Analog test interface for IEEE 1687 employing split SAR architecture to support embedded instrument dependability applications

Embedded instruments have become ubiquitous in modern day System-on-Chips for test and monitoring purposes. IEEE 1687 or IJTAG addresses the standardization of access and operation of these embedded instruments. Recently, there has been a lot of interest in employing embedded instruments for dependability purposes. Many of these embedded instruments are required to monitor physical quantities which are analog in nature. A cost-effective architecture to integrate these analog instruments into the IEEE 1687 infrastructure is a bottleneck and has not yet been standardized. This paper presents a time and area efficient architecture to interface analog embedded instruments onto the IEEE 1687 network especially for dependability applications. The architecture mitigates the drawbacks associated with utilizing an analog test bus and enables periodic sampling with minimal hardware overhead. The simulations to illustrate the concept have been conducted with TSMC 40nm CMOS technology

Predicting Wireless sensor readings with Neural network

Wireless sensor networks are becoming a part of our daily lives, as they act as a bridge between the physical world and the virtual world.One of the problems encountered by this type of networks while trying to fulfill their goals is the rate of energy consumption. The approach considered in this paper was that of an artificial neural network with the aim of reducing the rate of power consumption and thereby increasing the performance and durability of the network. Support vector machines backed artificial neural model was the best of all models picked. It was then compared with a linear regression model to see if there would be any good reasons to migrate to the this new approach. At the end, it was observed that the chosen network performed slightly above the level of the existing model. The implications of the observed results were that another form of prediction model can replace the existing one or alternated with one another in the process of operation of a wireless sensor network.Juhtmevabadest sensorvõrkudest on saamas osa meie igapäevalust. Tegemist on sillaga füüsilise ja virtuaalse maailma vahel. Üheks probleemiks seda laadi võrkude puhul on aga energia tarbimise määr. Käesolevas lõputöös uuriti lähenemist, kus kasutatakse tehisneurovõrke eesmärgiga vähendada energiatarvet ja seeläbi parendada sensorvõrgu efektiivsust ning vastupidavust. Tugivektormasinatega toetatud tehisneuromudel valiti välja kui parim vaatluse all olnud mudel. Seda mudelit võrreldi lineaarse regressiooni mudeliga, et näha kas väljavalitud mudeli puhul leidub mõjuvaid põhjuseid just seda eelistada. Lõpuks selgitati välja, et uuritava mudeli efektiivsus oli veidi kõrgem kui võrreldaval mudelil. Töö tulemustest järeldub, et olemasolevaid ennustusmudeleid võib asendada alternatiivsetega või kasutada neid vaheldumisi juhtmevaba sensorvõrgu töö käigus

Pushing the Boundaries of Spacecraft Autonomy and Resilience with a Custom Software Framework and Onboard Digital Twin

This research addresses the high CubeSat mission failure rates caused by inadequate software and overreliance on ground control. By applying a reliable design methodology to flight software development and developing an onboard digital twin platform with fault prediction capabilities, this study provides a solution to increase satellite resilience and autonomy, thus reducing the risk of mission failure. These findings have implications for spacecraft of all sizes, paving the way for more resilient space missions

Real Time Non-Invasive Hemodynamic Assessment of Ventricular Tachycardia

Hemodynamically unstable ventricular tachycardia (VT) is a critical cardiac arrhythmia associated with hemodynamic compromise that requires immediate cardioversion to prevent sudden cardiac death. Since unnecessary cardioverter defibrillators shocks damage the heart and increase the risk of mortality, the discrimination between unstable (i.e. requiring cardioversion) and stable (i.e. not requiring cardioversion) VT is of paramount importance. The aim of this study was to propose and assess non-invasive identification of hemodynamically unstable VT using photoplethysmography (PPG). Seventy-five (n = 75) episodes of VT were recorded in 14 patients undergoing invasive electrophysiological studies for VT catheter ablation. Invasive continuous arterial blood pressure (ABP), PPG and electrocardiogram (ECG) were simultaneously recorded. VTs were classified as unstable if during the first 10 seconds from onset, the mean ABP (PVT < 60PVT) was PVT < 60PVT <60 mmHg or if PVT dropped more than 30% with respect to a 10 seconds baseline (i.e. ratio RABP <0.70). Five PPG morphological features were derived and compared to the heart rate from the ECG. PPG markers detected hemodynamically unstable VT with accuracy as high as 86% and were more accurate than the heart rate. The mean absolute slope was the best PPG parameter for classification of PVT< 60PVT <60PVT < 60 mmHg (AUC = 0.85, Sensitivity = 72%, Specificity = 86%) and RABP <0.70RABP< 0.70 (AUC = 0.90, Sensitivity = 83%, Specificity = 89%) and it was automatically selected in the best two-variables logistic regression, for which AUC = 0.94. In conclusion, PPG analysis can accurately identify haemodynamically unstable VTs and has potential to enable optimization of VT therapy and reduce unnecessary and harmful cardioversion shocks