Polyimide (PI) and polydimethylsiloxane (PDMS) are widely used materials in biomedical sensor development. The hydrophobic property of PDMS makes it difficult to bind PDMS with other sensor materials. This paper employs chemical functionalization of the PDMS and PI surfaces via epoxy-thiol click chemistry to achieve irreversible bonding. To demonstrate the importance of strong bonding, a wireless pressure sensor is developed. The sensor masks are directly printed on the copper-coated polyimide sheets using a LaserJet printer. A wet etching technique is used to etch the sensor patterns. The plasma treatment is performed to achieve the hydroxylation on the surface of PDMS and patterned polyimide sheet. To chemically functionalize the surfaces, plasma-treated prefabricated PDMS sheets and electrode-patterned PI sheets were immersed in 2% v/v (3-glycidyloxypropyl) trimethoxysilane (GPTMS, 98%) in methanol and 2% v/v (3-mercaptopropyl) trimethoxysilane (MPTMS, 95%) in methanol, respectively. The final assembly was created after chemical treatment, and the sensor was then placed under pressure for 24 hours at room temperature. The bonding strength between the PDMS and PI is tested using a peel-off test method where adhesive and cohesive failures are observed. The sensor is tested for cyclic pressures over 1 million cycles and no bonding failure is observed. This irreversible bonding can improve sensor integrity, reliability, and stability, especially for biomedical applications.

Air temperature is an important meteorological parameter and is used for numerous purposes. Air temperature is usually observed using a radiation shield with ventilation, to obtain proper measurements by providing shade from direct solar radiation and increasing the heat exchange between the sensor and atmosphere. In rural areas, such auxiliary equipment is not available and it is still a challenge to obtain the air temperature accurately without aspiration. In this study, we describe a novel qualified CMOS-MEMS low-cost sensor, dubbed Digital TMOS, for remote temperature sensing of air temperature. The novel key ideas of this study are (i) the use of the Digital-TMOS, (ii) a narrow optical band pass filter (4.26um+/-90nm) corresponding to the CO2 carbon dioxide absorption band; (iii) measuring simultaneously the weather parameters.

Analysing the morphological features of the drainage basin helps in understanding its hydrological characteristics and the association of water, with soil, topography, and vegetation of the catchment. Morphometric analysis reveals the linear, areal, and relief aspects of a drainage basin. In this study, morphometric analysis has been performed using geospatial techniques to evaluate the hydrological characteristics of the Suswa River basin. The SRTM (Shutter Radar Topography Mission) DEM at 30m resolution has been used to delineate the basin and drainage network in the Arc GIS Software with the help of Spatial Analysis Tools. From this research, we have derived that the basin is having sub-dendritic to dendritic drainage pattern, and the average drainage density of the basin is 2.84 km/km². The elongation ratio of the Suswa River basin is 0.46 which implies that the basin is elongated in shape with moderate relief. This study concludes that morphometric analysis based on GIS & remote sensing techniques is a competent tool for hydrological studies. The present study would be beneficial to various managers and decision-makers for the organization working on watershed management and sustainable natural resources management.

An FPGA based hardware accelerator for bio-signal digital filtering in a neonatal heart rate monitoring system employing electric potential sensors (EPS) is presented. These active sensors provide a non-contact alternative to traditional ECG electrodes, but are more susceptible to noise such as power line interference and motion artefacts, therefore additional filtering capacity is required.

The proposed system contains a single hardware filter stage for antialiasing, with the remaining digital signal processing required to provide a clinical standard ECG performed on an FPGA (National Instruments myRIO 1900). This is compared with a previous microprocessor version (Raspberry Pi 3, BCM2837 processor) containing a dual hardware/software filtering scheme, with the aim of simplifying the analog front end and allowing for reconfigurable filtering in the digital domain. A custom neonate phantom was employed to emulate real world conditions and ambient noise.

The developed FPGA system was shown to have a signal quality comparable with the microprocessor implementation, with an average signal to noise ratio loss of 2%. A 12 dB increase in attenuation of the predominant 50 Hz noise and a 90% reduction in energy per sample filtered was shown compared to the microprocessor version, indicating both efficiency and filter effectiveness gains. The proposed system accurately calculated the heart rate of a simulated neonatal ECG signal, with lower heart rate variation than the microprocessor system. Finally, the phantom was used to broadcast data from the preterm infant cardio-respiratory signals database (PICSDB) and the FPGA filtering scheme was shown to remove the majority of the ambient 50 Hz noise with an average reduction of 30 db, and provide a clean ECG signal.

