Experimental studies were conducted to determine if the stresses occurring during the manufacturing process of fibre metal laminates cause irreversible damage to electronic components. This is especially interesting for electronic systems to be embedded into such FML for later structural health monitoring purposes. Depending on the requirements of the used prepreg material, required temperatures and pressures for manufacturing can be quite high. First studies were conducted on electronic components separately, to validate their functionality after 3.5 hours at elevated temperatures and pressures, exceeding manufacturers specifications. The functionality tests were performed afterwards for every tested component. Further experiments will be conducted investigating the influence on a fully functional, programmed electronic system under the same conditions to investigate the influence on memory and soldering joints as well.

Cylindrical structures analysis is important in several areas and this can be performed through the analysis of the diameter changes of these structures. Two important areas can be mentioned: pipelines for oil or gas distribution and, condition and growth of trees. In the diameter tree changes, the monitoring is directly related to irrigation, since it depends on the soil water deficit and trees are important in the global circulation of heat and water. This diameter can change in the order of 5 mm for some species. In this paper, it is proposed and experimentally investigated a strain gauge sensor based on a core diameter mismatch technique for diameter measurement. The sensor structure is formed by splicing an uncoated short section of MMF between two standard SMFs called SMF-MMF-SMF (SMS), the MMF length is 15 mm. Two cylindric structures were developed on a 3D printer, with different diameter sizes (DS: 80 mm and 110 mm), to assist in monitoring the diameter changes. The SMS sensor was placed on the printed structure and fixed at two points such that by reducing the diameter of the structure, the sensor presents dip or peak shift of transmittance spectrum due to induced curvature and strain force. Two values were used for the spacing between the fixations points (FP): 5 mm and 10 mm. For DS = 80 mm, the sensor presents respectively: a) a sensitivity of -0.876 nm/mm, R² of 0.9909 and a dynamic range of 5 mm; b) a sensitivity of -0.3892 nm/mm, R² of 0.9954 and a dynamic range of 4 mm. For DS = 110 mm, the sensor presents respectively: a) a sensitivity of -0.22 nm/mm, R² of 0.9979 and a dynamic range of 8 mm; b) a sensitivity of -0.2284 nm/mm, R² of 0.9888 and a dynamic range of 6 mm.

Seismocardiography (SCG) is a non-invasive method that measures local vibrations created by the mechanical cardiovascular exercises on the chest wall. Thereby, mechanical movements of the heart are recorded in real-time from vibration sensors positioned on the chest of the subject, to further compute the heart rate and retrieve the SCG waveform.

Although such events have been widely studied, robust signal processing methods, analogous to electrocardiography (ECG), remain a challenging task. On the other hand, the use of piezoelectric sensors has been favored in recent years due to its features and low-cost. However, robust data processing techniques should be developed to increase their performance and reliability.

In this work, we propose an attractive method for SCG data processing based on the k-means clustering algorithm to automatically label waveform events. Interestingly, the SCG signals are recovered from a custom-made device built around an ultra-low-cost piezoelectric sensor. Once the signals are measured, they are pre-processed by spectral filtering using the power spectral density (PSD) representation. Afterwards, the signal spectrum is used to filter out the useful components to compute the heart rate (HR) in the range from 50 – 120 Beats Per Minute (BPM). Thereby, the filtered signal is sequentially segmented, and every frame is processed by a light-weight k-means algorithm.

Finally, we show the performance of the smart seismocardiography by analyzing SCG waveforms at different physiological conditions.

Ambulatory cardiac rhythm monitoring is a clinical tool indicated mainly for detecting arrhythmias, but it turns out that measuring, recording, and displaying information in a patient for prolonged periods is often tedious and complicated. In this paper, we propose investigating the ability to integrate a portable ECG device to commercial platforms for analysis and visualization of information hosted in the cloud, considering that this could complement the device and raise a more convenient application of ambulatory monitoring. Our proposed ECG system based on the ADX8232 microchip has been tested in continuous and extended-time heart rate monitoring in healthy subjects, showing results of a similar degree to a desktop clinical ECG. The new functionality tests were performed at monitoring intervals of 1, 2, 12, 12, 24, and 36 hours on cloud service platforms and their consequent viewing, independent of location, to investigate whether they maintain reliable ECG records. Some positive results will allow the system to be considered a valuable tool to follow up a patient remotely.

