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Global positioning system (GPS) and inertial measurement units (IMUs) are often combined to produce navigation systems for airborne imaging platforms. The current state-of-the-art radar technology allows for radars to pulse at very high rates. GPS and IMU update rates are not fast enough to accurately report the platform position for each radar pulse. Independent GPS and IMUs cannot provide positional accuracy for long term stability. Traditional techniques, such as the Kalman and particle filter, are used to fuse GPS and IMU measurements. The Kalman filter excels for linear and Gaussian systems whereas the particle filter excels at non-linear and non-Gaussian systems. Sensor fusion techniques are used to help correct for IMU errors and provide the positional accuracy required for synthetic aperture radar (SAR) imaging applications. However, SAR requires the fusion algorithms to provide faster update rates. This paper explores the use of an up-sampled particle filter (UPF) for SAR to provide highly accurate position estimates at sampling frequencies comparable to radar pulse rates and overcome the limitations of standard interpolation techniques. This up-sampled particle filter is proven through simulations and instrumentation with a NovAtel GPS and IMU. The UPF technique allows for the GPS/IMU sampling rate to be different from the radar pulse repetition frequency (PRF) while providing accurate position solutions for each radar pulse capable of compensating for the phase history required for focusing a SAR image. The algorithms are instrumented in a SAR system and the position estimates are further validated and demonstrated through captured SAR images.

Rollators are widely used by people with mobility problems, but previous studies have been limited to self-report approaches when evaluating their real-world effectiveness. To support studies based on more robust datasets, a method to estimate mobility parameters, such as gait speed and distance traveled, in the real world is needed. Body-worn sensors offer one approach to the problem, but rollator-mounted sensors have some practical advantages providing direct insight into patterns of walking device used, an under-researched area. We present a novel method to estimate speed and distance traveled from a single rollator-mounted IMU. The method was developed using data collected from ten rollator users performing a series of walking tasks including obstacle negotiation. The IMU data is first pre-processed to account for noise, orientation offset, and rotation-induced accelerations. The method then uses a two-stage approach. First, activity classification is used to separate the rollator data into one of three classes (movement, turning, or other). Subsequently, the speed of movement and distance traveled is estimated, using a separate estimation model for each of the three classes. The results showed high classification accuracy (precision, recall, and F1 statistics all >0.9). Speed estimation showed mean absolute errors below 0.2 m/s. Estimates for distance traveled showed errors which ranged from 5% (straight line walking) to over 70%. The results showed some promise but further work with a larger data set is needed to confirm the performance of our approach.

By processing in the frequency domain (FD), massive MIMO systems can approach the theoretical per-user capacity using a single carrier modulation (SCM) waveform with a cyclic prefix. Minimum mean squared error (MMSE) detection and zero forcing (ZF) precoding have been shown to effectively cancel multi-user interference while compensating for inter-symbol interference. In this paper, we present a modified downlink precoding approach in the FD based on regularized zero forcing (RZF), which reuses the matrix inverses calculated as part of the FD MMSE uplink detection. By reusing these calculations, the computational complexity of the RZF precoder is drastically lowered, compared to the ZF precoder. Introduction of the regularization in RZF leads to a bias in the detected data symbols at the user terminals. We show this bias can be removed by incorporating a scaling factor at the receiver. Furthermore, it is noted that user powers have to be optimized to strike a balance between noise and interference seen at each user terminal. The resulting performance of the RZF precoder exceeds that of the ZF precoder for low and moderate input signal-to-noise ratio (SNR) conditions, and performance is equal for high input SNR. These results are established and confirmed by analysis and simulation., Comment: 6 pages, 2 figure, accepted by IEEE ICC 2021. Not final version

In vehicle-to-everything (V2X) communications, ensuring the scalability is one of the most important aspects of V2X safety applications. This paper analyzes and compares the scalability performance of dedicated short-range communication (DSRC) based on IEEE 802.11p and 3GPP Rel-14 LTE-V2X PC5 mode 4 (i.e., direct communication mode without the help of base stations) in transmitting the Basic Safety Messages using SAE congestion control mechanisms in freeway scenarios with up to 4000 vehicles over a 5-km road. Simulation results show that for V2V distances below 300 m, DSRC over 10-MHz bandwidth is comparable with LTE-V2X over 20-MHz bandwidth with HARQ retransmission. Also, we reveal that LTE-V2X has a consecutive packet loss issue due to its semi-persistent scheduling mechanism. The results show that the use of a wider bandwidth and packet retransmission in LTE-V2X can partly mitigate a consecutive packet loss issue.

