Model-based Design of a Line-tracking Algorithm for a Low-cost Mini Drone through Vision-based Control

This Thesis research project aims to design a Line-tracking algorithm for a low-cost mini drone through Vision-based control with image processing techniques. The design process is the application of the principles of Model-based software design, which is a modern technique to design control systems, based on the development of a model of the plant and the controller with enough detail to have a realistic representation of its behavior to accomplish the specifications. The designed model is tested in a simulation environment (Model-in-the-loop phase). Then, if it satisfies the requirements, it is tested in real-time, deploying the algorithm on the Hardware to evaluate if its performances are still acceptable or if it requires to be updated. A significant advantage that characterizes this technique is the auto-code generation, which allows us to automatically translate the blocks of the model built through Simulink into a C-code executable by the hardware, instead of writing it manually. This research project is adapted from a competition organized by Mathworks, which aims to make a drone follow a line of a specific color and land at the end of it on a circle. The task should be accomplished in as little time as possible but at the same time remaining stable and following the path as precisely as possible (within the low-cost limits of the mini drone used). The environment used to design and develop the control system is MATLAB, with Simulink and their add-on toolboxes like Aerospace blockset, image processing, computer vision, and Hardware support package for Parrot mini drone, which is the specific company that made the drone model of this project. Firstly, the preliminary goal is the accomplishment of the stabilization of flight maneuvers through a suitable control system architecture and PID controllers tuning. Then, the Flight Control System design proceeds with Image processing and Path planning subsystems design. The line-tracking algorithm implementations developed are two. The first one is based on the analysis of the pixels of the image acquired from the downward-facing camera and elaboration through image processing techniques like color thresholding and edge detection. The path planning logic was implemented through Stateflow, which is an add-on tool of Simulink, useful for State machines design. This first designed control system also has another simplified version, useful because it is computationally lighter on the hardware compared to the first standard version. The second algorithm, instead, is realized by using user-defined functions, like thresholding operation for noise removal in the binary image, or like the function that searches and detects the path and the line angles, and by some other already existing functions provided by the computer vision toolbox. Finally, their performances were both tested on the hardware and then analyzed and compared. The validation phase was discussed, commenting on their limits, and highlighting other issues encountered, not previously noticed within the simulation 3D environment during the Model-in-the-loop test

miR-34a and let-7 are functional opposites of p53-responsiveness.

<p>(<i>A</i>) Identification of the top 10 miRNAs differentially expressed between p53 responsive and unresponsive cells. All let-7 family members are highlighted in yellow and miR-34a in light blue. (<i>B</i>) Quantitative real-time PCR analysis of miR-34a, miR-34b, and miR-34c in MCF7 cells expressing either empty vector (vec) or CD95 (CD95) after 12 hr treatment with 10 µM etoposide.</p

miR-34a and CD95 are p53 transcriptional targets that are functionally connected.

<p>(<i>A</i>) Western blot analysis of p53 and CD95 in HCT116 parental wild type (wt) and p53^−/− cells. For quantitative real-time PCR analysis of miR-34a, the same cells were treated with either control medium (−) or 10 µM etoposide (+) for 12 hrs. (<i>B</i>) MCF7, CAKI-1, and HCT116 cells were treated with control medium (−) or 10 µM etoposide (+) for 12 hrs and subjected to Western blot analysis. Band intensities were quantified relative to actin for each lane. (<i>C</i>) Apoptosis of HCT116 wt and p53^−/− cells upon LzCD95L treatment (1 µg/ml) for 18 hrs. (<i>D</i>) Key p53 targets positively correlating with the sensitivity of NCI60 cells to CD95-mediated apoptosis. (<i>E</i>) Genes whose expression positively correlates with p53 responsiveness in NCI60 cells.</p

CD95 positively affects p53 expression.

