A Self-Organizing Neural Network for Learning a Body-Centered Invariant Representation of 3-D Target Position

This paper describes a self-organizing neural network that rapidly learns a body-centered representation of 3-D target positions. This representation remains invariant under head and eye movements, and is a key component of sensory-motor systems for producing motor equivalent reaches to targets (Bullock, Grossberg, and Guenther, 1993).National Science Foundation (IRI-87-16960, IRI-90-24877); Air Force Office of Scientific Research (F49620-92-J-0499

Neural Representations for Sensory-Motor Control, III: Learning a Body-Centered Representation of 3-D Target Position

A neural model is described of how the brain may autonomously learn a body-centered representation of 3-D target position by combining information about retinal target position, eye position, and head position in real time. Such a body-centered spatial representation enables accurate movement commands to the limbs to be generated despite changes in the spatial relationships between the eyes, head, body, and limbs through time. The model learns a vector representation--otherwise known as a parcellated distributed representation--of target vergence with respect to the two eyes, and of the horizontal and vertical spherical angles of the target with respect to a cyclopean egocenter. Such a vergence-spherical representation has been reported in the caudal midbrain and medulla of the frog, as well as in psychophysical movement studies in humans. A head-centered vergence-spherical representation of foveated target position can be generated by two stages of opponent processing that combine corollary discharges of outflow movement signals to the two eyes. Sums and differences of opponent signals define angular and vergence coordinates, respectively. The head-centered representation interacts with a binocular visual representation of non-foveated target position to learn a visuomotor representation of both foveated and non-foveated target position that is capable of commanding yoked eye movementes. This head-centered vector representation also interacts with representations of neck movement commands to learn a body-centered estimate of target position that is capable of commanding coordinated arm movements. Learning occurs during head movements made while gaze remains fixed on a foveated target. An initial estimate is stored and a VOR-mediated gating signal prevents the stored estimate from being reset during a gaze-maintaining head movement. As the head moves, new estimates arc compared with the stored estimate to compute difference vectors which act as error signals that drive the learning process, as well as control the on-line merging of multimodal information.Air Force Office of Scientific Research (F49620-92-J-0499); National Science Foundation (IRI -87-16960, IRI-90-24877); Office of Naval Research (N00014-92-J-l309

Advanced detectors and signal processing

Continued progress is reported toward development of a silicon on garnet technology which would allow fabrication of advanced detection and signal processing circuits on bubble memories. The first integrated detectors and propagation patterns have been designed and incorporated on a new mask set. In addition, annealing studies on spacer layers are performed. Based on those studies, a new double layer spacer is proposed which should reduce contamination of the silicon originating in the substrate. Finally, the magnetic sensitivity of uncontaminated detectors from the last lot of wafers is measured. The measured sensitivity is lower than anticipated but still higher than present magnetoresistive detectors

Heartworms In Iowa

Canine heartworm infection, caused by Dirofilaria immitis, has made an alarming incursion into Iowa during the past 4 to 5 years. Review of clinical records at Iowa State University for the past 5 years show 46 cases of heartworm infection. Now it has gotten to the stage where veterinarians in some areas of Iowa are regularly encountering the disease and routinely check for its presence

Scheduling for Space Tracking and Heterogeneous Sensor Environments

This dissertation draws on the fields of heuristic and meta-heuristic algorithm development, resource allocation problems, and scheduling to address key Air Force problems. The world runs on many schedules. People depend upon them and expect these schedules to be accurate. A process is needed where schedules can be dynamically adjusted to allow tasks to be completed efficiently. For example, the Space Surveillance Network relies on a schedule to track objects in space. The schedule must use sensor resources to track as many high-priority satellites as possible to obtain orbit paths and to warn of collision paths. Any collisions that occurred between satellites and other orbiting material could be catastrophic. To address this critical problem domain, this dissertation introduces both a single objective evolutionary tasker algorithm and a multi-objective evolutionary algorithm approach. The aim of both methods is to produce space object tracking schedules to ensure that higher priority objects are appropriately assessed for potential problems. Simulations show that these evolutionary algorithm techniques effectively create schedules to assure that higher priority space objects are tracked. These algorithms have application to a range of dynamic scheduling domains including space object tracking, disaster search and rescue, and heterogeneous sensor scheduling

Comparison of sugar and sodium nitrate flotation methods for detection of parasites in dog feces

At Iowa State University, the standard flotation method used to detect parasite eggs and cysts in dog feces has long been centrifugation using sugar (Sheather\u27s) solution. In recent years, several kits using sodium nitrate have been available commercially. The purpose of this study was to compare the sugar centrifugation and sodium nitrate flotation methods in their ability to detect parasite eggs and cysts in dog feces

Parasites in Domestic Animals in Iowa

Because the profit margin in the livestock industry is narrowing year by year, the importance of subclinical disease ever increases. Of major importance in the realm of subclinical disease is parasitism. Although parasites are capable of causing severe morbidity and morality, the usual situation seen from day to day is the insidious, nonspectacular stress placed on animals by subclinical infections by various parasites

Observations on Myiasis Caused by Wohlfahrtia (Skin Bots)

During the fly season, myiasis, or the presence of dipterid larvae in tissues, is a common and troublesome affliction of mammals. One of the most commonly encountered forms of myiasis is cutaneous myiasis, where the larvae occur in the skin. Many genera of flies are associated with cutaneous myasis., including the awesome primary screwworm (Callitroga hominivorax), various blow flies, flesh flies (Sarcophaga spp.), and skin bots (Cuterebra spp., Wohlfahrtia spp.)

Network Security Toolkit Including Heuristic Solutions for Trust System Placement and Network Obfuscation

For Part I, a supervisory control and data acquisition (SCADA) network consists of a group stations and substations in a portion of the power grid. The use of Internet technology in SCADA communications as well as other factors has caused vulnerabilities. One idea to help mitigate this risk is to strategically place trust nodes to compartmentalize and secure the SCADA systems without disturbing its finely honed processes. The trust nodes combine firewall and intrusion detection technology to provide more secure communication. An optimal solution to this problem has already been developed using a mixed-integer linear programming model. Because the problem is provably NP-Hard, a heuristic solution is presented in this part. The heuristic can find good, but not optimal, solutions. Experiments are promising that the proposed heuristic technique is close to optimal while arriving at results much quicker. For Part II, dynamically modifying the defense structure could be used to prevent adversaries from gathering intelligence, seriously inhibiting their ability to conduct attacks successfully. Work has already been done using a mixed-integer linear programming model (MILPM) to solve the multi-commodity capacitated network design problem (MCNDP) to create dynamically change routes and possibly topologies within a network. Information flows in the network can be periodically routed on different paths through the network so that traffic patterns change and adversaries have to work much harder to map the network. The MILPM solution offers a good baseline for caparison of any heuristic trying to solve the same problem. In this part, a heuristic approach to network obfuscation is proposed. The heuristic shows favorable results when compared to the MILPM solution