These results demonstrate that FPGA filtered EPS ECGs have comparable signal quality to the combined HW/SW filtering implementation, with a reduction in complexity and power consumption.

The Fourier transform infrared (FTIR) spectroscopy is a non-invasive technique that is largely used for investigating different samples of biomedical interest. In particular, it is one of the most common and up-to-date techniques for studying lipidomics. Most cell membranes are made up of proteins and lipids (proteins 55%, lipids 42%, and carbohydrates 3%). Lipids are a primary class of biological molecules that play numerous vital roles in various processes inducing apoptosis, differentiation, chemotaxis, and other responses.

In the present work, we adopted FTIR spectroscopy for monitoring lipid extraction efficiency of different methods that have been used for extracting lipids from hepatocarcinoma cells (HepG2). The traditional Bligh and Dyer method, a gold standard for total lipid determination based on a mixture of chloroform/methanol (1:2, v/v) followed by the addition of water to create a biphasic system, has been compared with three methods involving different extraction solvents: Bume modified method, a new rapid and simple chloroform-free method based on butanol/methanol mixture (BUME); a simple and robust two steps method with the addition of KOH for the detection of sphingosine-1-phosphate (S1P) and related sphingolipids [4] and finally a method based on a mixture of isopropanol:water: ethyl acetate (30:10:60, v/v/v) for analysis of broad categories of sphingolipids.

Infrared spectra have been obtained in the 4000-600 cm^-1 wavenumber range. The spectra acquired from samples obtained with the cited methods showed the contributions of different functional groups. A qualitative comparison among them indicated that all the spectra exhibited similar lipid species profiles. The ratio values estimated using the absorbance of selected bands related to different cell lipid constituents have been evaluated for a quantitative comparison of the efficiency of the different extraction methods.

Dichloromethane (DCM) or methyl chloride is a volatile organic compounds (VOC) infamous for its carcinogenic properties. The gas mainly used in industrial solvents is found to cause lung and liver cancers in animal experiments, whereas they are proven to cause cancers of the brain, liver, and a few types of blood cancers including Non-Hodgkin’s lymphoma in humans. The deteriorative effects are found to exposure as low as 200 ppm for a few continuous hours, whereas exposure above 1000 ppm is found to cause cancers in mammals. Among the various techniques available today for the detection of gases in atmospheric air the SAW (Surface Acoustic Wave) sensors are highly accurate. SAW offers higher sensitivity, simplicity of fabrication, rapid response time, room temperature operation, and/or the possibility of wireless operation at low costs.

In this paper, FEM design and analysis of the Surface Acoustic Wave technology based on love waves was used for detecting volatile organic gases. The 3D gas sensor was composed of interdigitated transducers modeled on a piezoelectric substrate and covered by a guiding layer of SiO₂, and on top of that was a film of polyisobutylene (PIB) that served as the sensing layer. The material used for the piezoelectric substrate was 64⁰YZ-cut Lithium Niobate (LiNbO₃) for love wave generation, and the lightweight electrodes were made of Aluminium (Al). Analytical simulations were conducted using COMSOL Multiphysics 6.0 software based on the Finite Element Method (FEM).

The impact of mass loading on the sensing layer was utilized to detect volatile organic gases. The resonant frequency of the SAW device was determined, and simulations were performed by exposing the sensor to dichloromethane gas at concentrations ranging from 0 to 1000 ppm. This work also described the analysis of various parameters of the SAW sensor such as the quality factor, coupling coefficient, equivalent circuit components, S parameter, and admittance. The simulation result exhibited a linear frequency shift of the sensor with dichloromethane gas concentration and explained the behavior of the sensor through its equivalent circuit.

