Portable multi-purpose device for monitoring of physiological informations

Unconstrained system that measures physiological information as skin temperatures and heart rate per unit time of a human subject was developed. The system contained portable device included memory control unit, instrumentation unit, timer and batteries, read-out unit, test unit and verify unit. Total number of data and channels, and interval were selected by switches in the memory control unit. The data from the instrumentation unit were transferred to memory control unit and stored in the Erasable Programmable ROM (EPROM). After measurement, EPROM chip was taken off the memory control unit and put on the read-out unit which transferred the data to the microcomputer. The data were directly calculated and analyzed by microcomputer. In application of the instrumentation unit, 8-channel skin thermometer was developed and tested. After amplification, 8 analog signals were multiplexed and converted into the binary codes. The digital signals were sequentially transferred to memory control unit and stored in the EPROM under controlled signal. The accuracy of the system is determined primarily by the accuracy of the sensor of instrumentation unit. The overall accuracy of 8-channel skin thermometer is conservatively stated within 0.1 degree C. This may prove to be useful in providing an objective measurement of human subjects, and can be used in studying environmental effect for human body and sport activities in a large population setting.     

Multi-analyte assay for triazines using cross-reactive antibodies and neural networks

A biosensor system based on total internal reflectance fluorescence (TIRF) was used to discriminate a mixture of the triazines atrazine and simazine. Only cross-reactive antibodies were available for these two analytes. The biosensor is fully automated and can be regenerated allowing several hundreds of measurements without any user input. Even a remote control for online monitoring in the field is possible. The multivariate calibration of the sensor signal was performed using artificial neural networks, as the relationship between the sensor signals and the concentration of the analytes is highly non-linear. For the development of a multi-analyte immunoassay consisting of two polyclonal antibodies with cross-reactivity to atrazine and simazine and different derivatives immobilised on the transducer surface, the binding characteristics between these substances like binding capacity and cross-reactivity were characterised. The examination of three different measurement procedures showed that a two-step measurement using only one antibody per step allows a quantification of both analytes in a mixture with limits of detection of 0.2 microg/l for atrazine and 0.3 microg/l for simazine. The biosensor is suitable for online monitoring in the field and remote control is possible.     

Two-dimensional fluorescence spectroscopy: a novel approach for controlling fed-batch cultivations

In industrial fed-batch cultivations it is often necessary to control substrate concentrations at a low level to prevent the production of overflow metabolites and thus optimize the biomass yield. A new method for on-line monitoring and fed-batch control based on fluorescence measurements has been developed. Via instantaneous in situ measurements and multivariate data analysis a chemometric model has been established, which enables the rapid detection of ethanol production at aerobic Saccharomyces cerevisiae fed-batch cultivations. The glucose feed rate is controlled by predicting the metabolic state directly from the fluorescence intensities. Thus, ethanol production could be avoided completely while increasing the biomass yield accordingly. The robust instrumentation is suitable for industrial applications.     

On-line monitoring and controlling system for fermentation processes

A personal computer-based on-line monitoring and controlling system was developed for the fermentation of microorganism. The on-line HPLC system for the analysis of glucose and ethanol in the fermentation broth was connected to the fermenter via an auto-sampling equipment, which could perform the pipetting, filtration and dilution of the sample and final injection onto the HPLC through automation based on a programmed procedure. The A/D and D/A interfaces were equipped in order to process the signals from electrodes and from the detector of HPLC, and to direct the feed pumps, the motor of stirrer and gas flow-rate controller. The software that supervised the control of the stirring speed, gas flow-rate, pH value, feed flow-rate of medium, and the on-line measurement of glucose and ethanol concentration was programmed by using Microsoft Visual Basic under Microsoft Windows. The signal for chromatographic peaks from on-line HPLC was well captured and processed using an RC filter and a smoothing algorithm. This monitoring and control system was demonstrated to be effective in the ethanol fermentation of Zymomonas mobilis operated in both batch and fed-batch modes. In addition to substrate and product concentrations determined by on-line HPLC, the biomass concentration in Z. mobilis fermentation could also be on-line estimated by using the pH control and an implemented software sensor. The substrate concentration profile in the fed-back fermentation followed well the set point profile due to the fed-back action of feed flow-rate control.     

