Fuzzy angel: uma arquitetura distribuída de telemedicina para monitoramento de pacientes com esclerose lateral amiotrófica

The Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle weakness that leads the patient to death, usually due to respiratory complications. Thus, as the disease progresses the patient will require noninvasive ventilation (NIV) and constant monitoring. This paper presents a distributed architecture for homecare monitoring of nocturnal NIV in patients with ALS. The implementation of this architecture used single board computers and mobile devices placed in patient’s homes, to display alert messages for caregivers and a web server for remote monitoring by the healthcare staff. The architecture used a software based on fuzzy logic and computer vision to capture data from a mechanical ventilator screen and generate alert messages with instructions for caregivers. The monitoring was performed on 29 patients for 7 con-tinuous hours daily during 5 days generating a total of 126000 samples for each variable monitored at a sampling rate of one sample per second. The system was evaluated regarding the rate of hits for character recognition and its correction through an algorithm for the detection and correction of errors. Furthermore, a healthcare team evaluated regarding the time intervals at which the alert messages were generated and the correctness of such messages. Thus, the system showed an average hit rate of 98.72%, and in the worst case 98.39%. As for the message to be generated, the system also agreed 100% to the overall assessment, and there was disagreement in only 2 cases with one of the physician evaluators.

Coding Strategies and Implementations of Compressive Sensing

This dissertation studies the coding strategies of computational imaging to overcome the limitation of conventional sensing techniques. The information capacity of conventional sensing is limited by the physical properties of optics, such as aperture size, detector pixels, quantum efficiency, and sampling rate. These parameters determine the spatial, depth, spectral, temporal, and polarization sensitivity of each imager. To increase sensitivity in any dimension can significantly compromise the others.

This research implements various coding strategies subject to optical multidimensional imaging and acoustic sensing in order to extend their sensing abilities. The proposed coding strategies combine hardware modification and signal processing to exploiting bandwidth and sensitivity from conventional sensors. We discuss the hardware architecture, compression strategies, sensing process modeling, and reconstruction algorithm of each sensing system.

Optical multidimensional imaging measures three or more dimensional information of the optical signal. Traditional multidimensional imagers acquire extra dimensional information at the cost of degrading temporal or spatial resolution. Compressive multidimensional imaging multiplexes the transverse spatial, spectral, temporal, and polarization information on a two-dimensional (2D) detector. The corresponding spectral, temporal and polarization coding strategies adapt optics, electronic devices, and designed modulation techniques for multiplex measurement. This computational imaging technique provides multispectral, temporal super-resolution, and polarization imaging abilities with minimal loss in spatial resolution and noise level while maintaining or gaining higher temporal resolution. The experimental results prove that the appropriate coding strategies may improve hundreds times more sensing capacity.

Human auditory system has the astonishing ability in localizing, tracking, and filtering the selected sound sources or information from a noisy environment. Using engineering efforts to accomplish the same task usually requires multiple detectors, advanced computational algorithms, or artificial intelligence systems. Compressive acoustic sensing incorporates acoustic metamaterials in compressive sensing theory to emulate the abilities of sound localization and selective attention. This research investigates and optimizes the sensing capacity and the spatial sensitivity of the acoustic sensor. The well-modeled acoustic sensor allows localizing multiple speakers in both stationary and dynamic auditory scene; and distinguishing mixed conversations from independent sources with high audio recognition rate.

A Reliable Downscaling of ECG Signals for the Detection of T wave Heterogeneity Features

In cardiovascular disease the definition and the detection of the ECG parameters related to repolarization dynamics in post MI patients is still a crucial unmet need. In addition, the use of a 3D sensor in the implantable medical devices would be a crucial mean in the assessment or prediction of Heart Failure status, but the inclusion of such feature is limited by hardware and firmware constraints. The aim of this thesis is the definition of a reliable surrogate of the 500 Hz ECG signal to reach the aforementioned objective. To evaluate the worsening of reliability due to sampling frequency reduction on delineation performance, the signals have been consecutively down sampled by a factor 2, 4, 8 thus obtaining the ECG signals sampled at 250, 125 and 62.5 Hz, respectively. The final goal is the feasibility assessment of the detection of the fiducial points in order to translate those parameters into meaningful clinical parameter for Heart Failure prediction, such as T waves intervals heterogeneity and variability of areas under T waves. An experimental setting for data collection on healthy volunteers has been set up at the Bakken Research Center in Maastricht. A 16 – channel ambulatory system, provided by TMSI, has recorded the standard 12 – Leads ECG, two 3D accelerometers and a respiration sensor. The collection platform has been set up by the TMSI property software Polybench, the data analysis of such signals has been performed with Matlab. The main results of this study show that the 125 Hz sampling rate has demonstrated to be a good candidate for a reliable detection of fiducial points. T wave intervals proved to be consistently stable, even at 62.5 Hz. Further studies would be needed to provide a better comparison between sampling at 250 Hz and 125 Hz for areas under the T waves.

