Sensory Data Processing Research Articles

Consciousness

Consciousness

Adaptive restriction rules provide functional and safe stimulation pattern for foot drop correction.

We report on our advances in sensory feedback data processing and control system design for functional electrical stimulation (FES) assisted correction of foot drop. We have applied 2 methods of signal purification on the bin integrated electroneurogram (i.e., optimized low pass filtering and wavelet denoising) before training adaptive logic networks (ALN). ALN generated stimulation control pulses, which correspond to the swing phase of the impaired leg when dorsal flexion of the foot is necessary to provide safe ground clearance. However, the obtained control signal contained sporadic stimulation spikes in the stance phase, which can collapse the subject, and infrequent broken stimulation pulses in the swing phase, which can result in unpredictable consequences. In this study, we have introduced adaptive restriction rules (ARR), which are initially used as previously reported and then dynamically adapted during the use of the system. Our results suggest that ARR provide a safer and more reliable stimulation pattern than fixed restriction rules.

Modelling the brain processes of conscious states

Based on a precise definition of consciousness in terms of awareness and volition, a simple model is developed to simulate the essential activity of the conscious brain. The model is based on the concepts of cognitive science but is consistent with the known neuroanatomical structure of the primate cortex. Awareness and volition are identified with the gain and creation of information at the microscopic level in channels of the neural membrane, and a method is devised for the computer simulation of the quantum mechanical indeterminacy of these processes. A simple computer program is described and listed to simulate the cognitive processing of sensory data and the concomitant initiation and excecution of voluntary activity.

Effects of synthetic delta9-tetrahydrocannabinol on binocular depth inversion of natural and artificial objects in man.

Binocular depth inversion represents an illusion of visual perception that is sensitive to various behavioural and psychiatric conditions. It is affected by cannabinoids, reflecting associated changes in perception. The present study investigated the differences in binocular depth inversion of different classes of natural and artificial objects and the effect of synthetic delta9-tetrahydrocannabinol (Dronabinol) on these illusionary perceptions. Using this model, the effects of orally administered Dronabinol on binocular depth inversion were investigated in 17 healthy male volunteers. Pictures from natural and artificial objects were presented stereoscopically and the depth perception of the volunteers was scored in an operationalized way. The timecourse of the effects of Dronabinol on binocular depth inversion was analyzed with regard to the stimulus classes (natural and synthetic objects). Significant differences in binocular depth inversion of the different groups of stimuli were revealed. Objects with a higher degree of everyday familiarity were generally seen as more illusionary than those with a lower degree of everyday familiarity. A strong impairment of binocular depth inversion due to Dronabinol was found in most classes of objects. Analysis of different stimulus classes provides further information on the underlying perceptual processing of binocular depth inversion. An impairment of top-down processing of visual sensory data by Dronabinol is suggested. The anandamidergic system seems to be involved in areas of visual information processing.

A Reactive Neuromorphic Controller for Local Robot Navigation

This paper presents and discusses a neuromorphic approach to designing a control law for locally guided robot navigation which allows for instantaneous response to the changes in the robot environment. The control algorithm is defined in the discrete domain appropriate for dedicated VLSI implementation and direct processing of discrete sensory data, such as obtained from a CCD camera. The control law is based on the principle of virtual force fields. The virtual forces (repulsive and circulation vectors) guiding the robot in the vicinity of obstacles are derived from the gradient fields associated with discrete representation of visual information. Discretization and computational tasks are assigned to parallel neuromorphic processors, which emulate the gradient operations. The particularities of discrete geometrical representations of the world and the adjustable control parameters essential for flexible and robust controller operation are discussed in detail. Finally, a detailed scheme of a tunable navigation controller specifying the parameters generated internally by the network and those that have to be provided from an external source (e.g., the operator, or a supervisory knowledge-based controller) is provided.

