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Estimation of pH is vital to assess the biochemical and biological processes in a wide variety of applications ranging from water to soil, health, and environment monitoring. This work reports a screen-printed flexible and disposable pH sensor using the impedimetric method. The pH sensor was fabricated by screen printing Graphene-Carbon modified Zinc Oxide based active layer on a paper substrate and shows nearly three orders of change in impedance magnitude in the pH range of 2 – 9. The sensor was carefully designed using COMSOL® Multiphysics software to understand the influence of electrode geometry and the electrical potential developed across the structure. The developed sensor was used for pH monitoring of soil and exhibited high sensitivity of 5.27 kΩ /pH (2-8) with a correlation coefficient (R 2 ) of 0.99. Finally, an IoT-enabled smart pH detection system was implemented for continuous pH monitoring for potential application in digital agriculture. The outcome demonstrates that the presented flexible and disposable pH sensor could open new opportunities for monitoring of water, product process, human health, and chemical (or bio) reactions even using small volumes of samples.

Sorting and measuring blood by cell type is extremely valuable clinically and provides physicians with key information for diagnosing many different disease states including: leukemia, autoimmune disorders, bacterial infections, etc. Despite the value, the present methods are unnecessarily costly and inhibitive particularly in resource poor settings, as they require multiple steps of reagent and/or dye additions and subsequent rinsing followed by manual counting using a hemocytometer, or they require a bulky, expensive equipment such as a flow cytometer. While direct on-paper imaging has been considered challenging, paper substrate offers a strong potential to simplify such reagent/dye addition and rinsing. In this work, three-layer paper-based device is developed to automate such reagent/dye addition and rinsing via capillary action, as well as separating white blood cells (WBCs) from whole blood samples. Direct onpaper imaging is demonstrated using a commercial microscope attachment to a smartphone coupled with a blue LED and 500 nm long pass optical filter. Image analysis is accomplished using an original MATLAB code, to evaluate the total WBC count, as well as differential WBC count, i.e., granulocytes (primarily neutrophils) vs. agranulocytes (primarily lymphocytes). Only a finger-prick of whole blood is required for this assay. The total assay time from finger-prick to data collection is under five minutes. Comparison with a hemocytometry-based manual counting corroborates the accuracy and effectiveness of the proposed method. This approach could be potentially used to help make blood cell counting technologies more readily available, especially in resource poor, point-of-care settings.

Millimeter-wave imaging may be used to assist in\ud the problem of wastepaper recognition during the sorting and\ud recycling process. Currently, there are a variety of techniques,\ud which are used within materials recovery facilities to sort waste\ud paper and card; however, improvements in sorting capability can\ud be realized using a multiple sensors to provide complementary\ud (orthogonal) data. This paper discusses the issues related to\ud the processes, which are currently used for sorting of recycled\ud wastepaper and the phenomenology for millimeter-wave imaging\ud of a wastepaper material. Experimental measurements and\ud images in the millimeter wave band are presented, which support\ud the proposed application.

Accurate measurement and monitoring of respiration is vital in patients affected by severe acute respiratory syndrome coronavirus - 2 (SARS-CoV-2). Patients with severe chronic diseases and pneumonia need continuous respiration monitoring and oxygenation support. Existing respiratory sensing techniques require direct contact with the human body along with expensive and heavy Holter monitors for continuous real-time monitoring. In this work, we propose a low-cost, non-invasive and reliable paper-based wearable screen printed sensor for human respiration monitoring as an effective alternative of existing sensing systems. The proposed sensor was fabricated using traditional screen printing of multi-walled carbon nanotubes (MWCNTs) and polydimethylsiloxane (PDMS) composite based interdigitated electrodes on paper substrate. The paper substrate was used as humidity sensing material of the sensor. The hygroscopic nature of paper during inhalation and exhalation causes a change in dielectric constant, which in turn changes the capacitance of the sensor. The composite interdigitated electrode configuration exhibited better response times with a rise time of 1.178s being recorded during exhalation and fall time of 0.88s during inhalation periods. The respiration rate of sensor was successfully examined under various breathing conditions such as normal breathing, deep breathing, workout, oral breathing, nasal breathing, fast breathing and slow breathing by employing it in a wearable mask, a mandatory wearable product during the current COVID-19 pandemic situation.Thus, the above proposed sensor may hold tremendous potential in wearable/flexible healthcare technology with good sensitivity, stability, biodegradability and flexibility at this time of need.

Monitoring blood coagulation in response to an anticoagulant (heparin) and its reversal agent (protamine) is essential during and after surgery, especially with cardiopulmonary bypass. A current clinical standard is the use of activated clotting time, where the mechanical movement of a plunger through a whole blood-filled channel is monitored to evaluate the endpoint time of coagulation. As a rapid, simple, low-volume, and cost-effective alternative, we have developed a paper microfluidic assay and Raspberry Pi-based device with the aim of quantifying the extent of blood coagulation in response to varying doses of heparin and protamine. The flow rate of blood through the paper microfluidic channel is automatically monitored using the Python-coded edge detection algorithm. For each set of the assay, 8- $ \mu \text{L}$ of fresh human whole blood (untreated and undiluted) from human subjects is loaded onto each of eight sample pads, which have been preloaded with varying amounts of heparin or protamine. The total assay time is 3–5 min including the time for sample loading and incubation.

Since its inception in 2002, the IEEE Sensors Conference has come a long way in establishing itself as a premier conference in the world on sensors and sensing technologies. With the entire world moving rapidly toward a ubiquitous intelligence society, the emphasis on deployment of trillions of sensors in the near future is driving the world of sensor technology like never before. It is reflected in the rapidly increasing research and development activities on sensors throughout the world today. The IEEE Sensors Council has done a remarkable job in promoting this area and providing a consistent platform for sharing and dissemination of knowledge related to all aspects of sensing and sensor development among researchers, manufacturers, and the users of sensors. The idea of holding the IEEE Sensors Conference in different parts of the world actively supports this promotion over different geographies. It could not have been more timely to hold SENSORS 2018, for the first time, in one of the largest and fastest developing economies of the world - India. The 2018 edition of the Sensors Conference was held on October 28–31 in New Delhi — the capital city of India that has been inhabited for six thousand years and has seen a great many upheavals in history.

