Simultaneous blood perfusion and oxygenation monitoring is crucial for patients undergoing a transplant procedure. This becomes of great importance during the surgical recovery period of a transplant procedure when uncorrected loss of perfusion or reduction in oxygen saturation can result in patient death. Pulse oximeters are standard monitoring devices which are used to obtain the perfusion level and oxygen saturation using the optical absorption properties of hemoglobin. However, in cases of varying perfusion due to hemorrhage, blood clot or acute blockage, the oxygenation results obtained from traditional pulse oximeters are erroneous due to a sudden drop in signal strength. The long term goal of the project is to devise an implantable optical sensor which is able to perform better than the traditional pulse oximeters with changing perfusion and function as a local warning for sudden blood perfusion and oxygenation loss. In this work, an optical sensor based on a pulse oximeter with an additional source at 810nm wavelength has been developed for in situ monitoring of transplant organs. An algorithm has been designed to separate perfusion and oxygenation signals from the composite signal obtained from the three source pulse oximetry-based sensor. The algorithm uses 810nm reference signals and an adaptive filtering routine to separate the two signals which occur at the same frequency. The algorithm is initially applied to model data and its effectiveness is further tested using in vitro and in vivo data sets to quantify its ability to separate the signals of interest. The entire process is done in real time in conjunction with the autocorrelation-based time domain technique. This time domain technique uses digital filtering and autocorrelation to extract peak height information and generate an amplitude measurement and has shown to perform better than the traditional fast Fourier transform (FFT) for semi-periodic signals, such as those derived from heart monitoring. In particular, in this paper it is shown that the two approaches produce comparable results for periodic in vitro perfusion signals. However, when used on semi periodic, simulated, perfusion signals and in vivo data generated from an optical perfusion sensor the autocorrelation approach clearly (Standard Error, SE = 0.03) outperforms the FFT-based analysis (Standard Error, SE = 0.62).

Gemeinsamer Workshop von Universitaetsrechenzentrum und Professur Rechnernetze und verteilte Systeme der Fakultaet fuer Informatik der TU Chemnitz. Service Monitoring ist eine Voraussetzung für zuverlässige Dienste. Ausgehend von einer Einführung in die Techniken des Real-Time Monitoring wird das System "Big Brother" und der Einsatz im Universitätsrechenzentrum der TUC vorgestellt. Abschliesend werden einige Aspekte der Benachrichtigung beim Auftreten kritischer Ereignisse und Zustände diskutiert. bei der Alarmierung

Bayesian process control is a statistical process control (SPC) scheme that uses the posterior state probabilities as the control statistic. The key issue is to decide when to restore the process based on real-time observations. Such problems have been extensively studied in the framework of partially observable Markov decision processes (POMDP), with particular emphasis on the structure of optimal control policy. Almost all existing structural results on the optimal policies are limited to the two-state processes, where the class of control-limit policy is optimal. However, the two-state model is a gross simplification, as real production processes almost always involve multiple states. For example, a machine in the production system often has multiple failure modes differing in their effects; the deterioration process can often be divided into multiple stages with different degradation levels; the condition of a complex multi-unit system also requires a multi-state representation. We investigate the optimal control policies for multi-state processes with fixed sampling scheme, in which information about the process is represented by a belief vector within a high dimensional probability simplex. It is well known that obtaining structural results for such high-dimensional POMDP is challenging. Firstly, we prove that for an infinite-horizon process subject to multiple competing assignable causes, a so-called conditional control limit policy is optimal. The optimal policy divides the belief space into two individually connected regions, which have analytical bounds. Next, we address a finite-horizon process with at least one absorbing state and show that a structured optimal policy can be established by transforming the belief space into a polar coordinate system, where a so-called polar control limit policy is optimal. Our model is general enough to include many existing models in the literature as special cases. The structural results also lead to significantly efficient algorithms for computing the optimal policies. In addition, we characterize the condition for some out-of-control state to be more desirable than the in-control state. The existence of such counterintuitive situation indicates that multi-state process control is drastically different from the two-state case.

