| 1. |
- 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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| 2. |
- Maioli, Andrea, et al.
(författare)
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Virtual Memory for Intermittent Computing
- 2021
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Ingår i: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450390972 ; , s. 261-273
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Konferensbidrag (refereegranskat)
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| 3. |
- Tavallaie, O., et al.
(författare)
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Throughput Maximization in Low-Power IoT Networks via Tuning the Size of the TSCH Slotframe
- 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 (ACM). - 9781450390972 ; , s. 401-402
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Konferensbidrag (refereegranskat)abstract
- Time-Slotted Channel Hopping (TSCH) was standardized as a part of IEEE 802.15.4e to address the strict reliability and timeliness requirements of low-power Internet of Things (IoT) applications. Setting the size of the TSCH slotframe has a considerable effect on the performance of scheduling algorithms used in IoT networks. Although IETF and IEEE standards define general mechanisms for communication of TSCH nodes, finding the optimal size of the TSCH slotframe has been left open and unresolved. In this poster, we propose an algorithm called S-TSCH to find the optimal size of the TSCH slotframe for maximizing network throughput based on 1) the number of nodes placed in the topology, 2) the data generation rate of applications running on IoT nodes, 3) and the maximum rate of generating TSCH/RPL control packets. To evaluate the performance of our contribution, we implement S-TSCH on Zolerita Firefly IoT motes and the Contiki-NG operating system. Evaluation results show that our proposed method improves the performance of distributed TSCH scheduling algorithms in terms of reliability and delay.
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| 4. |
- Villani, F., et al.
(författare)
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RF Power Transmission : Energy Harvesting for Self-Sustaining Miniaturized Sensor Nodes
- 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. 592-593
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Konferensbidrag (refereegranskat)abstract
- Radio Frequency (RF) energy transfer is an emerging technology to supply perpetually the new generation of internet of things devices. The proposed work shows the design and implementation of RF power transmission circuits the possible usages as a power source for batteryless devices. The realized circuits can receive power in the order of 1-10 mW depending on the distance from the transmitter, size, and antenna efficiency, allowing the deployment of these rectification circuits in any low power sensing network that requires a reliable and controllable power source. This paper will introduce and illustrate the preliminary results achieve for the work done for in a life-demo. © 2021 Owner/Author.
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| 5. |
- Zhu, S., et al.
(författare)
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Dataset : A Low-resolution infrared thermal dataset and potential privacy-preserving applications
- 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. 552-555
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Konferensbidrag (refereegranskat)abstract
- This paper presents a low-resolution infrared thermal dataset of people and thermal objects, such as a working laptop, in indoor environments. The dataset was collected by a far infrared thermal camera (32x24 pixels), which can capture the position and shape information of thermal objects without privacy issues that enable trustworthy computer vision applications. The dataset consists of 1770 thermal images with high-quality annotation collected from an indoor room with around 15°C. We implemented a privacy-preserving human detection method and trained a multiple object detection (MOD) model based on the dataset. The human detection method reaches 90.3% accuracy. On the other hand, the MOD model achieved 56.8% mean average precision (mAP). Researchers can implement interesting applications based on our dataset, for example, privacy-preserving people counting systems, occupancy estimation systems for smart buildings, and social distance detectors.
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