Since 2002, the IEEE Sensors Conference has been held in a different country every year during the last two decades, keeping a tradition of going to different geographical regions of IEEE. During this time, the IEEE Sensors Conference has become one of the premier global events on sensors and addressing the whole range from principles, design and fabrication to interfacing, data-extraction, applications, and systems. The most recent conference of this series, the IEEE Sensors 2017 Conference was held October 29 – November 1, 2017, at Scottish Events Campus (SEC) in Glasgow, Scotland, U.K. This was the first visit of the IEEE Sensors conference to the U.K. Glasgow is the largest city in Scotland, famous for its scientific and engineering heritage. The city is home to great inventions, from the development of penicillin to the first long distance television transmission and is a world-leading research city.

Over the years, the IEEE SENSORS Conference has become one of the premier, truly global event on novelties in any discipline related to sensors and addressing the whole range from principles, design and fabrication to interfacing, data-extraction, applications, and systems. The most recent conference of this series, the IEEE SENSORS 2016 Conference was held October 30 – November 2, 2016, in Orlando, Florida, USA. In a major departure from previous years, IEEE SENSORS 2016 solicited peer-reviewed 3-page conference papers for the first time, which was received with great enthusiasm by our community. A total of 1049 submissions were received, close to the largest in the SENSORS conference history, of which 617 were accepted (58.82%). Track Chairs handled the review process. All accepted papers went through the same peer-review process, each receiving 5 reviews, and those accepted were published in the conference proceedings in IEEE Xplore. However, they were divided into oral (198) and poster (420) presentations at the SENSORS conference depending upon author preference, suitability, session planning and similar considerations. Authors of 31 conference papers identified for the Best Paper Award from the 16 applicable Tracks were invited to submit expanded versions of their conference papers to this “Selected Papers from the IEEE SENSORS 2016 Conference” Special Issue of the IEEE Sensors Journal, of whom 18 submitted. After rigorous peer review according to the editorial procedures of the Journal, the Guest Editors of this Special Issue accepted 12 for publication and included them in the collection on the following pages.

Unmanned aerial vehicles (UAVs) in cellular networks have garnered considerable interest. One of their applications is as flying base stations (FBSs), which can increase coverage and quality of service (QoS). Because FBSs are battery-powered, regulating their energy usage is a vital aspect of their use; and therefore the appropriate placement and trajectories of FBSs throughout their operation are critical to overcoming this challenge. In this paper, we propose a method of solving a multi-FBS 3D trajectory problem that considers FBS energy consumption, operation time, flight distance limits, and inter-cell interference constraints. Our method is divided into two phases: FBS placement and FBS trajectory. In taking this approach, we break the problem into several snapshots. First, we find the minimum number of FBSs required and their proper 3D positions in each snapshot. Then, between every two snapshots, the trajectory phase is executed. The optimal path between the origin and destination of each FBS is determined during the trajectory phase by utilizing a proposed binary linear problem (BLP) model that considers FBS energy consumption and flight distance constraints. Then, the shortest path for each FBS is determined while taking obstacles and collision avoidance into consideration. The number of FBSs needed may vary between snapshots, so we present an FBS set management (FSM) technique to manage the set of FBSs and their power. The results demonstrate that the proposed approach is applicable to real-world situations and that the outcomes are consistent with expectations., This paper is a draft of a submitted paper to the IEEE Sensors journal

COVID-19 pandemic has a catastrophic consequence globally since its first case was detected in December 2019, with an aggressive spread. Currently an exponential growth is expected. If not diagnosed at the proper time, COVID-19 may lead to death of the infected individuals. Thus, continuous screening, early diagnosis and prompt actions are crucial to control the spread and reduce the mortality. In this paper we focus on developing a Medical Diagnosis Humanoid (MDH) which is a cost effective, safety critical mobile robotic system that provides a complete diagnostic test to check whether an individual is infected by Covid-19 or not. This paper highlights the development of a system based on Artificial Intelligence for Medical Science, where humanoids can navigate through desired destinations, diagnose an individual for Covid-19 through various parameters and make a survey of a locality for the same. The humanoid uses the concept of real time data sensing and processing through machine learning produced by various sensors used in the context.

A standard method for distance determination is light detection and ranging (LiDAR), which relies on the emission and detection of reflected laser pulses. When LiDAR systems become common for every vehicle, many simultaneous laser signals will produce mutual LiDAR interference between LiDAR systems. In this paper, we analyze the possibility to recognize mutual interference in time-correlated single photon counting (TCSPC) LiDAR with particular focus on flash systems. We evaluate the LiDAR interference appearance by deriving the expected event distribution for ego and aggressor signal. From that, we calculate the probability of photon detection within each measured signal. This paper shows the high potential of different pulse repetition frequencies to reduce LiDAR interference. Using signal-to-noise ratio (SNR), we define the extinction distance, beyond which the aggressor signal completely extinguishes the ego signal. Applied on different background and laser event rates, we find the connection between ideal LiDAR system designs and lowest probability for unrecognized LiDAR interference. Furthermore, we show the relationship to a specific LiDAR design, which must fulfill eye safety condition and receives lower intensities with increasing target distances. Finally, we present different solutions for the recognition and reduction of LiDAR interference based on our previous results.

Pedestrian positioning with wearable devices is a significant application of attitude tracking. It tracks the attitude (with heading angle being the most important part) of the device in real time and provides positioning services for users based on the information of step length provided by Pedestrian Dead-Reckon (PDR), which is a cheap and efficient positioning method at present. However, amid a train of positioning methods, the joint estimate of tracking is given by a train of methods based on the direction of gravity and the earths magnetic field direction. Considering the measurement of gravity that the gravity accelerometer is exposed to heavy noise due to the complex movement of human body during walking with uniform swing arm posture and forward speed, this paper proposed a novel estimate method based on the Kalman filter with multi-state constraints and the usage of low-cost sensors, which fulfills the estimation with the sequential observation of magnetic field. Compared with other related work, this method proposed in this paper eliminates the dependence on gravity direction, avoiding the influence of heavy noise caused by additional linear acceleration in motion state, and reduces the influence of insufficient observation when using magnetic field observation alone. The performance of the proposed method is evaluated by real-world experimentation results.

