Photonic integrated circuit for all-optical millimeter-wave signal generation

Generation of millimeter-wave electronic signals and power is required for high-frequency communication links, RADAR, remote sensing and other applications. However, in the 30 to 300 GHz mm-wave regime, signal sources are bulky and inefficient. All-optical generation of mm-wave signals promises to improve efficiency to as much as 30 to 50 percent with output power as high as 100 mW. All of this may be achieved while taking advantage of the benefits of monolithic integration to reduce the overall size to that of a single semiconductor chip only a fraction of a square centimeter in size. This report summarizes the development of the first monolithically integrated all-optical mm-wave signal generator ever built. The design integrates a mode-locked semiconductor ring diode laser with an optical amplifier and high-speed photodetector into a single optical integrated circuit. Frequency generation is demonstrated at 30, 60 and 90 Ghz

Autonomous Robotics at Trinity University (A.R.T.U.) IEEE Robotics Competition

This senior design project sought to construct an autonomous robot such that it would be capable of competing in the 2006 IEEE Region 5 robotics competition. This required the robot to locate and distribute a red, blue, green, and yellow can to its appropriate position based on the color of the can, all within a 3 minute time limit. The purpose of this report is to describe the design goals and specifications, outline the procedures taken to design the hardware and software of the robot, and to relate all results and conclusions for the project. At the competition, the robot successfully delivered all four cans to the correct locations, avoided hitting any human workers, completed the course with a time of 2.08 minutes (125 seconds), and received the 4th position for the competition. Based on these results, the project was a success as it met all design criteria. For future improvements, the group recommends using DC motors instead of servos in the drive system to achieve faster speeds on the course, and an automated calibration procedure would also be very useful in future projects

Nuclear Fuel Cycle Reasoner: PNNL FY13 Report

In Fiscal Year 2012 (FY12) PNNL implemented a formal reasoning framework and applied it to a specific challenge in nuclear nonproliferation. The Semantic Nonproliferation Analysis Platform (SNAP) was developed as a preliminary graphical user interface to demonstrate the potential power of the underlying semantic technologies to analyze and explore facts and relationships relating to the nuclear fuel cycle (NFC). In Fiscal Year 2013 (FY13) the SNAP demonstration was enhanced with respect to query and navigation usability issues

Nuclear Fuel Cycle Reasoner: PNNL FY12 Report

Building on previous internal investments and leveraging ongoing advancements in semantic technologies, PNNL implemented a formal reasoning framework and applied it to a specific challenge in nuclear nonproliferation. The Semantic Nonproliferation Analysis Platform (SNAP) was developed as a preliminary graphical user interface to demonstrate the potential power of the underlying semantic technologies to analyze and explore facts and relationships relating to the nuclear fuel cycle (NFC). In developing this proof of concept prototype, the utility and relevancy of semantic technologies to the Office of Defense Nuclear Nonproliferation Research and Development (DNN R&D) has been better understood

Foramen ovale blood flow and cardiac function after main pulmonary artery occlusion in fetal sheep

The foramen ovale (FO) accounts for the majority of fetal left ventricular (LV) output. Increased right ventricular afterload can cause a redistribution of combined cardiac output between the ventricles. To understand the capability of the FO to increase its volume blood flow and thus LV output, we mechanically occluded the main pulmonary artery in seven chronically instrumented near-term sheep fetuses. We hypothesized that FO volume blood flow and LV output would increase during main pulmonary artery occlusion. Fetal cardiac function and haemodynamics were assessed by pulsed and tissue Doppler at baseline, 15 and 60 min after occlusion of the main pulmonary artery and 15 min after occlusion was released. Fetal ascending aorta and central venous pressures and blood gas values were monitored. Main pulmonary artery occlusion initially increased fetal heart rate (P <0.05) from [mean (SD)] 158 (7) to 188 (23) beats min(-1) and LV cardiac output (P <0.0001) from 629 (198) to 776 (283) ml min(-1). Combined cardiac output fell (P <0.0001) from 1524 (341) to 720 (273) ml min(-1). During main pulmonary artery occlusion, FO volume blood flow increased (P <0.001) from 507 (181) to 776 (283) ml min(-1). This increase was related to fetal tachycardia, because LV stroke volume did not change. Fetal ascending aortic blood pressure remained stable. Central venous pressure was higher (P <0.05) during the occlusion than after it was released. During the occlusion, fetal pH decreased and PCO2 increased. Left ventricular systolic dysfunction developed while LV diastolic function was preserved. Right ventricular systolic and diastolic function deteriorated after the occlusion. In conclusion, the FO has a limited capacity to increase its volume blood flow at near-term gestation.Peer reviewe

Ontological Annotation with WordNet

Semantic Web applications require robust and accurate annotation tools that are capable of automating the assignment of ontological classes to words in naturally occurring text (ontological annotation). Most current ontologies do not include rich lexical databases and are therefore not easily integrated with word sense disambiguation algorithms that are needed to automate ontological annotation. WordNet provides a potentially ideal solution to this problem as it offers a highly structured lexical conceptual representation that has been extensively used to develop word sense disambiguation algorithms. However, WordNet has not been designed as an ontology, and while it can be easily turned into one, the result of doing this would present users with serious practical limitations due to the great number of concepts (synonym sets) it contains. Moreover, mapping WordNet to an existing ontology may be difficult and requires substantial labor. We propose to overcome these limitations by developing an analytical platform that (1) provides a WordNet-based ontology offering a manageable and yet comprehensive set of concept classes, (2) leverages the lexical richness of WordNet to give an extensive characterization of concept class in terms of lexical instances, and (3) integrates a class recognition algorithm that automates the assignment of concept classes to words in naturally occurring text. The ensuing framework makes available an ontological annotation platform that can be effectively integrated with intelligence analysis systems to facilitate evidence marshaling and sustain the creation and validation of inference models