Autonomous Navigation for Mobile Robots: Machine Learning-based Techniques for Obstacle Avoidance

Department of System Design and Control EngineeringAutonomous navigation of unmanned aerial vehicles (UAVs) has posed several challenges due to the limitations regarding the number and size of sensors that can be attached to the mobile robots. Although sensors such as LIDARs that directly obtain distance information of the surrounding environment have proven to be effective for obstacle avoidance, the weight and cost of the sensor contribute to the restrictions on usage for UAVs as recent trends require smaller sizes of UAVs. One practical option is the utilization of monocular vision sensors which tend to be lightweight and have a relatively low cost, yet still the main drawback is that it is difficult to draw a certain rule from the sensor data. Conventional methods regarding visual navigation makes use of features within the image data or estimate the depth of the image using various techniques such as optical flow. These features and methodologies however still rely on human-based rules and features, meaning that robustness can become an issue. A more recent approach to vision-based obstacle avoidance exploits heuristic methods based on artificial intelligence such as deep learning technologies, which have shown state-of-the-art performance in fields such as image processing or voice recognition. These technologies are capable of automatically selecting important features for classification or prediction tasks, hence allowing superior performance. Such heuristic methods have proven to be more efficient as the rules and features that are drawn from the image are automatically determined, unlike conventional methods where the rules and features are explicitly determined by humans. In this thesis, we propose an imitation learning framework based on deep learning technologies that can be applied to the obstacle avoidance of UAVs, where the neural networks in this framework are trained upon the flight data obtained from human experts, extracting the necessary features and rules to carry out designated tasks. The system introduced in this thesis mainly consists of three parts: the data acquisition and preprocessing phase, the model training phase, and the model application phase. A CNN (Convolutional Neural Network), 3D-CNN, and a DNN (Deep Neural Network) will each be applied to the framework and tested with respect to the collision ratios to validate the obstacle avoidance performance.ope

Primary biliary cholangitis presenting as acute ischemic stroke: A rare association

Primary biliary cholangitis is associated with hyperlipidemia, but studies show that the condition does not increase cardiovascular risks. The case presents acute ischemic stroke with no underlying risk factors and subsequent new diagnosis of primary biliary cholangitis, which can suggest possible association between primary biliary cholangitis and acute stroke

Recapitulation of complex transport and action of drugs at tumor microenvironment using tumor-microenvironment-on-chip

Targeted delivery aims to selectively distribute drugs to targeted tumor tissue but not to healthy tissue. This can address many of clinical challenges by maximizing the efficacy but minimizing the toxicity of anti-cancer drugs. However, complex tumor microenvironment poses various barriers hindering the transport of drugs and drug delivery systems. New tumor models that allow for the systematic study of these complex environments are highly desired to provide reliable test beds to develop drug delivery systems for targeted delivery. Recently, research efforts have yielded new in vitro tumor models, the so called tumor-microenvironment-on-chip, that recapitulate certain characteristics of the tumor microenvironment. These new models show benefits over other conventional tumor models, and have the potential to accelerate drug discovery and enable precision medicines. However, further research is warranted to overcome their limitations and to properly interpret the data obtained from these models. In this article, key features of the in vivo tumor microenvironment that are relevant to drug transport processes for targeted delivery was discussed, and the current status and challenges for developing in vitro transport model systems was reviewed

Control-Oriented Modeling and Layer-to-Layer Spatial Control of Powder Bed Fusion Processes

Powder Bed Fusion (PBF) is an important Additive Manufacturing (AM) process that is seeing widespread utilization. However, due to inherent process variability, it is still very costly and time consuming to certify the process and the part. This has led researchers to conduct numerous studies in process modeling, in-situ monitoring and feedback control to better understand the PBF process and decrease variations, thereby making the process more repeatable. In this study, we develop a layer-to-layer, spatial, control-oriented thermal PBF model. This model enables a framework for capturing spatially-driven thermal effects and constructing layer-to-layer spatial controllers that do not suffer from inherent temporal delays. Further, this framework is amenable to voxel-level monitoring and characterization efforts. System output controllability is analyzed and output controllability conditions are determined. A spatial Iterative Learning Controller (ILC), constructed using the spatial modeling framework, is implemented in two experiments, one where the path and part geometry are layer-invariant and another where the path and part geometry change each layer. The results illustrate the ability of the controller to thermally regulate the entire part, even at corners that tend to overheat and even as the path and part geometry change each layer

Tight versus standard blood pressure control on the incidence of myocardial infarction and stroke: an observational retrospective cohort study in the general ambulatory setting

