Estimation of automobile-driver describing function from highway tests using the double steering wheel

The automobile-driver describing function for lateral position control was estimated for three subjects from frequency response analysis of straight road test results. The measurement procedure employed an instrumented full size sedan with known steering response characteristics, and equipped with a lateral lane position measuring device based on video detection of white stripe lane markings. Forcing functions were inserted through a servo driven double steering wheel coupling the driver to the steering system proper. Random appearing, Gaussian, and transient time functions were used. The quasi-linear models fitted to the random appearing input frequency response characterized the driver as compensating for lateral position error in a proportional, derivative, and integral manner. Similar parameters were fitted to the Gabor transformed frequency response of the driver to transient functions. A fourth term corresponding to response to lateral acceleration was determined by matching the time response histories of the model to the experimental results. The time histories show evidence of pulse-like nonlinear behavior during extended response to step transients which appear as high frequency remnant power

Musculoskeletal modelling of an ostrich (Struthio camelus) pelvic limb: influence of limb orientation on muscular capacity during locomotion

We developed a three-dimensional, biomechanical computer model of the 36 major pelvic limb muscle groups in an ostrich (Struthio camelus) to investigate muscle function in this, the largest of extant birds and model organism for many studies of locomotor mechanics, body size, anatomy and evolution. Combined with experimental data, we use this model to test two main hypotheses. We first query whether ostriches use limb orientations (joint angles) that optimize the moment-generating capacities of their muscles during walking or running. Next, we test whether ostriches use limb orientations at mid-stance that keep their extensor muscles near maximal, and flexor muscles near minimal, moment arms. Our two hypotheses relate to the control priorities that a large bipedal animal might evolve under biomechanical constraints to achieve more effective static weight support. We find that ostriches do not use limb orientations to optimize the moment-generating capacities or moment arms of their muscles. We infer that dynamic properties of muscles or tendons might be better candidates for locomotor optimization. Regardless, general principles explaining why species choose particular joint orientations during locomotion are lacking, raising the question of whether such general principles exist or if clades evolve different patterns (e.g., weighting of muscle force–length or force–velocity properties in selecting postures). This leaves theoretical studies of muscle moment arms estimated for extinct animals at an impasse until studies of extant taxa answer these questions. Finally, we compare our model’s results against those of two prior studies of ostrich limb muscle moment arms, finding general agreement for many muscles. Some flexor and extensor muscles exhibit self-stabilization patterns (posture-dependent switches between flexor/extensor action) that ostriches may use to coordinate their locomotion. However, some conspicuous areas of disagreement in our results illustrate some cautionary principles. Importantly, tendon-travel empirical measurements of muscle moment arms must be carefully designed to preserve 3D muscle geometry lest their accuracy suffer relative to that of anatomically realistic models. The dearth of accurate experimental measurements of 3D moment arms of muscles in birds leaves uncertainty regarding the relative accuracy of different modelling or experimental datasets such as in ostriches. Our model, however, provides a comprehensive set of 3D estimates of muscle actions in ostriches for the first time, emphasizing that avian limb mechanics are highly three-dimensional and complex, and how no muscles act purely in the sagittal plane. A comparative synthesis of experiments and models such as ours could provide powerful synthesis into how anatomy, mechanics and control interact during locomotion and how these interactions evolve. Such a framework could remove obstacles impeding the analysis of muscle function in extinct taxa

Segmentation of fluorescence microscopy images using three dimensional active contours with inhomogeneity correction

Image segmentation is an important step in the quantitative analysis of fluorescence microscopy data. Since fluorescence microscopy volumes suffer from intensity inhomogeneity, low image contrast and limited depth resolution, poor edge details, and irregular structure shape, segmentation still remains a challenging problem. This paper describes a nuclei segmentation method for fluorescence microscopy based on the use of three dimensional (3D) active contours with inhomogeneity correction. The correction information utilizes 3D volume information while addressing intensity inhomogeneity across vertical and horizontal directions. Experimental results demonstrate that the proposed method achieves better performance than other reported methods

A Subject-Specific EMG-Driven Musculoskeletal Model for the Estimation of Moments in Ankle Plantar-Dorsiflexion Movement

