A 94 GHz Millimeter-Wave Radar System for Remote Vehicle Height Measurement to Prevent Bridge Collisions.

One of the main challenges of modern astronomical instruments like adaptive optics (AO) systems or fringe trackers is to deal with the very low photons flux detection scenarios. The typical timescale of atmospheric turbulences being in the range of some tens of milliseconds, infrared wavefront sensors for AO systems needs frame rates higher than 1 KHz leading to integration times lower than 1 ms. This integration time associated with a low irradiance results in a few number of integrated photons per frame per pixel. To preserve the information coming from this weak signal, the focal plane array (FPA) has to present a low read out noise, a high quantum efficiency and a low dark current. Up to now, the output noise of high speed near infrared sensors is limited by the silicon read out circuit noise. The use of HgCdTe avalanche photodiodes with high gain at moderate reverse bias and low excess noise seems then a logical way to reduce the impact of the read noise on images signal to noise ratio. These low irradiance passive imaging applications with integration times in the millisecond range needs low photodiode dark current and low background current. These requirements lead to the choice of the photodiode cut off wavelength. The short wave infrared (SWIR) around 3 μm is a good compromise between the gain that can be obtain for a given APD bias and the background and dark current. The CEA LETI HgCdTe APD technology, and a fine analysis of the gain curve characteristic are presented in [1] and won’t be detailed here. The response time of the APD is also a key factor for a high frame rate FPA. This parameter has been evaluated in [2] and the results shows cut off frequencies in the GHz range. In this communication we report the performances of a SWIR APD FPA designed and fabricated by CEA LETI and SOFRADIR for astrophysical applications. This development was made in the frame of RAPID, a 4 years R&D project funded by the French FUI (Fond Unique Interministeriel). This project involves industrial and academic partners from the field of advanced infrared focal plane arrays fabrication (SOFRADIR and CEA LETI) and of astronomical/defense institutes (IPAG, LAM, ONERA). The goal of this program is to develop a fast and low noise SWIR camera for astronomical fast applications like adaptive optics wavefront sensing and fringe tracking for astronomical interferometers [3]. The first batch of FPA’s was based on liquid-phase epitaxy (LPE) grown photodiode arrays with 3 μm cut off wavelength. In order to get higher avalanche gain for a given photodiode reverse bias voltage, we have made a second batch with a cadmium composition leading to 3.3 μm cut off wavelength (λ c ). This paper described the read out circuit in the next section. The aim section III is to find the critical parameter that has to be measured to evaluate the signal to noise ratio (SNR) of an APD FPA. The main electro optical characteristics of an FPA based on 3.3μm cut off wavelength APDs are reported in “Rapid FPAs characterisation” section. The dark current evolution with temperature of a 3 μm FPA high and low APD bias is also detailed in this section.

IR detector integration time is determined by a combination of the scene or target radiance, the noise of the sensor, and the sensor sensitivity. Typical LWIR detectors such as those used in most U.S. military systems can operate effectively with integration times in the microsecond region. MWIR detectors require much longer integration times (up to several milliseconds) under some conditions to achieve good Noise Equivalent Temperature Difference (NETD). Emerging 3rd Generation FLIR systems incorporate both MWIR and LWIR detectors. The category of sensors know as uncooled LWIR require thermal time constants, similar to integration time, in the millisecond range to achieve acceptable good NETD. These longer integration times and time constants would not limit performance in a purely static environment, but target or sensor motion can induce blurring under some circumstances. A variety of tasks and mission scenarios were analyzed to determine the integration time requirements for combinations of sensor platform movement and look angle. These were then compared to the typical integration times for MWIR and LWIR detectors to establish the suitability of each band for the functions considered.

