Optimal linear and nonlinear feature extraction based on the minimization of the increased risk of misclassification

General classes of nonlinear and linear transformations were investigated for the reduction of the dimensionality of the classification (feature) space so that, for a prescribed dimension m of this space, the increase of the misclassification risk is minimized

Vision technology/algorithms for space robotics applications

The thrust of automation and robotics for space applications has been proposed for increased productivity, improved reliability, increased flexibility, higher safety, and for the performance of automating time-consuming tasks, increasing productivity/performance of crew-accomplished tasks, and performing tasks beyond the capability of the crew. This paper provides a review of efforts currently in progress in the area of robotic vision. Both systems and algorithms are discussed. The evolution of future vision/sensing is projected to include the fusion of multisensors ranging from microwave to optical with multimode capability to include position, attitude, recognition, and motion parameters. The key feature of the overall system design will be small size and weight, fast signal processing, robust algorithms, and accurate parameter determination. These aspects of vision/sensing are also discussed

Virtual Patient/Family Communication in the Acute Care Setting

Patient and family-centered care strategies see patients and families as valuable healthcare team members. Such strategies thus treat these groups as essential clinical partners in providing safe, high-quality care. Participation, collaboration, and shared decision-making are central to this framework. Historically, hospitals have relied on physical presence at the bedside as a prerequisite to engaging families in the shared decision-making process. Visitor restrictions of the COVID-19 pandemic removed the primary strategy for family participation: physical presence. Healthcare organizations rapidly deployed mobile devices to help minimize the exposure of healthcare providers and provide video visits for family members. This deployment was often rushed, with minimal workflow analysis, role definition, or standard operating procedures. These deficiencies led to low adoption rates, poor user satisfaction, and often unanticipated clinician distress when used at patients’ end of life. A better understanding of these virtual tools is necessary to ensure high-quality patient care.The present quality improvement project aims to understand workflow; organizational barriers to adoption; and provider, family, and patient-related barriers to successfully using virtual communication in the acute care setting. The setting of this project was the intermediate care unit of a 140-bed community hospital that is part of a not-for-profit health system in the southwestern United States. Semi-structured interviews were performed to capture the lived experiences of family members and healthcare workers who used the virtual visit intervention during hospital visitor restrictions. The findings from these interviews, a literature review, and a workflow analysis identified several themes of the current tool\u27s benefits, barriers, and enhancements. These themes were mapped to the sociotechnical model of healthcare information technology adoption to identify and suggest successful design criteria for a standardized virtual intervention. This intervention could be applied when external interactions are limited, like the pandemic or individual family circumstances. Overall, participants found that the intervention during the COVID-19 pandemic provided comfort and closure, facilitated family-shared decision-making, and reduced patient loneliness. Areas of opportunity include device availability, features, application usability, virtual communication techniques, and standardized workflow. Although hospital visitor restrictions due to the COVID-19 pandemic are no longer in place, the lessons learned, and the criteria identified can help standardize and improve family-centered communication strategies when family members cannot be physically present in the hospital

Rice-obot 1: An intelligent autonomous mobile robot

The Rice-obot I is the first in a series of Intelligent Autonomous Mobile Robots (IAMRs) being developed at Rice University's Cooperative Intelligent Mobile Robots (CIMR) lab. The Rice-obot I is mainly designed to be a testbed for various robotic and AI techniques, and a platform for developing intelligent control systems for exploratory robots. Researchers present the need for a generalized environment capable of combining all of the control, sensory and knowledge systems of an IAMR. They introduce Lisp-Nodes as such a system, and develop the basic concepts of nodes, messages and classes. Furthermore, they show how the control system of the Rice-obot I is implemented as sub-systems in Lisp-Nodes

A contribution to laser range imaging technology

The goal of the project was to develop a methodology for fusion of a Laser Range Imaging Device (LRID) and camera data. Our initial work in the project led to the conclusion that none of the LRID's that were available were sufficiently adequate for this purpose. Thus we spent the time and effort on the development of the new LRID with several novel features which elicit the desired fusion objectives. In what follows, we describe the device developed and built under contract. The Laser Range Imaging Device (LRID) is an instrument which scans a scene using a laser and returns range and reflection intensity data. Such a system would be extremely useful in scene analysis in industry and space applications. The LRID will be eventually implemented on board a mobile robot. The current system has several advantages over some commercially available systems. One improvement is the use of X-Y galvonometer scanning mirrors instead of polygonal mirrors present in some systems. The advantage of the X-Y scanning mirrors is that the mirror system can be programmed to provide adjustable scanning regions. For each mirror there are two controls accessible by the computer. The first is the mirror position and the second is a zoom factor which modifies the amplitude of the position of the parameter. Another advantage of the LRID is the use of a visible low power laser. Some of the commercial systems use a higher intensity invisible laser which causes safety concerns. By using a low power visible laser, not only can one see the beam and avoid direct eye contact, but also the lower intensity reduces the risk of damage to the eye, and no protective eyeware is required

A technique for 3-D robot vision for space applications

An extension of the MIAG algorithm for recognition and motion parameter determination of general 3-D polyhedral objects based on model matching techniques and using Moment Invariants as features of object representation is discussed. Results of tests conducted on the algorithm under conditions simulating space conditions are presented

Classification by means of B-spline potential functions with applications to remote sensing

A method is presented for using B-splines as potential functions in the estimation of likelihood functions (probability density functions conditioned on pattern classes), or the resulting discriminant functions. The consistency of this technique is discussed. Experimental results of using the likelihood functions in the classification of remotely sensed data are given

Classification improvement by optimal dimensionality reduction when training sets are of small size

A computer simulation was performed to test the conjecture that, when the sizes of the training sets are small, classification in a subspace of the original data space may give rise to a smaller probability of error than the classification in the data space itself; this is because the gain in the accuracy of estimation of the likelihood functions used in classification in the lower dimensional space (subspace) offsets the loss of information associated with dimensionality reduction (feature extraction). A number of pseudo-random training and data vectors were generated from two four-dimensional Gaussian classes. A special algorithm was used to create an optimal one-dimensional feature space on which to project the data. When the sizes of the training sets are small, classification of the data in the optimal one-dimensional space is found to yield lower error rates than the one in the original four-dimensional space

Demonstration of a 3D vision algorithm for space applications

This paper reports an extension of the MIAG algorithm for recognition and motion parameter determination of general 3-D polyhedral objects based on model matching techniques and using movement invariants as features of object representation. Results of tests conducted on the algorithm under conditions simulating space conditions are presented

An algorithm for optimal single linear feature extraction from several Gaussian pattern classes

A computational algorithm is presented for the extraction of an optimal single linear feature from several Gaussian pattern classes. The algorithm minimizes the increase in the probability of misclassification in the transformed (feature) space. Numerical results on the application of this procedure to the remotely sensed data from the Purdue Cl flight line as well as LANDSAT data are presented. It was found that classification using the optimal single linear feature yielded a value for the probability of misclassification on the order of 30% less than that obtained by using the best single untransformed feature. Also, the optimal single linear feature gave performance results comparable to those obtained by using the two features which maximized the average divergence