Time-of-flight Research Articles

A Novel Rolling Horizon Based Solution Framework for Scheduling Airplane Maintenance

Maintenance, Repair and Overhaul (MRO) activities on aircraft and systems in aviation is a service sector that relies heavily on skilled workforce. The output of MRO activities is basically bringing the system reliability values, which decrease because of the use of aircraft and systems in certain flight times and landing and take-off numbers, to the default levels determined during the design phase. MRO companies are accountable to the civil aviation authorities who directly authorize them to ensure the required levels of reliability of their products. Airline companies request MRO services at the most convenient time and cost. However, the maintenance of an aircraft is a process that can take up to five weeks, includes plenty of jobs some of which may have stochastic durations, and many over-costed qualified technicians spend thousands of man-hours. In addition, each MRO company is involved in the maintenance of several airplanes arriving at different time intervals. In the study such a problem faced by an MRO company is addressed. The company’s aim is to schedule several incoming airplane maintenance projects. A framework that employs an integer programming (IP) model working on a rolling horizon (RH) setting is used.

Vertical and temporal flight patterns of coffee berry borer (Coleoptera: Curculionidae) in Hawaii.

Coffee berry borer (Hypothenemus hampei Ferrari) (Coleoptera: Curculionidae) is the most damaging insect pest of coffee worldwide, causing significant losses in coffee yields and quality. Knowledge of vertical and temporal flight patterns in coffee berry borer could be used to optimize spray timing and precision targeting of areas within the coffee tree, which may be more susceptible. In the present study, we estimated the vertical distribution of coffee berry borer females using traps set at 1-m intervals up to 5 m in height. We also quantified coffee berry borer infestation in the low, mid, and high canopy and documented fruit availability. Temporal flight patterns were estimated using timer traps, and correlation analyses were conducted to determine the relationship between the timing of daily flight and weather variables. Across the 4 study sites, we observed that 77%-84% of the trap catch was at 1 m, 11%-20% was at 2 m, and 1%-4% was at 3-5 m in height. Fruit infestation was significantly higher in the low branches (35%) relative to the high branches (17%). Flight height remained the same year-round, regardless of fruit availability. Coffee berry borer flew in low numbers during the day and night but peaked from 12 to 4 PM. Daily flight was positively correlated with an increase in air temperature and wind speed and negatively correlated with relative humidity. Findings from this study suggest that pesticide sprays should target low- to mid-level branches at 1-2 m in height and aim to be conducted in the early afternoon when coffee berry borer are actively flying and most vulnerable to chemical controls.

Combining Sound and Deep Neural Networks for the Measurement of Jump Height in Sports Science.

Jump height tests are employed to measure lower-limb muscle power of athletic and non-athletic populations. The most popular instruments for this purpose are jump mats and, in recent years, smartphone apps, which compute jump height through the manual annotation of video recordings and recently automatically using the sound produced during the jump to extract the flight time. In a previous work, the afore-mentioned sound systems were presented by the authors in which the take-off and landing events from the audio recordings of jump executions were obtained using classical signal processing. In this work, a more precise, noise-immune, and robust system, capable of working in the most unfavorable environments, is presented. The system uses a deep neural network trained specifically for this purpose. More than 300 jumps were recorded to train and validate the network performance. The ground truth was a jump mat, providing a slightly better accuracy in quiet and medium quiet environments but excellent accuracy in noisy and complicated ones. The developed audio-based system is a trustworthy instrument for measuring jump height accurately in any kind of environment, providing a perfect measurement tool that can be accessed through a mobile phone in the form of an app.