Development of EMG-based mode and intent recognition algorithms for a computer-controlled above-knee prosthesis

Conventional above-knee prostheses are unable to replace normal leg function due to the lack of adaptive control. Computer-controlled prostheses offer the possibility to implement such adaptive control. For a particular locomotion mode, control algorithms can generate appropriate damping profiles as a function of selected sensory inputs and stored information on normal walking. However, it is essential in such systems that the locomotor modes can be determined accurately from the inputs of the control system. Recognition of the intent to change from one mode to another is also a necessity because the control system has to account for such transitions. The possibility of using EMG signals from hip muscles and muscle residuals of the stump for this purpose is investigated. The modes tested are level walking, ramp ascent and ramp descent with slopes of 6° and 9°. EMG activity curves for three muscles: gluteus maximus, gluteus medius and tensor fasciae latae, are presented for the different modes. The results are obtained from two reference groups of 12 normal individuals and from a prosthetic patient. The results show the possibility of discriminating between modes. A discussion is made of the implementation of the results obtained by a finite state approach and the difficulties relating to the use of EMG signals for control purposes.     

Adaptive Methods of Trend Detection and Their Application in Analysing Biosignals

In the study estimations of the linear trend and its slope of discrete deterministic and stochastic signals are introduced, which are based on adaptive methods of mean estimation. Because of its simple recursive nature both the trend and the slope estimation may be realized in real time and thus they may be used for on-line procedures e.g. in monitoring systems of intensive care units. The adaptive estimation functions are self-learning, that means they are able to adapt automatically to the new conditions after structure changes of the time series and thus may be also used for detecting points of structure fracture. The properties of the adaptive trend- and slope estimations are demonstrated by some characteristic simulation examples and the possibilities of their applications for analysing biological signals are investigated in three different medical cases.     

Motor imaginary-based brain–machine interface design using programmable logic controllers for the disabled

Aiming at the implementation of brain–machine interfaces (BMI) for the aid of disabled people, this paper presents a system design for real-time communication between the BMI and programmable logic controllers (PLCs) to control an electrical actuator that could be used in devices to help the disabled. Motor imaginary signals extracted from the brain's motor cortex using an electroencephalogram (EEG) were used as a control signal. The EEG signals were pre-processed by means of adaptive recursive band-pass filtrations (ARBF) and classified using simplified fuzzy adaptive resonance theory mapping (ARTMAP) in which the classified signals are then translated into control signals used for machine control via the PLC. A real-time test system was designed using MATLAB for signal processing, KEP-Ware V4 OLE for process control (OPC), a wireless local area network router, an Omron Sysmac CPM1 PLC and a 5 V/0.3 A motor. This paper explains the signal processing techniques, the PLC's hardware configuration, OPC configuration and real-time data exchange between MATLAB and PLC using the MATLAB OPC toolbox. The test results indicate that the function of exchanging real-time data can be attained between the BMI and PLC through OPC server and proves that it is an effective and feasible method to be applied to devices such as wheelchairs or electronic equipment.     

Pediatric Anesthesia Monitoring with the Help of EEG and ECG

This paper presents research regarding the monitoring of the brain and the adequacy of anesthesia during surgery. Particular variables are derived from EEG and ECG signals and are correlated to anesthetic gas (sevoflurane) concentration, in pediatric anesthesia. The methods used for parameter extraction are based on change detection theory and time-frequency representation. Preliminary results show that the expired anesthetic gas concentration modulates both the heart rate variability and the duration of the burst suppression. Monitors of the central nervous system and autonomic nervous system activities can be expected to be based on these variables.     

