embedded sensing
Recently Published Documents


TOTAL DOCUMENTS

115
(FIVE YEARS 40)

H-INDEX

10
(FIVE YEARS 3)

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
Stefan Nedelcu ◽  
Kishan Thodkar ◽  
Christofer Hierold

AbstractCustomizable, portable, battery-operated, wireless platforms for interfacing high-sensitivity nanoscale sensors are a means to improve spatiotemporal measurement coverage of physical parameters. Such a platform can enable the expansion of IoT for environmental and lifestyle applications. Here we report a platform capable of acquiring currents ranging from 1.5 nA to 7.2 µA full-scale with 20-bit resolution and variable sampling rates of up to 3.125 kSPS. In addition, it features a bipolar voltage programmable in the range of −10 V to +5 V with a 3.65 mV resolution. A Finite State Machine steers the system by executing a set of embedded functions. The FSM allows for dynamic, customized adjustments of the nanosensor bias, including elevated bias schemes for self-heating, measurement range, bandwidth, sampling rate, and measurement time intervals. Furthermore, it enables data logging on external memory (SD card) and data transmission over a Bluetooth low energy connection. The average power consumption of the platform is 64.5 mW for a measurement protocol of three samples per second, including a BLE advertisement of a 0 dBm transmission power. A state-of-the-art (SoA) application of the platform performance using a CNT nanosensor, exposed to NO2 gas concentrations from 200 ppb down to 1 ppb, has been demonstrated. Although sensor signals are measured for NO2 concentrations of 1 ppb, the 3σ limit of detection (LOD) of 23 ppb is determined (1σ: 7 ppb) in slope detection mode, including the sensor signal variations in repeated measurements. The platform’s wide current range and high versatility make it suitable for signal acquisition from resistive nanosensors such as silicon nanowires, carbon nanotubes, graphene, and other 2D materials. Along with its overall low power consumption, the proposed platform is highly suitable for various sensing applications within the context of IoT.


2022 ◽  
pp. 107936
Author(s):  
Ashutosh Sharma ◽  
Mikhail Georgi ◽  
Maxim Tregubenko ◽  
Alexey Tselykh ◽  
Alexander Tselykh

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6797
Author(s):  
Salvatore Bruno ◽  
Giulia Del Serrone ◽  
Paola Di Mascio ◽  
Giuseppe Loprencipe ◽  
Eugenio Ricci ◽  
...  

Airport pavements should ensure regular and safe movements during their service life; the management body has to monitor the functional and structural characteristics, and schedule maintenance work, balancing the often conflicting goals of safety, economic and technical issues. This paper presents a remote monitoring system to evaluate the structural performance of a runway composed of concrete thresholds and a flexible central runway. Thermometers, strain gauges, and pressure cells will be embedded at different depths to continuously monitor the pavement’s response to traffic and environmental loads. An innovative system allows data acquisition and processing with specific calculation models, in order to inform the infrastructure manager, in real time, about the actual conditions of the pavement. In this way, the authors aim to develop a system that provides useful information for the correct implementation of an airport pavement management system (APMS) based on real-life data. Indeed, it permits comprehensive monitoring functions to be performed, based on the embedded sensing network.


2021 ◽  
Vol 11 (16) ◽  
pp. 7723
Author(s):  
Alex Mazursky ◽  
Jeong-Hoi Koo ◽  
Taylor Mason ◽  
Sam-Yong Woo ◽  
Tae-Heon Yang

We present a miniature haptic module based on electrorheological fluid, designed for conveying combined stiffness and vibrotactile sensations at a small scale. Haptic feedback is produced through electrorheological fluid’s controllable resistive force and varies with the actuator’s deformation. To demonstrate the proposed actuator’s feedback in realistic applications, a method for measuring the actuator’s deformation must be implemented for active control. To this end, in this study, we incorporate a sensor design based on a bend-sensitive resistive film to the ER haptic actuator. The combined actuator and sensor module was tested for its ability to simultaneously actuate and sense the actuator’s state under indentation. The results show that the bend sensor can accurately track the actuator’s displacement over its stroke. Thus, the proposed sensor may enable control of the output resistive force according to displacement, which may lead to more informed and engaging combined kinesthetic and tactile feedback.


