An FPGA Multiprocessor System for Undergraduate Study

We present our experiences using multiple soft processor cores on an FPGA to study advanced computer architecture at the undergraduate level. Our system instantiates multiple processor cores on a single FPGA device using the Altera Nios® II soft processor and associated CAD tools. With an easy to use development environment and powerful tools to quickly generate designs, an FPGA platform provides the necessary flexibility to quickly produce a working system. Students are able to easily modify and adapt their designs for a specific application. We demonstrate that multiprocessor systems can be developed, implemented and studied by undergraduate students due to the availability and accessibility of design tools and FPGA development boards. Further, these systems enhance the learning of multiprocessors and aptly compliment advanced computer architecture courses covering topics to include shared memory, synchronization, sequential consistency, and memory coherency

A Monte Carlo Approach to Closing the Reality Gap

We propose a novel approach to the 'reality gap' problem, i.e., modifying a robot simulation so that its performance becomes more similar to observed real world phenomena. This problem arises whether the simulation is being used by human designers or in an automated policy development mechanism. We expect that the program/policy is developed using simulation, and subsequently deployed on a real system. We further assume that the program includes a monitor procedure with scalar output to determine when it is achieving its performance objectives. The proposed approach collects simulation and real world observations and builds conditional probability functions. These are used to generate paired roll-outs to identify points of divergence in behavior. These are used to generate {\it state-space kernels} that coerce the simulation into behaving more like observed reality. The method was evaluated using ROS/Gazebo for simulation and a heavily modified Traaxas platform in outdoor deployment. The results support not just that the kernel approach can force the simulation to behave more like reality, but that the modification is such that an improved control policy tested in the modified simulation also performs better in the real world

Comparing Feedback Linearization and Adaptive Backstepping Control for Airborne Orientation of Agile Ground Robots using Wheel Reaction Torque

In this paper, two nonlinear methods for stabilizing the orientation of a Four-Wheel Independent Drive and Steering (4WIDS) robot while in the air are analyzed, implemented in simulation, and compared. AGRO (the Agile Ground Robot) is a 4WIDS inspection robot that can be deployed into unsafe environments by being thrown, and can use the reaction torque from its four wheels to command its orientation while in the air. Prior work has demonstrated on a hardware prototype that simple PD control with hand-tuned gains is sufficient, but hardly optimal, to stabilize the orientation in under 500ms. The goal of this work is to decrease the stabilization time and reject disturbances using nonlinear control methods. A model-based Feedback Linearization (FL) was added to compensate for the nonlinear Coriolis terms. However, with external disturbances, model uncertainty and sensor noise, the FL controller does not guarantee stability. As an alternative, a second controller was developed using backstepping methods with an adaptive compensator for external disturbances, model uncertainty, and sensor offset. The controller was designed using Lyapunov analysis. A simulation was written using the full nonlinear dynamics of AGRO in an isotropic steering configuration in which control authority over its pitch and roll are equalized. The PD+FL control method was compared to the backstepping control method using the same initial conditions in simulation. Both the backstepping controller and the PD+FL controller stabilized the system within 250 milliseconds. The adaptive backstepping controller was also able to achieve this performance with the adaptation law enabled and compensating for offset noisy sinusoidal disturbances.Comment: First Submission to IEEE Letters on Control Systems (L-CSS) with the American Controls Conference (ACC) Optio

Assessing the EE Program Outcome Assessment Process

Program outcome assessment is an integral part of systematic curriculum review and improvement. Accrediting commissions expect each student to achieve program outcomes by the time of graduation. Programs undergoing accreditation must have an assessment process that demonstrates program outcome achievement. Documenting and assessing just how graduates are meeting program outcomes can become a tedious and data intensive process. We report on our “assessment” of our assessment process that resulted in more streamlined procedures by targeting performance indicators. Our methodology included the development of a learn, practice and demonstrate model for each outcome that focuses performance indicators at the appropriate point in development. We target actual outcome achievement during the “demonstrate” phase with rubrics to detail the level of mastery on a modified Likert scale. We originally used seventy-eight embedded performance indicators spread throughout the curriculum. We reduced to thirty indicators using a mixture of internal and external measures such as individual classroom events and fundamentals of engineering exam topical area results. We also emplaced guidelines targeting a single outcome measurement per indicator. For example, in our capstone senior design course, virtually every assignment was being reviewed by one of our outcome monitors. By targeting performance indicators at specific sub-events and looking at those which had to be assessed during the course versus indicators assessed by advisors or senior faculty, we were able to reduce the embedded performance indicators by a factor of three. We applied similar techniques to reduce individual course director workload. We have found that by streamlining the outcome process and using a rubric approach applied across multiple outcomes, we can greatly reduce the number of performance indicators yet preserve our ability to accurately assess our program. Reduced workload assessing the program has enabled us to place more effort into improving the program

Cascaded Neural Networks for Identification and Posture-Based Threat Assessment of Armed People

