Structural optimization of Z-axis tuning-fork MEMS gyroscopes for enhancing reliability and resolution

Inertial sensors such as gyroscopes and accelerometers are normally considered as the most important sensors in a navigation system. Especially in the underwater or under-ice applications, the accuracy of the entire navigation system has to mainly rely on the precision of the inertial sensors due to inapplicability of global positioning systems. For MEMS-based inertial sensors, fabrication variation and environmental disturbance are among the major error sources. To address these challenges, in this paper we propose an optimization methodology by using parametric analysis on a reference design for improving sensor reliability and resolution. Apart from studying the resolution improvement by deploying an alternative sensing scheme, the effects of changing location, shape, and size of critical cantilevers have been thoroughly explored. By using this method, we have derived an improved mechanical structure for tuning-fork gyroscopes. Our numerical analyses show that the bandwidth of the proposed structure, which is the most important stability measure in the vibratory gyroscopes with slightly mismatched resonant frequencies, is over 1.7 times more immune to fabrication imperfection than the other structural alternatives. The drive and sense resonant amplitude robustness against fabrication imperfection is also improved in the proposed structure. In addition, this structure is able to provide at least 2.3 times larger sense-mode capacitance response to external rotation compared to the previously published designs. More important, it is observed that there is always non-negligible room for improving performance of gyroscopes if our proposed structural optimization methodology is integrated into the conventional MEMS-based inertial sensor design flow.     

Use of a dark object concept and support vector machines to automate forest cover change analysis

An automated method was developed for mapping forest cover change using satellite remote sensing data sets. This multi-temporal classification method consists of a training data automation (TDA) procedure and uses the advanced support vector machines (SVM) algorithm. The TDA procedure automatically generates training data using input satellite images and existing land cover products. The derived high quality training data allow the SVM to produce reliable forest cover change products. This approach was tested in 19 study areas selected from major forest biomes across the globe. In each area a forest cover change map was produced using a pair of Landsat images acquired around 1990 and 2000. High resolution IKONOS images and independently developed reference data sets were available for evaluating the derived change products in 7 of those areas. The overall accuracy values were over 90% for 5 areas, and were 89.4% and 89.6% for the remaining two areas. The user's and producer's accuracies of the forest loss class were over 80% for all 7 study areas, demonstrating that this method is especially effective for mapping major disturbances with low commission errors. IKONOS images were also available in the remaining 12 study areas but they were either located in non-forest areas or in forest areas that did not experience forest cover change between 1990 and 2000. For those areas the IKONOS images were used to assist visual interpretation of the Landsat images in assessing the derived change products. This visual assessment revealed that for most of those areas the derived change products likely were as reliable as those in the 7 areas where accuracy assessment was conducted. The results also suggest that images acquired during leaf-off seasons should not be used in forest cover change analysis in areas where deciduous forests exist. Being highly automatic and with demonstrated capability to produce reliable change products, the TDA-SVM method should be especially useful for quantifying forest cover change over large areas.     

A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS

Investigating the temporal and spatial pattern of landscape disturbances is an important requirement for modeling ecosystem characteristics, including understanding changes in the terrestrial carbon cycle or mapping the quality and abundance of wildlife habitats. Data from the Landsat series of satellites have been successfully applied to map a range of biophysical vegetation parameters at a 30 m spatial resolution; the Landsat 16 day revisit cycle, however, which is often extended due to cloud cover, can be a major obstacle for monitoring short term disturbances and changes in vegetation characteristics through time.The development of data fusion techniques has helped to improve the temporal resolution of fine spatial resolution data by blending observations from sensors with differing spatial and temporal characteristics. This study introduces a new data fusion model for producing synthetic imagery and the detection of changes termed Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH). The algorithm is designed to detect changes in reflectance, denoting disturbance, using Tasseled Cap transformations of both Landsat TM/ETM and MODIS reflectance data. The algorithm has been tested over a 185 × 185 km study area in west-central Alberta, Canada. Results show that STAARCH was able to identify spatial and temporal changes in the landscape with a high level of detail. The spatial accuracy of the disturbed area was 93% when compared to the validation data set, while temporal changes in the landscape were correctly estimated for 87% to 89% of instances for the total disturbed area. The change sequence derived from STAARCH was also used to produce synthetic Landsat images for the study period for each available date of MODIS imagery. Comparison to existing Landsat observations showed that the change sequence derived from STAARCH helped to improve the prediction results when compared to previously published data fusion techniques.     

Using ICESat's Geoscience Laser Altimeter System (GLAS) to assess large-scale forest disturbance caused by hurricane Katrina

In 2005, hurricane Katrina resulted in a large disturbance to U.S. forests. Recent estimates of damage from hurricane Katrina have relied primarily on optical remote sensing and field data. This paper is the first large-scale study to use satellite-based lidar data to quantify changes in forest structure from that event. GLAS data for the years prior to and following hurricane Katrina were compared to wind speed, forest cover, and damage data to assess the adequacy of sensor sampling, and to estimate changes in Mean Canopy Height (MCH) over all areas that experienced tropical force winds and greater. Statistically significant decreases in MCH post-Katrina were found to increase with wind intensity: Tropical Storm ?MCH = − 0.5 m, Category 1 ?MCH = − 2 m, and Category 2 ?MCH = − 4 m. A strong relationship was also found between changes in non-photosynthetic vegetation (?NPV), a metric previously shown to be related to storm damage, and post-storm MCH. The season of data acquisition was shown to influence calculations of MCH and MCH loss, but did not preclude the detection of major large-scale patterns of damage. Results from this study show promise for using space-borne lidar for large-scale assessments of forest disturbance, and highlight the need for future data on vegetation structure from space.     

