DynStatF: An Efficient Feature Fusion Strategy for LiDAR 3D Object Detection

Augmenting LiDAR input with multiple previous frames provides richer semantic information and thus boosts performance in 3D object detection, However, crowded point clouds in multi-frames can hurt the precise position information due to the motion blur and inaccurate point projection. In this work, we propose a novel feature fusion strategy, DynStaF (Dynamic-Static Fusion), which enhances the rich semantic information provided by the multi-frame (dynamic branch) with the accurate location information from the current single-frame (static branch). To effectively extract and aggregate complimentary features, DynStaF contains two modules, Neighborhood Cross Attention (NCA) and Dynamic-Static Interaction (DSI), operating through a dual pathway architecture. NCA takes the features in the static branch as queries and the features in the dynamic branch as keys (values). When computing the attention, we address the sparsity of point clouds and take only neighborhood positions into consideration. NCA fuses two features at different feature map scales, followed by DSI providing the comprehensive interaction. To analyze our proposed strategy DynStaF, we conduct extensive experiments on the nuScenes dataset. On the test set, DynStaF increases the performance of PointPillars in NDS by a large margin from 57.7% to 61.6%. When combined with CenterPoint, our framework achieves 61.0% mAP and 67.7% NDS, leading to state-of-the-art performance without bells and whistles.Comment: Accepted to CVPR2023 Workshop on End-to-End Autonomous Drivin

Foreground Scattering Elimination by Inverse Lock-in-Like Spatial Modulation

We describe a simple approach to enhance vision, which is impaired by close range obscuring and/or scattering structures. Such structures may be found on a dirty windscreen of a car, or by tree branches blocking the vision of objects behind. The main idea is to spatially modulate the obscuration, either by periodically moving the detector/eye or by letting the obscuration modulate itself, such as branches swinging in the wind. The approach has similarities to electronic lock-in techniques, where the feature of interest is modulated to enable it to be isolated from the strong perturbing background, but now, we modulate the background instead to isolate the static feature of interest. Thus, the approach can be denoted as “inverse lock-in-like spatial modulation”. We also apply a new digital imaging processing technique based on a combination of the Interframe Difference and Gaussian Mixture models for digital separation between the objects of interest and the background, and make connections to the Gestalt vision psychology field

Foreground scattering elimination by inverse lock-in-like spatial modulation

We describe a simple approach to enhance vision, which is impaired by close range obscuring and/or scattering structures. Such structures may be found on a dirty windscreen of a car, or by tree branches blocking the vision of objects behind. The main idea is to spatially modulate the obscuration, either by periodically moving the detector/eye or by letting the obscuration modulate itself, such as branches swinging in the wind. The approach has similarities to electronic lock-in techniques, where the feature of interest is modulated to enable it to be isolated from the strong perturbing background, but now, we modulate the background instead to isolate the static feature of interest. Thus, the approach can be denoted as “inverse lock-in-like spatial modulation”. We also apply a new digital imaging processing technique based on a combination of the Interframe Difference and Gaussian Mixture models for digital separation between the objects of interest and the background, and make connections to the Gestalt vision psychology field

Identification of flying insects in the spatial, spectral, and time domains with focus on mosquito imaging

Insects constitute a very important part of the global ecosystem and include pollinators, disease vectors, and agricultural pests, all with pivotal influence on society. Monitoring and control of such insects has high priority, and automatic systems are highly desirable. While capture and analysis by biologists constitute the gold standard in insect identification, optical and laser techniques have the potential for high-speed detection and automatic identification based on shape, spectroscopic properties such as reflectance and fluorescence, as well as wing-beat frequency analysis. The present paper discusses these approaches, and in particular presents a novel method for automatic identification of mosquitos based on image analysis, as the insects enter a trap based on a combination of chemical and suction attraction. Details of the analysis procedure are presented, and selectivity is discussed. An accuracy of 93% is achieved by our proposed method from a data set containing 122 insect images (mosquitoes and bees). As a powerful and cost-effective method, we finally propose the combination of imaging and wing-beat frequency analysis in an integrated instrument

Real-Time Measurement of CH₄ in Human Breath Using a Compact CH₄/CO₂ Sensor

The presence of an elevated amount of methane (CH4) in exhaled breath can be used as a non-invasive tool to monitor certain health conditions. A compact, inexpensive and transportable CH4 sensor is thus very interesting for this purpose. In addition, if the sensor is also able to simultaneously measure carbon dioxide (CO2), one can extract the end-tidal concentration of exhaled CH4. Here, we report on such a sensor based on a commercial detection module using tunable diode laser absorption spectroscopy. It was found that the measured CH4/CO2 values exhibit a strong interference with water vapor. Therefore, correction functions were experimentally identified and validated for both CO2 and CH4. A custom-built breath sampler was developed and tested with the sensor for real-time measurements of CH4 and CO2 in exhaled breath. As a result, the breath sensor demonstrated the capability of accurately measuring the exhaled CH4 and CO2 profiles in real-time. We obtained minimum detection limits of ~80 ppbv for CH4 and ~700 ppmv for CO2 in 1.5 s measurement time

