We identify a novel method of using feedback to provide enhanced information-theoretical security in the presence of an eavesdropper. This method begins with a fixed linear coset code providing both secrecy and error detection/correction, as has been described by several authors. The legitimate receiver then sends the syndrome information for the received codeword, and based on this feedback, the transmitter can provide further error correction information specifically tailored to assist only the legitimate receiver. We show that this method allows secure communication with the legitimate receiver even when the eavesdropper’s channel is superior to that of the legitimate receiver.

Timely and accurate stress detection is crucial for effective healthcare monitoring and intervention. Existing methods for stress detection often rely on invasive or subjective measures, limiting their use. Here, we propose StressVision; a non-invasive and automated transformer-based deep learning approach that uses thermal video analysis to capture and analyze facial thermal patterns, and enables objective and continuous stress detection. We validate our approach by applying StressVision to two datasets comprised of healthy human adult participants who were exposed to an acute stressor (ice-cold water) while thermal video of their faces and electrocardiography were recorded. One of these datasets was collected specifically for the purpose of this work (n=36) and the other dataset was acquired from a previous study (n=42). With StressVision we were able to achieve state-of-the-art stress detection performance, such that stress state could be classified (i.e., stress, no-stress) with accuracy = 0.8748. We make the StressVision source code available on GitHub along with our new dataset, which will serve as a valuable resource for stress-detection research and allow for bench-marking against other methods.

This paper explores the application of telemetry to the control of a 6-axis robotic arm, designed for a prototype rover competing in the 2023 University Rover Challenge. The arm utilizes brushed DC motors, controlled by H-bridge circuits, to move the axes and dual end-effector. A central circuit board facilitates both open and closed loop control of the system via an integrated Teensy microcontroller, with signals transmitted to and from the remote Basestation over the 900 MHz, 2.4 GHz, or 5.8 GHz bands. Data received by the arm consists of motor output demands, joint angle targets, coordinate targets for inverse kinematic control, and a variety of other control signals, while data transmitted by the arm provides sensor feedback and status information to the remote operator. This telemetry allows operators to make informed decisions on control scheme selection, which is ideal for teleoperation.

Modern day computer vision applications are frequently implemented using machine learning approaches. While these implementations can perform very well, the performance is heavily dependent on sufficient and accurate training data. Due to a lack of adequate training data, the Arizona Autonomous Vehicles Club (AZA) decided to implement the generalized hough transform to detect shapes in a live video feed from an unmanned aerial system (UAS). The hough transform is computationally intensive and since real-time performance is required, a serial approach may not have the execution speed necessary for the application. Image processing techniques include matrix multiplication and convolution operations which are highly parallelizable. Therefore, the algorithm was parallelized and implemented on a graphics processing unit (GPU). Performance profiling was done on both machine learning and traditional approaches where execution time and accuracy were compared.

Several years ago, the IADS group at Curtiss-Wright Defense Solutions set about designing a unified platform for post-test analysis. This design aimed to satisfy a collection of core requirements that were gathered from meeting with a number of engineering groups throughout the flight test community at that time. The ultimate goal was to create a standardized system for post-test analysis from those requirements that could be used (and reused) by different flight test disciplines across different projects. While the IADS team has made significant progress implementing these requirements in recent years, the team has also received valuable feedback from the flight test community on ways to improve this system. This presentation will explore some of the obstacles and challenges encountered using such a system in a real-world environment, as well as some solutions that have been used to overcome these obstacles. Topics will include strategies for fast and efficient data visualization of entire flights, the importance of a common API for reading different data types, as well as managing large amounts of data in a cloud environment.

Integration of 5G cellular technology into our test ranges will create myriad opportunities to expand capabilities for the tester. One aspect of aeronautical mobile telemetry that will be profoundly transformed by the exploitation of 5G radios is the collection, distribution, and use of data in the control room during tests. The advent of bi-directional, packet-based telemetry with dynamically managed spectrum opens the door to more flexible and more efficient testing. It also presents challenges, some of which are technical in nature and others that are more procedural and cultural in nature. This paper will discuss some of the opportunities and challenges of incorporating 5G data feeds into the modern control room.

