Edge detection and characterization of digitized images

Pattern Analysis and Applications - We present a method for determining which of a large set of pixels are inside or on the boundary of a polygon. The method works much quicker than the standard...     

Epinephrine doses delivered from auto-injectors stored at excessively high temperatures

Context: Prompt injection of epinephrine (adrenaline) from epinephrine auto-injectors (EAIs) is the primary treatment for anaphylaxis in out-of-hospital settings. Storage of EAIs at room temperature (25?°C) is advised; however, storage at excessively high temperatures sometimes occurs. To our knowledge, there are no previous publications on the doses of epinephrine ejected from EAIs after storage at such temperatures.

Objective: We examined the epinephrine doses delivered from activated EAIs stored constantly or cyclically at 70?°C.

Methods: Twenty-five in-date EAIs were stored constantly or cyclically at 70?°C (excessive heat) or 25?°C (controls) for 5?d or 10?d. EAIs were activated and the epinephrine doses in the ejected solutions were measured using HPLC-UV. The enantiomeric purity of epinephrine was also measured by HPLC-UV.

Results: Control EAIs ejected a volume of 0.300?±?0.006?mL containing 103.7?±?3.3% of labeled dose (LD). After 5?d or 10?d of constant storage at 70?°C and activation at 70?°C, EAIs ejected a volume of 0.367?±?0.008?mL containing 96.8?±?3.8% LD and 0.373?±?0.007?mL containing 77.7?±?3.3% LD, respectively. After 5?d of cyclic storage at 70?°C and cooling to 25?°C before activation, EAIs ejected a volume of 0.311?±?0.008?mL containing 87.2?±?1.9% LD. Under the experimental conditions of this study, the resultant chromatographic peaks of epinephrine solutions from all EAIs represented only the pure l-enantiomer of epinephrine.

Conclusion: EAIs should be stored under recommended conditions of the manufacturer. EAIs stored at excessively high temperatures cannot be used to treat humans while still hot, and when cooled, cannot be relied on to deliver the labeled epinephrine dose in anaphylaxis.     

Emission polarization of roughened glass and aluminum surfaces

Ellipsometer measurements of the effective complex refractive index at a wavelength of 10.6 μm are made on a series of glass and aluminum surfaces of increasing surface roughness. The measured values are then used to calculate the degree of emission polarization and are shown to be in agreement with the experimentally determined values when depolarization is small. Comparisons are also made with calculations based on the Kirchhoff scattering theory. Both the theory and the experimental results indicate that it is the local surface slope and not the roughness magnitude that is the prime factor in determining the degree of emission polarization from the samples studied.     

Integrated diffractive andrefractive elements for spectrum shaping

Diffractive elements can be designed for spectrum shaping in the Fourier or Fresnel plane by iterative methods. It is necessary to use a Fourier lens and the wavelength for which the diffractive elements were designed to get the required spectrum shaping at the Fourier plane. Using a different wavelength will cause chromatic aberration. We deal with the combination of refractive and diffractive elements and two or more different diffractive elements on the same element to get appropriate beam shaping of light sources with a multiple spectral output. Simulations are preformed that transform the profile of a He-Ne laser with a Nd:YAG laser source, and shape the trapezoidal beam profile of an excimer laser into a Gaussian beam is also considered.     

Editorial: Special issue on approximate dynamic programming and reinforcement learning

We are extremely pleased to present this special issue of the Journal of Control Theory and Applications.Approximate dynamic programming (ADP) is a general and effective approach for solving optimal control and estimation problems by adapting to uncertain environments over time.ADP optimizes the sensing objectives accrued over a future time interval with respect to an adaptive control law,conditioned on prior knowledge of the system,its state,and uncertainties.A numerical search over the present value of the control minimizes a Hamilton-Jacobi-Bellman (HJB) equation providing a basis for real-time,approximate optimal control.     

Closed-loop drip irrigation control using a hybrid wireless sensor and actuator network

In this research, a closed-loop drip irrigation control hybrid wireless sensor and actuator network (HWSAN) prototype were developed and deployed in a crop field for soil property precise measurement and precision irrigation in accordance with the measured soil property. The HWSAN was composed of a wireless sensor and actuator network (WSAN) used for in-field soil property monitoring and irrigation control and a laboratory supervising system. The WSAN included ten sensor nodes, five irrigation control nodes...     

Physics-Based Person Tracking Using the Anthropomorphic Walker

We introduce a physics-based model for 3D person tracking. Based on a biomechanical characterization of lower-body dynamics, the model captures important physical properties of bipedal locomotion such as balance and ground contact. The model generalizes naturally to variations in style due to changes in speed, step-length, and mass, and avoids common problems (such as footskate) that arise with existing trackers. The dynamics comprise a two degree-of-freedom representation of human locomotion with inelastic ground contact. A stochastic controller generates impulsive forces during the toe-off stage of walking, and spring-like forces between the legs. A higher-dimensional kinematic body model is conditioned on the underlying dynamics. The combined model is used to track walking people in video, including examples with turning, occlusion, and varying gait. We also report quantitative monocular and binocular tracking results with the HumanEva dataset.     

Action selection and task sequence learning for hybrid dynamical cognitive agents

As a foundation for action selection and task-sequencing intelligence, the reactive and deliberative subsystems of a hybrid agent can be unified by a single, shared representation of intention. In this paper, we summarize a framework for hybrid dynamical cognitive agents (HDCAs) that incorporates a representation of dynamical intention into both reactive and deliberative structures of a hybrid dynamical system model, and we present methods for learning in these intention-guided agents. The HDCA framework is based on ideas from spreading activation models and belief–desire–intention (BDI) models. Intentions and other cognitive elements are represented as interconnected, continuously varying quantities, employed by both reactive and deliberative processes. HDCA learning methods—such as Hebbian strengthening of links between co-active elements, and belief–intention learning of task-specific relationships—modify interconnections among cognitive elements, extending the benefits of reactive intelligence by enhancing high-level task sequencing without additional reliance on or modification of deliberation. We also present demonstrations of simulated robots that learned geographic and domain-specific task relationships in an office environment.     

A rapid computational filter for predicting the rate of human renal clearance

In silico models that predict the rate of human renal clearance for a diverse set of drugs, that exhibit both active secretion and net re-absorption, have been produced using three statistical approaches. Partial Least Squares (PLS) and Random Forests (RF) have been used to produce continuous models whereas Classification And Regression Trees (CART) has only been used for a classification model. The best models generated from either PLS or RF produce significant models that can predict acids/zwitterions, bases and neutrals with approximate average fold errors of 3, 3 and 4, respectively, for an independent test set that covers oral drug-like property space. These models contain additional information on top of any influence arising from plasma protein binding on the rate of renal clearance. Classification And Regression Trees (CART) has been used to generate a classification tree leading to a simple set of Renal Clearance Rules (RCR) that can be applied to man. The rules are influenced by lipophilicity and ion class and can correctly predict 60% of an independent test set. These percentages increase to 71% and 79% for drugs with renal clearances of < 0.1 ml/min/kg and > 1 ml/min/kg, respectively. As far as the authors are aware these are the first set of models to appear in the literature that predict the rate of human renal clearance and can be used to manipulate molecular properties leading to new drugs that are less likely to fail due to renal clearance.