Analysis of Clinic Layouts and Patient-Centered Procedural Innovations Using Discrete-Event Simulation

Abstract

Given that healthcare costs in the U.S. continue to climb, engineering managers can help address this situation by identifying designs for cost savings. Using discrete-event simulation, the layout of four different healthcare clinic scenarios considered in a redesign program named Patient Aligned Care Teams were studied. Model 1 is an existing layout in many clinics and Model 2 is staff-centered, while Models 3 and 4 provide additional space including common hallway(s). Our main conclusion is that Model 4 reduces patient travel distances and average queueing waits, while also providing the best privacy in accordance with HIPAA and the most security for the staff.     

Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations

Like other human-induced landcover changes, urbanization represents a response to specific economic, demographic, or environmental conditions. We use the Washington D.C. area as a case study to relate satellite-derived estimates of urban growth to these economic and demographic drivers. Using the Landsat data archive we have created a three epoch timeseries for urban growth for the period 1973-1996. This map is based on a NDVI-differencing approach for establishing urban change filtered with a landcover classification to minimize confusion with agriculture. Results show that the built-up area surrounding Washington DC has expanded at a rate of ～22km² per year during this period, with notably higher growth during the late-1980s. Comparisons with census data indicate that the physical growth of the urban plan, observable from space, can be reasonably correlated with regional and national economic patterns.     

Cognitive behavioral therapy for 4- to 7-year-old children with anxiety disorders: A randomized clinical trial.

Objective: To examine the efficacy of a developmentally appropriate parent–child cognitive behavioral therapy (CBT) protocol for anxiety disorders in children ages 4–7 years. Method: Design: Randomized wait-list controlled trial. Conduct: Sixty-four children (53% female, mean age 5.4 years, 80% European American) with anxiety disorders were randomized to a parent–child CBT intervention (n = 34) or a 6-month wait-list condition (n = 30). Children were assessed by interviewers blind to treatment assignment, using structured diagnostic interviews with parents, laboratory assessments of behavioral inhibition, and parent questionnaires. Analysis: Chi-square analyses of outcome rates and linear and ordinal regression of repeated measures, examining time by intervention interactions. Results: The response rate (much or very much improved on the Clinical Global Impression Scale for Anxiety) among 57 completers was 69% versus 32% (CBT vs. controls), p     

Study on multi‐objective optimization of load dispatch including renewable energy and CCS technologies

This paper mainly studies the multi‐objective optimization of load dispatch of power systems including renewable energy and CO₂ capture and storage (CCS) technologies. The improved environmental/economic load dispatch model for the power system is constructed, considering the renewable energy utilization and CCS technologies. A novel singular weighted method (SWM) has been proposed in this paper for solving this kind of multi‐objective and multi‐constraint optimization problem. A power system with five generators has been applied in one case study to test the model and SWM. It was concluded that the share that each unit takes is not linear; however, the optimal results are largely relevant to the characteristics of the units. In addition, the research results showed that with the increment of the weight coefficient for a certain objective function, the optimization result was closer to the single optimization result for that objective function; and with the increase of forecast demand load, a 35 MW wind energy unit and a 200 MW water energy unit should be built. Copyright © 2009 John Wiley & Sons, Ltd.     

Mapping impervious surface expansion using medium-resolution satellite image time series: a case study in the Yangtze River Delta,China

Cities have been expanding rapidly worldwide, especially over the past few decades. Mapping the dynamic expansion of impervious surface in both space and time is essential for an improved understanding of the urbanization process, land-cover and land-use change, and their impacts on the environment. Landsat and other medium-resolution satellites provide the necessary spatial details and temporal frequency for mapping impervious surface expansion over the past four decades. Since the US Geological Survey opened the historical record of the Landsat image archive for free access in 2008, the decades-old bottleneck of data limitation has gone. Remote-sensing scientists are now rich with data, and the challenge is how to make best use of this precious resource. In this article, we develop an efficient algorithm to map the continuous expansion of impervious surface using a time series of four decades of medium-resolution satellite images. The algorithm is based on a supervised classification of the time-series image stack using a decision tree. Each imerpervious class represents urbanization starting in a different image. The algorithm also allows us to remove inconsistent training samples because impervious expansion is not reversible during the study period. The objective is to extract a time series of complete and consistent impervious surface maps from a corresponding times series of images collected from multiple sensors, and with a minimal amount of image preprocessing effort. The approach was tested in the lower Yangtze River Delta region, one of the fastest urban growth areas in China. Results from nearly four decades of medium-resolution satellite data from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper plus (ETM+) and China–Brazil Earth Resources Satellite (CBERS) show a consistent urbanization process that is consistent with economic development plans and policies. The time-series impervious spatial extent maps derived from this study agree well with an existing urban extent polygon data set that was previously developed independently. The overall mapping accuracy was estimated at about 92.5% with 3% commission error and 12% omission error for the impervious type from all images regardless of image quality and initial spatial resolution.