Recurrent Neural Networks and Matrix Methods for Cognitive Radio Spectrum Prediction and Security

In this work, machine learning tools, including recurrent neural networks (RNNs), matrix completion, and non-negative matrix factorization (NMF), are used for cognitive radio problems. Specifically addressed are a missing data problem and a blind signal separation problem. A specialized RNN called Cellular Simultaneous Recurrent Network (CSRN), typically used in image processing applications, has been modified. The CRSN performs well for spatial spectrum prediction of radio signals with missing data. An algorithm called soft-impute for matrix completion used together with an RNN performs well for missing data problems in the radio spectrum time-frequency domain. Estimating missing spectrum data can improve cognitive radio efficiency. An NMF method called tuning pruning is used for blind source separation of radio signals in simulation. An NMF optimization technique using a geometric constraint is proposed to limit the solution space of blind signal separation. Both NMF methods are promising in addressing a security problem known as spectrum sensing data falsification attack

Transfer Learning Using Infrared and Optical Full Motion Video Data for Gender Classification

This work is a review and extension of our ongoing research in human recognition analysis using multimodality motion sensor data. We review our work on hand crafted feature engineering for motion capture skeleton (MoCap) data, from the Air Force Research Lab for human gender followed by depth scan based skeleton extraction using LIDAR data from the Army Night Vision Lab for person identification. We then build on these works to demonstrate a transfer learning sensor fusion approach for using the larger MoCap and smaller LIDAR data for gender classification

Physician and Clinical Integration Among Rural Hospitals

The pressures for closer alignment between physicians and hospitals in both rural and urban areas are increasing. This study empirically specifies independent dimensions of physician and clinical integration and compares the extent to which such activities are practiced between rural and urban hospitals and among rural hospitals in different organizational and market contexts. Results suggest that both rural and urban hospitals practice physician integration, although each emphasizes different types of strategies. Second, urban hospitals engage in clinical integration with greater frequency than their rural counterparts. Finally, physician integration approaches in rural hospitals are more common among larger rural hospitals, those proximate to urban facilities, those with system affiliations, and those not under public control.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72074/1/j.1748-0361.1998.tb00637.x.pd

Track E Implementation Science, Health Systems and Economics

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138412/1/jia218443.pd

Survey on Deep Neural Networks in Speech and Vision Systems

This survey presents a review of state-of-the-art deep neural network architectures, algorithms, and systems in speech and vision applications. Recent advances in deep artificial neural network algorithms and architectures have spurred rapid innovation and development of intelligent speech and vision systems. With availability of vast amounts of sensor data and cloud computing for processing and training of deep neural networks, and with increased sophistication in mobile and embedded technology, the next-generation intelligent systems are poised to revolutionize personal and commercial computing. This survey begins by providing background and evolution of some of the most successful deep learning models for intelligent speech and vision systems to date. An overview of large-scale industrial research and development efforts is provided to emphasize future trends and prospects of intelligent speech and vision systems. Robust and efficient intelligent systems demand low-latency and high fidelity in resource-constrained hardware platforms such as mobile devices, robots, and automobiles. Therefore, this survey also provides a summary of key challenges and recent successes in running deep neural networks on hardware-restricted platforms, i.e. within limited memory, battery life, and processing capabilities. Finally, emerging applications of speech and vision across disciplines such as affective computing, intelligent transportation, and precision medicine are discussed. To our knowledge, this paper provides one of the most comprehensive surveys on the latest developments in intelligent speech and vision applications from the perspectives of both software and hardware systems. Many of these emerging technologies using deep neural networks show tremendous promise to revolutionize research and development for future speech and vision systems