IntelliChair: a non-intrusive sitting posture and sitting activity recognition system

Current Ambient Intelligence and Intelligent Environment research focuses on the interpretation of a subject’s behaviour at the activity level by logging the Activity of Daily Living (ADL) such as eating, cooking, etc. In general, the sensors employed (e.g. PIR sensors, contact sensors) provide low resolution information. Meanwhile, the expansion of ubiquitous computing allows researchers to gather additional information from different types of sensor which is possible to improve activity analysis. Based on the previous research about sitting posture detection, this research attempts to further analyses human sitting activity. The aim of this research is to use non-intrusive low cost pressure sensor embedded chair system to recognize a subject’s activity by using their detected postures. There are three steps for this research, the first step is to find a hardware solution for low cost sitting posture detection, second step is to find a suitable strategy of sitting posture detection and the last step is to correlate the time-ordered sitting posture sequences with sitting activity. The author initiated a prototype type of sensing system called IntelliChair for sitting posture detection. Two experiments are proceeded in order to determine the hardware architecture of IntelliChair system. The prototype looks at the sensor selection and integration of various sensor and indicates the best for a low cost, non-intrusive system. Subsequently, this research implements signal process theory to explore the frequency feature of sitting posture, for the purpose of determining a suitable sampling rate for IntelliChair system. For second and third step, ten subjects are recruited for the sitting posture data and sitting activity data collection. The former dataset is collected byasking subjects to perform certain pre-defined sitting postures on IntelliChair and it is used for posture recognition experiment. The latter dataset is collected by asking the subjects to perform their normal sitting activity routine on IntelliChair for four hours, and the dataset is used for activity modelling and recognition experiment. For the posture recognition experiment, two Support Vector Machine (SVM) based classifiers are trained (one for spine postures and the other one for leg postures), and their performance evaluated. Hidden Markov Model is utilized for sitting activity modelling and recognition in order to establish the selected sitting activities from sitting posture sequences.2. After experimenting with possible sensors, Force Sensing Resistor (FSR) is selected as the pressure sensing unit for IntelliChair. Eight FSRs are mounted on the seat and back of a chair to gather haptic (i.e., touch-based) posture information. Furthermore, the research explores the possibility of using alternative non-intrusive sensing technology (i.e. vision based Kinect Sensor from Microsoft) and find out the Kinect sensor is not reliable for sitting posture detection due to the joint drifting problem. A suitable sampling rate for IntelliChair is determined according to the experiment result which is 6 Hz. The posture classification performance shows that the SVM based classifier is robust to “familiar” subject data (accuracy is 99.8% with spine postures and 99.9% with leg postures). When dealing with “unfamiliar” subject data, the accuracy is 80.7% for spine posture classification and 42.3% for leg posture classification. The result of activity recognition achieves 41.27% accuracy among four selected activities (i.e. relax, play game, working with PC and watching video). The result of this thesis shows that different individual body characteristics and sitting habits influence both sitting posture and sitting activity recognition. In this case, it suggests that IntelliChair is suitable for individual usage but a training stage is required.

Controladores repetitivos aplicados a filtros ativos de potência sob variações de carga e de frequência da rede

This work presents the development and modification of techniques to reduce the effects of load variation and mains frequency deviation in repetitive controllers applied to active power filters. To minimize the effects of aperiodic signals resulting from the connection or disconnection of non-linear loads is developed a technique which recognizes linear and nonlinear loads, and operates to reset the controller only when the error due to the transition of considerable value, and the transition is from non-linear to linear load. An algorithm to adapt the gain of the repetitive controller, based on a sigmoid function adaptation, in order to minimize the effects caused by random noise in the measurement system is also used. This work also analyzes the effects of frequency variation and presents the main methods to cope with this situation. Some solutions are the change in the number of samples per period and the variation of the sampling rate. The first has the advantage of using linear design techniques and results in a time invariant system. The second method changes the sampling frequency and leads to a time variant system that demands a difficult analysis of stability. The proposed algorithms were tested using the methods of truncation of the number of samples and the method of changing the sampling rate of the system to compensate possible frequency variations of the grid. Experimental results are presented to validate the proposal.