2021 ◽  
Author(s):  
Andrea Albanese ◽  
matteo nardello ◽  
Davide Brunelli

Artificial intelligence has smoothly penetrated several economic activities, especially monitoring and control applications, including the agriculture sector. However, research efforts toward low-power sensing devices with fully functional machine learning (ML) on-board are still fragmented and limited in smart farming. Biotic stress is one of the primary causes of crop yield reduction. With the development of deep learning in computer vision technology, autonomous detection of pest infestation through images has become an important research direction for timely crop disease diagnosis. This paper presents an embedded system enhanced with ML functionalities, ensuring continuous detection of pest infestation inside fruit orchards. The embedded solution is based on a low-power embedded sensing system along with a Neural Accelerator able to capture and process images inside common pheromone-based traps. Three different ML algorithms have been trained and deployed, highlighting the capabilities of the platform. Moreover, the proposed approach guarantees an extended battery life thanks to the integration of energy harvesting functionalities. Results show how it is possible to automate the task of pest infestation for unlimited time without the farmer's intervention.


2021 ◽  
Author(s):  
Andrea Albanese ◽  
matteo nardello ◽  
Davide Brunelli

Artificial intelligence has smoothly penetrated several economic activities, especially monitoring and control applications, including the agriculture sector. However, research efforts toward low-power sensing devices with fully functional machine learning (ML) on-board are still fragmented and limited in smart farming. Biotic stress is one of the primary causes of crop yield reduction. With the development of deep learning in computer vision technology, autonomous detection of pest infestation through images has become an important research direction for timely crop disease diagnosis. This paper presents an embedded system enhanced with ML functionalities, ensuring continuous detection of pest infestation inside fruit orchards. The embedded solution is based on a low-power embedded sensing system along with a Neural Accelerator able to capture and process images inside common pheromone-based traps. Three different ML algorithms have been trained and deployed, highlighting the capabilities of the platform. Moreover, the proposed approach guarantees an extended battery life thanks to the integration of energy harvesting functionalities. Results show how it is possible to automate the task of pest infestation for unlimited time without the farmer's intervention.


Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3975
Author(s):  
Lorraine C. Nagle ◽  
Amelie Wahl ◽  
Vladimir Ogourstov ◽  
Ian Seymour ◽  
Fiona Barry ◽  
...  

The emergence of specific drug–device combination products in the inhalable pharmaceutical industry demands more sophistication of device functionality in the form of an embedded sensing platform to increase patient safety and extend patent coverage. Controlling the nebuliser function at a miniaturised, integrated electrochemical sensing platform with rapid response time and supporting novel algorithms could deliver such a technology offering. Development of a nanoporous gold (NPG) electrochemical sensor capable of creating a unique fingerprint signal generated by inhalable pharmaceuticals provided the impetus for our study of the electrooxidation of salbutamol, which is the active bronchodilatory ingredient in VentolinTM formulations. It was demonstrated that, at NPG-modified microdisc electrode arrays, salbutamol is distinguishable from the chloride excipient present at 0.0154 M using linear sweep voltammetry and can be detected amperometrically. In contrast, bare gold microdisc electrode arrays cannot afford such discrimination, as the potential for salbutamol oxidation and chloride adsorption reactions overlap. The discriminative power of NPG originates from the nanoconfinement effect for chloride in the internal pores of NPG, which selectively enhances the electron transfer kinetics of this more sluggish reaction relative to that of the faster, diffusion-controlled salbutamol oxidation. Sensing was performed at a fully integrated three-electrode cell-on-chip using Pt as a quasi-reference electrode.