<p>(<i>A</i>) Left panel: MCF7 parental, vector or CD95 expressing cells treated with 10 µM etoposide for 12 hrs analyzed by western blotting for CD95, p53, and p21 expression. Right panel: p21 mRNA upregulation after 12 hrs of 20 µM etoposide treatment in vector or CD95 transfected cells. (<i>B</i>) Left panel: Effect of CD95 knockdown with a lentiviral shRNA (shR#6) on etoposide induction in CAKI-1 cells. Right panel: Effect of CD95 knockdown on etoposide induction in HCT116 cells. In both panels cells treated with 10 µM etoposide for 12 hrs. Band intensities were quantified relative to actin for each lane. (<i>C</i>) miR-34a (upper panel) and p21 (lower panel) expression analysis by quantitative real-time PCR in CAKI-1 vector or CD95 knockdown cells using shR#6 after a 12 hrs treatment with of etoposide (10 µM).</p

CD95 is a negative regulator of let-7.

<p>(<i>A</i>) Let-7d expression in apoptosis resistant Type I and Type II cells among the NCI60 cell lines. (<i>B</i>) Let-7c expression determined by quantitative real-time PCR in cells with modulated CD95 expression; left panel: MCF7 cells overexpressing CD95; center panel: CAKI-1 cells with CD95 knockdown with shR#6; right panel (left): HCT116 wt cells expressing either vector or shR#6, and (right) wt and p53^−/− cells.</p

miR-34a is a marker for cells sensitive to CD95-mediated apoptosis.

<p>(<i>A</i>) Top 10 miRNAs differentially expressed between NCI60 cells sensitive and resistant to CD95-mediated apoptosis. (<i>B</i>) Dot plot showing miR-34a expression in 21 NCI60 cell lines sensitive and 36 cell lines resistant to CD95-mediated apoptosis. (<i>C</i>) Dot plot showing miR-34a expression in 33 NCI60 cell lines sensitive and 24 cell lines resistant to CD95-mediated apoptosis. (<i>D</i>) The miRNAs differentially expressed (p<0.05) between NCI60 cells sensitive and resistant to TRAIL-induced apoptosis. (<i>E</i>) HCT116 cells were either left untreated (/), transfection reagent treated (siPORT), or transfected with either scrambled pre-miR (scr) or pre-miR-34a and incubated with the indicated amounts of leucine-zipper tagged CD95 ligand (LzCD95L). Apoptotic cells were quantified 18 hrs after LzCD95L treatment. Inset shows real-time PCR analysis of miR-34a expression in the transfected cells. (<i>F</i>) Top: phase contrast image of HCT116 cells treated with scr, or pre-miR-34a and LzCD95L as described in C with and with out pretreatment with zVAD-fmk. Bottom: Sub-G1 peak analysis using PI staining and flow cytometry of HCT116 treated with pre-miR-34a and LzCD95L minus plus zVAD-fmk. (<i>G</i>) Surface staining of CD44 or CD95 in HCT116 cells 18 hours after transfection with either scr or pre-miR-34a. (<i>H</i>) Western blot analysis of c-Met and p53 in HCT116 cells three days after transfection with either scr or pre-miR-34a. Band intensities were quantified relative to actin for each lane.</p

Model of proposed regulatory network.

<p>The three signaling branches of p53 leading to opposing outcomes of p53 activation. Experiments in this paper suggest different activation thresholds for p21 and miR-34a upon altering CD95 expression as p21 was equally efficiently induced despite modulation in CD95, whereas miR-34a was responsive to CD95 changes. Nonapoptotic activities of CD95 mentioned in this figure were discussed in recent reviews <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049636#pone.0049636-Peter4" target="_blank">[77]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049636#pone.0049636-Peter5" target="_blank">[78]</a>.</p

Supplementary Figures S1-S4 from Loss of miR-200c Expression Induces an Aggressive, Invasive, and Chemoresistant Phenotype in Non–Small Cell Lung Cancer

Supplementary Figures S1-S4.</p

Supplementary Figure 1 from Effects of Src kinase inhibition induced by dasatinib in non–small cell lung cancer cell lines treated with cisplatin

Supplementary Figure 1 from Effects of Src kinase inhibition induced by dasatinib in non–small cell lung cancer cell lines treated with cisplati

Supplementary Table 1 from Effects of Src kinase inhibition induced by dasatinib in non–small cell lung cancer cell lines treated with cisplatin

Supplementary Table 1 from Effects of Src kinase inhibition induced by dasatinib in non–small cell lung cancer cell lines treated with cisplati