Wireless Kitchen Fire Prevention System Using Electrochemical Carbon Dioxide Gas Sensor for Smart HomeKweon, Soon-Jae;Park, Jeong-Ho;Park, Chong-Ook;Yoo, Hyung-Joun;Ha, Sohmyung
doi: 10.3390/s22113965pmid: 35684586
This paper presents a wireless kitchen fire prevention system that can detect and notify the fire risk caused by gas stoves. The proposed system consists of two modules. The sensor module detects the concentration of carbon dioxide (CO2) near the gas stove and transmits the monitoring results wirelessly. The alarm module, which is placed in other places, receives the data and reminds the user of the stove status. The sensor module uses a cost-efficient electrochemical CO2 sensor and embeds an in situ algorithm that determines the status of the gas stove based on the measured CO2 concentration. For the wireless communication between the modules, on-off keying (OOK) is employed, thereby achieving a longer battery lifetime of the alarm module, low cost, and simple implementation. To increase the lifetime further, a wake-up function based on passive infrared (PIR) sensing is employed in the alarm module. Our system can successfully detect the on state of the stove within 40 s and the off state within 80 s. Thanks to the low-power implementation, in situ algorithm, and wake-up function, the alarm module’s expected battery lifetime is extended to about two months.
A Two-Step Approach for Classification in Alzheimer’s DiseaseDe Falco, Ivanoe;De Pietro, Giuseppe;Sannino, Giovanna
doi: 10.3390/s22113966pmid: 35684587
The classification of images is of high importance in medicine. In this sense, Deep learning methodologies show excellent performance with regard to accuracy. The drawback of these methodologies is the fact that they are black boxes, so no explanation is given to users on the reasons underlying their choices. In the medical domain, this lack of transparency and information, typical of black box models, brings practitioners to raise concerns, and the result is a resistance to the use of deep learning tools. In order to overcome this problem, a different Machine Learning approach to image classification is used here that is based on interpretability concepts thanks to the use of an evolutionary algorithm. It relies on the application of two steps in succession. The first receives a set of images in the inut and performs image filtering on them so that a numerical data set is generated. The second is a classifier, the kernel of which is an evolutionary algorithm. This latter, at the same time, classifies and automatically extracts explicit knowledge as a set of IF–THEN rules. This method is investigated with respect to a data set of MRI brain imagery referring to Alzheimer’s disease. Namely, a two-class data set (non-demented and moderate demented) and a three-class data set (non-demented, mild demented, and moderate demented) are extracted. The methodology shows good results in terms of accuracy (100% for the best run over the two-class problem and 91.49% for the best run over the three-class one), F_score (1.0000 and 0.9149, respectively), and Matthews Correlation Coefficient (1.0000 and 0.8763, respectively). To ascertain the quality of these results, they are contrasted against those from a wide set of well-known classifiers. The outcome of this comparison is that, in both problems, the methodology achieves the best results in terms of accuracy and F_score, whereas, for the Matthews Correlation Coefficient, it has the best result over the two-class problem and the second over the three-class one.
Security and Privacy Analysis of Youth-Oriented Connected DevicesSolera-Cotanilla, Sonia;Vega-Barbas, Mario;Pérez, Jaime;López, Gregorio;Matanza, Javier;Álvarez-Campana, Manuel
doi: 10.3390/s22113967pmid: 35684588
Under the Internet of Things paradigm, the emergence and use of a wide variety of connected devices and personalized telematics services have proliferated recently. As a result, along with the penetration of these devices in our daily lives, the users’ security and privacy have been compromised due to some weaknesses in connected devices and underlying applications. This article focuses on analyzing the security and privacy of such devices to promote safe Internet use, especially by young people. First, the connected devices most used by the target group are classified, and an exhaustive analysis of the vulnerabilities that concern the user is performed. As a result, a set of differentiated security and privacy issues existing in the devices is identified. The study reveals that many of these vulnerabilities are related to the fact that device manufacturers often prioritize functionalities and services, leaving security aspects in the background. These companies even exploit the data linked to the use of these devices for various purposes, ignoring users’ privacy rights. This research aims to raise awareness of severe vulnerabilities in devices and to encourage users to use them correctly. Our results help other researchers address these issues with a more global perspective.