Anti-inflammatory activity and structural analysis of fucoidan extracted from Sargassum fusiforme via ESI-TOF MS spectrometry

This study used Celluclast-assisted hydrolysis and DEAE-cellulose anion-exchange chromatography to extract, purify, and assess the anti-inflammatory activity of fucoidan derived from Sargassum fusiforme. The Celluclast-assisted hydrolysate was used for obtaining ethanol-precipitated S. fusiforme fucoidans (SFF) which were rich in polysaccharides and sulfates. Five fractions obtained through DEAE-cellulose chromatography, notably SFF-F5, displayed high sulfate content (29.16 ± 0.15%). The anti-inflammatory potential of these fractions was tested in lipopolysaccharide-induced RAW 264.7 macrophages. SFF-F5 was found to significantly reduce NO production at non-toxic concentrations of 12.5, 25, and 50 μg/mL. Structural analysis using Matrix assisted laser desorption/ionization time-of flight mass spectrometry (MALDI-TOF MS) revealed repetitive patterns in the fucoidan fractions. And Electrospray ionization mass spectrometry (ESI-TOF MS) spectrum of SFF fractions identified various structures of glycans bound with fucose and sulfate (Fuc1(SO3)1, Fuc2(SO3)1, Hex5Fuc1(SO3)1). In-silico evaluation based on the detected glycan structures indicated that fucoidan fractions could inhibit the dimerization of the TLR4-MD2 complex, demonstrating their potential as anti-inflammatory agents. This result confirm that the anti-inflammatory activity varies depending on the glycan composition of fucoidan. This study provides a foundation for further exploration into the relationship between the structure and anti-inflammatory activity of S. fusiforme-derived fucoidan, facilitating its effective industrial application.

Metabolic flexibility associated with flight time among combat pilots of the Brazilian air force.

Fighter pilots must support the effects of many stressors, including physical and psychological exertion, circadian disturbance, jet lag, and environmental stress. Despite the rigorous selection of military pilots, those factors predispose to failures in physiological compensatory mechanisms and metabolic flexibility. We compared through NMR-based metabolomics the metabolic profile of Brazilian F5 fighter pilots with different flight experiences vs. the control group of non-pilots. We hypothesized that combat pilots have metabolic flexibility associated with combat flight time. We evaluated for the first time 34 Brazilian fighter pilots from Santa Cruz Air Base (Rio de Janeiro, RJ) allocated into three groups: pilots with lower total accumulated flight experience < 1,100h (PC1, n = 7); pilots with higher total accumulated flight experience ≥ 1,100h (PC2, n = 6); military non-pilots (CONT, n = 21). Data collection included anthropometric measurements, total blood count, lipidogram, markers of oxidative stress, and serum NMR-based metabolomics. In comparison with controls (p < 0.05), pilots exhibited decreased levels of white blood cells (-13%), neutrophils (-15%), lymphocytes (-20%), alfa-glucose (-13%), lactate (-26%), glutamine (-11%), histidine (-20%), and tyrosine (-11%), but higher isobutyrate (+ 10%) concentrations. Significant correlations were found between lactate vs. amino acids in CONT (r = 0.55-0.68, p < 0.001), and vs. glutamine in PC2 (r = 0.94, p = 0.01). Fighter pilots with lower experience showed a dysregulation in immune-metabolic function in comparison with controls, which seemed to be counteracted by the accumulation of flight hours. Those findings might have implications for the health preservation and operational training of fighter pilots.

MeSat Mission: Exploring Martian Environment with THz Radiometer Payload and Optimal Trajectory

