A hydraulic excavator consists of multiple electrohydraulic actuators (EHA) to control the movement of its manipulator. However, uncertainties and nonlinear behaviors of its hydraulic components always make it challenging to design a proper control strategy for successful excavation. This paper proposes a collaborative tracking control strategy to overcome these difficulties and minimize tracking errors of the bucket tip for autonomous excavation.

The proposed control strategy has two decoupled layers to tackle the complex problem of tracking control of a hydraulic excavator. As the first component, a PID controller was used to compensate for misplacement of the bucket tip on the working plane, and thus can contribute to minimizing tracking errors. Meanwhile, three separate fuzzy controllers as the second component were adopted to maintain the desired stroke of each EHA by adjusting the position of servo valves, which allows for tracking control during excavation operations due to its capability dealing with highly nonlinear and uncertain dynamics of the hydraulic components.

To validate the performance of the developed controllers, a multi-domain simulation model was created for co-simulations, which includes the control algorithms and the excavator’s mechanical and hydraulic system designed in Matlab and Amesim, respectively. Simulations indicate that the proposed method enables achieving accurate tracking control for autonomous excavation with small tracking errors despite the nonlinear characteristics of the hydraulic excavator system.

Recent works proved the possibility of using common low-cost plastic polymers like polyethylene naphthalate (PEN) as a scintillator for radiation sensing applications. Scintillators are transparent materials that emit light upon excitation by energetic charged particles and are used to detect energetic particles and measure their properties. PEN offers excellent scintillation properties like high density (1.33g/cm³), a peak emission wavelength at ~ 425 nm and a light yield of roughly 10⁴ photons/MeV. Because of these good properties and of its ease of manufacture and low cost, PEN is a very attractive solution for many applications, from dosimetry purposes in irradiation facilities or nuclear medicine to particles energy measurement in High Energy Physics (HEP) or Space experiments. Many of these applications require the instrumentation to operate in very high radiation environments and have pushed the problem of the detector's radiation hardness to the high priority level.

In this work, we investigate the radiation hardness of a PEN thin-film scintillator. Several samples have been irradiated in air with a 11 MeV proton beam and with a 1 MeV electron beam at the maximum doses of 15 Mrad and 80 Mrad, respectively. The radiation-induced damage has been measured in terms of light-yield loss as a function of the dose. Post-irradiation measurements have been performed by coupling the scintillator samples to a Photo-Multiplier Tube and by measuring the light induced by excitation with Am-241 and Cs-137 sources. This investigation revealed a reduction of the light yield emission, with similar trends for both irradiation beams. At the maximum dose of 80 Mrad, a light yield loss of ~35% has been observed. The results described in this work enrich the literature on radiation hardness studies on plastic scintillators, providing useful studies for the introduction of PEN scintillators in nuclear, space and HEP applications.

Metal-oxide (MOX) sensors offer a low-cost solution to detect volatile organic compound (VOC) mixtures. However, their operation involves time-consuming heating cycles, leading to a slower data collection and data classification process. This work introduces a few-shot learning (FSL) approach that promotes rapid classification. In this approach, a model trained on several base classes is finetuned to recognize a novel class using a small number (n = 5, 25, 50, and 75) of randomly selected novel class measurements/shots. The used dataset comprises MOX sensor measurements of four different juices (apple, orange, blackcurrant and multivitamin) and air, collected over 10-minute phases using a pulse heater signal. While a high average accuracy of 82.46 is obtained for 5-class classification using 75 shots, the model’s performance depends on the juice type. One-shot validation showed that not all measurements within a phase are representative, forcing careful shot selection to achieve a high classification accuracy. Error analysis revealed contamination of some measurements by the previously measured juice, a characteristic of MOX sensor data that is often overlooked and equivalent to mislabelling. Three strategies are adopted to overcome this: (E1) and (E2) fine-tune after dropping initial/final measurements and the first half of each phase, respectively, (E3) pretrained with data from the second half of each phase. Results show that each of the strategies performs best for a specific number of shots. E3 results in the highest performance for 5-shot learning (accuracy 63.69), whereas E2 yields best results for 25-/50-shot learning (accuracies 79/87.1) and E3 predicts best for 75-shot learning (accuracy 88.6). Error analysis also showed that for all strategies more than 50% of air misclassifications resulted from contamination, but E2 was affected the least. This work demonstrates how strongly data quality can affect prediction performance especially for FSL methods and that a data-centric approach can improve results.