This paper presents a near real-time, multi-stage classifier which identifies people and handguns in images, and then further assesses the threat-level that a person poses based on their body posture. The first stage consists of a convolutional neural network (CNN) that determines whether a person and a handgun are present in an image. If so, a second stage CNN is then used to estimate the pose of the person detected to have a handgun. Lastly, a feed-forward neural network (NN) makes the final threat assessment based on the joint positions of the person’s skeletal pose estimate from the previous stage. On average, this entire pipeline requires less than 1 second of processing time on a desktop computer. The model was trained using approximately 2,000 images and achieved a pistol and person detection rate of 22% and 55%, respectively. The final stage NN correctly identified the severity of the threat with 84% accuracy. The images used to train each stage of our multi-classifier model are available online. With an expanded dataset the accuracy of detecting people and pistols can likely be improved in the future

Wait, I\u27m tagged?! Toward AR in Project Aquaticus

Human-robot teaming to perform complex tasks in a large environment is limited by the human’s ability to make informed decisions. We aim to use augmented reality to convey critical information to the human to reduce cognitive workload and increase situational awareness. By bridging previous Project Aquaticus work to virtual reality in Unity 3D, we are creating a testbed to easily and repeatedly measure the effectiveness of augmented reality information display solutions to support competitive gameplay. We expect the human-robot teaming performance to be improved due to the increased situational awareness and reduced stress that the augmented reality data display provides

Large Observatory for x-ray Timing (LOFT-P): a Probe-class mission concept study

LOFT-P is a concept for a NASA Astrophysics Probe-Class (<$1B) X-ray timing mission, based on the LOFT concept originally proposed to ESAs M3 and M4 calls. LOFT-P requires very large collecting area (>6 m^2, >10x RXTE), high time resolution, good spectral resolution, broad-band spectral coverage (2-30 keV), highly flexible scheduling, and an ability to detect and respond promptly to time-critical targets of opportunity. It addresses science questions such as: What is the equation of state of ultra dense matter? What are the effects of strong gravity on matter spiraling into black holes? It would be optimized for sub-millisecond timing to study phenomena at the natural timescales of neutron star surfaces and black hole event horizons and to measure mass and spin of black holes. These measurements are synergistic to imaging and high-resolution spectroscopy instruments, addressing much smaller distance scales than are possible without very long baseline X-ray interferometry, and using complementary techniques to address the geometry and dynamics of emission regions. A sky monitor (2-50 keV) acts as a trigger for pointed observations, providing high duty cycle, high time resolution monitoring of the X-ray sky with ~20 times the sensitivity of the RXTE All-Sky Monitor, enabling multi-wavelength and multi-messenger studies. A probe-class mission concept would employ lightweight collimator technology and large-area solid-state detectors, technologies which have been recently greatly advanced during the ESA M3 study. Given the large community interested in LOFT (>800 supporters, the scientific productivity of this mission is expected to be very high, similar to or greater than RXTE (~2000 refereed publications). We describe the results of a study, recently completed by the MSFC Advanced Concepts Office, that demonstrates that LOFT-P is feasible within a NASA probe-class mission budget.Comment: Proc. SPIE 9905, Space Telescopes and Instrumentation 2016: Ultraviolet to Gamma Ray, 99054Y (July 18, 2016

Compressed representation of a partially defined integer function over multiple arguments

In OLAP (OnLine Analitical Processing) data are analysed in an n-dimensional cube. The cube may be represented as a partially defined function over n arguments. Considering that often the function is not defined everywhere, we ask: is there a known way of representing the function or the points in which it is defined, in a more compact manner than the trivial one

Patients with type 1 diabetes show signs of vascular dysfunction in response to multiple high-fat meals

Peer reviewe

Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study

The prevalence of hypertension in African Americans (AAs) is higher than in other US groups; yet, few have performed genome-wide association studies (GWASs) in AA. Among people of European descent, GWASs have identified genetic variants at 13 loci that are associated with blood pressure. It is unknown if these variants confer susceptibility in people of African ancestry. Here, we examined genome-wide and candidate gene associations with systolic blood pressure (SBP) and diastolic blood pressure (DBP) using the Candidate Gene Association Resource (CARe) consortium consisting of 8591 AAs. Genotypes included genome-wide single-nucleotide polymorphism (SNP) data utilizing the Affymetrix 6.0 array with imputation to 2.5 million HapMap SNPs and candidate gene SNP data utilizing a 50K cardiovascular gene-centric array (ITMAT-Broad-CARe [IBC] array). For Affymetrix data, the strongest signal for DBP was rs10474346 (P= 3.6 × 10−8) located near GPR98 and ARRDC3. For SBP, the strongest signal was rs2258119 in C21orf91 (P= 4.7 × 10−8). The top IBC association for SBP was rs2012318 (P= 6.4 × 10−6) near SLC25A42 and for DBP was rs2523586 (P= 1.3 × 10−6) near HLA-B. None of the top variants replicated in additional AA (n = 11 882) or European-American (n = 69 899) cohorts. We replicated previously reported European-American blood pressure SNPs in our AA samples (SH2B3, P= 0.009; TBX3-TBX5, P= 0.03; and CSK-ULK3, P= 0.0004). These genetic loci represent the best evidence of genetic influences on SBP and DBP in AAs to date. More broadly, this work supports that notion that blood pressure among AAs is a trait with genetic underpinnings but also with significant complexit