Pinhole SPECT imaging: compact projection/backprojection operator for efficient algebraic reconstruction

We describe the efficient algebraic reconstruction (EAR) method, which applies to cone-beam tomographic reconstruction problems with a circular symmetry. Three independant steps/stages are presented, which use two symmetries and a factorization of the point spread functions (PSFs), each reducing computing times and eventually storage in memory or hard drive. In the case of pinhole single photon emission computed tomography (SPECT), we show how the EAR method can incorporate most of the physical and geometrical effects which change the PSF compared to the Dirac function assumed in analytical methods, thus showing improvements on reconstructed images. We also compare results obtained by the EAR method with a cubic grid implementation of an algebraic method and modeling of the PSF and we show that there is no significant loss of quality, despite the use of a noncubic grid for voxels in the EAR method. Data from a phantom, reconstructed with the EAR method, demonstrate 1.08-mm spatial tomographic resolution despite the use of a 1.5-mm pinhole SPECT device and several applications in rat and mouse imaging are shown. Finally, we discuss the conditions of application of the method when symmetries are broken, by considering the different parameters of the calibration and nonsymmetric physical effects such as attenuation.     

Nanoimprinted,Submicrometric, MOF‐Based 2D Photonic Structures: Toward Easy Selective Vapors Sensing by a Smartphone Camera

In this work, a soft‐lithographic approach to fabricate submicrometer metal organic framework (MOF)‐based 2D photonic structures is described. Nanometric zeolitic imidazole framework material ZIF‐8 (zinc) is chosen as the sensible MOF material because of its chemical stability and its vapor selective adsorption properties. Two different systems are fabricated: nanopatterned colloidal ZIF‐8 homo‐ and ZIF‐8/TiO₂ heterostructures. Several features (stripes, squares, etc.) with dimensions of 200 nm are replicated on different substrates such as silicon, flexible plastics, and even aluminum cans, over relatively large surfaces (up to 1 cm²). In addition, the use of these photonic MOF‐heterostructures as very low‐cost sensing platforms compatible with smartphone technology is demonstrated. This method relies on the evaluation of the change in diffraction efficiency of the photonic MOF‐patterns, induced by the MOF refractive index variation, which is simply detected by a charge coupled device (CCD) camera, as those integrated in smartphones, without need for complex optical instrumentations for transduction data processing. Performances of the sensors are first evaluated using isopropyl alcohol adsorption/desorption cycling as a model case. In addition, a “real” environmental issue is tackled. Selective detection of styrene in presence of interfering water is demonstrated at concentrations below the human permissible exposure limit. In situ ellispometric analyses are also carried out in order to confirm the sensor performances and to propose a mechanism for styrene uptake into the nanoMOFs.     

Reduction of a single granule drying model: An essential step in preparation of a population balance model with a continuous growth term

The development of a Population Balance Model (PBM) for a pharmaceutical granule drying process requires a continuous growth term; the latter actually represents the drying process as the moisture content is the internal coordinate of the PBM. To establish such a PBM, a complex drying model for a single granule needs reduction in complexity. The starting point is a detailed model that describes the drying behavior of single pharmaceutical granules. A Global Sensitivity Analysis (GSA) was performed to detect the most sensitive degrees of freedom in the model as these need to be retained in the reduced model. Simulations of the complex drying model were, in a next phase, used to develop the reduced model, which describes the decrease of the moisture content in function of the gas temperature. The developed reduced model was then included in a Population Balance Equation (PBE) to describe the drying behavior of a population of granules. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1127–1138, 2013     

Regularized variational Bayesian learning of echo state networks with delay&sum readout

In this work, a variational Bayesian framework for efficient training of echo state networks (ESNs) with automatic regularization and delay&sum (D&S) readout adaptation is proposed. The algorithm uses a classical batch learning of ESNs. By treating the network echo states as fixed basis functions parameterized with delay parameters, we propose a variational Bayesian ESN training scheme. The variational approach allows for a seamless combination of sparse Bayesian learning ideas and a variational Bayesian space-alternating generalized expectation-maximization (VB-SAGE) algorithm for estimating parameters of superimposed signals. While the former method realizes automatic regularization of ESNs, which also determines which echo states and input signals are relevant for "explaining" the desired signal, the latter method provides a basis for joint estimation of D&S readout parameters. The proposed training algorithm can naturally be extended to ESNs with fixed filter neurons. It also generalizes the recently proposed expectation-maximization-based D&S readout adaptation method. The proposed algorithm was tested on synthetic data prediction tasks as well as on dynamic handwritten character recognition.     