Despite the advances in natural language processing (NLP), neural networks and deep learning in recent years, biometric privacy is still an issue with daily used smart mobile devices. Despite the functions implemented in the devices where users can manually register biometrics to train the device, anyone can emulate the same biometrics registered used nowadays. Here, we introduce a long-term wearable, wireless stethoscope device that implements heart sound recognition as a biometric during continuous cardiac monitoring leveraging a series of feature extraction through signal processing to overcome these challenges. The wireless, skin-mounted wearable stethoscope that integrates soft electronics for continuous and remote cardiac auscultation and more secure authorization for mobile devices generates a stronger, ambulatory security system. Furthermore, the Band-Aid-like stethoscope leverages ultra-thin, highly soft, biocompatible elastomers for sensitive skin and is lightweight to give users minimum burden on the chest. Collectively, the presented study details the proposed mechanics of the envisioned design and its mechanics.

This Paper describes for the first time incorporation of ZnO nanorods on porous graphitic carbon nitride (g-C₃N₄) matrix for the efficient hydrogen gas sensing. Researchers have explored g-C₃N₄ for various applications such as a catalyst for water splitting, photoluminescence, storage because of its relatively low cost, easy synthesis, and ready availability. For the synthesis of g-C₃N₄, urea was used as a precursor at 550-600 ^°C and at ambient pressure under a muffle furnace without add-on support. ZnO nanorods were synthesis by the direct thermal pyrolysis of Zinc acetate hexahydrate. g-C₃N₄ and ZnO/g-C₃N₄ were characterized by X-ray diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), scanning electron microscope (SEM), transmission electron microscopy (TEM), Photoluminescence spectroscopy (PL), Fourier transform infrared spectroscopy (FTIR), and energy-dispersive X-ray spectroscopy (EDS). ZnO/g-C₃N₄ film was deposited on an interdigitated carbon electrode. Significant change in the resistance was observed during the presence and absence of hydrogen gas. The response and recovery time of the sensor were calculated for the sensor at various different concentrations of analyte gases.

Recently, significant efforts have been made to apply autonomy to off-road vehicles and machinery. For this, a LiDAR sensor has played an important role in a variety of related applications due to its merits of providing high-resolution and accurate information about the environment. However, its detection performance significantly degrades under dusty conditions. Specifically, measured data can be corrupted due to light backscattering from the dust particles, and thus it makes the whole perception of the vehicles prone to failure.

To deal with this problem, we designed a de-dust filter using a LIOR (Low-Intensity Outlier Removal) filtering technique that offers a viable solution to eliminate dust particles from measurement data. The proposed method employs a two-step filtering procedure. The first step is based on the fact that dust particles have a lower intensity than other non-dust objects. A threshold intensity was identified by analyzing the gathered data, which can be used to distinguish dust from non-dust objects. Then, points with intensity values below the threshold were eliminated through this filtering process. As a second step, a statistical outlier removal filter was applied to the points identified as outliers in the previous step in order to preserve non-dust object points that had low intensity but were incorrectly classified as dust.

Experimental results confirm that the proposed method is robust to dust particles by successfully removing them from the measured point cloud with good filtering accuracy while maintaining rich information about the environment. Therefore, this method can be applied to LiDAR sensors mounted on vehicles in various industrial fields with dust exposure, such as construction, mining, and agriculture.

Colorectal cancer is among the top contributors to cancer death, but that is only due to the lack of efficient screening methods. Specific metabolites in urine have been identified as a factor in cancer detection, but a laboratory NMR device is required to identify these metabolites; however, the cost of these devices starts at 25 thousand dollars and requires trained professionals to operate. To increase the accessibility of colorectal cancer screening, a solution was proposed that the absorbance of metabolite-specific assays is to be measured using a portable and inexpensive metabolic biosensor. The first step was to create low-cost quantitative color-based metabolite assays that use redox-sensitive dye or colorimetric reactions that change color intensity when reacting with specific concentrations of these metabolites in urine. To complete the experiments, a device that utilizes the TCS34725 color sensor was created. The sensor is placed 1cm above a microfluidic cartridge with a channel that can hold 100microliter of liquid, and a diffused LED light source is placed 1cm under the cartridge to provide light to the sample liquid in the cartridge channel. All of this is then placed inside of a black box environment to complete the setup. A laboratory microplate reader is used for reference on the quantitative color-based Creatinine assay, and a linear trend was observed with an R2 of 0.999. The sensor device observed a polynomial curve due to light saturation at higher concentrations, but an R2 of 0.992 was obtained. In conclusion, the device can distinguish between the different concentrations of metabolites within samples, thus creating the possibility of an affordable and easy-to-use biosensor platform that can benefit the population within rural and inaccessible communities. In the Future, this portable metabolic biosensor system has the potential to be modified such that screening for other diseases can be done.

