Cardiovascular disease is a group of disorders of the heart and blood vessels, including coronary heart disease, cerebrovascular disease, and rheumatic heart disease, etc. It is one of the most common causes of death in the world. In 2013, cardiovascular disease claimed a total of 17.3 million lives (31.% of total deaths), which is a significant increase from 12.3 million in 1990 (25.8% of total deaths).

There is an increasing demand for a reliable heart monitoring system to capture intermittent abnormalities and detect serious heart behaviors, leading to sudden death in extreme cases. In addition to cardiac monitoring, there is a growing need to capture the respiratory function in several contexts such as sleep apneas. For example, “Internet-of-Medical-Things (IoMT)” are now considered to be a good strategy for monitoring the abnormalities of breathing and cardiac rate. A wearable monitoring strain sensor detects the accidents and sends immediately the information to the medical staff.

Therefore, there is a need to develop a highly sensitive, durable, and biocompatible strain sensor. In this paper, a new one-dimensional nanomaterial based strain sensor will be introduced for the respiratory rate monitoring of an anesthetized rat, followed by the fabrication process and the experimental platform. The dedicated sensitive sensor, based on carbon nanotubes mixed with poly(3,4-ethylenedioxythiophene) polystyrene sulfonate, was attached above the rat chest. A Wheatstone bridge electrical circuit, associated with a multifunction portable device, was connected to the strain sensor. The change of the strain sensor’s resistance value, induced by the mechanical deformability during the rat respiration, was detected and transformed into a voltage signal. The respiration information could be thus extracted and analyzed.

In many fields of science, the aim is to obtain structures characterized by small sizes of the order of micro / nanometers. Systems with small dimensions can have countless applications in various industries, such as cosmetology, medicine, and nutrition technology. Multiple techniques are used to obtain spheres as small as possible. These include interference in the composition, the use of surfactants, or mechanical interference: rapid mixing, increased pressure, and ultrasounds. Ultrasonic waves of high intensity can improve many processes, starting from producing the material and its subsequent processing. The use of ultrasound in creating systems can be an effective method to reduce their size, as well as it can affect the functions they represent. An important aspect here is the time during which ultrasound is used. In this work, the impact of ultrasound on the produced chemosensory microspheres was examined and compared. The measurements were made for two types of microspheres: anion-selective and cation-selective. After removing the organic solvent from the manufactured suspension of the microspheres, an ultrasonic scrubber bath was applied as an ultrasound source for time-controlled impact influencing the microstructures. The obtained results of chemosensory properties of the developed micro-optodes provided new insight into the microstructures manufacturing process and, to a certain extent, allowed for their improvement and optimization.

This work was financially supported by National Science Centre (Poland) within the framework of the SONATA BIS project No. UMO-2018/30/E/ST4/00481. Aleksandra Kalinowska acknowledges financial support from IDUB project (Scholarship Plus programme).

Bilirubin (BR) is clinically confirmed as a biomarker for liver health and is used to assess the prognosis of cirrhosis. Optical and chemical methods have been utilized for blood BR biosensing. While optical methods offer real-time monitoring and are handy and immune to infection, measurements may not be practical due to the instrument complexity and space requirements. This study investigated the dual-wavelength (DWL) technique for BR estimation using a system-on-chip (SoC). The SoC includes an optical module with blue (455 nm) and green (530 nm) LEDs which were used for DWL measurement. Porcine blood was used as a surrogate of human blood and BR levels were kept within the pathophysiological ranges projected from healthy individuals (<1.2 mgdL) to a cirrhotic patient (up to 50 mg/dL). Our findings show a high BR sensitivity in blood and this lays the groundwork for point-of-care testing for BR levels primarily for hyperbilirubinemia infants and cirrhotic adults out in homes or in-community settings.

Excavator’s main tasks include digging, trenching, and ground leveling tasks at construction sites, and work efficiency and safety can be improved by using an autonomous excavator. A prerequisite step to achieving an autonomous excavation is to obtain a sound perception of the surrounding ground. For this, a LiDAR sensor has been widely used to scan the environment. However, the point cloud generated by the LiDAR is not ideal for surface reconstruction to generate a ground map, as it suffers from flaws such as noise and outlier points. Thus, a series of enhancements must be done before fitting a surface on the point cloud data.

To tackle this issue, our paper proposes advanced methodologies to improve the surface reconstruction for the group map generation, which are applied to the raw data obtained from the lidar sensor before applying the surface reconstruction algorithms.