Kawasaki Heavy Industries, Ltd. has been researching on two-way communication using iNET since 2014. We started to conduct performance tests in 2015. Firstly, we used a tethered aerostat to evaluate our system in stable conditions. In this step, we got results as expected, and we moved to the next step, flight tests using aircrafts. For the first 2 years, we had struggled to demonstrate the performance due to some problems of our ground facility, etc. But finally, we successfully demonstrated our two-way communication system in 2018. After that, we began considering that our system can be applicable not only to the flight test but also to the field of disaster prevention and rescue. From this point of view, we integrated a 4K video transmission function which we think is useful for assessing the detailed conditions of affected areas into our system. This result was presented at ITC 2022. In this paper, we describe the summary of these test results and the future plans.

Regardless of which telemetry RF band (L, S, C band) is used, available bandwidth is becoming progressively limited. This is partly due to the constantly growing need for various vehicle test operations but is also happening at a time when the amount of measurement data that is being generated and recorded during those flight operations is growing. This is in part due to several factors: Measurement points where measured values are digitized with higher sampling rates, Higher available signal dynamic range, with 20 or 24-bit resolution, and Video from onboard cameras and other video sources. Completely new ways of working would become available if it was possible to transmit from the test vehicle to the ground station only those measurement data points which are important for a specific test and if the system could be reconfigured easily, quickly and securely from the ground station for the next test point. This sort of change would require a bi-directional telemetry connection between the ground station and the aircraft. The cooperation between JDA Systems and imc Test & Measurement has resulted in a new generation of flight test solutions that are based on IRIG and other industry standards. Part of the overall solution includes the telemetry link itself which is based on IRIG 106 Chapter 4/7. One of the fundamental innovations is that the telemetry not only transmits the measurement data from the aircraft to the ground station, but above all enables the telemetry data stream to be reconfigured, at short notice, during flight operations. imc Test & Measurement GmbH is an established manufacturer of high-end measurement and automation systems based in Berlin / Germany and has more than 30 years of experience in the field of mobile and stationary testing technology. The company is part of the Axiometrix Solutions Group, a leading provider of test solutions that includes globally recognized measurement technology brands. JDA Systems is a long-established, privately owned telemetry company with a wide range of proven products for telemetry hardware and software providing the engine for the telemetry links between the aircraft and the ground station, and the evaluation of the measurement data transmitted to the ground station. JDA Systems has worked with the IRIG 106 standards for decades and were a founder member of the IRIG 106 Chapter 10 committee.

This paper discusses the battery management system (BMS) and power distribution system used on a teleoperated rover used in the Mars Society University Rover Challenge competition (URC). The BMS monitors telemetry generated battery and can also receive commands from operators at a remote base station. The system is responsible for monitoring current draw of the entire rover, and voltage present in the battery array. The system can autonomously reconfigure the rover systems to isolate and recover from fault conditions. Operators at the remote base station can also command the systems to reconfigure the rover power distribution configuration.

In the world of motorsports, data is paramount for competition. Obtaining information about the track, the environment, and the car allows a team to better understand and tune their car to be as optimal as possible. This project created a modular system that transmitted data about the behavior of the car to the pitlane in real-time and could be configured to work with a variety of sensors. The system is centered around two Raspberry Pi computers that communicate with each other through RF transceivers. One Raspberry Pi is located onboard the race car and the other is in the pit lane. Connected to the onboard computer are many sensors: GPS, accelerometer, and strain gauges, all gathering information on the behavior of the car. This data is transmitted to the remote computer where it creates graphs and other visualizations about the car’s behavior in real-time using software for the pit crew to analyze.