4,744 research outputs found

    SmartHeart CABG Edu

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    The paper reports on the SmartHeart CABG Edu Android app. The app was conceived to be an innovative and up-to-date tool for patient education, the first of its kind in the Italian context. In particular, the app was developed to provide educational material for patients about to undergo Coronary Artery Bypass Graft (CABG) surgery, a set of self-assessment tools concerning health status (i.e., BMI calculator, LDL cholesterol calculator and anxiety assessment tool) and usability questionnaires (i.e., SEQ and SUS). The paper initially describes the app, then reports on its evaluation, concerning both the app usability and the pre-operative anxiety, and ends by showing the improvements -- derived from the usability evaluation -- put into practice

    Parachute emergency landing simulation and enhanced composite material characterization for General Aviation aircraft

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    General Aviation (GA) aircraft crashworthiness of the vehicle when it hits the ground after the parachute deployment is an important issue. The current dynamic emergency landing regulation (CS 23.562) defines the maximum human tolerant accelerations under both vertical and horizontal directions. This article aims to compare two different aircraft configurations: metal low-wing and composite high-wing ones. Both are two-seats and single-engine GA aircraft. The purpose of the analysis is to check whether the seats and restraint systems met human injury tolerance standards and to determine the possible impact on passengers in the cabin space due to shock loads. Finite element analysis of the fuselage sections for both configurations is performed using the commercial LS-Dyna solver. An extensive campaign of experimental tests has been performed on the composite samples for tuning and validating the model and to find the transition from an undamaged up to totally collapsed sample. The material of the composite fuselage has been characterized through experimental tests. The adopted material model has been refined to match with the performed experimental analysis, allowing high-fidelity modeling. A parametric analysis has been performed to determine the optimal impact angle in terms of lumbar injuries and loads transmitted by the seat belt due to aircraft contact with the ground, thereby increasing the level of safety. The investigations carried out may be an important indicator of the design of the parachute system

    ColibriUAV: An Ultra-Fast, Energy-Efficient Neuromorphic Edge Processing UAV-Platform with Event-Based and Frame-Based Cameras

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    The interest in dynamic vision sensor (DVS)-powered unmanned aerial vehicles (UAV) is raising, especially due to the microsecond-level reaction time of the bio-inspired event sensor, which increases robustness and reduces latency of the perception tasks compared to a RGB camera. This work presents ColibriUAV, a UAV platform with both frame-based and event-based cameras interfaces for efficient perception and near-sensor processing. The proposed platform is designed around Kraken, a novel low-power RISC-V System on Chip with two hardware accelerators targeting spiking neural networks and deep ternary neural networks.Kraken is capable of efficiently processing both event data from a DVS camera and frame data from an RGB camera. A key feature of Kraken is its integrated, dedicated interface with a DVS camera. This paper benchmarks the end-to-end latency and power efficiency of the neuromorphic and event-based UAV subsystem, demonstrating state-of-the-art event data with a throughput of 7200 frames of events per second and a power consumption of 10.7 \si{\milli\watt}, which is over 6.6 times faster and a hundred times less power-consuming than the widely-used data reading approach through the USB interface. The overall sensing and processing power consumption is below 50 mW, achieving latency in the milliseconds range, making the platform suitable for low-latency autonomous nano-drones as well

    ColibriES: A Milliwatts RISC-V Based Embedded System Leveraging Neuromorphic and Neural Networks Hardware Accelerators for Low-Latency Closed-loop Control Applications

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    End-to-end event-based computation has the potential to push the envelope in latency and energy efficiency for edge AI applications. Unfortunately, event-based sensors (e.g., DVS cameras) and neuromorphic spike-based processors (e.g., Loihi) have been designed in a decoupled fashion, thereby missing major streamlining opportunities. This paper presents ColibriES, the first-ever neuromorphic hardware embedded system platform with dedicated event-sensor interfaces and full processing pipelines. ColibriES includes event and frame interfaces and data processing, aiming at efficient and long-life embedded systems in edge scenarios. ColibriES is based on the Kraken system-on-chip and contains a heterogeneous parallel ultra-low power (PULP) processor, frame-based and event-based camera interfaces, and two hardware accelerators for the computation of both event-based spiking neural networks and frame-based ternary convolutional neural networks. This paper explores and accurately evaluates the performance of event data processing on the example of gesture recognition on ColibriES, as the first step of full-system evaluation. In our experiments, we demonstrate a chip energy consumption of 7.7 \si{\milli\joule} and latency of 164.5 \si{\milli\second} of each inference with the DVS Gesture event data set as an example for closed-loop data processing, showcasing the potential of ColibriES for battery-powered applications such as wearable devices and UAVs that require low-latency closed-loop control

    Axial eccentric SynRel and SPM Motors analytical models validation using 3D finite element

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    This paper deals with the uniform and non-uniform axial eccentricity analyses of the surface mounted permanent magnet and synchronous reluctance machines. The analyses are carried out using an analytical model for each considered machine. Being the axial eccentricity a 3D physical phenomenon, the standard sliding approach used in the analytical models has been validated through accurate 3D FE simulations. The results presented in this paper verify the effectiveness of the analytical approaches quantifying the results deviations respect to the computational expensive 3D FE simulations. The results also confirms that synchronous reluctance machines show higher radial forces compared to the surface permanent magnet machines for the same eccentricity level, main geometry and operating condition
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