Sensors

Continuous-wave Doppler radar (CWDR) can be used to remotely detect physiological parameters, such as respiration and heart signals. However, detecting and separating multiple targets remains a challenging task for CWDR. While complex transceiver architectures and advanced signal processing algorithms have been demonstrated as effective for multiple target separations in some scenarios, the separation of equidistant sources within a single antenna beam remains a challenge. This paper presents an alternative phase tuning approach that exploits the diversity among target distances and physiological parameters for multi-target detection. The design utilizes a voltage-controlled analog phase shifter to manipulate the phase correlation of the CWDR and thus create different signal mixtures from the multiple targets, then separates them in the frequency domain by suppressing individual signals sequentially. We implemented the phase correlation system based on a 2.4 GHz single-channel CWDR and evaluated it against multiple mechanical and human targets. The experimental results demonstrated successful separation of nearly equidistant targets within an antenna beam, equivalent to separating physiological signals of two people seated shoulder to shoulder. Full article

In biosensing applications, the exploitation of organic transistors gated via a liquid electrolyte has increased in the last years thanks to their enormous advantages in terms of sensitivity, low cost and power consumption. However, a practical aspect limiting the use of these devices in real applications is the contamination of the organic material, which represents an obstacle for the realization of a portable sensing platform based on electrolyte-gated organic transistors (EGOTs). In this work, a novel contamination-free microfluidic platform allowing differential measurements is presented and validated through finite element modeling simulations. The proposed design allows the exposure of the sensing electrode without contaminating the EGOT device during the whole sensing tests protocol. Furthermore, the platform is exploited to perform the detection of bovine serum albumin (BSA) as a validation test for the introduced differential protocol, demonstrating the capability to detect BSA at 1 pM concentration. The lack of contamination and the differential measurements provided in this work can be the first steps towards the realization of a reliable EGOT-based portable sensing instrument. Full article

In precision grazing, pasture allocation decisions are made continuously to ensure demand-based feed allowance and efficient grassland utilization. The aim of this study was to evaluate existing prediction models that determine feed scarcity based on changes in dairy cow behavior. During a practice-oriented experiment, two groups of 10 cows each grazed separate paddocks in half-days in six six-day grazing cycles. The allocated grazing areas provided 20% less feed than the total dry matter requirement of the animals for each entire grazing cycle. All cows were equipped with noseband sensors and pedometers to record their head, jaw, and leg activity. Eight behavioral variables were used to classify herbage sufficiency or scarcity using a generalized linear model and a random forest model. Both predictions were compared to two individual-animal and day-specific reference indicators for feed scarcity: reduced milk yields and rumen fill scores that undercut normal variation. The predictive performance of the models was low. The two behavioral variables “daily rumination chews” and “bite frequency” were confirmed as suitable predictors, the latter being particularly sensitive when new feed allocation is present in the grazing set-up within 24 h. Important aspects were identified to be considered if the modeling approach is to be followed up. Full article

Deep learning approaches for facial Emotion Recognition (ER) obtain high accuracy on basic models, e.g., Ekman’s models, in the specific domain of facial emotional expressions. Thus, facial tracking of users’ emotions could be easily used against the right to privacy or for manipulative purposes. As recent studies have shown that deep learning models are susceptible to adversarial examples (images intentionally modified to fool a machine learning classifier) we propose to use them to preserve users’ privacy against ER. In this paper, we present a technique for generating Emotion Adversarial Attacks (EAAs). EAAs are performed applying well-known image filters inspired from Instagram, and a multi-objective evolutionary algorithm is used to determine the per-image best filters attacking combination. Experimental results on the well-known AffectNet dataset of facial expressions show that our approach successfully attacks emotion classifiers to protect user privacy. On the other hand, the quality of the images from the human perception point of view is maintained. Several experiments with different sequences of filters are run and show that the Attack Success Rate is very high, above 90% for every test. Full article

