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A paper based chemiresistor has been fabricated to selectively sense ethanol in human breath. The chemiresistor was composed of a sensing mixture of multiwall-carbon-nano-tubes (MWCNTs), poly (diallyl-dimethyl-ammonium chloride) (PDDA), alcohol dehydrogenase (ADH), and coenzyme (NADH). The aluminum electrode was deposited on the paper surface, followed by drop-casting of the aforementioned sensing mixture. The resistance of the sensors was measured by exposing the same in gas-vapor mixture as well as the sample solution. The surface-modified MWCNTs specifically broke down ethanol present in the gas-vapor mixture or in a solution to generate a quantitative electronic response proportional to the ethanol concentration. Subsequently, the interference of other volatile organic materials was also tested to prove the selectivity and sensitivity of the sensor towards ethanol in the presence of different volatile organic compounds (VOCs). The variation of the resistance during the interaction between sensor and ethanol was also characterized by measuring the surface potential of the channel material under ethanol exposure using Kelvin probe force microscopy (KPFM). The sensor was integrated with a voltage divider circuit, a display, and a microcontroller unit to make a proof-of-concept prototype for the point-of-care (POC) detection of ethanol in human breath.

Sorting and measuring blood by cell type is extremely valuable clinically and provides physicians with key information for diagnosing many different disease states including: leukemia, autoimmune disorders, bacterial infections, etc. Despite the value, the present methods are unnecessarily costly and inhibitive particularly in resource poor settings, as they require multiple steps of reagent and/or dye additions and subsequent rinsing followed by manual counting using a hemocytometer, or they require a bulky, expensive equipment such as a flow cytometer. While direct on-paper imaging has been considered challenging, paper substrate offers a strong potential to simplify such reagent/dye addition and rinsing. In this work, three-layer paper-based device is developed to automate such reagent/dye addition and rinsing via capillary action, as well as separating white blood cells (WBCs) from whole blood samples. Direct onpaper imaging is demonstrated using a commercial microscope attachment to a smartphone coupled with a blue LED and 500 nm long pass optical filter. Image analysis is accomplished using an original MATLAB code, to evaluate the total WBC count, as well as differential WBC count, i.e., granulocytes (primarily neutrophils) vs. agranulocytes (primarily lymphocytes). Only a finger-prick of whole blood is required for this assay. The total assay time from finger-prick to data collection is under five minutes. Comparison with a hemocytometry-based manual counting corroborates the accuracy and effectiveness of the proposed method. This approach could be potentially used to help make blood cell counting technologies more readily available, especially in resource poor, point-of-care settings.

Wearable electrochemical sensors have attracted tremendous attention in recent years. Here, an integrated three-dimensional paper-based microfluidic electrochemical device (3D-PMED) with flexible wireless circuits is demonstrated for real-time monitoring of sweat potassium. The paper-based microfluidic pad is fabricated by printing wax patterns on cellulose paper and then folding the pre-patterned paper four times to form a five-layer stacked structure: sweat collector, vertical channel, transverse channel, electrode layer, and sweat evaporator. Also, we have discussed the different properties with three swear collector types. The sweat monitoring device is realized by integrating a screen-printed potassium ion-selective sensor on the PET substrate with the fabricated paper microfluidic pad. The sweat flow in 3D-PMED is modeled with red ink to generate the flow pathway of sweat and the capability of sweat storage. The detection range of the potassium ion-selective sensor is 1–32 mM, and the electrode response potential is 61.79 mV per decade of K+ concentration. The device has a small size that is suitable for everywhere on the body, and also have shown good selectivity for both anion and cation, and a stable performance within 1 week. This 3D-PMED has provided a simple, low-cost way for real-time dynamic sweat potassium monitoring when exercising.

The excellent characteristics of nanocellulose fiber-based papers have made them one of the most attractive and innovative materials for the development of electrochemical paper-based analytical devices (ePADs). Here, the authors describe a new ePAD based on cellulose nanofibers (CNs) for the determination of glucose concentration from whole blood samples. Cellulose acetate (CA) nanofibers were prepared by the electrospinning method. Then, the paper layer regenerated to cellulose by deacetylation in alkaline solution. To obtain a smooth and continuous CNs layer, it was treated with trimethyl chitosan (TMC). The formation of CA layer, its alkaline regeneration and TMC modification was confirmed by atomic force microscopy, scanning electron microscopy, Fourier-transform infrared spectroscopy, and energy dispersiveX-ray. Screen-printed three electrodes (SPEs) were fabricated by sputtering a thick layer of gold (Au) on the TMC/CNs substrate. Subsequently, reduced graphene oxide (rGO) was used to modify the surface of the working electrode. The fabrication steps of rGO-Au-SPE were characterized using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and scanning electron microscop. The results showed that design, construction and modification process was well achieved. Next, glucose oxidase is successfully immobilized onto the regenerated CNs layer which has been previously located in the sensing area of the rGO-Au-SPEs. The ePAD glucose assay exhibited high sensitivity of $9.9\times 10^{-4}~\text{K}\Omega ^{-1} \cdot \text{mM} ^{-1}$ to glucose in the range 3.3 – 27.7 mM ( $\text{R}^{2} =0.99$ ) with a detection limit of 0.1 mM, high reproducibility (RSD = 0.57-1.59%) and outstanding selectivity as well as stability. This biosensing motif represents a general platform for the analysis of other biomarkers.

