In this work we investigate the electrospray technique for the preparation of graphene layers for use in chemiresistive gas sensors. A dispersion of reduced graphene oxide (rGO) in isopropanol (0,1 mg/ml) is electrosprayed and the rGO sheets are deposited onto a polymeric substrate with printed interdigitated electrodes. The size of the rGO spot is determined to a large extent by the distance between the needle tip and the substrate, whereas the deposition pattern strongly depends upon the applied voltage and the flow rate, which determine the electrospray operation regime. The optimal process parameters for obtaining uniform rGO layers over the entire surface of the interdigitated electrodes (active area of the sensor) have been determined. The performance of the sensors based on electroprayed rGO for the detection at room temperature of sub-ppm levels of nitrogen oxide (NO₂), ozone (O₃) and carbon monoxide (CO) in air has been assessed, including the effects of air humidity and UV light irradiation.

The paper is focused on the analysis of use of the photoplethysmographic (PPG) optical sensor for non-invasive acquisition of vital information about the cardiovascular system from different parts (finger, ear, wrist, etc.) of the human skin surface. For description and comparison of PPG signal properties the energetic and temporal parameters were determined. The PPG waveform was next used to determine the instantaneous heart rate (HR). Mapping of spectral features and stability of HR values was evaluated by their statistical parameters. In our experiments, two basic types of PPG sensors working in transmission and reflection modes were tested. Two realizations of the transmission-type PPG sensor (finger-ring and ear-clip) were first used in our experiments. Then, the reflection PPG sensor was applied for PPG signal measurement. This type of the PPG sensor is more universal, we test practical placement on fingers (from pinkie to thumb) and on a wrist. In all these experiments, the analog interface connected by a cable to the PC as a storage device was used for PPG signal recording. In the third comparison, we test the reflection PPG sensor integrated in a smart watch device which can display HR values on a smartphone with the running remote application. The final aim was to find conditions, limitations, and general recommendations for development of a wearable PPG sensor with wireless connection enabling measurement in the magnetic field environment with radio-frequency and electromagnetic disturbance. This situation occurs when a tested person is examined in the scanning area of the running magnetic resonance tomograph.

The thermopile-based thermoelectric sensor has emerged as an important approach for microwave power measurement. It employs the Seebeck effect, which converts the microwave power into the heat and generates the thermovoltage. However, the sensitivity of the current existed planar thermopile-based sensor is low and thus constrains its wide application. This is mainly caused by the heat losses of the substrate in the conversion process of microwave power-heat-electricity. In this paper, a novel embedded power sensor based on the InP DHBT process in transferred-substrate technology is presented. The thermopile is embedded in the Benzocyclobutene (BCB) to reduce heat loss. The electromagnetic and thermal co-simulation method is used to evaluate sensor performance. The proposed sensor could operate in a wide frequency bandwidth with a low port reflection loss and high sensitivity. The simulation results show that the port return loss is less than −18.6 dB from 20 GHz to 200 GHz. Meanwhile, the average sensitivity is higher than 1.02 mV/mW with excellent linearity.

COVID-19 pandemic has brought several limitations regarding physical distancing in order to reduce the interactions among large groups that could have prolonged close contact. For health reasons, such physical distancing requirements should be guaranteed in private and public spaces. In Spain, occupancy is restricted by law but in practice, certain spaces may become overcrowded. In practice there are law infringements and some places rely on movement estimations that may not be accurate. For instance, although it is known the number of passengers that enter a public transportation service, it is difficult to determine the actual occupancy of such a vehicle, since it is commonly unknown when and where the passengers descend. Despite existing a number counting systems, they are either prone to counting errors in overcrowded scenarios or require the active involvement of a person in the in/out process or even the user himself/herself (e.g., going through a lathe or tapping a card when entering or exiting a vehicle). This paper presents a novel IoT occupancy system that allows for estimating in real time the people occupancy level of public spaces like buildings, classrooms, businesses or moving transportation vehicles. The proposed system is based on autonomous wireless devices that do not need active actions from the passengers/users and require a minimum amount of infrastructure.