A Unified Local–Global Feature Extraction Network for Human Gait Recognition Using Smartphone SensorsDas, Sonia;Meher, Sukadev;Sahoo, Upendra Kumar
doi: 10.3390/s22113968pmid: 35684589
Smartphone-based gait recognition has been considered a unique and promising technique for biometric-based identification. It is integrated with multiple sensors to collect inertial data while a person walks. However, captured data may be affected by several covariate factors due to variations of gait sequences such as holding loads, wearing types, shoe types, etc. Recent gait recognition approaches either work on global or local features, causing failure to handle these covariate-based features. To address these issues, a novel weighted multi-scale CNN (WMsCNN) architecture is designed to extract local to global features for boosting recognition accuracy. Specifically, a weight update sub-network (Ws) is proposed to increase or reduce the weights of features concerning their contribution to the final classification task. Thus, the sensitivity of these features toward the covariate factors decreases using the weight updated technique. Later, these features are fed to a fusion module used to produce global features for the overall classification. Extensive experiments have been conducted on four different benchmark datasets, and the demonstrated results of the proposed model are superior to other state-of-the-art deep learning approaches.
A Chip Antenna for Bluetooth Earphones with Cross-Head Interference Tested from Received-Signal SensingSeo, Yejune;Cho, Junghyun;Lee, Yejin;Jang, Jiyeon;Kwon, Hyung-Wook;Kahng, Sungtek
doi: 10.3390/s22113969pmid: 35684593
In this paper, a novel chip antenna and its function in wireless connectivity are presented for Bluetooth (BLT) earphones. The chip antenna is a metamaterial so compact (<λ/8), as the size of 4.9 × 13.0 × 2.0 mm3, that when it is mounted on the realistic PCB, it can be held in the enclosure of the BLT earphone. This setting does not degrade the resonance (S11 < −10 dB) of the proposed antenna. As two earphones in a pair are demanded to communicate with each other, one shares an RF signal with the other and they take turns as the master and slave. The received signal sensing is conducted with the latest model of human head-ear-phantom located between the earphones to mimic the real use-case and cross-head interference. Electromagnetic simulation of the antenna is done and verified by fabrication and measurement. Particularly, received-signal strength indications between the proposed antennas in the earphones are experimentally obtained as −67.5 dBm and −70 dBm without and with the head-ear-phantom, respectively, much greater than −120 dBm, the limit of detection, and implying acceptable connectivity and invulnerability over cross-head-interference problems.
Temperature-Compensated Multi-Point Strain Sensing Based on Cascaded FBG and Optical FMCW InterferometryFeng, Zhiyu;Cheng, Yu;Chen, Ming;Yuan, Libo;Hong, Deng;Li, Litong
doi: 10.3390/s22113970pmid: 35684592
We proposed a novel temperature-compensated multi-point strain sensing system based on cascaded FBG and optical FMCW interferometry. The former is used for simultaneous sensing of temperature and strain, and the latter is used for position information reading and multiplexing. In the experiment, a narrow linewidth laser with continuous frequency-sweeping was used as the light source. After demodulating the beat-frequency signal, the link information of the 16 m fiber was obtained, and the measured result was identical to the actual position. The measurement accuracy reached 50.15 mm, and the dynamic range was up to 22.68 dB. Meanwhile, we completed the sensing experiments for temperature range from 20 °C to 90 °C and strain range from 0 με to 7000 με. The sensitivity of the sensing system to temperature was 10.21 pm/°C, the sensitivity and accuracy to strain were as high as 1.163 pm/με and 10 με, respectively. Finally, the measured strain and temperature values were obtained using the sensing matrix. The sensing system has important practical significance in the field of quasi-distributed strain measurement.