Pencil (graphite) on paper (PoP) electronics has gained significant attention due to its low cost and versatility in developing customizable disposable electronics. Despite the convenience of the PoP methodology in device fabrication its application in developing electronics is limited due to the absence of a bandgap in graphite. The present work is the first demonstration of the direct writing of 2D MoS2 trace on paper which was utilized as a pressure and strain sensor. Detailed characterization studies revealed the formation of rhombohedral and hexagonal phased MoS2 micro-flowers. XPS studies revealed the formation of mixed 1T (metallic) and 2H (semiconducting) phases of MoS2. With the increase in the number of traces of 2D MoS2 on paper, the resistance of the channel decreased owing to the increase in conducting channels. The as-fabricated pressure sensor displayed a sensitivity of 2.21 kPa−1 and strain sensor displayed a GF of 13. Also, the sensor displayed an excellent durability for 1600 cycles. The superior response of the sensor can be attributed to the synergistic properties of micro fibrous paper substrate and piezo-dependent electron transfer of 2D-MoS2. The presented technique enables “make it yourself” multifunctional device that have numerous potential applications in the field of consumer electronics and personal healthcare monitoring.

This paper describes a simple and low-cost test strip for on-site monitoring of methiocarb insecticide. Hydrophilic filter paper soaked in agarose solution was bounded by hydrophobic solid wax and then was coated with unmodified gold nanoparticles (AuNPs). AuNPs aggregation caused by methiocarb served as colorimetric response. We demonstrated detection capability of methiocarb both in solution- and substrate-based sensor. A good linear relationship was obtained between the colorimetric response and the concentration of methiocarb ranging from 20 to 80 ng mL $^{-1}$ with a limit of detection of 5 ng mL $^{-1}$ . Excellent selectivity toward methiocarb was observed among various pesticides and cationic and anionic ions. Furthermore, the proposed assay could successfully detect methiocarb in paddy and tap water with satisfactory results. It would be of interest for use as rapid, portable, and technically simple test strip in water quality monitoring.

Cardiac biomarker detection helps in early diagnosis of cardiac syndromes. However, a rapid, cost-effective, reliable, and easy-to-use sensor is still unavailable. Herein, we report a disposable, label free, impedance-based biosensor for the detection of two common cardiac biomarkers, Myoglobin (Myo), and Myeloperoxidase (MPO). The sensor is fabricated on Whatman filter paper coated with a conducting polymer, polyaniline (PAni). The PAni-coated paper is functionalized with glutaraldehyde, a homo-bi functional crosslinker, prior to the covalent attachment of antibodies specific to each biomarker protein. Non-specific active regions of the sensor are blocked by treatment with bovine serum albumin. Conductive silver paste is used to define electrodes for AC impedance measurement on the PAni coated filter paper. Binding of the target protein (Myo or MPO) to the specific antibody changes the impedance between the electrodes; this is detected using AC impedance spectroscopy. The impedance is measured using a frequency response analyzer with an AC excitation voltage of 100 mV in the frequency range of 100 Hz–100 kHz. We can successfully detect Myo and MPO in buffer solution within the concentration range of 100 ng/mL– $50~\mu \text{g}$ /mL. Detection limit obtained for these cardiac biomarkers spiked in human serum is $1~\mu \text{g}$ /mL. Further blocking using human serum improves the lower detection limit to 500 ng/mL. The developed biosensor uses inexpensive materials and fabrication techniques, detects cardiac biomarkers in clinically relevant concentrations rapidly (i.e., within 20 mins), and can be disposed in an environment friendly manner, thus making the sensor suitable for diagnostic applications.

This report demonstrates the fabrication of SnS/paper based low-cost and flexible pressure sensor, employing a facile solvothermal synthesis followed by vacuum filtration, for artificial electronic skin and touchpad applications. The fabricated pressure sensor displayed a high sensitivity of 3.18 kPa $^{- {\sf 1}}$ and a response time $\sim 300$ msec. Inspired by the high value of sensitivity and an appreciable flexibility, a large-area pressure sensor array ( ${\sf 3} \times {\sf 3}$ ) was fabricated and integrated onto a human arm for demonstrating real-time applications. The fabricated sensor array was able to consistently detect and track random localized points of contact, the shape of an unknown object placed on top of it, and an index finger trajectory along the surface with high accuracy and precision. The fabricated device showed a negligible change in the performance over 500 bending cycles which indicates the highly robust nature of the device. Successful fabrication of such a cost-effective large-area pressure sensor array is a major step in flexible electronics, and holds immense potential in artificial electronic skin, security, personal healthcare applications.

Conductivity is an important parameter which has definite correlation with the amount of moisture present within the transformer insulation. However, in existing methods conductivity is usually estimated from time domain (PDC) measurement, which is inadvertently noise prone and time consuming. Considering this issue, in this article presents a novel technique for conductivity estimation for moisture sensing of oil-paper insulation using frequency domain spectroscopy measurement (FDS). To this end, FDS measurement of oil-paper insulation was performed from 50mHz to 1kHz to obtain the imaginary complex permittivity $\varepsilon ''$ ( $\omega$ ), which is a function of conductivity ( $\sigma$ ) as well as imaginary complex susceptibility $\chi ''$ ( $\omega$ ). From the knowledge of frequency variation of both $\sigma $ and $\chi ''$ ( $\omega$ ), we developed an electrical equivalent model of the insulation. The equivalent model parameters were optimized until $\varepsilon ''$ ( $\omega$ ) computed from the proposed model and that obtained from original FDS measurement were almost identical. The conductivity values obtained from the equivalent model showed good agreement when compared with PDC measurement. To validate the practicability of the proposed method, experiments were conducted on various test samples and also on a few real life transformers. Most importantly, the measurement time was found to be reduced by 78.5%, enabling diagnosis of transformer insulation at a significantly reduced time.