Spaceborne sensors are now providing invaluable datasets for the Earth’s surface studies. Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) with Advanced Topographic Laser Altimeter System (ATLAS) instrument was launched by NASA on September 15, 2018, to measure the elevation of Earth’s surface using laser wavelength of 532 nm and pulse repetition frequency of 10kHz giving footprint of approximately 70cm on the ground. The ICESat-2 datasets are used in this study for the visualization and investigation of the complex Himalayan terrain in the parts of the Kinnaur district and surroundings, which are prone to landslides due to the geology of the region as observed during the recent landslide events. The ICESat-2 elevation data sets were compared with the openly accessible DEM datasets namely, ALOS PALSAR RTC HR (12.5m) and TanDEM-X (90m) at ICESat-2 footprint locations. Preprocessing of datasets was done for selecting ICESat-2 footprints (Track ID: 325, 1270, 828, 386) at locations of high-quality datasets for analysis. The analysis of pre-processed 19,755 ICESat-2 footprints (out of 20,948 footprints) was done with ALOS PALSAR RTC HR (12.5m) and TanDEM-X (90m) datasets. The visualization of the region in the Google earth and OpenAltimetry 3D viewer depicts that the mountain slopes are very steep indicating rugged terrain difficult to access and challenging for construction of transport facilities. The results of Track ID: 325, show that the range of elevations in ICESat-2 elevation values in the study area is from 3409.75m to 5976.31m. The standard deviation representing terrain ruggedness using ICESat-2 elevation values is found as 432.06m. Considering higher accuracy ICESat-2 values for the difficult terrain as a reference, the mean error (ME), mean absolute error (MAE), and RMSE for TanDEM-X were found as 0.26m, 12.92m, and 17.4m respectively. Whereas the ME, MAE, and RMSE for ALOS PALSAR RTC HR DEM were found as 0.20m, 9.50m, and 13.88m respectively. Thus for the study site, using ICESat-2 ATL06 products, ALOS PALSAR RTC HR DEM is found more suitable than TanDEM-X 90m openly accessible datasets for any kind of application.

Digital Elevation Model (DEM) is mostly used to extract the terrain parameters for surface and elevation analysis for representing the topography of earth surface in the best possible way. Nowadays smart devices such as smartphones, tablets employed with GPS chipsets are easily available in the market and can be used for positioning and navigation. These smart devices can measure elevation data and are cost-effective. The plain areas of Ratlam City (Madhya Pradesh) was the study area. Vivo 1606 smartphone incorporated with Assisted-GPS is used with GPS utility App called Mobile Topographer to collect the ground coordinates and elevation data. The ground control points (GCPs) were collected in parts of urban areas, such as open grounds, streets, parks, and other uniformly distributed GCP locations. Using smartphone derived GCPs as a reference the two openly accessible DEMs namely, CartoDEM V3 R1 (30m) and TanDEM-X (90m) were evaluated statistically. Statistical parameters such as Mean Error (ME), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were computed for comparative quality analysis between CartoDEM V3 R1, and TanDEM-X 90m, using the observed GPS elevation data. The ME (4.60 m), MAE (6.12 m), and RMSE (7.15 m) for TanDEM-X were higher than that of CartoDEM V3 R1, ME (3.09 m), MAE(50.5 m), and RMSE (6.17 m) respectively. The results revealed that the accuracy of CartoDEM V3 R1 is higher and it statistically performs better than TanDEM-X 90m in plain areas of Ratlam.

