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Controller design for nonlinear systems with Control Lyapunov Function (CLF) based quadratic programs has recently been successfully applied to a diverse set of difficult control tasks. These existing formulations do not address the gap between design with continuous time models and the discrete time sampled implementation of the resulting controllers, often leading to poor performance on hardware platforms. We propose an approach to close this gap by synthesizing sampled-data counterparts to these CLF-based controllers, specified as quadratically constrained quadratic programs (QCQPs). Assuming feedback linearizability and stable zero-dynamics of a system's continuous time model, we derive practical stability guarantees for the resulting sampled-data system. We demonstrate improved performance of the proposed approach over continuous time counterparts in simulation., 6 pages, 1 figure, submitted to IEEE L-CSS / CDC 2021

The efficient utilization of available resources while simultaneously achieving control objectives is a primary motivation in the event-triggered control paradigm. In many modern control applications, one such objective is enforcing the safety of a system. The goal of this paper is to carry out this vision by combining event-triggered and safety-critical control design. We discuss how a direct transcription, in the context of safety, of event-triggered methods for stabilization may result in designs that are not implementable on real hardware due to the lack of a minimum interevent time. We provide a counterexample showing this phenomena and, building on the insight gained, propose an event-triggered control approach via Input to State Safe Barrier Functions that achieves safety while ensuring that interevent times are uniformly lower bounded. We illustrate our results in simulation., Comment: 6 pages, 2 figures, submitted to L-CSS + CDC 2020

Reverse forward scatter radar, an unusual alternative form to the traditional forward scatter radar, is investigated to demonstrate its viability and practicality. The power budget is illustrated graphically and derived by finding the maximum possible baselines for various targets, including a human. The two-ray path and the free space propagation model are considered. Reverse forward scatter radar is shown to have clear potential to be used in practice.

This paper presents a hybrid adaptive law for safety-critical adaptive control of constrained continuous-time systems under the effect of unknown disturbances. The proposed adaptive law features a hybrid update law that, using a hysteresis-type mechanism, appropriately resets the estimate of the disturbance. In contrast to continuous-time adaptation laws for safety-critical control, our hybrid adaptive law relaxes the assumption typically imposed on the unknown disturbances as well as the behavior usually imposed around the boundary of the safe region. We illustrate the benefits of the proposed hybrid law in an adaptive cruise control problem.

As autonomous vehicle technology starts to become more common in daily life, a degree of uncertainty arises with respect to pedestrian safety. As the technology gets more and more advanced, there becomes greater possibility for lapses in connectivity and miscommunications between pedestrians and vehicles. Hence, it is important that society finds the best way for pedestrians to interact with all types of vehicles in order to keep them safe, in the midst of a continuous increase in pedestrian incidents. Thusly, the focus of this investigation is to determine the feasibility of crossing intersections reliant on pedestrian to vehicle connectivity. As vehicles increase in complexity with respect to their interaction with other vehicles, pedestrians, and the environment, intersections may not require physical infrastructure - autonomous vehicles may be built to recognize pedestrians and to stop accordingly, and signal timing may be periodically uploaded in the form of continuously changing datasets. Such intersections have ambiguous effects on pedestrian and vehicle interactions, partially due to the uncertainty of afore-mentioned connectivity dynamics. Connected and Autonomous Vehicles (CAVs) rely primarily on obstacle detection systems, a main connectivity dynamic between pedestrians and vehicles, to tell them to stop for pedestrians. These systems, many still deep in the development phase, may be flawed. This paper describes an experiment developed with the intention of comparing a traditional obstacle detection approach to pedestrian crossing with one in which vehicles stop based on pedestrians notifying them of their presence. More specifically, in order to cross, pedestrians must "signal" to vehicles that they are in the crossing area (in the real world, this could be done on a mobile phone) so that the vehicles will stop for them. The model, developed using Simio, outputs queue timing and density values for the vehicles and pedestrians. Data was gathered related to average wait times and flow of pedestrians and vehicles over a simulated time frame of a 4 week period. The results indicate that utilizing a pedestrian to vehicle communication framework, either in place of or in addition to traditional obstacle detection and avoidance systems, would prove beneficial in developing more efficient intersections.

