| 1. |
- Akbari-Saatlu, Mehdi, et al.
(author)
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H2S gas sensing based on SnO2thin films deposited by ultrasonic spray pyrolysis on Al2O3substrate
- 2021
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In: 2021 IEEE Sensors Applications Symposium (SAS). - 9781728194318
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Conference paper (peer-reviewed)abstract
- H2S gas is harmful for human health and environment, therefore novel gas sensors for real time and fast detection with high precision have been sought. Metal oxides are already known as promising candidate for this purpose. This article presents the performance of a gas sensor consists of a microheater and active layer formed on single alumina substrate for operating at high temperature applications. Ultrasonic spray pyrolysis deposition method was used to make both thick layer of SnO2 for microheater and thin and porous crystalline layer of SnO2 as sensing layer. The prepared sensor showed suitable dynamic response towards 10 to 50 ppm of H2S gas both in humid and dry conditions at 450 °C. In these experiments, the cross sensitivity of the sensor was also checked for other interfering gases e.g. CH4 and NO2.
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| 2. |
- Depari, A., et al.
(author)
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Versatile and low-cost sensor interface for IoT-ready odor monitoring in wastewater management
- 2021
-
In: 2021 IEEE Sensors Applications Symposium, SAS 2021 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728194318
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Conference paper (peer-reviewed)abstract
- The monitoring of pollutants in industrial plants is a major concern, in order to satisfy the requirements dictated by the related norms. Recently, the problem of odor monitoring gained importance since, despite the generally low dangerous nature of the emission, people usually correlate bad smell to unhealthy air condition. In this paper, we focus on the wastewater treatment application scenario and propose a versatile air pollution control solution. In particular, a distributed eNose, based on low-cost, but highly configurable sensing probes, is suggested. The Internet of Things (IoT) paradigm has been followed, thanks to the adoption of a communication infrastructure based on the widely adopted LoRaWAN technology. Such an approach, on one hand, would allow to easily cope with the administration and local population; on the other hand, it paves the way to use analytics to predict emission events in advance. The need for managing multiple transducers per each probe has been solved using an innovative acquisition strategy, exploiting both the volt-amperometric and resistance-to-time (integral) approaches, implemented by low-cost microcontroller and ancillary electronics. A proof-of-concept prototype has been realized and preliminary experiments have been carried out. Experimental results have demonstrated the solidity of the proposed approach, with relative error below 1% and relative standard deviation below 0.5% over the whole considered resistive range of more than two decades. © 2021 IEEE.
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| 3. |
- Giordano, M., et al.
(author)
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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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In: 2021 IEEE Sensors Applications Symposium, SAS 2021 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728194318
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Conference paper (peer-reviewed)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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| 4. |
- Krug, Silvia, et al.
(author)
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Comparing BLE and NB-IoT as communication options for smart viticulture IoT applications
- 2021
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In: 2021 IEEE Sensors Applications Symposium (SAS). - 9781728194318
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Conference paper (peer-reviewed)abstract
- Choosing the appropriate communication technology for outdoor applications has been a challenge over years and let to many different options. This makes it difficult for designers and users to chose the best option for their setup as each option has unique pros and cons. In this paper, we evaluate and compare Narrow Band Internet of Things (NB-IoT) and Bluetooth Low Energy (BLE) regarding their applicability for a smart viticulture scenario. We study how the node density and system energy consumption varies for various configurations and are thus able to highlight challenges in deployments as well as tradeoffs between the technologies.
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| 5. |
- Spirjakin, Denis, et al.
(author)
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Novel Method of Temperature Modulation for Enhancing Catalytic Gas Sensor Selectivity
- 2021
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In: 2021 IEEE Sensors Applications Symposium (SAS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728194318 - 9781728194318
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Conference paper (peer-reviewed)abstract
- Catalytic gas sensors are among the most widespread gas sensors for combustible gas concentration measurements. However, their selectivity is low. In this research, the results of machine learning techniques application to enhance catalytic gas sensor selectivity are presented. The measurements of sensor signal are performed using the multistage heat pulse method described in our previous works. Contrary to the previous works, the number of heating stages was increased from 2 to 55, which corresponds to the heating voltage range of 125 m V to 1.5 V with a 25 m V step. This change enriches sensor signal with information about gas compositions. Methane and vapors of acetone, ethanol and gasoline are used as target gases. A support vector machine method is used to train two models. The first one was trained based on the plain normalized data. It was used for a microcontroller implementation of the method. The second model used the data transformed by principal component analysis technique. This model was used to visualize the method proposed. The results show that the application of proposed method allows to identify gases by single catalytic sensor. These principles can be used to design selective gas detectors which will react only to target gases.
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| 6. |
- Spirjakin, Denis, et al.
(author)
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Passive Methane Gas Sensor Node
- 2021
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In: 2021 IEEE Sensors Applications Symposium (SAS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728194318 - 9781728194318
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Conference paper (peer-reviewed)abstract
- A passive methane sensor based on a catalytic sensor was developed and investigated. The sensor is activated when an NFC signal source (mobile phone) approaches it. In the case when the radio frequency field becomes strong enough, the NFC energy is accumulated in the supercapacitor. After the required voltage value is reached on the supercapacitor, power is supplied to the sensor control circuit and a single measurement is performed. The energy value, which is necessary to perform measurements is approximately 88.7 mJ. A mobile phone acts also as the sensor reader.
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| 7. |
- Wang, X., et al.
(author)
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SmartHand : Towards embedded smart hands for prosthetic and robotic applications
- 2021
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In: 2021 IEEE Sensors Applications Symposium, SAS 2021 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728194318
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Conference paper (peer-reviewed)abstract
- The sophisticated sense of touch of the human hand significantly contributes to our ability to safely, efficiently, and dexterously manipulate arbitrary objects in our environment. Robotic and prosthetic devices lack refined tactile feedback from their end-effectors, leading to counterintuitive and complex control strategies. To address this lack, tactile sensors have been designed and developed, but they are either expensive and not scalable or offer an insufficient spatial and temporal resolution. This paper focuses on overcoming these issues by designing a smart embedded system, called SmartHand, enabling the acquisition and real-time processing of high-resolution tactile information from a hand-shaped multi-sensor array for prosthetic and robotic applications. We acquire a new tactile dataset consisting of 340,000 frames while interacting with 16 objects from everyday life and the empty hand, i.e., a total of 17 classes. The design of the embedded system minimizes response latency in classification, by deploying a small yet accurate convolutional neural network on a high-performance ARM Cortex-M7 microcontroller. Compared to related work, our model requires one order of magnitude less memory and 15.6× fewer computations while achieving similar inter-session accuracy and up to 98.86% and 99.83% top-1 and top-3 cross-validation accuracy, respectively. Experimental results of the designed prototype show a total power consumption of 505 mW and a latency of only 100 ms. © 2021 IEEE.
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