Perception systems for robust autonomous navigation in natural environments

2022 Spring.Includes bibliographical references.As assistive robotics continues to develop thanks to the rapid advances of artificial intelligence, smart sensors, Internet of Things, and robotics, the industry began introducing robots to perform various functions that make humans' lives more comfortable and enjoyable. While the principal purpose of deploying robots has been productivity enhancement, their usability has widely expanded. Examples include assisting people with disabilities (e.g., Toyota's Human Support Robot), providing driver-less transportation (e.g., Waymo's driver-less cars), and helping with tedious house chores (e.g., iRobot). The challenge in these applications is that the robots have to function appropriately under continuously changing environments, harsh real-world conditions, deal with significant amounts of noise and uncertainty, and operate autonomously without the intervention or supervision of an expert. To meet these challenges, a robust perception system is vital. This dissertation casts light on the perception component of autonomous mobile robots and highlights their major capabilities, and analyzes the factors that affect their performance. In short, the developed approaches in this dissertation cover the following four topics: (1) learning the detection and identification of objects in the environment in which the robot is operating, (2) estimating the 6D pose of objects of interest to the robot, (3) studying the importance of the tracking information in the motion prediction module, and (4) analyzing the performance of three motion prediction methods, comparing their performances, and highlighting their strengths and weaknesses. All techniques developed in this dissertation have been implemented and evaluated on popular public benchmarks. Extensive experiments have been conducted to analyze and validate the properties of the developed methods and demonstrate this dissertation's conclusions on the robustness, performance, and utility of the proposed approaches for intelligent mobile robots

Machine learning for omics data analysis.

In proteomics and metabolomics, to quantify the changes of abundance levels of biomolecules in a biological system, multiple sample analysis steps are involved. The steps include mass spectrum deconvolution and peak list alignment. Each analysis step introduces a certain degree of technical variation in the abundance levels (i.e. peak areas) of those molecules. Some analysis steps introduce technical variations that affect the peak areas of all molecules equally while others affect the peak areas of a subset of molecules with varying degrees. To correct these technical variations, some existing normalization methods simply scale the peak areas of all molecules detected in one sample using a single normalization factor or fit a regression model based on different assumptions. As a result, the local technical variations are ignored and may even be amplified in some cases. To overcome the above limitations, we developed a molecule specific normalization algorithm, called MSN, which adopts a robust surface fitting strategy to minimize the molecular profile difference of a group of house-keeping molecules across samples. The house-keeping molecules are those molecules whose abundance levels were not affected by the biological treatment. We also developed an outlier detection algorithm based on Fisher Criterion to detect and remove noisy data points from the experimental data. The applications of the MSN method on two different datasets showed that MSN is a highly efficient normalization algorithm that yields the highest sensitivity and accuracy compared to five existing normalization algorithms. The outlier detection algorithm\u27s application on the same datasets has also shown to be efficient and robust

Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction

In this paper, we present an end-to-end future-prediction model that focuses on pedestrian safety. Specifically, our model uses previous video frames, recorded from the perspective of the vehicle, to predict if a pedestrian will cross in front of the vehicle. The long term goal of this work is to design a fully autonomous system that acts and reacts as a defensive human driver would --- predicting future events and reacting to mitigate risk. We focus on pedestrian-vehicle interactions because of the high risk of harm to the pedestrian if their actions are miss-predicted. Our end-to-end model consists of two stages: the first stage is an encoder/decoder network that learns to predict future video frames. The second stage is a deep spatio-temporal network that utilizes the predicted frames of the first stage to predict the pedestrian's future action. Our system achieves state-of-the-art accuracy on pedestrian behavior prediction and future frames prediction on the Joint Attention for Autonomous Driving (JAAD) dataset

Laurus nobilis, Zingiber officinale and Anethum graveolens essential oils: Composition, antioxidant and antibacterial activities against bacteria isolated from fish and shellfish

Several bacterial strains were isolated from wild and reared fish and shellfish. The identification of these strains showed the dominance of the Aeromonas hydrophila species in all seafood samples, followed by Staphylococcus spp., Vibrio alginolyticus, Enterobacter cloacae, Klebsiella ornithinolytica, Klebsiella oxytoca and Serratia odorifera. The isolates were studied for their ability to produce exoenzymes and biofilms. The chemical composition of the essential oils from Laurus nobilis leaves, Zingiber officinale rhizomes and Anethum graveolens aerial parts was studied by GC and GC/MS. The essential oils' antioxidant and antibacterial activities against the isolated microorganisms were studied. Low concentrations of the three essential oils were needed to inhibit the growth of the selected bacteria and the lowest MBCs values were obtained for the laurel essential oil. The selected essential oils can be used as a good natural preservative in fish food due to their antioxidant and antibacterial activities

Phytochemical analysis, antimicrobial and antioxidant activities of Allium roseum var. odoratissimum (Desf.) Coss extracts

Polyphenolic extracts from fresh aerial parts (stalk, leaf, and flower) and bulbs of Allium roseum var. odoratissimum (Rosy garlic) harvested from Tunisian sandy soil were analyzed by high performance liquid chromatography (HPLC)-diode array detector (DAD) coupled with tandem mass spectrometry (MS), and tested for their antioxidant, antimicrobial, cytotoxic and antiviral activities. Leaves, stalks and flowers showed the highest total antioxidant capacity, scavenger activity against stable DPPH radical, and β-carotene bleaching capacity, with leaves and flowers extracts showing the highest total polyphenolic (84.39 mg gallic acid equivalents/g plant organs dry residue) and total flavonoid [5.88 mg (+)-catechin equivalents/g plant organs dry residue] contents, respectively. The metabolic profiles registered for the polyphenolic extracts revealed the presence of one hydroxycinnamic acid derivative, three flavones, and ten flavonols never detected before, with the only exception of kaempferol-3-O-glucuronide. Overall, all the tested polyphenolic extracts possessed high activity against Gram positive and Gram negative bacteria, and Candida spp. Strains, generally recognized as the most important pathogens affecting food dishes. Conversely, no toxicity on VERO cells line and no antiviral activity against Coxsakievirus B-3 and Herpes Simplex Virus type 2 were registered. This study gives a better insight into the potential healthy effects of Rosy garlic and the possibility of using it in food dishes to prevent contamination by the most common bacteria

Development of an Optimized Process for Functional Recombinant SARS-CoV-2 Spike S1 Receptor-Binding Domain Protein Produced in the Baculovirus Expression Vector System

To map the spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and evaluate immune response variations against this virus, it is essential to set up efficient serological tests locally. The SARS-CoV-2 immunogenic proteins were very expensive and not affordable for lower- middle-income countries (LMICs). For this purpose, the commonly used antigen, receptor-binding domain (RBD) of spike S1 protein (S1RBD), was produced using the baculovirus expression vector system (BEVS). In the current study, the expression of S1RBD was monitored using Western blot under different culture conditions. Different parameters were studied: the multiplicity of infection (MOI), cell density at infection, and harvest time. Hence, optimal conditions for efficient S1RBD production were identified: MOI 3; cell density at infection 2–3 × 106 cells/mL; and time post-infection (tPI or harvest time) of 72 h and 72–96 h, successively, for expression in shake flasks and a 7L bioreactor. A high production yield of S1RBD varying between 4 mg and 70 mg per liter of crude cell culture supernatant was achieved, respectively, in the shake flasks and 7L bioreactor. Moreover, the produced S1RBD showed an excellent antigenicity potential against COVID-19 (Wuhan strain) patient sera evaluated by Western blot. Thus, additional serological assays, such as in-house ELISA and seroprevalence studies based on the purified S1RDB, were developed