Bayesian image reconstruction and adaptive scene sampling in single-photon LiDAR imaging

Three-Dimensional multispectral Light Detection And Ranging (LiDAR) used with time-correlated Single-Photon (SP) detection has emerged as a key imaging modality for high-resolution depth imaging due to its high sensitivity and excellent surface-to-surface resolution. This allowed depth imaging through adversarial conditions with a prime role in numerous applications. However, several practical challenges currently limit the use of LiDAR in real-world conditions. Large data volume constitutes a major challenge for multispectral SP-LiDAR imaging due to the acquisition of millions of events per second that are usually gathered in large histogram cubes. This challenge is more evident when the useful signal photons are attenuated and the background noise is amplified as a result of imaging through a scattering environment such as underwater or fog. Another limitation includes the detection of multiple-surfaces-per pixel which usually occurs when imaging through semi-transparent materials (e.g., windows, camouflage), or in long-range profiling. This thesis proposes robust and fast computational solutions to improve the acquisition and processing of LiDAR data while measuring uncertainty on high-dimensional data. A smart task-based sampling framework is proposed to improve the acquisition process and reduce data volume. In addition, the processing was improved using a Bayesian approach to different types of inverse problems (e.g. spectral classification, and scene reconstruction). The contributions of this thesis enables fast and robust 3D reconstruction of complex scenes, paving the way for the extensive use of single-photon imaging in real-world applications

Technological Trends and Key Communication Enablers for eVTOLs

The world is looking for a new exciting form of transportation that will cut our travel times considerably. In 2021, the time has come for flying cars to become the new transportation system of this century. Electric vertical take-off and landing (eVTOL) vehicles, which are a type of flying cars, are predicted to be used for passenger and package transportation in dense cities. In order to fly safely and reliably, wireless communications for eVTOLs must be developed with stringent eVTOL communication requirements. Indeed, their communication needs to be ultra-reliable, secure with ultra-high data rate and low latency to fulfill various tasks such as autonomous driving, sharing a massive amount of data in a short amount of time, and high-level communication security. In this paper, we propose major key communication enablers for eVTOLs ranging from the architecture, air-interface, networking, frequencies, security, and computing. To show the relevance and the impact of one of the key enablers, we carried out comparative simulations to show the superiority compared to the current technology. We compared the usage of an air-based communication infrastructure with a tower mast in a realistic scenario involving eVTOLs, delivery drones, pedestrians, and vehicles.Comment: 8 pages, 10 figure

Harnessing the Potential of Optical Communications for the Metaverse

The Metaverse is a digital world that offers an immersive virtual experience. However, the Metaverse applications are bandwidth-hungry and delay-sensitive that require ultrahigh data rates, ultra-low latency, and hyper-intensive computation. To cater for these requirements, optical communication arises as a key pillar in bringing this paradigm into reality. We highlight in this paper the potential of optical communications in the Metaverse. First, we set forth Metaverse requirements in terms of capacity and latency; then, we introduce ultra-high data rates requirements for various Metaverse experiences. Then, we put forward the potential of optical communications to achieve these data rate requirements in backbone, backhaul, fronthaul, and access segments. Both optical fiber and optical wireless communication (OWC) technologies, as well as their current and future expected data rates, are detailed. In addition, we propose a comprehensive set of configurations, connectivity, and equipment necessary for an immersive Metaverse experience. Finally, we identify a set of key enablers and research directions such as analog neuromorphic optical computing, optical intelligent reflective surfaces (IRS), hollow core fiber (HCF), and terahertz (THz)

3D Target Detection and Spectral Classification for Single-photon LiDAR Data

3D single-photon LiDAR imaging has an important role in many applications. However, full deployment of this modality will require the analysis of low signal to noise ratio target returns and a very high volume of data. This is particularly evident when imaging through obscurants or in high ambient background light conditions. This paper proposes a multiscale approach for 3D surface detection from the photon timing histogram to permit a significant reduction in data volume. The resulting surfaces are background-free and can be used to infer depth and reflectivity information about the target. We demonstrate this by proposing a hierarchical Bayesian model for 3D reconstruction and spectral classification of multispectral single-photon LiDAR data. The reconstruction method promotes spatial correlation between point-cloud estimates and uses a coordinate gradient descent algorithm for parameter estimation. Results on simulated and real data show the benefits of the proposed target detection and reconstruction approaches when compared to state-of-the-art processing algorithm

Chemical composition, vasorelaxant, antioxidant and antiplatelet effects of essential oil of Artemisia campestris L. from Oriental Morocco

Background: Artemisia campestris L. (Asteraceae) is a medicinal herb traditionally used to treat hypertension and many other diseases. Hence, this study is aimed to analyze the essential oil of A. campestris L (AcEO) and to investigate the antiplatelet, antioxidant effects and the mechanisms of its vasorelaxant effect. Methods: The chemical composition of AcEO was elucidated using GC/MS analysis. Then, the antioxidant effect was tested on DPPH radical scavenging and on the prevention of β-carotene bleaching. The antiplatelet effect was performed on the presence of the platelet agonists: thrombin and ADP. The mechanism of action of the vasorelaxant effect was studied by using the cellular blockers specified to explore the involvement of NO/GC pathway and in the presence of calcium channels blockers and potassium channels blockers. Results: AcEO is predominated by the volatiles: spathulenol, ß-eudesmol and p-cymene. The maximal antioxidant effect was obtained with the dose 2 mg/ml of AcEO. The dose 1 mg/ml of AcEO showed a maximum antiplatelet effect of, respectively 49.73% ±9.54 and 48.20% ±8.49 on thrombin and ADP. The vasorelaxation seems not to be mediated via NOS/GC pathway neither via the potassium channels. However, pretreatment with calcium channels blockers attenuated this effect, suggesting that the vasorelaxation is mediated via inhibition of L-type Ca2+ channels and the activation of SERCA pumps of reticulum plasma. Conclusion: This study confirms the antioxidant, antiplatelet and vasorelaxant effects of A.campestris L essential oil. However, the antihypertensive use of this oil should be further confirmed by the chemical fractionation and subsequent bio-guided assays

Generalized Extended Riemann-Liouville type fractional derivative operator

In this paper, we aim to present new extensions of incomplete gamma, beta, Gauss hypergeometric, confluent hypergeometric function and Appell-Lauricella hypergeometric functions, by using the extended Bessel function due to Boudjelkha [4]. Some recurrence relations, transformation formulas, Mellin transform and integral representations are obtained for these generalizations. Further, an extension of the Riemann-Liouville fractional derivative operator is established