Mosquito counting system based on optical sensing

Mosquitos, sometimes carrying deadly diseases such as malaria, zika, and dengue fever, cause much concern. To control mosquitos, it is important to effectively monitor their presence and behavioral trends. We have constructed two optical sensing systems for insects based on light attenuation and light backscattering, respectively. The systems, which were tested with the potentially dangerous Aedes albopictus and Culex pipiens, were able to extract the wing-beat frequency, when they passed impinging light, derived from light-emitting diodes. We could achieve distinction between the sexes of A. albopictus and C. pipiens based on the wing-beat frequency. Finally, we propose a statistical method suitable for the system to improve the accuracy of counting

A Bi-objective Robust Dynamic Bayesian Network Method for Supply Chain Performance Evaluation

International audienceEvaluating supply chain (SC) disruption risks in the context of data scarcity and ripple effects is essential because uncertain disruptions can propagate throughout the SC, resulting in negative impacts on SC performance. Considering that different decision-makers have varying risk tolerances, this can affect the expected disruption risk assessment results. To tackle the problem, this paper proposes a methodology that employs probability intervals to capture uncertain parameters, utilizes dynamic Bayesian networks (DBNs) to model risk propagation, and incorporates risk deviation variables to quantify decision-makers' risk tolerance. Subsequently, a bi-objective optimization model is developed to evaluate the optimal Pareto front with respect to both SC disruption risk and deviation budget. For solving the studied problem, the linearisation and ϵ-constraint methods are developed. To demonstrate the feasibility and effectiveness of the proposed model, the numerical experiment is carried out, and the results are analysed to draw managerial insights

Gas monitoring in human frontal sinuses-stability considerations and gas exchange studies

Acute rhinosinusitis is a common infectious disease, which, in more than 90% of cases, is caused by viruses rather than by bacteria. Even so, antibiotics are often unnecessarily prescribed, and in the long run this contributes to the alarming level of antibiotics resistance. The reason is that there are no good guiding tools for defining the background reason of the infection. One main factor for the clearance of the infection is if there is non-obstructed ventilation from the sinus to the nasal cavity. Gas in Scattering Media Absorption Spectroscopy (GASMAS) has potential for diagnosing this. We have performed a study of frontal sinuses of volunteers with a focus on signal stability and reproducibility over time, accurate oxygen concentration determination, and assessment of gas transport through passages, naturally and after decongestant spray administration. Different from earlier studies on frontal sinuses, water vapor, serving the purpose of oxygen signal normalization, was measured at 818 nm rather than earlier at 937 nm, now closer to the 760 nm oxygen absorption band and thus resulting in more reliable results. In addition, the action of decongestants was objectively demonstrated for the first time. Evaluated oxygen concentration values for left-and right-hand side sinus cavities were found to agree within 0.3%, and a left-right geometrical asymmetry parameter related to anatomical differences was stable within 10%

Bi-objective optimization for supply chain ripple effect management under disruption risks with supplier actions

International audienceIn practice, supplier actions are often taken to reduce the impact of disruption propagation in the supply chain and ensure continuity of material flows. However, these actions can be very costly. The selection of appropriate supplier actions to reduce the disruption risk is of great interest to both academics and practitioners. However, there is no study on optimally selecting supplier actions to find the best balance between the cost of these actions and the disruption risk. This work investigates a new bi-objective supply chain ripple effect management problem, considering supplier actions. The two objectives are to minimize the manufacturer's disruption risk and the expected total action cost. To efficiently address the problem, an integrated approach that combines Markov decision process (MDP), dynamic Bayesian network (DBN), and bi-objective nonconvex mixed-integer programming model, along with optimization techniques, is designed. From this study, the following managerial insights can be drawn: (i) for different desired risk reductions, cost-effective supplier actions are different and can be identified by the proposed approach to support decision-making; (ii) the risk decreases with the increase of the total action cost before the risk threshold is achieved, and the disruption risk cannot be smaller than the risk threshold, even if more costly actions are taken; (iii) the costs of supplier actions have no impact on the risk threshold, while the state probability distributions of suppliers and the manufacturer affect the risk threshold

Gas in Scattering Media Absorption Spectroscopy on Small and Large Scales: Towards the Extension of Lung Spectroscopic Monitoring to Adults

Numerous natural materials are porous, contain free gas, and are scattering light strongly. Scattering brings about a strong trapping of light and an associated prolonged transit time for photons through a medium. In contrast to the matrix materials, gas enclosures require very narrow‐band laser radiation for probing. We have in the present study used the gas in scattering media absorption spectroscopy (GASMAS) method to study free oxygen in thin (cm) samples utilizing a tunable diode laser, while a pulsed dye laser was employed in corresponding measurements on larger samples, up to the meter scale. Time‐resolved spectroscopy was in both cases used to assess the temporal distribution of the detected photons, mapping the path lengths through the media, which ranged between few centimeters up to 100 m. This study explores the feasibility to extend recent successful monitoring of gases in neonatal infant lungs to the case of larger children or even adults, which could have very important applications, for example, in ventilator setting optimization for severely ill patients, suffering, for example, from SARS‐CoV‐2. The conclusion of our work is that this goal most realistically can be reached by applying intra‐tracheal laser light illumination at the 1 W power level, employing a tapered amplifier, injected with a distributed feed‐back diode‐laser oscillator output and combined with wavelength‐modulation spectroscopy