Space exploration presents vast prospects for scientific, industrial, and economic progress. This paper introduces the MeSat mission as a pioneering approach to Mars exploration. The MeSat aims to deepen our understanding of Martian conditions and resources by employing an optimized Earth-to-Mars trajectory, enabling a comprehensive study of the Martian atmosphere and surface. The mission comprises a cargo microsatellite hosting three 6U CubeSats and two 3U CubeSats, deployed into four separate Mars orbits to form a constellation. Each CubeSat carries distinct payloads: a THz radiometer for Martian water vapor atmospheric observation, a high-resolution surface camera, a high-tech spectrometer, and a Fourier transform spectrometer (FTS) for wind speed readings. This paper includes the majority of the key parameters; however, we focus our discussion more on two aspects of this pioneering mission: the first aspect contains the proposal of four distinct payloads for the study of Mars’ atmosphere and the second aspect proposes an optimal mission design algorithm that analyzes a fuel-efficient low-thrust trajectory from Earth to Mars. Regarding the payloads, the THz radiometer requires a specific design; hence, we explain this payload in more depth; the rest of the payloads, we suggest utilizing commercially available elements for the cost-effective manufacture of a whole system. For mission trajectory optimization, the study employs a dual-step hybrid optimization algorithm (PSO-homotopy) to analyze fuel-efficient low-thrust trajectories from Earth to Mars, incorporating the ephemeris dynamics model to account for gravitational perturbations in the entire Solar System. In practical mission design, crucial factors like hyperbolic excess velocity, diverse opportunities for Earth launch and Mars rendezvous, varied propulsion systems, and time of flight (TOF) play vital roles in trajectory optimization. In summary, for the MeSat mission, we propose a comprehensive Mars environmental mission design. We consider all aspects of the mission from trajectory design to engineering detail design, since we would like to inspire future Mars missions with a complete report.

First measurement in a magnetic confinement fusion experiment of the H3+H3→He5+n intermediate two-body resonant reaction

We report on the first experimental measurements made at a magnetic confinement fusion device of the tritium(T)-tritium(T) reaction T+T→He4+2n indicating the presence of the intermediate two-body resonant reaction T+T→He5+n. During the second deuterium-tritium campaign (DTE2) at the Joint European Torus, measurements of fusion plasmas with high tritium concentrations, nT/(nT+nD)≈0.99, heated with tritium neutral beam injection, were performed using the neutron time-of-flight (TOF) spectrometer TOFOR. We detect a peak in the neutron emission TOF spectrum consistent with the two-body resonant reaction. The TT neutron emission energy spectrum is modeled using an R-matrix framework where the distributions of the most likely model parameters given our experimental TOF data are determined utilizing a Markov chain Monte Carlo approach. We compare our best estimate of the T+T neutron emission energy spectrum with results obtained at inertial confinement fusion experiments at the OMEGA facility and find a spectral shape that is consistent with the energy dependency in the neutron spectrum observed at OMEGA. Published by the American Physical Society 2024

Non-systematic noise reduction framework for ToF camera

Time-of-flight (ToF) cameras enable a diverse range of applications due to their high frame rate, high resolution, and low cost. However, these cameras suffer from non-systematic noise during the acquisition of high-quality depth images, severely affecting their range accuracy. In this paper, we propose a non-systematic noise reduction framework named “DCS2Noise” to address this issue. This framework comprises a three-stage denoising strategy, involving noise standardization, deep learning-based differential correlation sampling (DCS) denoising and further enhancement with pairwise noise suppression. This framework directly captures and denoises DCS images during the ToF imaging process, making it more suitable for non-systematic noise reduction in ToF cameras. Compared to traditional methods, our approach significantly reduces the root mean squared error (RMSE) and improves the noise reduction ratio, peak signal to noise ratio (PSNR), and structural similarity index measure (SSIM). We believe that this study provides new insights into understanding noise in ToF cameras and offers effective references for reducing non-systematic noise in three-dimensional measuring instruments.

Constrained Parameterized Differential Dynamic Programming for Waypoint-Trajectory Optimization

