Exploration of Deep Learning Applications on an Autonomous Embedded Platform (Bluebox 2.0)

Indiana University-Purdue University Indianapolis (IUPUI)An Autonomous vehicle depends on the combination of latest technology or the ADAS safety features such as Adaptive cruise control (ACC), Autonomous Emergency Braking (AEB), Automatic Parking, Blind Spot Monitor, Forward Collision Warning or Avoidance (FCW or FCA), Lane Departure Warning. The current trend follows incorporation of these technologies using the Artificial neural network or Deep neural network, as an imitation of the traditionally used algorithms. Recent research in the field of deep learning and development of competent processors for autonomous or self-driving car have shown amplitude of prospect, but there are many complexities for hardware deployment because of limited resources such as memory, computational power, and energy. Deployment of several mentioned ADAS safety feature using multiple sensors and individual processors, increases the integration complexity and also results in the distribution of the system, which is very pivotal for autonomous vehicles. This thesis attempts to tackle two important adas safety feature: Forward collision Warning, and Object Detection using the machine learning and Deep Neural Networks and there deployment in the autonomous embedded platform. 1. A machine learning based approach for the forward collision warning system in an autonomous vehicle. 2. 3-D object detection using Lidar and Camera which is primarily based on Lidar Point Clouds. The proposed forward collision warning model is based on the forward facing automotive radar providing the sensed input values such as acceleration, velocity and separation distance to a classifier algorithm which on the basis of supervised learning model, alerts the driver of possible collision. Decision Tress, Linear Regression, Support Vector Machine, Stochastic Gradient Descent, and a Fully Connected Neural Network is used for the prediction purpose. The second proposed methods uses object detection architecture, which combines the 2D object detectors and a contemporary 3D deep learning techniques. For this approach, the 2D object detectors is used first, which proposes a 2D bounding box on the images or video frames. Additionally a 3D object detection technique is used where the point clouds are instance segmented and based on raw point clouds density a 3D bounding box is predicted across the previously segmented objects

A real-world clinical experience on the effectiveness of remogliflozin etabonate in management of Indian patients with type II diabetes mellitus

Background: The aim of the study was to evaluate effectiveness and safety of remogliflozin etabonate in a real-world outpatient setting in type 2 diabetes mellitus (T2DM) patients in India.Methods: A retrospective, observational, single-center study wherein medical records of adult patients (≥18 years old) with T2DM managed with remogliflozin 100 mg for at least three months at the diabetes care center in Jharkhand were retrieved. The effectiveness was assessed in terms of change from baseline in glycated hemoglobin (HbA1c), fasting plasma glucose (FPG), postprandial plasma glucose (PPG), total body weight, blood pressure (BP, systolic and diastolic), kidney function tests, and lipid parameters after three months of treatment. Safety was assessed by adverse events (AEs) and serious AEs.Results: Half of the patients received ≥3 concomitant antidiabetic drugs, common being sulphonylureas (92%), and metformin (91%). Remogliflozin treatment resulted in a significant mean reduction from baseline in HbA1c [-1.99 (0.12%); p<0.001], FPG [-52.3 (4.31) mg/dl; p<0.001] and PPG [-103.6 (7.10) mg/dl; p<0.001). Bodyweight reduction was not statistically significant [-0.1 (10.12) kg]. A significant reduction was observed in the systolic BP [-15.9 (2.21) mmHg; p<0.001] and diastolic BP [-3.3 (0.95) mmHg; p=0.001]. Commonly reported AE was heartburn (51.4%) and urinary tract infections (34.2%). No serious AEs were reported. The mean estimated glomerular filtration rate showed a statistically significant reduction of -1.55 (0.61) ml/min. The lipid parameter findings were non-significant.Conclusions: The real-world experience of remogliflozin administered concomitantly with other antidiabetic drugs was effective and well-tolerated in Indian patients with T2DM.

