AlGaN/GaN HEMTs have been shown to be efficient sensors for a broad range of physical parameters, in either liquid or dry condition, such as pressure sensor [1,2], gas detection [3,4], pH sensor [5], and more recently used as biosensors for the rapid detection of viruses [6]. These achievements could pave the way for the use of these HEMT transistors in electronic nose development particularly useful for volatile organic compound (VOC) detection. Among VOCs, acetone is one of the most important elements because it can be used as a biomarker for early disease, such as lung cancer, detection. For this purpose, sensors with responsivity in the range below 1 ppm are desired [7]. Up to now, only few works have been reported on acetone detection with rather contradictory observations between its detection at room temperature (RT) [8] or high temperature [9]. In this work, we report on AlGaN/GaN HEMT sensors for acetone concentration below 100 ppm and in a broad range of the sensor temperature varying from (RT) to 300°C.

At RT, in presence of acetone, a smooth and monotonic decrease of the current is observed with a rather large responsivity of 15µA/ppm and with large response time (several minutes) and memory effect. This decrease of the current can be explained by the electrostatic interaction between the 2D gas in the HEMT with the dipolar moment of the acetone molecules as described by Neuberger et al. [8]. This decrease is also in agreement with the work of Neuberger. et al [8].

At high temperature (300°C), in contrary to what has been reported in [9], a current decrease is first observed just after the acetone injection and then followed by an increase which saturates and stabilizes at a constant value. In order to clarify this unexpected behavior, a detailed study of the sensors response versus the temperature and acetone injection mode has been carried out. The output of this investigation is that a competition between the current variations induced by both the sensor and gas flow temperature difference from one side and acetone dipolar moment from the other side can explain this transient. At high temperature, the gas flow (especially for high acetone concentration) tends to cool down the sensor inducing an increase of the current, whereas the true acetone effect leads to a decrease of the current.

AlGaN/GaN HEMTs – based sensors are shown to allow for very sensitive acetone detection at both room and high temperature. Nevertheless, care must be taken during the characterization and operation of such sensors especially at high operating temperature. Increasing the latter, can help to improve the sensor response suppress the memory effect, but requires the control or the cancellation of the current transient due to the temperature difference between the gas flow and the transistor gate.

References

[1] Kang, B.S.; Kim, S.; Kim, J.; Mehandru, R.; Ren, F.; Baik, K.; Pearton, S.J.; Gila, B.P.; Abernathy, C.R.; Pan, C.C.; Chen, G.T.; Chyi, J.I.; Chandrasekaran, V.; Sheplak, M.; Nishida, T.; Chu, S.N.G., AlGaN/GaN high electron mobility transistor structures for pressure and pH sensing. physica status solidi (c) 2005, 2, 2684–2687. doi:10.1002/pssc.200461269.

[2]. Gajula, D.; Jahangir, I.; Koley, G., High Temperature AlGaN/GaN Membrane Based Pressure Sensors, Micromachines 2018, 9, 207. doi:10.3390/mi9050207.

[3] Bishop, C.; Halfaya, Y.; Soltani, A.; Sundaram, S.; Li, X.; Streque, J.; Gmili, Y.E.; Voss, P.L.; Salvestrini, J.P.; Ougazzaden, A., Experimental Study and Device Design of NO, NO₂, and NH₃ Gas Detection for a Wide Dynamic and Large Temperature Range Using Pt/AlGaN/GaN HEMT, IEEE Sensors Journal 2016, 16, 6828–6838. doi:10.1109/jsen.2016.2593050.

[4] Halfaya, Y.; Bishop, C.; Soltani, A.; Sundaram, S.; Aubry, V.; Voss, P.; Salvestrini, J.P.; Ougazzaden, A. Investigation of the Performance of HEMT-Based NO, NO₂ and NH₃ Exhaust Gas Sensors for Automotive Antipollution Systems. Sensors 2016, 16, 273. doi:10.3390/s16030273.

[5] Sama, N.Y.; Bouhnane, H.; Gautier, S.; Ahaitouf, A.; Matray, J.M.; Salvestrini, J.P.; Ougazzaden, A.; Hathcock, A.; He, D.; Vuong, T.Q.P.; Karrakchou, S.; Ayari, T.; Mballo, A.; Bishop, C.; Halfaya, Y., Investigation of Sc₂O₃ Based All-Solid-State EIS Structure for AlGaN/GaN HEMT pH Sensor, 2019 IEEE SENSORS. IEEE, 2019. doi:10.1109/sensors43011.2019.8956762.

[6] Yang, J.; Carey, P.; Ren, F.; Mastro, M.A.; Beers, K.; Pearton, S.J.; Kravchenko, I.I., Zika virus detection using antibody-immobilized disposable cover glass and AlGaN/GaN high electron mobility transistors, Applied Physics Letters 2018, 113, 032101. doi:10.1063/1.5029902.

