| 1. |
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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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| 3. |
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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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| 11. |
- Giordano, M., et al.
(författare)
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A Battery-Free Long-Range Wireless Smart Camera for Face Recognition
- 2021
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Ingår i: SenSys 2021 - Proceedings of the 2021 19th ACM Conference on Embedded Networked Sensor Systems. - New York, NY, USA : Association for Computing Machinery, Inc. - 9781450390972 ; , s. 594-595
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Konferensbidrag (refereegranskat)abstract
- In this demo we present a battery-free smart camera that exploits aggressive power management and energy harvesting to achieve face recognition in an energy-neutral fashion. A novel hardware accelerator for Convolution Neural Networks is employed to speed up the inference of the Tiny Machine Learning algorithm. The recognized face, and not the entire image, is sent via LoRa in a sensor network-like scenario. Experimental results demonstrated the capability of the developed sensor node to start and work perpetually with only a small photovoltaic panel array. © 2021 Owner/Author.
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| 12. |
- 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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| 13. |
- Reichmuth, M., et al.
(författare)
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A Non-invasive Wearable Bioimpedance System to Wirelessly Monitor Bladder Filling
- 2020
-
Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9783981926347 ; , s. 338-341
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Konferensbidrag (refereegranskat)abstract
- Monitoring of renal function can be crucial for patients in acute care settings. Commonly during postsurgical surveillance, urinary catheters are employed to assess the urine output accurately. However, as with any external device inserted into the body, the use of these catheters carries a significant risk of infection. In this paper, we present a non-invasive method to measure the fill rate of the bladder, and thus rate of renal clearance, via an external bioimpedance sensor system to avoid the use of urinary catheters, thereby eliminating the risk of infections and improving patient comfort. We design and propose a 4-electrode front-end and the whole wearable and wireless system with low power and accuracy in mind. The results demonstrate the accuracy of the sensors and low power consumption of only 80μW with a duty cycling of 1 acquisition every 5 minutes, which makes this battery-operated wearable device a long-term monitor system. © 2020 EDAA.
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| 14. |
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| 15. |
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| 16. |
- Scherer, M., et al.
(författare)
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Towards Always-on Event-based Cameras for Long-lasting Battery-operated Smart Sensor Nodes
- 2021
-
Ingår i: Conference Record - IEEE Instrumentation and Measurement Technology Conference. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728195391
-
Konferensbidrag (refereegranskat)abstract
- A recent and promising approach to minimize the power consumption of always-on battery-operated sensors is to perform 'smart' detection of events to trigger processing. This approach effectively reduces the data bandwidth and power consumption at the system-level and increases the lifetime of sensor nodes. This paper presents an always-on, event-driven ultra-low-power camera platform for motion detection applications. The platform exploits an event-driven VGA imager that features a motion detection mode based on a tunable scene background subtraction algorithm and a grayscale imaging mode. To reduce the power consumption in the motion detection mode, the platform implements a configurable refresh rate which allows for adaption to sensing requirements by trading off between power consumption and detection sensitivity. With accurate experimental evaluation the paper demonstrates that the proposed approach reduces the system-level power consumption for always-on motion sensing applications by switching between an active 15 FPS imaging mode, consuming 5.5 mW and a low-power motion detection mode consuming 1.8 mW. We further estimate the power consumption for a single-chip solution and show that the system-level power budget can be reduced to 2.4 mW in imaging, and 400W in detection mode. © 2021 IEEE.
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| 17. |
- Wang, X., et al.
(författare)
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An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing
- 2020
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Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728153865
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Konferensbidrag (refereegranskat)abstract
- This paper presents an accurate and robust embedded motor-imagery brain-computer interface (MI-BCI). The proposed novel model, based on EEGNet [1], matches the requirements of memory footprint and computational resources of low-power microcontroller units (MCUs), such as the ARM Cortex-M family. Furthermore, the paper presents a set of methods, including temporal downsampling, channel selection, and narrowing of the classification window, to further scale down the model to relax memory requirements with negligible accuracy degradation. Experimental results on the Physionet EEG Motor Movement/Imagery Dataset show that standard EEGNet achieves 82.43%, 75.07%, and 65.07% classification accuracy on 2-, 3-, and 4-class MI tasks in global validation, outperforming the state-of-the-art (SoA) convolutional neural network (CNN) by 2.05%, 5.25%, and 6.49%. Our novel method further scales down the standard EEGNet at a negligible accuracy loss of 0.31% with 7.6× memory footprint reduction and a small accuracy loss of 2.51% with 15× reduction. The scaled models are deployed on a commercial Cortex-M4F MCU taking 101 ms and consuming 4.28 mJ per inference for operating the smallest model, and on a Cortex-M7 with 44 ms and 18.1 mJ per inference for the medium-sized model, enabling a fully autonomous, wearable, and accurate low-power BCI. © 2020 IEEE.