Recently, several methods for SARS-CoV-2 detection have been developed to obtain rapid, portable, cheap, and easy-to-use diagnostic tools. This review paper summarizes and discusses studies on the development of point-of-care devices for SARS-CoV-2 diagnosis with comparisons between them from several aspects. Various detection methods of the recently developed portable COVID-19 biosensor will be presented in this review. The discussion is divided into four major classifications based on the target biomarkers of SARS-CoV-2, such as antibodies, nucleic acids, antigens, and metabolic products. An overview of the potential development for future study is also provided. Moreover, basic knowledge of biosensors is also explained for tutoring the implementation of theory into the research of COVID-19 biosensors. This review paper is aimed to provide a tutorial by collecting the information on the development of a point-of-care device for SARS-CoV-2 detection to provide information for further research and propose the new COVID-19 portable diagnostic tool.

There has been a significant increase in the number of total knee replacement (TKR) surgeries over the past few years, particularly among active young and elderly people suffering from knee pain. Continuous and optimal monitoring of the load on the knee is highly desirable for designing more reliable knee implants. This paper focuses on designing a smart knee implant consisting of a triboelectric energy harvester and a frontend electronic system to process the harvested signal for monitoring the knee load. The harvester produces an AC signal with peak voltages ranging from 10 V to 150 V at different values of knee cyclic loads. This paper demonstrates the measurement results of a PCB prototype of the frontend electronic system fabricated to verify the functionality and feasibility of the proposed approach for a small range of cycling load. The frontend electronic system consists of a voltage processing unit to attenuate high peak voltages, a rectifier and a regulator to convert the input AC signal into a stabilized DC signal. The DC voltage signal provides biasing for the delta-sigma analog-to-digital converter (ADC). Thus, the output of the triboelectric harvester acts as both the power signal that is rectified/regulated and data signal that is digitized. The power consumption of the proposed PCB design is approximately $5.35~\mu \text{W}$ . Next, the frontend sensor circuitry is improved to accommodate a wider range of cyclic load. These results demonstrate that triboelectric energy harvesting is a promising technique for self-monitoring the load inside knee implants.

IoT sensors are becoming increasingly important supplement to traditional monitoring systems, particularly for in-situ based monitoring. Data collected using IoT sensors are often plagued with missing values occurring as a result of sensor faults, network failures, drifts and other operational issues. Missing data can have substantial impact on in-field sensor calibration methods. The goal of this research is to achieve effective calibration of sensors in the context of such missing data. To this end, two objectives are presented in this paper. 1) Identify and examine effective imputation strategy for missing data in IoT sensors. 2) Determine sensor calibration performance using calibration techniques on data set with imputed values. Specifically, this paper examines the performance of Variational Autoencoder (VAE), Neural Network with Random Weights (NNRW), Multiple Imputation by Chain Equations (MICE), Random Forest-based Imputation (missForest) and K-Nearest Neighbour (KNN) for imputation of missing values on IoT sensors. Furthermore, the performance of sensor calibration via different supervised algorithms trained on the imputed dataset were evaluated. The analysis showed VAE technique to outperform the other methods in imputing the missing values at different proportions of missingness on two real-world datasets. Experimental results also showed improved calibration performance with imputed dataset.

This paper describes the design, implementation, and evaluation of an environmental logging microsystem (ELM) for operation at elevated pressure and in corrosive environments, at temperatures up to 125°C. The ELM units are intended to be deployed in large quantities, allowed to collect data, and then retrieved, interrogated, and re-charged. Powered by a rechargeable battery embedded within the system, each ELM incorporates pressure and temperature sensors, control electronics, optical communication elements, and power management and battery re-charging circuits. The pressure sensor is a customized capacitive transducer chip on a sapphire substrate; details are provided in a companion paper. The electronic components and battery used in ELM are selected on the basis of functionality and form factor; packaged components are selected for ease of assembly and for added protection against the environment. The pressure sensor, electronics and battery are assembled on a flexible circuit board, folded into a stack with dimensions 7.2 mm $\times\,\,6.6$ mm $\times\,\,6.5$ mm, and encapsulated in a steel tube filled with optically transparent silicone caulk. This encapsulation provides mechanical protection against shock and abrasion, as well as chemical protection against high salinity environments, while allowing the ambient pressure and temperature to be transferred to the sensing elements. Results are reported from high-temperature and high-pressure tests reaching 125°C and 7,250 psi in brine and other corrosive environments in laboratory conditions. Field tests that were conducted in a brine well to a maximum depth of 1,235 m are also described. The recorded data were post-processed to interpret the environmental pressure and temperature.

In this paper, we propose a theoretical framework for cooperative abnormality detection and localization systems by exploiting molecular communication setup. The system consists of mobile sensors in a fluidic medium, which are injected into the medium to search the environment for abnormality. Some fusion centers (FC) are placed at specific locations in the medium, which absorb all sensors arrived at their locations, and by observing its state, each FC decides on the abnormality existence and/or its location. To reduce the effects of sensor imperfection, we propose a scheme where the sensors release some molecules (i.e., markers) into the medium after they sense an abnormality. If the goal is abnormality detection, the released molecules are used to cooperatively activate other sensors. If the goal is abnormality localization, the released molecules are used by the FCs to determine the location. In our model, both sensors' imperfection and markers background noise are taken into account. For the detection phase, we consider two sensor types based on their activation strategy by markers. To make the analysis tractable, we assume some ideal assumptions for the sensors' model. We investigate the related binary hypothesis testing problem and obtain the probabilities of false alarm and miss-detection. It is shown that using sensors with the ability of cooperatively activating each other can significantly improve the detection performance in terms of probability of error. For the localization phase, we consider two types of FCs based on their capability in reading sensors' storage levels. We study their performance and obtain the optimal and sub-optimal decision schemes and also the probability of localization error for both perfect and imperfect sensing regimes., This paper is accepted for publication in the IEEE Sensors Journal

In this review paper, we focus on the 3D printing technologies that consist of the extruding of fluid material in lines to form structures for electro- and biomechanical applications. Our paper reviews various 3D print technologies, materials, sensing technologies and applications of extrusion-based 3D printing. We also discuss how to overcome some of the challenges with 3D printed sensors, such as the anisotropy of the conductors as well as the drift and nonlinearity of the materials.

