How can you combine professional-quality research with discovery-based undergraduate education? The UCLA Undergraduate Consortium for Functional Genomics provides the answer

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Abstract-A broad range of embedded networked sensor (ENS) systems for critical environmental monitoring applications now require complex, high peak power dissipating sensor devices, as well as on-demand high performance computing and high bandwidth communication. Embedded computing demands for these new platforms include support for computationally intensive image and signal processing as well as optimization and statistical computing. To meet these new requirements while maintaining critical support for low energy operation, a new multiprocessor node hardware and software architecture, Low Power Energy Aware Processing (LEAP), has been developed. This architecture integrates fine-grained energy dissipation monitoring and sophisticated power control scheduling for all subsystems including sensor subsystems. The LEAP architecture enables complex energy-aware algorithm design by providing a simple interface to control numerous platform and sensor power modes and report detailed energy usage information. This paper also describes experimental results of a new distributed node testbed based on LEAP demonstrating that by exploiting high energy efficiency components and enabling proper on-demand scheduling, the LEAP architecture meets both sensing performance and energy dissipation objectives for a broad class of applications. This testbed including the network of distributed LEAP nodes and a system producing physical, mobile events provides a development environment for LEAP-hosted algorithms. New design principles, detailed implementation, and in-network programming and remote debugging capabilities of this platform are also described. While this is the first report of the LEAP system, it has been deployed for nearly one year with 50 users developing energy aware systems.Keywords-embedded wireless networked sensor, energy-aware multprocessor platform, sensor platform hardware and software architecture

Using a large consortium of undergraduate students in an organized program at the University of California, Los Angeles (UCLA), we have undertaken a functional genomic screen in the Drosophila eye. In addition to the educational value of discovery-based learning, this article presents the first comprehensive genomewide analysis of essential genes involved in eye development. The data reveal the surprising result that the X chromosome has almost twice the frequency of essential genes involved in eye development as that found on the autosomes.

Falls are currently a leading cause of death from injury in the elderly. The usage of the conventional assistive cane devices is critical in reducing the risk of falls and is relied upon by over 4 million patients in the U.S.. While canes provide physical support as well as supplementary sensing feedback to patients, at the same time, these conventional aids also exhibit serious adverse effects that contribute to falls. The falls due to the improper usage of the canes are particularly acute in the elderly and disabled where reduced cognitive capacity accompanied by the burden of managing cane motion leads to increased risk. This paper describes the development of the SmartCane assistive system that encompasses broad engineering challenges that will impact general development of individualized, robust assistive and prosthetic devices. The SmartCane system combines advances in signal processing, embedded computing, and wireless networking technology to provide capabilities for remote monitoring, local signal processing, and real-time feedback on the cane usage. This system aims to reduce risks of injuries and falls by enabling training and guidance of patients in proper usage of assistive devices.