Spectral imaging in preclinical research and clinical pathology.

Spectral imaging methods are attracting increased interest from researchers and practitioners in basic science, pre-clinical and clinical arenas. A combination of better labeling reagents and better optics creates opportunities to detect and measure multiple parameters at the molecular and cellular level. These tools can provide valuable insights into the basic mechanisms of life, and yield diagnostic and prognostic information for clinical applications. There are many multispectral technologies available, each with its own advantages and limitations. This chapter will present an overview of the rationale for spectral imaging, and discuss the hardware, software and sample labeling strategies that can optimize its usefulness in clinical settings

Dynamic Modeling, Predictive Control and Optimization of a Rapid Pressure Swing Adsorption System

Rapid Pressure Swing Adsorption (RPSA) is a gas separation technology with an important commercial application for Medical Oxygen Concentrators (MOCs). MOCs use RPSA technology to produce high purity oxygen (O2) from ambient air, and provide medical oxygen therapy to Chronic Obstructive Pulmonary Disease (COPD) patients. COPD is a lung disease which prevents O2 from entering a patient\u27s blood, and reduces the blood oxygen level. The standard therapy for COPD is to provide the patient with high purity (~90%) O2. MOCs have become more popular than traditional O2 gas cylinders due to their improved safety, and smaller device size and weight. The MOC market is growing rapidly and was expected to grow from $358 million in 2011 to$1.8 billion in 2017. Recently, a novel, single-bed MOC design was developed and tested to further reduce the size and weight of the device, and provide a continuous supply of O2 to the patient. This single-bed design uses a complex RPSA cyclic process with many nonlinear effects. Flow reversals, discrete valve switching, nonlinear adsorption effects, and complex fluid dynamics all make operating the RPSA system very challenging. Feedback control is necessary in a final commercial product to ensure the device operates reliably, but feedback control of PSA systems is not well studied in the current literature.In this work, a study of dynamic modeling, predictive control and optimization of this single-bed RPSA device is presented. A detailed, nonlinear plant model of the RPSA device is used to study the dynamics of the system as well as design a Model Predictive Controller (MPC) for the RPSA system. The plant model is a fully coupled, nonlinear set of Partial and Ordinary Differential Equations (PDEs and ODEs) which act as a representation of reality when design and evaluating the MPC. A sub-space model identification technique using Pseudo-Random Binary Sequence (PRBS) input signals generate a linear model which reduces the computational cost of MPC, and allows the algorithm to be implemented as an embedded controller for the RPSA device. The multivariable MPC independently manipulates the RPSA cycle step durations to control both the product composition and pressure. This MPC strategy was designed and tested in simulation before being implemented on a lab-scale device.The MPC is implemented onto a lab-scale MOC prototype using Raspberry Pi hardware, and evaluated using several MOC-relevant disturbance scenarios. The MPC is also expanded using piece-wise linear modeling to improve the performance of an RPSA device for other concentrated O2 applications. The embedded MPC features a convex quadratic optimization problem which is solved in real time using online output measurements. Additional hardware in the embedded controller operates the RPSA cycle and implements control actions supplied by the MPC.Design and optimization of RPSA systems remains an active area of research, and many PSA models have been used to optimize RPSA cycles in simulation. In this work, a model-free steady state optimization approach using the embedded hardware is presented which does not require a detailed process model, and uses experimental data and a nonlinear solver to optimize the RPSA operation given various objectives

Restructurable Controls

Restructurable control system theory, robust reconfiguration for high reliability and survivability for advanced aircraft, restructurable controls problem definition and research, experimentation, system identification methods applied to aircraft, a self-repairing digital flight control system, and state-of-the-art theory application are addressed

SYSTEM IDENTIFICATION AND MODEL PREDICTIVE CONTROL FOR INTERACTING SERIES PROCESS WITH NONLINEAR DYNAMICS

This thesis discusses the empirical modeling using system identification technique and the implementation of a linear model predictive control with focus on interacting series processes. In general, a structure involving a series of systems occurs often in process plants that include processing sequences such as feed heat exchanger, chemical reactor, product cooling, and product separation. The study is carried out by experimental works using the gaseous pilot plant as the process. The gaseous pilot plant exhibits the typical dynamic of an interacting series process, where the strong interaction between upstream and downstream properties occurs in both ways. The subspace system identification method is used to estimate the linear model parameters. The developed model is designed to be robust against plant nonlinearities. The plant dynamics is first derived from mass and momentum balances of an ideal gas. To provide good estimations, two kinds of input signals are considered, and three methods are taken into account to determine the model order. Two model structures are examined. The model validation is conducted in open-loop and in closed-loop control system. Real-time implementation of a linear model predictive control is also studied. Rapid prototyping of such controller is developed using the available equipments and software tools. The study includes the tuning of the controller in a heuristic way and the strategy to combine two kinds of control algorithm in the control system. A simple set of guidelines for tuning the model predictive controller is proposed. Several important issues in the identification process and real-time implementation of model predictive control algorithm are also discussed. The proposed method has been successfully demonstrated on a pilot plant and a number of key results obtained in the development process are presented

Aeronautical Engineering: A continuing bibliography, supplement 120

This bibliography contains abstracts for 297 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980