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| 18. |
- 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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| 19. |
- 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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| 20. |
- Denward, C M T, et al.
(författare)
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Solar radiation effects on decomposition of macrophyte litter in a lake littoral
- 2001
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Ingår i: Archiv für Hydrobiologie. - 0003-9136. ; 152:1, s. 69-80
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Tidskriftsartikel (refereegranskat)abstract
- Ambient solar radiation effects on Phragmites australis leaf and culm litter were investigated in a shallow and eutrophic south Swedish lake littoral. Leaf and culm litter was exposed to natural sunlight in the lake at a water depth of a few centimeters. For the leaf litter, an additional subset of the experiment was exposed to solar radiation in the air, to evaluate effects of the solar radiation on the leaf litter in a dry state. Radiation treatments (shaded, photosynthetic active radiation [PAR], PAR + ultraviolet-A [UVA] and PAR + UVA + ultraviolet-B [UVB]) were achieved by screening with Plexiglas and Mylar film. Decomposition was measured as dry weight loss, and fungal and bacterial abundance on the detritus was estimated as ergosterol and bacterial numbers, respectively. We found no differences in either weight loss or bacterial abundance among radiation treatments. The fungal biomass in the dry leaf litter was unaffected by the radiation. In the wet leaf litter, however, the ergosterol content in PAR, PAR + UVA and PAR + UVA + UVB treated samples was about one third of the amounts found in the initial material and in the samples kept darkened. Similarly, the fungal biomass associated with the culm litter was negatively affected by solar PAR + UVA + UVB radiation, but in culms exposed only to PAR or to PAR + UVA it was not significantly different from the fungal biomass in darkened samples. These results suggest that the net effects of radiation differ between fungi and bacteria, with the fungi being more susceptible to suppression by solar radiation than the bacteria. Our experiments mimic more closely than previously published studies the conditions that can be expected in natural environments. Hence, we propose that previous reports of strong radiation effects on aquatic liner degradation should be applied very carefully to natural conditions.
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| 21. |
- Dheman, K., et al.
(författare)
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Wireless, Artefact Aware Impedance Sensor Node for Continuous Bio-Impedance Monitoring
- 2020
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Ingår i: IEEE Transactions on Biomedical Circuits and Systems. - : Institute of Electrical and Electronics Engineers Inc.. - 1932-4545 .- 1940-9990. ; 14:5, s. 1122-1134
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Tidskriftsartikel (refereegranskat)abstract
- Body bio-impedance is a unique parameter to monitor changes in body composition non-invasively. Continuous measurement of bio-impedance can track changes in body fluid content and cell mass and has widespread applications for physiological monitoring. State-of-the-art implementation of bio-impedance sensor devices is still limited for continuous use, in part, due to artefacts arising at the skin-electrode (SE) interface. Artefacts at the SE interface may arise due to various factors such as motion, applied pressure on the electrode surface, changes in ambient conditions or gradual drying of electrodes. This paper presents a novel bio-impedance sensor node that includes an artefact aware method for bio-impedance measurement. The sensor node enables autonomous and continuous measurement of bio-impedance and SE contact impedance at ten frequencies between 10 kHz to 100 kHz to detect artefacts at the SE interface. Experimental evaluation with SE contact impedance models using passive 2R1C electronic circuits and also with non-invasive in vivo measurements of SE contact impedance demonstrated high accuracy (with maximum error less than 1.5%) and precision of 0.6 ω. The ability to detect artefacts caused by motion, vertically applied pressure and skin temperature changes was analysed in proof of concept experiments. Low power sensor node design achieved with 50mW in active mode and only 143 μW in sleep mode estimated a battery life of 90 days with a 250 mAh battery and duty-cycling impedance measurements every 60 seconds. Our method for artefact aware bio-impedance sensing is a step towards autonomous and unobtrusive continuous bio-impedance measurement for health monitoring at-home or in clinical environments. © 2007-2012 IEEE.
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| 22. |
- Fischer, R., et al.
