The Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is a multiyear engineering research center established by the National Science Foundation for the development of small, inexpensive, low-power radars designed to improve the scanning of the lowest levels (,3 km AGL) of the atmosphere. Instead of sensing autonomously, CASA radars are designed to operate as a network, collectively adapting to the changing needs of end users and the environment; this network approach to scanning is known as distributed collaborative adaptive sensing (DCAS). DCAS optimizes the low-level volume coverage scanning and maximizes the utility of each scanning cycle. A test bed of four prototype CASA radars was deployed in southwestern Oklahoma in 2006 and operated continuously while in DCAS mode from March through June of 2007.This paper analyzes three convective events observed during April-May 2007, during CASA's intense operation period (IOP), with a special focus on evaluating the benefits and weaknesses of CASA radar system deployment and DCAS scanning strategy of detecting and tracking low-level circulations. Data collected from nearby Weather Surveillance Radar-1988 Doppler (WSR-88D) and CASA radars are compared for mesoscyclones, misocyclones, and low-level vortices. Initial results indicate that the dense, overlapping coverage at low levels provided by the CASA radars and the high temporal (60 s) resolution provided by DCAS give forecasters more detailed feature continuity and tracking. Moreover, the CASA system is able to resolve a whole class of circulations-misocyclones-far better than the WSR-88Ds. In fact, many of these are probably missed completely by the WSR-88D. The impacts of this increased detail on severe weather warnings are under investigation. Ongoing efforts include enhancing the DCAS data quality and scanning strategy, improving the DCAS data visualization, and developing a robust infrastructure to better support forecast and warning operations.

This paper investigates the feasibility of using inexpensive, general-purpose automated methods for recognition of worker activity in manufacturing processes. A novel aspect of this study is that it is based on live data collected from an operational manufacturing cell without any guided or scripted work. Activity in a single-worker cell was recorded using the Microsoft Kinect, a commodity-priced sensor that records depth data and includes built-in functions for the detection of human skeletal positions, including the positions of all major joints. Joint position data for two workers on different shifts was used as input to a collection of learning algorithms with the goal of classifying the activities of each worker at each moment in time. Results show that unsupervised and semisupervised algorithms, such as unsupervised hidden Markov models, show little loss of accuracy compared to supervised methods trained with ground truth data. This conclusion is important because it implies that automated activity recognition can be accomplished without the use of ground truth labels, which can only be obtained by time-consuming manual review of videos. The results of this study suggest that intelligent manufacturing can now include detailed process-control measures of human workers with systems that are affordable enough to be installed permanently for continuous data collection.

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Experimental weather radars are being developed that could enhance the severe weather warning process by providing higher resolution data sensed closer to the ground and with faster update rates. Because wind speed is an important criterion in the issuance of severe thunderstorm warnings, this research investigates the impact of adding these new data to the forecaster decision-making process. In a static case review setting, 30 National Weather Service (NWS) forecasters evaluated six convective weather cases under two conditions: (1) using (conventional) WSR-88D weather radar data, and, (2) using both WSR-88D and additional data from an experimental four-radar network. Forecasters' predictions of ground level wind gusts, 2-5 min into the future, were compared to measurements from ground-based wind sensors. When provided with the additional radar data participants significantly improved the accuracy of their wind speed assessments (absolute error reduced from 5.9 m s −1 to 4.0 m s −1 ; p < 0.001), increased their assessment confidence ratings (p < 0.001), forecasted significantly greater wind speeds (20.4 m s −1 as opposed to 17.1 m s −1 ; p < 0.001), and increased the number of affirmative decisions to warn from 15 to 35 (p = 0.001). While the addition of high resolution, low altitude, rapidly updating radar data is shown to have both qualitative and quantitative benefits, training and warning policy implications for the incorporation of new technology must also be carefully considered as increased accuracy, confidence and higher wind speed estimates may lead to more warnings.

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