Optimal spatio‐temporal hybrid sampling designs for ecological monitoring

Question: Static sampling designs for collecting spatial data efficiently are being readily utilized by ecologists, however, most ecological systems involve a multivariate spatial process that evolves dynamically over time. Efficient monitoring of such spatio‐temporal systems can be achieved by modeling the dynamic system and reducing the uncertainty associated with the effect of design choice at future observation times. However, can we combine traditional techniques with dynamic methods to find optimal dynamic sampling designs for monitoring the succession of a herbaceous community? Location: Lower Hamburg Bend Conservation Area, Missouri, USA (40°34′42″ lat. 95°45′38″ long.). Methods: The dynamic nature of the system under study is modeled in such a way that uncertainty in the measurements and temporal process can both be accounted for. Both fixed and roving monitoring locations were used in conjunction with a spatio‐temporal statistical model to efficiently determine optimal locations of roving monitors over time based on the reduction of uncertainty in predictions. Results: During the first 3 years of the study, roving monitors where held at fixed locations to allow for statistical parameter estimation from which to make predictions. Optimal monitoring locations for the remaining 2 years were selected based on the overall reduction in prediction uncertainty. Conclusions: The dynamic and adaptive vegetation monitoring scheme allowed for the efficient collection of data that will be utilized for many future ecological studies. By optimally placing an additional set of monitoring locations, we were able to utilize information about the system dynamics when informing the data collection process.     

Particle Size, Moisture, and Fluidization Variations Described by Indirect In-line Physical Measurements of Fluid Bed Granulation

The aim of this study was to evaluate an instrumentation system for a bench scale fluid bed granulator to determine the parameters expressing the changing conditions during the spraying phase of a fluid bed process. The study focused mainly on four in-line measurements (dependent variables): fluidization parameter (calculated by inlet air flow rate and rotor speed), pressure difference over the upper filters, pressure difference over the granules (lower filter), and temperature of the fluidizing mass. In-line particle size measured by the spatial filtering technique was an essential predictor variable. Other physical process measurements of the automated granulation system, 25 direct and 12 derived parameters, were also utilized for multivariate modeling. The correlation and partial least squares analyses revealed significant relationships between various process parameters highlighting the particle size, moisture, and fluidization effect. Fluidization parameter and pressure difference over upper filters were found to correlate with in-line particle size and therefore could be used as estimates of particle size during granulation. The pressure difference over the granules and the temperature of the fluidizing mass expressed the moisture conditions of wet granulation. The instrumentation system evaluated here is an invaluable aid to gaining more control for fluid bed processing to obtain repeatable granules for further processing.     

Identification and control of dissolved oxygen in hybridoma cell culture in a shear sensitive environment

The productivity of mammalian cells can be enhanced by facilitating adequate oxygen transfer into the cultivation medium. However, current methods of controlling dissolved oxygen (DO) fail to account for alterations in medium composition during the course of the fermentation. These changes, which directly affect gas solubility and overall mass transfer coefficient, may be significant and deteriorate controller's performance in the long run. In this paper, the applications of Generalized Predictive Controllers (GPC) to DO control were investigated in a shear sensitive environment and compared to PID and Model Predictive Controllers (MPC). Input and output data for system identification were initially generated by varying the composition of oxygen fed into the bioreactor from 0 to 0.21 mol % while keeping the total inlet gas flow rate at 8.75 vvm. The process was identified using an AutoRegressive model with eXogeneous inputs (ARX) model and tested on different data sets. The model parameters were then correlated with the overall mass transfer coefficients. In simulation tests, the output of the PID controller switched from minimum to maximum values while more continuous control signals were obtained with the MPC and GPC controllers. When tested in a cell-free medium, all three controllers were able to track setpoint changes with some chattering observed in the control signals. The GPC outperformed the MPC and PID controllers when applied to the cultivation of hybridoma cells.     

Raman spectroscopy and chemometrics for on‐line control of glucose fermentation by Saccharomyces cerevisiae

This work presents the use of Raman spectroscopy and chemometrics for on‐line control of the fermentation process of glucose by Saccharomyces cerevisiae. In a first approach, an on‐line determination of glucose, ethanol, glycerol, and cells was accomplished using multivariate calibration based on partial least squares (PLS). The PLS models presented values of root mean square error of prediction (RMSEP) of 0.53, 0.25, and 0.02% for glucose, ethanol and glycerol, respectively, and RMSEP of 1.02 g L^?1 for cells. In a second approach, multivariate control charts based on multiway principal component analysis (MPCA) were developed for detection of fermentation fault‐batch. Two multivariate control charts were developed, based on the squared prediction error (Q) and Hotelling's T². The use of the Q control chart in on‐line monitoring was efficient for detection of the faults caused by temperature, type of substrate and contamination, but the T² control chart was not able to monitor these faults. On‐line monitoring by Raman spectroscopy in conjunction with chemometric procedures allows control of the fermentative process with advantages in relation to reference methods, which require pretreatment, manipulation of samples and are time consuming. Also, the use of multivariate control charts made possible the detection of faults in a simple way, based only on the spectra of the system. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Development of an automation system for a tablet coater

