rate, light weight, and small dimension. A dynamic river test was conducted to validate the underwater navigation performance of SINS/LDV integrated system. The experimental results show that during the voyage of 4493s and 5271.8 m, the maximum horizontal positioning errors of the proposed SINS/LDV integrated underwater navigation system is 27.8 m and the relative position error is less than 0.6% with respect to total distance. Therefore, the water track LDV is practical to aid SINS in underwater navigation environment.

UI-MoCap: An Integrated UWB-IMU Circuit Enables 3D Positioning and Enhances IMU Data Transmission.

While inertial measurement unit (IMU)-based motion capture (MoCap) systems have been gaining popularity for human movement analysis, they still suffer from long-term positioning errors due to accumulated drift and inefficient data transmission via Wi-Fi or Bluetooth. To address this problem, this study introduces an integrated ultrawideband (UWB)-IMU system, named UI-MoCap, designed for simultaneous 3D positioning as well as wireless IMU data transmission through UWB pulses. The UI-MoCap comprises mobile UWB tags and hardware-synchronized UWB base stations. Each UWB tag, a compact circular PCB with a 3.4cm diameter, houses a nine-axis IMU unit and a UWB transceiver for data transmission. The base stations are equipped with a UWB transceiver and an Ethernet controller, ensuring efficient reception and management of messages from multiple tags. Experiments were conducted to evaluate the system's validity and reliability of 3D positioning and IMU data transmission. The results demonstrate that UI-MoCap achieves centimeter-level 3D positioning accuracy and maintains consistent positioning performance over time. Moreover, UI-MoCap exhibits high update rates and a minimal packet loss rate for IMU data transmission, significantly outperforming Wi-Fi-based transmission techniques. Future work will explore the fusion of UWB and IMU technologies to further enhance positioning performance, with a focus on human movement analysis and rehabilitation applications.

A Comparative Study of Control Laws for a Maritime Surface Vessel Tracking an Underwater Vehicle

This paper compares the tradeoffs between performance and implementation efforts of three pure pursuit control laws; a proportional-integral-derivative (PID), nonlinear model predictive (NMPC) and a deep reinforcement learning (DRL) trained neural network (NN). The control laws are employed on a target tracking problem where the issue is to keep an unmanned surface vessel (USV) within proximity to a submerged autonomous underwater vessel (AUV) for optical communication to operate. The results from a simulation case study, show that the PID settled with a steady error of 2.0 m from the target, the NMPC at 2.4 m and the NN at 2.3 m. The relative control effort is highest for the PID at 100%, the NMPC settled at 98% and the NN at 96%. The PID represents a methodology that has transparent and time-efficient tuning rules together with a high update rate of 11,5 kHz. The NMPC provides a flexible tuning method but requires an accurate dynamic model and has the slowest update rate of 5.2 Hz. The DRL stand as a viable solution to solve the control problem and has an update rate of 4,2 kHz, but it bears the most time-consuming implementation and tuning of the three alternatives presented here.

Design of Intelligent Channelizer for Extracting Signals of Arbitrary Centre Frequency and Bandwidth

There are two standard approaches to the problem of wideband signal channelization, namely those based upon the use of a digital down conversion (DDC) unit and those based upon the use of a polyphase discrete Fourier transform (DFT) or PDFT. There are clear advantages and disadvantages with both approaches, however, in that: a) with the DDC approach, optimal performance and flexibility is obtained but at the expense of a heavy computational load; whereas b) with the PDFT approach, a sub optimal and less flexible performance is obtained but at a greatly reduced computational cost through the exploitation of a suitably defined fast Fourier transform (FFT). An intelligent channelizer is described herein which possesses a flexible design able to exploit the merits of both approaches for the case where the input data comprises real-valued samples. The two key design features are: a) optimal setting of the PDFT parameters to ensure that for every signal of interest there is at least one channel completely containing it; and b) simultaneous computation of two real-data FFTs: the first as required by the PDFT and the second, a high-resolution FFT with high update rate, able to accurately direct the application of the low-rate DDC units to the relevant PDFT channel outputs.