New Approaches in Automation and Robotics

The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book

LED Selection for Spectral (Multispectral) Imaging

Research was performed to design an LED-based spectral imaging system having channels, commonly referred to as a multispectral imaging system. The first part tackled the evaluation of a camera model in predicting the signals of a 10 LED LEDmotive Technologies Spectra Tunelab coupled with a Finger Lakes Instrumentation panchromatic camera. The camera model was shown to be valid and effective in predicting the camera signal taking into account the color transformation noise. The second part involved the computational selection of 10 LEDs in order to determine the optimum combination for a custom Spectra Tunelab. The computational selection used the spectral data provided by the manufacturer for their 37 available LEDs. The LEDs were grouped according to a specified wavelength range. The binning process helped in decreasing the computational cost and time; the possible combinations were reduced to 110,592 from the initial calculated value of 348,330,136 possible combinations. The combinations were further reduced to 1000 according to spectral reflectance Root-Mean-Square-Error (RMSE). The Euclidean and score ranking methods were then used to evaluate color transformation noise, spectral error and colorimetric accuracy. Goodness of Fit Coefficient and Throughput were calculated as well to further evaluate the combinations. A compromise among the values were reached to identify the best possible LED combination. The optimal combination has peak wavelengths at 390 nm, 450 nm, 475 nm, 505 nm, 540 nm, 550 nm, 590 nm, 620 nm, 660 nm, and 745 nm. All the LEDs were narrow band except the LED with its peak wavelength at 550 nm. This particular LED was similar to the human visual system’s luminous efficiency function. Its inclusion was important for colorimetric accuracy and small color transformation noise. When evaluating a large color-gamut target made using commonly used commercial pigments and several artist pigments, the following quality metrics were achieved: average ∆E00 of 0.12, total Noise, N of 3.35, a lightness noise (∆L) of 1.22, spectral reflectance RMSE of 6.4 x10-3, GFC of 0.97 and a total throughput of 646.85

Propulsion Controls and Diagnostics Research at NASA Glenn Research Center

With the increased emphasis on aircraft safety, enhanced performance and affordability, and the need to reduce the environmental impact of aircraft, there are many new challenges being faced by the designers of aircraft propulsion systems. Also the propulsion systems required to enable the National Aeronautics and Space Administration (NASA) Vision for Space Exploration in an affordable manner will need to have high reliability, safety and autonomous operation capability. The Controls and Dynamics Branch (CDB) at NASA Glenn Research Center (GRC) in Cleveland, Ohio, is leading and participating in various projects in partnership with other organizations within GRC and across NASA, the U.S. aerospace industry, and academia to develop advanced controls and health management technologies that will help meet these challenges through the concept of Intelligent Propulsion Systems. This paper describes the current activities of the CDB under the NASA Aeronautics Research and Exploration Systems Missions. The programmatic structure of the CDB activities is described along with a brief overview of each of the CDB tasks including research objectives, technical challenges, and recent accomplishments. These tasks include active control of propulsion system components, intelligent propulsion diagnostics and control for reliable fault identification and accommodation, distributed engine control, and investigations into unsteady propulsion systems

Advances in Youla-Kucera parametrization: A Review

International audienceYoula-Kucera (YK) parametrization was formulated decades ago for obtaining the set of controllers stabilizing a linear plant. This fundamental result of control theory has been used to develop theoretical tools solving many control problems ranging from stable controller switching, closed-loop identification, robust control, disturbance rejection, adaptive control to fault tolerant control.This paper collects the recent work and classifies the maccording to the use of YK parametrization, Dual YK parametrization or both, providing the latest advances with main applications indifferent control fields. A final discussion gives some insights on the future trends in the field

Analog dithering techniques for highly linear and efficient transmitters

The current thesis is about investigation of new methods and techniques to be able to utilize the switched mode amplifiers, for linear and efficient applications. Switched mode amplifiers benefit from low overlap between the current and voltage wave forms in their output terminals, but they seriously suffer from nonlinearity. This makes it impossible to use them to amplify non-constant envelope message signals, where very high linearity is expected. In order to do that, dithering techniques are studied and a full linearity analysis approach is developed, by which the linearity performance of the dithered amplifier can be analyzed, based on the dithering level and frequency. The approach was based on orthogonalization of the equivalent nonlinearity and is capable of prediction of both co-channel and adjacent channel nonlinearity metrics, for a Gaussian complex or real input random signal. Behavioral switched mode amplifier models are studied and new models are developed, which can be utilized to predict the nonlinear performance of the dithered power amplifier, including the nonlinear capacitors effects. For HFD application, self-oscillating and asynchronous sigma delta techniques are currently used, as pulse with modulators (PWM), to encode a generic RF message signal, on the duty cycle of an output pulse train. The proposed models and analysis techniques were applied to this architecture in the first phase, and the method was validated with measurement on a prototype sample, realized in 65 nm TSMC CMOS technology. Afterwards, based on the same dithering phenomenon, a new linearization technique was proposed, which linearizes the switched mode class D amplifier, and at the same time can reduce the reactive power loss of the amplifier. This method is based on the dithering of the switched mode amplifier with frequencies lower than the band-pass message signal and is called low frequency dithering (LFD). To test this new technique, two test circuits were realized and the idea was applied to them. Both of the circuits were of the hard nonlinear type (class D) and are integrated CMOS and discrete LDMOS technologies respectively. The idea was successfully tested on both test circuits and all of the linearity metric predictions for a digitally modulated RF signal and a random signal were compared to the measurements. Moreover a search method to find the optimum dither frequency was proposed and validated. Finally, inspired by averaging interpretation of the dithering phenomenon, three new topologies were proposed, which are namely DLM, RF-ADC and area modulation power combining, which are all nonlinear systems linearized with dithering techniques. A new averaging method was developed and used for analysis of a Gilbert cell mixer topology, which resulted in a closed form relationship for the conversion gain, for long channel devices