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In this paper we present an approximate division scheme for Scaled Population (SP) arithmetic, a technique that improves on the limitations of stochastic computing (SC). SP arithmetic circuits are designed (a) to perform all operations with a constant delay, and (b) they use scaling operations to help reduce errors compared to SC circuits. As part of this work, we also present a method to correlate two SP numbers with a constant delay. We compare our SP divider with SC dividers, as well as fixed-point dividers (in terms of area, power and delay). Our 512-bit SP divider has a delay (power) that is 0.08× (0.06×) that of the equivalent fixed-point binary divider. Compared to a equivalent SC divider, our power-delay-product is 13× better. Index Terms—Approximate Arithmetic, Stochastic Computing, Computer Arithmetic, Approximate Division, Fast Division 

This paper addresses the robustness problem of visual-inertial state estimation for underwater operations. Underwater robots operating in a challenging environment are required to know their pose at all times. All vision-based localization schemes are prone to failure due to poor visibility conditions, color loss, and lack of features. The proposed approach utilizes a model of the robot's kinematics together with proprioceptive sensors to maintain the pose estimate during visual-inertial odometry (VIO) failures. Furthermore, the trajectories from successful VIO and the ones from the model-driven odometry are integrated in a coherent set that maintains a consistent pose at all times. Health-monitoring tracks the VIO process ensuring timely switches between the two estimators. Finally, loop closure is implemented on the overall trajectory. The resulting framework is a robust estimator switching between model-based and visual-inertial odometry (SM/VIO). Experimental results from numerous deployments of the Aqua2 vehicle demonstrate the robustness of our approach over coral reefs and a shipwreck. 

This research involves developing a drone control system that functions by relating EEG and EMG from the forehead to different facial movements using recurrent neural networks (RNN) such as long-short term memory (LSTM) and gated recurrent Unit (GRU). As current drone control methods are largely limited to handheld devices, regular operators are actively engaged while flying and cannot perform any passive control. Passive control of drones would prove advantageous in various applications as drone operators can focus on additional tasks. The advantages of the chosen methods and those of some alternative system designs are discussed. For this research, EEG signals were acquired at three frontal cortex locations (fp1, fpz , fp2 ) using electrodes from an OpenBCI headband and observed for patterns of Fast Fourier Transform (FFT) frequency-amplitude distributions. Five different facial expressions were repeated while recording EEG signals of 0-60Hz frequencies with two reference electrodes placed on both earlobes. EMG noise received during EEG measurements was not filtered away but was observed to be minimal. A dataset was first created for the actions done, and later categorized by a mean average error (MAE), a statistical error deviation analysis and then classified with both an LSTM and GRU neural network by relating FFT amplitudes to the actions. On average, the LSTM network had classification accuracy of 78.6%, and the GRU network had a classification accuracy of 81.8%. 

The Fearless Steps Apollo (FS-APOLLO) resource is a collection of over 150,000 hours of audio, associated meta-data, and supplemental technological toolkit intended to benefit the (i) speech processing technology, (ii) communication science, team-based psychology, and history, and (iii) education/STEM, preservation/archival communities. The FSAPOLLO initiative which started in 2014 has since resulted in the preservation of over 75,000 hours of NASA Apollo Missions audio. Systems created for this audio collection have led to the emergence of several new Speech and Language Technologies (SLT). This paper seeks to provide an overview of the latest advancements in the FS-Apollo effort and explore upcoming strategies in big-data deployment, outreach, and novel avenues of K-12 and STEM education facilitated through this resource. 

We investigate the feasibility of targeted privacy attacks using only information available in physical channels of LTE mobile networks and propose three privacy attacks to demonstrate this feasibility: mobile-app fingerprinting attack, history attack, and correlation attack. These attacks can reveal the geolocation of targeted mobile devices, the victim's app usage patterns, and even the relationship between two users within the same LTE network cell. An attacker also may launch these attacks stealthily by capturing radio signals transmitted over the air, using only a passive sniffer as equipment. To ensure the impact of these attacks on mobile users' privacy, we perform evaluations in both laboratory and real-world settings, demonstrating their practicality and dependability. Furthermore, we argue that these attacks can target not only 4G/LTE but also the evolving 5G standards. 

This paper presents fabrication and experimental measurements of broadband terahertz (THz) photoconductive antennas (PCAs), based on the conventional low temperature gallium arsenide (LT-GaAs) material. Various antenna electrode geometries, that were previously designed through computer simulations, are fabricated using the electron beam lithography (EBL). The generated time domain pulse is measured using a time domain spectroscopy system (TDS). The bandwidth of each emitting device is obtained using the fast Fourier transform of the generated electric field pulse.