The ratio of protein to creatinine (PCR) in urine is a measure of kidney health of a human body. We report the development of a point-of-care (POC) kidney function test (KFT) kit, which is composed of a paper-based PCR sensor, an opto-electrochemical measurement unit, a display, and a facility to transfer data to a smartphone. For the total protein sensor, the pieces of filter papers were coated with a mixture of alcoholic bromophenol blue and citric buffer leading to a chrome-yellow coloration. While for the creatinine sensor the papers were coated with alkaline picrate solution leading to a lemon-yellow coloration of the surface. Dispensing protein (creatinine) solution of known concentration on the respective sensors led to the conversion of chrome-yellow to bluish-green coloration (lemon-yellow to orange coloration) for the total protein (creatinine) sensor. The intensity of such colorations varied with the concentration of the analytes in the solutions. The variations in the intensities of colors with analyte concentration were quantified by integrating a light emitting diode (LED) at one side of each sensor and a light intensity sensor (LIS) on the other side. The intensity of the transmitted rays, passing through the sensors, was found to vary the electrical output of the LIS monotonically with the loading of the total protein or creatinine in the analyte. This helped in obtaining the calibration plots for the PCR sensors. Further, the sensors were employed for the on-spot detection of the unknown PCR in the real human urine samples.

Slow or non-healing wounds in chronic diabetic foot diseases are often associated with co-morbid circumstances, inevitably leading to lower limb amputations. The wound bed in chronic foot ulcers are prone to bacterial infections, with Staphylococcus aureus ( S. aureus ) acting as the major culprit in resisting the normal wound healing process. This study reports the development of a flexible screen-printed wound dressing based on a composite of Zeolitic Imidazolate Framework (ZIF 67) and carbon nitride (C3 N4) conjugated with S. aureus probe DNA for the detection of its corresponding target DNA. The nano-wound dressing was assessed using Electrochemical Impedance Spectroscopy (EIS) for the quantification of S. aureus target DNA within 1 fM to $10~\mu \text{M}$ . A direct proportionality between the charge transfer resistance ( $\text{R}_{\text {ct}}$ ) and S. aureus target DNA concentration. Circuit simulations detailing the quantifications of interfacial parameters, involved in the recognition event, were performed by curve fitting of Nyquist spectra in order to explain the variation of $\text{R}_{\text {ct}}$ with target DNA concentrations. The Limit of Detection (LoD), sensitivity and response time of the developed wound dressing-based sensor were found to be 0.46 fM, 0.25 $\text{k}\Omega $ /fM/mm2 and ~ 10 s respectively at 21.04 kHz, while being selective only towards S. aureus target DNA. The sensor was further validated in human serum samples and was shown to possess a shelf life of 35 days, thereby exhibiting tremendous potential to be implemented in connected health applications.

Biological compounds like Dopamine (D), Xanthine (X), Ascorbic acid (AA) and Uric acid (UA) play a vital role in food, clinical and human metabolism. Since these compounds are present in biological fluids and have close standard potentials values, it is imperative to choose a method to sense these fluids without any electrochemical interference. Toray paper, with a porous gas diffusion layer imbibed with denser carbon fibers, is an excellent option that can be utilized as a working electrode for such sensing applications. Herein, the electroactivity of these compounds was tested using cyclic voltammetry and square wave voltammetry without any interference. The linear ranges for the compounds (D, X, AA, UA) are 10- $1000~\mu \text{M}$ , 7- $300~\mu \text{M}$ , 100- $1000~\mu \text{M}$ and 30- $1000~\mu \text{M}$ respectively, while the limits of detection are $9.67~\mu \text{M}$ , $6.54~\mu \text{M}$ , $97.12~\mu \text{M}$ and $28.74~\mu \text{M}$ respectively with S/N ratio of 1.5. Finally, the electrode was tested with human serum samples for the identification of D, X, AA and UA manifesting exceptional reproducibility.

There is an increasing demand for measuring respiratory rate (RR) to monitor the progression of clinical events such as cardiac arrest or during the admission to the intensive care unit (ICU). Current methods of respiration monitoring are complex, invasive, expensive and therefore require alternative techniques. In this paper, we utilized wearable and flexible technology to develop humidity sensors for long-term continuous monitoring of respiration. Using manual drawing and printing techniques, the sensors were made based on graphite trace and silver nanoparticles as two different sensing materials. The surface morphologies of the devices were characterized by Scanning Electron Microscope (SEM) to show the construction of the sensors. Additionally, the material characterization was performed to obtain the Raman Spectra. Paper was chosen as a flexible substrate owing to its biodegradability, porosity, and disposability. The humidity sensing characteristics of the sensors indicated the best sensitivity of 0.0564% for Graphite on Paper (GOP) sensor. Furthermore, the hygroscopic nature of the paper enables converting the humidity changes during inhalation and exhalation into electrical signals. The electrical signals are transmitted via an interface to a computer using a simple data-logging set-up. A set of normal, deep and apnea breathing conditions could be easily differentiated from these signals along with information on respiration rate and pattern. The functionality of sensors was also tested after light and rigorous exercises. The proposed sensor combines the advantages of being low cost, non-invasive, highly sensitive, stable and holds tremendous potential for wearable/flexible healthcare technology.

