• COGNITIVE EQUALIZATION FOR HF CHANNELS

      Teku, Noel; Bose, Tamal; Univ Arizona, Dept Elect & Comp Engn (International Foundation for Telemetering, 2018-11)
      In the High Frequency (HF) band, ranging from 3-30 MHz, long-range communications can be obtained by bouncing signals off the ionosphere without any significant infrastructure. However, the ionosphere changes rapidly, which can cause potentially harmful effects to the transmitted signal. This has motivated research into using adaptive equalization in this band to reverse these effects. However, a disadvantage of this technique is that based on the equalizer model and learning algorithm used, the error propagation may become significantly large, resulting in insufficient equalization to respond to these variations. To counter this, we investigate the usage of cognitive equalization, where an adaptive equalizer is equipped with the ability to change its structure (i.e. number of taps, step size, etc.) based on the current channel conditions and use probability of error to characterize its performance.
    • Constraint Gain for Two Dimensional Magnetic Recording Channels

      Bahrami, Mohsen; Vasic, Bane; Marcellin, Michael; Univ Arizona, Dept Elect & Comp Engn (International Foundation for Telemetering, 2018-11)
      In this paper, we study performance gains of constrained codes in Two dimensional Magnetic Recording (TDMR) channels using the notion of constraint gain. We consider Voronoi based TDMR channels with realistic grain, bit, track and magnetic-head dimensions. Specifically, we investigate the constraint gain for two-dimensional no-isolated-bit constraint over Voronoi based TDMR channels. We focus on schemes that employ the generalized belief propagation algorithm for obtaining information rate estimates for TDMR channels.
    • AN IMPROVED TELEMETRY SYSTEM FOR MONITORING AN OFF-ROAD RACECAR

      Anderson, Kohl; Boyer, Kyle; Brubaker, Laura; Fuehrer, Daniel; Herriman, Richard; Houston, Paul; Ruckle, Sean; Marcellin, Michael; Univ Arizona, Dept Elect & Comp Engn (International Foundation for Telemetering, 2018-11)
      The University of Arizona Baja Racing Team competes annually in a grueling off-road racing competition designed to test the durability of each team’s vehicle. For the last several years, we have been developing a custom telemetry system to monitor and analyze the performance of the vehicle in order to provide live diagnostics to the pit crew and driver, as well as to inform future designs. This year, we have redesigned the core of the system to be more modular and use more COTS parts in order to allow easier upgrade and repair, and have upgraded many existing sensors, added sensors to monitor driver vitals, improved the driver’s display, and embedded USB hubs in our power distribution boards to allow programming of all microcontrollers on the vehicle over a single USB interface. These changes will make future development easier and will produce far more data than we have had in previous generations.
    • INTELLIGENT JAMMING USING DEEP Q-LEARNING

      Thurston, Noah; Vanhoy, Garrett; Bose, Tamal; Univ Arizona, Dept Elect & Comp Engn (International Foundation for Telemetering, 2018-11)
      The threat of a malicious user interfering with network traffic so as to deny access to resources is an inherent vulnerability of wireless networks. To combat this threat, physical layer waveforms that are resilient to interference are used to relay critical traffic. These waveforms are designed to make it difficult for a malicious user to both deny access to network resources and avoid detection. If a malicious user has perfect knowledge of the waveform being used, it can avoid detection and deny network throughput, but this knowledge is naturally limited in practice. In this work, the threat of a malicious user that can implicitly learn the nature of the waveform being used simply by observing reactions to its behavior is analyzed and potential mitigation techniques are discussed. The results show that using recurrent neural networks to implement deep Q-learning, a malicious user can converge on an optimal interference policy that simultaneously minimizes the potential for it to be detected and maximizes its impediment on network traffic.
    • Remote Monitoring of Forces on Head for Detection of Traumatic Brain Injuries on Amusement Park Rides

      Camp, Laura; Marcellin, Stephanie; Rickel, Jodi; Rubenow, Tierny; Univ Arizona, Dept Elect & Comp Engn (International Foundation for Telemetering, 2018-11)
      The ASTM F24 Committee pays substantial attention to the potential safety risks that roller coasters pose to riders. Although the G-forces exerted on rides are strictly controlled to prevent traumatic brain injury and other conditions, operators may wish to monitor the impact forces guests experience to determine if they need to be removed from the ride. We have designed a system to monitor data and relay the findings to the operators. To measure the effect roller coasters have on the brains of guests, we used a combination of gyroscopes, accelerometers, and impact force sensors are incorporated into a headpiece worn by the guest. During the ride, the sensor data is wirelessly transmitted to a base station where it can be monitored in real time by an operator. The system compares the gathered data with limits based on pre-existing research on traumatic brain injuries, and then alerts the operator to potential issues.