Leal C., Lopes P.A., Serra A., Coelho J., Almeida A.T., Tavakoli M.
ACS Appl. Mater. Interfaces 2019
2019
Article
Abstract:
Stretchable electronics stickers that adhere to the human skin and collect biopotentials are becoming increasingly popular for biomonitoring applications. Such stickers should include electrodes, stretchable interconnects, silicon chips for processing and communication, and batteries. Here, we demonstrate a material architecture and fabrication technique for a multi-layer, stretchable, low-cost, rapidly deployable and disposable sticker that integrates skin interfacing hydrogel electrodes, stretchable interconnects, and Ag2O-Zn (Silver Oxide – Zinc) battery. In addition, the application of a printed biphasic current collector (AgInGa) for the Ag2O-Zn battery is reported for the first time. Surprisingly, and unlike previously reported batteries, the battery capacity increases after being subject to strain cycles and reaches to a record-breaking areal capacity of 6.88 mAh cm-2 post stretch. As a proof of concept, an application of heart rate monitoring is presented. The disposable patch is interfaced with a miniature battery-free electronics circuit for data acquisition, processing, and wireless transmission. A version of the patch partially covering the patient´s chest can supply enough energy for continuous operation for ~6 days.
Araújo T., Aresta G., Galdran A., Costa P., Mendonça A.M., Campilho A.
DLMIA 2018, ML-CDS 2018: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
2018
Conference Paper
Abstract:
We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images. UOLO consists of an object segmentation module which intermediate abstract representations are processed and used as input for object detection. The resulting system is optimized simultaneously for detecting a class of objects and segmenting an optionally different class of structures. UOLO is trained on a set of bounding boxes enclosing the objects to detect, as well as pixel-wise segmentation information, when available. A new loss function is devised, taking into account whether a reference segmentation is accessible for each training image, in order to suitably backpropagate the error. We validate UOLO on the task of simultaneous optic disc (OD) detection, fovea detection, and OD segmentation from retinal images, achieving state-of-the-art performance on public datasets.
Ferreira M., Conceicao H., Fernandes R., Tonguz O.K.
IEEE Vehicular Technology Conference
2009
Conference Paper
Abstract:
Connectivity analysis of Vehicular Ad Hoc Networks (VANETs) involves the study of static and dynamic characteristics of the network. Realistic mobility models are essential for both aspects, in order to produce valid configurations of the position of vehicles in one static instant, and to dynamically move such vehicles in an interval of time. Focusing on static connectivity evaluation, we extend the state-of-the-art of the realism of urban mobility models for VANETs, by proposing and implementing an analysis based on a stereoscopic aerial survey over a large European city. Our static connectivity metrics show important differences compared to the model and simulation based results described in the VANET literature.
This paper examines the extent to which users in developing countries innovate, the factors that enable these innovations and whether they are meaningful on a global stage. To study this issue, we conducted an empirical investigation into the origin and types of innovations in financial services offered via mobile phones, a global, multi-billion-dollar industry in which developing economies play an important role. We used the complete list of mobile financial services, as reported by the GSM Association, and collected detailed histories of the development of the services and their innovation process. Our analysis, the first of its kind, shows that 85% of the innovations in this field originated in developing countries. We also conclude that, at least 50% of all mobile financial services were pioneered by users, approximately 45% by producers, and the remaining were jointly developed by users and producers. The main factors contributing to these innovations to occur in developing countries are the high levels of need, the existence of flexible platforms, in combination with increased access to information and communication technology. Additionally, services developed by users diffused at more than double the rate of producer-innovations. Finally, we observe that three-quarters of the innovations that originated in non-OECD countries have already diffused to OECD countries, and that the (user) innovations are therefore globally meaningful. This study suggests that the traditional North-to-South diffusion framework fails to explain these new sources of innovation and may require re-examination.
