Understanding a Dynamic World: Dynamic Motion Estimation for Autonomous Driving Using LIDAR

In a society that is heavily reliant on personal transportation, autonomous vehicles present an increasingly intriguing technology. They have the potential to save lives, promote efficiency, and enable mobility. However, before this vision becomes a reality, there are a number of challenges that must be solved. One key challenge involves problems in dynamic motion estimation, as it is critical for an autonomous vehicle to have an understanding of the dynamics in its environment for it to operate safely on the road. Accordingly, this thesis presents several algorithms for dynamic motion estimation for autonomous vehicles. We focus on methods using light detection and ranging (LIDAR), a prevalent sensing modality used by autonomous vehicle platforms, due to its advantages over other sensors, such as cameras, including lighting invariance and fidelity of 3D geometric data. First, we propose a dynamic object tracking algorithm. The proposed method takes as input a stream of LIDAR data from a moving object collected by a multi-sensor platform. It generates an estimate of its trajectory over time and a point cloud model of its shape. We formulate the problem similarly to simultaneous localization and mapping (SLAM), allowing us to leverage existing techniques. Unlike prior work, we properly handle a stream of sensor measurements observed over time by deriving our algorithm using a continuous-time estimation framework. We evaluate our proposed method on a real-world dataset that we collect. Second, we present a method for scene flow estimation from a stream of LIDAR data. Inspired by optical flow and scene flow from the computer vision community, our framework can estimate dynamic motion in the scene without relying on segmentation and data association while still rivaling the results of state-of-the-art object tracking methods. We design our algorithms to exploit a graphics processing unit (GPU), enabling real-time performance. Third, we leverage deep learning tools to build a feature learning framework that allows us to train an encoding network to estimate features from a LIDAR occupancy grid. The learned feature space describes the geometric and semantic structure of any location observed by the LIDAR data. We formulate the training process so that distances in this learned feature space are meaningful in comparing the similarity of different locations. Accordingly, we demonstrate that using this feature space improves our estimate of the dynamic motion in the environment over time. In summary, this thesis presents three methods to aid in understanding a dynamic world for autonomous vehicle applications with LIDAR. These methods include a novel object tracking algorithm, a real-time scene flow estimation method, and a feature learning framework to aid in dynamic motion estimation. Furthermore, we demonstrate the performance of all our proposed methods on a collection of real-world datasets.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147587/1/aushani_1.pd

Uptake of heavy metal Cd(II) from aqueous solutions using brown algae Sargassum myriocystum

207-216The uptake of brown marine algae, Sargassum myriocystum has been utilized for the exclusion of Cd(II) metal from wastewater. Various parameters such as solution pH, optimum temperature, biomass concentration, the contact time and agitation speed have been analyzed for the effective biosorption of Cd(II). Desorption studies of Cd(II) have been performed with various desorbent such as H3PO4, HNO3, HCL, H2SO4, NaOH, NaCl, and H2O among which 0.1M/L HCl is found to be the better desorbing agent. SEM-EDX and FTIR analyses were utilized for metal-algal interaction study. The thermodynamic parameters such as free energy change (ΔG°), enthalpy change (∆Hº) and entropy change (∆Sº) have been calculated through the Van’t Hoff plot. A positive value of 14.72 ∆S Jmol-1K-1, a negative value of free energy (ΔG°) and 42.21 kJ/mol of ∆H kJmol-1 at all the temperatures indicated that the process is feasible and spontaneous. Hence, S. myriocystum is an effective and prosperous cost-effective biosorbent approach for Cd(II) from various industrial wastes due to its fast sorption rate, high selectivity and great uptake capacity

Uptake of heavy metal Cd(II) from aqueous solutions using brown algae Sargassum myriocystum

The uptake of brown marine algae, Sargassum myriocystum has been utilized for the exclusion of Cd(II) metal from wastewater. Various parameters such as solution pH, optimum temperature, biomass concentration, the contact time and agitation speed have been analyzed for the effective biosorption of Cd(II). Desorption studies of Cd(II) have been performed with various desorbent such as H3PO4, HNO3, HCL, H2SO4, NaOH, NaCl, and H2O among which 0.1M/L HCl is found to be the better desorbing agent. SEM-EDX and FTIR analyses were utilized for metal-algal interaction study. The thermodynamic parameters such as free energy change (ΔG°), enthalpy change (∆Hº) and entropy change (∆Sº) have been calculated through the Van’t Hoff plot. A positive value of 14.72 ∆S Jmol-1K-1, a negative value of free energy (ΔG°) and 42.21 kJ/mol of ∆H kJmol-1 at all the temperatures indicated that the process is feasible and spontaneous. Hence, S. myriocystum is an effective and prosperous cost-effective biosorbent approach for Cd(II) from various industrial wastes due to its fast sorption rate, high selectivity and great uptake capacity

Sensorimotor integration and motor learning during a novel force-matching task in young adults with attention-deficit/hyperactivity disorder