Ultrasonic- and IMU-Based High-Precision UAV Localization for the Low-Cost Autonomous Inspection in Oil and Gas Pressure Vessels

With the increasing demands for unmanned aerial vehicle (UAV) based autonomous inspections in the oil and gas industry, one of the challenging issues for 3D UAV positioning has emerged due to the satellite signal blocking. Considering the existing characteristics of the ultrasonic based technique, such as the low cost, extremely lightweight and high positioning accuracy, it can be promising as the potential solution. Nevertheless, the low position update rate and vulnerable positioning performance to the changing environment still limit its applications on UAV. Therefore, in this article, an ultrasonic and inertial measurement unit (IMU) based localisation algorithm and low cost UAV autonomous inspection system are presented. With the incorporation of the IMU, the position update rate, accuracy and stability of the algorithm can all be significantly improved. This is done by the adaptively estimated noise covariance matrices through the proposed adaptive extended Kalman filter (AEKF) algorithm and the added weighting factors. Followed by, an additional virtual observation process is presented to overcome the unavailability of the observation information for further performance improvement. Finally, extensive numerical results and field tests demonstrate that the proposed algorithm and system can achieve the high update rate, reliable, accurate and precision UAV positioning in oil and gas pressure vessels and are feasible for the UAV autonomous inspection in these environments.

Multi-GNSS precise point positioning with predicted orbits and clocks

Global Navigation Satellite Systems (GNSS) Real-time Precise Point Positioning (RT-PPP) strongly relies on the precise satellite orbits and clocks, especially the latter requires high update rate, e.g., five seconds, due to its limited prediction accuracy. Such a high-rate update frequency is a burden to both the data analysis and communicating, and interruption in communicating is almost unavoidable. For the new constellations such as Galileo and BDS-3 with high-stability hydrogen clocks onboard, it is possible to predict satellite clocks to a longer arc. Moreover, recent developments in multi-GNSS ultra-rapid precise orbit determination enables the half-hourly update, ensuring the availability with a prediction arc of 30–60 min. We investigate multi-GNSS RT-PPP using half-hourly predicted products and demonstrate that a 3-D accuracy of 2.9 and 11.3 cm can be achieved for static and simulated kinematic solutions, respectively. We present the different clock prediction accuracies of different types of satellites and propose a satellite-specific weighting strategy in PPP, which exploits the benefits of the satellites of good performance. The method is based on the prediction accuracy of both, satellites and clocks, and shows an improvement of 15 to 60% compared to those without satellite-specific weighting or with simplified weighting strategies. We also demonstrate that Galileo satellites contribute the most in the quad-constellation solution, thanks to the highly stable satellite clocks.

Closed Loop Static Control of Multi-Magnet Soft Continuum Robots

This paper discusses a novel static control approach applied to magnetic soft continuum robots (MSCRs). Our aim is to demonstrate the control of a multi-magnet soft continuum robot (SCR) in 3D. The proposed controller, based on a simplified yet accurate model of the robot, has a high update rate and is capable of real-time shape control. For the actuation of the MSCR, we employ the dual external permanent magnet (dEPM) platform and we sense the shape via fiber Bragg grating (FBG). The employed actuation system and sensing technique makes the proposed approach directly applicable to the medical context. We demonstrate that the proposed controller, running at approximately 300 Hz, is capable of shape tracking with a mean error of 8.5% and maximum error of 35.2% . We experimentally show that the static controller is 25.9% more accurate than a standard PID controller in shape tracking and is able to reduce the maximum error by 59.2%.

Dynamic Model-Based Safety Margins for High-Density Matrix Headlight Systems

Real-time masking of vehicles in a dynamic road environment is a demanding task for adaptive driving beam systems of modern headlights. Next-generation high-density matrix headlights enable precise, high-resolution projections, while advanced driver assistance systems enable detection and tracking of objects with high update rates and low-latency estimation of the pose of the ego-vehicle. Accurate motion tracking and precise coverage of the masked vehicles are necessary to avoid glare while maintaining a high light throughput for good visibility. Safety margins are added around the mask to mitigate glare and flicker caused by the update rate and latency of the system. We provide a model to estimate the effects of spatial and temporal sampling on the safety margins for high-and low-density headlight resolutions and different update rates. The vertical motion of the ego-vehicle is simulated based on a dynamic model of a vehicle suspension system to model the impact of the motion-to-photon latency on the mask. Using our model, we evaluate the light throughput of an actual matrix headlight for the relevant corner cases of dynamic masking scenarios depending on pixel density, update rate, and system latency. We apply the masks provided by our model to a high beam light distribution to calculate the loss of luminous flux and compare the results to a light throughput approximation technique from the literature.

