A threshold insensitive method for locating the forest canopy top with waveform lidar

Lidars have the unique ability to make direct, physical measurements of forest height and vertical structure in much denser canopies than is possible with passive optical or short wavelength radars. However the literature reports a consistent underestimate of tree height when using physically based methods, necessitating empirical corrections. This bias is a result of overestimating the range to the canopy top due to background noise and failing to correctly identify the ground.This paper introduces a method, referred to as “noise tracking”, to avoid biases when determining the range to the canopy top. Simulated waveforms, created with Monte-Carlo ray tracing over geometrically explicit forest models, are used to test noise tracking against simple thresholding over a range of forest and system characteristics. It was found that noise tracking almost completely removed the bias in all situations except for very high noise levels and very low (< 10%) canopy covers. In all cases noise tracking gave lower errors than simple thresholding and had a lower sensitivity to the initial noise threshold.Finite laser pulses spread out the measured signal, potentially overriding the benefit of noise tracking. In the past laser pulse length has been corrected by adding half that length to the signal start range. This investigation suggests that this is not always appropriate for simple thresholding and that the results for noise tracking were more directly related to pulse length than for simple thresholding. That this effect has not been commented on before may be due to the possible confounding impacts of instrument and survey characteristics inherent in field data. This method should help improve the accuracy of waveform lidar measurements of forests, whether using airborne or spaceborne instruments.     

Matrix approach to deadlock-free dispatching in multi-class finite buffer flowlines

For finite-buffer manufacturing systems, the major stability issue is "deadlock," rather than "bounded-buffer-length stability." The paper introduces the concept of "system deadlock," defined rigorously in Petri net terms, and system operation with uninterrupted part-flow is characterized in terms of the absence of this condition. For a large class of finite-buffer multiclass re-entrant flowline systems, an analysis of "circular waits" yields necessary and sufficient conditions for the occurrence of "system deadlock." This allows the formulation of a maximally permissive one-step-look-ahead deadlock-avoidance control policy for dispatching jobs, while maximizing the percent utilization of resources. The result is a generalized kanban dispatching strategy, which is more general than the standard multiclass last buffer first serve (LBFS) dispatching strategies for finite buffer flowlines that typically under-utilize the resources. The problem of computational complexity associated with Petri net (PN) applications is overcome by using certain sub-matrices of the PN incidence matrix. Computationally efficient matrix techniques are given for implementing the deadlock-free dispatching policy.     

Robust Adaptive Control of Robots Using Neural Network: Global Stability

A desired compensation adaptive law‐based neural network (DCAL‐NN) controller is proposed for the robust position control of rigid‐link robots. The NN is used to approximate a highly nonlinear function. The controller can guarantee the global asymptotic stability of tracking errors and boundedness of NN weights. In addition, the NN weights here are tuned on‐line, with no offline learning phase required. When compared with standard adaptive robot controllers, we do not require linearity in the parameters, or lengthy and tedious preliminary analysis to determine a regression matrix. The controller can be regarded as a universal reusable controller because the same controller can be applied to any type of rigid robots without any modifications. A comparative simulation study with different robust and adaptive controllers is included.     

Polarized light imaging of white matter architecture

Polarized light imaging (PLI) is a method to image fiber orientation in gross histological brain sections based on the birefringent properties of the myelin sheaths. The method uses the transmission of polarized light to quantitatively estimate the fiber orientation and inclination angles at every point of the imaged section. Multiple sections can be assembled into a 3D volume, from which the 3D extent of fiber tracts can be extracted. This article describes the physical principles of PLI and describes two major applications of the method: the imaging of white matter orientation of the rat brain and the generation of fiber orientation maps of the human brain in white and gray matter. The strengths and weaknesses of the method are set out.     

The second order local-image-structure solid

Characterization of second order local image structure by a 6D vector (or jet) of Gaussian derivative measurements is considered. We consider the affect on jets of a group of transformations-affine intensity-scaling, image rotation and reflection, and their compositions-that preserve intrinsic image structure. We show how this group stratifies the jet space into a system of orbits. Considering individual orbits as points, a 3D orbifold is defined. We propose a norm on jet space which we use to induce a metric on the orbifold. The metric tensor shows that the orbifold is intrinsically curved. To allow visualization of the orbifold and numerical computation with it, we present a mildly-distorting but volume-preserving embedding of it into euclidean 3-space. We call the resulting shape, which is like a flattened lemon, the second order local-image-structure solid. As an example use of the solid, we compute the distribution of local structures in noise and natural images. For noise images, analytical results are possible and they agree with the empirical results. For natural images, an excess of locally 1D structure is found.     

The normal of a fractal surface

  We have discovered a class of fractal functions that are differentiable. Fractal interpolation functions have been used for over a decade to generate rough functions passing through a set of given points. The integral of a fractal interpolation function remains a fractal interpolation function, and this new fractal interpolation function is differentiable. Tensor products of pairs of these fractal functions form fractal surfaces with a well-defined tangent plane. We use this surface normal to shade fractal surfaces, and demonstrate its use with renderings of fractal mirrors.     

Activation of the mouse heart adenosine 5',5"'-P1-P4-tetraphosphate receptor

We have previously demonstrated that mouse brain membrane fractions have a specific, saturable receptor for diadenylated nucleotides. Binding is specific for two adenosines, and the length of the phosphate bridge is critical, with four phosphates being optimal [Hilderman et al. (1991) J. Biol. Chem. 266, 6915-6918]. In this report, we demonstrate that adenosine 5',5"'-P1,P4-tetraphosphate (Ap4A) binding to its receptor is dependent upon an activation step that requires divalent cations and a serine protease. Monoclonal antibodies (Mabs) are identified that inhibit Ap4A binding to its membrane receptor. These antibodies recognize a 212-kDa membrane protein. However, SDS-PAGE analysis of Ap4A cross-linked to membrane fractions reveals that Ap4A is not attached to the 212-kDa peptide but to a 30-kDa polypeptide. Appearance of the 30-kDa polypeptide is dependent on the activation step, and one of the inhibitory antibodies blocks its appearance. We suggest that the protease-dependent processing step involves cleavage of the 212-kDa component with the appearance of an active 30-kDa receptor.     

Computational complexity of determining resource loops inre-entrant flow lines

This paper presents a comparison study of the computational complexity of the general job shop protocol and the more structured flow line protocol in a flexible manufacturing system. It is shown that the representative problem of finding resource invariants is NP-complete in the case of the job shop, while in the flow line case it admits a closed form solution. The importance of correctly selecting part flow and job routing protocols in flexible manufacturing systems to reduce complexity is thereby conclusively demonstrated