passive sensing
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2022 ◽  
Vol 270 ◽  
pp. 112866
Author(s):  
Andrea Monti-Guarnieri ◽  
Clement Albinet ◽  
Alessandro Cotrufo ◽  
Niccolò Franceschi ◽  
Marco Manzoni ◽  
...  
Keyword(s):  

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 189
Author(s):  
Juan A. Besada ◽  
Ivan Campaña ◽  
David Carramiñana ◽  
Luca Bergesio ◽  
Gonzalo de Miguel

Noncollaborative surveillance of airborne UAS (Unmanned Aerial System) is a key enabler to the safe integration of UAS within a UTM (Unmanned Traffic Management) ecosystem. Thus, a wide variety of new sensors (known as Counter-UAS sensors) are being developed to provide real-time UAS tracking, ranging from radar, RF analysis and image-based detection to even sound-based sensors. This paper aims to discuss the current state-of-the art technology in this wide variety of sensors (both academically and commercially) and to propose a set of simulation models for them. Thus, the review is focused on identifying the key parameters and processes that allow modeling their performance and operation, which reflect the variety of measurement processes. The resulting simulation models are designed to help evaluate how sensors’ performances affect UTM systems, and specifically the implications in their tracking and tactical services (i.e., tactical conflicts with uncontrolled drones). The simulation models cover probabilistic detection (i.e., false alarms and probability of detection) and measurement errors, considering equipment installation (i.e., monostatic vs. multistatic configurations, passive sensing, etc.). The models were integrated in a UTM simulation platform and simulation results are included in the paper for active radars, passive radars, and acoustic sensors.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhiping Wan ◽  
Zhiming Xu ◽  
Jiajun Zou ◽  
Shaojiang Liu ◽  
Weichuan Ni ◽  
...  

Passive sensing networks can maintain the operation of the network by capturing energy from the environment, thereby solving the energy limitation problem of network nodes. Therefore, passive sensing networks are widely used in data collection in complex environments. However, the complexity of the network deployment environment makes passive sensing nodes unable to obtain stable energy from the surroundings. Therefore, better routing strategies are needed to save network energy consumption. In response to this problem, this paper proposes an IPv6 passive-aware network routing algorithm for the Internet of Things. This method is based on the characteristics of passive sensing networks. By analyzing the successful transmission rate of the network node transmission link, transmission energy consumption, end-to-end transmission delay, and waiting delay of IPv6 packets, the utility evaluation function of the route is obtained. After the utility evaluation function is obtained, the network routing is selected through the utility evaluation function. Then, the utility value and the deep neural network method are combined to train the classification model. The classification model assigns the best routing strategy according to the characteristics of the current network, thereby improving the energy consumption and delay performance of the network.


2021 ◽  
Vol 13 (21) ◽  
pp. 4369
Author(s):  
Daniel Duane ◽  
Chenyang Zhu ◽  
Felix Piavsky ◽  
Olav Rune Godø ◽  
Nicholas C. Makris

Attenuation from fish can reduce the intensity of acoustic signals and significantly decrease detection range for long-range passive sensing of manmade vehicles, geophysical phenomena, and vocalizing marine life. The effect of attenuation from herring shoals on the Passive Ocean Acoustic Waveguide Remote Sensing (POAWRS) of surface vessels is investigated here, where concurrent wide-area active Ocean Acoustic Waveguide Remote Sensing (OAWRS) is used to confirm that herring shoals occluding the propagation path are responsible for measured reductions in ship radiated sound and corresponding detection losses. Reductions in the intensity of ship-radiated sound are predicted using a formulation for acoustic attenuation through inhomogeneities in an ocean waveguide that has been previously shown to be consistent with experimental measurements of attenuation from fish in active OAWRS transmissions. The predictions of the waveguide attenuation formulation are in agreement with measured reductions from attenuation, where the position, size, and population density of the fish groups are characterized using OAWRS imagery as well as in situ echosounder measurements of the specific shoals occluding the propagation path. Experimental measurements of attenuation presented here confirm previous theoretical predictions that common heuristic formulations employing free space scattering assumptions can be in significant error. Waveguide scattering and propagation theory is found to be necessary for accurate predictions.


2021 ◽  
Vol 12 ◽  
Author(s):  
Jihui Lee ◽  
Nili Solomonov ◽  
Samprit Banerjee ◽  
George S. Alexopoulos ◽  
Jo Anne Sirey

Late-life depression is heterogenous and patients vary in disease course over time. Most psychotherapy studies measure activity levels and symptoms solely using self-report scales, administered periodically. These scales may not capture granular changes during treatment. We introduce the potential utility of passive sensing data collected with smartphone to assess fluctuations in daily functioning in real time during psychotherapy for late life depression in elder abuse victims. To our knowledge, this is the first investigation of passive sensing among depressed elder abuse victims. We present data from three victims who received a 9-week intervention as part of a pilot randomized controlled trial and showed a significant decrease in depressive symptoms (50% reduction). Using a smartphone, we tracked participants' daily number of smartphone unlocks, time spent at home, time spent in conversation, and step count over treatment. Independent assessment of depressive symptoms and behavioral activation were collected at intake, Weeks 6 and 9. Data revealed patient-level fluctuations in activity level over treatment, corresponding with self-reported behavioral activation. We demonstrate how passive sensing data could expand our understanding of heterogenous presentations of late-life depression among elder abuse. We illustrate how trajectories of change in activity levels as measured with passive sensing and subjective measures can be tracked concurrently over time. We outline challenges and potential solutions for application of passive sensing data collection in future studies with larger samples using novel advanced statistical modeling, such as artificial intelligence algorithms.


