| 1. |
- 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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| 2. |
- Reichmuth, M., et al.
(författare)
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A Non-invasive Wearable Bioimpedance System to Wirelessly Monitor Bladder Filling
- 2020
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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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| 3. |
- Salami, B., et al.
(författare)
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LEGaTO: Low-Energy, Secure, and Resilient Toolset for Heterogeneous Computing
- 2020
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Ingår i: PROCEEDINGS OF THE 2020 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2020). - 1530-1591. - 9783981926347 ; , s. 169-174
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Konferensbidrag (refereegranskat)abstract
- The LEGaTO project leverages task-based programming models to provide a software ecosystem for Made in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC, balanced with the security and resilience challenges. LEGaTO is an ongoing three-year EU H2020 project started in December 2017.
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| 4. |
- Sun, Jinghao, et al.
(författare)
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On the Volume Calculation for Conditional DAG Tasks : Hardness and Algorithms
- 2020
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Ingår i: PROCEEDINGS OF THE 2020 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2020). - NEW YORK, USA. - 9783981926347 ; , s. 204-209
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Konferensbidrag (refereegranskat)abstract
- The hardness of analyzing conditional directed acyclic graph (DAG) tasks remains unknown so far. For example, previous researches asserted that the conditional DAG's volume can be solved in polynomial time. However, these researches all assume well-nested structures that are recursively composed by single-source-single-sink parallel and conditional components. For conditional DAGs in general that do not comply with this assumption, the hardness and algorithms of volume computation are still open. In this paper, we construct counterexamples to show that previous work cannot provide a safe upper bound of the conditional DAG's volume in general. Moreover, we prove that the volume computation problem for conditional DAGs is strongly NP-hard. Finally, we propose an exact algorithm for computing the conditional DAG's volume. Experiments show that our method can significantly improve the accuracy of the conditional DAG's volume estimation.
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