[7] Wilson, A., Advances in Electronic-Nose Technologies for the Detection of Volatile Biomarker Metabolites in the Human Breath, Metabolites 2015, 5, 140–163. doi:10.3390/metabo5010140.

[8] Neuberger, R.; Müller, G.; Ambacher, O.; Stutzmann, M., High-Electron-Mobility AlGaN/GaN Transistors (HEMTs) for Fluid Monitoring Applications, physica status solidi (a) 2001, 185, 85–89. doi:10.1002/1521-396x(200105)185:1<85::aid-pssa85>3.0.co;2-u.

[9] Sun, J.; Sokolovskij, R.; Iervolino, E.; Santagata, F.; Liu, Z.; Sarro, P.M.; Zhang, G., Characterization of an Acetone Detector Based on a Suspended WO₃-Gate AlGaN/GaN HEMT Integrated With Microheater, IEEE Transactions on Electron Devices 2019, 66, 4373–4379. doi:10.1109/ted.2019.2936912.

[10] Rabbaa, S.; Stiens, J. Validation of a triangular quantum well model for GaN-based HEMTs used in pH and dipole moment sensing. Journal of Physics D: Applied Physics 2012, 45, 475101. doi:10.1088/0022-3727/45/47/475101.

Knowing what individuals are doing when they are exposed to elevated levels of pollution is crucial to implement plans to reduce possible harm. Merging new sensing technologies with machine learning methods can be used as a tool to recognize complex activities. In this work, a novel approach of providing a wearable airborne particulate matter and ambient sensor in combination with a motion tracking wrist device to 97 individuals involved in the ICARUS H2020 project in Ljubljana, Slovenia, was used. They were instructed to wear the devices for 7 days, while they manually recorded their hourly activities. The compiled dataset was cleaned and separated into a training and testing data set (each containing unique participants). As the activity data was in hourly intervals and sensor data in minute values, two approaches were used: a) transforming each hourly activity to 60-minute iterations and b) averaging sensor minute data to hourly values. These data sets were used in three different models, based on three classification algorithms: k-Nearest Neighbors (IBk), decision tree (J48) and random forest (RandomForest). The results of the models for hourly values showed an accuracy of 31.0%, 28.6% and 35.7% for IBk, J48 and RandomForest, respectively, and for minute values 23.1%, 22.0% and 23.0% for IBk, J48 and RandomForest, respectively. As expected, most misclassified instances were observed for activities with vague definitions, such as resting and playing. Low accuracy can also be explained with the differences in time scales. The accuracy could be improved by more clearly defining the activities and collecting minute value data. To this end, this research provides a crucial first step in determining the possibilities of combining information coming from various new sensing technologies for complex activity recognition.

Atrial fibrillation (AF) is a common irregular heart rhythm associated with a fivefold increase in stroke risk. It is often not recognised as it can occur intermittently and without symptoms. A promising approach to detect AF is to use a handheld electrocardiogram sensor (ECG, measuring heart activity) multiple times a day for 1-4 weeks. However, the ECG recordings must be manually reviewed, which is time-consuming and costly. Our aims were to: (i) evaluate the manual review workload; and (ii) evaluate strategies to reduce the workload.

2,141 older adults were asked to record their ECG four times per day for 1-3 weeks in the SAFER Feasibility Study, producing 162,515 recordings. Patients with AF were identified by: (i) an algorithm identifying recordings exhibiting anything other than a high quality signal and a regular rhythm; (ii) a nurse reviewing recordings to correct algorithm misclassifications; and (iii) two cardiologists independently reviewing recordings from patients with any evidence of irregular heart beats.

A total of 30,165 reviews were anticipated to be required (20,155 by the nurse, and 5,005 by each cardiologist). After cardiologists reviewed a subset of recordings to identify AF patients, 813 recordings were found with AF. The number of reviews would have been reduced to: 25,160 by using only one cardiologist reviewer; 18,144 by using a stricter algorithm to only identify recordings with an irregular rhythm throughout; and 14,946 by using one cardiologist and the stricter algorithm. These changes would reduce the number of reviews per AF recording from 35 to 29, 24, and 19 respectively. The number of AF patients identified would not have fallen considerably: from 54 to 54, 53 and 53.

In conclusion, it may be possible to reduce the manual workload by almost half whilst still identifying a very similar number of patients with undiagnosed, clinically relevant AF.