Diabetes is a chronic diseases that affects nearly half a billion people around the globe, and is almost always associated with a number of complications, including kidney failure, blindness, stroke, and heart attack. An important step towards improved diabetes care is to accurately predict the risk of diabetes complications and to identify the corresponding risk factors associated with the onset of each complication. In this paper, we study the problem of risk prediction and outcome-specific risk factor identification from readily available patient medical record data. We adopt a private-shared multi-task learning (MTL) model, which jointly models multiple complications with each task corresponding to the risk modeling of one complication. The MTL formulation not only boosts prediction performance but also enables identification of outcome-specific risk factors. Specifically, we decompose the coefficient matrix, in which each column (vector) corresponds to the coefficient of one complication risk model, into a shared component and an outcome-specific private component. The shared component is assumed to be low-rank to capture the relationships among complications in terms of overall diabetes health condition. The private component is assumed to be non-overlapping and sparse so that they are discriminative among the different complication outcomes. Further, the shared component and the private component for the same complication are assumed to be orthogonal. Extensive experimental results on a type 2 diabetes cohort extracted from a large electronic medical claims database show that the proposed method outperforms baseline models by a significant margin. Also the identified outcome-specific risk factors provide meaningful clinical insights. The results demonstrate that simultaneously modeling multiple risks through MTL not only improves prediction performance but also enables identification of outcome-specific risk factors.

Low Gain Avalanche Detectors (LGADs) have recently been studied for applications in high energy physics. They provide the advantages of built-in gain and fast read-out. However, radiation hardness remains a problem, reduced effective boron doping concentration (acceptor removal) after hadron irradiation that dramatically reduces LGAD gain. We propose a new LGAD process flow that allows for creation of a very steep boron profile in the multiplication region, reducing the fractional acceptor removal and resulting performance degradation. The new LGAD process flow requires a low temperature silicon-silicon wafer bonding process, which is currently under development. TCAD process simulations are used to demonstrate feasibility of the new structure, and TCAD device simulations are used to characterize LGAD performance before and after irradiation.

New free electron lasers, such as SLAC's LCLS-II, will provide unique scientific imaging opportunities with soft xrays from 250eV-1.5KeV. In order to fully utilize these facilities, we need to develop detectors with both shallow entrance windows and adequate signal-to noise ratios to utilize the soft x-rays effectively. Low Gain Avalanche Detectors (LGADs) have the potential for improved signal-to-noise ratio due to the built-in amplification. However, the conventional LGAD structure is not conducive to a shallow entrance window. In this work, we propose a new LGAD device structure that can incorporate a shallow entrance window utilizing microwave annealing to activate the dopant in the entrance window. Process and device simulations are used to demonstrate the feasibility and performance of the proposed structure.

This paper presents a high sampling frequency pulse-Doppler radar that was constructed using commercially available components. The system architecture is explained with emphasis placed on the digital transceiver subsystem. A calibration technique is presented to mitigate range ambiguity due to digital latency variation between resets of the system. The pulse-Doppler operation of the system is then evaluated through the range-Doppler mapping of moving and stationary targets, confirming the radar's potential for synthetic aperture radar imaging.

