Dennis Lee Tebbe

3D heterogeneous sensor system on a chip for defense and security applications

This paper describes a new concept for ultra-small, ultra-compact, unattended multi-phenomenological sensor systems for rapid deployment, with integrated classification-and-decision-information extraction capability from a sensed environment. We discuss a unique approach, namely a 3-D Heterogeneous System on a Chip (HSoC) in order to achieve a minimum 10X reduction in weight, volume, and power and a 10X or greater increase in capability and reliability -- over the alternative planar approaches. These gains will accrue from (a) the avoidance of long on-chip interconnects and chip-to-chip bonding wires, and (b) the cohabitation of sensors, preprocessing analog circuitry, digital logic and signal processing, and RF devices in the same compact volume. A specific scenario is discussed in detail wherein a set of four types of sensors, namely an array of acoustic and seismic sensors, an active pixel sensor array, and an uncooled IR imaging array are placed on a common sensor plane. The other planes include an analog plane consisting of transductors and A/D converters. The digital processing planes provide the necessary processing and intelligence capability. The remaining planes provide for wireless communications/networking capability. When appropriate, this processing and decision-making will be accomplished on a collaborative basis among the distributed sensor nodes through a wireless network.

Signal Processing and Neural Network Simulator

The signal processing and neural network simulator (SPANNS) is a digital signal processing simulator with the capability to invoke neural networks into signal processing chains. This is a generic tool which will greatly facilitate the design and simulation of systems with embedded neural networks. The SPANNS is based on the Signal Processing WorkSystemTM (SPWTM), a commercial-off-the-shelf signal processing simulator. SPW provides a block diagram approach to constructing signal processing simulations. Neural network paradigms implemented in the SPANNS include Backpropagation, Kohonen Feature Map, Outstar, Fully Recurrent, Adaptive Resonance Theory 1, 2, & 3, and Brain State in a Box. The SPANNS was developed by integrating SAICs Industrial Strength Neural Networks (ISNN) Software into SPW.

Adaptive Power Allocation in a Nonlinear Satellite Repeater

Adaptive power allocation in a frequency multiplexed satellite transponder is designed to utilize satellite-transmitted power more effectively. When a large number of communication links share a satellite transponder, adaptive power control can significantly improve the communication capacity which is a function of the specified network availability. This paper analyzes a general mathematical model of power control in a nonlinear transponder. It is shown that a single inequality is a necessary and sufficient condition for being able to simultaneously satisfy all of the communication link requirements. If link assignments do not exceed the adaptive capacity represented by this inequality, then adaptive satellite power control can maintain the specified network availability while increasing communication capacity.