Chemical Oxygen Demand (COD) is a widely used parameter in analysing and controlling the degree of pollution in water. Methods of analysis based on electrochemical sensors are increasingly being used for COD quantitation, because they could be simple, accurate, sensitive and environmentally friendly. Electro-oxidizing the organic contaminants to completely transform them into CO₂ and H₂O is considered the best method for COD estimation using sensors. In this sense, copper electrodes have been reported based on the fact that copper in alkaline media acts as a powerful electrocatalyst for oxidation of aminoacids and carbohydrates, which are believed to be the major culprits for organic pollution. Cyclic voltammetry was the technique used to obtain the voltammetric responses. Commonly, different organic compounds show different shapes of cyclic voltammograms and different current intensity in different concentrations. In this work, four kinds of copper (Cu) and copper oxide (CuO) electrodes were studied employing the cyclic voltammetry technique: Nafion film covered electrodeposited CuO/Cu electrode (NfCuO/Cu), Cu nanoparticle-graphite composite electrode (Cu-NP), CuO nanoparticle-graphite composite electrode (CuO-NP) and Ni/Cu alloy nanoparticle-graphite composite electrode (Ni/Cu-NP). Actual COD estimations are based on the measurement of oxidation currents of organic compounds. Glucose, glycine, potassium hydrogen phthalate (KHP) and ethylene glycol were chosen to be the standard substances to observe the responses, and correlate current intensity vs. COD values. From the obtained cyclic voltammograms, we can see that glucose is very easy to be oxidized by those four electrodes and electrode NfCuO/Cu shows the best calibration curve of current intensity vs. COD values with a linear range of 19.2~1120.8 mg/L and limit of detection of 27.5 mg/ L (calculated based on the formula 3σ/k). However, the compound KHP is very difficult to be oxidized. Besides, the obtained voltammetric profiles presented different shapes with the tested organic compounds, suggesting this can be used as a potential fingerprint for distinguishing the organic compounds. Consequently, Principle Component Analysis (PCA) technique was applied to try to do multivariate examination. Ongoing work is focused on optimizing measuring condition, modifying electrodes and detecting the COD values of real samples.

Recently, there is a big demand on contactless devices for health safety, therefore developing a low-cost contactless breathing rate sensor will have a great benefit for many patients and healthcare workers. In this paper, we propose a contactless sub-6~GHz breathing rate sensor with an implementation using a low-cost USRP device B205-mini. A detailed performance analysis of the proposed system with different sensor algorithms and local oscillator frequencies is also presented. The proposed system estimates the channel phase shift at a randomly selected frequency and detects the presence of low frequency oscillations in the estimated phase shift. Compared to 24~GHz or 77~GHz FMCW radar based systems using distance measurements, the proposed system is simpler, can be built by using more economical RF components, and requires lower sampling frequencies. Another key advantage of the proposed system is that, even a very narrow unused frequency band is enough for the operation of the sensor. When operated at frequencies shared by other devices, the proposed system can turn off the transmitter at randomly selected intervals to detect the presence of other transmission activities, and then can switch to a different operating frequency. We provide both Python and Octave/Matlab based implementations which are available in a public GitHub repo. Finally, we demonstrate the effectiveness of the proposed system using real measurements.

Recording and processing of terrestrial multispectral information can be of significant value for built heritage studies. The efficient adoption of sensing techniques at the visible, near-infrared, and long-wavelength infrared spectra, and the integrated analyses of the produced data, is essential for the observation of historical architecture; especially for the implementation of non-destructive preliminary surveys, which can provide with a general idea for the state-of-preservation of a structure and indicate areas of interest for more detailed diagnostic procedures. The technological developments regarding high-resolution thermal imaging, near-infrared imaging, terrestrial laser scanning (TLS), and the advances in digital photogrammetry and visual analytics play a great part in this. The presented work focuses on the fusion and the combined analysis of two-dimensional (2D) multispectral results to study architectural heritage. Spectral images-captured with a modified DSLR camera-, thermograms, photogrammetrically produced orthophoto-maps, and spatial raster data produced from TLS point clouds, are fused, and analyzed. The results are evaluated from the scope of mapping surface materials, deterioration patterns, and hidden defects, towards the employment of advanced geomatics approaches to effectively monitor cultural heritage. The described methods and techniques are implemented using as case studies the façades of the Castle of Valentino in Turin, an important architectural monument included in the list of UNESCO World Heritage Sites.