Using a shoulder harness and control cable, a\ud person can control the opening and closing of a bodypowered prosthesis prehensor. In many setups the cable\ud does not pass adjacent to the shoulder joint center allowing\ud shoulder flexion on the prosthetic side to be used for\ud prehensor control. However, this makes cable setup a\ud difficult compromise as prosthesis control is dependent on\ud arm posture; too short and the space within which a person\ud can reach may be unduly restricted, too long and the user\ud may not be able to move their shoulder sufficiently to take\ud up the inevitable slack at some postures and hence have no\ud control over prehensor movement. Despite the fundamental\ud importance of reachable workspace to users, to date there\ud have been no studies in prosthetics on this aspect. Here, a\ud methodology is presented to quantify the reduction in the\ud reachable volume due to the harness, and to record the\ud range-of-motion of the prehensor at a series of locations\ud within the reachable workspace. Ten anatomically intact\ud participants were assessed using a body-powered prosthesis\ud simulator. Data was collected using a 3D motion capture\ud system and an electronic goniometer. The harnessed\ud reachable workspace was 38-85% the size of the\ud unharnessed volume with participants struggling to reach\ud across the body and above the head. Across all arm postures\ud assessed, participants were only able to achieve full\ud prehensor range-of-motion in 9%. The methodologies\ud presented could be used to evaluate future designs of both\ud body-powered and myoelectric prostheses.

SiGe alloys have been widely used as stressors in source/drain (S/D) regions for advanced complementary metal-oxide-semiconductor (CMOS) technologies to enhance channel mobility and boost device performance. Many previous studies were mainly focused on investigation of the main epitaxial SiGe layer’s growth mechanism, and its impact on the downstream process and device performance. In this work, instead of focusing on the main epitaxial SiGe layer, we present a method for tuning the device performance through adjustment of the epitaxial SiGe seed layer growth time/thickness. Experiments on patterned wafers show that the SiGe seed layer thickness has a strong impact on device performance while not affecting the subsequent epitaxial growth of SiGe S/D. This demonstrates that SiGe seed layer thickness can be a promising knob for tuning the device performance.

Unmanned Aerial Systems (UAS) are vulnerable to a number of cyber-attacks. To navigate and avoid collisions, they rely on accurate information from GPS and Automatic Dependent Surveillance-Broadcast devices. Signals of these devices are can be jammed, spoofed, deleted, and modified. Identifying the location of an attacker can detect and mitigate cyber-attacks. This paper proposes a novel approach to identifying the angle of arrival of a signal. The approach consists of using a spinning radome with an attenuating, parasitic, section. The attenuating section spins around to create a periodic amplitude modulation on the received signal while not breaking the communication link. The amplitude of the signal drops when the attenuating section is in-line with the transmitter attacker. When the received signal is the lowest, the angle of the radome attenuator corresponds to the angle of arrival of the signal of interest. The results show that by using two measurements of the angle of arrival at different locations, one can calculate the attacker/transmitter's location with good precision. Two radomes were used for measurements at various locations.

Cyclic prefix direct sequence spread spectrum (CP-DSSS) is a novel waveform that positions itself well as a secondary network to relieve the congested wireless spectrum. The underlying structure of CP-DSSS allows for efficient and effective multiaccess capabilities through frequency and time division schemes. The sum-rate capacity of the system is maximized when the spectrum is divided and allocated to users with the best signal-to-noise (SNR) ratio for the given channel slice. We propose an algorithm for dividing and allocating portions of the spectrum to multiple users with the final goal of maximizing the sum-rate capacity of the network. We also propose and develop a precoding/equalization technique that reduces the length of the channel impulse response. This, when used along with a matched filter detector, leads to a noticeable improvement in the sum-rate capacity of the network.

Nonlinearities in Radio Frequency Power Transistors can be attributed to mechanisms such as non-linear transconductances and nonlinear capacitances at the device terminals. In this paper, we extend the work in by Ladhani et al to include the impact of the nonlinear gate-source capacitance in a MOSFET (Metal Oxide Semiconductor Field Effect Transistor) and discuss its contribution (along with the gate baseband impedance) to the amplifier’s intermodulation distortion and linearizability. Measured results show that for a 140W (P1dB (1dB Compression Point)) Class AB LDMOS (Laterally Diffused Metal Oxide Semiconductor Field Effect Transistor) PA, an optimized gate baseband impedance provides 6dB improvement in corrected ACPR (Adjacent Channel Power Ratio) and 7dB improvement in corrected Alt-1 (First Adjacent Channel Power Ratio) at 7dB backoff from peak power using a 4-Carrier WCDMA (Wideband Code Division Multiple Access) signal.