| 1. |
- Thoma, B, et al.
(författare)
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An international, interprofessional investigation of the self-reported podcast listening habits of emergency clinicians: A METRIQ Study
- 2020
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Ingår i: CJEM. - : Springer Science and Business Media LLC. - 1481-8043 .- 1481-8035. ; 22:1, s. 112-117
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Tidskriftsartikel (refereegranskat)abstract
- ObjectivesPodcasts are increasingly being used for medical education. A deeper understanding of usage patterns would inform both producers and researchers of medical podcasts. We aimed to determine how and why podcasts are used by emergency medicine and critical care clinicians.MethodsAn international interprofessional sample (medical students, residents, physicians, nurses, physician assistants, and paramedics) was recruited through direct contact and a multimodal social media (Twitter and Facebook) campaign. Each participant completed a survey outlining how and why they utilize medical podcasts. Recruitment materials included an infographic and study website.Results390 participants from 33 countries and 4 professions (medicine, nursing, paramedicine, physician assistant) completed the survey. Participants most frequently listened to medical podcasts to review new literature (75.8%), learn core material (75.1%), and refresh memory (71.8%). The majority (62.6%) were aware of the ability to listen at increased speeds, but most (76.9%) listened at 1.0 x (normal) speed. All but 25 (6.4%) participants concurrently performed other tasks while listening. Driving (72.3%), exercising (39.7%), and completing chores (39.2%) were the most common. A minority of participants used active learning techniques such as pausing, rewinding, and replaying segments of the podcast. Very few listened to podcasts multiple times.ConclusionsAn international cohort of emergency clinicians use medical podcasts predominantly for learning. Their listening habits (rarely employing active learning strategies and frequently performing concurrent tasks) may not support this goal. Further exploration of the impact of these activities on learning from podcasts is warranted.
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| 2. |
- Valiente-Dobon, J. J., et al.
(författare)
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Conceptual design of the AGATA 2 pi array at LNL
- 2023
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Ingår i: Nuclear Instruments and Methods in Physics Research Section A. - : Elsevier BV. - 0168-9002 .- 1872-9576. ; 1049
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Tidskriftsartikel (refereegranskat)abstract
- The Advanced GAmma Tracking Array (AGATA) has been installed at Laboratori Nazionali di Legnaro (LNL), Italy. In this installation, AGATA will consist, at the beginning, of 13 AGATA triple clusters (ATCs) with an angular coverage of 1n,and progressively the number of ATCs will increase up to a 2 pi angular coverage. This setup will exploit both stable and radioactive ion beams delivered by the Tandem-PIAVE-ALPI accelerator complex and the SPES facility. The new implementation of AGATA at LNL will be used in two different configurations, firstly one coupled to the PRISMA large-acceptance magnetic spectrometer and lately a second one at Zero Degrees, along the beam line. These two configurations will allow us to cover a broad physics program, using different reaction mechanisms, such as Coulomb excitation, fusion-evaporation, transfer and fission at energies close to the Coulomb barrier. These setups have been designed to be coupled with a large variety of complementary detectors such as charged particle detectors, neutron detectors, heavy-ion detectors, high-energy gamma-ray arrays, cryogenic and gasjet targets and the plunger device for lifetime measurements. We present in this paper the conceptual design, characteristics and performance figures of this implementation of AGATA at LNL.
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| 3. |
- Carnohan, Shane, et al.
(författare)
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Next generation application of DPSIR for sustainable policy implementation
- 2023
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Ingår i: Current Research in Environmental Sustainability. - : Elsevier B.V.. - 2666-0490. ; 5
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Tidskriftsartikel (refereegranskat)abstract
- As our societies and natural systems are becoming ever more interconnected, it is critical that sustainable management can adapt to new knowledge from both the ecological and the social domains, and act on it in a timely and effective manner. This need is amplifying in the Anthropocene as we are approaching the limit for humanity's safe operating space, leading to irreversible change to ecosystem function. This urgently requires increased attention and concern regarding the information feedbacks between the silos of science, policy and society. A web of policies is in place to protect the health of people and the planet, but to ensure that they are effective we need frameworks to make sense of real-world complexities and interlinkages between multiple factors. The Drivers-Pressures-State-Impacts-Response (DPSIR) framework was created for this purpose, however, its' implicit focus on 1) analytical and 2) procedural aspects must be made explicit, to enable coordination across silos and studies. Continued creation of new DPSIR derivatives may limit its impact, while more explicit coordination between these two aspects can improve the effectiveness of DPSIR while retaining its flexibility. We thus propose five elements to support sustainable policy development and implementation using DPSIR: 1) iteration; 2) risk, uncertainty and analytical bias; 3) flexible integration; 4) use of quantitative methods, and; 5) clear and standard definitions for DPSIR. We illustrate these elements in four cases: Three highlight missing feedbacks when DPSIR elements are not made explicit and a fourth case – on per-and-polyfluorinated alkyl substances (PFAS) – showing a potential roadmap to successful policy implementation using DPSIR. © 2022 The Authors
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| 4. |
- Cerutti, G., et al.