In traditional rehabilitation process, ankle movement ability is only qualitatively estimated by its motion performance, however, its movement is actually achieved by the forces acting on the joints produced by muscles contraction. In this paper, the musculoskeletal model is introduced to provide a more physiologic method for quantitative muscle forces and muscle moments estimation during rehabilitation. This paper focuses on the modeling method of musculoskeletal model using electromyography (EMG) and angle signals for ankle plantar-dorsiflexion (P-DF) which is very important in gait rehabilitation and foot prosthesis control. Due to the skeletal morphology differences among people, a subject-specific geometry model is proposed to realize the estimation of muscle lengths and muscle contraction force arms. Based on the principle of forward and inverse dynamics, difference evolutionary (DE) algorithm is used to adjust individual parameters of the whole model, realizing subject-specific parameters optimization. Results from five healthy subjects show the inverse dynamics joint moments are well predicted with an average correlation coefficient of 94.21% and the normalized RMSE of 12.17%. The proposed model provides a good way to estimate muscle moments during movement tasks

Segmentation of biological images containing multitarget labeling using the jelly filling framework

Biomedical imaging when combined with digital image analysis is capable of quantitative morphological and physiological characterizations of biological structures. Recent fluorescence microscopy techniques can collect hundreds of focal plane images from deeper tissue volumes, thus enabling characterization of three-dimensional (3-D) biological structures at subcellular resolution. Automatic analysis methods are required to obtain quantitative, objective, and reproducible measurements of biological quantities. However, these images typically contain many artifacts such as poor edge details, nonuniform brightness, and distortions that vary along different axes, all of which complicate the automatic image analysis. Another challenge is due to "multitarget labeling," in which a single probe labels multiple biological entities in acquired images. We present a "jelly filling" method for segmentation of 3-D biological images containing multitarget labeling. Intuitively, our iterative segmentation method is based on filling disjoint tubule regions of an image with a jelly-like fluid. This helps in the detection of components that are "floating" within a labeled jelly. Experimental results show that our proposed method is effective in segmenting important biological quantities

Boundary Segmentation For Fluorescence Microscopy Using Steerable Filters

Fluorescence microscopy is used to image multiple subcellular structures in living cells which are not readily observed using conventional optical microscopy. Moreover, two-photon microscopy is widely used to image structures deeper in tissue. Recent advancement in fluorescence microscopy has enabled the generation of large data sets of images at different depths, times, and spectral channels. Thus, automatic object segmentation is necessary since manual segmentation would be inefficient and biased. However, automatic segmentation is still a challenging problem as regions of interest may not have well defined boundaries as well as non-uniform pixel intensities. This paper describes a method for segmenting tubular structures in fluorescence microscopy images of rat kidney and liver samples using adaptive histogram equalization, foreground/background segmentation, steerable filters to capture directional tendencies, and connected-component analysis. The results from several data sets demonstrate that our method can segment tubular boundaries successfully. Moreover, our method has better performance when compared to other popular image segmentation methods when using ground truth data obtained via manual segmentation

Tubule Segmentation of Fluorescence Microscopy Images Based on Convolutional Neural Networks With Inhomogeneity Correction

Fluorescence microscopy has become a widely used tool for studying various biological structures of in vivo tissue or cells. However, quantitative analysis of these biological structures remains a challenge due to their complexity which is exacerbated by distortions caused by lens aberrations and light scattering. Moreover, manual quantification of such image volumes is an intractable and error-prone process, making the need for automated image analysis methods crucial. This paper describes a segmentation method for tubular structures in fluorescence microscopy images using convolutional neural networks with data augmentation and inhomogeneity correction. The segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods. Experimental results indicate that the proposed method has better performance with correctly segmenting and identifying multiple tubular structures compared to other methods

Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks

Fluorescence microscopy enables one to visualize subcellular structures of living tissue or cells in three dimensions. This is especially true for two-photon microscopy using near-infrared light which can image deeper into tissue. To characterize and analyze biological structures, nuclei segmentation is a prerequisite step. Due to the complexity and size of the image data sets, manual segmentation is prohibitive. This paper describes a fully 3D nuclei segmentation method using three dimensional convolutional neural networks. To train the network, synthetic volumes with corresponding labeled volumes are automatically generated. Our results from multiple data sets demonstrate that our method can successfully segment nuclei in 3D

Four Dimensional Image Registration For Intravital Microscopy

Increasingly the behavior of living systems is being evaluated using intravital microscopy since it provides subcellular resolution of biological processes in an intact living organism. Intravital microscopy images are frequently confounded by motion resulting from animal respiration and heartbeat. In this paper we describe an image registration method capable of correcting motion artifacts in three dimensional fluorescence microscopy images collected over time. Our method uses 3D B-Spline non-rigid registration using a coarse-to-fine strategy to register stacks of images collected at different time intervals and 4D rigid registration to register 3D volumes over time. The results show that our proposed method has the ability of correcting global motion artifacts of sample tissues in four dimensional space, thereby revealing the motility of individual cells in the tissue