The ability to record brain signals during daily life activities would allow to deepen our understanding of the human brain, its related pathologies (such as epilepsy and Alzheimer’s dis- ease) or develop practical and discreet brain-computer interfaces. In this paper, we present a new miniature wearable electroencephalography (EEG) sensor, of which multiple copies can be de- ployed on the scalp in a wireless mesh configuration to record brain activity. Each node in this wire- less EEG sensor network (WESN) performs a local EEG acquisition which is transmitted via Blue- tooth low energy to a smartphone. To keep coherent signals over time and avoid drifting, the nodes exchange synchronization pulses within the WESN via human body communication.

Flow sensors are essential for a variety of applications in fluidic industries. This paper proposes a liquid flow sensor using a microfluidic channel for macrotubular architectures such as pipes. The sensor comprised a firm poly(methyl methacrylate) microfluidic channel bridge on a mechanically flexible polydimethylsiloxane (PDMS) platform installed on the inner wall of tubular systems. The flexible platform was compatible with various tubular architectures and adopted curvatures. In addition, the microscale fluidic channel surpassed the primary disadvantages of common bulky and rigid flowmeters that cause flow streams disturbance and significant pressure drops in tubular systems. Moreover, the microchannel flow sensor is based on detecting the dominated dynamic pressure generated from the fluid velocity inside the microchannel since the tube flow rate is proportional to the flow velocity inside the channel. The pressure sensors for the microchannel flowmeter displayed a sensitivity of 10 pF/kPa and were fabricated inside the PDMS platform. In particular, the pressure was measured using a capacitive pressure sensor owing to its compatibility with flexible electronics and low power consumption. The capacitive pressure sensor inside the microchannel measure the flowrate based on the force generated on the internal walls from the fluid flow velocity inside the channel. Furthermore, the flow sensor behavior was studied for the overall tubular system and validated using a simulation model for volume flow rate ranging from 500–2000 ml/min.

Construction manipulator is the key component of construction machinery to complete the operation task, thus its assembly link directly affects the product quality and operation performance of the whole machinery. In order to solve the problems of low assembly efficiency and the inability to fully reflect the assembly process indexes and product characteristics in the traditional construction machinery arm assembly, this paper studies assembly process modeling and simulation for construction machinery arm based on assembly data and digital twin. By extracting and processing the assembly resource data and field measurement data of mechinery arm, the assembly process information database under the digital twin environment is constructed, which lays the foundation for the virtual assembly model construction of mechinery arm. Through the real-time data interaction between virtual space and physical space, a complete assembly digital twin space is formed. Finally, taking the assembly line of an excavator arm as an example, it is shown that the digital twin-based assembly simulation can monitor the assembly process in real time and optimize its configuration to improve assembly efficiency. Thereby, an effective closed-loop feedback mechanism is constructed for the whole assembly process of construction machinery arm.

Recent technological developments of sweat sensor systems offer new opportunities to unobtrusively monitor athletes, to optimize their performance and minimize injury risk. However, from physiological literature it can be concluded that for most sweat constituents, little is known yet about how these constituents change over time and how they relate to the physical status of an athlete. A parameter of interest is ammonium in sweat. Sweat [NH₄⁺] can potentially be used as a marker for muscle fatigue. To investigate the use of NH₄⁺ as a biomarker, real-time NH₄⁺ monitoring systems are required. NH₄⁺ will quickly evaporate from sweat, which makes it challenging to measure from sweat samples. Therefore, we present a new approach of monitoring NH₃ that is evaporated from sweat during exercise. A metal oxide gas sensor is placed in a capsule that is connected to the skin. This capsule is ventilated with dry air at low flow rates (0.2 -1.2 l/min). The capsule also contains a temperature and humidity sensor, to compensate for temperature and humidity effects. Experimental results prove that NH₃ sensors show good sensitivity from 27 mV/ppm to 1.1 mV/ppm in the desired measurement range of 1 to 30 ppm respectively. As expected, the baseline resistance varies between each gas sensor. Humidity changes will influence baseline resistance and sensitivity significantly. This confirms the design choice to measure humidity and temperature changes in the capsule. In future experiments, the setup will be tested in-situ, to validate if NH₃ levels can be measured in an exercise setting.