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.

Abstract
Perception algorithms are essential for autonomous or semi-autonomous vehicles to perceive the semantics of their surroundings, including object detection, panoptic segmentation, and tracking. Decision-making in case of safety-critical situations, like autonomous emergency braking and collision avoidance, relies on the outputs of these algorithms. This makes it essential to correctly assess such perception systems before their deployment and to monitor their performance when in use. It is difficult to test and validate these systems, particularly at runtime, due to the high-level and complex representations of their outputs. This paper presents an overview of different existing metrics used for the evaluation of LiDAR-based perception systems, emphasizing particularly object detection and tracking algorithms due to their importance in the final perception outcome. Along with generally used metrics, we also discuss the impact of Planning KL-Divergence (PKL), Timed Quality Temporal Logic (TQTL), and Spatio-temporal Quality Logic (STQL) metrics on object detection algorithms. In the case of panoptic segmentation, Panoptic Quality (PQ) and Parsing Covering (PC) metrics are analysed resorting to some pretrained models. Finally, it addresses the application of diverse metrics to evaluate different pretrained models with the respective perception algorithms on publicly available datasets. Besides the identification of the various metrics being proposed, their performance and influence on models are also assessed after conducting new tests or reproducing the experimental results of the reference under consideration.

Abstract

Abstract
Heart failure (HF) is a multifactorial chronic syndrome with progressive increasing incidence causing a huge financial burden worldwide. Remote monitoring should, in theory, improve HF management, but given increasing morbidity and mortality, a question remains: are we monitoring it properly? Device-based home monitoring enables objective and continuous measurement of vital variables and non-invasive devices should be first choice for elderly patients. There is no shortage of literature on the subject, however, most studies were designed to monitor a single variable or class of variables that were not properly assembled and, to the best of our knowledge, there are no large randomized studies about their impact on HF patient management. To overcome this problem, we carefully selected the most critical possible HF decompensating factors to design MONITORIA, a non-invasive device for comprehensive HF home monitoring. MONITORIA stands for MOnitoring Non-Invasively To Overcome mortality Rates of heart Insufficiency on Ambulatory, and in this paper, which is part I of a series of three articles, we discuss the theoretical basis for its design. MONITORIA and its inherent follow-up strategy will optimize HF patient care as it is a promising device, which will essentially adapt innovation not to the disease but rather to the patients. (C) 2020 Sociedade Portuguesa de Cardiologia. Published by Elsevier Espana, S.L.U.

Abstract
Introduction: Heart failure (HF) represents a huge financial and economic burden worldwide. Some authors advocate that remote monitoring should be implemented to improve HF management, but given its increasing incidence, as well as its morbidity and mortality, a question still remains: are we monitoring it properly? There is no shortage of literature on home monitoring devices, however, most of them are designed to monitor an unsuitable array of variables and, to the best of our knowledge, there are no large randomized studies about their impact on morbidity/mortality of HF patients. Objective: Description of a novel monitoring device. Methods: As a solution, we designed MONITORIA (MOnitoring NonInvasively To Overcome mortality Rates of heart Insufficiency on Ambulatory). Results: This is a multimodal device that will provide real time monitoring of vital, electrophysiological, hemodynamic and chemical signs, transthoracic impedance, and physical activity levels. The device is meant to perform continuous analysis and transmission of all data. Significant alterations in a patient's variable will alert the attending physician and, in case of potentially life-threatening situations, the national emergency medical system. The MONITORIA device will, also, have a function that sends shocks or functions as a pacemaker to treat certain arrhythmias/blockades. This function can be activated the very first time the patient utilizes it, based on their risk of sudden cardiac death. Discussion/Conclusions: MONITORIA is a promising device mostly because it is included in a follow-up program that takes into account a multi-perspective feature of HF development and is based on the real world patient, adapting innovations not to the disease but rather to the patients. (C) 2021 Sociedade Portuguesa de Cardiologia. Published by Elsevier Espana, S.L.U.