On the Protection of a High Performance Load Balancer Against SYN Attacks** This is an extended journal version of [2

SYN flooding is a simple and effective denial-of-service attack. In this attack, many TCP SYN requests are sent to the targeted server, in an attempt to consume its resources and make it unresponsive to legitimate traffic. While SYN attacks have traditionally targeted web servers, they are also known to be very harmful to intermediate cloud devices, and in particular to stateful load balancers (LBs). Fighting against a SYN attack without negatively affecting legitimate connections is not easy, especially if the LB needs to perform frequent server pool updates during the attack, which is very likely since attacks can often last for many hours or even days. This paper is the first to propose LB schemes that guarantee high throughput of one million connections per second, while supporting a high pool update rate without breaking connections and fighting against a high rate SYN attack. Using an analysis and a proof of concept, we show that the LB can handle up to 10 million fake SYNs per second when the RTT is 10ms, and up to 5 million fake SYNs per second when the RTT is 20ms.

Эффективность применения динамического метода оценивания состояния параметров режима электроэнергетической системы

The results of the estimation of power system mode parameters are used to solve important technological tasks by real-time hardware-software packages (HSPs), for instance, the calculation of maximum allowed power flows (MAPFs) via sections by a Control System of Stability Margin (HSP CSSM). Now, in the HSP CSSM the state estimation is realized by the static method. Remote measurements (RMs) obtained from an operative informational complex are used as initial data. With the introduction of Wide-Area Measurement Systems and the possibility to obtain synchronized phasor measurements (SPMs) with a high update rate, it becomes possible to apply and improve state estimation dynamic methods. Even though, many researchers pay attention to the state estimation dynamic method, but practical application of this method and obtained results are presented in papers insufficiently. The goal of the study is to improve the state estimation dynamic method based on the extended Kalman filter and analyze the effectiveness in determining the mode parameters of electric power system. The studies are performed by the developed algorithm of the state estimation dynamic method based on extended Kalman filter. С# is the language for software code. Practical evaluation of the state estimation algorithm has been carried out on the basis of a power system model containing 55 nodes and 76 branches. An improved dynamic method to estimate the state of mode parameters is proposed. The test results show that in steady-state modes, when RMs are not updated on time, the developed dynamic method demonstrates high accuracy for the estimation of mode parameters and MAPFs. The estimation error of a voltage and an active power is low, therefore MAPFs are more specifically than MAPFs obtained by CSSM. Also, this method operates with high accuracy in the post emergency states, but only for that part of the power system, where the topology and mode have not been changed. For the part, where the topology and mode affected, the best result shows the static state estimation method by RMs and SPMs. In post emergency states the static state estimation method offers to form the transfer matrix for the dynamic method, therefore, static and dynamic state estimation methods must be used simultaneously in real-time HSPs. It is an undoubted fact that the use of synchronized phasor measurements as input data increases the accuracy of estimation. These results are expected to implement in the software of HSPs, involving the state estimation component.

Identifying mangroves through knowledge extracted from trained random forest models: An interpretable mangrove mapping approach (IMMA)

Black-box algorithms are among the dominant mangrove mapping approaches with complex decision-making procedures. Model internals and tacit knowledge were neglected, such as a large number of decision rules provided by random forest (RF) analyses. Explainable artificial intelligence (XAI) has emerged to emphasize the interpretability of an approach. However, current knowledge-based mangrove mapping approaches rely on extensive experiments. Thus, they cannot be easily updated to accommodate new issues, such as prevalent false positives resulting from insufficient consideration of the spectral mixture of vegetation and water in existing studies. To combine the advantages of black-box-based approaches with high update rates and knowledge-based approaches with high interpretability, this study developed a knowledge extraction method by parsing trained RF models, reconstructing decision rules to incorporate the ensemble procedure, and selecting the optimal decision rule as the target. Using this method, an interpretable mangrove mapping approach (IMMA) consisting of five features was constructed, which derived from Sentinel-2 image bands and a digital elevation model: B12 < 0.06 & B8/B2 > 3.50 & elevation < 4.70 & mangrove vegetation index (MVI) > 2.92 & normalized difference index4 (NDI) < 0.07. The study achieved an overall accuracy (OA) of 82.3% along the entire coast of China using test samples. Comparatively, it achieved an OA of 78.8% in south Florida, with no training samples for the RF models. The IMMA approach had a limited number of false positives compared with the black-box-based and knowledge-based approaches. By analyzing, we found B12 < 0.06 & B8/B2 > 3.50 & elevation < 4.70 dominated the IMMA to achieve comparable classification results to the existing studies, and B8/B2 > 3.50 was the key to suppressing the false positives resulting from the spectral mixture. The IMMA provided a bridge between training samples and interpretable decision rules, a tool to discover new knowledge, a key to improving fundamental scientific understanding of mangrove mapping, and an alternative to black-box algorithms in the XAI era expandable to various fields.