Contamination of water bodies due to the disposal of natural and anthropogenic pollutants is one of the major global concerns in the recent times. Regular on-spot monitoring of water quality has become mandatory wherein diverse classes of materials such as organic, inorganic, heavy metals, or biological wastes are detected employing standard protocols. However, most of the existing laboratory techniques are either costly and/or time consuming owing to the process involving the expert driven sophisticated analysis techniques. Herein, we report the development of three paper based colorimetric sensors for the on-spot quantitative detection of the levels of fluoride (F−), lead (Pb2+), and pH in drinking water. The variations in the color-intensities of the paper sensors with the variation in the concentration of fluoride (F−), lead (Pb2+), and pH were converted into electronic signals to enable the development of a point-of-care-testing (POCT) device. For this purpose, initially, the sensors were characterized and calibrated with known concentrations of contaminates before successful validations with real samples. Thereafter, the sensors were illuminated with a light emitting diode from one side while the transmitted rays passing through the sensor was captured by a light dependent resistor (LDR) from the other side. The variation in the color intensity of the paper-sensor with the contaminant loading was found linearly varying with the resistance of the LDR. The sensor was then translated into a specific, stable and user friendly POCT device, which enabled inexpensive spot detection of pollutants in drinking water.

Monitoring blood coagulation in response to an anticoagulant (heparin) and its reversal agent (protamine) is essential during and after surgery, especially with cardiopulmonary bypass. A current clinical standard is the use of activated clotting time, where the mechanical movement of a plunger through a whole blood-filled channel is monitored to evaluate the endpoint time of coagulation. As a rapid, simple, low-volume, and cost-effective alternative, we have developed a paper microfluidic assay and Raspberry Pi-based device with the aim of quantifying the extent of blood coagulation in response to varying doses of heparin and protamine. The flow rate of blood through the paper microfluidic channel is automatically monitored using the Python-coded edge detection algorithm. For each set of the assay, 8- $ \mu \text{L}$ of fresh human whole blood (untreated and undiluted) from human subjects is loaded onto each of eight sample pads, which have been preloaded with varying amounts of heparin or protamine. The total assay time is 3–5 min including the time for sample loading and incubation.

Wireless, passive, and cost-efficient monitoring of moisture within packaged food and medicine is important to ensure their safety and freshness. This paper presents paper-based single spiral inductance–capacitance (LC) sensors and a distance-insensitive readout system. The parasitic capacitance of the single-spiral coil is used to form an LC resonant circuit with its inductance. The printing paper is utilized as a humidity sensing material. A model for the LC sensor is analyzed and compared with HFSS simulation, showing the relative error within 6.3%. Nano-silver paste for the single-spiral coil was screen printed on the printing paper. The LC sensors with 4, 6, and 8-turns coils were characterized at relative humidity levels of 15%-90%RH by a readout coil at a fixed position. Comparison shows that the 8-turn sensor has the best linearity with respect to humidity. The sensitivity and response time were measured to be 120 kHz/%RH and 60 minutes, respectively, with the hysteresis errors less than 0.9%. During the practical monitoring, the tested signal frequency will have a shift if the coil’s relative position alters. To weaken the influence of the coil’s position, a readout coil, working at the quasi-resonant with the LC sensor was utilized to enhance the readout, resulting in more stable signal. According to the experimental results, the improved readout system can restrain the frequency shift within 1.1% at a readout distance from 2–50 mm when the ambient humidity is 60%RH.

Paper-based gas sensors represent an emerging important class of devices in flexible electronics. Colloidal nanocrystals unite large surface-to-volume ratio with excellent solution processability, offering avenues to high-performance paper-based gas sensors. One of the factors limiting the performance of paper-based gas sensors is the sphere-like morphology of semiconductor nanocrystals, making it difficult to construct a stable sensing network to ensure efficient carrier transport and mechanical robustness. Here we demonstrated sensitive and flexible gas sensor via the spray coating of PbS nanowires onto paper substrates at room temperature. The pencil-drawn graphite electrode was employed to simplify the sensor design and fabrication. Unlike the sphere-like PbS nanocrystals that usually assemble into compact thin-film solids, the PbS nanowires-on-paper sensor exhibits a porous network microstructure, which not only offers efficient pathway for gas adsorption and diffusion but also possesses inherent flexibility for superior mechanical bendability. The response of the PbS nanowires-on-paper sensor toward 50 ppm of NO2 at room temperature was 17.5, with the response and recovery time being 3 and 148 s, respectively. The sensor shows only a slight decrease in response (6% of the initial value) and identical temporal response when subject to 500 bending and unbending cycles. The competitive adsorption of NO2 with O2 on PbS surfaces is proposed as the sensing mechanism accounts for the high sensitivity and good reversibility at room temperature. Our results highlight the significance of the solution-processable nanowires as the ideal building blocks for the flexible paper-based gas sensors.