Xavier

Office delivery robots have to perform many tasks such as picking up and delivering mail or faxes, returning library books, and getting coffee. They have to determine the order in which to visit locations, plan paths to those locations, follow paths reliably, and avoid static and dynamic obstacles in the process. Reliability and efficiency are key issues in the design of such autonomous robot systems. They must deal reliably with noisy sensors and actuators and with incomplete knowledge of the environment. They must also act efficiently, in real time, to deal with dynamic situations. To achieve these objectives, we have developed a robot architecture that is composed of four layers: obstacle avoidance, navigation, path planning, and task planning. The layers are independent, communicating processes that are always active, processing sensory data and status information to update their decisions and actions. A version of our robot architecture has been in nearly daily use in our building since December 1995. As of January 1997, the robot has traveled more than 110 kilometers (65 miles) in service of over 2500 navigation requests that were specified using our World Wide Web interface.

Cerebellum implicated in sensory acquisition and discrimination rather than motor control.

Recent evidence that the cerebellum is involved in perception and cognition challenges the prevailing view that its primary function is fine motor control. A new alternative hypothesis is that the lateral cerebellum is not activated by the control of movement per se, but is strongly engaged during the acquisition and discrimination of sensory information. Magnetic resonance imaging of the lateral cerebellar output (dentate) nucleus during passive and active sensory tasks confirmed this hypothesis. These findings suggest that the lateral cerebellum may be active during motor, perceptual, and cognitive performances specifically because of the requirement to process sensory data.

A robotic system for handling textile and non rigid flat materials

This paper presents a robotic system incorporating vision and force/torque sensing for handling flat textile materials. The methodology followed consists in tackling representative problems related to a broad range of materials. Representative tasks have been selected which are further analyzed into simple operations. Algorithms for sensory data processing and control have been developed. Finally, conclusions from experimentation are presented.

Spatial cues serving the tactile directional sensibility of the human forearm.

1. Tactile directional sensibility is considered to rely on the parallel processing of direction-contingent sensory data that depend on skin stretching caused by friction, and spatial cues that vary with time. A temperature-controlled airstream stimulus that prevented the activation of stretch receptors was used to investigate directional sensibility for the skin of the forearm. 2. The dependence on contact load and distance of movement was determined for normal subjects with a two-alternative forced-choice method. Testing was performed under two conditions, elbow bent or straight. Bracing the skin by straightening the arm did not alter the accuracy of the directional sensibility, in contrast to previous findings with stimuli that caused friction. 3. The accuracy of directional sensibility was correlated linearly to the logarithm of the distance of movement of the air jet. No correlation was found between accuracy and contact load, unlike findings with stimuli that cause friction. 4. Measurements were made with different subjects to determine the threshold distance at constant load. On average, subjects were able to distinguish direction with movements of < or = 8 mm. This acuity is sharper than has been reported with static stimuli. There was no correlation between subjects' threshold distances for judging direction and spatial acuity measured with absolute point localization. 5. The ability to distinguish direction was poor for the airstream stimulus compared with stimuli causing frictional contact with hairy skin. Nevertheless, the present findings are consistent with the suggestion that cutaneous spatial acuity is better for dynamic than for static stimuli.

Data fusion with artificial neural networks for classification of earth surface from microwave satellite measurements

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven Special Sensor Microwave Imager (SSMI) multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both horizontal and vertical polarizations (only vertical at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including nonscattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (a) preprocessing to extract feature vectors and enhance separability among detected classes; (b) preliminary classification of Earth surface patterns using two separate and parallel-acting classifiers: back-propagation neural network and binary decision tree classifiers; and (c) data fusion of results from preliminary classifiers to obtain the optimal performance in over-all classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4% are used as the training set and 96% as the testing set. After training, this classification system is able to bring up the detection accuracy to 94% compared with 88% for back-propagation artificial neural networks and 80% for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR).