Unmanned aerial vehicles (UAVs) are required to pass through multiple important waypoints as quickly as possible in courier delivery, enemy reconnaissance and other tasks to eventually reach the target position. There are two important problems to be solved in such tasks: constraining the trajectory to pass through intermediate waypoints and optimizing the flight time between these waypoints. A constrained parameterized differential dynamic programming (C-PDDP) algorithm is proposed for meeting multiple waypoint constraints and free-time constraints between waypoints to deal with these two issues. By considering the intermediate waypoint constraints as a kind of path state constraint, the penalty function method is adopted to constrain the trajectory to pass through the waypoints. For the free-time constraints, the flight times between waypoints are converted into time-invariant parameters and updated at the trajectory instants corresponding to the waypoints. The effectiveness of the proposed C-PDDP algorithm under waypoint constraints and free-time constraints is verified through numerical simulations of the UAV multi-point reconnaissance problem with five different waypoints. After comparing the proposed algorithm with fixed-time constrained DDP (C-DDP), it is found that C-PDDP can optimize the flight time of the trajectory with three segments to 7.35 s, 9.50 s and 6.71 s, respectively. In addition, the maximum error of the optimized trajectory waypoints of the C-PDDP algorithm is 1.06 m, which is much smaller than that (7 m) of the C-DDP algorithm used for comparison. A total of 500 Monte Carlo tests were simulated to demonstrate how the proposed algorithm remains robust to random initial guesses.

Distance Measurement of Contra-Rotating Rotor Blades with Ultrasonic Transducers.

Coaxial rotor helicopters have great potential in civilian and commercial uses, with many advantages, but challenges remain in the accurate measurement of rotor blades' distance to prevent blade collision. In this paper, a blade tip distance measurement method based on ultrasonic measurement window and phase triggering is proposed, and the triggering time of the transmitter is studied. Due to the complexity of the measured signal, bandpass filtering and a time-of-flight (TOF) estimation based on the power density of the received signal are utilised. The method is tested on an experimental test platform with a pair of 200 kHz ultrasonic transducers. The experimental results show that the maximum ranging error is less than 1.0% for the blade tip distance in a range of 100-1000 mm. Compared with the amplitude threshold method, the proposed TOF estimation method works well on the received signal with a low SNR and improves the ranging accuracy by about 5 mm when the blade tip distance is larger than 500 mm. This study provides a good reference for the accurate measurement of rotor blade tip distance, and gives a solution for ranging high-speed rotating objects.

Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize.

Field-based phenomic prediction employs novel features, like vegetation indices (VIs) from drone images, to predict key agronomic traits in maize, despite challenges in matching biomarker measurement time points across years or environments. This study utilized functional principal component analysis (FPCA) to summarize the variation of temporal VIs, uniquely allowing the integration of this data into phenomic prediction models tested across multiple years (2018-2021) and environments. The models, which included 1 genomic, 2 phenomic, 2 multikernel, and 1 multitrait type, were evaluated in 4 prediction scenarios (CV2, CV1, CV0, and CV00), relevant for plant breeding programs, assessing both tested and untested genotypes in observed and unobserved environments. Two hybrid populations (415 and 220 hybrids) demonstrated the visible atmospherically resistant index's strong temporal correlation with grain yield (up to 0.59) and plant height. The first 2 FPCAs explained 59.3 ± 13.9% and 74.2 ± 9.0% of the temporal variation of temporal data of VIs, respectively, facilitating predictions where flight times varied. Phenomic data, particularly when combined with genomic data, often were comparable to or numerically exceeded the base genomic model in prediction accuracy, particularly for grain yield in untested hybrids, although no significant differences in these models' performance were consistently observed. Overall, this approach underscores the effectiveness of FPCA and combined models in enhancing the prediction of grain yield and plant height across environments and diverse agricultural settings.

Optimization of hybrid delivery by vehicle and drones

In this paper, we introduce the optimization of hybrid delivery by vehicle and drones. Hybrid delivery by vehicle and drones is advantageous for improving O2O last-mile delivery efficiency. However, vehicle stop selection and drone delivery sortie arrangement are challenging in the optimization of hybrid delivery by vehicle and drones because cyclic sorties and forward sorties jointly influence the total makespan of the delivery. To minimize the delivery makespan, an MILP model for hybrid delivery is formulated considering both the cyclic sorties and forward sorties of drones. In the proposed model, the intertwined influences of the flight times of cyclic and forward sorties and their further effects on total delivery makespan are depicted. Additionally, we introduce an ACO based on set covering for the selection of vehicle stops and a customized HGSADC for the arrangement of drone sorties. A study based on a simulated case and experiments based on 40 generated instances at different scales are explored to assess the optimization of hybrid delivery by vehicle and drones.