Optical frequency combs generate precise optical frequencies tightly linked to stable microwave references. Dual-comb spectroscopy (DCS) utilizes the coherent interference of two combs with appropriately controlled different repetition frequencies. To improve its performance far beyond the trade-off relationship between its frequency resolution and measurement speed, we present a complementary hybridization approach consisting of a fiber-laser-based comb and an electro-optic-modulator-based comb with a near-integer multiple repetition frequency relationship. We suppressed relative phase noise by locking two distant electro-optic modulator (EOM) comb modes to one of the fiber comb modes and a cavity-locked laser, which was an optical reference of the fiber comb, and achieved a figure of merit of 4.3 × 107 Hz1/2. Finally, we revisit signal to noise ratio theory to derive a relationship applicable to this configuration and discuss future perspective as regards performance improvement.

A three-dimensional surface measurement using fringe projection technique has received attention among the researches for the last few decades.However choosing the best method is the challenge because one needs to consider the cost, measurement speed, system complexity, measurement accuracy and data reliability to ensure the success of the measurement. This paper focused on the successful manipulation of non-collimated light source and three step phase shifting for height measurement of a lead frame.The measurement done based on pixel determination from the saw tooth image. In addition, the scaling factor technique has been employed for the measurement accuracy. The experimental results achieved a high precision of measurement with simple system and high speed capability.

This article deals with design and partial realization of experimental aerometric system for general aviation. The height is determined by the barometric principle. However, the primary measurement of the pressure is performed by differential pressure sensor which is added by a low-cost absolute pressure sensor. This system allows a measurement of height, vertical speed and temperature of the environment. The accuracy of the system fulfills demands of civil aviation standards at low-cost. The article describes principle and advantages of height measurement by a differential pressure sensor, a design of an aerometric system and its components, and achieved measurement results. The article also outlines a possible future utilization of the system in the sector of ultralight planes and modern gliders.

The present paper proposes a sensing system for the speed, the height and the direction of sea waves. The measurement of the three quantities is achieved using three optical range sensors which are adequately located over the sea near the platform. For realizing this, the sensor outputs are modeled as outputs of a linear dynamic system and a Kalman filter and maximum likelihood method are applied. Not only a case of one sea wave but also a case of two overlapping sea waves with different directions are considered. The validity of the system is finally verified with simulations and experiments

This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, represents the first coordinated multivariate ensemble of 21st Century global wind-wave climate projections available (henceforth COWCLIP2.0). COWCLIP2.0 comprises general and extreme statistics of significant wave height (HS), mean wave period (Tm), and mean wave direction (θm) computed over time-slices 1979–2004 and 2081–2100, at different frequency resolutions (monthly, seasonally and annually). The full ensemble comprising 155 global wave climate simulations is obtained from ten CMIP5-based state-of-the-art wave climate studies and provides data derived from alternative wind-wave downscaling methods, and different climate-model forcing and future emissions scenarios. The data has been produced, and processed, under a specific framework for consistency and quality, and follows CMIP5 Data Reference Syntax, Directory structures, and Metadata requirements. Technical comparison of model skill against 26 years of global satellite measurements of significant wave height has been undertaken at global and regional scales. This new dataset provides support for future broad scale coastal hazard and vulnerability assessments and climate adaptation studies in many offshore and coastal engineering applications.

A dynamic range enhancement pixel architecture based on a standard CMOS image sensor (CIS) process with a pinned photodiode (PPD) is proposed, combining dual conversion gain (DCG) and logarithmic response. Due to a unique charge compensation mechanism of the proposed pixel, in a single exposure, three images are captured with the same integration time, related to different incident light levels. As a result, an over 140 dB dynamic range image is able to be obtained by merging these images. Photo response measurements show the image sensor exhibits over 92 dB linear dynamic range and more than 51 dB logarithmic dynamic range, without signal-to-noise ratio (SNR) performance degradation under low light conditions.