Developments in the field of artificial intelligence have made great strides in the field of automatic semantic segmentation, both in the 2D (image) and 3D spaces. Within the context of 3D recording technology it has also seen application in several areas, most notably in creating semantically rich point clouds which is usually performed manually. In this paper, we propose the introduction of deep learning-based semantic image segmentation into the photogrammetric 3D reconstruction and classification workflow. The main objective is to be able to introduce semantic classification at the beginning of the classical photogrammetric workflow in order to automatically create classified dense point clouds by the end of the said workflow. In this regard, automatic image masking depending on pre-determined classes were performed using a previously trained neural network. The image masks were then employed during dense image matching in order to constraint the process into the respective classes, thus automatically creating semantically classified point clouds as the final output. Results show that the developed method is promising, with automation of the whole process feasible from input (images) to output (labelled point clouds). Quantitative assessment gave good results for specific classes e.g., building facades and windows, with IoU scores of 0.79 and 0.77 respectively. Full article

This paper considers the application of signal processing methods to passive indoor positioning with acoustics microphones. The key aspect of this problem is time-delay estimation (TDE) that is used to get the time difference of arrival of the source’s signal between the pair of distributed microphones. This paper studies the approach based on generalized phase spectrum (GPS) TDE methods. These methods use frequency-domain information about the received signals that make them different from widely applied generalized cross-correlation (GCC) methods. Despite the more challenging implementation, GPS TDE methods can be less demanding on computational resources and memory than conventional GCC ones. We propose an algorithmic implementation of a GPS estimator and study the various frequency weighting options in applications to TDE in a small room acoustic environment. The study shows that the GPS method is a reliable option for small acoustically dead rooms and could be effectively applied in presence of moderate in-band noises. However, GPS estimators are far less efficient in less acoustically dead environments, where other TDE options should be considered. The distinguishing feature of the proposed solution is the ability to get the time delay using a limited number of the adjusted bins. The solution could be useful for passively locating moving emitters of narrow-band continual noises using computationally simple frequency detection algorithms. Full article

The modern era of technology contains a myriad of high-speed standards and proprietary serial digital protocols, which evolve alongside the microwave and RF realm. The increasing data rate push the requirements for hardware design, including modern printed circuit boards (PCB). One of these requirements for modern high-speed PCB interfaces are a homogenous track impedance all the way from the source to the load. Even though some high-speed interfaces don’t require any external components embedded into the interconnects, there are others which require either passive or active components—or both. Usually, component package land-pads are of fixed size, thus, if not addressed, they create discontinuities and degrade the transmitted signal. To solve this problem, impedance compensation techniques such as reference plane cut-out are employed for multiple case studies covering this topic. This paper presents an original method of finding the optimal cut-out size for the maximum characteristic impedance compensation in high-density multilayer PCB designs, which has been verified via theoretical estimation, computer simulation, and practical measurement results. Track-to-discontinuity ratios of 1:1.75, 1:2.5, and 1:5.0 were selected in order to resemble most practical design scenarios on a 6-layer standard thickness PCB. The measurements and simulations revealed that the compensated impedance saturation occurs at (150–250%) cut-out widths for a 50 Ω microstrip. Full article

In this study, a novel distance sensing method is presented by using a semiconductor laser (SL) with optical feedback (OF) and operating the SL at a switching status happened between two nonlinear dynamic states (stable state and period-one state). In this case, without the need for any electronic or optical modulation devices, the laser intensity can be modulated in a square wave form due to the switching via utilizing the inherent SL dynamics. The periodicity in the switching enables us to develop a new approach for long-distance sensing compared to other SL with OF-based distance measurement systems and lift the relevant restrictions that existed in the systems. Moreover, the impact of system controllable parameters on the duty cycle of the square wave signals generated was investigated on how to maintain the proposed system robustly operating at the switching status. Both simulation and experiment verified the proposed sensing approach. Full article

Recent tragedies around the world have shown how accidents in the cable-stayed bridges can wreak havoc on the society. To ensure the safety of the cable-stayed bridges, several studies have estimated the cable tension force using the vibration of cables. Most of these methods for estimating the tension of a cable start with measuring the displacement of the cable. Recent development of commercial cameras provide opportunity for more convenient and efficient method for measuring the displacement of cable. However, traditional vision-based displacement measurement methods require the assumption that the movement of the cable should be measured in parallel to the camera plane. This assumption limits the installation location of the camera when measuring the displacement of a cable. Therefore, this study introduces a new vision-based cable displacement measurement system that can measure the displacement of a cable in various locations even when the camera is installed in the side of the cable. The proposed method consists of three phases: (1) camera projection matrix estimation, (2) cable tracking in the image coordinate, and (3) cable displacement estimation in the world coordinate. To validate the performance of the proposed method, a simulation-based validation test, a lab-scale validation test, and an on-site validation test were conducted. The simulation-based validation test verified the performance of the proposed method in an ideal condition, and the lab-scale validation test showed the performance of the method in physical environment. Finally, the on-site validation test showed that the proposed method can measure the cable displacement with a side view camera. Full article