The system does not collect any personal information to ensure user privacy and includes a decentralized traceability subsystem based on blockchain, which guarantees the availability, security and immutability of the collected information in order to share it among smart city stakeholders (e.g., health authorities, insurance companies, mobility service providers, infrastructure operators, payment system providers, research institutions) to secure public safety and then deliver transparent decision-making based on data-driven analysis and planning.

Off-shore oil and gas platforms present a harsh environment for their installed infrastructure, with pipelines that are both subjected to a corrosive atmosphere and transport aggressive chemicals being the most critical. These conditions have prompted the industry to substitute metallic pipelines for composite counterparts, often made of fiber reinforced plastics assembled with bonded joints. Various technologies have emerged in the recent years to assess the health of these composite pipelines. In particular, robust speckle metrology techniques such as shearography have produced very satisfactory results. However, these inspection techniques require specialized equipment and trained personnel to be flown to off-shore platforms, which can incur in non-trivial inspection costs. In this paper we propose and demonstrate a robust and cost-effective approach to monitor pipeline bonded joints during assembly and operation using fiber Bragg grating (FBG) sensors embedded into the joints’ adhesive layer. This has been achieved by instrumenting bonded joints between 4” nominal diameter industrial grade Chlorinated Polyvinyl Chloride (CPVC) pipes with 12 sensing fibers, each fiber contains 3 FBGs for a total of 36 strain measurement points. To validate this approach, we present and compare data collected from both structurally sound joints and joints with controlled defects for various situations, including joint and pipeline assemble and operation. Computed tomography has been used as the control technique to evaluate structural soundness of tested joints. This approach allows for informed decisions on when to perform targeted in-depth inspections based on both real-time and long-term feedback of the FBG sensor data, resulting on lower monitoring costs, a severe increase on monitoring uptime (up to full uptime), and increased operational security.

Heating of the properties generates 56 percent of the carbon emissions in the city of Helsinki. Privately owned and rental properties have seen very few improvements in energy efficiency in recent years and as part of their climate programs, the cities look for initiatives and incentives to tackle the issue. In some research (1), reasons for the lack of action in rental properties are also a range of market barriers and market failures including misinformation, split incentives and an uneven power dynamic between renters and landlords. Finland has quite a rare model on the way how the private buildings are formed as a single legal body that owns the apartments. Instead of owning an apartment as a property, the right to hold the apartment is given by owning shares in the limited liability housing company. This structure may sometimes affect decision making on not only investments but also all the spending, including consulting and analysis that might prove some actions to be reasonable. It is expected that standardized analytical methods on commonly available data such as room sensor temperature and humidity values could provide a way to identify the first steps on energy efficiency measures in ways that could be replicated to larger scales, even city-wide analysis. For the sensors, data quality definition was created by implementing the ISO 19157 requirements and the goal is to create self-explanatory datastreams that can be processed live with meaningful results being achieved focusing on the three analyses described in this study. The three methods together should result with 10-20% savings on primary energy consumption without additional investment on equipment.

During the last years the education sector has incorporated in regular classrooms the use of new technologies and computing devices (e.g., laptops, tablets, smartphones), which allowed for implementing new ways for enhancing teaching and learning.
Among such new technologies is Augmented Reality (AR), which makes use of diverse sensors and actuators to enable creating experiences that mix reality and virtual elements in an attractive and visual way, thus helping teachers to foster student interest in learning certain subjects and abstract concepts in novel visual ways.
This paper proposes to harness the potential of the latest AR devices in order to enable giving AR-enabled lectures and hands-on labs. Specifically, this paper first proposes an architecture for providing low-latency AR education services in a classroom or a laboratory. Such a low-latency is achieved thanks to the use of edge computing devices, which offload the cloud from the traditional tasks that are required by dynamic AR applications (e.g., near real-time data processing, communications among AR devices).
Depending on the specific AR application and the number of users, the wireless link (usually Wi-Fi) could be overloaded if the network has not been properly designed, and the overall performance of the application can be compromised, leading to high latency and even to wireless communication failure. In order to tackle this issue, radio channel measurements and simulation results have been obtained by means of an in-house developed 3D ray-launching tool, which is able to model and simulate the behaviour of an AR-enabled classroom/laboratory in terms of radio propagation and quality of service. To corroborate the obtained theoretical results, a Microsoft HoloLens 2 teaching application was used to carry out an empirical measurement campaign whose results are compared in order to validate the proposed approach.