Synthesizing 4D Magnetic Resonance Angiography From 3D Time-of-Flight Using Deep Learning: A Feasibility Study.

Objective and background This study aimed to develop a deep convolutional neural network (DCNN) model capable of generating synthetic 4D magnetic resonance angiography (MRA) from 3D time-of-flight (TOF) images, allowing estimation of temporal changes in arterial flow. TOF MRA provides static information about arterial structures through maximum intensity projection (MIP) processing, but it does not capture the dynamic information of contrast agent circulation, which is lost during MIP processing. Considering the principles of TOF, it is hypothesized that dynamic information about arterial blood flow is latent within TOF signals. Although arterial spin labeling (ASL) can extract dynamic arterial information, ASL MRA has drawbacks, such as longer imaging times and lower spatial resolution than TOF MRA. This study's primary aim is to extend the utility of TOF MRA by training a machine-learning model on paired TOF and ASL data to extract latent dynamic information from TOF signals. Methods A DCNN combining a modified U-Net and a long-short-term memory (LSTM) network was trained on a dataset of 13 subjects (11 menand two women, aged 42-77 years) using paired 3D TOF MRA and 4D ASL MRA images. Subjects had no history of cerebral vessel occlusion or significant stenosis. The dataset was acquired using a 3T MRI system with a 32-channel head coil. Preprocessing involved resampling and intensity normalization of TOF and ASL images, followed by data augmentation and arterial mask generation. The model learned to extract flow information from TOF images and generate 8-phase 4D MRA images. The precision of flow estimation was evaluated using the coefficient of determination (R²) and Bland-Altman analysis. A board-certified neuroradiologist validated the quality of the images and the absence of significant stenosis in the major cerebral arteries. Results The generated 4D MRA images closely resembled the ground-truth ASL MRA data, with R² values of 0.92, 0.85, and 0.84 for the internal carotid artery (ICA), proximal middle cerebral artery (MCA), and distal MCA, respectively. Bland-Altman analysis revealed a systematic error of -0.06, with 95% agreement limits ranging from -0.18 to 0.12. Additionally, the model successfully identified flow abnormalities in a subject with left MCA stenosis, displaying a delayed peak and subsequent flattening distal to the stenosis, indicative of reduced blood flow. Visualization of the predicted arterial flow overlaid on the original TOF MRA images highlighted the spatial progression and dynamics of the flow. Conclusions The DCNN model effectively generated synthetic 4D MRA images from TOF images, demonstrating its potential to estimate temporal changes in arterial flow accurately. This non-invasive technique offers a promising alternative to conventional methods for visualizing and evaluating healthy and pathological flow dynamics. It has significant potential to improve the diagnosis and treatment of cerebrovascular diseases by providing detailed temporal flow information without the need for contrast agents or invasive procedures. The practical implementation of this model could enable the extraction of dynamic cerebral blood flow information from routine brain MRI examinations, contributing to the early diagnosis and management of cerebrovascular disorders.

Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements

Indoor location services often use Bluetooth low energy (BLE) devices for their low energy consumption and easy implementation. Applications like device monitoring, ranging, and asset tracking utilize the received signal strength (RSS) of the BLE signal to estimate the proximity of a device from the receiver. However, in multipath environments, RSS-based solutions may not provide an accurate estimation. In such environments, receivers with antenna arrays are used to calculate the difference in time of flight (ToF) and therefore calculate the direction of arrival (DoA) of the Bluetooth signal. Other techniques like triangulation have also been used, such as having multiple transmitters or receivers as a network of sensors. To find a lost item, devices like Tile© use an onboard beeper to notify users of their presence. In this paper, we present a system that uses a single-measurement device and describe the method of measurement to estimate the location of a BLE device using RSS. A BLE device is configured as an Eddystone beacon for periodic transmission of advertising packets with RSS information. We developed a smartphone application to read RSS information from the beacon, designed an algorithm to estimate the DoA, and used the phone’s internal sensors to evaluate the DoA with respect to true north. The proposed measurement method allows for asset tracking by iterative measurements that provide the direction of the beacon and take the user closer at every step. The receiver application is easily deployable on a smartphone, and the algorithm provides direction of the beacon within a 30° range, as suggested by the provided results.