A TCAD device technology co-optimization workflow for MRAM devices is presented using multi-scale modelling. Seamless transfer of magnetic parameters within the workflow allows first-principles MRAM stack design and optimization, offering an end-to-end solution. TCAD DTCO enables stack engineering to meet technology specific design targets in high-volume manufacturing. Impact of etch and integration steps is accurately analyzed using Micromagnetic simulation and physical characterization. These process steps alter STT switching dynamics and their implications on Write Error Rates (WER) are discussed. These results provide critical insight for optimizing the WER at reduced pulse-widths needed by Last Level Cache (LLC) applications.

Sliding Mode Control (SMC) systems are developed for a hydraulic manipulator. The control model is obtained via state-dependent parameter (SDP) system identification. In contrast to previous research using discrete-time SDP models, in which the model coefficients are functions of the sampling interval, the present work develops a new continuous-time approach. It is well known that for conventional SMC there is a trade-off between chattering and robust performance. Hence, a recently developed approach to address this problem is investigated, in which the controller is designed via a fractional exponent of the sliding surface. The approach is developed for both conventional and Nonsingular Terminal SMC (NTSMC). The new continuous version of the NTSMC algorithm successfully reduces chattering and provides the best overall performance of various SMC designs. However, for the preliminary experiments reported in this article, a PID lead-lag controller yields the lowest absolute errors, albeit at the cost of a higher control effort. Hence, given that dead-zone and other uncertainties provide the main motivation for use of SMC in this application, further research into the robustness of the new algorithm is required.

Uncertain time-delays can reduce the performance of hydraulic manipulator control systems. Variations in the time delay between, for example, an applied voltage and the associated manipulator movement, may be caused by the internal dynamics of the system and other nonlinear characteristics, such as fluid compressibility, dead-band of the pump, valve flow properties and friction characteristics. Robot control that addresses system time-delays, nonlinearities and uncertainty is the subject of much research but, whilst the specific concept of varying time delays in hydraulic systems is sometimes acknowledged, it appears to be less widely investigated than other types of uncertainty. The present article discuses some of the issues involved, exemplified by a dual manipulator device used in the laboratory for research into nuclear decommissioning, and presents a review of the relevant control literature in this area.

Adaptive Control Lyapunov Functions (aCLFs) were introduced 20 years ago, and provided a Lyapunov-based methodology for stabilizing systems with parameter uncertainty. The goal of this paper is to revisit this classic formulation in the context of safety-critical control. This will motivate a variant of aCLFs in the context of safety: adaptive Control Barrier Functions (aCBFs). Our proposed approach adaptively achieves safety by keeping the systems state within a safe set even in the presence of parametric model uncertainty. We unify aCLFs and aCBFs into a single control methodology for systems with uncertain parameters in the context of a Quadratic Program (QP) based framework. We validate the ability of this unified framework to achieve stability and safety in an adaptive cruise control (ACC) simulation., 8 pages, 2 figures, submitted to 2020 IEEE American Control Conference (ACC)

In this work we propose long wave infrared (LWIR) imagery as a viable supporting modality for semantic segmentation using learning-based techniques. We first address the problem of RGB-thermal camera calibration by proposing a passive calibration target and procedure that is both portable and easy to use. Second, we present PST900, a dataset of 894 synchronized and calibrated RGB and Thermal image pairs with per pixel human annotations across four distinct classes from the DARPA Subterranean Challenge. Lastly, we propose a CNN architecture for fast semantic segmentation that combines both RGB and Thermal imagery in a way that leverages RGB imagery independently. We compare our method against the state-of-the-art and show that our method outperforms them in our dataset., 6 pages