The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication. The millimeter wave spectrum is part of 5G and covers frequencies between 30 and 300 GHz corresponding to wavelengths ranging from 10 to 1 mm. Although millimeter wave is often considered as a communication medium, it has also proved to be an excellent 'sensor', thanks to its narrow beams, operation across a wide bandwidth, and interaction with atmospheric constituents. In this paper, which is to the best of our knowledge the first review that completely covers millimeter wave sensing application pipelines, we provide a comprehensive overview and analysis of different basic application pipeline building blocks, including hardware, algorithms, analytical models, and model evaluation techniques. The review also provides a taxonomy that highlights different millimeter wave sensing application domains. By performing a thorough analysis, complying with the systematic literature review methodology and reviewing 165 papers, we not only extend previous investigations focused only on communication aspects of the millimeter wave technology and using millimeter wave technology for active imaging, but also highlight scientific and technological challenges and trends, and provide a future perspective for applications of millimeter wave as a sensing technology., 36 pages, submitted to IEEE Sensors Journal

Lithium represents the main drug for the treatment of Bipolar Disorder (BD) as it acts as a mood stabilizer. However, because of its narrow therapeutic window, psychiatric patients are obliged to frequent tests in hospital to control their lithium blood concentration during therapy. In this paper, we fabricate for the first time a complete non-invasive system for the Therapeutic Drug Monitoring (TDM) of lithium in sweat in people suffering from BD. The wearable electrochemical sensing platform includes a paper fluidics and a stable Reference Electrode (RE). Lithium detection is based on the use of a potentiometric Ion-Selective Electrode (ISE) with a nanostructured solid contact in order to increase the potential stability of the sensor. A chemical chlorination combined with the use of a PVC membrane doped with an Ionic Liquid (IL) is used to minimize possible potential drifts of the RE. The Nernstian response (56.8±3.9 mV /decade) of the system in the range of clinical interest is then proved in aqueous solution and in sweat. The integration of the sensing platform with a paper fluidics transporting fresh sweat sample to the sensing area is also considered. The already tested sweat is collected in a reservoir made of slow adsorbing paper. The complete wearable system is then successfully tested in a simulated setup by using a mannequin. The measured potential is well compared to the predicted ones in the therapeutic window of lithium drug, proving the good sensing capability in artificial sweat.

[EN] Organic electrochemical transistors (OECTs) have been recognized as a major emerging technology in the area of flexible electronics in the last decade. Although they have yet to be incorporated in common electronic fabrication technologies, they have considerably advanced as an emerging platform for biosensing applications. The paper provides a comprehensive and critical review of the most important advances in the field of OECT-based biosensors. A brief description of the device physics is given with the most important equations and a comparison has been made with the conventional MOSFET devices and characteristic equations. The use of OECTs as an emerging biosensing platform has been explored and their application as biomolecule, enzyme, bacteria, viruses, cells, nucleotide detectors as well as electrophysiological and wearable sensors has been reported. Furthermore, trends have been extracted and described in the paper in terms of fabrication technologies, electrode materials and most importantly, the semiconducting polymer. Additionally, future perspectives on the development and fabrication technologies of these devices have been further explored., This work was supported in part by the University of Cyprus Internal Funds, Advanced Researchers Article 102, the Research and Innovation Foundation of Cyprus, under Grant POST-DOC/0718/0163 and in part by the Spanish Government/FEDER for Funds (MINECO/FEDER) under Grant RTI2018-100910-BC43.

The non-contact continuous monitoring of biomarkers comprising breathing detection and heart rate are essential vital signs to evaluate the general physical health of a patient. As compared to existing methods that need dedicated equipment (such as wearable sensors), the radio frequency (RF) signals can be synthesised to continuously monitor breathing rate in a contact-less setting. In this paper, we proposed the contact less breathing rate detection using universal software radio peripheral (USRP) platform without any wearable sensor. Our system leverage on the channel state information (CSI) to record the minute movement caused by breathing over orthogonal frequency division multiplexing (OFDM) in multiple sub-carriers. We presented a comparison of our breathing rate detection with wearable sensor (ground truth) results for single human subject. In this paper, we used wireless data to train, validate and test different machine learning (ML) algorithms to classify USRP data into normal, shallow and elevated breathing depending on the breathing rate. Although different ML models were developed using the K-Nearest Neighbor (KNN), Discriminant Analysis (DA), Naive Bayes (NB) and Decision Tree (DT) algorithms, however results showed KNN based model provided the highest accuracy for our data (91%) each time the trial was made. DT (71.131%), DA (59.72%) and NB (48.99%). Results presented in this paper showed that USRP based breathing rate is comparable to the wearable sensor demonstrating the potential application of our method to accurately monitor breathing rate of patients in primary or acute setting.

This paper proposes an integrated layered doped and undoped amorphous silicon thin-film based temperature sensing device. Temperature sensing performance has been measured for thin film p-i-n (p-type- intrinsic-n-type) configuration-based diode. Linear dependency of voltage on the temperature for forward-biased diode at a constant bias current is demonstrated in the temperature range of 30 – 200 °C. Further, the same device has been introduced with double-walled carbon nanotubes (DWCNTs) to improve the linearity of the sensor. Comparative performance of two configurations p-i-n and p-i-n/DWCNTs for temperature sensing application has been studied. Moreover, this paper discussed the effect of the DWCNTs on the sensor parameters such as sensitivity, S and coefficient of determination, R2. The maximum sensitivity of the sensor, 22.34 mV/ °C for p-i-n configured device and 21.06 mV/°C for p-i-n/DWCNTs configuration in a biasing current range of 10– 60 mA have been found. We achieved a maximum value of the coefficient of determination equal to 0.99889 for a p-i-n configuration and 0.99922 for a p-i-n/DWCNTs configured device.