Time division multiplexing based multi-spectral semantic camera for LiDAR applications

The recent progress in the development of measurement systems for autonomous recognition had a substantial impact on emerging technology in numerous fields, especially robotics and automotive applications. In particular, time-of-flight (TOF) based light detection and ranging (LiDAR) systems enable to map the surrounding environmental information over long distances and with high accuracy. The combination of advanced LiDAR with an artificial intelligence platform allows enhanced object recognition and classification, which however still suffers from limitations of inaccuracy and misidentification. Recently, multi-spectral LiDAR systems have been employed to increase the object recognition performance by additionally providing material information in the short-wave infrared (SWIR) range where the reflection spectrum characteristics are typically very sensitive to material properties. However, previous multi-spectral LiDAR systems utilized band-pass filters or complex dispersive optical systems and even required multiple photodetectors, adding complexity and cost. In this work, we propose a time-division-multiplexing (TDM) based multi-spectral LiDAR system for semantic object inference by the simultaneous acquisition of spatial and spectral information. By utilizing the TDM method, we enable the simultaneous acquisition of spatial and spectral information as well as a TOF based distance map with minimized optical loss using only a single photodetector. Our LiDAR system utilizes nanosecond pulses of five different wavelengths in the SWIR range to acquire sufficient material information in addition to 3D spatial information. To demonstrate the recognition performance, we map the multi-spectral image from a human hand, a mannequin hand, a fabric gloved hand, a nitrile gloved hand, and a printed human hand onto an RGB-color encoded image, which clearly visualizes spectral differences as RGB color depending on the material while having a similar shape. Additionally, the classification performance of the multi-spectral image is demonstrated with a convolution neural network (CNN) model using the full multi-spectral data set. Our work presents a compact novel spectroscopic LiDAR system, which provides increased recognition performance and thus a great potential to improve safety and reliability in autonomous driving.

Valuation of advanced Dosimetry in Transarterial Radioembolization of Hepatocellular Carcinoma with 90Y microspheres

Objective : The aim of our study was to analyze the relationships between tumor (T) and normal tissue (N) absorbed dose in relation to the clinical outcomes in hepatocellular carcinoma (HCC) patients treated with 90Y microspheres. Materials and methods : After transarterial radioembolization of HCC with 90Y microspheres, 62 patients (10 females: 52 males, mean age 68.2±8.2 years) were imaged using a four-ring, time-of-flight (TOF), PET/CT system. Low dose, non-diagnostic CT images from PET/CT were utilized for localization of the 90Y microspheres and attenuation correction of PET images. Response assessment was conducted using mRECIST criteria on MRI one month post treatment and subsequently every three months after 90Y treatment. Results : Among 62 patients, mean liver, tumor, and normal tissue doses (mean ± SD) were 51.38±23.32 Gy, 682.60±785.19 Gy and 45.54±21.19 Gy, respectively. Out of these patients, 39 exhibited complete response (CR), 11 showed partial response (PR), 2 had stable disease (SD), and 10 showed progression of the disease (PD). For CR+PR patients the mean T was 824.63±833.10 Gy, whereas for PD patients, the mean T was significantly lower at 205.70±183.22 Gy. The mean liver and normal tissue doses were comparable; for CR+PR patients had liver and normal tissue doses of 51.01±22.55 Gy and 45.18±20.68 Gy, respectively, and for PD patients, these values were 54.34±26.73 Gy and 49.10±22.97 Gy, respectively. Conclusion : Despite not considering the partial volume effect and having a limited number of PD cases, our data indicates a statistically significant lower (P = 0.0001) tumor dose in patients with disease progression compared to those with complete and partial response. These results suggest that post-therapy personalized, and image-based dosimetry provides promising predictive outcomes in 90Y microsphere radiation therapy for liver cancers.