An instrumentation and automation system for a side-vented pan coater with a novel air-flow rate measurement system for monitoring the film-coating process of tablets was designed and tested. The instrumented coating system was tested and validated by film-coating over 20 pilot-scale batches of tablets with aqueous-based hydroxypropyl methylcellulose (HPMC). Thirteen different process parameters were continuously measured and monitored, and the most significant ones were logged for analysis. Laser profilometry was used to measure the surface roughness of the coated tablets. The instrumentation system provided comprehensive and quantitative information on the process parameters monitored. The measured process parameters and the responses of the film-coated tablet batches showed that the coating process is reproducible. The inlet air-flow rate influenced the coating process and the subsequent quality of the coated tablets. Increasing the inlet flow rate accelerated the drying of the tablet surface. At high inlet flow rate, obvious film-coating defects (ie, unacceptable surface roughness of the coated tablets) were observed and the loss of coating material increased. The instrumented and automated pancoating system described, including historical data storage capability and a novel air-flow measurement system, is a useful tool for controlling and characterizing the tablet film-coating process. Monitoring of critical process parameters increases the overall coating process efficiency and predictability.     

A Linear Domain System Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Genetic Algorithm

<正> This paper proposes a new adaptive linear domain system identification method for small unmanned aerial rotorcraft.Byusing the flash memory integrated into the micro guide navigation control module, system records the data sequences of flighttests as inputs (control signals for servos) and outputs (aircraft's attitude and velocity information).After data preprocessing, thesystem constructs the horizontal and vertical dynamic model for the small unmanned aerial rotorcraft using adaptive geneticalgorithm.The identified model is verified by a series of simulations and tests.Comparison between flight data and the one-stepprediction data obtained from the identification model shows that the dynamic model has a good estimation for real unmannedaerial rotorcraft system.Based on the proposed dynamic model, the small unmanned aerial rotorcraft can perform hovering,turning, and straight flight tasks in real flight tests.     

Schistosoma mansoni: sex-specific modulation of parasite growth by host immune signals

Development of female schistosomes from infectious cercariae to mature egg-producing adults requires both male schistosomes and an intact adaptive immune system. By examining single sex infections in immunodeficient mice, we provide evidence that female schistosome development is not directly influenced by the adaptive immune system, whereas male development is. Our data are consistent with a sequential model of schistosome development, where the adaptive immune system signals development of mature males, which subsequently stimulate development of mature females. The male schistosome therefore appears to play a central role both in transducing signals from the adaptive immune system and in facilitating female development.     

An adaptive modeling approach using spiking-augmentation method to improve chemometric model performance in bioprocess monitoring

Intensified and continuous processes require fast and robust methods and technologies to monitor product titer for faster analytical turnaround time, process monitoring, and process control. The current titer measurements are mostly offline chromatography-based methods which may take hours or even days to get the results back from the analytical labs. Thus, offline methods will not meet the requirement of real time titer measurements for continuous production and capture processes. FTIR and chemometric based multivariate modeling are promising tools for real time titer monitoring in clarified bulk (CB) harvests and perfusate lines. However, empirical models are known to be vulnerable to unseen variability, specifically a FTIR chemometric titer model trained on a given biological molecule and process conditions often fails to provide accurate predictions of titer in another molecule under different process conditions. In this study, we developed an adaptive modeling strategy: the model was initially built using a calibration set of available perfusate and CB samples and then updated by augmenting spiking samples of the new molecules to the calibration set to make the model robust against perfusate or CB harvest of the new molecule. This strategy substantially improved the model performance and significantly reduced the modeling effort for new molecules.     

