Protall (An Intelligent, multi-sensor, comprehensive obstacle avoidance system for automobiles and UAVs)

In this era of Artificial Intelligence and Automation, manufacturing and testing of self-driving cars and autonomous delivery of parcels to the desired location with the help of UAVs have a considerable amount of growth in the industry. This advancement in technology also raises safety issues due to the failure of sensors to detect the object or sometimes because of the dynamic environment. Protall (Protect-all) is an integrated solution for UAV and automobile vehicles to provide ultimate safety to both itself and its surroundings. With the strategic sensor integration and its intelligent processing, it aims to produce controlled output and hence, ensures to prevent any possible failures from occurring. The system constantly reacts to the environment and maintains comprehensive interaction with the user thus, enabling it to handle any dynamic situation and hence, it emerges as an optimal solution for Vehicles and UAVs.

DEVELOPMENT OF A GENERAL SENSOR SYSTEM MODEL TO DESCRIBE THE FUNCTIONALITY AND THE UNCERTAINTY OF SENSING MACHINE ELEMENTS

Sensor integration as close to the process as possible provides advantages in the quality of the measurement results as well as the possibility to implement completely new sensor principles and to measure novel quantities of interest. Sensor integration at positions close to the process can be made possible, for example, through the development and application of Sensing Machine Elements (SME). In the first part of this contribution, a general sensor system model is proposed. It is based on the concept of measuring chains and allows the uniform description of functions and uncertainties within a conventional sensor or SME application. For this purpose, essential quantities are defined, which are required for a uniform understanding. In the second part, the presented sensor system model is applied to a load measuring strain gauge on a drive shaft. This enables the condition monitoring of the shaft and drive train by measuring the electrical resistance of the strain gauge and thus allowing conclusions about the acting drive torque. The individual functions and uncertainties of the strain gauge integration are presented in the system model. This example shows the applicability of the presented system model for sensors and SME.

Ultrasound for Gaze Estimation—A Modeling and Empirical Study

Most eye tracking methods are light-based. As such, they can suffer from ambient light changes when used outdoors, especially for use cases where eye trackers are embedded in Augmented Reality glasses. It has been recently suggested that ultrasound could provide a low power, fast, light-insensitive alternative to camera-based sensors for eye tracking. Here, we report on our work on modeling ultrasound sensor integration into a glasses form factor AR device to evaluate the feasibility of estimating eye-gaze in various configurations. Next, we designed a benchtop experimental setup to collect empirical data on time of flight and amplitude signals for reflected ultrasound waves for a range of gaze angles of a model eye. We used this data as input for a low-complexity gradient-boosted tree machine learning regression model and demonstrate that we can effectively estimate gaze (gaze RMSE error of 0.965 ± 0.178 degrees with an adjusted R2 score of 90.2 ± 4.6).