Autonomous flight through unknown environments in the presence of obstacles is a challenging problem for micro aerial vehicles (MAVs). A majority of the current state-of-art research assumes obstacles as opaque objects that can be easily sensed by optical sensors such as cameras or LiDARs. However in indoor environments with glass walls and windows, or scenarios with smoke and dust, robots (even birds) have a difficult time navigating through the unknown space.In this paper, we present the design of a new class of micro aerial vehicles that achieves autonomous navigation and are robust to collisions. In particular, we present the Tiercel MAV: a small, agile, light weight and collision-resilient robot powered by a cellphone grade CPU. Our design exploits contact to infer the presence of transparent or reflective obstacles like glass walls, integrating touch with visual perception for SLAM. The Tiercel is able to localize using visual-inertial odometry (VIO) running on board the robot with a single downward facing fisheye camera and an IMU. We show how our collision detector design and experimental set up enable us to characterize the impact of collisions on VIO. We further develop a planning strategy to enable the Tiercel to fly autonomously in an unknown space, sustaining collisions and creating a 2D map of the environment. Finally we demonstrate a swarm of three autonomous Tiercel robots safely navigating and colliding through an obstacle field to reach their objectives.

The goal of this paper is to understand the impact of learning on control synthesis from a Lyapunov function perspective. In particular, rather than consider uncertainties in the full system dynamics, we employ Control Lyapunov Functions (CLFs) as low-dimensional projections. To understand and characterize the uncertainty that these projected dynamics introduce in the system, we introduce a new notion: Projection to State Stability (PSS). PSS can be viewed as a variant of Input to State Stability defined on projected dynamics, and enables characterizing robustness of a CLF with respect to the data used to learn system uncertainties. We use PSS to bound uncertainty in affine control, and demonstrate that a practical episodic learning approach can use PSS to characterize uncertainty in the CLF for robust control synthesis.

The growing sophistication of experimental data analysis at DOE scientific user facilities is poised to outstrip local resources for storage and computing. At the same time, experimental facilities generally underuse DOE supercomputers for analysis because 1) logging into a supercomputer to schedule computations complicates the experimental workflow, and 2) scientists must often interpret data on-the-fly and cannot abide by batch queueing policies. In this work, we describe how the Balsam edge service has enabled near-real-time analysis of data collected at the Advanced Photon Source (APS) with an X-ray Photon Correlation Spectroscopy (XPCS) application running on Argonne Leadership Computing Facility (ALCF) resources. Balsam provides a uniform API for remote job submission and can leverage transfer protocols like Globus to orchestrate data movement. The Balsam service then interfaces with the system scheduler to perform automated job submission and process tasks in a high-throughput, fault-tolerant fashion. We highlight the effectiveness of backfill queues when used with Balsam to process XPCS data on idle nodes with relatively low latency.

This article concerns a previously developed, Fractional–order, Generalised Predictive Control (FGPC) approach to control system design. Here, fractional order concepts are utilised to extend the design flexibility of the FGPC algorithm, in comparison to conventional Generalised Predictive Control (GPC). The performance of FGPC is investigated via Monte Carlo simulation. With a focus on plots of the closed-loop eigenvalues, these results are utilised to develop recommendations for how to optimise the extra design coefficients introduced in the fractional order case. One of the simulation examples involves a hydraulically actuated robotic manipulator. Finally, FGPC methods are applied to control airflow in a laboratory 1 m by 2 m by 2 m forced ventilation environmental test chamber.