(författare)
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WindNode : A long-lasting and long-range bluetooth wireless sensor node for pressure and acoustic monitoring on wind turbines
- 2021
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Ingår i: Proceedings - 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems, ICPS 2021. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728162072 ; , s. 393-399
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Konferensbidrag (refereegranskat)abstract
- This paper presents a low power, flexible and energy-efficient wireless sensor node for aerodynamic and acoustic measurements on wind turbine blades and other industrial structures. It comprises 40 high-accuracy absolute MEMS pressure sensors, ten MEMS microphones, a data processing system, a wireless transmitter based on Bluetooth Low Energy 5 tuned for long-range and high throughput while maintaining energy efficiency. The sensor node has been designed and implemented to test the range of communication, the impact on energy efficiency, the functionality, and the estimated lifetime. Experimental tests outdoor in realistic conditions revealed that the system can sustain a data rate of 850kbps over 438m. The node power consumption while streaming all measured data from a multi-MW wind turbine is only 46mW, enabling lifetimes of a full month even in the worst-case scenario of streaming all sensor data using an 8.7Ah Li-Ion battery. © 2021 IEEE.
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| 23. |
- Flueratoru, L., et al.
(författare)
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On the energy consumption and ranging accuracy of ultra-wideband physical interfaces
- 2020
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Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728182988
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Konferensbidrag (refereegranskat)abstract
- Ultra-wideband (UWB) communication is attracting increased interest for its high-accuracy distance measurements. However, the typical current consumption of tens to hundreds of mA during transmission and reception might make the technology prohibitive to battery-powered devices in the Internet of Things. The IEEE 802.15.4 standard specifies two UWB physical layer interfaces (PHYs), with low- and high-rate pulse repetition (LRP and HRP, respectively). While the LRP PHY allows a more energy-efficient implementation of the UWB transceiver than its HRP counterpart, the question is whether some ranging quality is lost in exchange. We evaluate the trade-off between power and energy consumption, on the one hand, and distance measurement accuracy and precision, on the other hand, using UWB devices developed by Decawave (HRP) and 3db Access (LRP). We find that the distance measurement errors of 3db Access devices have at most 12 cm higher bias and standard deviation in line-of-sight propagation and 2-3 times higher spread in non-line-of-sight scenarios than those of Decawave devices. However, 3db Access chips consume 10 times less power and 125 times less energy per distance measurement than Decawave ones. Since the LRP PHY has an ultra-low energy consumption, it should be preferred over the HRP PHY when energy efficiency is critical, with a small penalty in the ranging performance. © 2020 IEEE.
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| 24. |
- Frank, DN, et al.
(författare)
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Otitis media susceptibility and shifts in the head and neck microbiome due to SPINK5 variants
- 2021
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Ingår i: Journal of medical genetics. - : BMJ. - 1468-6244 .- 0022-2593. ; 58:7, s. 442-452
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Tidskriftsartikel (refereegranskat)abstract
- Otitis media (OM) susceptibility has significant heritability; however, the role of rare variants in OM is mostly unknown. Our goal is to identify novel rare variants that confer OM susceptibility.MethodsWe performed exome and Sanger sequencing of >1000 DNA samples from 551 multiethnic families with OM and unrelated individuals, RNA-sequencing and microbiome sequencing and analyses of swabs from the outer ear, middle ear, nasopharynx and oral cavity. We also examined protein localisation and gene expression in infected and healthy middle ear tissues.ResultsA large, intermarried pedigree that includes 81 OM-affected and 53 unaffected individuals cosegregates two known rare A2ML1 variants, a common FUT2 variant and a rare, novel pathogenic variant c.1682A>G (p.Glu561Gly) within SPINK5 (LOD=4.09). Carriage of the SPINK5 missense variant resulted in increased relative abundance of Microbacteriaceae in the middle ear, along with occurrence of Microbacteriaceae in the outer ear and oral cavity but not the nasopharynx. Eight additional novel SPINK5 variants were identified in 12 families and individuals with OM. A role for SPINK5 in OM susceptibility is further supported by lower RNA counts in variant carriers, strong SPINK5 localisation in outer ear skin, faint localisation to middle ear mucosa and eardrum and increased SPINK5 expression in human cholesteatoma.ConclusionSPINK5 variants confer susceptibility to non-syndromic OM. These variants potentially contribute to middle ear pathology through breakdown of mucosal and epithelial barriers, immunodeficiency such as poor vaccination response, alteration of head and neck microbiota and facilitation of entry of opportunistic pathogens into the middle ear.
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