An analog neural network solution to the inverse problem of 'early taction'

The authors examine an application of analog neural networks to low-level processing of tactile sensory data. In analogy to the term early vision, the authors call the first level of processing required in tactile sensing early taction. The problem of deblurring or deconvolution of data provided by an array of tactile sensors that is also assumed to be corrupted by noise is addressed. It is noted that this inverse problem is ill posed and that the technique of regularization may be used to obtain solutions. The theory of nonlinear electrical networks is utilized to describe energy functions for a class of nonlinear networks and to show that the equilibrium states of the proposed network correspond to regularized solutions of the deblurring problem. An entropy regularizer is incorporated into the energy function of the network for the recovery of normal stress distributions. An integrated circuit prototype of the proposed network is discussed. >

Entropy-driven on-line control of autonomous robots

A new approach to on-line path planning is derived in this paper. The planning algorithm is motivated by robot navigation and manipulation tasks in uncertain, unstructured, dynamic environments. A minimum entropy evidential classifier is used to recognize targets and obstacles in the environment. An iterative Newton scheme is then used to generate a sequence of knot points that guide the motion of the robot. The acquisition and processing of sensory data continue during the motion, thus reducing the uncertainty about the environment. The classification of targets and obstacles is updated, and the path is replanned (locally) to adapt to those changes. A graphical tool based on the concept of Julia sets is used to ensure the predictability and smoothness of the paths.

Histidine phosphorylation and phosphoryl group transfer in bacterial chemotaxis.

A cascade of protein phosphorylation, initiated by autophosphorylation of the CheA protein, may be important in the signal transduction pathway of bacterial chemotaxis. A proteolytic fragment of CheA cannot autophosphorylate, but can still transfer phosphate to proteins that generate excitation and adaptation signals. The site of CheA phosphorylation is His 48; mutants altered at this position are non-chemotactic. Similar mechanisms of transient protein phosphorylation and phosphoryl group transfer seem to be involved in processing sensory data and in activating specific gene expression.

Asymmetrical regional changes in energy metabolism of the central nervous system during walking

Cats were injected with 2-deoxy-[ 14C]glucose (2-DG) while walking on a moving treadmill (experimental group), or sitting down on a stationary one (controls). After a 45-min equilibration period they were anesthetized, and their central nervous system (CNS) was removed rapidly and frozen. The tissue blocks were sectioned serially, and X-ray film exposed to the sections was used for quantitative densitometric analysis by Sokoloff's method. The utilization of glucose in a CNS region (LCMRg) was regarded as a measure of that region's energy metabolic activity and — indirectly — of its functional status. The walking cats exhibited significantly higher LCMRg in many but not all places of the neuraxis, compared to the control group. Also, LCMRg was symmetrical (side to side) in the control group but significantly asymmetrical in certain regions of the CNS in the experimental group. In all but one of these cats the LCMRg was greater in the right side of the gray matter of the cervical spinal cord and in the left visual and motor cortices and caudate nucleus. The finding that the motor cortex and other supraspinal regions become more active during walking suggests they may contribute to the control of locomotion and/or processing of related sensory data. The side to side assymetry in the spinal cord and hemispheres during walking may be related to the phenomenon of lateral dominance.

Robotic Realities: Near-Term Prospects and Problems

Industrial robots are automation, but with a difference. Other machine tools are extensions of human capabilities, while robots are seen mainly as substitutes for human workers. Robots will find most of their industrial applications during the next decade or two in the metal-working sectors, where they will begin to displace semiskilled machine operatives in medium to large batch production operations. They cannot substitute for skilled machinists or other workers doing nonroutine jobs, or specialized, dedicated hard automation used in mass production. The current generation of robots, lacking sensory data processing and interpretation capabilities, can potentially replace up to 1.3 million manufacturing jobs. The next generation, with crude vision or tactile senses; will potentially displace about 3 million more. However, only relatively large firms can profitably utilize many robots at present; it may be 20 years or more before these usage rates are achieved in practice. A shift from stand-alone machine tools, to manufacturing cells consisting of several machine tools served by a robot and controlled by a computer, will accelerate the practical use of robots in the 1990s.