(författare)
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Sound event detection with binary neural networks on tightly power-constrained IoT devices
- 2020
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Ingår i: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery. - 9781450370530
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Konferensbidrag (refereegranskat)abstract
- Sound event detection (SED) is a hot topic in consumer and smart city applications. Existing approaches based on deep neural networks (DNNs) are very effective, but highly demanding in terms of memory, power, and throughput when targeting ultra-low power always-on devices. Latency, availability, cost, and privacy requirements are pushing recent IoT systems to process the data on the node, close to the sensor, with a very limited energy supply, and tight constraints on the memory size and processing capabilities precluding to run state-of-The-Art DNNs. In this paper, we explore the combination of extreme quantization to a small-footprint binary neural network (BNN) with the highly energy-efficient, RISC-V-based (8+1)-core GAP8 microcontroller. Starting from an existing CNN for SED whose footprint (815 kB) exceeds the 512 kB of memory available on our platform, we retrain the network using binary filters and activations to match these memory constraints. (Fully) binary neural networks come with a natural drop in accuracy of 12-18% on the challenging ImageNet object recognition challenge compared to their equivalent full-precision baselines. This BNN reaches a 77.9% accuracy, just 7% lower than the full-precision version, with 58 kB (7.2× less) for the weights and 262 kB (2.4× less) memory in total. With our BNN implementation, we reach a peak throughput of 4.6 GMAC/s and 1.5 GMAC/s over the full network, including preprocessing with Mel bins, which corresponds to an efficiency of 67.1 GMAC/s/W and 31.3 GMAC/s/W, respectively. Compared to the performance of an ARM Cortex-M4 implementation, our system has a 10.3× faster execution time and a 51.1× higher energy-efficiency. © 2020 ACM.
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| 5. |
- Magno, M., et al.
(författare)
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InfiniWolf : Energy Efficient Smart Bracelet for Edge Computing with Dual Source Energy Harvesting
- 2020
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Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9783981926347 ; , s. 342-345
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Konferensbidrag (refereegranskat)abstract
- This work presents InfiniWolf, a novel multi-sensor smartwatch that can achieve self-sustainability exploiting thermal and solar energy harvesting, performing computationally high demanding tasks. The smartwatch embeds both a System-on-Chip (SoC) with an ARM Cortex-M processor and Bluetooth Low Energy (BLE) and Mr. Wolf, an open-hardware RISC-V based parallel ultra-low-power processor that boosts the processing capabilities on board by more than one order of magnitude, while also increasing energy efficiency. We demonstrate its functionality based on a sample application scenario performing stress detection with multi-layer artificial neural networks on a wearable multi-sensor bracelet. Experimental results show the benefits in terms of energy efficiency and latency of Mr. Wolf over an ARM Cortex-M4F micro-controllers and the possibility, under specific assumptions, to be self-sustainable using thermal and solar energy harvesting while performing up to 24 stress classifications per minute in indoor conditions. © 2020 EDAA.
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| 6. |
- Polonelli, T., et al.
(författare)
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H-Watch : An open, connected platform for AI-enhanced CoViD19 infection symptoms monitoring and contact tracing.
- 2021
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Ingår i: Proceedings - IEEE International Symposium on Circuits and Systems. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728192017
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Konferensbidrag (refereegranskat)abstract
- The novel COVID-19 disease has been declared a pandemic event. Early detection of infection symptoms and contact tracing are playing a vital role in containing COVID-19 spread. As demonstrated by recent literature, multi-sensor and connected wearable devices might enable symptom detection and help tracing contacts, while also acquiring useful epidemiological information. This paper presents the design and implementation of a fully open-source wearable platform called H-Watch. It has been designed to include several sensors for COVID-19 early detection, multi-radio for wireless transmission and tracking, a microcontroller for processing data on-board, and finally, an energy harvester to extend the battery lifetime. Experimental results demonstrated only 5.9 mW of average power consumption, leading to a lifetime of 9 days on a small watch battery. Finally, all the hardware and the software, including a machine learning on MCU toolkit, are provided open-source, allowing the research community to build and use the H-Watch. © 2021 IEEE
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| 7. |
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| 8. |
- Wang, X., et al.
