| Patent Number |
Title Of Patent |
Date Issued |
| 7548576 |
Self organization of wireless sensor networks using ultra-wideband radios |
June 16, 2009 |
| A novel UWB communications method and system that provides self-organization for wireless sensor networks is introduced. The self-organization is in terms of scalability, power conservation, channel estimation, and node synchronization in wireless sensor networks. The UWB receiver in |
| 7305052 |
UWB communication receiver feedback loop |
December 4, 2007 |
| A novel technique and structure that maximizes the extraction of information from reference pulses for UWB-TR receivers is introduced. The scheme efficiently processes an incoming signal to suppress different types of UWB as well as non-UWB interference prior to signal detection. Suc |
| 7277644 |
Fade-resistant forward error correction method for free-space optical communications systems |
October 2, 2007 |
| Free-space optical (FSO) laser communication systems offer exceptionally wide-bandwidth, secure connections between platforms that cannot other wise be connected via physical means such as optical fiber or cable. However, FSO links are subject to strong channel fading due to atmosphe |
| 7194019 |
Multi-pulse multi-delay (MPMD) multiple access modulation for UWB |
March 20, 2007 |
| A new modulation scheme in UWB communications is introduced. This modulation technique utilizes multiple orthogonal transmitted-reference pulses for UWB channelization. The proposed UWB receiver samples the second order statistical function at both zero and non-zero lags and matches |
| 5840040 |
Encephalolexianalyzer |
November 24, 1998 |
| The encephalolexianalyzer uses digital signal processing techniques on electroencephalograph (EEG) brain waves to determine whether or not someone is thinking about moving, e.g., tapping their fingers, or, alternatively, whether someone is actually moving, e.g., tapping their fingers |
| 5490062 |
Real-time neural network earthquake profile predictor |
February 6, 1996 |
| A neural network has been developed that uses first-arrival energy to predict the characteristics of impending earthquake seismograph signals. The propagation of ground motion energy through the earth is a highly nonlinear function. This is due to different forms of ground motion as |
| 5373486 |
Seismic event classification system |
December 13, 1994 |
| In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Self-organizing neural networks (SONNs) can be used for classifying |