Mechanoreceptors of the insects plays a vital role in insects to sense and monitor the Environmental parameters like flow, tactile pressure, etc. In this paper, we studied the morphology of the mechanoreceptor of the insect Blattella Asahinai (Scientific name of Coachroach), which are a hair-like structure known as Trichoid sensilla, under the Scanning electron microscope and Confocal laser microscope. The scanning images showed the detail sensilla component’s, in which hair is embedded in the socket which is connected with the cuticle and joint membrane, the dendrite touches at the base of the hair and passing through the cuticle layers. Images also showed that the tubular bodies and microtubules are tightly compacted inside the dendrite. This paper also presents how the sensilla works when the external stimulus occurs on it. The hair deflects with the disturbance of the cuticle and joint membrane and this deformed hair lean on the dendrite which is attached at the base of this hair which in turn presses the tubular bodies and microtubules which develops negative ions passes down through this dendrite to the neuron which provides the information as an electric signal to the brain of an insect so that it takes necessary action. These morphological studies, sensing mechanism techniques, material properties of the components. design principles are considered for the development of the artificial bio-inspired sensors. The model of the artificial bioinspired mechanosensor is presented.

Periodic monitoring of breath sounds is essential for early screening of several lung diseases caused due to the inflammation of the airways by viruses such as COVID-19. These tests require external medical equipment to be used or necessitate people to visit a hospital. During the current pandemic situation like COVID-19, it is difficult for a large number of people to undergo such tests. Fortunately, smartphones are ubiquitous and their microphone can be leveraged for recording breath sound data. We present a smartphone-based solution for monitoring breath sounds from the user via the in-built microphone together with and our AI-based anomaly detection engine, for preliminary screening for lung diseases.

The two major tasks involved in this project are breath sound detection followed by anomaly detection. For the breath sound detector module, multiple machine learning algorithms are used to detect whether the recorded sound is a breath sound. Subsequently, in the anomaly detection engine, various machine learning algorithms are employed by extracting various features from the audio signal. Considering the effectiveness of deep learning in classifying images, we have used the spectrogram image generated using Fast Fourier transform with Convolutional Neural Network (CNN), an Ensembled CNN, and Gated Convolutional Recurrent Neural Network (Gated CRNN). Data for this project is obtained from various sources including the RALE database, which has sounds of wheezes, crackles, etc. For the breath detector module, we have achieved an average accuracy of about 97% and for anomaly detection, 94% is obtained. We have developed an android application supported by a cloud-based implementation allowing the use of AI algorithms. This app can effectively be used as a remote screening tool for lung diseases by using breath sounds as a biomarker.

Operating photovoltaic (PV) modules are frequently shaded by nearby structures, vegetation, droppings, etc., and this reduces the effective incident solar radiation received by the modules. Shading also reduces the power output of PV modules and, under certain conditions, causes the formation of hotspots. In this study, a wide variety of partial shading scenarios were investigated to evaluate their effects on the output current, voltage and efficiencies, and hotspot formation in mono-crystalline and poly-crystalline PV modules operating under the ambient conditions experienced at Nsukka, Nigeria. Sixteen shading cases were considered, including 20%, 40%, 60% and 80% of the modules' surface areas shaded parallel to the long sides, parallel to the short sides, diagonally and randomly. Test ambient conditions, module outputs and surface thermal patterns were simultaneously monitored using a digital solarimeter, multimeter and infrared thermal imager, respectively. The outputs of the modules reduced to almost zero when as little as 40% of the module surfaces were shaded, with the reductions in performance being more severe in the mono-crystalline modules than in the poly-crystalline modules. The infrared thermography revealed the thermal patterns under the different shading conditions and showed that the random shading of the modules was the most likely to result in hotspots.

Nowadays, the implementation of automated manufacturing processes within a wide variety of industrial environments is not understood without the Industry 4.0 concept and the context-aware possibilities given by the Industrial Internet of Things (IIoT). In this sense, Wireless Sensor Networks (WSN) play a key role due to their inherent mobility, ease of deployment and maintenance, scalability and low power consumption, among others. However, in complex industrial scenarios where a lot of obstacles as well as metallic objects are present, the radio propagation can be severely affected. In addition, specific machinery (such as laser or soldering machines) can cause potential interferences on the wireless radio channel. This work proposes the deployment and optimization of a WSN in the facilities of Galeo Enrollables Company, located in Navarre (Spain), in order to optimize the manufacturing processes. The company is specialized in the manufacturing, design and innovation of technical and solar protection curtains. The company is in the process of integrating a new Enterprise Resource Planning (ERP) system to control and monitor manufacturing times, the stock of the warehouse and administrative tasks, leading to the calculation of operating costs. In this way, the company is interested in deploying WSNs in order to acquire real-time data provided by the workshop machinery with the aim of including it in the new ERP system. For that purpose, radio propagation measurements as well as 3D Ray Launching simulations have been performed in order to characterize the wireless channel of this harsh industrial environment. Low cost sensors and actuators have been selected to prepare different wireless motes corresponding to the different machines present within the scenario. The information gathered by the motes is then transmitted to a central node, which conditions the data for input into the ERP system.