EVALUATION OF LIPOSOMAL GOSSYPIN IN ANIMAL MODELS OF EPILEPSY

Objective: Epilepsy is a chronic neurological disorder affecting 1 % of the population worldwide. A number of studies have reported neuroprotective, anticonvulsant and anti-oxidant activity of gossipin a bioflavonoid isolated from Hibiscus vitifolius. The present study was carried out to evaluate the acute effects of liposome entrapped gossypin on Increasing Current electroshock seizures (ICES) test; Pentylenetetrazole (PTZ) induced seizures and status epilepticus in mice.Methods: Gossypin liposomes were prepared by film hydration technique, and the effect of liposomal Gossypin formulations was studied in two doses i.e. 2.5 mg/kg and 5 mg/kg given per oral on ICES test and PTZ induced seizures in mice. Same doses of the formulation were administered by intravenous route during PTZ induces status epilepticus in mice. Results: The results indicated that liposome entrapped Gossypin in doses 2.5 mg/Kg and 5 mg/Kg demonstrated significant increase in seizure threshold and latency to generalized seizures in ICES test and PTZ induced seizures respectively. Oxidative stress parameters like malondialdehyde (MDA) and glutathione were estimated in brain tissues in mice. Increased levels of MDA and glutathione were reduced and liposomal Gossypin suppressed the progression of kindling in mice. These results suggest that liposomal Gossypin appears to possess protective activity against kindling in mice.Conclusion: To conclude, the study supports that liposomal Gossypin offers protection against PTZ kindling in mice. Liposomal Gossypin administration significantly reduced the progression of kindling in mice therefore it could be a promising candidate to control both development of seizures and oxidative stress during epilepsy.Keywords: Epilepsy, Gossypin, Liposomes, ICES and PTZ induced seizures, Status epilepticu

Improving the rate of Early Initiation of breastfeeding in a busy Government Hospital of Central India–A Quality Improvement Study

Background:Early Initiation of Breastfeeding (within 1st hour)&nbsp; will improve the success with latch/suckling in first days ,timely switch from colostrum to milk production , Improved milk volume at 4 days , 4 weeks, 4 months.Delayed initiation , results in failure to breastfeed leading to faulty IYCF practices and later in life leads to Severemalnutrition. The present rates of early initiation of breastfeeding is 21 % in our setup so there is a need of Quality improvement in this subject.Objectives: To Increase the rate of early initiation of breastfeeding in normal babies (i.e, babies with birthweight&gt; 1800 grams with no perinatal complications)&nbsp; in a very busy Government Hospital from existing 21% to 60% by 30 days.Design: Quality improvement study. Setting: Labor Room, Operation Theatre and PNWs and Post-Ceaserian wards of a very busy tertiary care Government hospital.Procedure: A team of Final year Post Graduate students of Obstetrics and Gynecology, Post-Graduate student of Pediatrics, analyzed possible reasons for delayed initiation of breastfeeding by Process flow mapping and Fish bone analysis .Various change ideas were tested through sequential Plan-Do-Study-Act (PDSA) cycles.Outcome measure: Proportion of eligible babiesbreast fed within 1 hour of delivery. Results: The rate of first-hour initiation of breastfeeding increased from 21% to 36.7% over the study period. The result was sustained even after the last PDSA cycle, without any additional resources. The study did not achieve the expected goal because of following reasons Time given was less and needed more time for PDSA cycle. Need more training of team members. Lack of communication among team members. Perinatology includes the team of Obstetricians, Pediatricians and Anesthetists coordinate and complementation needed. Conclusion:Proper communication, training, Reinforcements with Posters and Counselling may help in increasing the Early Initiation of Breastfeeding in a busy Government Hospitals. Keywords: Quality Improvement, Breast Feeding, Plan-Do-Study-Act cycle