Heavy precipitation and storm surges often co-occur and compound together to form sudden and severe flooding events. However, we lack comprehensive observational tools with high temporal and spatial resolution to capture these fast-evolving hazards. Remotely sensed images provide extensive spatial coverage, but they may be limited by adverse weather conditions or platform revisiting schedule. River gauges could provide frequent water height measurement but they are sparsely distributed. Riverine flood and storm surge models, depending on input data quality and calibration process, have various uncertainties. These lead to inevitable temporal and spatial gaps in monitoring inundation dynamics. To fill in the observation gaps, this paper proposes a probabilistic method to estimate daily inundation probability by combining the information from multiple sources, including satellite remote sensing, riverine flood depth, storm surge height, and land cover. Each data source is regarded as a spatial evidence layer, and the weight of evidence is calculated by assessing the association between the evidence presence and inundation occurrence. Within a Bayesian model, the fusion results are daily inundation probability whenever at least one data source is available. The proposed method is applied to estimate daily inundation in Harris, Texas, impacted by Hurricane Harvey. The results agree with the reference water extent, high water mark, and extracted tweet locations. This method could help to further understand flooding as an evolving time-space process and support response and mitigation decisions.

A robust, optimized power system stabilizer (PSS) is crucial for oscillation damping, and thus improving electrical network stability. Additionally, real-time testing methods are required to significantly reduce the likelihood of software failure in a real-world setting at the user location. This paper presents an Antlion-based proportional integral derivative (PID) PSS to improve power system stability during real-time constraints. The Antlion optimization (ALO) is developed with real-time testing methodology, using hardware-in-the-loop (HIL) that can communicate multiple digital control schemes with real-time signals. The dynamic power system model runs on the dSPACE DS1104, and the proposed PSS runs on the field programmable gate arrays (FPGA) (NI SbRIO-9636 board). The optimized PSS performance was compared with a modified particle swarm optimization (MPSO)-based PID-PSS, through different performance indices. The test cases include other step load perturbations and several short circuit faults at various locations. Twelve different test cases have been applied, through real-time constraints, to prove the robustness of the proposed PSS. These include 5 and 10% step changes through 3 different operating conditions and single, double, and triple lines to ground short circuits through 3 different operating conditions, and at various locations of the system transmission lines. The analysis demonstrates the effectiveness of ALO and MPSO in regaining the system’s stability under the three loading conditions. The integral square of the error (ISE), integral absolute of the error (IAE), integral time square of the error (ITSE), and integral time absolute of the error (ITAE) are used as performance indices in the analysis stage. The simulation results demonstrate the effectiveness of the proposed PSS, based on the ALO algorithm. It provides a robust performance, compared to the traditional PSS. Regarding the applied indices, the proposed PSS, based on the ALO algorithm, obtains significant improvement percentages in ISE, IAE, ITSE, and ITAE with 30.919%, 23.295%, 51.073%, and 53.624%, respectively.