In this work, we present an information based exploration strategy tailored for the generation of high resolution 3D maps. We employ RGBD panoramas because they have been shown to provide memory efficient high quality representations of space. Robots explore the environment by selecting locations with maximal Cauchy-Schwarz Quadratic Mutual Information (CSQMI) computed on an angle enhanced occupancy grid to collect these RGBD panoramas. By employing the angle enhanced occupancy grid, the resulting exploration strategy emphasizes perspective in addition to binary coverage. Furthermore, the goal selection strategy is improved by using image morphology to reduce the search space over which CSQMI is computed. We present experimental results demonstrating the improved performance in perception related tasks by capturing panoramas using this approach, near frontier exploration, and a control of logging images at regular intervals while teleoperating the robot through the workspace. Collect imagery was passed through an object detection library with our perspective aware approach yielding a greater number of successful detections compared to near frontier exploration.

The robotic platform in this study is being used for research into assisted tele-operation for common nuclear decommissioning tasks, such as remote pipe cutting. It has dual, seven-function, hydraulically actuated manipulators mounted on a mobile base unit. For the new visual servoing system, the user selects an object from an on-screen image, whilst the computer control system determines the required position and orientation of the manipulators; and controls the joint angles for one of these to grasp the pipe and the second to position for a cut. Preliminary testing shows that the new system reduces task completion time for both inexperienced and experienced operators, in comparison to tele-operation. In a second contribution, a novel state-dependent parameter (SDP) control system is developed, for improved resolved motion of the manipulators. Compared to earlier SDP analysis of the same device, which used a rather ad hoc scaling method to address the dead -zone, a state-dependent gain is used to implement inverse dead-zone control. The new approach integrates input signal calibration, system identification and nonlinear control design, allowing for straightforward recalibration when the dynamic characteristics have changed or the actuators have deteriorated due to age.

The non-destructive testing (NDT) of components is very important to the aerospace industry. Welds in these components may contain porosities and other defects. These reduce the fatigue life of components and may result in catastrophic accidents if they end up in the aircraft. Currently such welds are inspected by humans studying radiographs of the welds. We describe an automatic system for detecting defects in welds, with the aim of creating a triage system to reduce the workload on human inspectors. Given an X-ray image of the aerospace weld, the system locates the weld line, then analyses the region around the line to identify abnormalities. Our results show that the weld can be precisely extracted from X-ray images and the defect detection operation can identify 83% of defects with fewer than 3 false positives per image, and thus may be useful for prompting human inspectors to reduce their workload.

We have probed straightforwardly, thermoelectric (TE) properties of bismuth telluride (Bi 2 Te 3 ) nanoplates grown via solvothermal method, without any modifications or optimization. The material was synthesized, its crystalline structure characterized, and prepared for TE measurements via sintering via Gleeble. Electron microscopy and diffraction data show highly crystalline hexagonal nanoplates with lateral growth direction. The thermoelectric properties of the sintered sample exhibit Seebeck coefficient of 21.118 uV/K which is in range of most optimized and engineered Bi 2 Te 3 based TE devices. The results presented in this proceeding may prove invaluable in research and development of next-generation of low-dimension, low-costing thermoelectric devices and power generators.

Physicians are often required to insert needles into a patient's body for procedures including drug injection or biopsy. This task is extremely delicate as incorrect insertion could cause serious damage to the patient. An abdominal phantom was designed for physicians to practice needle insertion. The phantom was inexpensive and manufactured with 3D printing. Computed tomography (CT) scans of the phantom were taken to show the visibility of the phantom inside a CT scanner. Medical trainees practiced needle insertion with the phantom and hit the target lesion on 76.9% of insertion attempts.

We describe experimental measurements and simulations of a switchable THz metasurface device in a slab waveguide geometry. The device exhibits giant phase modulation of the TE 1 mode at particular frequencies determined by the metamaterial geometry. Simulations predict that a 2π phase shift can be realized in an interaction length of only a few millimeters.