Alarm Test: A Novel Chemical-Free Behavioural Assessment Tool for Zebrafish

Zebrafish (ZF) is an incredible animal for the study of neurological disorders. Its behaviour is like higher vertebrate animals, which makes it gainful and robust. Understanding the psychological and biological implications of housing settings for ZFs is very crucial in improving the replicability and dependability of ZF behavioural research. Individual housing triggers depression-like symptoms that suggest that housing conditions have negative effects on ZF and can result in the data discrepancy. Based on various behavioural analyses, we have evaluated that the ZFs kept in isolation and the ZFs kept in herd conditions exhibit different behavioural patterns. Interestingly, normal isolated subjects exhibit similar behavioural patterns as Alzheimer disease (AD)-induced subjects; hence, this can have serious implications on any study concerning behaviour of ZFs. Therefore, we have reported a new behavioural test named “Alarm Test”, which effectively discriminates normal isolated subjects from AD subjects. Alarm Test is observed to be better than other tests used for studying fear and anxiety in ZFs as it uses the indigenous compound released by ZFs during fear and makes use of the same for analysis. This can reduce the involvement of chemicals during behavioural analysis as well as sacrifice of ZFs for collection of alarm substance

Effect of classroom intervention on student food selection and plate waste: Evidence from a randomized control trial

peer-reviewedBackground U.S. children are failing to meet the recommended daily 4 cups of fruits and vegetables. New federal guidelines were implemented for healthier school lunches for the National School Lunch Programs (NSLP). Consequently, students waste large amounts of fruits and vegetables. Several organizations advocate implementation of classroom nutrition education programs as a school nutrition policy. Methods We conducted a randomized control trial to evaluate the effectiveness of a classroom nutrition education on food consumption behavior of public elementary school students. Our intervention was designed to improve students’ preferences for fruits and vegetables. We collected data using digital-photography, and estimated the amount of fruits and vegetables selected and wasted using ordinary least squares. Results The nutrition education program had no impact on the amount of fruits and vegetables selected by the students in the treatment group. We also find no significant difference in the amount of fruits and vegetables wasted by students in the treatment and control group. Conclusion Nutrition education did not change students’ consumption behavior, implying the proposed policy might not be optimal. Inducing a behavioral change in elementary school students is an intricate process and might require more than classroom lessons to change their dietary habits

Challenges and solutions for antennas in Vehicle-to-Everything services

Autonomous vehicle is being developed for widespread deployment. Its reliability and safety are critically dependent on advanced wireless technologies, e.g., vehicle-to-everything (V2X) communication. The frontend of a V2X system needs an antenna module that enables the vehicle to reliably connect to all other networks. Designing V2X antenna is challenging due to the complex in-vehicle environment, trend for hidden antenna solution, long simulation time and need for omnidirectional coverage. In this article, we survey these challenges as well as existing V2X antenna solutions. In view of the drawbacks in the existing solutions, we propose an efficient design methodology for V2X antennas to provide the desired coverage. The method utilizes a simple geometrical model of the vehicle that captures the shadowing effects of the vehicle body to obtain candidate antenna locations that offer the best coverage via multi-antenna diversity. Hence, complex full-wave simulation can be avoided. The approach is validated through comprehensive full-wave simulations and pattern measurements on two car models. The results confirm that, at 5.9GHz, line-of-sight shadowing has more dominant effect on the received power than multipath propagation due to the car body. In cases of strong diffraction and surface waves, a simple rule-of-thumb can be devised to improve the accuracy of the method

Deep Learning for the Matrix Element Method

Extracting scientific results from high-energy collider data involves the comparison of data collected from the experiments with synthetic data produced from computationally-intensive simulations. Comparisons of experimental data and predictions from simulations increasingly utilize machine learning (ML) methods to try to overcome these computational challenges and enhance the data analysis. There is increasing awareness about challenges surrounding interpretability of ML models applied to data to explain these models and validate scientific conclusions based upon them. The matrix element (ME) method is a powerful technique for analysis of particle collider data that utilizes an \textit{ab initio} calculation of the approximate probability density function for a collision event to be due to a physics process of interest. The ME method has several unique and desirable features, including (1) not requiring training data since it is an \textit{ab initio} calculation of event probabilities, (2) incorporating all available kinematic information of a hypothesized process, including correlations, without the need for feature engineering and (3) a clear physical interpretation in terms of transition probabilities within the framework of quantum field theory. These proceedings briefly describe an application of deep learning that dramatically speeds-up ME method calculations and novel cyberinfrastructure developed to execute ME-based analyses on heterogeneous computing platforms.Comment: 6 pages, 3 figures. Contribution to the Proceedings of the ICHEP 2022 Conferenc