Connecting AUTOSAR VFB to Simulink Environment

This thesis was conducted as a part of the Architecture For Future Electric-vehicles (AFFE) project. AFFE project is a research project funded by VINNOVA to bring out the next generation electric vehicles based on Automotive Open System Architecture (AUTOSAR). The primary aim of the thesis is to analyze and demonstrate the possibility of connecting model-based design environments to the AUTOSAR Virtual Function Bus (VFB) to make the process of development of complex automotive systems easier. The tools chosen for this purpose were Mecel’s Picea suite for the VFB implementation and Mathworks’ Simulink as the model-based design environment. The outcome of the project is that a scalable solution to connect the two disparate programs was created using two different Interprocess Communication (IPC) methods

A novel weather parameters prediction scheme and their effects on crops

Weather forecast is significantly imperative in today’s smart technological world. A precise forecast model entails a plentiful data in order to attain the most accurate predictions. However, a forecast of future rainfall from historical data samples has always been challenging and key area of research. Hence, in modern weather forecasting a combo of computer models, observation, and knowledge of trends and patterns are introduced. This research work has presented a fitness function based adaptive artificial neural network scheme in order to forecast rainfall and temperature for upcoming decade (2021-2030) using historical weather data of 20 different districts of Karnataka state. Furthermore, effects of these forecasted weather parameters are realized over five major crops of Karnataka namely rice, wheat, jowar, maize, and ragi with the intention of evaluation for efficient crop management in terms of the passing relevant messages to the farmers and alternate measures such as suggesting other geographical locations to grow the same crop or growing other suitable crops at same geographical location. A graphical user interface (GUI) application has been developed for the proposed work in order to ease out the flow of work

Benchmarking LiDAR Sensors for Development and Evaluation of Automotive Perception

Environment perception and representation are some of the most critical tasks in automated driving. To meet the stringent needs of safety standards such as ISO 26262 there is a need for efficient quantitative evaluation of the perceived information. However, to use typical methods of evaluation, such as comparing using annotated data, is not scalable due to the manual effort involved. There is thus a need to automate the process of data annotation. This paper focuses on the LiDAR sensor and aims to identify the limitations of the sensor and provides a methodology to generate annotated data of a measurable quality. The limitations with the sensor are analysed in a Systematic Literature Review on available academic texts and refined by unstructured interviews with experts. The main contributions are 1) the SLR with related interviews to identify LiDAR sensor limitations and 2) the associated methodology which allows us to generate world representations

Defining Traffic States using Spatio-temporal Traffic Graphs

Intersections are one of the main sources of congestion and hence, it is important to understand traffic behavior at intersections. Particularly, in developing countries with high vehicle density, mixed traffic type, and lane-less driving behavior, it is difficult to distinguish between congested and normal traffic behavior. In this work, we propose a way to understand the traffic state of smaller spatial regions at intersections using traffic graphs. The way these traffic graphs evolve over time reveals different traffic states - a) a congestion is forming (clumping), the congestion is dispersing (unclumping), or c) the traffic is flowing normally (neutral). We train a spatio-temporal deep network to identify these changes. Also, we introduce a large dataset called EyeonTraffic (EoT) containing 3 hours of aerial videos collected at 3 busy intersections in Ahmedabad, India. Our experiments on the EoT dataset show that the traffic graphs can help in correctly identifying congestion-prone behavior in different spatial regions of an intersection.Comment: Accepted in 23rd IEEE International Conference on Intelligent Transportation Systems September 20 to 23, 2020. 6 pages, 6 figure

Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning

Federated Learning (FL) is a collaborative learning paradigm enabling participants to collectively train a shared machine learning model while preserving the privacy of their sensitive data. Nevertheless, the inherent decentralized and data-opaque characteristics of FL render its susceptibility to data poisoning attacks. These attacks introduce malformed or malicious inputs during local model training, subsequently influencing the global model and resulting in erroneous predictions. Current FL defense strategies against data poisoning attacks either involve a trade-off between accuracy and robustness or necessitate the presence of a uniformly distributed root dataset at the server. To overcome these limitations, we present FedZZ, which harnesses a zone-based deviating update (ZBDU) mechanism to effectively counter data poisoning attacks in FL. Further, we introduce a precision-guided methodology that actively characterizes these client clusters (zones), which in turn aids in recognizing and discarding malicious updates at the server. Our evaluation of FedZZ across two widely recognized datasets: CIFAR10 and EMNIST, demonstrate its efficacy in mitigating data poisoning attacks, surpassing the performance of prevailing state-of-the-art methodologies in both single and multi-client attack scenarios and varying attack volumes. Notably, FedZZ also functions as a robust client selection strategy, even in highly non-IID and attack-free scenarios. Moreover, in the face of escalating poisoning rates, the model accuracy attained by FedZZ displays superior resilience compared to existing techniques. For instance, when confronted with a 50% presence of malicious clients, FedZZ sustains an accuracy of 67.43%, while the accuracy of the second-best solution, FL-Defender, diminishes to 43.36%.Comment: 14 pages, 11 figures, 5 tables, Accepted in ACM CODASPY 202

Factors determining poor prognosis in scorpion sting in coastal Andhra Pradesh

Introduction: Scorpion stings are a major public health problem and a common emergency in India and other tropical countries. In India, red scorpions are more prevalent, and their venom is more likely to cause cardiovascular complications and mortality. Objective: There are very few studies regarding predictors of poor prognosis. Hence, this study was designed which aimed to identify these factors. Materials and Methods: In this hospital-based, prospective study, children admitted to scorpion sting between December 2009 and November 2010 were included. Relevant information was collected and analyzed. Results: Scorpion stings account for 1 in every 36 admissions. Maximum cases were in 0-3 and 7-9 years age groups with mean of 6.78 years. Red scorpion stings were more common and are associated with more severe presentations. There was considerable variation in sting-prazosin interval (SPI) ranging from 2 to 28 h. If the child’s age was below 6 years, red scorpion sting and SPI >8 h were very significantly associated with a higher incidence of complications such as peripheral circulatory failure, congestive cardiac failure, myocarditis, and acute pulmonary edema. Conclusion: Younger age, red scorpion, and delay in administration of prazosin are predictors of poor prognosis in scorpion stings in children, a common and fatal medical emergency in India