2021 ◽  
Author(s):  
Yashodhan Athavale

The Internet of Things (IoT) is a trending model in the wake of recent advancements in ubiquitous sensors and smart devices, and is rapidly being deployed in communications, infrastructure, transportation and healthcare services. The Internet of Medical Things (IoMT) is a subset of the IoT and provides a layered architecture for connecting individuals with mobile devices and wearables, such that their vital physiological data can be captured and analyzed non-invasively using smart sensors embedded within these devices. Currently available wearables have embedded sensing modules for measuring movement, direction, light and pressure. Actigraphs are one such type of wearables which exclusively employ the use of accelerometers for capturing human movement-based vibration data. The main intention of this research work is the analysis of unstructured, non-stationary actigraphy signals. This study aims to address three key objectives: (i) enabling compression and denoising of actigraphy data during acquisition; (ii) extracting regions of interest from the actigraphy data, and; (iii) deriving actigraphy specific features for improving the activity classification accuracy. These have been achieved through three key contributions, namely: (A) a signal encoding framework for data compression and denoising, (B) two novel adaptive segmentation schemes which help in extracting specific movement information from actigraphy data, and (C) two key actigraphy specific features, which quantify limb movements, and hence provide a better classification accuracy using machine learning algorithms. The outcome of this research work is a device-independent actigraphy analysis application for estimating the severity of neuromuscular diseases, identifying types of daily activity, and classify joint degeneration severity, which has been tested on five different actigraphy datasets from wake and sleep states. Compared to traditional signal processing methods, the proposed algorithms ensured a 20-90% increase in SNR (signal-to-noise ratio), 50-80% reduction in bit rate, 50-90% data compression, and an increase in activity recognition accuracy by over 5-20%. Results from this systematic investigation indicate that data analysis conducted at the acquisition source, optimizes signal denoising, memory and power usage, and activity recognition, thereby promoting an edge computing approach to physiological signal analysis using wearables in a low resource environment.


2021 ◽  
Author(s):  
Yashodhan Athavale

The Internet of Things (IoT) is a trending model in the wake of recent advancements in ubiquitous sensors and smart devices, and is rapidly being deployed in communications, infrastructure, transportation and healthcare services. The Internet of Medical Things (IoMT) is a subset of the IoT and provides a layered architecture for connecting individuals with mobile devices and wearables, such that their vital physiological data can be captured and analyzed non-invasively using smart sensors embedded within these devices. Currently available wearables have embedded sensing modules for measuring movement, direction, light and pressure. Actigraphs are one such type of wearables which exclusively employ the use of accelerometers for capturing human movement-based vibration data. The main intention of this research work is the analysis of unstructured, non-stationary actigraphy signals. This study aims to address three key objectives: (i) enabling compression and denoising of actigraphy data during acquisition; (ii) extracting regions of interest from the actigraphy data, and; (iii) deriving actigraphy specific features for improving the activity classification accuracy. These have been achieved through three key contributions, namely: (A) a signal encoding framework for data compression and denoising, (B) two novel adaptive segmentation schemes which help in extracting specific movement information from actigraphy data, and (C) two key actigraphy specific features, which quantify limb movements, and hence provide a better classification accuracy using machine learning algorithms. The outcome of this research work is a device-independent actigraphy analysis application for estimating the severity of neuromuscular diseases, identifying types of daily activity, and classify joint degeneration severity, which has been tested on five different actigraphy datasets from wake and sleep states. Compared to traditional signal processing methods, the proposed algorithms ensured a 20-90% increase in SNR (signal-to-noise ratio), 50-80% reduction in bit rate, 50-90% data compression, and an increase in activity recognition accuracy by over 5-20%. Results from this systematic investigation indicate that data analysis conducted at the acquisition source, optimizes signal denoising, memory and power usage, and activity recognition, thereby promoting an edge computing approach to physiological signal analysis using wearables in a low resource environment.


Sign in / Sign up

Export Citation Format

Share Document