Uso de agentes inteligentes no controle simultâneo da largura e do reforço dos cordões de solda no processo GMAW-S

Tese (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2016.

Structural Health Monitoring Inside Concrete and Grout Using the Wireless Identification and Sensing Platform (WISP)

This research investigates the implementation of battery-less RFID sensing platforms inside lossy media, such as, concrete and grout. Both concrete and novel grouts can be used for nuclear plant decommissioning as part of the U.S. Department of Energy’s (DOE’s) cleanup projects. Our research examines the following: (1) material characterization, (2) analytical modeling of transmission and propagation losses inside lossy media, (3) maximum operational range of RFID wireless sensors embedded inside concrete and grout, and (4) best positioning of antennas for achieving longer communication range between RFID antennas and wireless sensors. Our research uses the battery-less Wireless Identification and Sensing Platform (WISP) which can be used to monitor temperature, and humidity inside complex materials. By using a commercial Agilent open-ended coaxial probe (HP8570B), the measurements of the dielectric permittivity of concrete and grout are performed. Subsequently, the measured complex permittivity is used to formulate analytical Debye models. Also, the transmission and propagation losses of a uniform plane wave inside grout are calculated. Our results show that wireless sensors will perform better in concrete than grout. In addition, the maximum axial and radial ranges for WISP are experimentally determined. Our work illustrates the feasibility of battery-less wireless sensors that are embedded inside concrete and grout. Also, our work provides information that can be used to optimize the power management, sampling rate, and antenna design of such sensors.

Intrapartum fetal scalp lactate sampling for fetal assessment in the presence of a non-reassuring fetal heart rate trace (Protocol)

This is the protocol for a review and there is no abstract. The objectives are as follows: To evaluate the effectiveness and risks of fetal scalp lactate sampling in the assessment of fetal wellbeing during labour, compared with no testing or alternative additional testing (pH, fetal pulse oximetry, etc) for women exhibiting a non-reassuring cardiotocograph trace. A secondary objective of the review is to determine whether effectiveness and risks of intrapartum fetal scalp lactate sampling is influenced by the following: stage of labour; gestation less than 37 completed weeks, greater than or equal to 37 completed weeks; additional tests performed to confirm the presence or absence of fetal acidemia during labour.

Experimental determination of benzene uptake rate in Tenax TA diffusive samplers

This paper proposes a methodology to predict benzene uptake rate in ambient air, using passive samplers with Tenax TA. Variations in the uptake rate were found to occur as a function of the sampling time; and were greater at the beginning of sampling. An empirical model was obtained and values for uptake rate agree with literature. Concentration prediction errors can be minimized by using sampling times of 4 to 14 days, thus avoiding the influence of excessive uptake rates in the initial days and the influence of back diffusion at the end of the sampling period.

A sampling device to quantify offspring release of sessile marine invertebrates

Quantifying the rate of propagule release is of most importance to estimate reproductive output of natural populations, but simple methods to obtain such data are seldom reported. We designed and tested an inexpensive apparatus capable of reliably measure the release of gametes, eggs or larvae of sessile marine invertebrates in vertical walls. A population of the acom barnacle Chthamalus bisinuatus was sampled with this trap over 68d to obtain a time series of naupliar release. An apparent semilunar trend is shown, indicating the effectiveness of this sampling method.

Test procedure for variable rate fertilizer on coffee

The objective was to develop and test a procedure for applying variable rates of fertilizers and evaluate yield response in coffee (Coffea arabica L.) with regard to the application of phosphorus and potassium. The work was conducted during the 2004 season in a 6.4 ha field located in central Sao Paulo state. Two treatments were applied with alternating strips of fixed and variable rates during the whole season: one following the fertilizing procedures recommended locally, and the other based on a grid soil sampling. A prototype pneumatic fertilizer applicator was used, carrying two conveyor belts, one for each row. Harvesting was done with a commercial harvester equipped with a customized volumetric yield monitor, separating the two treatments. Data were analyzed based on geostatistics, correlations and regressions. The procedure showed to be feasible and effective. The area that received fertilizer applications at a variable rate showed a 34% yield increase compared to the area that received a fixed rate. The variable rate fertilizer resulted in a savings of 23% in phosphate fertilizer and a 13% increase in potassium fertilizer, when compared to fixed rate fertilizer. Yield in 2005, the year after the variable rate treatments, still presented residual effect from treatments carried out during the previous cycle.