This paper focuses on fabricating cost effective and time-efficient bioelectrodes for enzymatic bio fuel cells by immobilizing the enzymes, glucose oxidase, and laccase on the surface of buckeye composite Bucky paper (BP) without any redox cofactors. Electrochemical studies including linear sweep voltammetry, cyclic voltammetry open circuit potential, and electrochemical impedance spectroscopy were carried out to evaluate the performance of the prepared electrodes. Maximum current density of 9.79 mA/cm2 at 0.4 V and 2 mA/cm2 at 0.3 V was observed for anode and cathode, respectively, at a scan rate of 10 mV/s at 40-mM glucose concentration. Morphological studies using scanning electron microscope revealed uniform dispersion of the enzymes on the surface of the BP electrodes facilitating the presence of enzyme active sites for catalytic reactions. The absence of redox cofactors in this approach dramatically reduces the cost and fabrication cycle time and also preserves their biocompatible nature. The fabricated BP-based bioelectrodes have shown excellent performance and further encourage toward future studies at the microfluidics level.

This paper presents the design of a wideband Hilbert fractal antenna for the purpose of detecting and classifying different common partial discharge (PD) types in an oil-paper insulated system. Three common types of PDs are considered for the multi-class classification problem, namely, PD from a sharp point to ground plane, surface discharge, and PD from a void in the insulation. The different PD types showed variation in the detected frequency contents. The collected samples were processed using pattern recognition techniques to identify their corresponding PD types. A recognition rate of 95% was achieved when K-nearest neighbors was used as the classifier.

A course on the emerging topic “Smart Sensors and Internet of Things (IoT)” has been designed, developed, and delivered from 21st March 2016 to 31st March 2016 in the Department of Electrical Engineering, Jamia Millia Islamia (Central University), New Delhi-110025, India. This course was organized under a program initiated by the Government of India, entitled “Global Initiative of Academic Networks” for higher education. The basic objective is to bring the internationally and nationally acclaimed scientists on a single platform to motivate young minds of India so that India’s scientific and technological capacity reach global excellence. The course was designed to develop the technical skills for the students, the researchers, the engineers, and the faculties, who desire to address some of the issues faced by the people around the world in the fields of smart sensors and IoTs with applications in smart homes, smart cities, smart grids, smart environment, smart transport, and so on. The assessment of the course by the participants shows extreme usefulness of it.

Seamless integration of functional nanomaterials on paper can boost the functionality of paper-based flexible sensors and electronics. In this paper, we report the systematic study of a low-cost hydrothermal process for growing zinc oxide nanowires (ZnO NWs) on cellulose paper substrates. To control the competition between homogeneous and heterogeneous nucleation and obtain ZnO NWs with superior morphology and high growth efficiency, we tune the critical growth parameters including temperature, assistant chemicals, and seeding layer. We experimentally confirm the necessity of ammonium hydroxide as assistant chemical in the growth solution, and achieve a condition that generates the highest weight growth percentage of 40% in the tested range. We quantify the weight growth percentage of ZnO NWs over growth time, measure the electrical resistance of the ZnO-NW paper, and eventually establish an experimental guideline for preparing ZnO-NW paper with desired electrical property. To demonstrate potential applications of the ZnO-NW paper, we use the obtained ZnO-NW paper for sensing of ultraviolet light and mechanical touch. This paper provides experimental insights into hydrothermal growth of ZnO NWs on paper, and could further inspire novel utilization of ZnO-NW paper for flexible sensors and electronics.

In the above paper [1] , some abbreviations of chemicals were misspelled and should be corrected as follows. 1. Caption of TABLE I, Page 6101.

Detection of bacterial colonies in water is a pressing issue in recent times owing to its implications on human health, leading to an urgent need for low cost, disposable sensors. In this work, we report a disposable impedimetric sensor for detection of total bacterial colonies present in water samples. The sensor consists of a polyaniline coated filter paper functionalized with a homo-bi functional crosslinker, glutaraldehyde. Prior to the functionalization step, two Ag electrodes are deposited on the filter paper using conductive silver paste. The aldehyde groups of glutaraldehyde bind with the amine functional groups present on bacterial cell wall resulting in a change in dielectric constant, and consequently, the capacitive effect between the two Ag electrodes. Also, bacterial metabolism affects the medium conductivity and in turn, the medium resistance changes. The combined effect of changes in capacitive effect as well as resistance is reflected as a change in AC impedance measured using a frequency response analyzer (FRA). E. coli and Pseudomonas aeruginosa bacteria are detected in PBS buffer up to ~ 500 cfu/mL. Testing is also carried out by spiking tap water and grey water with known concentrations of bacteria samples. The detection limits obtained are 500 – 1000 cfu/mL with a response time of ~ 20 minutes. The developed sensor provides rapid, moderately sensitive, cost-effective solution for detecting total bacterial load in water samples.

Malaysia, Sarawak state in particular produces superior quality black pepper compared to other producing countries. While Sarawak black pepper deserves higher price in the global market, from year 2015 onwards, its price has been plummeting due to global oversupply of pepper. Physical, chemical and biological profiling of Sarawak pepper is conducted to differentiate Sarawak pepper against adulteration and to justify its higher market price. However, the analysis of pepper was performed using time-consuming, non-portable, expensive and invasive laboratory equipment and methodologies. This paper reports an attempt to develop a black pepper geo-tracing system using metal oxide semiconductor (MOS) gas sensors array. The pneumatic system to contain and purge the sample volatile organic compounds (VOCs) is designed, simulated, fabricated and proven to be contain and purge sample VOCs effectively. A total of 200 black pepper samples from Malaysia and India are sampled using non-invasive method for the MOS gas sensors response and the sensors response are analyzed using Principal Component Analysis (PCA). The PCA scatter plots shows that Malaysia and India black pepper can be discriminated based on the MOS gas sensor response towards different VOCs composition in the black pepper samples. The PCA result are then used for geo-tracing of a total of 40 samples (20 from Malaysia; 20 from India), which shows an overall accuracy of 92.5%. The geo-tracing system developed performs rapidly with 260–600 s sampling cycle compared to gas chromatography-mass spectroscopy (GC-MS) equipment which requires at least 30 minutes sampling time, and is portable, cheaper and non-invasive.

