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. This proposal has been validated with two different datasets, achieving, in WEMAC, WESAD 3-classes, and WESAD 2-classes, F1-score results of 78.13%, 88.07%, and 99.60%, respectively, and 79.90%, 89.12%, and 99.60% in accuracy. Furthermore, the paper demonstrates the feasibility of implementing the proposed method on the Coral Edge TPU device, prepared to make inferences on the edge.

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. However, some cases of large vessel disease involvement have been documented. ANCA-associated aortitis is a rare entity poorly described in the literature. Due to the rarity of this pathology, there is no Level I evidence available regarding diagnosis and treatment. We present the rare case of an 80-year-old male presenting with ANCA-associated aortitis complicated by acute dissection of the left common iliac artery. His case was successfully managed by corticosteroid therapy and endovascular stenting of the involved iliac artery. ANCA-associated aortitis is a rare entity that has not been described well in the current literature. We believe this case to be the first involving ANCA-associated aortitis presenting with an acute dissection.

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. We present a rare case of the oldest documented patient with a true brachial artery aneurysm with idiopathic etiology. PRESENTATION OF CASE: An 83-year-old gentleman presented with left upper extremity pain, erythema, and swelling for 1 week. He denied trauma to the area. Examination revealed a pulsatile mass of the antecubital fossa and decreased distal pulses. Imaging illustrated a 9mm aneurysm of the brachial artery with stenosis of the radial artery and non-enhancement of the origin of the ulnar artery. The patient underwent a brachial aneurysm excision, radial and ulnar embolectomy, and brachial to ulnar and radial artery bypass. Postoperatively, palpable pulses were appreciated in the radial and ulnar arteries. Pathology demonstrated a true aneurysm. The patient's postoperative course was uneventful and follow-up 6 months later revealed normal perfusion. DISCUSSION: This case highlights the importance of maintaining a high index of suspicion coupled with a thorough history and physical examination when encountering neurovascular complaints of the upper extremities. Operative intervention even in asymptomatic patients is warranted due to a high complication rate of 33%. CONCLUSION: More research into the pathophysiology of this rare pathology is needed to further understand, prevent, or mitigate its complications.

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. Rather than generating usable data straight away, complex sensors demand prior calculations to supply meaningful information. In addition, the integration of complex sensors in real applications requires a coordinated development from hardware and software teams that need a common framework to reduce development times. In this work, we present an edge and fog computing platform capable of providing seamless integration of complex sensors, with the implementation of an efficient data fusion strategy. It uses a symbiotic hardware/software design approach based on a novel messaging system running on a modular hardware platform. We have applied this platform to integrate Bluetooth vehicle identifiers and radar counters in a specific mobility use case, which exhibits an effective end-to-end integration using the proposed solution.

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. However, they usually fail in user accessibility and ease of development. In this paper, an integrated framework to develop FPGA-based high-performance embedded systems for Edge Computing in Cyber-Physical Systems is presented. This framework provides a hardware-based processing architecture, an automated toolchain, and a runtime to transparently generate and manage reconfigurable systems from high-level system descriptions without additional user intervention. Moreover, it provides users with support for dynamically adapting the available computing resources to switch the working point of the architecture in a solution space defined by computing performance, energy consumption and fault tolerance. Results show that it is indeed possible to explore this solution space at run time and prove that the proposed framework is a competitive alternative to software-based edge computing platforms, being able to provide not only faster solutions, but also higher energy efficiency for computing-intensive algorithms with significant levels of data-level parallelism.

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. The tools are integrated in a single platform which promotes software reuse by automatically selecting suitable library components for application synthesis and the abstraction of the underlying architecture through the use of a middleware layer. The second objective of the project was to test the effectiveness of the toolset for the development of two case studies in different domains, one for detecting the occupancy state of parking lots and one for monitoring air concentration of harmful gasses near an industrial site.

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. At this point, the use of hardware based, and in particular FPGA solutions, might appear as a candidate technology, since though power use is higher compared with lower power devices, execution time is reduced, so energy could be reduced overall. In order to demonstrate this, an innovative WSN node architecture is proposed. This architecture is based on a high performance high capacity state-of-the-art FPGA, which combines the advantages of the intrinsic acceleration provided by the parallelism of hardware devices, the use of partial reconfiguration capabilities, as well as a careful power-aware management system, to show that energy savings for certain higher-end applications can be achieved. Finally, comprehensive tests have been done to validate the platform in terms of performance and power consumption, to proof that better energy efficiency compared to processor based solutions can be achieved, for instance, when encryption is imposed by the application requirements.