The research behind this article primarily concerns the development of mobile robots for nuclear decommissioning. The robotic platform under study has dual, seven-function, hydraulically actuated manipulators, for which the authors are developing a vision based, assisted teleoperation interface for common decommissioning tasks such as pipe cutting. However, to improve safety, task execution speed and operator training-time, high performance control of the nonlinear manipulator dynamics is required. Hence, the present article focuses on an associated dynamic model, and addresses the challenging generic task of parameter estimation for a highly non-convex and nonlinear system. A novel approach for estimation of the fundamental parameters of the manipulator, based on the idea of multi-objectivization, is proposed. Here, a single objective output error identification problem is converted into a multi-objective optimization problem. This is solved using a multi-objective genetic algorithm with non-dominated sorting. Numerical and experimental results using the nuclear decommissioning robot, show that the performance of the proposed approach, in terms of both the output error index and the accuracy of the estimated parameters, is superior to the previously studied single-objective identification problem.

Early plant disease detection and treatment could dramatically increase crop yield. However, even experts cannot visually distinguish various diseases with certainty, limiting image based diagnosis. In this paper, we present a novel small unmanned aircraft system (sUAS) for phytobiopsy. This platform can remove a leaf section with visual symptoms and transport it to a lab for precise disease analysis. We present the design of a gripper and an arm mechanism, and we discuss best practices for using the system. Results are presented from extensive experimental evaluation of the gripper assembly and 21 indoor manual flight trials, demonstrating efficacy. Our sUAS utilizes its inherent mobility and range to solve a pressing agricultural problem, improving food production and disease detection capabilities. A short video of our system can be found at https://goo.gl/xomYUO.

Surge arresters in HVDC converter stations are subjected to continuous operating voltages with very different waveforms compared to arresters for normal a.c. applications. Since the protection levels achievable are directly proportional to how high the arresters can be stressed continuously, accurate knowledge of long-term stability and continuous power losses for the applied voltage waveforms are decisive. In the past it has been difficult to produce and perform tests on metal-oxide (MO) resistors with these waveforms, but today they can be readily produced by a high-voltage power amplifier system. More accurate knowledge of the performance of the MO resistors thus forms the basis for obtaining accurate designs of the arresters.

This paper presents a nested marsupial robotic system and its execution of a notional disaster response task. Human supervised autonomy is facilitated by tightly-coupled, high-level user feedback enabling command and control of a bimanual mobile manipulator carrying a quadrotor unmanned aerial vehicle that carries a miniature ground robot. Each robot performs a portion of a mock hazardous chemical spill investigation and sampling task within a shipping container. This work offers an example application for a heterogeneous team of robots that could directly support first responder activities using complementary capabilities of autonomous dexterous manipulation and mobility, autonomous planning and control, and teleoperation. The task was successfully executed during multiple live trials at the DARPA Robotics Challenge Technology Expo in June 2015. A key contribution of the work is the application of a unified algorithmic approach to autonomous planning, control, and estimation supporting vision-based manipulation and non-GPS-based ground and aerial mobility, thus reducing algorithmic complexity across this capability set. The unified algorithmic approach is described along with the robot capabilities, hardware implementations, and human interface, followed by discussion of live demonstration execution and results.

In this paper we propose a new approach to recovering the relative position and orientation of a pair of platforms moving on the plane by fusing the kinds of estimates provided by visual odometry systems with distance measurements obtained from ranging radio systems. By combining these two complementary sources of information we are able to provide estimates that could not be obtained using either source separately. We show that the localization problem can be phrased as an optimization problem where we are interested in maximizing a convex function subject to a set of convex constraints and we propose an efficient and effective solution scheme that leverages the structure of the problem. Experimental results are provided to demonstrate that the proposed method performs creditably on actual robotic platforms.

