Perceptual compression of magnitude-detected synthetic aperture radar imagery

A perceptually-based approach for compressing synthetic aperture radar (SAR) imagery is presented. Key components of the approach are a multiresolution wavelet transform, a bit allocation mask based on an empirical human visual system (HVS) model, and hybrid scalar/vector quantization. Specifically, wavelet shrinkage techniques are used to segregate wavelet transform coefficients into three components: local means, edges, and texture. Each of these three components is then quantized separately according to a perceptually-based bit allocation scheme. Wavelet coefficients associated with local means and edges are quantized using high-rate scalar quantization while texture information is quantized using low-rate vector quantization. The impact of the perceptually-based multiresolution compression algorithm on visual image quality, impulse response, and texture properties is assessed for fine-resolution magnitude-detected SAR imagery; excellent image quality is found at bit rates at or above 1 bpp along with graceful performance degradation at rates below 1 bpp

Parametric analysis of dynamic postural responses

A detailed theoretical understanding of postural control mechanisms must be preceded by careful quantification of both the deterministic and stochastic aspects of postural behavior of normal and abnormal subjects under various dynamic conditions. Toward this end, concise parametric transfer function plus noise models were derived for both shoulder and waist position data obtained by applying a linear anteriorposterior bandlimited pseudorandom disturbance to the base of support of human subjects. Model orders as well as model parameters were determined empirically. One advantage of this modeling procedure is the conciseness of the postural models, permitting easy statistical analysis of the data obtained under different dynamic conditions from many subjects. Model features, including pole and zero locations, from 6 normal subjects each tested on 5 consecutive days under 3 input amplitudes and eyes open and closed conditions are presented. The resulting transfer function models consist of only 1 or 2 poles near the integration position on the Z plane unit circle and 0 to 2 zeros. Locations of the poles indicate that the eyes closed responses are more oscillatory, less damped, and with higher gains than the eyes open responses. These transfer functions are similar to nonparametric ones of other authors. The noise model orders are also small. Their spectra are those of low pass systems. Also, the quantity and frequency range of the postural noise is positively related to the amplitude of platform motion as well as related to the presence or absence of vision.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47433/1/422_2004_Article_BF00346137.pd

User manual for IDENT, a parametric and nonparametric linear systems identification package

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/24919/1/0000346.pd

Short‐term effects of smoking marijuana on balance in patients with multiple sclerosis and normal volunteers

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110106/1/cptclpt199433.pd

Dynamic posture analysis of Spacelab-1 crew members

Dynamic posture testing was conducted on the science crew of the Spacelab-1 mission on a single axis linear motion platform. Tests took place in pre- and post-flight sessions lasting approximately 20 min each. The pre-flight tests were widely spaced over the several months prior to the mission while the post-flight tests were conducted over the first, second, fourth, and sixth days after landing. Two of the crew members were also tested on the day of landing. Consistent with previous postural testing conducted on flight crews, these crew members were able to complete simple postural tasks to an acceptable level even in the first few hours after landing. Our tests were designed to induce dynamic postural responses using a variety of stimuli and from these responses, evaluate subtle changes in the postural control system which had occurred over the duration of the flight. Periodic sampling post-flight allowed us to observe the time course of readaptation to terrestrial life. Our observations of hip and shoulder position, when subjected to careful analysis, indicated modification of the postural response from pre- to post-flight and that demonstrable adjustments in the dynamic control of their postural systems were taking place in the first few days after flight. For transient stimuli where the platform on which they were asked to stand quickly moved a few centimeters fore or aft then stopped, ballistic or open loop ‘programs’ would closely characterize the response. During these responses the desired target position was not always achieved and of equal importance not always properly corrected some 15 seconds after the platform ceased to move. The persistent observation was that the subjects had a much stronger dependence on visual stabilization post-flight than pre-flight. This was best illustrated by a slow or only partial recovery to an upward posture after a transient base-of-support movement with eyes open. Postural responses to persistent wideband pseudorandom base-of-support translation stimuli were modeled as time invarient linear systems arrived at by Kaiman adaptive filter techniques. Derived model parame ters such as damping factor and fundamental frequency of the closed loop system showed significant modification between pre- and post-flight. This phenomenon is best characterized by movement of the poles toward increasing stability. While pre-flight data tended to show shoulders and hips moving in phase with each other, post-flight data showed a more disjoint behavior. One can speculate that this change illustrates a shattered postural organization or an acquired strategy not designed to stabilize terrestrial posture but as a carry over from optimum inflight postural control. Given our observations one can never be certain if these changes represent modifications in the physiology of posture of purposeful changes in strategy. As in other examples of motion behavior, the time domain analysis as represented by the step changes in position is not always reconcilable with the system modeling of pseudorandum responses and subsequent frequency domain analysis as represented by the pseudorandom noise stimuli. We present the observed data with arguments and some contradictions as to the nature of the adaptive changes which occur in the postural control system.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46556/1/221_2004_Article_BF00237754.pd