Laser scanners recording a huge number of data points from different surfaces are widely used to capture the exact geometry of objects. These large amounts of data require intelligent solutions to be examined and processed efficiently. Deep learning based approaches have found their way into many data analytic applications to process such large datasets, categorize them, or even determine the most informative portion of the data. This research focuses on 3D deep learning techniques directly applied to point clouds to determine the most important features of a 3D shape. More specifically this research adopts Pointnet as a backbone architecture for feature extraction from 3D point clouds and computes a Gradient-Based Class Activation Mapping on each object to create a 3D importance map for each object. Experiments confirm the success of the proposed approach in determination of important features of 3D objects as compared with the ground truth.

The primary function of sugar is to provide the energy that our body needs, in addition to contributing to the performance of different organs such as the brain and muscles. However, its excessive consumption can lead to serious health problems including body weight gain as well as metabolic and cardiovascular disorders. Therefore, monitoring sugar content both in physiological conditions and in the food industry is of critical importance. In this work, we report the fabrication and testing of a fiber optics sensor based on multimodal interference (MMI) that is capable of measuring sugar concentration in aqueous solutions. The sensor has a simple singlemode-multimode-singlemode (SMS) architecture, consisting of a segment of multimode fiber spliced between two single-mode fibers. The sensor's operating mechanism is based on the spectral shift due to changes in the effective refractive index (RI) of the solution under test. Thus, when the sensor is immersed in binary sugar-water solutions with different concentrations, the spectral response of the sensor shifts according to the RI of the mixture. The optical sensor was tested with fructose and sucrose, diluted in distilled water. The range of concentrations in which the sensor was tested was from 0% to 18.5%. Preliminary results indicate that the sensor exhibit a linear response with a sensitivity around 0.17524nm/% and 0.16321nm/% for sucrose and fructose, respectively. The optical sensor presented has the advantage of simple construction, low cost, linear response, it does not require additional processes or coatings on the optical fiber, and it has the capability for performing real-time measurements, which makes it suitable for quality control applications.

Lateral flow assays (LFAs; aka. Rapid Tests) are inexpensive paper-based devices for rapid and specific detection of analyte of interest (e.g., COVID virus) in fluidic samples. Areas of application of LFAs cover a broad spectrum, spanning from agriculture, to food/water safety, to point-of-care medical testing, and most recently, detection of COVID-19 infection. While these low-cost and rapid tests are specific to the target analyte, their sensitivity and limit of detection is far inferior to their laboratory based counterparts. In addition, rapid tests normally cannot quantify the concentration of target analyte and only provide qualitative/binary detection. We have developed a low-cost, end-user sensing platform that significantly improves the sensitivity of the rapid test. The developed platform is based on Arduino and utilizes a low-cost far infrared, single-element detector to offer sensitive and semi-quantitative results from commercially-available rapid tests. The developed sensing paradigm is based on radiometric detection of photothermal responses of rapid tests in the frequency domain when exposed to modulated laser excitation. As a proof of principle, we studied commercially-available rapid tests for detection of THC (the principal psychoactive constituent of cannabis) in oral fluid with different concentrations of control positive solutions and subsequently read them with the developed sensor. Results suggest that the developed end-user sensor is not only able to improve the detection limit of the rapid test by approximately an order of magnitude (from 25ng/ml to 5ng/ml), but also offer the ability to obtain semi-quantitative insight into concentration of THC in the fluidic samples (<5ng/mL, 5-10ng/mL, 10-25ng/mL, >25ng/mL vs <25ng/mL,>25ng/mL).

Temperature is a critical factor for performance and operation of photovoltaic (PV) generators, whose efficiency and electrical generation decreases as the temperature rises. For this reason, it is essential to sensor the PV modules in order to continuously measure and track their operating temperature. This paper presents a network of digital temperature sensors (DS18B20) and a set of analog sensors (PT-100) for a 1.1 kW polycrystalline PV array. The physical layout of the sensors on the modules is different depending on the nature of sensor for comparison purposes. These sensors are described in terms of their implementation and configuration, as well as the advantages and disadvantages of each installation. In addition, a model that estimates cell temperature from ambient temperature and incident solar irradiance is incorporated. Regarding data acquisition, an industrial controller and a remote input/output unit gather analog measurements, whereas a low-cost Arduino board retrieves data from the digital sensors network. Both sensor signals are stored in a database, so experimental measurements and estimated data are visualised simultaneously using a web-based IoT software (Grafana) in real time. Finally, results under real operating conditions are reported and the data are analysed, proving the suitability between each sensor type and the model.