Increased ovulation rate in Merino ewes immunized against small synthetic peptide fragments of the inhibin alpha subunit

Four experiments were carried out in Merino ewes during a period of 4 years to determine the long-term effects of immunization against different synthetic peptides mimicking the amine terminal of the or subunit of porcine inhibin. Peptides were conjugated to human serum albumin and 100-200 mu g emulsified in Freund's complete adjuvant for the primary immunization. Usually two booster injections were given at monthly intervals with 50-100 mu g conjugated peptide using either incomplete Freund's adjuvant or Montanide : Marcel. In some experiments a further immunization was carried in the next year. Blood samples were taken 10 days after each immunization, during the luteal phase, for estimation of gonadotrophin concentrations and determination of inhibin antibody titres. One day after blood sampling cloprostenol was used to induce luteolysis and laparoscopy was performed in the subsequent oestrous cycle. Immunization of ewes with synthetic peptides 1-32, 1-26, 7-26 and 8-30 resulted in large increases in the ovulation rate (OR). An approximately two-fold increase in OR was observed following the first booster immunization with these peptides and a three- to five-fold increase after the second booster immunization. Immunization with these large peptides resulted in a sustained increase in OR for a period of at least 1 year after the second booster immunization. Of the shorter peptides, peptides 10-26 and 13-26 gave a reasonable ovulatory response, although it was more difficult to obtain a response with peptides 1-16, 8-22, 13-25, 8-19 and 10-19; peptides 7-13 and 1-6 gave no response (but were examined for one breeding season only). The smaller peptides led to lower inhibin antibody titres that were not necessarily associated with increased follicle-stimulating hormone (FSH) or OR. More intensive blood sampling in one experiment showed that following primary immunization against peptide 1-32 there was a transient increase in plasma FSH which did not lead to an increased OR. Moreover, a prolonged period of raised FSH after the first booster was significantly correlated with increased OR. In these animals antibody titres were only slightly increased after primary immunization, but after the first booster immunization higher titres were observed that were significantly correlated with trough FSH values and the subsequent OR. These results are interpreted as showing that (1) to obtain an increase in OR peptides 1-32, 1-26 and 7-26 are suitable as immunogens; (2) smaller peptides are less reliable, often require multiple injections, and the response may be delayed; and (3) an extended period of raised plasma FSH is needed to give a large ovulatory response.

A Comparison of Bayesian and Sampling Theory Inferences in a Probit Model

HE PROBIT MODEL IS A POPULAR DEVICE for explaining binary choice decisions in econometrics. It has been used to describe choices such as labor force participation, travel mode, home ownership, and type of education. These and many more examples can be found in papers by Amemiya (1981) and Maddala (1983). Given the contribution of economics towards explaining such choices, and given the nature of data that are collected, prior information on the relationship between a choice probability and several explanatory variables frequently exists. Bayesian inference is a convenient vehicle for including such prior information. Given the increasing popularity of Bayesian inference it is useful to ask whether inferences from a probit model are sensitive to a choice between Bayesian and sampling theory techniques. Of interest is the sensitivity of inference on coefficients, probabilities, and elasticities. We consider these issues in a model designed to explain choice between fixed and variable interest rate mortgages. Two Bayesian priors are employed: a uniform prior on the coefficients, designed to be noninformative for the coefficients, and an inequality restricted prior on the signs of the coefficients. We often know, a priori, whether increasing the value of a particular explanatory variable will have a positive or negative effect on a choice probability. This knowledge can be captured by using a prior probability density function (pdf) that is truncated to be positive or negative. Thus, three sets of results are compared:those from maximum likelihood (ML) estimation, those from Bayesian estimation with an unrestricted uniform prior on the coefficients, and those from Bayesian estimation with a uniform prior truncated to accommodate inequality restrictions on the coefficients.