Validity and reliability of My Jump 2® app to measure the vertical jump on elite women beach volleyball players.

The aim of this study was to assess the reliability and validity of the My Jump 2® app in measuring jump height, flight time, and peak power among elite women beach volleyball players on sand surfaces. Eleven elite female beach volleyball players (aged 23.6 ± 6.2 years; weight 66.3 ± 5.8 kg; height 174.4 ± 5.8 cm; with 8.4 ± 4.8 years of professional experience) participated in this study. Each player performed six countermovement jumps in a wooden box filled with sand on a force platform while simultaneously recording a video for subsequent analysis using the My Jump 2® app. We found excellent agreement for flight time, jump height and peak power between observers (ICC = 0.92, 0.91 and 0.97, respectively). No significant differences between force platform and My Jump 2® app were detected in the values obtained for the three variables (P > 0.05). For the force platform and the My Jump 2® app, we found a good agreement measuring jump height and flight time (ICC = 0.85 and 0.85, respectively). However, we only found a moderate agreement for peak power (ICC = 0.64). The difference in jump height showed a limit of agreement between -4.10 and 4.74 cm in Bland-Altman, indicating a high level of agreement between the two measurement tools. Based on our findings, the My Jump 2® app reveals a valid tool for measuring jump height and flight time of CMJ on sand surfaces. However, more caution is needed when measuring peak power.

Decision-making strategies implemented in SolFinder 1.0 to identify eco-efficient aircraft trajectories: application study in AirTraf 3.0

Abstract. The optimization of aircraft trajectories involves balancing operating costs and climate impact, which are often conflicting objectives. To achieve compromised optimal solutions, higher-level information such as preferences of decision-makers must be taken into account. This paper introduces the SolFinder 1.0 module, a decision-making tool designed to identify eco-efficient aircraft trajectories, which allow for the reduction of the flight's climate impact with limited cost penalties compared to cost-optimal solutions. SolFinder 1.0 offers flexible decision-making options that allow users to select trade-offs between different objective functions, including fuel use, flight time, NOx emissions, contrail distance, and climate impact. The module is included in the AirTraf 3.0 submodel, which optimizes trajectories under atmospheric conditions simulated by the ECHAM/MESSy Atmospheric Chemistry model. This paper focuses on the ability of the module to identify eco-efficient trajectories while solving a bi-objective optimization problem that minimizes climate impact and operating costs. SolFinder 1.0 enables users to explore trajectory properties at varying locations of the Pareto fronts without prior knowledge of the problem results and to identify solutions that limit the cost of reducing the climate impact of a single flight.

Enabling resilient UAV swarms through multi-hop wireless communications

In the last decade, the popularity of UAVs, colloquially known as drones, has increased tremendously. Nowadays, drones are used in a wide range of use-cases, such as precision agriculture, surveillance, and photography. Many of these use-cases can be made more efficient if multiple UAVs are used cooperatively (i.e., in a swarm). To achieve this, communication between the UAVs is paramount. To ensure communication, many works rely on the existing infrastructure (e.g., 4G). However, in many rural areas, this infrastructure does not exist. In those cases, an ad hoc (Wi-Fi) network is the most adequate alternative. Yet, due to the limited communication range of Wi-Fi, it is not possible to let UAVs in a swarm to communicate over a long distance. To solve this issue a relay approach is necessary. Despite general solutions to relay messages between (mobile) nodes already exist, many UAV swarms rely on master–slave communication. Thus, a specific solution for this type of communication might be more efficient. Hence, in this work, we propose a strategy to efficiently relay messages for UAV swarms adopting the master–slave communication paradigm. Our approach seeks to introduce a very small message overhead to avoid congestion of the network, and to provide more bandwidth for the actual applications of the UAV swarm. We tested our approach using a realistic UAV simulator called ArduSim. Our results show that our approach is capable of detecting all the nodes in the network within a few seconds. Furthermore, we applied our message relay approach on an existing swarm application (where a swarm of UAVs had to follow a mission), and our results show that, now, the communication range of the UAVs can be much larger, without impacting other aspects of the mission (such as flight time).

