Survey Automation: Report and Workshop Proceedings

Product Description
For over 100 years, the evolution of modern survey methodology using the theory of representative sampling to make interferences from a part of the population to the whole has been paralleled by a drive toward automation, harnessing technology and computerization to make parts of the survey process easier, faster, and better. The availability of portable computers in the late 1980s ushered in computer-assisted personal interviewing (CAPI), in which interviewers administer a survey instrument to respondents using a computerized version of the questionnaire on a portable laptop computer. Computer assisted interviewing (CAI) methods have proven to be extremely useful and beneficial in survey administration. However, the practical problems encountered in documentation and testing CAI instruments suggest that this is an opportune time to reexamine not only the process of developing CAI instruments but also the future directions of survey automation writ large.

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The Effects of Highlighting, Validity, and Feature Type on Air-to-GroundTarget Acquisition Performance

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This is a AIR FORCE INST OF TECH WRIGHT-PATTERSONAFB OH report procured by the Pentagon and made available for public release. It has been reproduced in the best form available to the Pentagon. It is not spiral-bound, but rather assembled with Velobinding in a soft, white linen cover. The Storming Media report number is A747053. The abstract provided by the Pentagon follows: Air-to-ground target acquisition is an important part of any flight task, whether during navigation, landing at an airport, or during military bombing missions. This task usually involves visual searches to cross-check between a map and the forward field of view (FFOV). Previous research has implied that cultural (man-made) targets and lead-in features may yield better performance than natural targets and lead-in features. Also, since the target acquisition task often involves visual searches in complex visual fields (for both the map and the FFOV), previous research suggests that highlighting a target on the map can automate the search process by increasing the target’s salience. However, as with any automation, the possibility of failure exists and is often associated with drastic consequences. This study examined the differences in performance between natural and cultural feature types as targets and lead-in features under highlighted and nonhighlighted conditions. Also, performance under highlighted and nonhighlighted conditions were compared to determine if highlighting did facilitate the target acquisition task. When targets were highiighted, the highlighting was either valid, invalid with the wrong target highlighted, or invalid with the wrong target highlighted and the correct target absent from the FFOV. The target highlighting occurred under 60% validity conditions; therefore, in some cases, a nearby lead-in feature was highlighted (always validly) to minimize or eliminate any costs of highlighting invalidity.

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Automated Pick-and-Place of 30µm glass sphere

Automated Pick & Place of glass spheres with a diameter of 30µm. We presented this microassembly setup on the 2006 Hannover Fair International (HMI). The video demonstrates the major steps needed for successfull automation on the microscale.

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