A Deep Learning Approach for Fear Recognition on the Edge Based on Two-Dimensional Feature Maps.

Applying affective computing techniques to recognize fear and combining them with portable signal monitors makes it possible to create real-time detection systems that could act as bodyguards when users are in danger. With this aim, this paper presents a fear recognition method based on physiological signals obtained from wearable devices. The procedure involves creating two-dimensional feature maps from the raw signals, using data augmentation and feature selection algorithms, followed by deep learning-based classification models, taking inspiration from those used in image processing.

Antineutrophil Cytoplasmic Antibody (ANCA)-Associated Aortitis Presenting With Acute Dissection: Case Report and Comprehensive Literature Review.

Description Vasculitides are a group of diseases that produces vasculitis, which is characterized by inflammatory infiltrates within blood vessel walls and results in intimal injury as well as progressive mural destruction. Infiltrates are characterized per the Chapel Hill classification, into large, medium, and small-vessel vasculitides. ANCA-associated vasculitis (AAV) is a disease that has been described as involving small-sized vessels.

True brachial artery aneurysm: A case report and review of literature.

Introduction: A true brachial artery aneurysm is a rare pathology with an incidence of 0.17% of all peripheral artery aneurysms. This pathology can manifest devastating complications if overlooked, however, a high index of suspicion coupled with a thorough history and physical allows easy diagnosis.

Edge and Fog Computing Platform for Data Fusion of Complex Heterogeneous Sensors.

The explosion of the Internet of Things has dramatically increased the data load on networks that cannot indefinitely increment their capacity to support these new services. Edge computing is a viable approach to fuse and process data on sensor platforms so that information can be created locally. However, the integration of complex heterogeneous sensors producing a great amount of diverse data opens new challenges to be faced.

FPGA-Based High-Performance Embedded Systems for Adaptive Edge Computing in Cyber-Physical Systems: The ARTICo³ Framework.

Cyber-Physical Systems are experiencing a paradigm shift in which processing has been relocated to the distributed sensing layer and is no longer performed in a centralized manner. This approach, usually referred to as Edge Computing, demands the use of hardware platforms that are able to manage the steadily increasing requirements in computing performance, while keeping energy efficiency and the adaptability imposed by the interaction with the physical world. In this context, SRAM-based FPGAs and their inherent run-time reconfigurability, when coupled with smart power management strategies, are a suitable solution.

Integrated Toolset for WSN Application Planning, Development, Commissioning and Maintenance: The WSN-DPCM ARTEMIS-JU Project.

In this article we present the main results obtained in the ARTEMIS-JU WSN-DPCM project between October 2011 and September 2015. The first objective of the project was the development of an integrated toolset for Wireless sensor networks (WSN) application planning, development, commissioning and maintenance, which aims to support application domain experts, with limited WSN expertise, to efficiently develop WSN applications from planning to lifetime maintenance. The toolset is made of three main tools: one for planning, one for application development and simulation (which can include hardware nodes), and one for network commissioning and lifetime maintenance.

Using SRAM based FPGAs for power-aware high performance wireless sensor networks.

While for years traditional wireless sensor nodes have been based on ultra-low power microcontrollers with sufficient but limited computing power, the complexity and number of tasks of today's applications are constantly increasing. Increasing the node duty cycle is not feasible in all cases, so in many cases more computing power is required. This extra computing power may be achieved by either more powerful microcontrollers, though more power consumption or, in general, any solution capable of accelerating task execution.