2015 IEEE Global Communications Conference, GLOBECOM 2015
2016
Conference Paper
Abstract:
As VoD systems migrate to the Cloud, new challenges emerge in managing user Quality-of- Experience (QoE). The complexity of the cloud system due to virtualization and resource sharing complicates the QoE management. Operational failures in the Cloud could be challenging for QoE as well. We believe that end users have the best perception of system performance in terms of their QoE. We propose a QoE based adaptive control system for VoD in the Cloud. The system learns server performance from the user QoE and then adaptively selects servers for users accordingly. We deploy our proposed system in Google Cloud and evaluate it with hundreds of clients deployed all over the world. Results show that given the same amount of resources, our system provides 9% to 30% more users with QoE above the Mean Opinion Score (MOS) “good” level than the existing measurement based server selection systems. The system guarantees a better QoE (above 6% better) for 90% users. Additionally, our system discovers operational failures by monitoring QoE and prevents streaming session crashes. A computational overhead analysis shows that our system can easily scale to large VoD systems containing thousands of servers.
2020 IEEE 25th Pacific Rim International Symposium on Dependable Computing (PRDC)
2020
Article
Abstract:
Containers revolutionized cloud applications, as they are lightweight, highly portable and ideal for microservices. Although they are being adopted in business-critical scenarios, they introduce security concerns which are exacerbated in multi-tenant environments. Intrusion detection techniques can help, but they have received limited attention in this context. This paper presents an approach that uses attack injection to evaluate the effectiveness of intrusion detection in container-based systems. We use a TPC-C workload, with a database engine running as a container, while monitoring its system calls. First, the algorithms are submitted to benign workloads to learn the application profile. Then, we execute a set of attack injection experiments with diverse attacks, and we verify whether the algorithms report them. An experiment was designed to evaluate the algorithms in Docker and LXC containers, and in a traditional OS deployment for comparison. The results show that the approach is effective in evaluating the algorithms in different scenarios. The algorithms consistently detect most of the attacks (89+%). The precision values show more variance, but with careful tuning and richer workloads, this problem can be mitigated.
25th IEEE Pacific Rim International Symposium on Dependable Computing (PRDC 2020)
2020
Conference Paper
Abstract:
Containers revolutionized cloud applications, as they are lightweight, highly portable and ideal for microservices. Although they are being adopted in business-critical scenarios, they introduce security concerns which are exacerbated in multi-tenant environments. Intrusion detection techniques can help, but they have received limited attention in this context. This paper presents an approach that uses attack injection to evaluate the effectiveness of intrusion detection in container-based systems. We use a TPC-C workload, with a database engine running as a container, while monitoring its system calls. First, the algorithms are submitted to benign workloads to learn the application profile. Then, we execute a set of attack injection experiments with diverse attacks, and we verify whether the algorithms report them. An experiment was designed to evaluate the algorithms in Docker and LXC containers, and in a traditional OS deployment for comparison. The results show that the approach is effective in evaluating the algorithms in different scenarios. The algorithms consistently detect most of the attacks (89+%). The precision values show more variance, but with careful tuning and richer workloads, this problem can be mitigated.
Luis N., Pereira T., Fernandez S., Moreira A., Borrajo D., Veloso M.
Nerea Luis1 et al.
2018
Article
Abstract:
Many real-world robotic scenarios require performing task planning to decide courses of actions to be executed by (possibly heterogeneous) robots. A classical centralized planning approach has to find a solution inside a search space that contains every possible combination of robots and goals. This leads to inefficient solutions that do not scale well. Multi-Agent Planning (MAP) provides a new way to solve this kind of tasks efficiently. Previous works on MAP have proposed to factorize the problem to decrease the planning effort i.e dividing the goals among the agents (robots). However, these techniques do not scale when the number of agents and goals grow. Also, in most real world scenarios with big maps, goals might not be reached by every robot so it has a computational cost associated. In this paper we propose a combination of robotics and planning techniques to alleviate and boost the computation of the goal assignment process. We use Actuation Maps (AMs). Given a map, AMs can determine the regions each agent can actuate on. Thus, specific information can be extracted to know which goals can be tackled by each agent, as well as cheaply estimating the cost of using each agent to achieve every goal. Experiments show that when information extracted from AMs is provided to a multi-agent planning algorithm, the goal assignment is significantly faster, speeding-up the planning process considerably. Experiments also show that this approach greatly outperforms classical centralized planning.
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