Schizophrenia is a challenging mental health disorder.¹ While various antipsychotics have been used to treat schizophrenia, monitoring schizophrenia treatment requires patients to frequently travel to hospitals in order to test and maintain efficacious levels. Yet, current technologies for antipsychotic drug monitoring require benchtop equipment and long sample preparation time, impeding the ability to rapidly measure various antipsychotics levels at the point-of-care. For example, clozapine is the most effective antipsychotic medication for schizophrenia, but it is dramatically underutilized due to a burdensome monitoring scheme. We propose to overcome the analytical challenges by designing an intelligent multi-sensor array that will be modified with micro/nanometers-thick films.² The films are based on 2D materials (reduced graphene oxide, MoS₂ and WS₂) that increase the electrocatalytic activity of the sensors and the underlying variability of the electrochemical signals generated by the antipsychotics. Here, we have shown the development of microelectrodes modified with 2D materials; 2) the development of an intelligent multi-electrode array framework; and 3) the proof-of-concept extraction of antipsychotic levels from schizophrenia patients by using intelligent chemometric models. By rapidly deciphering the electrochemical signals in whole blood and quantifying the levels of the antipsychotics, better schizophrenia treatment outcome can be enabled.

Acknowledgement:

The authors thank the Brain and Behavior Research Foundation NARSAD Young Investigator and the Jeanne Marie Lee Investigator Grant (Grant number 26038) for funding the project. We also thank the Helmsley Charitable Trust through the Agricultural, Biological and Cognitive Robotics Initiative for the financial support in this research at the Ben-Gurion University of the Negev. The authors also thank the Kreitman School for the Mid-way Negev fellowship for their support. The authors also thank Professor Deanna L. Kelly for supplying the patients samples and guidance.

References:

• A. Gok, V. Duyan, Int. J. Soc. Psychiatry. 2020, 66, 249.
• P. Shukla, R. Cazelles, D. L. Kelly, H. Ben-Yoav, Talanta, 2020, 209. 1205604.

Switched-mode power supply and semiconductor devices have been gaining more and more applicability in the industrial scenario. With the massive use of these devices, several problems related to power quality and equipment degradation have been arising, caused mainly by the harmonic content present in current and voltage. Power quality problems have direct implications for business productivity, causing high economic losses. Therefore, it is mandatory to develop solutions that mitigate these problems. Active Power Filters (APF) are power electronic equipment capable of compensating power quality problems and present the ability to dynamically adjust their modes of operation in response to changes in load or the power systems. Active power filters have the economical advantage of using the same current and voltage sensors already existing in a power system. Aiming to develop an APF for single-phase applications, this work proposes a shunt controlled active power filter using hall effect current and voltage sensor aiming to compensate harmonic currents provided by nonlinear loads. It was proposed two proportional-integral compensators, where the external loop is responsible for link-CC control and the inner loop is responsible for current compensation. The results show the applicability of the proposed APF since all harmonic content was filtered.

The most common way for accessing healthcare and monitoring physiological signals is based on commercial devices. Most of them are, in general, expensive, highly invasive, and require sophisticated infrastructure for operating. Nowadays, wearable devices (WD) offer an attractive technology for circumventing the limitations of classic medical devices. The design of WD, however, remains a challenging task to reach high-performance, reliability, and to be ergonomic. In this work, we develop, to the best of our knowledge, a novel WD with two main highlights. (i) Our device is based on a low-power 32-bit microcontroller, embedding a Bluetooth Low Energy (BLE) module for wireless data streaming with a mobile application for signal monitoring and recording, alongside a warning notification system. (ii) The proposed WD has a modular and flexible design, such that the user can increase the number of sensors by sharing the acquisition and processing system, thus reducing the hardware requirements and exhibiting a minimally invasive arrangement. For all the WD stages, we show their design methodology, the tests for characterizing their performance, and the results obtained from a case of study. For this latter, we consider two sensor prototypes for measuring the corporal temperature with a passive sensor, as well as the breath and heart rates via photoplethysmography signals. Results show that our WD is a cost-effective alternative and a promising tool for healthcare monitoring, as it operates in agreement with physiological levels with high-reliability.