Thermal sensor array (TSA) offers privacy-preserving, low-cost, and non-invasive features, which makes it suitable for various indoor applications such as anomaly detection, health monitoring, home security, and monitoring energy efficiency applications. Previous approaches to human-centred applications using the TSA usually relied on the use of a fixed sensor location to make the human-sensor distance and the human presence shape fixed. However, placing this sensor in different locations and new indoor environments can pose a significant challenge. In this paper, a novel framework based on a deep convolutional encoder-decoder network is proposed to address this challenge in real-life deployment. The framework presents a semantic segmentation of the human presence and estimates the occupancy in indoor-environment. It is also capable to segment the human presence and counts the number of people from different sensor locations, indoor environments, and human to sensor distance. Furthermore, the impact of the distance on the human presence using the TSA is investigated. The framework is evaluated to estimate the occupancy in different sensor locations, the number of occupants, environments, and human distance with classification and regression machine learning approaches. This paper shows that the classification approach using the adaptive boosting algorithm is an accurate approach which has achieves an accuracy of 98.43% and 100% from vertical and overhead sensor locations respectively.

Recently, convolutional neural networks (CNNs) have set latest state-of-the-art on various human activity recognition (HAR) datasets. However, deep CNNs often require more computing resources, which limits their applications in embedded HAR. Although many successful methods have been proposed to reduce memory and FLOPs of CNNs, they often involve special network architectures designed for visual tasks, which are not suitable for deep HAR tasks with time series sensor signals, due to remarkable discrepancy. Therefore, it is necessary to develop lightweight deep models to perform HAR. As filter is the basic unit in constructing CNNs, it deserves further research whether re-designing smaller filters is applicable for deep HAR. In the paper, inspired by the idea, we proposed a lightweight CNN using Lego filters for HAR. A set of lower-dimensional filters is used as Lego bricks to be stacked for conventional filters, which does not rely on any special network structure. The local loss function is used to train model. To our knowledge, this is the first paper that proposes lightweight CNN for HAR in ubiquitous and wearable computing arena. The experiment results on five public HAR datasets, UCI-HAR dataset, OPPORTUNITY dataset, UNIMIB-SHAR dataset, PAMAP2 dataset, and WISDM dataset collected from either smartphones or multiple sensor nodes, indicate that our novel Lego CNN with local loss can greatly reduce memory and computation cost over CNN, while achieving higher accuracy. That is to say, the proposed model is smaller, faster and more accurate. Finally, we evaluate the actual performance on an Android smartphone., Comment: 11 pages, 11 figures

As reported in the stereo literature, confidence estimation represents a powerful cue to detect outliers as well as to improve depth accuracy. Purposely, we proposed a strategy enabling us to achieve state-of-the-art results by learning a confidence measure in the disparity domain only with a CNN. Since this method does not require the cost volume, it is very appealing because potentially suited for any depth-sensing technologies, including, for instance, those based on deep networks. By following this intuition, in this paper, we deeply investigate the performance of confidence estimation methods, known in the literature and new ones proposed in this paper, neglecting the use of the cost volume. Specifically, we estimate from scratch confidence measures feeding deep networks with raw depth estimates and optionally images and assess their performance deploying three datasets and three stereo algorithms. We also investigate, for the first time, their performance with disparity maps inferred by deep stereo end-to-end architectures. Moreover, we move beyond the stereo matching context, estimating confidence from depth maps generated by a monocular network. Our extensive experiments with different architectures highlight that inferring confidence prediction from the raw reference disparity only, as proposed in our previous work, is not only the most versatile solution but also the most effective one in most cases.

Today’s modern vehicles contain anywhere from sixty to one-hundred sensors and exhibit the characteristics of Cyber-Physical-Systems (CPS). There is a high degree of coupling, cohesiveness, and interactions among vehicle’s CPS components (e.g., sensors, devices, systems, systems-of-systems) across sensing, communication, and control layers. Cyber-attacks in the sensing or communication layers can compromise the security of the control layer. This paper provides a detailed review of potential cyber threats related to the sensing layer. Notably, the focus is mainly towards two categories of sensors: vehicle dynamics sensors (e.g., Tire Pressure Monitoring Systems (TPMS), magnetic encoders, and inertial sensors) and environment sensors (e.g., Light Detection and Ranging (LiDAR), ultrasonic, camera, Radio Detection and Ranging (Radar) systems, and Global Positioning System (GPS) units). The paper also offers perspectives through existing countermeasures from literature and stresses the need for data-driven cybersecurity solutions.

The deployment of transducers to perform in situ inspections of industrial components can be complicated, and in many cases is still performed manually by a team of operators, which involves significant costs and can be dangerous. Robots capable of deploying probes in difficult to access locations are becoming available. Electromagnetic acoustic transducers (EMAT) are well suited to be used with robots since they are non-contact transducers that do not require a coupling medium, and can easily perform scans. However, existing acquisition systems for EMATs are generally not suitable to be directly mounted on robots. In this paper, a new wireless acquisition system for EMATs is presented. The system is standalone, it transmits the inspection data over WiFi, and is compatible with the robotics operating system (ROS). In addition, it is designed to be modular, small and lightweight so that it can be easily mounted on robots. The system design in terms of hardware and software is described in this paper. The resulting performance of the system is also reported.