Deep learning-based platform performs high detection sensitivity of intracranial aneurysms in 3D brain TOF-MRA: An external clinical validation study

PurposeTo evaluate the diagnostic efficacy of a developed artificial intelligence (AI) platform incorporating deep learning algorithms for the automated detection of intracranial aneurysms in time-of-flight (TOF) magnetic resonance angiography (MRA). MethodThis retrospective study encompassed 3D TOF MRA images acquired between January 2023 and June 2023, aiming to validate the presence of intracranial aneurysms via our developed AI platform. The manual segmentation results by experienced neuroradiologists served as the “gold standard”. Following annotation of MRA images by neuroradiologists using InferScholar software, the AI platform conducted automatic segmentation of intracranial aneurysms. Various metrics including accuracy (ACC), balanced ACC, area under the curve (AUC), sensitivity (SE), specificity (SP), F1 score, Brier Score, and Net Benefit were utilized to evaluate the generalization of AI platform. Comparison of intracranial aneurysm identification performance was conducted between the AI platform and six radiologists with experience ranging from 3 to 12 years in interpreting MR images. Additionally, a comparative analysis was carried out between radiologists’ detection performance based on independent visual diagnosis and AI-assisted diagnosis. Subgroup analyses were also performed based on the size and location of the aneurysms to explore factors impacting aneurysm detectability. Results510 patients were enrolled including 215 patients (42.16 %) with intracranial aneurysms and 295 patients (57.84 %) without aneurysms. Compared with six radiologists, the AI platform showed competitive discrimination power (AUC, 0.96), acceptable calibration (Brier Score loss, 0.08), and clinical utility (Net Benefit, 86.96 %). The AI platform demonstrated superior performance in detecting aneurysms with an overall SE, SP, ACC, balanced ACC, and F1 score of 91.63 %, 92.20 %, 91.96 %, 91.92 %, and 90.57 % respectively, outperforming the detectability of the two resident radiologists. For subgroup analysis based on aneurysm size and location, we observed that the SE of the AI platform for identifying tiny (diameter＜3mm), small (3 mm ≤ diameter＜5mm), medium (5 mm ≤ diameter＜7mm) and large aneurysms (diameter ≥ 7 mm) was 87.80 %, 93.14 %, 95.45 %, and 100 %, respectively. Furthermore, the SE for detecting aneurysms in the anterior circulation was higher than that in the posterior circulation. Utilizing the AI assistance, six radiologists (i.e., two residents, two attendings and two professors) achieved statistically significant improvements in mean SE (residents: 71.40 % vs. 88.37 %; attendings: 82.79 % vs. 93.26 %; professors: 90.07 % vs. 97.44 %; P < 0.05) and ACC (residents: 85.29 % vs. 94.12 %; attendings: 91.76 % vs. 97.06 %; professors: 95.29 % vs. 98.82 %; P < 0.05) while no statistically significant change was observed in SP. Overall, radiologists’ mean SE increased by 11.40 %, mean SP increased by 1.86 %, and mean ACC increased by 5.88 %, mean balanced ACC promoted by 6.63 %, mean F1 score grew by 7.89 %, and Net Benefit rose by 12.52 %, with a concurrent decrease in mean Brier score declined by 0.06. ConclusionsThe deep learning algorithms implemented in the AI platform effectively detected intracranial aneurysms on TOF-MRA and notably enhanced the diagnostic capabilities of radiologists. This indicates that the AI-based auxiliary diagnosis model can provide dependable and precise prediction to improve the diagnostic capacity of radiologists.