Abstract
Blindness and visual impairment are commonly associated with social and functional limitations, almost 45 million people in the world have blindness, and 135 million have any visual impairment. This condition has a significant impact on the quality of life and brings many challenges to the individual, one of which is the navigation and positioning tasks. Although there are already apps capable of helping visually impaired people (VIP) for mobility purposes, most of them focus on detecting obstacles and, therefore, on avoiding dangerous situations. However, mobility of VIP involves many more tasks, such as knowing their exact position and staying informed along an entire route. For this purpose, a standalone and customizable solution is proposed that uses traditional visual recognition of landmarks to process the surroundings of the current location of the visually impaired person using a smartphone and informing about the nearby places assuring the user a sense of the site. For feature detection, it used the oriented features from accelerated segment test (FAST) and rotated binary robust-independent elementary feature (BRIEF) (ORB) algorithm, and for feature matching, it used the brute-force method with the k-nearest neighbor (KNN) algorithm. Results show that the proposed solution can analyze pictures in fractions of a second with satisfactory accuracy.

Abstract
To develop a full battery model in view to accurate battery management, Li-ion cell dynamics is modelled by a capacitor in series with a simplified Randles circuit. The open circuit voltage is the voltage at the capacitor terminals, allowing, in this way, for the dependence of the open circuit voltage on the state-of-charge to be embedded in its capacitance. The Randles circuit is recognised as a trusty description of a cell dynamics. It contains a semi-integrator of the current, known as the Warburg impedance, that is a special case of a fractional integrator. To enable the formulation of a time-domain system identification algorithm, the Warburg impedance impulse response was calculated and normalised, in order to derive a finite order state-space approximation, using the Ho-Kalman algorithm. Thus, this Warburg impedance LTI model, with known parameters (normalised impedance) in series with a gain block, is suitable for system identification, since it has only one unknown parameter. A LTI System identification Algorithm was formulated to estimate the model parameters and the initial values of both the open circuit voltage and the states of the normalised Warburg impedance. The performance of the algorithm was very satisfactory on the whole state-of-charge region and when compared with low order Thevenin models. Once it is understood the parameters variability on the state-of-charge, temperature and ageing, we envisage to continue the work using parameter-varying algorithms.

Abstract
It is well known that conditional heteroscedasticity is exhibited by many economic and financial time series such as stock prices or returns. Empirical analysis is often based on a subseries obtained through systematically sampling from an underlying time series and we analyze how that can affect testing for heteroscedasticity. The results show the distribution of the test statistics is changed by systematic sampling, causing a serious power loss that increases with the sampling interval. Consequently, the tests often fail to reject the hypothesis of no conditional heteroscedasticity, leading to the wrong decision and missing the true nature of the data-generating process.

Abstract
Segmentation of the left ventricle (LV) in echocardiography is an important task for the quantification of volume and mass in heart disease. Continuing advances in echocardiography have extended imaging capabilities into the 3D domain, subsequently overcoming the geometric assumptions associated with conventional 2D acquisitions. Nevertheless, the analysis of 3D echocardiography (3DE) poses several challenges associated with limited spatial resolution, poor contrast-to-noise ratio, complex noise characteristics, and image anisotropy. To develop automated methods for 3DE analysis, a sufficiently large, labeled dataset is typically required. However, ground truth segmentations have historically been difficult to obtain due to the high inter-observer variability associated with manual analysis. We address this lack of expert consensus by registering labels derived from higher-resolution subject-specific cardiac magnetic resonance (CMR) images, producing 536 annotated 3DE images from 143 human subjects (10 of which were excluded). This heterogeneous population consists of healthy controls and patients with cardiac disease, across a range of demographics. To demonstrate the utility of such a dataset, a state-of-the-art, self-configuring deep learning network for semantic segmentation was employed for automated 3DE analysis. Using the proposed dataset for training, the network produced measurement biases of -9 +/- 16 ml, -1 +/- 10 ml, -2 +/- 5 %, and 5 +/- 23 g, for end-diastolic volume, end-systolic volume, ejection fraction, and mass, respectively, outperforming an expert human observer in terms of accuracy as well as scan-rescan reproducibility. As part of the Cardiac Atlas Project, we present here a large, publicly available 3DE dataset with ground truth labels that leverage the higher resolution and contrast of CMR, to provide a new benchmark for automated 3DE analysis. Such an approach not only reduces the effect of observer-specific bias present in manual 3DE annotations, but also enables the development of analysis techniques which exhibit better agreement with CMR compared to conventional methods. This represents an important step for enabling more efficient and accurate diagnostic and prognostic information to be obtained from echocardiography.

Abstract
The architectural style of microservices has received much attention from both business and academia and converting a monolithic application into a microservice-based one has become a regular practice. However, companies struggle with migrating their existing monolithic applications to microservices and software engineers frequently face challenges due to a lack of awareness of alternative migration methodologies, making the migration process even harder. In this paper, we present a framework to help software engineers during the migration process by addressing gaps in understanding various migration tools and approaches, allowing for easy comparison between multiple options. Our tool combines multiple existing approaches into one platform, allowing a comprehensive visualization of migration proposals and comparing different options offered by already existing approaches.

Abstract
The demonstration of robotic technologies in real environments is essential for technology developers and end-users to fully showcase the benefits of theirs solutions, and contributes to the promotion of the transition of inspection and maintenance methodologies towards automated robotic strategies. However, before allowing technologies to be demonstrated in real, operating offshore wind-farms, there is a need to de-risk the technology, to ensure its safe operation offshore. As part of the ATLANTIS project, a pioneer pilot infrastructure, the ATLANTIS Test Centre, was installed in Viana do Castelo, Portugal. This infrastructure will allow the demonstration of key enabling robotic technologies for offshore inspection and maintenance. The Test Centre is composed of two distinct testbeds, and a supervisory control centre, enabling the de-risking, testing, validation and demonstration of technologies, in both near-real and real environments. This paper presents the details of the Coastal Testbed of the ATLANTIS Test Centre, from implementation to available resources and infrastructures and environment details.