©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Low pressure environments, situate insulation systems in a challenging position since partial discharges (PDs), corona and arc tracking are more likely to develop. Therefore, specific solutions are required to detect such harmful phenomena before major failure occurrence. This paper deals with three low-cost and small-size sensing methods, i.e., a single loop antenna, a visible-UV imaging sensor and the measurement of the leakage current to detect corona in the early stage, thus anticipating the appearance of severer effects such as arc tracking or disruptive breakdown. The three studied methods can be applied for an on-line monitoring of corona activity under low pressure environments, thus being compatible with predictive maintenance approaches. This on-line monitoring can be used to develop improved electrical protection devices able to detect such effects in an initial stage, thus improving current solutions which are unable to do so. All three studied sensors give consistent linear responses within the studied pressure range, i.e., 10-100 kPa, with almost no drift. The sensitivity of the visible-UV imaging sensor is slightly lower than that of the others, but it has the advantage of directly locating the discharge points. Results presented in this paper can be very useful for the more electrical aircraft (MEA), which is forcing electrical distribution systems to operate at higher voltage levels. Due to the little experience and scarcity of published data, the experimental results presented in this paper can be valuable for a better understanding of the combined action of high voltage and low pressure environments. This work was supported in part by the Generalitat de Catalunya under Project 2017 SGR 967 & in part by the Spanish Ministry of Science and Innovation under Project RTC-2017-6297-3. J.-R. Riba is with the Universitat Politècnica de Catalunya

Micro-processor controlled prosthetic legs (MPCPL) offer better functionality than conventional prosthetic legs as they use actuators to replace missing joint function. This potentially reduces the user’s metabolic energy consumption and normal walking gait can be mimicked as closely as possible. However, MPCPL require a good control system to perform efficiently, and one of the essential components is the system of sensors. The sensory system must satisfy two important criteria; the practicality in donning and doffing the prosthesis, i.e. the process of putting on and taking off the prosthesis by the amputee user, and the quality in the information provided. In this paper, a comprehensive review was conducted on studies related to the state of the art of sensory system adopted in MPCPL. The publications were searched using four electronics databases within the last 13 years. A total of 31 papers were reviewed. The articles were classified into three main categories: prosthetic-device oriented, user’s-biological-input oriented and neuro-mechanical fusion sensory system. Types of sensors used and their application to the prosthetic system were analyzed. This review indicates that the sensors technology reported in the literature still does not fulfil the criteria of an efficient sensory system. Hence, a sensory system that eases the don and doff process of the prosthesis, yet informative in terms of providing enough useful data to effectively control the prosthesis, is needed for a successful MPCPL.

Remote healthcare monitoring (RHM) is an emerging trend in the biomedical field that leverages on the advancement of Internet of Things (IoT) to enhance the coverage of healthcare services around the world. An RHM system with the electromyography (EMG) measurement capability has great potential in improving the efficiency of medical treatment and physical therapy for chronic inflammatory myopathies. Conventional EMG measurement is often invasive, complex and require an ever-present medical officer. This paper proposes a capacitive electromyography (CEMG) monitoring system to overcome the limitations of the contact EMG monitoring system by using the capacitive sensing methodology. The system architecture and circuit design are detailed in this paper and the hardware implementation with experimental results are presented. This CEMG monitoring system achieved extremely low noise floor of less than 2 mV. Its performance also highly correlates with the conventional wet contact electrode without requiring any post signal processing. Therefore, this CEMG monitoring system is ideal for remote healthcare monitoring applications. 2001-2012 IEEE. Manuscript received November 11, 2019; revised November 25, 2019; accepted November 25, 2019. Date of publication December 2, 2019; date of current version February 14, 2020. This work was supported in part by Research University Grant, Univer-siti Kebangsaan Malaysia, under Grant GUP-2018-141, and in part by the Qatar National Research Foundation (QNRF) under Grant NPRP12s-0227-190164. The associate editor coordinating the review of this article and approving it for publication was Prof. Tarikul Islam. (Corresponding author: Charn Loong Ng.) C. L. Ng and M. B. I. Reaz are with the Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia (e-mail: charnloong@gmail.com; mamun.reaz@gmail.com). Scopus

Mammals like rats, who live in dark burrows, heavily depend on tactile perception obtained through the vibrissal system to move through gaps and to discriminate textures. The organization of a mammalian whisker follicle contains multiple sensory receptors and glands strategically organized to capture tactile sensory stimuli of different frequencies. In this paper, we used a controllable stiffness soft robotic follicle to test the hypothesis that the multimodal sensory receptors together with the controllable stiffness tissues in the whisker follicle form a physical structure to maximize tactile information. In our design, the ring sinus and ringwulst of a biological follicle are represented by a linear actuator connected to a stiffness controllable mechanism in-between two different frequency-dependent data capturing modules. In this paper, we show for the first time the effect of the interplay between the stiffness and the speed of whisking on maximizing a difference metric for texture classification.

This paper investigates a rotational data collection strategy for planar array electrical capacitance tomography (ECT) to improve the imaging of complex-shaped objects in scenarios where access to the object is limited. Planar array of ECT can produce 2D and 3D images of changes in the dielectric permittivity of the object, but detecting complex-shaped objects may require more electrodes in the array, which can decrease the quality of capacitance measurements and make it challenging to detect objects at greater depths. With the air background as calibration measurements, by rotating the sensor array or the object, more independent data can be obtained, which can enhance image quality compared to traditional planar array ECT. The paper presents experimental results of image enhancement through visual reconstruction of known-shaped dielectric test samples and quantitative analysis against the ground truth. These findings suggest that rotational data collection has the potential to improve image quality in planar array ECT for detecting complex-shaped objects.

One of the critical advantages of a high-altitude airship (HAA) is its ability to carry multiple-task payloads for remote sensing monitoring on the plateau. We developed a multi-payload observation system based on the HAA (ASQ-MPHAAS) for remote sensing monitoring of the Qinghai plateau. In this paper, we demonstrate the design, development, implementation of the ASQ-MPHAAS. The ASQ-MPHAAS includes two parts; one part is our HAA (ASQ-HAA380), and the other part is our multi-payload observation system (ASQ-MPOS-1). The ASQ-MPOS-1 consists of two hyperspectral push-broom imaging sensors, a multispectral imaging sensor, a high-resolution RGB camera, a video sensor, a Position and Orientation System (POS), and other components. The ASQ-MPOS-1 is installed under the ASQ-HAA380. We also introduce some key methods of data processing we propose to solve unique problems brought by HAA. Four real-world field experiments are presented in this paper to demonstrate that the ASQ-MPHAAS can capture different kinds of remote sensing data at a high spatial resolution of a few or dozen centimeters.

