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| 2. |
- Ruilope, LM, et al.
(författare)
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Design and Baseline Characteristics of the Finerenone in Reducing Cardiovascular Mortality and Morbidity in Diabetic Kidney Disease Trial
- 2019
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Ingår i: American journal of nephrology. - : S. Karger AG. - 1421-9670 .- 0250-8095. ; 50:5, s. 345-356
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Tidskriftsartikel (refereegranskat)abstract
- <b><i>Background:</i></b> Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. <b><i>Patients and</i></b> <b><i>Methods:</i></b> The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate ≥25 mL/min/1.73 m<sup>2</sup> and albuminuria (urinary albumin-to-creatinine ratio ≥30 to ≤5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level α = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. <b><i>Conclusions:</i></b> FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049.
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| 4. |
- Giordano, M., et al.
(författare)
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SmartTag : An ultra low power asset tracking and usage analysis IoT device with embedded ML capabilities
- 2021
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Ingår i: 2021 IEEE Sensors Applications Symposium, SAS 2021 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728194318
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Konferensbidrag (refereegranskat)abstract
- Assessing power tools usage helps to prolong their life cycle, as well as indicate targeted maintenance interventions after a particular series of events, e.g. drops. In this work, we propose a low power multi-sensors hardware-software co-design for extremely long shelf life, and a long operating lifecycle. The designed device is based on a Bluetooth Low Energy (BLE) system on chip (SoC) to exchange data with a gateway. NFC has been chosen to wake up the device without adding any additional power consumption. The system on a chip includes an ARM Cortex-M4F core to further process the information achieving low latency and high energy efficiency. The device hosts a temperature and humidity sensor used to monitor the storage conditions, and an accelerometer is used for condition and activity monitoring. This paper provides a proof-of-concept approach to continuously assess the usage of a power tool and detect potential mis-usages, e.g., drops. The architecture, thought to be flexible, can host both traditional signal processing and novel tiny machine learning workloads, offering a future-proof platform for several application scenarios. Experimental results highlight the advanced processing capabilities at low power consumption enabling a long lifetime of up to 4 years with a small coin battery. © 2021 IEEE.
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| 5. |
- Scherer, M., et al.
(författare)
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TinyRadarNN : Combining Spatial and Temporal Convolutional Neural Networks for Embedded Gesture Recognition with Short Range Radars
- 2021
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers Inc.. - 2327-4662. ; 8:13, s. 10336-10346
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Tidskriftsartikel (refereegranskat)abstract
- This work proposes a low-power high-accuracy embedded hand-gesture recognition algorithm targeting battery-operated wearable devices using low-power short-range RADAR sensors. A 2-D convolutional neural network (CNN) using range-frequency Doppler features is combined with a temporal convolutional neural network (TCN) for time sequence prediction. The final algorithm has a model size of only 46 thousand parameters, yielding a memory footprint of only 92 KB. Two data sets containing 11 challenging hand gestures performed by 26 different people have been recorded containing a total of 20'210 gesture instances. On the 11 hand gesture data set, accuracies of 86.6% (26 users) and 92.4% (single user) have been achieved, which are comparable to the state of the art, which achieves 87% (10 users) and 94% (single user), while using a TCN-based network that is $7500\times $ smaller than the state of the art. Furthermore, the gesture recognition classifier has been implemented on a parallel ultralow power processor, demonstrating that real-time prediction is feasible with only 21 mW of power consumption for the full TCN sequence prediction network, while a system-level power consumption of less than 120 mW is achieved. We provide open-source access to example code and all data collected and used in this work on tinyradar.ethz.ch. © 2014 IEEE.
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| 6. |
- Baumann, N., et al.
(författare)
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Piepser 2.0 : A Self-Sustaining Smartwatch to Maximize the Paragliders Flytime
- 2020
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Ingår i: IEEE Transactions on Instrumentation and Measurement. - : Institute of Electrical and Electronics Engineers Inc.. - 0018-9456 .- 1557-9662. ; 69:4, s. 1445-1454
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Tidskriftsartikel (refereegranskat)abstract
- The main motivation of paraglider pilots is to stay in the air for as long as possible. Therefore, paraglider pilots are always searching for thermal upwind that allow them to gain altitude. These thermal lifts are difficult to detect. Therefore, sensors and devices that indicate the vertical speed (so-called variometers) are widely used among paraglider pilots. This article presents the design and the implementation of an ultralow-power, self-sustaining, high-precision, wrist-worn variometer with a minimal form factor but infinite lifetime, which can visually and acoustically indicate the vertical velocity. This article demonstrates the benefits of combining multisource energy harvesting (EH) for wearable devices with low power design, exploiting a novel near-threshold ARM Cortex-M4F microcontroller, the Ambiq Apollo2, for the onboard processing. The experimental results show a power consumption of only 17.12~\mu \text{W} in sleep mode and 1937.21~\mu \text{W} in the worst case scenario when processing the data and outputting an audio feedback. Measurements confirmed that combining both thermal and solar EH makes the designed electronics self-sustaining. Without EH, the system will be operational for up to 372 h in always-on mode (worst case scenario) supplied by a 200-mAh lithium-ion battery. © 1963-2012 IEEE.
