Abstract
The estimation of the harmonic content of an ADC output is fundamental to evaluate its suitability to perform according the requirements specified for an application. The use of the traditional frequency analysis leads to a large hardware overhead due to the amount of on-chip processing being involved, or to a large quantity of data to be transferred in case the processing is performed in a tester. This paper presents an algorithm capable of estimating the harmonics with similar accuracy but with the advantage of being more suitable for a BIST implementation, since it requires a reduced number of on-chip operations, and that only a small set of values has to be supplied outside the chip for further processing. It relies, on the fact that harmonics generated by an ADC are mathematical related with the polynomial coefficients of its transfer function. ADC offset and gain errors can also be measured.

Abstract
This paper describes the design of a processor specific for testing cores embedded in system-on-chip. This processor which can be implemented within a system's reconfigurable area, shall be responsible for scheduling and control test operations and perform preliminary data processing, as well as to provide the interface with an external tester Building these test operations on-chip allows for simplifying external tester interface and to reduce testing time. The testing procedure and the infrastructure required to test an AID converter is described as an example.

Abstract
A procedure is proposed to estimate an analogue-to-digital converter's signal-to-noise plus distortion ratio using the histogram method. The procedure provides results that are in close agreement with the ones obtained with the spectral analysis and sinewave fitting methods. It is shown that the errors obtained by using former implementations of the histogram method are due to not considering the input stimulus probability density function, and it is shown how these errors can be rectified.

Abstract
High levels of dependability are required to promote the adherence by public and medical communities to wearable medical devices. The study presented herein addresses fault detection and diagnosis in these systems. The main objective resides on correctly classifying the captured physiological signals, in order to distinguish whether the actual cause of a detected anomaly is a wearer health condition or a system functional flaw. Data fusion techniques, namely fuzzy logic, artificial neural networks, decision trees and naive Bayes classifiers are employed to process the captured data to increase the trust levels with which diagnostics are made. Concerning the wearer condition, additional information is provided after classifying the set of signals into normal or abnormal (e.g., arrhythmia, tachycardia and bradycardia). As for the monitoring system, once an abnormal situation is detected in its operation or in the sensors, a set of tests is run to check if actually the wearer shows a degradation of his health condition or if the system is reporting erroneous values. Selected features from the vital signals and from quantities that characterize the system performance serve as inputs to the data fusion algorithms for Patient and System Status diagnosis purposes. The algorithms performance was evaluated based on their sensitivity, specificity and accuracy. Based on these criteria the naive Bayes classifier presented the best performance.

Abstract

In the last decade the advances in wearable technology have driven and transformed performance monitoring in fitness and wellness applications, surveillance in extreme (working) conditions, and management of chronic diseases. These innovations have opened a whole new perspective on health and social care, challenged by vast expenditures in ageing societies.

Abstract
The increased demand for and use of autonomous driving and advanced driver assistance systems has highlighted the issue of abnormalities occurring within the perception layers, some of which may result in accidents. Recent publications have noted the lack of standardized independent testing formats and insufficient methods with which to analyze, verify, and qualify LiDAR (Light Detection and Ranging)-acquired data and their subsequent labeling. While camera-based approaches benefit from a significant amount of long-term research, images captured through the visible spectrum can be unreliable in situations with impaired visibility, such as dim lighting, fog, and heavy rain. A redoubled focus upon LiDAR usage would combat these shortcomings; however, research involving the detection of anomalies and the validation of gathered data is few and far between when compared to its counterparts. This paper aims to contribute to expand the knowledge on how to evaluate LiDAR data by introducing a novel method with the ability to detect these patterns and complement other performance evaluators while using a statistical approach. Although it is preliminary, the proposed methodology shows promising results in the evaluation of an algorithm's confidence score, the impact that weather and road conditions may have on data, and fringe cases in which the data may be insufficient or otherwise unusable.