BACKGROUND: The 2017 American College of Cardiology and American Heart Association guideline defined hypertension as blood pressure (BP) ≥ 130/80 mmHg compared to the traditional definition of ≥140/90 mmHg. This change raised much controversy. We conducted this study to compare the impact of tight (TBPC) versus standard BP control (SBPC) on the incidence of myocardial infarction (MI) and stroke. METHODS: We retrospectively identified all hypertensive patients in an ambulatory setting based on the diagnostic code for 1 year at our institution who were classified by the range of BP across 3 years into 2 groups of TBPC (\u3c 130 mmHg) and SBPC (130-139 mmHg). We compared the incidence of new MI and stroke between the 2 groups across a 2-year follow-up. Multivariate analysis was done to identify independent predictors for the incidence of new MI and stroke. RESULTS: Of 5640 study patients, the TBPC group showed significantly less incidence of stroke compared to the SBPC group (1.5% vs. 2.7%, P \u3c 0.010). No differences were found in MI incidence between the 2 groups (0.6% vs. 0.8%, P = 0.476). Multivariate analysis showed that increased age independently increased the incidence of both MI (OR 1.518, 95% CI 1.038-2.219) and stroke (OR 1.876, 95% CI 1.474-2.387), and TBPC independently decreased the incidence of stroke (OR 0.583, 95% CI 0.374-0.910) but not of MI. CONCLUSIONS: Our observational study suggests that TBPC may be beneficial in less stroke incidence compared to SBPC but it didn\u27t seem to affect the incidence of MI. Our study is limited by its retrospective design with potential confounders

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

The burden of prostatic calculi is more important than the presence

Prostatic calculi are a common finding on transrectal prostate ultrasound. However, it remains unclear whether they are significantly associated with lower urinary tract symptoms (LUTS). Our objective was to evaluate the association between prostatic calculi and LUTS with a focus on calculi burden because no studies have investigated prostatic calculi using calculi burden as an indicator. A total of 606 participants who received transrectal prostate ultrasound were divided into two groups according to the presence of prostatic calculi. Calculi burden was defined as the sum of the transverse diameters of all visible calculi within the prostate. The International Prostatic Symptom Score (IPSS) and a quality of life (QoL) score were collected. Both groups were compared, and a multivariate analysis was performed to predict moderate/severe LUTS. Linear correlation was evaluated between calculi burden and IPSS in the calculi group. No differences in total IPSS, voiding IPSS, or QoL score were detected between the two groups, but storage IPSS was significantly higher in the calculi group than that of controls. The multivariate analysis showed that the presence of prostatic calculi was not an independent predictor of moderate/severe LUTS. A positive linear correlation was detected between calculi burden and storage IPSS in calculi group (r = 0.148). However, no correlation was found between calculi burden and total IPSS, voiding IPSS, or QoL score. Our results showed that the presence of prostatic calculi was not a significant factor predicting moderate/severe LUTS. However, an increased calculi burden may be associated with aggravating storage symptoms

Target Classification and Prediction of Unguided Rocket Trajectories Using Deep Neural Networks

This paper deals with classification and prediction of low-altitude rocket targets using deep neural networks. Conventionally, model-based methods such as the Kalman filter are widely used. However, they can lack robustness due to unexpected situations and noisy sensor measurements. To address this issue, this study proposes the use of various data-driven methods which are powerful on situations where no model is available. Specifically, three types of neural networks are used: DNN (deep neural network), CNN (convolutional neural network) and RNN (recurrent neural network). To verify the benefit and robustness of the proposed algorithms, comparisons with the model-based method are performed on several scenario

Association of midline prostatic cysts and lower urinary tract symptoms: A caseâ control analysis of 606 transrectal ultrasound findings

ObjectiveTo evaluate the association between midline prostatic cysts (MPCs) and lower urinary tract symptoms (LUTS).MethodsA total of 606 patients who underwent transrectal ultrasound of the prostate (TRUS) were retrospectively reviewed. Patients were divided into two groups based on the presence of MPCs for comparison. We used the International Prostate Symptom Score (IPSS) as a LUTS parameter. Multivariate analysis was performed to find out independent predictors for moderate to severe LUTS. An MPC subgroup analysis was done to look for linear correlation between the size of MPCs and LUTS.ResultsPatients with no MPCs were of higher age, had more history of diabetes, were taking more urological medications, and had more IPSS storage symptoms. No significant differences were found in body mass index, total IPSS, voiding IPSS, bother score, total prostateâ specific antigen level, and the prostate size. Multivariate analysis revealed age, history of diabetes, taking urological medications, and the prostate size as independent predictors of moderate to severe LUTS. The presence of MPCs was not an independent factor. Subgroup analysis failed to show significant correlation between the size of MPCs and the LUTS scores.ConclusionsThe presence of MPCs is not an independent factor for moderate to severe LUTS, and the size of the MPCs does not have any correlation to LUTS scores either.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152931/1/luts12288.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152931/2/luts12288_am.pd