Comprehensive Automation for Specialty Crops: Year 1 results and lessons learned

Comprehensive Automation for Specialty Crops is a project focused on the needs of the specialty crops sector, with a focus on apples and nursery trees. The project’s main thrusts are the integration of robotics technology and plant science; understanding and overcoming socio-economic barriers to technology adoption; and making the results available to growers and stakeholders through a nationwide outreach program. In this article, we present the results obtained and lessons learned in the first year of the project with a reconfigurable mobility infrastructure for autonomous farm driving. We then present sensor systems developed to enable three real-world agricultural applications—insect monitoring, crop load scouting, and caliper measurement—and discuss how they can be deployed autonomously to yield increased production efficiency and reduced labor costs.     

Fault tolerant control design via hybrid petri nets

This paper proposes a novel fault tolerant control (FTC) scheme for hybrid systems modeled by hybrid Petri nets (HPNs). The HPNs model consists of discrete and continuous PNs. The faults are represented by unobservable discrete transitions or the normal observable discrete transitions with abnormal firing time in discrete PNs. First, an observer‐based fault diagnosis method is proposed to estimate the marking in discrete places with unknown initial marking and diagnose the faulty behavior simultaneously. Then, an adaptive fault tolerant controller is designed to maintain the general mutual exclusion constraints (GMEC) of discrete PNs, and a scheme that adjusts firing speeds of continuous transitions is provided to maintain the optimality of continuous PNs. Finally, an example of an intelligent transportation system consisting of automated vehicles on a bridge is included to demonstrate the effectiveness of our developed techniques. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Matching and alpha-equivalence check for nominal terms

Nominal terms generalise first-order terms by including abstraction and name swapping constructs. α-equivalence can be easily axiomatised using name swappings and a freshness relation, which makes the nominal approach well adapted to the specification of systems that involve binders. Nominal matching is matching modulo α-equivalence and has applications in programming languages, rewriting, and theorem proving. In this paper, we describe efficient algorithms to check the validity of equations involving binders and to solve matching problems modulo α-equivalence, using the nominal approach.     

Transport properties of alumina nanofluids

Recent studies have showed that nanofluids have significantly greater thermal conductivity compared to their base fluids. Large surface area to volume ratio and certain effects of Brownian motion of nanoparticles are believed to be the main factors for the significant increase in the thermal conductivity of nanofluids. In this paper all three transport properties, namely thermal conductivity, electrical conductivity and viscosity, were studied for alumina nanofluid (aluminum oxide nanoparticles in water). Experiments were performed both as a function of volumetric concentration (3-8%) and temperature (2-50?°C). Alumina nanoparticles with a mean diameter of 36?nm were dispersed in water. The effect of particle size was not studied. The transient hot wire method as described by Nagaska and Nagashima for electrically conducting fluids was used to test the thermal conductivity. In this work, an insulated platinum wire of 0.003?inch diameter was used. Initial calibration was performed using de-ionized water and the resulting data was within 2.5% of standard thermal conductivity values for water. The thermal conductivity of alumina nanofluid increased with both increase in temperature and concentration. A maximum thermal conductivity of 0.7351?W?m(-1)?K(-1) was recorded for an 8.47% volume concentration of alumina nanoparticles at 46.6?°C. The effective thermal conductivity at this concentration and temperature was observed to be 1.1501, which translates to an increase in thermal conductivity by 22% when compared to water at room temperature. Alumina being a good conductor of electricity, alumina nanofluid displays an increasing trend in electrical conductivity as volumetric concentration increases. A microprocessor-based conductivity/TDS meter was used to perform the electrical conductivity experiments. After carefully calibrating the conductivity meter's glass probe with platinum tip, using a standard potassium chloride solution, readings were taken at various volumetric concentrations. A 3457.1% increase in the electrical conductivity was measured for a small 1.44% volumetric concentration of alumina nanoparticles in water. The highest value of electrical conductivity, 314?μS?cm(-1), was recorded for a volumetric concentration of 8.47%. In the determination of the kinematic viscosity of alumina nanofluid, a standard kinematic viscometer with constant temperature bath was used. Calibrated capillary viscometers were used to measure flow under gravity at precisely controlled temperatures. The capillary viscometers were calibrated with de-ionized water at different temperatures, and the resulting kinematic viscosity values were found to be within 3% of the standard published values. An increase of 35.5% in the kinematic viscosity was observed for an 8.47% volumetric concentration of alumina nanoparticles in water. The maximum kinematic viscosity of alumina nanofluid, 2.901?42?mm(2)?s(-1), was obtained at 0?°C for an 8.47% volumetric concentration of alumina nanoparticles. The experimental results of the present work will help researchers arrive at better theoretical models.