Development of Low-Cost Solder Paste Hand Dispenser

Nanášení pájecí pasty pomocí šablonového tisku je v případě prototypování a kusové výroby drahé a nelze rychle reagovat na změnu designu desky. V těchto případech se proto používají manuální nebo automatické dávkovače. Tato práce se zabývá vývojem nízko-nákladového ručního dávkovače, který využívá jednoduchý pružinový systém a plastovou konstrukci, uzpůsobenou pro výrobu metodou 3D tisku. Navrhované řešení umožňuje na rozdíl od jiných dávkovacích systémů kontinuální vytlačování pasty. Jak ukázal test, depozice pájecí pasty je stejně kvalitní jako u tlakového dávkovače, přitom ale výrobní náklady nečiní více než 1 euro, což je zanedbatelná částka oproti jiným, komerčním řešením.In prototyping and piece-production, stencil printing of the solder paste is expensive, and with the use of the stencil, it is not possible to react fast on changes in the design. Therefore, automatic or manual dispensers are used. This work aimed to develop a low-cost dispensing system based on a simple principle that uses torsion springs and its plastic construction is printable by a 3D printer. Unlike other dispensing systems, the presented dispenser allows continuous deposition of the solder paste. As the performance test showed, the deposition can be as good as with a commercial dispenser. Furthermore, the cost of the dispenser does not exceed 1€. In comparison with other commercial solutions, it is a negligible amount

What impact can hospitalization environment produce on the ANS functioning in patients with Unresponsive Wakefulness Syndrome?–24-hour monitoring

Objectives: Studies showed that the recovery of patients with Unresponsive Wakefulness Syndrome (UWS) is also correlated to the recovery of circadian rhythms. In this study, we observed the correlations between patients with UWS biometrical and ambient parameters. Methods: A dedicated monitoring system was realized to record and correlate the level of noise and luminosity with biometric Heart Rate (HR), Heart Rate Variability (HRV) and Breath Rate (BR) parameters. Eleven patients with UWS were recruited and monitored for 13 ± 7 days. Correlation of ambient and biometric parameters was analyzed by Spearman’s test. Wilcoxon’s test was used to compare the biometric parameters in two different moments of daily activity in the rehabilitation unit (night and day). Patients showed a moderate negative or positive correlation between biometric and ambient parameters. Results: Significant differences between night and morning (0.0001 \u3c p ≤ 0.001) were found for HR, HRV and BR in seven, five and four patients, respectively, at Wilcoxon’s test. HR and BR were higher during the night while HRV was lower. Conclusion: In patients with UWS, lower HRV and higher HR and BR during the night might be indicative of interference in sleep/wake cycles. The modifications of the environment surrounding the patient due to the unit procedures of the staff and/or some interaction modalities of the relatives may have an effect on residual endogenous mechanisms of self-regulation. However, differences between night and day in the biometrical parameters are not necessarily linked to the changes in the environment care unit

Sleep stage classification with ECG and respiratory effort

Automatic sleep stage classification with cardiorespiratory signals has attracted increasing attention. In contrast to the traditional manual scoring based on polysomnography, these signals can be measured using advanced unobtrusive techniques that are currently available, promising the application for personal and continuous home sleep monitoring. This paper describes a methodology for classifying wake, rapid-eye-movement (REM) sleep, and non-REM (NREM) light and deep sleep on a 30 s epoch basis. A total of 142 features were extracted from electrocardiogram and thoracic respiratory effort measured with respiratory inductance plethysmography. To improve the quality of these features, subject-specific Z-score normalization and spline smoothing were used to reduce between-subject and within-subject variability. A modified sequential forward selection feature selector procedure was applied, yielding 80 features while preventing the introduction of bias in the estimation of cross-validation performance. PSG data from 48 healthy adults were used to validate our methods. Using a linear discriminant classifier and a ten-fold cross-validation, we achieved a Cohen's kappa coefficient of 0.49 and an accuracy of 69% in the classification of wake, REM, light, and deep sleep. These values increased to kappa = 0.56 and accuracy = 80% when the classification problem was reduced to three classes, wake, REM sleep, and NREM sleep