In the last decade, multi-rotor Micro Aerial Vehicles (MAVs) have attracted great attention from robotics researchers. Offering affordable agility and maneuverability, multi-rotor aircrafts have become the most commonly used platforms for robotics applications. Amongst the most promising applications are inspection of power-lines, cell-towers, large and constrained infrastructures and precision agriculture. While GPS offers an easy solution for outdoor autonomy, using on-board sensors is the only solution for autonomy in constrained indoor environments. In this paper, we present our results on autonomous inspection of completely dark, featureless, symmetric dam penstocks using cameras and range sensors. We use a hex-rotor platform equipped with an IMU, four cameras and two lidars. One of the cameras tracks features on the walls using the on-board illumination to estimate the position along the tunnel axis unobservable to range sensors while all of the cameras are used for panoramic image construction. The two lidars estimate the remaining degrees of freedom (DOF). Outputs of the two estimators are fused using an Unscented Kalman Filter (UKF). A moderately trained operator defines waypoints using the Remote Control (RC). We demonstrate our results from Carters Dam, GA and Glen Canyon Dam, AZ which include panoramic images for cracks and rusty spot detection and 6-DOF estimation results with ground truth comparisons. To our knowledge ours is the only study that can autonomously inspect environments with no geometric cues and poor to no external illumination using MAVs.

We present an electrically modulated nonlinear metamaterial at terahertz frequencies. The device consists of a planar array of split-ring resonators (SRRs) fabricated on n-type GaAs. Increasing the incident THz field strength induces carriers in the GaAs substrate, shorting the SRR capacitive gap and modulating the MM resonance. The application of a 15V bias to the MM reduces the net field modulation by ∼60%.

We have used ultrafast THz spectroscopy to both excite and probe low energy excitations in two-dimensional electron gases, topological insulators, and multiferroics.

We describe experimental characterization of switchable terahertz metasurfaces using broadband ellipsometry. A theoretical framework based on generalized sheet transition conditions is developed to extract surface susceptibility as a function of frequency and applied DC bias. This will allow us to make a priori predictions of the performance of these switchable devices in device applications, enabling rational design of active components.

A Hybrid Electric Vehicle longitudinal dynamics model for the control of energy management is developed. The model is implemented using Simulink® and consists of a transitional vehicle speed input parameterized by, for example, the New European Driving Cycle. It is a backward looking model in that engine and motor on/off states are determined by the controller, dependent on wheel torque requirements and output targets. The objective of the simulation is to calculate tractive effort and resistance forces to determine longitudinal net vehicle force at the road. This article addresses model development and initial investigations of its dynamic behaviour in order to establish appropriate energy management strategies for the Hybrid Electric system. In particular, All Wheel Drive, Front Wheel Drive and Rear Wheel Drive drivetrain architectures are evaluated to determine minimum fuel usage and battery state of charge. The use of a logic controller allows a reduction of simulation time and ensures accurate results for charge depletion and harvesting. Simulated fuel consumption is within 1% of actual usage.

A doctor palpating with his or her fingertips can easily determine whether a patient's tissue contains a solid tumor, regardless of extraneous factors such as the patient's pose. We want to create automatic palpation tools that achieve this same level of robustness, but real tactile sensor signals are sensitive to small physical variations and tend to drift over time. To investigate ways of achieving tactile perceptual invariance, we collected two nearly identical data sets from a SynTouch BioTac palpating at 10,260 points distributed across an artificial tissue sample made from silicone rubber with rigid embedded lumps. Each palpation interaction was distilled down to 20 numbers (19 electrode impedances and one DC pressure). Fitting a multivariate Gaussian distribution to examples recorded far from the lumps achieved near-perfect accuracy, precision, and recall in recognizing other background examples recorded the same day. These models performed miserably, though, when used to classify the other data set. Inspired by robust perceptual methods from computer vision, we transformed the 20 tactile sensor array readings into 190 binary pairwise comparisons and used the log likelihood of observing a given binary pattern to determine whether an example was far from the embedded lumps. This novel approach achieved accuracy, precision, and recall of about 80% on reserved testing data from the same day and yielded roughly similar levels of accuracy and recall with excellent precision on the data set recorded on the other day. Pairwise comparisons between tactile sensor array readings may hold promise for achieving robust automatic tactile perception.