Miniaturization and on-chip integration are major lines of research in many branches of science and technology developments, undoubtedly in sensor technology. Fluxgate magnetometers are very sensitive, and accurate magnetic sensors able to detect weak fields both AC and DC, which in recent years saw a great effort in minimizing their dimensions, weight, and power consumption. The physics behind the fluxgate principle is rather complex and makes simulations difficult and only partially used in the literature. The limited physical access to micro sensors for measurements and the need to optimize the entire integrated system, including the sensor geometry and the excitation and readout circuits, make numerical analyses particularly useful in the design of miniaturized sensors. After a thorough review of the miniaturized solutions proposed so far, the present paper examines in detail the possibility of adopting a model based approach for designing miniaturized fluxgate sensors. The model of the fluxgate effect of two different technologies proposed in the literature has been implemented to benchmark simulation results with real data. In addition to the advantages for an optimized design, the implementation and computational challenges of the numerical analyses are precisely outlined. Full article

Floods are a major cause of loss of lives, destruction of infrastructure, and massive damage to a country’s economy. Floods, being natural disasters, cannot be prevented completely; therefore, precautionary measures must be taken by the government, concerned organizations such as the United Nations Office for Disaster Risk Reduction and Office for the coordination of Human Affairs, and the community to control its disastrous effects. To minimize hazards and to provide an emergency response at the time of natural calamity, various measures must be taken by the disaster management authorities before the flood incident. This involves the use of the latest cutting-edge technologies which predict the occurrence of disaster as early as possible such that proper response strategies can be adopted before the disaster. Floods are uncertain depending on several climatic and environmental factors, and therefore are difficult to predict. Hence, improvement in the adoption of the latest technology to move towards automated disaster prediction and forecasting is a must. This study reviews the adoption of remote sensing methods for predicting floods and thus focuses on the pre-disaster phase of the disaster management process for the past 20 years. A classification framework is presented which classifies the remote sensing technologies being used for flood prediction into three types, which are: multispectral, radar, and light detection and ranging (LIDAR). Further categorization is performed based on the method used for data analysis. The technologies are examined based on their relevance to flood prediction, flood risk assessment, and hazard analysis. Some gaps and limitations present in each of the reviewed technologies have been identified. A flood prediction and extent mapping model are then proposed to overcome the current gaps. The compiled results demonstrate the state of each technology’s practice and usage in flood prediction. Full article

Vertically oriented graphene (VG), owing to its sharp edges, non-stacking morphology, and high surface-to-volume ratio structure, is promising as a consummate material for the application of photoelectric detection. However, owing to high defect and fast photocarrier recombination, VG-absorption-based detectors inherently suffer from poor responsivity, severely limiting their viability for light detection. Herein, we report a high-performance photodetector based on a VG/indium tin oxide (ITO) composite structure, where the VG layer serves as the light absorption layer while ITO works as the carrier conduction channel, thus achieving the broadband and high response nature of a photodetector. Under the illumination of infrared light, photoinduced carriers generated in VG could transfer to the floating ITO layer, which makes them separate and diffuse to electrodes quickly, finally realizing large photocurrent detectivity. This kind of composite structure photodetector possesses a room temperature photoresponsivity as high as ~0.7 A/W at a wavelength of 980 nm, and it still maintains an acceptable performance at temperatures as low as 87 K. In addition, a response time of 5.8 s is observed, ~10 s faster than VG photodetectors. Owing to the unique three-dimensional morphology structure of the as-prepared VG, the photoresponsivity of the VG/ITO composite photodetector also presented selectivity of incidence angles. These findings demonstrate that our novel composite structure VG device is attractive and promising in highly sensitive, fast, and broadband photodetection technology. Full article