As the first step, a sensor configured to report the furthest reflection of the fired laser helps reduce noisy data. The next step is to remove outliers from a data set. Further, a region of interest around the digging area was defined to exclude any unnecessary environment and objects, which can significantly reduce the computation time. Finally, the data points enhanced (i.e., more suitable for surface reconstruction algorithm) through the previous steps were used to fit or approximate the ground surface. The advanced front surface reconstruction method was applied to generate the surface because it does not require the normal vectors of each point on top of being robust to noisy data.

Implementing the proposed surface reconstruction methods in the excavation application will allow for better identification of the ground shape and provide a solid foundation for the generation of optimal trajectory, accurate tracking control, and safety evaluation that are required for completing a successful autonomous excavation.

Due to increased passenger traffic and more frequent load exertion on the railway infrastructure, the rate of deterioration for track systems has increased. Switches and Crossings (S&Cs), which are the track components that help trains change direction are highly susceptible to degradation and would thus benefit from continuous condition monitoring using physical sensors. However, the critical locations for the placement of sensors need to be determined to obtain the most useful data for condition monitoring. To this end, Multi-Body Simulations (MBS) and Finite Element (FE) Analysis have been carried out for the interaction between a train and a railway switch. From the outputs of the MBS simulations, damage prediction models were used to determine the locations with the highest risk of damage along the length of the switch. Parametric simulations for different train speeds, wheel-rail friction conditions, axle loads, and fault introduction were carried out through the detailed 3D solid element FE model of the local region of the switch. It was observed from previous field experimentation that a more linear relationship is obtained between the wheel-rail contact force and sensor measurements for strain gauges than accelerometers in the presence of surface crossing faults. Therefore, stress/strain signals obtained from the simulations were post-processed to determine strain sensor placement. Ideal sensor placement locations have been determined through the algorithm that has been developed whilst considering the material fatigue life as well as the mutual information between the fault occurrence and the sensor placement locations. The virtual strain measurements from these validated simulation models, which can be turned into live Digital Twins can also be used for sensor selection for condition monitoring.

Operational modal analysis (OMA) is required for the maintenance of large-scale civil structures. This paper developed a novel methodology of non-contact-based blind identification of modal frequency of a vibrating structure from its video measurement. The developed methodology uses computer vision techniques and signal separation techniques for modal frequency identification. There are two stages in the proposed methodology, first stage is extracting the motion data of the vibrating structure from its video using a multiscale decomposition computer vision technique known as complex steerable pyramid. Second stage consists of a statistical technique popularly known as principal component analysis (PCA) for dimension reduction on the motion data extracted from the video and a signal separation technique based on Hilbert transform known as analytical mode decomposition (AMD) for separating the modal frequencies. The second stage of methodology is validated by a 10-DOF numerical model. The proposed methodology is applied on the real-life video of the London Millennium bridge and an accuracy of 99% is achieved in identifying the modal frequency. This paper proves that the results of benchmark experiments reveal the competence of the recommended technique in blindly extracting the modal frequencies of the structure precisely in a non-contact computer vision-based measurement.

Capacitive MEMS accelerometers have a high thermal sensitivity that drifts the output when subjected to changes in temperature. To improve its performance in applications with thermal variations, it is necessary to compensate for these effects. These drifts can be compensated by a lightweight algorithm by knowing the characteristic thermal parameters of an accelerometer (Temperature Drift of Bias and Temperature Drift of Scale Factor). These parameters vary in each accelerometer and axis, making an individual calibration necessary.

In this work, a simple calibration method is proposed that allows the characteristic parameters of the three axes to be obtained simultaneously through a test of less than three hours. This method is based on the study of two specific orientations, each at two temperatures. By means of the suitable selection of the orientations and the temperature points, the data obtained can be extrapolated to the entire working range of the accelerometer. Only a mechanical anchor and a heat source are required to perform the calibration. This technique can be scaled to calibrate multiple accelerometers simultaneously. A lightweight algorithm is used to analyze the test data and obtain the characteristic parameters. This algorithm stores only the most relevant data, reducing memory and computing power requirements. This way it can be run on real-time on a low-cost microcontroller during testing to obtain compensation parameters immediately. This method is aimed at mass factory calibration, where individual calibration with traditional methods may not be an adequate option. The proposed method has been compared with a traditional calibration using a six-sided orthogonal die and a thermal camera. The average difference between the compensations according to both techniques is 0.64mg, calculated on an acceleration of 1G and a thermal variation of 20ºC; the maximum deviation being 1.1mg.

