ArchGenTool: a System-Independent Collaborative Tool for Robotic Architecture Design

Complex robotic architectures require a collaborative effort in design and adherence to the design in the implementation phse. ArchGentTool is a collaborative architecture generation tool which supports the design of the robotic architecture in a multi-level fashion. It comprises high-level conceptual analysis of the system to be designed, as well as low-level implementation breakdown of its functional components, acting complementary to the ROS framework. The tool facilitates reusability and expandability of the architecture to any robotic system, as it can be adapted to different specifications. A case study with the RAMCIP service robot is presente

WTA/TLA: A UAV-captured dataset for semantic segmentation of energy infrastructure

Automated inspection of energy infrastructure with Unmanned Aerial Vehicles (UAVs) is becoming increasingly important, exhibiting significant advantages over manual inspection, including improved scalability, cost/time effectiveness, and risks reduction. Although recent technological advancements enabled the collection of an abundance of vision data from UAVs’ sensors, significant efforts are still required from experts to interpret manually the collected data and assess the condition of energy infrastructure. Thus, semantic understanding of vision data collected from UAVs during inspection is a critical prerequisite for performing autonomous robotic tasks. However, the lack of labeled data introduces challenges and limitations in evaluating the performance of semantic prediction algorithms. To this end, we release two novel semantic datasets (WTA and TLA) of aerial images captured from power transmission networks and wind turbine farms, collected during real inspection scenarios with UAVs. We also propose modifications to existing state-of-the-art semantic segmentation CNNs to achieve improved trade-off between accuracy and computational complexity. Qualitative and quantitative experiments demonstrate both the challenging properties of the provided dataset and the effectiveness of the proposed networks in this domain.The dataset is available at: https://github.com/gzamps/wta_tla_dataset

Evaluation of Various Dynamic Issues During Transient Operation of Turbocharged Diesel Engine with Special Reference to Friction Development

Copyright © 2007 SAE International The modeling of transient turbocharged diesel engine operation appeared in the early seventies and continues to be in the focal point of research, due to the importance of transient response in the everyday operating conditions of engines. The majority of research has focused so far on issues concerning thermodynamic modeling, as these directly affect heat release predictions and consequently performance and pollutants emissions. On the other hand, issues concerning the dynamics of transient operation are often disregarded or over-simplified, possibly for the sake of speeding up program execution time. In the present work, an experimentally validated transient diesel engin

Feature extraction based on bio-inspired model for robust emotion recognition

Emotional state identification is an important issue to achieve more natural speech interactive systems. Ideally, these systems should also be able to work in real environments in which generally exist some kind of noise. Several bio-inspired representations have been applied to artificial systems for speech processing under noise conditions. In this work, an auditory signal representation is used to obtain a novel bio-inspired set of features for emotional speech signals. These characteristics, together with other spectral and prosodic features, are used for emotion recognition under noise conditions. Neural models were trained as classifiers and results were compared to the well-known mel-frequency cepstral coefficients. Results show that using the proposed representations, it is possible to significantly improve the robustness of an emotion recognition system. The results were also validated in a speaker independent scheme and with two emotional speech corpora.Fil: Albornoz, Enrique Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin

Primary care professionals’ experiences during the first wave of the COVID-19 pandemic in Greece: a qualitative study

Background: The coronavirus outbreak (COVID-19) tested health care systems worldwide. This qualitative study aimed to explore and understand the experiences, beliefs and concerns of Primary Care Professionals (PCPs) regarding the preparedness and response of primary care to the first wave of the pandemic in Greece, a country where a public structured primary care system has been developing. Methods: We conducted semi-structured telephone interviews with 33 PCPs (General Practitioners, community General Internal Medicine Specialists, community Paediatricians and nurses) recruited from all regions of Greece after the first wave of the pandemic (June 2020). Interviews were transcribed verbatim, data were anonymised and analysed. Thematic analysis was applied developing a conceptual framework. Results: Four main themes were identified: a) Primary care unit adaptation and issues faced during the pandemic; b) Management of suspected COVID-19 cases; c) Management of non-suspected cases; d) Consequences of the pandemic. In the first phase of the pandemic, remote management of suspected cases and their referral to the hospital were preferred as a result of a shortage of personal protective equipment and inaccessibility to coronavirus testing in primary care. Due to the discontinuation of regular medical services and the limited in-person contact between doctors and patients, chronic disease management and prevention programmes were left behind. Social and emotional consequences of the pandemic, such as workplace stigma, isolation and social seclusion, deriving from fear of viral transmission, as well as burnout symptoms and exhaustion were commonly experienced among PCPs. Positive consequences of the pandemic were considered to be the recognition of the importance of an empowered public healthcare system by citizens and the valuable insight, knowledge and experience professionals gained in times of crisis. Conclusions: Primary care has a key role to play during and after the pandemic by using its information infrastructure to identify at-risk groups, detect new cases of COVID-19, provide care according to needs, and carry out vaccination programmes. Central coordination and empowerment of primary care will increase its effectiveness, via public awareness, holistic patient management, and unburdening of hospitals

Robust Human Pose Tracking For Realistic Service Robot Applications

Robust human pose estimation and tracking plays an integral role in assistive service robot applications, as it provides information regarding the body pose and motion of the user in a scene. Even though current solutions provide high-accuracy results in controlled environments, they fail to successfully deal with problems encountered under real-life situations such as tracking initialization and failure, body part intersection, large object handling and partial-view body-part tracking. This paper presents a framework tailored for deployment under real-life situations addressing the above limitations. The framework is based on the articulated 3D-SDF data representation model, and has been extended with complementary mechanisms for addressing the above challenges. Extensive evaluation on public datasets demonstrates the framework's state-of-the-art performance, while experimental results on a challenging realistic human motion dataset exhibit its robustness in real life scenarios

The changing epidemiology of the ageing thalassaemia populations: A position statement of the Thalassaemia International Federation

Therapeutic advances in β-thalassaemia have gradually lead to a significant improvement in prognosis over the past few decades. As a result, patients living in areas where disease-specific programmes offering access to modern therapy are in place experience a new era of prolonged survival that tends to reach that of the normal population. This ageing thalassaemia population, however, faces a new spectrum of comorbidities resulting from increasing age that may jeopardise the advances in prognosis provided by current therapy and thus poses new challenges in diagnosis, monitoring and treatment. In this position paper of the Thalassaemia International Federation, we review the changing epidemiology and clinical spectrum of patients with β-thalassaemia and propose actions to be undertaken in order to address the emerging spectrum of comorbidities resulting from ageing. © 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Lt

Design of novel screening environments for Mild Cognitive Impairment: Giving priority to elicited speech and language abilities

Recent cognitive decline screening batteries have highlighted the importance of language deficits related to semantic knowledge breakdown to reveal the incipient dementia. This paper proposes the introduction of novel enriched linguistic tests and examines the hypothesis that language can be a sensitive cognitive measure for Mild Cognitive Impairment (MCI). A group of MCI and healthy elderly were administered a set of proposed linguistic tests. Performance measures were made on both groups to indicate that concrete verbal production deficits such as impaired verb fluency can distinguish the MCI from normal aging. In addition, it was found that even in cases where the MCI subjects preserved scores, language tests took significantly more time compared to healthy controls. These findings indicate that language could be a sensitive cognitive marker in preclinical stages of MCI. © 2015 ICST