In this paper, an assessment of the uncertainty affecting a hybrid procedure (experimental/numerical) is carried out to validate it for industrial applications, at the least. The procedure in question serves to depict 3D incompressible flow fields by using 2D measurements of it and computing the third velocity component by means of the continuity equation. A quasi-3D test case of an incompressible flow has been inspected in the wake of a NACA 0012 airfoil immersed in a forced flow of water running in a rectangular open channel. Specifically, starting from a 2D measurement data in planes orthogonal to the stream-wise direction, the computational approach can predict the third flow velocity component. A 3D ADV instrument has been utilized to measure the flow field, but only two velocity components have been considered as measured quantities, while the third one has been considered as reference with which to compare the computed component from the continuity equation to check the accuracy and validity of the hybrid procedure. At this aim, the uncertainties of the quantities have been evaluated, according to the GUM, to assess the agreement between experiments and predictions, in addition to other metrics. This aspect of uncertainty is not a technical sophistication but a substantial way to bring to the use of a 1D and 2D measurement system in lieu of a 3D one, which is costly in terms of maintenance, calibration, and economic issues. Moreover, the magnitude of the most relevant flow indicators by means of experimental data and predictions have been estimated and compared, for further confirmation by means of a supervised learning classification. Further, the sensed data have been processed, by means of a machine learning algorithm, to express them in a 3D way along with accuracy and epoch metrics. Two additional metrics have been included in the effort to show paramount interest, which are a geostatistical estimator and Sobol sensitivity. The statements of this paper can be used to design and test several devices for industrial purposes more easily. Full article

Premature infants are among the most vulnerable patients in a hospital. Due to numerous complications associated with immaturity, a continuous monitoring of vital signs with a high sensitivity and accuracy is required. Today, wired sensors are attached to the patient’s skin. However, adhesive electrodes can be potentially harmful as they can damage the very thin immature skin. Although unobtrusive monitoring systems using cameras show the potential to replace cable-based techniques, advanced image processing algorithms are data-driven and, therefore, need much data to be trained. Due to the low availability of public neonatal image data, a patient phantom could help to implement algorithms for the robust extraction of vital signs from video recordings. In this work, a camera-based system is presented and validated using a neonatal phantom, which enabled a simulation of common neonatal pathologies such as hypo-/hyperthermia and brady-/tachycardia. The implemented algorithm was able to continuously measure and analyze the heart rate via photoplethysmography imaging with a mean absolute error of 0.91 bpm, as well as the distribution of a neonate’s skin temperature with a mean absolute error of less than 0.55 °C. For accurate measurements, a temperature gain offset correction on the registered image from two infrared thermography cameras was performed. A deep learning-based keypoint detector was applied for temperature mapping and guidance for the feature extraction. The presented setup successfully detected several levels of hypo- and hyperthermia, an increased central-peripheral temperature difference, tachycardia and bradycardia. Full article

A Drift-Free 3D Orientation and Displacement estimation method (DFOD) based on a single inertial measurement unit (IMU) is proposed and validated. Typically, body segment orientation and displacement methods rely on a constant- or zero-velocity point to correct for drift. Therefore, they are not easily applicable to more proximal segments than the foot. DFOD uses an alternative single sensor drift reduction strategy based on the quasi-cyclical nature of many human movements. DFOD assumes that the quasi-cyclical movement occurs in a quasi-2D plane and with an approximately constant cycle average velocity. DFOD is independent of a constant- or zero-velocity point, a biomechanical model, Kalman filtering or a magnetometer. DFOD reduces orientation drift by assuming a cyclical movement, and by defining a functional coordinate system with two functional axes. These axes are based on the mean acceleration and rotation axes over multiple complete gait cycles. Using this drift-free orientation estimate, the displacement of the sensor is computed by again assuming a cyclical movement. Drift in displacement is reduced by subtracting the mean value over five gait cycle from the free acceleration, velocity, and displacement. Estimated 3D sensor orientation and displacement for an IMU on the lower leg were validated with an optical motion capture system (OMCS) in four runners during constant velocity treadmill running. Root mean square errors for sensor orientation differences between DFOD and OMCS were 3.1 ± 0.4° (sagittal plane), 5.3 ± 1.1° (frontal plane), and 5.0 ± 2.1° (transversal plane). Sensor displacement differences had a root mean square error of 1.6 ± 0.2 cm (forward axis), 1.7 ± 0.6 cm (mediolateral axis), and 1.6 ± 0.2 cm (vertical axis). Hence, DFOD is a promising 3D drift-free orientation and displacement estimation method based on a single IMU in quasi-cyclical movements with many advantages over current methods. Full article