Due to the increased demand for a sustainable source of energy, the research on energy harvesting has increased in the last twenty years. Energy harvesting aims to gain energy from the ambient environment and converting this energy into electrical power. There are different kinds of renewable energy sources and vibration energy harvesting (VEH) is the most promising source owing to its low maintenance cost. This paper focuses on an electromagnetic vibration energy harvester based on the concept of rotational energy harvesting. The proposed device uses a rotating rotor with permanent magnets and moves the repulsive magnet block up and down. The block is connected to an antiphase harvester, which creates power by cutting the magnetic flux density. The antiphase has been proved to doubling the voltage when the antiphase is moving. To improve the vibration amplitude of the magnet block and the antiphase, springs are added to the proposed design. In the concept, four configurations with and without different spring positions are proposed. The experimental results showed that when the spring is placed in the upper and bottom part of the moving part or spring at the bottom would generate the largest vibration amplitude. Based on Faraday’s Law of Induction, the voltage is proportional to the velocity or vibration amplitude. Hence, for both cases, at least six times the voltage is generated compared to the design without added springs.

Whereas thermal comfort and air quality have direct bearing on the health and productivity of occupants, rarely do naturally ventilated buildings meet requirements defined in international standards. Student residences are even more so important as the health of students can impact their productivity while in school. The monitoring and evaluating these two conditions (thermal comfort and air quality) have gained more prominence since the Covid-19 pandemic.

This study assessed thermal and air quality conditions in student residences at the University of Nigeria, Nsukka. First, indoor thermal comfort parameters such as temperature, humidity, clothing level and metabolic rate of occupants in naturally ventilated sample rooms owned by the school and private individuals were either measured or observed, and recorded. Temperatures and humidity in the dwellings were measured and recorded in 15 minute intervals for 10 hours using a combined data logger with external sensor probe. The average values were then evaluated using the CBE thermal comfort tool for comfort range plots. The results which were evaluated using the adaptive model revealed a wider range of comfort band than the PMV model owing to adaptive behavior of occupants. The results showed that most school hostels fall outside the 90% acceptability limit for adaptive comfort while private hostels are within 80-90% of the adaptive comfort chart. One major reason is the difference in population of students within each room.

Next, levels of indoor PM₁.₀, PM_2.5, PM₁₀ and HCHO were measured using on-the-spot detectors. Twenty rooms in each of 6 different buildings were surveyed and their results were graphically compared with standards specified by the World Health Organization. Residences outside the university showed levels higher than stipulated as activities such as tobacco intake and cooking with combustible fuels were allowed.

Location Awareness and Location-Based Services facilitate our daily lives by involving in broad domain of applications, including commercial, healthcare, and life-saving services. Global Navigation Satellite Systems (GNSS) provide satisfactory outdoor localization services, in contrast, indoors, GNSS performance degrades significantly due to signal blockage and attenuation. Many Indoor Localization Systems (ILS) have been proposed aiming to improve the indoor localization performance. Pedestrian Dead Reckoning (PDR) using off-the-shelf inertial sensors has gained great popularity as low-cost autonomous technique. PDR provides a reliable short-term solution bridging outages of wireless techniques. Contrarily, its performance degrades rapidly due to the inherent errors of low-cost inertial sensors and the accumulated drift of the gyroscope heading over long period of time. Despite numerous research efforts, the heading problem and the unconstrained smartphones cases still limit the widespread use of PDR. In this paper, we propose robust PDR scheme to achieve better performance for long period navigation under different smartphone poses. To improve the length estimation, we propose a new step detection method based on a robust peak and valley algorithm and a nonlinear model to estimate a robust empirical length regardless smartphone pose. The magnetic field quasi-static periods are used to accurately calibrate the gyro heading. The pedestrian walking between two anchor nodes is exploited to further calibrate the step length and heading. Finally, a real-time PDR outlier filtering is proposed based on robust turn detection and Random Sample Consensus (RANSAC) algorithm to curb the PDR from the slight body shaking. Extensive experiments are conducted to evaluate each PDR component. Our results show that reliable step counting is achieved with different smartphone poses, additionally, the step length is estimated with a maximum error of less than 2% of the walked distance. Moreover, the estimated heading after 20 minutes walking drifted less than 5º. The overall performance of PDR is significantly improved in long-term scenarios with a mean and a 90% error of 1.83 and 4.1 m, respectively, over the entire 750-m continuously walking distance. Furthermore, when PDR is combined with Bluetooth Low Energy (BLE) beacons for step length, heading, and position calibration, the corresponding values are improved to 1.45 and 2.35 m, respectively.