We report the method for fabrication and validation of a standalone handheld optical biosensor for non-invasive monitoring of glucose using saliva. For this, a biodegradable, low-cost test strip for glucose was developed by co-immobilizing glucose oxidase enzyme and bromocresol purple pH sensitive dye. The enzymatic reaction produced pH change and, thus, a change in color of a filter paper strip, which was detected by a standalone electronic meter. The in-house developed meter consisted of an optically isolated region in the chassis for inserting the test strip along with the sample to avoid interferences from ambient wind and light. The developed biosensor had a wide detection range of detection between 32- and 516-mg/dL glucose concentration while the sensitivity of detection was 1.0 mg/dL/count at a limit of detection (LOD) of 32 mg/dL within a response time of 15 s. The device along with strips was validated on clinical samples while comparing salivary glucose level with that of blood glucose level using a commercial glucometer. A very good correlation was found for diabetic individuals (0.80), whereas it was less significant (0.53) for non-diabetic individuals. The device passed Clarke’s error grid analysis and hence should be medically acceptable. This simple and low-cost setup should be ideally suited for mass screening of diabetes, apart from being used as a self-monitoring device in a non-invasive and painless manner.

Fullerenes (C60) exhibits many advantageous properties but its poor solubility seriously restricts its applications. In this paper, C60 is dissolved in graphene oxide (GO) aqueous solution homogenously. This water-soluble composite material of GO/C60 is used for making a capacitive humidity sensor. This sensor shows a sensitivity of three times larger than that of the sensor based on C60 film. The possible reason is that C60 is uniformly adsorbed on the surface of GO, thus reducing the agglomeration of C60. This is supported by TEM, FTIR, and complex impedance test.

A novel ethanol gas sensor constructed with SnO2 nanoparticles doped with palladium and graphene is described. By incorporating 0.1 wt% graphene and 3 wt% PdCl2 into SnO2, the working temperature of the sensor can be reduced to 40 °C and the response is 4.6. The operation temperature can be decreased to near room temperature due to the increased oxygen adsorption by palladium and the high electron mobility by graphene.

A surface adsorbed reduced graphene oxide (rGO) on nylon-6 was prepared by a facile vacuum-assisted method. Spectroscopic, surface, and thermal characterization of the fabricated film revealed the formation of semi-ordered stacked rGO nanolayers, indicating physical interaction between the rGO and nylon 6 substrate. Electrical conductivity measurement data suggested a percolated system with a conducting network of nanosheets in the composite matrix. A low percolation threshold of 0.48% was observed, which can be attributed to the high conductivity and large surface to volume ratio of rGO. The conductivity of the nanocomposite was affected by the presence of trimethylamine (TMA) vapor, and the variation was dependent on the concentration of TMA in the vapor phase. This behavior proves the potential of the rGO/nylon6 composite as a promising sensing material for TMA.

High sensitivity and reliability of graphene oxide (GO) integrated with aluminum (Al) micro-interdigitated electrodes ( $\mu $ -IDEs) patterned on p-Si for the detection of aqueous fluoride anion (F−) is demonstrated. The strong molecular interaction, hydrogen bonding, and ionic conduction between the oxygen containing functional groups (epoxy (1, 2-ether), hydroxyl, carbonyl, and carboxyl) onto GO and F− are investigated by electrical and optical techniques. The GO/Al ( $\mu $ -IDEs)/p-Si sensor system shows $\sim 82$ % increase in the sensing signal for 0.1 ppm GO + F− solution with respect to GO. The response of the sensor for 1, 10, 100, and 1000 ppm of GO + F− solution shows almost 220, 415, 500, and 305 times increase in sensing signal with respect to GO. The significant enhancement in sensor response at lower concentration (0.1–100 ppm) of F− is observed. However, at high concentration (1000 ppm) of F−, the interlayer swelling and the expansion of GO dominate and result the reduction in sensing response of GO/Al ( $\mu $ -IDEs)/p-Si sensor. The Fourier transform infrared spectroscopy (FT-IR) spectra show the decrease in –OH, C–O–C, and CLO absorption peaks of GO with an increasing aqueous F− concentration, supporting the reduction in sensing response at 1000 ppm. The response of GO/Al ( $\mu $ -IDEs)/p-Si sensor is favorable for use in graphene-based electronics sensors.

COVID-19 pandemic has a catastrophic consequence globally since its first case was detected in December 2019, with an aggressive spread. Currently an exponential growth is expected. If not diagnosed at the proper time, COVID-19 may lead to death of the infected individuals. Thus, continuous screening, early diagnosis and prompt actions are crucial to control the spread and reduce the mortality. In this paper we focus on developing a Medical Diagnosis Humanoid (MDH) which is a cost effective, safety critical mobile robotic system that provides a complete diagnostic test to check whether an individual is infected by Covid-19 or not. This paper highlights the development of a system based on Artificial Intelligence for Medical Science, where humanoids can navigate through desired destinations, diagnose an individual for Covid-19 through various parameters and make a survey of a locality for the same. The humanoid uses the concept of real time data sensing and processing through machine learning produced by various sensors used in the context.