IntroductionAttention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that exhibits unique neurological and behavioral characteristics. Those with ADHD often have noted impairments in motor performance and coordination, including during tasks that require force modulation. The present study provides insight into the role of altered neural processing and SMI in response to a motor learning paradigm requiring force modulation and proprioception, that previous literature has suggested to be altered in those with ADHD, which can also inform our understanding of the neurophysiology underlying sensorimotor integration (SMI) in the general population.MethodsAdults with ADHD (n = 15) and neurotypical controls (n = 15) performed a novel force-matching task, where participants used their right-thumb to match a trace template that varied from 2–12% of their Abductor Pollicis Brevis maximum voluntary contraction. This motor task was completed in pre, acquisition, and post blocks. Participants also completed a retention test 24 h later. Median nerve somatosensory-evoked potentials (SEPs) were collected pre and post motor acquisition. SEPs were stimulated at two frequencies, 2.47 Hz and 4.98 Hz, and 1,000 sweeps were recorded using 64-electrode electroencephalography (EEG) at 2,048 Hz. SEP amplitude changes were normalized to each participant’s baseline values for that peak.ResultsBoth groups improved at post measures (ADHD: 0.85 ± 0.09; Controls: 0.85 ± 0.10), with improvements maintained at retention (ADHD: 0.82 ± 0.11; Controls: 0.82 ± 0.11). The ADHD group had a decreased N18 post-acquisition (0.87 ± 0.48), while the control N18 increased (1.91 ± 1.43). The N30 increased in both groups, with a small increase in the ADHD group (1.03 ± 0.21) and a more pronounced increase in controls (1.15 ± 0.27).DiscussionUnique neural differences between groups were found after the acquisition of a novel force-matching motor paradigm, particularly relating to the N18 peak. The N18 differences suggest that those with ADHD have reduced olivary-cerebellar-M1 inhibition when learning a novel motor task dependent on force-modulation, potentially due to difficulties integrating the afferent feedback necessary to perform the task. The results of this work provide evidence that young adults with ADHD have altered proprioceptive processing when learning a novel motor task when compared to neurotypical controls

Broad and strong memory CD4(+)and CD8(+)T cells induced by SARS-CoV-2 in UK convalescent individuals following COVID-19

The development of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines and therapeutics will depend on understanding viral immunity. We studied T cell memory in 42 patients following recovery from COVID-19 (28 with mild disease and 14 with severe disease) and 16 unexposed donors, using interferon-γ-based assays with peptides spanning SARS-CoV-2 except ORF1. The breadth and magnitude of T cell responses were significantly higher in severe as compared with mild cases. Total and spike-specific T cell responses correlated with spike-specific antibody responses. We identified 41 peptides containing CD4+ and/or CD8+ epitopes, including six immunodominant regions. Six optimized CD8+ epitopes were defined, with peptide–MHC pentamer-positive cells displaying the central and effector memory phenotype. In mild cases, higher proportions of SARS-CoV-2-specific CD8+ T cells were observed. The identification of T cell responses associated with milder disease will support an understanding of protective immunity and highlights the potential of including non-spike proteins within future COVID-19 vaccine design

IL-17+ CD8+ T cells:differentiation, phenotype and role in inflammatory disease

AbstractThe pro-inflammatory cytokine interleukin-17A (IL-17) has been the subject of research by many groups worldwide. IL-17 expression is often associated with a specific subset of CD4+ T cells (the so-called Th17 cells); however various other immune cell subsets can also synthesise and express IL-17, including CD8+ T cells. Here we review recent data regarding the presence of IL-17+ CD8+ T cells (also known as Tc17 cells) in human inflammatory disease, discuss current knowledge regarding the culture conditions required for the differentiation of these cells in humans and mice, and describe key phenotypic and functional features. Collectively, this information may shed light on the potential pathogenic role that IL-17+ CD8+ T cells may play in human inflammatory disease

Longitudinal Analysis of Mortality Risk Factors for Actuarial Valuation

Today, people are living longer and the world population is getting older. Recent statistics indicate that a 65-year-old female in the United States is estimated to live to 88.8 years old, while a 65-year-old male to 86.6 years old. This translates to about a two-year increase in life expectancy from birth compared to that more than a decade ago. Understanding these trends and their potential impact are ever more relevant to the insurance industry. In this thesis, we emphasize the longitudinal modeling framework of pension and long term care insurance using some advanced techniques to analyze patterns and trends in longevity and to investigate potential covariates to further characterize the nature of the risk. Using data from the Health and Retirement Study (HRS), our work finds that factors that incorporate demographic, health, lifestyle, and financial information help improve model projections of mortality. We used multiple state framework to develop models for understanding the utilization of long term care. Some key findings indicate that female tends to be more vulnerable to exposure for long term care needs, and so with low educated people. Finally, motivated by the data obtained from an insurer, this thesis also examined the effect of policy termination on the survival of policyholders with life insurance contracts. We modeled the time until a policy lapses and its subsequent mortality pattern and found some evidence of mortality selection. We subsequently examined the financial cost of policy termination. The lack of available data precluded us from extending this analysis to pension plans and long term care insurance products; such can be done as further studies