Robotic manipulators face significant challenges when handling objects of different sizes and shapes. Incorporating a sense of touch into these devices has the potential to improve performance and dexterity. In this paper, a bio-inspired approach is presented for slip detection and suppression during object manipulation. The method was inspired by the behavior of FA-I afferents located in the glabrous skin that encode sliding motion of objects over the skin. The proposed slip detection method encodes object motion captured by a slip sensor into spikes, following principles of neuromorphic sensing. The spikes are used as the feedback signal for an event-based closed-loop control system. The controller behaves in a reflex-like manner and actively engages the robotic fingers to increase grip force and suppress slip. A Dynamic Adaptive Threshold method was designed to improve slip detection for different surface properties of grasped objects. The performance of the method was evaluated following situations of dynamic slip caused by a sudden or gradual increase in object weight. The results demonstrated the feasibility of the proposed method. Slip events were suppressed before complete object slippage in 80% of all experimental trials. The response time ( Delta {\text {t}} < 85$ ms) was compatible with the time for grip force adjustments in humans. This paper explored event-based touch applicable to the problem of manipulation, which is less explored than event-based tactile perception and shows promising prospects for both robotics and prosthetics. - 2001-2012 IEEE. Scopus

Electrical impedance tomography (EIT) imaging modality has great potential on industrial applications with the advantages of being high temporal resolution. It is especially useful in cases, such as geophysical detection, landmine detection, and detections on non-transparent region, where the measurement data are only available from single surface, for data acquisition. Instead of the circular EIT model that uses the traditional circular electrode model, in this paper, planar array EIT is implemented, aiming to visualize a pipeline transporting a two-phase flow. The planar array can explore spatial information within its detectable region by producing 3D images, which have a higher spatial resolution in the axis direction than a traditional EIT with a dual-plane electrode sensor. However, in solving the inverse problem of a 3D subsurface EIT using a planar array, the images may be degraded, especially in cases where the location of the target is relatively deep. The total variation (TV) algorithm as block prior assumption-based regularization method has the potential to improve the image quality, and some works have shown that TV reconstructs sharper images, which provides an advantage when representing spatial information. In this paper, the performance of subsurface EIT using the TV algorithm for 3D visualization is presented based on simulations and experiments, and the results of quantitative measurement of depth are discussed.

This paper presents a novel soft tissue elasticity measurement technique based on the fusion of Magnetic, Angular Rate, and Gravity (MARG) sensors and fixed tactile sensors. This paper is intended both as a stand-alone technology and as an extension of traditional tactile imaging of the breast to allow for accurate diagnosis of breast lesions. A series of artificial silicone materials known to imitate soft biological breast tissues is characterized using the proposed system and compared against an Instron universal testing machine to determine the system accuracy and repeatability. Comparing the characteristics of ten distinct materials, with elasticities in the range 9-90 kPa, determined by the proposed system to those from the Instron yields accuracy within 4% over the full-scale range. Interexperimental repeatability is within 1.5%. The proposed system delivers absolute elasticity of materials to within 4%, which, when combined with its lack of moving parts and low implementation cost, can significantly improve the diagnostic capability of tactile imaging in the clinical environment. By applying this technique, to determine the background elasticity of breast tissue, in conjunction with the relative lesion elasticity result from tactile arrays, the full non-invasive diagnostic potential of tactile imaging can be realized with the effect of reducing benign biopsy rates, secondary care costs, and patient stress.

International audience; This paper presents a 3D printing technique based on stereolithography and direct light processing for the fabrication of low resonance frequency thin plates suitable for acoustic metamaterials applications. It was possible to achieve a better resolution with respect to other 3D printing methods such as fusion deposition modeling and to obtain plates with a thickness of 70 μm. The plates were characterized using three different methods: laser Doppler vibrometer supported by modal analysis, impedance tube measurements backed by a transfer matrix model and nanoindentation. All results are in good agreement. The physical parameters retrieved through the characterization methods can be used for future designs and integrated into finite element analysis to better predict the noise impact of these materials. Thanks to the small radius and thickness of the plates presented in this paper and to their low resonance frequency, it is suggested that they could be arranged in various configurations and used as unit cells in acoustic metamaterials applications for noise attenuation in small-scale electroacoustic devices.

Non-contact sensors are negating the use of wearables or cameras and providing a rewarding and accepting environment to assist in biomedical applications, such as physiological examinations, physiotherapy, home assistance, rehabilitation success determination, compliance, and health diagnostics. In this paper, physiological parameter identification of human gait has been demonstrated through an edge-based sensor and a heuristic approach. Impulse radio ultra-wide band (IR-UWB) pulsed Doppler radar has been employed with a focus on understanding human walking patterns. This paper extracts an individual’s gait trait from the associated biomechanical activity and differentiates lower limb movement patterns from other body areas via a radar transceiver. It is observed that Doppler shifts alone are not reliable to detect human gait because of frequency shifts taking place across the entire body (including breathing, heartbeat, and arm movements) where movement occurs. Thus, a heuristic spherical trigonometrical approach has been proposed to augment radar principles and short-term Fourier transformation (STFT) to identify the gait trait. The experiment presented includes data gathering from a number of male and female participants in both ideal and real environments. Subsequently, the proposed gait identification and parameter characterization has been analyzed, tested, and validated against popularly accepted smartphone applications where the variations are less than 5%.

Additive manufacturing (AM) offers new manufacturing solutions for the integration of smart functionalities in engineering structures. In this paper, an analytical model is presented for an embedded load sensing element based on a liquid-filled capillary. During the additive manufacturing process, the capillary is integrated in the region where the strain is to be determined. The embedded capillary deforms as the structure deforms under an applied load, as such altering the pressure inside the capillary. The monitoring of the capillary pressure allows monitoring the loads and thus usage of the component. This paper presents a model describing the behavior of the sensing element under uniform tensile stress. The sensitivity of the load sensing element per unit longitudinal strain depends on the bulk modulus of the liquid inside the capillary and the Poisson coefficient of the surrounding material. The current work further compares the analytical model against static tension-compression tests of powder bed fused stainless steel (AISI 316L) test specimen with an integrated capillary filled with a liquid (water). Similarly, the validation of the model is then checked against a dynamic four-point bending test on a Ti-6Al-4V specimen produced by powder bed fusion.