Zero-velocity update method (ZUPT) is widely used in inertial measurement unit (IMU)-based pedestrian navigation systems for mitigating sensor drifting error. In the basic pedestrian dead reckoning (PDR) system, especially in the foot-tie PDR system, zero-velocity update method with Kalman filter are two core algorithms. In the basic PDR system, ZUPT usually uses a single threshold to judge the gait of pedestrians. A single threshold, however, makes ZUPT unable to accurately judge the gait of pedestrians in different road conditions. In this paper, we propose a new, redesigned ZUPT method without using additional equipment and filter algorithms to further improve the accuracy of correction results. The method uses a sliding detection algorithm to help re-detect the zero-velocity intervals, aiming to remove the pseudo-zero velocity interval and the pseudo-motion interval, as well as improving the performance of the ZUPT method. The method was implemented in a shoe-mounted IMU-based navigation system. For walking step detection tests, the accuracy of the proposed modified ZUPT method reached 87.24%, 25% higher than the conventional methods. In a long-distance walking path tracking test, the mean error of the estimated path of our method is 0.61 m, an 81.69% reduction compared to the conventional ZUPT methods. The details of the improved ZUPT method presented in this paper not only enable the tracking technology to better track a pedestrian’s step changes during walking, but also provide better calculation conditions for subsequent filter operations.

Human Activity Recognition (HAR) is a field that infers human activities from raw time-series signals acquired through embedded sensors of smartphones and wearable devices. It has gained much attraction in various smart home environments, especially to continuously monitor human behaviors in ambient assisted living to provide elderly care and rehabilitation. The system follows various operation modules such as data acquisition, pre-processing to eliminate noise and distortions, feature extraction, feature selection, and classification. Recently, various state-of-the-art techniques have proposed feature extraction and selection techniques classified using traditional Machine learning classifiers. However, most of the techniques use rustic feature extraction processes that are incapable of recognizing complex activities. With the emergence and advancement of high computational resources, Deep Learning techniques are widely used in various HAR systems to retrieve features and classification efficiently. Thus, this review paper focuses on providing profound concise of deep learning techniques used in smartphone and wearable sensor-based recognition systems. The proposed techniques are categorized into conventional and hybrid deep learning models described with its uniqueness, merits, and limitations. The paper also discusses various benchmark datasets used in existing techniques. Finally, the paper lists certain challenges and issues that require future research and improvements.

This paper deals with the fault detection of a pneumatic control valve using canonical variate analysis (CVA). CVA can find the optimal linear combinations of p-window and f-window data, so that the correlation between these combinations can be maximized. Based on CVA, the p-window data is considered by traditional hotelling T2 statistic and squared prediction error (SPE) indicators, the corresponding fault detection rates (FDR) are low. In order to improve the FDR, a detection indicator based on SMD (square of the Mahalanobis distance) of the residual is proposed in this paper. The proposed indicator considers not only the information in the p-window data, but also that of the f-window data, which can improve the FDRs. The proposed techniques have been validated using a Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems (DAMADICS) benchmark. It concludes that 14 out of the 19 faults can be successfully detected using the proposed method (CVA-SMD). Simulation results have shown that the CVA-SMD can improve the FDR compared with existing CVA-T2 and CVA-SPE methods. Experiments based on real-world data have also demonstrated that the CVA-SMD has better performance than existing PCA-T2, PCA-SPE, PCA-SMD, CVA-T2 and CVA-SPE methods.

In this review paper, we focus on the 3D printing technologies that consist of the extruding of fluid material in lines to form structures for electro- and biomechanical applications. Our paper reviews various 3D print technologies, materials, sensing technologies and applications of extrusion-based 3D printing. We also discuss how to overcome some of the challenges with 3D printed sensors, such as the anisotropy of the conductors as well as the drift and nonlinearity of the materials.

This paper presents a review of Tactile Sensing Systems, with an emphasis on their application to the non-invasive characterization of tumors. Emulating the perceptual mechanism of the human skin, the Tactile Sensing Systems characterize tumors using touch sensors by quantifying the mechanical properties such as size and elasticity. The authors survey several tactile transduction methods: capacitive, piezoresistive, piezoelectric, magnetic, and optical. The advantages and disadvantages of different tactile sensors are discussed. The complex human sense of touch is emulated using tactile sensor data, novel data processing algorithms, and near real-time interpretation in a human-readable format. Tactile Sensing Systems utilize tactile sensors and other subsystems to come up with accurate mechanical properties of the touched objects. We review Elasticity Determination Systems, which are a special case of Tactile Sensing Systems. These systems are based on capacitive sensors, piezoelectric sensors, elastography, and optical tactile sensors. Then the optical Tactile Sensing System is discussed in detail; architecture, sensing principle, and algorithms to compute a risk score. Moreover, a survey of multimodal Tactile Sensing Systems, which broaden the capabilities of existing tactile sensing systems, is presented. The paper concludes with discussions and future research directions.