The spaceborne lidar dataset from Ice, Cloud, and Land Elevation Satellite (ICESat-2) provides highly accurate measurements of heights for the Earth's surface which helps in terrain analysis, visualization, and decision making for many applications. TanDEM-X 90 (90m) and CartoDEM V3R1 (30m) elevation are among the high-quality openly accessible DEM datasets for the plain regions in India. These two DEMs are validated against the ICESat-2 elevation datasets for the relatively plain areas of Ratlam City and its surroundings. The mean error (ME), mean absolute error (MAE), and root mean square error (RMSE) of TanDEM-X 90 DEM are 1.49m, 1.62m, and 0.21m respectively. The computed ME, MAE, and RMSE for CartoDEM V3R1 are 3.23m, 3.28m, and 0.36m respectively. The statistical results reveal that TanDEM-X 90 performs best in plain areas than CartoDEMV3R1. The study further indicates that these DEMs and spaceborne LiDAR datasets can be useful for planning various works requiring height as an important parameter such as the layout of pipelines or cut and fill calculations for various construction activities. The TanDEM-X 90 can assist planners in quick assessments for the terrain for infrastructural developments, which otherwise need time-consuming traditional surveys using a theodolite or a total station.

Urban monitoring using remote sensing is the most reliable and cost-effective method that provides high accuracy and multi-temporal data for studying urban expansion in horizontal and vertical dimensions. Vertical monitoring of urban areas includes mapping compactness, population growth, and study of urban surface geometry which plays an essential role in the applications of urban heat islands, generation of urban canopy layer, etc. The presented study uses the Ice, cloud, and land elevation satellite-2 (ICESat-2) ATL 03 photon data for building height estimation for a sample of 30 buildings in three experimental sites. The heights computed from the ICESat-2 profile were compared with google images of respective buildings for the accuracy assessment. The results when compared to the ICESat-2 reference data give an RMSE of 2.04 m. Another popular way to map the vertical dimension of terrain in urban areas, that are globally used by policymakers for resource management, planning, and maintenance is the Digital Elevation Model (DEM). It can be generated using various remote sensing techniques but the usage of active remote sensing procedures has an advantage over passive methods due to their capability to function both day and night irrespective of weather. Thus, the study further aims to assess the openly accessible DEM products available from TanDEM-X, which is a German Earth observation satellite that uses InSAR (Synthetic Aperture Radar Interferometry) technique, and Cartosat-1, an optical stereo acquisition satellite launched by the Indian Space Research Organization (ISRO). Taking ICESat-2 (ATL-08) Elevation data as reference, the accuracy of two study sites was checked by statistical measures such as Mean error (ME), Mean absolute error (MAE), Root mean square error (RMSE). In the urban area of Greater Hyderabad Municipal Corporation (GHMC), an RMSE of 5.29m and 7.48m were observed for TanDEM-X 90 and CartoDEM V3 R1 respectively, while the same showed an RMSE of 5.15 and 5.48 in the Bellampalli Mandal rural site respectively. The DEMs exhibited better results for the Bellampalli Mandal rural area of Telangana State as the built-up is sparse and the terrain is mostly flat as compared to the GHMC site. The results show that the accuracy of TanDEM-X is better as compared to the CartoDEM V3 R1. These results can assist the decision-makers and planners in choosing suitable DEMs for planning and management purposes for smart cities as well as rural settlements.

Histamine is a well-known biogenic amine, which is often contained in some fermented foods and drinks. High volumes of histamine contaminated food intake can lead to food poisoning and serious allergic reaction to the human body. In addition, the USA food and drug administration (FDA) set a guidance level and also informed, that higher histamine concentrations can cause histamine poisoning when the concentration level exceeds 200 ppm. In this condition, to ensure the food quality is highly necessary and important to detect and quantification of histamine levels from the food samples. In the present work, we have constructed a poly(azure A) (PAA) film manganese-hexacyanoferrate (MnHCF) complex modified screen-printed electrodes for rapid and online histamine analysis. The proposed sensor is used for histamine oxidation by electrochemical techniques such as cyclic voltammetry and chronoamperometry. The electrochemical techniques were carried out with histamine in an optimal condition such as supporting electrolyte, pH, working potential window, and scan rate. The PAA- MnHCF complex modified electrode showed oxidation potential of histamine at 0.9 V in phosphate buffer solution (0.1 M PBS, pH 7.4) at the scan rate of 50 mV/s. The bare screen-printed electrode showed oxidation potential of histamine at 1.1 V with a smaller current response at the same experimental condition. The interference study was performed by chronoamperometry, and the selected interferences (glucose, l-cystine, and putrescine) were tested with PAA- MnHCF complex modified electrodes. Based on the obtained results, it was found that the developed modified electrodes offer accuracy, fast analysis, selectivity, and reproducibility towards histamine analysis.