IEEE 802.15.6 is a wireless body area network (WBAN) standard proposed to facilitate the exponentially growing interest in the field of health monitoring. This standard is flexible and outlines multiple basic medium-access control (MAC) protocols that are contention based and collision free to meet the WBAN quality-of-service (QoS) challenges. Typically, current research trends in WBAN MAC focus on designing a hybrid MAC that is a combination of basic MAC protocols. In this paper, we provide a first detailed survey of existing hybrid MAC protocols based on the IEEE 802.15.6, which would be useful for the related research community. First, this paper lists the design challenges of a WBAN MAC. Second, it highlights the significance of hybrid MAC protocols in meeting the design challenges while comparing them to standard MAC protocols. Third, a critical and thorough comparison of existing hybrid MAC protocols is presented in terms of network QoS and WBAN specific parameters. Finally, we identify key open research areas that are often neglected in hybrid MAC design and further propose some possible directions for future research.

Electronic textiles (e-textiles) are an emerging technology comprised of electronic circuit systems embedded into the fabric using traditional textile techniques such as knitting and weaving. Such systems can be formed using off-the-shelf passive components and integrated circuits (ICs) available commercially. An e-textile switching mechanism can be provided by proximity and touch sensing input behavior from a human hand to switch circuits, for example, turning on an LED for visual verification of successful switching. Challenges to be overcome to achieve reliable systems include optimized circuit sampling rate, detection sensitivity, and sensing electrode dimensions when operating among textile fibers. This paper presents the development of a proximity and touch sensing e-textile flexible circuit system using a commercial capacitive detection chip PCF8883US by an NXP Semiconductor. Experiments determine the necessary sensitivity and sampling rate values for this chip to detect through fabric for its optimum capacitance performance value of 20 pF. This paper further details the fabrication of this $150~\mu \text{m}$ track width, $3.0~\text{mm}\times35.0~\text{mm}$ dimension flexible, copper-polyimide capacitive sensing circuit with a 0.0403 mm thick, 20:1 PDMS aqueous-resistant layer and its integration into two e-textile systems–a knitted textile yarn and woven textile fabric. Experiments revealed that when an electrode is strip-shaped for e-textile applications, its width is more influential than electrode length in enabling capacitive dual-functionality proximity and touch sensing. A square-shaped electrode offers a 58.3% increase in nominal proximity detection distance compared with multiple electrodes arranged in the same area.

Industrial automation relies on sensing the process parameters like liquid levels. Simultaneous sensing of multiple fluids is especially useful for many automation processes involving multiple ingredients. Some of the desirable features in any level sensor are its non-intrusiveness, high accuracy, and consistent performance. These features can be achieved with typical microwave radar-based level sensors. However, this paper presents a microwave resonance method instead of typical microwave reflectance (radar) to reliably sense the liquid level at fraction of the cost using a unique printed sensor. In addition to that, sensor design presented in this paper enables sensing liquid level of eight liquids simultaneously and independently utilizing its multiple channels. An array of eight modified microwave T-resonators has been compactly packed inside a metallic cylinder of just $4"$ diameter where the resonators share the same ground plane. Sensors’ performance, to measure the unknown level of eight different fluids (with dielectric constant ( $\in _{\mathbf {r}}$ ) ranging from 2.8 to 80), has been measured in real time using a custom-built software. Simple one point calibration procedure has also been proposed in this paper which has resulted into measurement accuracy of ~0.6% which is better than 1%, realizable with high-end and expensive radar based level sensors

WOS: 000472604000039 This paper focused on the experimental validation of diaphragm-based Fabry-Perot fiber optic pressure sensors (D-FP-FOPS) with ethylene propylene diene terpolymers (EPDM) diaphragm as designed and analyzed sensor tip theoretically. We also used self-adhesive EPDM rubber as a diaphragm for the first time in the literature. Analytical calculation and finite element method (FEM) analysis were carried out to obtain values of fundamental resonance frequency (f(0)) and deflection (d) with diaphragm specific values of Young's modulus and Poisson's ratio measured by tensile tests. We produced D-FP-FOPS tip with EPDM diaphragm and obtained experimental f(0) between 250-400 Hz from extended signal to noise ratio (SNR) plot which figured out with the help of theoretical values of f(0). We also analyzed minimum detectable pressure (MDP) mapping which is used to confirm SNR mapping for D-FP-FOPS. We noticed that our sensor could be operated up to 1.6 Pa pressure which confirms the mechanical limit given in the literature. Before production of D-FP-FOPS, a pioneering way which includes design, analytical calculation, FEM analysis, and experimental validation was demonstrated as a novel. Moreover, since we used self-adhesive EPDM tape as a diaphragm material, our sensor tip cost is less than 50 $ which capable to compete with commercial sensors. Research Funds for the TUBITAK [2170560] This work was supported by the Research Funds for the TUBITAK under Grant 2170560. The associate editor coordinating the review of this paper and approving it for publication was Dr. Carlos Marques.

This paper presents a measurement system based on the first harmonic in tunable diode laser absorption spectroscopy using a vertical-cavity surface-emitting laser to measure the atmospheric CO2 and H2O concentrations. The developed system separates the residual amplitude modulation signal from the harmonics and then eliminates it. A digital signal processing is developed to autonomously infer the wavelength and light intensities of the laser. The gas concentrations are determined without extra calibration. The long-term measurements are taken to validate the precision and accuracy of the system. Based on the Allan variance analysis, the broad wavelength scanning enhances the measurement precision, and the first-harmonic detection can achieve about two times as high precision as the traditional second-harmonic detection. The field measurements implemented in early spring of 2018 in Munich were compared with the commercial nondispersive infrared (NDIR) sensor. The outcomes have revealed that our system has high accuracy for the gas concentration measurements and high consistency with the NDIR sensor. The diurnal variations of CO2 concentration have demonstrated that CO2 concentration in urban areas is affected by the biosphere and meteorological conditions and the daily anthropogenic activities. Furthermore, the air trajectory analysis based on the HYSPLIT model has found that the CO2 emission sources primarily come from the southeast of Munich. The developed system described in this paper has a great potential for in situ trace gas concentration measurements, the analysis on the polluted gas distributions, and the verification of the pollutant transport model in urban areas.