Transportation accidents, are significantly affected by human factors, which account for a substantial proportion of incidents and fatalities. Factors such as fatigue, stress, illness, medication, and substance use impair pilot performance, leading to compromised decision-making, reduced situational awareness, and increased risk-taking behavior (Wingelaar-Jagt et al., 2021). While regulatory guidelines and medical evaluations exist to address these challenges, current measures often rely on self-reporting and subjective assessments that can be prone to bias. Artificial Intelligence (AI) driven facial recognition model has been used in other industries to assess human subjects’ health status (Chan et al., 2024) and cognitive workload (Iarlori et al., 2024). This research aims to develop an AI-driven facial recognition model to objectively assess pilot fitness to fly by analyzing micro expressions, facial symmetry, eye movement, and other biomarkers that reflect fatigue, stress, and impairment. The AI model will be trained using publicly available datasets containing facial images of individuals in varying conditions such as fatigue, drowsiness, stress, sadness, and under the influence of alcohol, drugs, or medication. Data preprocessing will employ facial landmark detection and attention-based image segmentation to isolate key facial regions, including the eyes (tracking movement and redness), mouth (symmetry, dryness, or tremor), and skin tone (color changes indicative of intoxication or stress)(Chan et al., 2024). Model training will leverage deep convolutional neural networks (CNNs), utilizing transfer learning techniques to enhance performance with smaller datasets. There are three tasks in this research. Task 1 focuses on model building using secondary data from publicly available facial image datasets in different conditions. Task 2 involves a laboratory-based experiment with healthy individuals to validate and refine the AI algorithm’s accuracy in detecting cognitive performance changes under stress. Participants will perform cognitive tasks under high-stress conditions, and facial images will be captured to fine-tune the algorithm. Task 3 includes a pilot simulation-based experiment to fine-tune the AI algorithm for aviation-specific applications. Licensed pilots will perform flight simulation tasks under high-workload or stressful conditions, such as emergency scenarios and adverse weather conditions. Data from facial images and simulator metrics like decision-making speed, navigation accuracy, and task prioritization will be analyzed to adapt the AI algorithm for real-time, aviation-specific assessments. The integration of this technology into preflight screening process will provide real-time, non-invasive assessments, complementing existing protocols and enhancing aviation safety by offering early warnings of performance degradation, thereby reducing accident risks and improving operational efficiency. Such AI facial recognition technology can also be utilized in-flight to detect subtle cues informing the pilot of their assessed condition. The authors would like to acknowledge Embry-Riddle Aeronautical University – FIRST Program for the funding provided. The authors would also like to acknowledge the consistent support from College of Aviation - School of Graduate Studies, and College of Engineering - Mechanical Engineering department.

A modeling and simulation framework for composite reliability assessment considering adverse weather conditions

Adaptive Random Testing (ART) aims to enhance the performance of pure random testing by detecting failure region in a software. The ART algorithm generates effective test cases which requires less number of test cases than that of pure random testing. However, all ART algorithms currently proposed are designed for the tests of monolithic system or unit level. In case of integrated system tests, ART approaches do not achieve same performances as those of ARTs applied to the unit or monolithic system. In this paper, we propose an extended ART algorithm which can be applied to the integrated system testing environment without degradation of performance. The proposed approach investigates an input distribution of the unit under a test with limited number of seed input data and generates information to be used to resizing input domain partitions. The simulation results show that our approach in an integration environment could achieve similar level of performance as an ART is applied to a unit testing. Results also show resilient effectiveness for various failure rates.

This paper assesses the sea surface height (SSH), significant wave height (SWH) and wind speed (U) measurements derived from Haiyang-2B (HY-2B) altimeter products from April 2019 to September 2019. Crossover analysis is used to assess to SSH measurements. The mean standard deviations of the SSH crossover differences for HY-2B is 5.09 cm, which is lower than Jason-2 (5.31 cm) and Jason-3 (5.27 cm). Compared with the National Data Buoy Center (NDBC) observations, the HY-2B SWH measurements show a root-mean-square error (RMSE) of 0.205 m with a positive bias of 0.150 m. The HY-2B wind speed measurements show a RMSE of 1.139 m/s with a negative bias of 0.563 m/s. The SSH, SWH and wind speed measurements from HY-2B products show high accuracy, but should be further calibrated.

In this work we extend our edge‐based stabilized finite element incompressible flow solver to turbulence modeling with the residual‐based variational multiscale (RB‐VMS) method. Using the advective‐form of the convection term of the Navier–Stokes equations, RB‐VMS is implemented as a straightforward extension of standard stabilized methods with a modified advective velocity. This requires minimum modification of the existing highly optimized code. Two test cases were solved to assess accuracy and performance of the present implementation. First, the laminar incompressible flow past a circular cylinder at Re = 100 and second, the fully turbulent incompressible flow in a lid‐driven cubic cavity at Re = 12000. Comparisons were made with standard stabilized finite element formulations, highly resolved numerical simulations and experimental data. Results have shown that the present implementation is able to achieve reasonable accuracy without performance degradation in different flow regimes. Copyright © 2008 John Wiley & Sons, Ltd.