Deep Neural Networks (DNNs)

Deep Neural Networks (DNNs) Deep Neural Networks (DNNs) are extremely powerful in performing machine learning tasks including image classification, speech recognition, and speech coding . However, training DNNs is computationally difficult. In particular, fine tuning DNNs requires stochastic gradient descent, which is unusually difficult to parallelize across machines. This lack … Continue Reading →

An Energy-Efficient Ant-Based Routing Algorithm for Wireless Sensor Networks

An Energy-Efficient Ant-Based Routing Algorithm for Wireless Sensor Networks ABSTRACT An Energy-Efficient Ant-Based Routing Algorithm for Wireless Sensor Networks.Optimal path selection in wireless sensor networks (WSNs) is one of the challenging tasks. Several efficient routing protocols are proposed for specific scenarios to achieve particular objectives in WSN. However, such networks … Continue Reading →

Combining Admission and Modulation Decisions for Wireless Embedded Systems

Combining admission and Modulation Decisions for Wireless Embedded Systems The advent of cognitive radio technology has enabled dramatically more options in the use of RF spectrum, allowing multiple transmitters to effectively share spectrum in ways that were previously unavailable (either due to technical limitations or regulatory restrictions). In this dissertation, … Continue Reading →

MIMO RADAR DIVERSITY WITH NEYMAN-PEARSON SIGNAL DETECTION IN NON-GAUSSIAN CIRCUMSTANCE WITH NON-ORTHOGONAL WAVEFORMS

MIMO RADAR DIVERSITY WITH NEYMAN-PEARSON SIGNAL DETECTION IN NON-GAUSSIAN CIRCUMSTANCE WITH NON-ORTHOGONAL WAVEFORMS: The diversity gain of a multiple-input multiple-output (MIMO) system adopting the Neyman-Pearson (NP) criterion is derived for a signal-present versus signal-absent scalar hypothesis test statistic and for a vector signal-present versus signal-absent hypothesis testing problem. The results are … Continue Reading →