Radiobots: Architecture, Algorithms and Realtime Reconfigurable Antenna Designs for Autonomous, Self-learning Future Cognitive Radios, Sudharman Jayaweera and Christos Christodoulou Stochastic Control for Smart Grid with Integrated Renewable Distributed Generators, Sudharman Jayaweera
JáJáĪ Synthesis of Gradient and Hamiltonian Dynamics Applied to Learning in Neural Networks, J.W. JáJáĪ Randomized Parallel Sorting Algorithm With an Experimental Study, D.R. The Neural Representation of Concepts at the Sensor Level, Michael John Healy and Thoms Preston CaudellĪ Categorical Model for Faceted Ontologies with Data Repositories, Michael Healy, Renzo Sanchez-Silva, and Thomas CaudellĪ New Deterministic Parallel Sorting Algorithm With an Experimental Evaluation, D.R. Neural Networks, Knowledge and Cognition: A Mathematical Semantic Model Based upon Category Theory, Michael John Healy and Thomas Preston Caudell
Temporal Sequencing via Supertemplates, Michael Healy and Thomas CaudellĪ Model of Human Categorization and Similarity Based Upon Category Theory, Michael Healy, Thomas Caudell, and Timothy GoldsmithĮpisodic Memory via Spans and Cospans: A Hierarchy of Spatiotemporal Colimits, Michael John Healy MS and Thomas Preston Caudell PhD Malveaux, Manish Bhattarai, Ramiro Jordan, and Manel Martinez-Ramon Mesh node communication system for fire figthers, Eric E.
KovatchĪn Emotion Model for Video Game AI, Thomas Caudell and Anthony Campisi Illendulaĭesign and Analysis of the Alliance / University of New Mexico Roadrunner Linux SMP SuperCluster, D.A. BaderĪn Experimental Comparison of Parallel Algorithms for Ear Decomposition of Graphs using Two Leading Paradigms, D.A. SIMPLE: A Methodology for Programming High Performance Algorithms on Clusters of Symmetric Multiprocessors (SMPs) (Preliminary Version), D.A. BaderĪ Practical Parallel Algorithm for Cycle Detection in Partitioned Digraphs, D.A. YangĪn Improved Randomized Selection Algorithm With an Experimental Study, D.A. AbdallahĪpplications of Quantifier Elimination Theory to Control Theory, C.T. New Approaches to High Power Microwave Computation and Experimentation, Chaouki T. The Time-to-Graduation Problem (Survival Analysis for Education Outcomes), Don R. Non-negative Quadratic Programming Total Variation Regularization for Poisson Vector-Valued Image Restoration, Paul A. Towards Formalizing Network Architectural Descriptions, Joud Khoury, Chaouki Abdallah, and Gregory Heileman Towards a Taxonomy of Inter-network Architectures, Joud Khoury and Chaouki Abdallah Pre Incident Indicator Analysis (PIIA) System, Frank Gilfeather Throughput Optimization inWireless Networks with Multi-packet, Jorge Crichigno, Min-Yo Wu, and Wei Shu Our results show that both detection schemes can be used to achieve high-performance vibrating-object detectors.A Dynamic Programming Approach for Routing in Wireless Mesh Networks, Jorge Crichigno, Joud Khoury, Min-Yo Wu, and Wei Shu The proposed detection algorithms are tested using both simulated and real SAR data. The second scheme is model-based, and uses a probabilistic model of the slow-time SAR signal, the Karhunen-Loeve expansion, and a likelihood-ratio detector. The first scheme is data-driven and utilizes features extracted with the help of the discrete fractional Fourier transform (DFrFT) to feed a random-forest detector. Two different vibration detection schemes are investigated. This paper focuses on the detection of vibrating objects by exploiting the phase modulation that a vibration causes in the received slow-time SAR data. This renders their use as unpractical for exploratory applications. However, these algorithms tend to be computationally demanding and, in addition, require knowledge of the exact location of the object a priori. Recently, synthetic aperture radar (SAR) has proven to be a versatile tool capable of performing vibrometry and high-precision vibration-estimation algorithms have been developed for reconstructing surface vibration waveforms from SAR images. Moreover, since vibration signatures observed from the exterior surfaces of structures are intrinsically linked to the type of machinery operating inside of them, the ability to monitor vibrations remotely can facilitate the detection and identification of the machinery. The vibratory response of buildings and machines carries key information that can be exploited to infer their operating conditions and to diagnose failures.
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