Digital Forensic Analysis to Improve User Privacy on AndroidKim, Hyungchan;Shin, Yeonghun;Kim, Sungbum;Jo, Wooyeon;Kim, Minju;Shon, Taeshik
doi: 10.3390/s22113971pmid: 35684591
The Android platform accounts for 85% of the global smartphone operating-system market share, and recently, it has also been installed on Internet-of-Things (IoT) devices such as wearable devices and vehicles. These Android-based devices store various personal information such as user IDs, addresses, and payment information and device usage data when providing convenient functions to users. Insufficient security for the management and deletion of data stored in the device can lead to various cyber security threats such as personal information leakage and identity theft. Therefore, research on the protection of personal information stored in the device is very important. However, there is a limitation that the current research for protection of personal information on the existing Android platform was only conducted on Android platform 6 or lower. In this paper, we analyze the deleted data remaining on the device and the possibility of recovery to improve user privacy for smartphones using Android platforms 9 and 10. The deleted data analysis is performed based on three data deletion scenarios: data deletion using the app’s own function, data deletion using the system app’s data and cache deletion function, and uninstallation of installed apps. It demonstrates the potential user privacy problems that can occur when using Android platforms 9 and 10 due to the leakage of recovered data. It also highlights the need for improving the security of personal user information by erasing the traces of deleted data that remain in the journal area and directory entry area of the filesystem used in Android platforms 9 and 10.
The Influence of the sEMG Amplitude Estimation Technique on the EMG–Force RelationshipRanaldi, Simone;Corvini, Giovanni;De Marchis, Cristiano;Conforto, Silvia
doi: 10.3390/s22113972pmid: 35684590
The estimation of the sEMG–force relationship is an open problem in the scientific literature; current methods show different limitations and can achieve good performance only on limited scenarios, failing to identify a general solution to the optimization of this kind of analysis. In this work, this relationship has been estimated on two different datasets related to isometric force-tracking experiments by calculating the sEMG amplitude using different fixed-time constant moving-window filters, as well as an adaptive time-varying algorithm. Results show how the adaptive methods might be the most appropriate choice for the estimation of the correlation between the sEMG signal and the force time course. Moreover, the comparison between adaptive and standard filters highlights how the time constants exploited in the estimation strategy is not the only influence factor on this kind of analysis; a time-varying approach is able to constantly capture more information with respect to fixed stationary approaches with comparable window lengths.
Microwave Ablation of Liver, Kidney and Lung Lesions: One-Month Response and Manufacturer’s Charts’ Reliability in Clinical PracticeFrandon, Julien;Akessoul, Philippe;Kammoun, Tarek;Dabli, Djamel;de Forges, Hélène;Beregi, Jean-Paul;Greffier, Joël
doi: 10.3390/s22113973pmid: 35684594
Background: Microwave ablation systems allow for performing tumoral destruction in oncology. The objective of this study was to assess the early response and reliability of the microwave ablation zone size at one month for liver, kidney and lung lesions, as compared to the manufacturer’s charts. Methods: Patients who underwent microwave ablation with the EmprintTM ablation system for liver, kidney and lung lesions between June 2016 and June 2018 were retrospectively reviewed. Local response and ablation zone size (major, L, and minor, l, axes) were evaluated on the one-month follow-up imaging. Results were compared to the manufacturers’ charts using the Bland–Altman analysis. Results: Fifty-five patients (mean age 68 ± 11 years; 95 lesions) were included. The one-month complete response was 94%. Liver ablations showed a good agreement with subtle, smaller ablation zones (L: −2 ± 5.7 mm; l: −5.2 ± 5.6 mm). Kidney ablations showed a moderate agreement with larger ablations for L (L: 8.69 ± 7.94 mm; l: 0.36 ± 4.77 mm). Lung ablations showed a moderate agreement, with smaller ablations for l (L: −5.45 ± 4.5 mm; l: −9.32 ± 4.72 mm). Conclusion: With 94% of early complete responses, the system showed reliable ablations for liver lesions, but larger ablations for kidney lesions, and smaller for lung lesions.