10.2196/29426 ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. e29426
Author(s):  
Natasha Wade ◽  
Joseph M Ortigara ◽  
Ryan M Sullivan ◽  
Rachel L Tomko ◽  
Florence J Breslin ◽  
...  

Background Concerns abound regarding childhood smartphone use, but studies to date have largely relied on self-reported screen use. Self-reporting of screen use is known to be misreported by pediatric samples and their parents, limiting the accurate determination of the impact of screen use on social, emotional, and cognitive development. Thus, a more passive, objective measurement of smartphone screen use among children is needed. Objective This study aims to passively sense smartphone screen use by time and types of apps used in a pilot sample of children and to assess the feasibility of passive sensing in a larger longitudinal sample. Methods The Adolescent Brain Cognitive Development (ABCD) study used passive, objective phone app methods for assessing smartphone screen use over 4 weeks in 2019-2020 in a subsample of 67 participants (aged 11-12 years; 31/67, 46% female; 23/67, 34% White). Children and their parents both reported average smartphone screen use before and after the study period, and they completed a questionnaire regarding the acceptability of the study protocol. Descriptive statistics for smartphone screen use, app use, and protocol feasibility and acceptability were reviewed. Analyses of variance were run to assess differences in categorical app use by demographics. Self-report and parent report were correlated with passive sensing data. Results Self-report of smartphone screen use was partly consistent with objective measurement (r=0.49), although objective data indicated that children used their phones more than they reported. Passive sensing revealed the most common types of apps used were for streaming (mean 1 hour 57 minutes per day, SD 1 hour 32 minutes), communication (mean 48 minutes per day, SD 1 hour 17 minutes), gaming (mean 41 minutes per day, SD 41 minutes), and social media (mean 36 minutes per day, SD 1 hour 7 minutes). Passive sensing of smartphone screen use was generally acceptable to children (43/62, 69%) and parents (53/62, 85%). Conclusions The results of passive, objective sensing suggest that children use their phones more than they self-report. Therefore, use of more robust methods for objective data collection is necessary and feasible in pediatric samples. These data may then more accurately reflect the impact of smartphone screen use on behavioral and emotional functioning. Accordingly, the ABCD study is implementing a passive sensing protocol in the full ABCD cohort. Taken together, passive assessment with a phone app provided objective, low-burden, novel, informative data about preteen smartphone screen use.


Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6730
Author(s):  
Md Shifatul Islam ◽  
Asimina Kiourti ◽  
Md Asiful Islam

The idea of passive biosensing through inductive coupling between antennas has been of recent interest. Passive sensing systems have the advantages of flexibility, wearability, and unobtrusiveness. However, it is difficult to build such systems having good transmission performance. Moreover, their near-field coupling makes them sensitive to misalignment and movements. In this work, to enhance transmission between two antennas, we investigate the effect of superstrates and metamaterials and propose the idea of dielectric fill in between the antenna and the superstrate. Preliminary studies show that the proposed method can increase transmission between a pair of antennas significantly. Specifically, transmission increase of ≈5 dB in free space and ≈8 dB in lossy media have been observed. Next, an analysis on a representative passive neurosensing system with realistic biological tissues shows very low transmission loss, as well as considerably better performance than the state-of-the-art systems. Apart from transmission enhancement, the proposed technique can significantly mitigate performance degradation due to misalignment of the external antenna, which is confirmed through suitable sensitivity analysis. Overall, the proposed idea can have fascinating prospects in the field of biopotential sensing for different biomedical applications.


Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6602
Author(s):  
Pinggang Jia ◽  
Jia Liu ◽  
Jiang Qian ◽  
Qianyu Ren ◽  
Guowen An ◽  
...  

An LC wireless passive pressure sensor based on a single-crystalline magnesium oxide (MgO) MEMS processing technique is proposed and experimentally demonstrated for applications in environmental conditions of 900 °C. Compared to other high-temperature resistant materials, MgO was selected as the sensor substrate material for the first time in the field of wireless passive sensing because of its ultra-high melting point (2800 °C) and excellent mechanical properties at elevated temperatures. The sensor mainly consists of inductance coils and an embedded sealed cavity. The cavity length decreases with the applied pressure, leading to a monotonic variation in the resonant frequency of the sensor, which can be retrieved wirelessly via a readout antenna. The capacitor cavity was fabricated using a MgO MEMS technique. This MEMS processing technique, including the wet chemical etching and direct bonding process, can improve the operating temperature of the sensor. The experimental results indicate that the proposed sensor can stably operate at an ambient environment of 22–900 °C and 0–700 kPa, and the pressure sensitivity of this sensor at room temperature is 14.52 kHz/kPa. In addition, the sensor with a simple fabrication process shows high potential for practical engineering applications in harsh environments.


2021 ◽  
Vol 3 ◽  
Author(s):  
Tim Kaiser ◽  
Björn Butter ◽  
Samuel Arzt ◽  
Björn Pannicke ◽  
Julia Reichenberger ◽  
...  

Food craving (FC) peaks are highly context-dependent and variable. Accurate prediction of FC might help preventing disadvantageous eating behavior. Here, we examine whether data from 2 weeks of ecological momentary assessment (EMA) questionnaires on stress and emotions (active EMA, aEMA) alongside temporal features and smartphone sensor data (passive EMA, pEMA) are able to predict FCs ~2.5 h into the future in N = 46 individuals. A logistic prediction approach with feature dimension reduction via Best Item Scale that is Cross-Validated, Weighted, Informative and Transparent (BISCWIT) was performed. While overall prediction accuracy was acceptable, passive sensing data alone was equally predictive to psychometric data. The frequency of which single predictors were considered for a model was rather balanced, indicating that aEMA and pEMA models were fully idiosyncratic.


2021 ◽  
Author(s):  
Amani Yousef Owda ◽  
Neil A. Salmon ◽  
Majdi Owda
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