(författare)
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FANN-on-MCU : An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things
- 2020
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers Inc.. - 2327-4662. ; 7:5, s. 4403-4417
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Tidskriftsartikel (refereegranskat)abstract
- The growing number of low-power smart devices in the Internet of Things is coupled with the concept of 'edge computing' that is moving some of the intelligence, especially machine learning, toward the edge of the network. Enabling machine learning algorithms to run on resource-constrained hardware, typically on low-power smart devices, is challenging in terms of hardware (optimized and energy-efficient integrated circuits), algorithmic, and firmware implementations. This article presents a FANN-on-MCU, an open-source toolkit built upon the fast artificial neural network (FANN) library to run lightweight and energy-efficient neural networks on microcontrollers based on both the ARM Cortex-M series and the novel RISC-V-based parallel ultralow-power (PULP) platform. The toolkit takes multilayer perceptrons trained with FANN and generates code targeted to low-power microcontrollers. This article also presents detailed analyses of energy efficiency across the different cores, and the optimizations to handle different network sizes. Moreover, it provides a detailed analysis of parallel speedups and degradations due to parallelization overhead and memory transfers. Further evaluations include experimental results for three different applications using a self-sustainable wearable multisensor bracelet. The experimental results show a measured latency in the order of only a few microseconds and power consumption of a few milliwatts while keeping the memory requirements below the limitations of the targeted microcontrollers. In particular, the parallel implementation on the octa-core RISC-V platform reaches a speedup of 22× and a 69% reduction in energy consumption with respect to a single-core implementation on Cortex-M4 for continuous real-time classification. © 2014 IEEE.
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| 9. |
- Wang, X., et al.
(författare)
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HR-SAR-Net : A Deep Neural Network for Urban Scene Segmentation from High-Resolution SAR Data
- 2020
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Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728148427
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Konferensbidrag (refereegranskat)abstract
- Synthetic aperture radar (SAR) data is becoming increasingly available to a wide range of users through commercial service providers with resolutions reaching 0.5 m/px. Segmenting SAR data still requires skilled personnel, limiting the potential for large-scale use. We show that it is possible to automatically and reliably perform urban scene segmentation from next-gen resolution SAR data (0.15 m/px) using deep neural networks (DNNs), achieving a pixel accuracy of 95.19% and a mean intersection-over-union (mIoU) of 74.67% with data collected over a region of merely 2.2km2. The presented DNN is not only effective, but is very small with only 63k parameters and computationally simple enough to achieve a throughput of around 500 Mpx/s using a single GPU. We further identify that additional SAR receive antennas and data from multiple flights massively improve the segmentation accuracy. We describe a procedure for generating a high-quality segmentation ground truth from multiple inaccurate building and road annotations, which has been crucial to achieving these segmentation results. © 2020 IEEE.
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| 10. |
- Di Mauro, A., et al.
(författare)
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FlyDVS : An Event-Driven Wireless Ultra-Low Power Visual Sensor Node
- 2021
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Ingår i: Proceedings -Design, Automation and Test in Europe, DATE. - : Institute of Electrical and Electronics Engineers Inc.. - 9783981926354 ; , s. 1851-1854
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Konferensbidrag (refereegranskat)abstract
- Event-based cameras, also called dynamic vision sensors (DVS), inspired by the human vision system, are gaining popularity due to their potential energy-saving since they generate asynchronous events only from the pixels changes in the field of view. Unfortunately, in most current uses, data acquisition, processing, and streaming of data from event-based cameras are performed by power-hungry hardware, mainly high-power FPGAs. For this reason, the overall power consumption of an event-based system that includes digital capture and streaming of events, is in the order of hundreds of milliwatts or even watts, reducing significantly usability in real-life low-power applications such as wearable devices. This work presents FlyDVS, the first event-driven wireless ultra-low-power visual sensor node that includes a low-power Lattice FPGA and, a Bluetooth wireless system-on-chip, and hosts a commercial ultra-low-power DVS camera module. Experimental results show that the low-power FPGA can reach up to 874 efps (event-frames per second) with only 17.6mW of power, and the sensor node consumes an overall power of 35.5 mW (including wireless streaming) at 200 efps. We demonstrate FlyDVS in a real-life scenario, namely, to acquire event frames of a gesture recognition data set. © 2021 EDAA.
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