Studies have been done and continue on the design and construction of a Shashlik detector using Radiation hard quartz capillaries filled with wavelength shifting liquid to collect the scintillation light from LYSO crystals for use as a calorimeter in the Phase II CMS upgrade at CERN. The work presented here focuses on the studies of the capillaries and liquids that would best suit the purpose of the detector. Comparisons are made of various liquids, concentrations, and capillary construction techniques will be discussed.

This work demonstrates the use of depth panoramas in the construction of detailed 3D models of extended environments. The paper describes an approach to the acquisition of such panoramic images using a robotic platform that collects sequences of depth images with a commodity depth sensor. These sequences are stitched into panoramic images that efficiently capture scene information while reducing noise in the captured imagery. Scene structure is extracted from the panoramas and used to register a collection of panoramas to obtain coverage of extended areas. The presented approach maintains fine geometry detail in 3D reconstructions, uses a modest amount of memory, and enjoys excellent scaling characteristics across large environments.

This paper presents a novel approach to solving optical flow problems using a discrete, tree-structured MRF derived from a hierarchical segmentation of the image. Our method can be used to find globally-optimal matching solutions even for problems involving very large motions. Experiments demonstrate that our approach is competitive on the MPI-Sintel dataset and that it can significantly outperform existing methods on problems involving large motions.

The Gain and Phase (GAP) measurement device is a sophisticated device, which is used to measure the gain and phase of PWM converters and various generic circuits. The device excites the system with a sinusoidal test signal and measures both the time histories of the test signal and the response signal. It sweeps different frequency values giving the phase and amplitude response (Bode plot). For a PWM converter the GAP measurement device has the capability of injecting a sinusoidal modulated digital PWM signal and measuring the response of the return signal to give us the Bode plot. For PWM converter measurements, the proposed device has the flexibility of sampling response in either off-time, on-time or any other time sampling. The device additionally gives the flexibility to measure the response of the system by breaking the loop anywhere in the loop path.

We consider the problem of online completion of ill-conditioned low-rank matrices. While many matrix completion algorithms have been proposed recently, they often struggle with ill-conditioned matrices and take a long time to converge. In this paper, we present a new algorithm called Polar Incremental Matrix Completion (PIMC) to address this problem. Our method is based on the GROUSE algorithm, and we show how a polar decomposition can be used to maintain an estimate of the singular value matrix to better deal with ill-conditioned problems. The method is also online, allowing it to be applied to streaming data. We evaluate our algorithm on both synthetic data and a real "structure from motion" dataset from the computer vision community, and show that PIMC outperforms similar methods.

We present an approach for 2D sensor network localization when only bearing measurements are available and no global coordinate frame is known. Our work builds off of the linear constraint given in Kennedy et al. (2012) for sets of nodes that form triangles. We extend that constraint to general networks and present methods for locally optimizing the resulting cost function. We also show how these methods can be used for 3D network localization when the vertical axis is known. The algorithms are evaluated on both synthetic and real datasets, and we also show how they can be applied to the “structure from motion” problem in the field of computer vision.

In response to the Presidential Memorandum on Wireless Innovation and the recommendation of the Presidential Council of Advisors on Science and Technology (PCAST), the federal government has begun the process of organizing a National Advanced Spectrum and Communications Test Network (NASCTN) under the auspices of the U.S. Department of Commerce (DOC). The intent of this organization is to provide a single place for information about and access to all federally owned, operated or funded spectrum test facilities. This paper describes the NASCTN effort and the processes that are being developed to support its role as a focal point for spectrum sharing testing in the federal government.

L3 Electron Devices has developed a new Ka-band NanoMPM ® , the M1871. Like its Ku-band predecessor, the amplifier is significantly smaller than current MPM offerings yet has improved power density compared to existing MPM and SSPA products. Saturated output power is 50 W from 29 to 31 GHz with greater than 30% efficiency in a 2.5 lb package.