a b s t r a c tThe development of bio-electronic prostheses, hybrid human-electronics devices and bionic robots has been the aim of many researchers. Although neurophysiologic processes have been widely investigated and bio-electronics has developed rapidly, the dynamics of a biological neuronal network that receive sensory inputs, store and control information is not yet understood. Toward this end, we have taken an interdisciplinary approach to study the learning and response of biological neural networks to complex stimulation patterns. This paper describes the design, execution, and results of several experiments performed in order to investigate the behavior of complex interconnected structures found in biological neural networks.The experimental design consisted of biological human neurons stimulated by parallel signal patterns intended to simulate complex perceptions. The response patterns were analyzed with an innovative artificial neural network (ANN), called ITSOM (Inductive Tracing Self Organizing Map). This system allowed us to decode the complex neural responses from a mixture of different stimulations and learned memory patterns inherent in the cell colonies. In the experiment described in this work, neurons derived from human neural stem cells were connected to a robotic actuator through the ANN analyzer to demonstrate our ability to produce useful control from simulated perceptions stimulating the cells.Preliminary results showed that in vitro human neuron colonies can learn to reply selectively to different stimulation patterns and that response signals can effectively be decoded to operate a minirobot. Lastly the fascinating performance of the hybrid system is evaluated quantitatively and potential future work is discussed.
The ability to rapidly detect and identify potential targets both fixed and mobile from multiple sensor feeds is a critical function in network centric warfare. In this paper we describe the use of Image Differencing and 3D terrain database editing in order to fuse oblique aerial photos, IR sensor imagery, and other non-traditional data sources to produce battlefield metrics that support network centric operations. Such metrics include target detection, recognition, and location, and improved knowledge of the target environment. Key to our approach is the rapid generation of target and background signatures from highresolution 1-meter object descriptor terrain databases. This technique utilizes the difference between measured and calculated sensor images to 1) update and correct knowledge of the terrain background, 2) register multi sensor imagery 3) identify potential/candidate targets based on residual image differencing and 3) measure and report target locations based on scene matching. The technique is especially suited for utilizing imagery from reconnaissance and remotely piloted vehicle sensors. It also holds promise for automation and real-time data reduction of battlefield sensor feeds and for improving now-time situational awareness.We will present the algorithms and approach utilized in the Image Differencing technique. We will also describe the software developed to implement the approach. Lastly we will present the results of experiments and benchmarks conducted to identify and measure target locations in test locations at Ft. Hood, TX and Ft. Hunter Liggett, CA.
The ability to rapidly and inexpensively generate terrain databases to replicate actual terrain is critical to insuring correlation between the results from live, virtual, and constructive simulations used in testing and evaluating weapons, sensors, and battlefield command and control systems. In this paper we describe a technique for producing battlefield terrain data sets from oblique aerial photos and other nontraditional data sources using image differencing and 3D terrain editing tools. This technique uses a feedback loop to calculate terrain data parameters from differences between actual sensor imagery and synthetic imagery of replicated terrain created by an image generator. The technique is especially well suited for updating knowledge of battlefield situations from reconnaissance and remotely piloted vehicle sensors. It also holds promise for automation and real-time data reduction of battlefield sensor feeds and improved now-time situational awareness.We will present the algorithms and approach utilized in the Image Differencing technique. We will also describe the software developed to implement the approach. Lastly we will present the results of experiments and benchmarks conducted to measure the effectiveness and progress made toward real-time terrain database generation.
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