Robustness of multimodal 3D object detection using deep learning approach fo autonomous vehicles

Dans cette thèse, nous étudions la robustesse d’un modèle multimodal de détection d’objets en 3D dans le contexte de véhicules autonomes. Les véhicules autonomes doivent détecter et localiser avec précision les piétons et les autres véhicules dans leur environnement 3D afin de conduire sur les routes en toute sécurité. La robustesse est l’un des aspects les plus importants d’un algorithme dans le problème de la perception 3D pour véhicules autonomes. C’est pourquoi, dans cette thèse, nous avons proposé une méthode pour évaluer la robustesse d’un modèle de détecteur d’objets en 3D. À cette fin, nous avons formé un détecteur d’objets 3D multimodal représentatif sur trois ensembles de données différents et nous avons effectué des tests sur des ensembles de données qui ont été construits avec précision pour démontrer la robustesse du modèle formé dans diverses conditions météorologiques et de luminosité. Notre méthode utilise deux approches différentes pour construire les ensembles de données proposés afin d’évaluer la robustesse. Dans une approche, nous avons utilisé des images artificiellement corrompues et dans l’autre, nous avons utilisé les images réelles dans des conditions météorologiques et de luminosité extrêmes. Afin de détecter des objets tels que des voitures et des piétons dans les scènes de circulation, le modèle multimodal s’appuie sur des images et des nuages de points 3D. Les approches multimodales pour la détection d’objets en 3D exploitent différents capteurs tels que des caméras et des détecteurs de distance pour détecter les objets d’intérêt dans l’environnement. Nous avons exploité trois ensembles de données bien connus dans le domaine de la conduite autonome, à savoir KITTI, nuScenes et Waymo. Nous avons mené des expériences approfondies pour étudier la méthode proposée afin d’évaluer la robustesse du modèle et nous avons fourni des résultats quantitatifs et qualitatifs. Nous avons observé que la méthode que nous proposons peut mesurer efficacement la robustesse du modèle.In this thesis, we study the robustness of a multimodal 3D object detection model in the context of autonomous vehicles. Self-driving cars need to accurately detect and localize pedestrians and other vehicles in their 3D surrounding environment to drive on the roads safely. Robustness is one of the most critical aspects of an algorithm in the self-driving car 3D perception problem. Therefore, in this work, we proposed a method to evaluate a 3D object detector’s robustness. To this end, we have trained a representative multimodal 3D object detector on three different datasets. Afterward, we evaluated the trained model on datasets that we have proposed and made to assess the robustness of the trained models in diverse weather and lighting conditions. Our method uses two different approaches for building the proposed datasets for evaluating the robustness. In one approach, we used artificially corrupted images, and in the other one, we used the real images captured in diverse weather and lighting conditions. To detect objects such as cars and pedestrians in the traffic scenes, the multimodal model relies on images and 3D point clouds. Multimodal approaches for 3D object detection exploit different sensors such as camera and range detectors for detecting the objects of interest in the surrounding environment. We leveraged three well-known datasets in the domain of autonomous driving consist of KITTI, nuScenes, and Waymo. We conducted extensive experiments to investigate the proposed method for evaluating the model’s robustness and provided quantitative and qualitative results. We observed that our proposed method can measure the robustness of the model effectively

Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges

Machine Learning algorithms have had a profound impact on the field of computer science over the past few decades. These algorithms performance is greatly influenced by the representations that are derived from the data in the learning process. The representations learned in a successful learning process should be concise, discrete, meaningful, and able to be applied across a variety of tasks. A recent effort has been directed toward developing Deep Learning models, which have proven to be particularly effective at capturing high-dimensional, non-linear, and multi-modal characteristics. In this work, we discuss the principles and developments that have been made in the process of learning representations, and converting them into desirable applications. In addition, for each framework or model, the key issues and open challenges, as well as the advantages, are examined

Common CT Findings of Novel Coronavirus Disease 2019 (COVID-19): A Case Series

Given the highly infectious nature of the coronavirus disease 2019 (COVID-19) virus and the lack of proven specific therapeutic drugs and licensed vaccines effective against it, early diagnosis of the disease is of paramount importance. The common chest CT imaging of confirmed COVID-19 cases is discussed here, which shows ground-glass opacity, crazy paving, and consolidation

THE EFFECTS OF OMEGA-3 FATTY ACIDS ON BLOOD HOMOCYS-TEINE LEVEL IN TYPE 2 DIABETIC PATIENTS

Abstract &nbsp;&nbsp; INTRODUCTION: Diabetes is regarded as serious condition for both the individual&nbsp;and the society.&nbsp;Its rapidly increasing global prevalence is&nbsp;a&nbsp;significant cause for concern. One of the most important reasons of mortality in diabetic patients is atherosclerosis. Many epidemiologic studies have shown that the total homocysteine concentration is a risk indicator for cardiovascular disease. Studies have shown that its concentration is increased considerably in diabetes mellitus. Epidemiological data indicate that the consumption of omega-3 unsaturated fatty acids (n-3FA) leads to a reduction in cardiovascular disorders and may protect against metabolic diseases. In recent years, many have studied omega-3 fatty acids but still, it cannot be used as an additive. This study aimed to evaluate the effects of &omega;3 on homocysteine in type 2 diabetic patients. &nbsp;&nbsp; METHODS: A randomized double blind placebo controlled clinical trial was conducted on 80 type 2 diabetic patients aged 45-85 years with diabetes for at least 2 years. Anthropometric indices including body mass index (BMI) and medical history were obtained. Diabetic patients were randomly assigned to either the case or the control group. Each subject received 3 capsules per day (omega-3 or placebo) for a period of 2 months. A sample of 10&nbsp;ml blood was collected from each subject at the beginning and at the end of the study. Serum homocysteine was measured by Hitachi autoanalyzer with the Enzymatic Cycling method. Nutrient intake was estimated using 24-hour dietary recall questionnaire at the beginning and at the end of the trial for 2 days and analyzed by FPII. T-test was also used to compare the groups. &nbsp;&nbsp; RESULTS: Comparison of mean&nbsp;&plusmn;&nbsp;SD (standard deviation) of BMI and food intake did not show any difference between the case and control groups. homocysteine levels were 3.10&nbsp;&micro;mol/lit and 0.126&nbsp;&micro;mol/lit in the case and control groups, respectively, and the difference was significant. &nbsp;&nbsp; CONCLUSION: Omega-3 fatty acids supplementation (3 g/per day) in the form of capsules can decrease homocysteine content in diabetic patients. &nbsp; &nbsp;&nbsp; Keywords: Type 2 diabetes mellitus, omega-3 fatty acid, homocysteine. &nbsp;</p

Memetic Algorithms for Business Analytics and Data Science: A Brief Survey

This chapter reviews applications of Memetic Algorithms in the areas of business analytics and data science. This approach originates from the need to address optimization problems that involve combinatorial search processes. Some of these problems were from the area of operations research, management science, artificial intelligence and machine learning. The methodology has developed considerably since its beginnings and now is being applied to a large number of problem domains. This work gives a historical timeline of events to explain the current developments and, as a survey, gives emphasis to the large number of applications in business and consumer analytics that were published between January 2014 and May 2018