Bayesian process control is a statistical process control (SPC) scheme that uses the posterior state probabilities as the control statistic. The key issue is to decide when to restore the process based on real-time observations. Such problems have been extensively studied in the framework of partially observable Markov decision processes (POMDP), with particular emphasis on the structure of optimal control policy. Almost all existing structural results on the optimal policies are limited to the two-state processes, where the class of control-limit policy is optimal. However, the two-state model is a gross simplification, as real production processes almost always involve multiple states. For example, a machine in the production system often has multiple failure modes differing in their effects; the deterioration process can often be divided into multiple stages with different degradation levels; the condition of a complex multi-unit system also requires a multi-state representation. We investigate the optimal control policies for multi-state processes with fixed sampling scheme, in which information about the process is represented by a belief vector within a high dimensional probability simplex. It is well known that obtaining structural results for such high-dimensional POMDP is challenging. Firstly, we prove that for an infinite-horizon process subject to multiple competing assignable causes, a so-called conditional control limit policy is optimal. The optimal policy divides the belief space into two individually connected regions, which have analytical bounds. Next, we address a finite-horizon process with at least one absorbing state and show that a structured optimal policy can be established by transforming the belief space into a polar coordinate system, where a so-called polar control limit policy is optimal. Our model is general enough to include many existing models in the literature as special cases. The structural results also lead to significantly efficient algorithms for computing the optimal policies. In addition, we characterize the condition for some out-of-control state to be more desirable than the in-control state. The existence of such counterintuitive situation indicates that multi-state process control is drastically different from the two-state case.

No description available.

Active flow control of a precessing jet is the focus of this work. A round jet confined by a round cavity exhibits a self-excited rotational motion, precession, for a specific range of cavity lengths. Active flow control of this unstable flow provides the ability to control near-field mixing of the precessing jet. Twelve micro-jets on the periphery of the nozzle inlet are used as actuation and near-field pressure data is measured by four pressure probes at the chamber exit to monitor the flow behavior. A phase plane, based on pressure signals, is used to find a Reynolds number and actuation frequency range where actuation stabilizes the flow motion. Phase-locked stereoscopic PIV is also used to validate the pressure processing tool. The results confirm the pressure measurement and micro-jet actuation can be employed to develop a future closed-loop flow control on a precessing jet.

Ring closing metathesis (RCM) is a chemical transformation that converts a bisalkene compound into a cycloalkene. It is catalyzed by transition metal complexes containing carbene ligands (that feature metal-carbon double bonds). The mechanism is well-understood, however, there are numerous details of the reaction that are less well understood, especially concerning catalyst activation and decomposition and formation of byproducts. This thesis takes a new approach to the study of RCM: analysis of the reaction using real-time mass spectrometric techniques. Electrospray ionization (ESI) mass spectrometry was employed in this study, and the real-time aspect was enabled by using pressurized sample infusion (PSI). Observation of the reactants and products was enabled using charge-tagged bis-alkenes of the general formula [Bu2N{(CH2)nCH=CH2}2]+ [PF6]–. These were synthesized in two steps using a generally applicable methodology to generate a wide range of ring sizes of the product, from 5- to 15-membered rings. Examination of their behavior under carefully optimized RCM conditions using Grubbs’ second-generation catalyst showed a wide variation in reaction rates and amount of byproducts, largely due to ring-strain effects (especially high for 5- and 9-membered rings). Byproducts always exhibited a 14 Da mass unit difference from starting materials or products, and Orbitrap MS analysis confirmed it was CH2. Isomerization was suspected to lead to byproducts. A pathway for byproducts via isomerization and cross metathesis was proposed. The source of actual isomerization catalyst was believed to be from the precatalyst itself as the evidence of precatalyst decomposition was observed. Finally, to prove our isomerization hypothesis, an authentic isomerization catalyst was deliberately added into a fast and clean reaction along with Grubbs’ second-generation catalyst, and it produced the expected byproducts. Only small amounts of oligomeric intermediates were observed, probably because of the low concentrations used. [ClPCy3]+ was a new short-lived decomposition product stemming from catalyst breakdown, along with already-known imidazolium and protonated phosphine decomposition products. Overall, the thesis provides deep new insights into the nature of RCM reactions, in particular revealing the importance of isomerization in RCM reactions that are slow due to ring strain effects and in uncovering a new decomposition pathway for important RCM catalysts. / Graduate

No description available.

Real-time water quality monitoring is crucial due to land utilization increases which can negatively impact aquatic ecosystems from surface water runoff. Conventional monitoring methodologies are laborious, expensive, and spatio-temporally limited. Autonomous surface vehicles (ASVs), equipped with sensors/instrumentation, serve as mobile sampling stations that reduce labor and enhance data resolution. However, ASV autopilot navigational accuracy is affected by environmental forces (wind, current, and waves) that can alter trajectories of planned paths and negatively affect spatio-temporal resolution of water quality data. This study demonstrated a commercially available solar powered ASV equipped with a multi-sensor payload ability to operate autonomously to accurately and repeatedly maintain established A-B line transects under varying environmental conditions, where lateral deviation from a planned linear route was measured and expressed as cross-track error (XTE). This work provides a framework for development of spatial/temporal resolution limitations of ASVs for real-time monitoring campaigns and future development of in-situ sampling technologies.

No description available.