Swimmers take great advantage by reducing the drag forces either in passive or active conditions. The purpose of this work is to determine the frontal area of swimmers by means of an automated vision system. The proposed algorithm is automated and also allows to determine lateral pose of the swimmer for training purposes. In this way, a step towards the determination of the instantaneous active drag is reached that could be obtained by correlating the effective frontal area of the swimmer to the velocity. This article shows a novel algorithm for estimating the frontal and lateral area in comparison with other models. The computing time allows to obtain a reasonable online representation of the results. The development of an automated method to obtain the frontal surface area during swimming increases the knowledge of the temporal fluctuation of the frontal surface area in swimming. It would allow the best monitoring of a swimmer in their swimming training sessions. Further works will present the complete device, which allows to track the swimmer while acquiring the images and a more realistic model of conventional active drag ones. Full article

Ocean science and hydroacoustic seafloor mapping rely on accurate navigation underwater. By exploiting terrain information provided by a multibeam echosounder system, it is possible to significantly improve map quality. This article presents an algorithm capable of improving map quality and accuracy by aligning consecutive pings to tiles that are matched pairwise. A globally consistent solution is calculated from these matches. The proposed method has the potential to be used online in addition to other navigation solutions, but is mainly targeted for post processing. The algorithm was tested using different parameter settings on an AUV and a ship-based dataset. The ship-based dataset is publicly available as a benchmark. The original accurate navigation serving as a ground truth, alongside trajectories that include an artificial drift, are available. This allows quantitative comparisons between algorithms and parameter settings. Full article

Ransomware has become an increasingly popular type of malware across the past decade and continues to rise in popularity due to its high profitability. Organisations and enterprises have become prime targets for ransomware as they are more likely to succumb to ransom demands as part of operating expenses to counter the cost incurred from downtime. Despite the prevalence of ransomware as a threat towards organisations, there is very little information outlining how ransomware affects Windows Server environments, and particularly its proprietary domain services such as Active Directory. Hence, we aim to increase the cyber situational awareness of organisations and corporations that utilise these environments. Dynamic analysis was performed using three ransomware variants to uncover how crypto-ransomware affects Windows Server-specific services and processes. Our work outlines the practical investigation undertaken as WannaCry, TeslaCrypt, and Jigsaw were acquired and tested against several domain services. The findings showed that none of the three variants stopped the processes and decidedly left all domain services untouched. However, although the services remained operational, they became uniquely dysfunctional as ransomware encrypted the files pertaining to those services. Full article

The coronavirus pandemic, also known as the COVID-19 pandemic, is an ongoing virus. It was first identified on December 2019 in Wuhan, China, and later spread to 192 countries. As of now, 251,266,207 people have been affected, and 5,070,244 deaths are reported. Due to the growing number of COVID-19 patients, the demand for COVID wards is increasing. Telemedicine applications are increasing drastically because of convenient treatment options. The healthcare sector is rapidly adopting telemedicine applications for the treatment of COVID-19 patients. Most telemedicine applications are developed for heterogeneous environments and due to their diverse nature, data transmission between similar and dissimilar telemedicine applications is a difficult task. In this paper, we propose a Tele-COVID system architecture design along with its security aspects to provide the treatment for COVID-19 patients from distance. Tele-COVID secure system architecture is designed to resolve the problem of data interchange between two different telemedicine applications, interoperability, and vendor lock-in. Tele-COVID is a web-based and Android telemedicine application that provides suitable treatment to COVID-19 patients. With the help of Tele-COVID, the treatment of patients at a distance is possible without the need for them to visit hospitals; in case of emergency, necessary services can also be provided. The application is tested on COVID-19 patients in the county hospital and shows the initial results. Full article