Photonic crystal slabs for low-cost biosensors

Biosensors are devices that utilize biological recognition elements to selectively detect and analyze specific biological and chemical analyte substances. In this work a technology platform for label-free optical biosensors based on surface-functionalized photonic crystal slabs is proposed. Using this technology platform, low-cost solutions for three biotechnical questions are presented

Knowledge visualization: From theory to practice

Visualizations have been known as efficient tools that can help users analyze com- plex data. However, understanding the displayed data and finding underlying knowl- edge is still difficult. In this work, a new approach is proposed based on understanding the definition of knowledge. Although there are many definitions used in different ar- eas, this work focuses on representing knowledge as a part of a visualization and showing the benefit of adopting knowledge representation. Specifically, this work be- gins with understanding interaction and reasoning in visual analytics systems, then a new definition of knowledge visualization and its underlying knowledge conversion processes are proposed. The definition of knowledge is differentiated as either explicit or tacit knowledge. Instead of directly representing data, the value of the explicit knowledge associated with the data is determined based on a cost/benefit analysis. In accordance to its importance, the knowledge is displayed to help the user under- stand the complex data through visual analytical reasoning and discovery

Information Outlook, August 2006

Volume 10, Issue 8https://scholarworks.sjsu.edu/sla_io_2006/1007/thumbnail.jp

Doctor of Philosophy

dissertationIn light of epidemic levels of self-objectification leading to a host of negative consequences for girls and women, intervention is crucial. This study in Self-Objectification Resilience (SOR) implemented a necessary next step in critical feminist scholarship by identifying emancipatory alternatives to the chronic experiences of female objectification and self-objectification. To investigate the successful promotion and cultivation of Self-Objectification Resilience through a model and intervention designed for this study, 50 women ages 18 to 35 completed a confidential, 4-week, online study. Based on a broad meta-analysis of research in self-objectification and resilience, as well as the analysis of the present study's intervention feedback, four important resilient traits most directly combat the negative consequences of self-objectification: self-actualization, self-compassion, embodied empowerment, and feminist beliefs. The feedback gleaned from study participants proved invaluable to the SOR research agenda; it contributed to research on the dismal state of female body image, with robust, qualitative data revealing 50% of study participants "hated" or were "severely dissatisfied" with their bodies and another 34% reported to be "generally dissatisfied." Results contributed important information on the epidemic of self-objectification, with 70% of participants reporting detailed experiences of currently isolating themselves from everyday life, including school, sexual intimacy, and physical activity, due to body shame. The 9 participants out of 50 who reported positive body satisfaction reflected and reinforced vital themes of the SOR model; they had experienced extremely painful "disruptions" that worked as a catalyst to greater self-objectification resilience. More than half had overcome an eating disorder or had loved ones who were presently battling one. In all, the present study on Self-Objectification Resilience contributes important research toward understanding how positive adaptation can be possible to provide emancipation for girls and women from the bodily prison of self-objectification

Enhanced online programming for industrial robots

The use of robots and automation levels in the industrial sector is expected to grow, and is driven by the on-going need for lower costs and enhanced productivity. The manufacturing industry continues to seek ways of realizing enhanced production, and the programming of articulated production robots has been identified as a major area for improvement. However, realizing this automation level increase requires capable programming and control technologies. Many industries employ offline-programming which operates within a manually controlled and specific work environment. This is especially true within the high-volume automotive industry, particularly in high-speed assembly and component handling. For small-batch manufacturing and small to medium-sized enterprises, online programming continues to play an important role, but the complexity of programming remains a major obstacle for automation using industrial robots. Scenarios that rely on manual data input based on real world obstructions require that entire production systems cease for significant time periods while data is being manipulated, leading to financial losses. The application of simulation tools generate discrete portions of the total robot trajectories, while requiring manual inputs to link paths associated with different activities. Human input is also required to correct inaccuracies and errors resulting from unknowns and falsehoods in the environment. This study developed a new supported online robot programming approach, which is implemented as a robot control program. By applying online and offline programming in addition to appropriate manual robot control techniques, disadvantages such as manual pre-processing times and production downtimes have been either reduced or completely eliminated. The industrial requirements were evaluated considering modern manufacturing aspects. A cell-based Voronoi generation algorithm within a probabilistic world model has been introduced, together with a trajectory planner and an appropriate human machine interface. The robot programs so achieved are comparable to manually programmed robot programs and the results for a Mitsubishi RV-2AJ five-axis industrial robot are presented. Automated workspace analysis techniques and trajectory smoothing are used to accomplish this. The new robot control program considers the working production environment as a single and complete workspace. Non-productive time is required, but unlike previously reported approaches, this is achieved automatically and in a timely manner. As such, the actual cell-learning time is minimal

Unified Role Assignment Framework For Wireless Sensor Networks

Wireless sensor networks are made possible by the continuing improvements in embedded sensor, VLSI, and wireless radio technologies. Currently, one of the important challenges in sensor networks is the design of a systematic network management framework that allows localized and collaborative resource control uniformly across all application services such as sensing, monitoring, tracking, data aggregation, and routing. The research in wireless sensor networks is currently oriented toward a cross-layer network abstraction that supports appropriate fine or course grained resource controls for energy efficiency. In that regard, we have designed a unified role-based service paradigm for wireless sensor networks. We pursue this by first developing a Role-based Hierarchical Self-Organization (RBSHO) protocol that organizes a connected dominating set (CDS) of nodes called dominators. This is done by hierarchically selecting nodes that possess cumulatively high energy, connectivity, and sensing capabilities in their local neighborhood. The RBHSO protocol then assigns specific tasks such as sensing, coordination, and routing to appropriate dominators that end up playing a certain role in the network. Roles, though abstract and implicit, expose role-specific resource controls by way of role assignment and scheduling. Based on this concept, we have designed a Unified Role-Assignment Framework (URAF) to model application services as roles played by local in-network sensor nodes with sensor capabilities used as rules for role identification. The URAF abstracts domain specific role attributes by three models: the role energy model, the role execution time model, and the role service utility model. The framework then generalizes resource management for services by providing abstractions for controlling the composition of a service in terms of roles, its assignment, reassignment, and scheduling. To the best of our knowledge, a generic role-based framework that provides a simple and unified network management solution for wireless sensor networks has not been proposed previously

Human-Machine Communication: Complete Volume. Volume 2

This is the complete volume of HMC Volume 2