An expert system for fault management assistance on a space sleep experiment

The expert system, Principal Investigator-in-a-box, or [PI], was designed to assist astronauts or other operators in performing experiments outside their expertise. Currently, the software helps astronauts calibrate instruments for a Sleep and Respiration Experiment without contact with the investigator on the ground. It flew on the Space Shuttle missions STS-90 and STS-95. [PI] displays electrophysiological signals in real time, alerts astronauts via the indicator lights when a poor signal quality is detected, and advises astronauts how to restore good signal quality. Thirty subjects received training on the sleep instrumentation and the [PI] interface. A beneficial effects of [PI] and training reduced troubleshooting time. [PI] benefited subjects on the most difficult scenarios, even though its lights were not 100% accurate. Further, questionnaires showed that most subjects preferred monitoring waveforms with [PI] assistance rather than monitoring waveforms alone. This study addresses problems of complex troubleshooting and the extended time between training and execution that is common to many human operator situations on earth such as in power plant operation, and marine exploration.     

The remapping of space in motor learning and human–machine interfaces

Studies of motor adaptation to patterns of deterministic forces have revealed the ability of the motor control system to form and use predictive representations of the environment. One of the most fundamental elements of our environment is space itself. This article focuses on the notion of Euclidean space as it applies to common sensory motor experiences. Starting from the assumption that we interact with the world through a system of neural signals, we observe that these signals are not inherently endowed with metric properties of the ordinary Euclidean space. The ability of the nervous system to represent these properties depends on adaptive mechanisms that reconstruct the Euclidean metric from signals that are not Euclidean. Gaining access to these mechanisms will reveal the process by which the nervous system handles novel sophisticated coordinate transformation tasks, thus highlighting possible avenues to create functional human–machine interfaces that can make that task much easier. A set of experiments is presented that demonstrate the ability of the sensory-motor system to reorganize coordination in novel geometrical environments. In these environments multiple degrees of freedom of body motions are used to control the coordinates of a point in a two-dimensional Euclidean space. We discuss how practice leads to the acquisition of the metric properties of the controlled space. Methods of machine learning based on the reduction of reaching errors are tested as a means to facilitate learning by adaptively changing he map from body motions to controlled device. We discuss the relevance of the results to the development of adaptive human–machine interfaces and optimal control.     

Proactive and Reactive Processes in the Medial Frontal Cortex: An Electrophysiological Study

The posterior medial frontal cortex (pMFC) is known to be involved in adaptive goal-directed behavior, but its specific function is not yet clear. Most theories have proposed that the pMFC monitors performance in a reactive manner only, but it is possible that the pMFC also contributes to performance monitoring in a proactive manner. To date, the evidence for proactive pMFC activity is equivocal. Here, we investigated pMFC activity before, during and after the performance of a challenging motor task. Participants navigated a cursor through narrow and wide mazes in randomly intermixed trials. On each trial, participants saw previews of the actual maze display prior to gaining control of the cursor. Event-related potentials (ERPs) to the preview displays were compared to ERPs elicited by no-go signals and errors. Compared to the wider maze, the preview display for the more challenging narrow maze elicited a medial-frontal negativity (MFN) similar to the ERP components elicited by no-go signals and errors. Like these known ERP components, the preview-elicited MFN appeared to be generated from a source in pMFC. This is consistent with the hypothesis that the pMFC participates in adaptive behavior whenever there is a need for increased effort to maintain successful task performance.     

Enzyme logic gates for the digital analysis of physiological level upon injury

A biocomputing system composed of a combination of AND/IDENTITY logic gates based on the concerted operation of three enzymes: lactate oxidase, horseradish peroxidase and glucose dehydrogenase was designed to process biochemical information related to pathophysiological conditions originating from various injuries. Three biochemical markers: lactate, norepinephrine and glucose were applied as input signals to activate the enzyme logic system. Physiologically normal concentrations of the markers were selected as logic 0 values of the input signals, while their abnormally increased concentrations, indicative of various injury conditions were defined as logic 1 input. Biochemical processing of different patterns of the biomarkers resulted in the formation of norepi-quinone and NADH defined as the output signals. Optical and electrochemical means were used to follow the formation of the output signals for eight different combinations of three input signals. The enzymatically processed biochemical information presented in the form of a logic truth table allowed distinguishing the difference between normal physiological conditions, pathophysiological conditions corresponding to traumatic brain injury and hemorrhagic shock, and abnormal situations (not corresponding to injury). The developed system represents a biocomputing logic system applied for the analysis of biomedical conditions related to various injuries. We anticipate that such biochemical logic gates will facilitate decision-making in connection to an integrated therapeutic feedback-loop system and hence will revolutionize the monitoring and treatment of injured civilians and soldiers.