Static Laser Feedback Interferometry-Based Gaze Estimation for Wearable Glasses

Fast and robust gaze estimation is a key technology for wearable glasses, as it enables novel methods of user interaction as well as display enhancement applications, such as foveated rendering. State-of-the-art video-based systems lack a high update rate, integrateability, slippage robustness, and low power consumption. To overcome these limitations, we propose a model-based fusion algorithm to estimate gaze from multiple static laser feedback interferometry (LFI) sensors, which are capable of measuring distance toward the eye and the eye’s rotational velocity. The proposed system is ambient light robust and robust to glasses slippage. During evaluation, a gaze accuracy of 1.79° at an outstanding update rate of 1 kHz is achieved, while the sensors consume only a fraction of the power compared with the state-of-the-art video-based system.

Compact Long-Range Single-Photon Underwater Lidar With High Spatial–Temporal Resolution

Oceanic lidar has emerged as a strong technology for oceanic three-dimensional remote sensing. However, most existing oceanic lidars are bulky and high-power consumption, thus difficult to enable underwater operation. Here we present a compact single-photon lidar system for long-range underwater measurement. A single-photon detector was adopted to achieve a high signal-to-noise ratio. Benefiting from the single-photon sensitivity in detection, long-range active detection was realized with a low pulse energy laser at 1 μJ and a small-aperture coupler at 12 mm. Moreover, a narrow linewidth picosecond fiber laser with high repetition rate was employed to guarantee a high spatial resolution and high update rate. A fiber-connected configuration was specially designed for the miniaturized and robust structure in an optical receiver. In an experimental demonstration, the profile of backscattered signal from clean water was obtained over 70 m with high spatial-temporal resolution to demonstrate the capability of this lidar system. The maximum detection distance of the single-photon lidar reaches ~3.6/ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K_d</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K_d</i> , diffuse attenuation coefficient) for waterbody and up to 5.5/ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K_d</i> for a hard target. Furthermore, it exhibits a high update rate capability and realizes the localization and quantification of underwater bubbles up to 26 m away at a high update rate of 100 Hz. These results indicate its potential in a variety of applications including remote sensing of marine biogeochemical parameters, the quantification of seabed gas emissions, and long-range underwater imaging.

Sequential Convex Programming Methods for Real-Time Optimal Trajectory Planning in Autonomous Vehicle Racing

Optimization problems for trajectory planning in autonomous vehicle racing are characterized by their nonlinearity and nonconvexity. Instead of solving these optimization problems, usually a convex approximation is solved instead to achieve a high update rate. We present a real-time-capable model predictive control (MPC) trajectory planner based on a nonlinear single-track vehicle model and Pacejka’s magic tire formula for autonomous vehicle racing. After formulating the general nonconvex trajectory optimization problem, we form a convex approximation using sequential convex programming (SCP). The state of the art convexifies track constraints using sequential linearization (SL), which is a method of relaxing the constraints. Solutions to the relaxed optimization problem are not guaranteed to be feasible in the nonconvex optimization problem. We propose sequential convex restriction (SCR) as a method to convexify track constraints. SCR guarantees that resulting solutions are feasible in the nonconvex optimization problem. We show recursive feasibility of solutions to the restricted optimization problem. The MPC is evaluated on a scaled version of the Hockenheimring racing track in simulation. The results show that MPC using SCR yields faster lap times than MPC using SL, while still being real-time capable.

Joint range-velocity estimation with continuous active sonar

Active sonars using linear frequency-modulated (LFM) continuous waveforms are typically processed in sub-bands to facilitate high target range update rates. Target range-rate is then estimated using incoherently processed matched-filter outputs from each sub-band as input to a tracker. Target returns can easily be confused with clutter because sub-band width is typically chosen to avoid decoherence caused by target and/or platform motion. In this paper, we present methods for joint range-velocity estimation which allows for coherent combination of sub-band outputs and accounts for target/platform motion. The approach involves pre-filtering beamformed sub-band outputs using the transmitted LFM waveform, match-filtering each frequency-domain pre-filtered sub-band under a range-velocity hypothesis, and coherently combining the results. A maximum likelihood estimate (MLE) of range-velocity is formed which involves adaptively suppressing clutter and noise using an estimate of sub-band clutter covariance matrices. Simulation results are presented which indicate that joint range-velocity estimation using coherent processing across sub-bands offers a significant improvement over conventional methods and allows for more flexible selection of sub-band processing bandwidth. Moreover, the approach lends itself to handling the case of partial coherence across sub-bands and offers the potential for improved discrimination of slowly moving targets versus clutter discretes. Work supported by ONR.