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| 7. |
- Dheman, K., et al.
(författare)
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ImpediSense:A long lasting wireless wearable bio-impedance sensor node
- 2021
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Ingår i: Sustainable Computing. - : Elsevier Inc.. - 2210-5379 .- 2210-5387. ; 30
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Tidskriftsartikel (refereegranskat)abstract
- Bio-impedance is a method to assess the body composition safely, non-invasively and inexpensively. This method finds application for assessing body fluid and body composition for multiple disease scenarios in clinical environments and for at-home monitoring of chronic ailments. Bio-impedance sensors require higher power than most other bio-signal acquisition systems due to need of high frequency current and voltage management. Currently used bio-impedance devices are bulky due to incorporation of large batteries and cannot be used for long term monitoring, especially for wearable applications. This limits the widespread implementation of bio-impedance measurement devices. We present the design and implementation of a wireless wearable bio-impedance sensor node, ImpediSense, which has a low power system design that achieves long duration operability without compromising on sensor measurement accuracy and precision. Experimental evaluation show a battery life of several months for measuring bio-impedance with power duty cycling every 1 min over ten frequencies in the range of 10 kHz–100 kHz, using a small form factor 250 mA h Li-ion battery. The lifetime is achieved due to several power optimization implemented in system design of hardware and firmware resulting in active power of 53 mW and idle power of 15.7 μW. Additionally, the presented sensor node shows high performance in terms of accuracy of impedance measurement with an error less than 1.5 % and precision of 0.6 Ω when measuring tetrapolar bio-impedance of the human body. With the inclusion of a small sized battery, ImpediSense has a compact form factor with dimensions 3 cm × 1.8 cm × 0.6 cm, making it more conducive for incorporation in wearable systems. © 2021
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| 8. |
- 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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| 9. |
- Djidi, N. E. H., et al.
(författare)
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How can wake-up radio reduce lora downlink latency for energy harvesting sensor nodes?
- 2021
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:3, s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- LoRa is popular for internet of things applications as this communication technology offers both a long range and a low power consumption. However, LoRaWAN, the standard MAC protocol that uses LoRa as physical layer, has the bottleneck of a high downlink latency to achieve energy efficiency. To overcome this drawback we explore the use of wake-up radio combined with LoRa, and propose an adequate MAC protocol that takes profit of both these heterogeneous and complementary technologies. This protocol allows an opportunistic selection of a cluster head that forwards commands from the gateway to the nodes in the same cluster. Furthermore, to achieve self-sustainability, sensor nodes might include an energy harvesting sub-system, for instance to scavenge energy from the light, and their quality of service can be tuned, according to their available energy. To have an effective self-sustaining LoRa system, we propose a new energy manager that allows less fluctuations of the quality of service between days and nights. Latency and energy are modeled in a hybrid manner, i.e., leveraging microbenchmarks on real hardware platforms, to explore the influence of the energy harvesting conditions on the quality of service of this heterogeneous network. It is clearly demonstrated that the cooperation of nodes within a cluster drastically reduces the latency of LoRa base station commands, e.g., by almost 90% compared to traditional LoRa scheme for a 10 nodes cluster. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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| 10. |
- Giordano, M., et al.
(författare)
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A Battery-Free Long-Range Wireless Smart Camera for Face Detection
- 2020
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Ingår i: Proceedings. - New York, NY, USA : Association for Computing Machinery, Inc. - 9781450381291 ; , s. 29-35
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Konferensbidrag (refereegranskat)abstract
- This paper presents a battery-free smart camera that combines tiny machine learning, long-range communication, power management, and energy harvesting. The smart sensor node has been implemented and evaluated in the field, showing both battery-less capabilities with a small-size photovoltaic panel and the energy efficiency of the proposed solution. We evaluated two different ARM Cortex-M4F microcontrollers, the Ambiq Apollo 3 that is an energy-efficient microcontroller, and a Microchip SAMD51 able to work in high radiation environments but with a higher power in active mode. Finally, a low power LoRa module provides the long-range wireless transmission capability. The tiny machine learning algorithm for face recognition has been optimized in terms of accuracy versus energy, achieving up to 97% accuracy recognizing five different faces. Experimental results demonstrated the capability of the developed sensor node to start from the cold start after 1 minute at a very low luminosity of 350 lux using a cm size flexible photovoltaic panels and work perpetually after the cold start. © 2020 ACM.
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