Considering the adverse impact of speed measurement on the accuracy of pose estimation after a mobile robot slips, collides, or abducts, this paper proposes a monocular inertial simultaneous localization and mapping algorithm that includes wheel speed anomaly detection. The algorithm adds wheel speed measurement to the least squares problem in a tightly coupled manner and uses a nonlinear optimization method to maximize the posterior probability to solve the optimal state estimation. For the speed control of the Mecanum wheel, because the existing closed-loop speed control method cannot calculate the motion constraint error, this paper reports a design of a control method of the Mecanum wheel moving chassis based on torque control, which can use the motion constraint error to estimate the credibility of the wheel speed measurement to detect whether the chassis movement status is abnormal; meanwhile, to prevent the chassis speed measurement error from adversely affecting the robot pose estimation, this paper uses three methods to actively detect whether the chassis movement is abnormal, and analyze the chassis movement status in real time. When it is determined that the chassis has abnormal motion, the wheel odometer pre-integration measurement of the current frame is actively removed from the state estimation equation, thereby ensuring the accuracy of the pose estimation. Experimental results show the feasibility and effectiveness of the method proposed in this paper, and the algorithm is robust.

A high-precision angular rate sensitive method of spacecraft based on deflection current and deflection angle measurement is proposed in this paper, in the view of this problem that the angular rate sensitive accuracy of spacecraft using Magnetically Suspended Control & Sensing Gyroscope (MSCSG) is affected by the deflection disturbance of the rotor relative to the stator. Firstly, the moment model of the rotor and the Lorentz force magnetic bearing (LFMB) is established, and the main factors affecting the sensitive accuracy of MSCSG are analyzed in detail; The inertial coupling terms which have great influence on the sensitivity accuracy are effectively retained by the rotor deflection angular velocity and angular acceleration estimation. The high precision analytical expression of attitude angular rate is derived and the error analysis is carried out; It is verified by simulation and experiment that the high precision sensitive method proposed in this paper has higher sensitive precision than the traditional measurement method, which greatly improves the zero bias stability index of MSCSG. The method proposed in this paper has important reference significance for improving the integration of magnetic suspension rotor control and sense performance.

Images and videos in multimedia data are typical representation methods that include various types of information, such as color, shape, texture, pattern, and other characteristics. Besides, in video data, information such as object movement is included. Objects may move with time, and spatial features can change, which is incorporated in spatio-temporal relations. Many research studies have been carried out over time on information recognition by computers using low-level data in this connection. There is a semantic gap between low-level and high-level information in vocabulary representing human thinking. A substantial amount of research has been conducted on reducing the semantic gap, and it is focused on representation methods of logic. The goal of this study is to understand object movement and define spatio-temporal relations through mapping between vocabulary and the object movements. Ontology mapping is a method used to bridge the gap between low-level and high-level information. In this case, the spatio-temporal relation consists of temporal relations obedient to the passage of time, directional relations obedient to changes in object movement direction, changes in object size relations, topological relations obedient to changes in object movement position, and velocity relations using concept relations between topology models. In this paper, an ontology is used to define the inference rules using the proposed spatio-temporal relations and the use of Markov Logic Networks (MLNs) for probabilistic reasoning. Finally, the performed experiment and evaluation prove the verification recognition and understanding of object movements based on video data. This paper can be extended to retrieval and comparison between object movements, automatic annotation, and video summarization. The contributions of this paper include definition of the spatio-temporal relations of a region-based object, recognition of the semantic movements of moving objects, designing and constructing a spatio-temporal ontology, and Understanding the semantic movement of moving objects.

Currently healthcare data packets do not get any special priority while routing through the Internet of Things (IoT) networks. These data packets flow through routers using conventional QoS process which does not guarantee that a patient’s critical health data traveling in congested IoT network will actually be routed to doctors office on time. This leaves the remote medical treatment at risk with possible threat to patients’ lives. In this paper, we studied the current healthcare packet routing process in IoT and its performance issues in congested IoT networks, then proposed a sensor driven solution to prioritize healthcare data routing in congested IoT networks. In our proposed system, we add a healthcare data identifier in IP packet header at the sensor level, modify QoS software at the network router level, and provide the highest priority to healthcare data packet routing based on the healthcare data identifier seen at router QoS. A prototype has been built and tested by using TI Launchpads. Test data shows that healthcare packets with an identifier can route to doctors at 80% less latency than healthcare packets routed without an identifier. Additionally, the proposed system saves medical diagnostic cost, and more importantly, reduces the risk of losing human lives. A shorter version of this analysis has also been presented to IEEE conference. The detail analysis in this paper opens up multiple avenues for further research.

While the Internet of Things (IoT) has been instrumental in healthcare data transmission, it also presents vulnerabilities and security risks to patients’ personalized health information for remote medical treatment. Currently most published security solutions available for healthcare data are not focused on data flow all the way from IoT sensor devices placed on a patient’s body through network routers to doctor’s offices. In this paper, we studied how the IoT network facilitates healthcare data transmission for remote medical treatment, explored security risks associated with unsecured data transmission, especially between IoT sensor devices and network routers, and then proposed an encrypted security solution initiated at IoT sensor devices. Our proposed solution provides a cryptography algorithm embedded into the sensor device such that the packets generated with patient’s health data are encrypted right at the sensor device before being transmitted. The proof of concept has been verified using a lab setup with two level encryption at the IoT sensor level and two level decryption at the receiving end at the doctor’s office. Test results are promising for an end-to-end security solution of healthcare data transmission in IoT. This paper also opens up further research avenues on IoT sensor driven security.

The most daunting and conflicting challenges accompanying the wireless sensor networks are energy and security. And data aggregation and compression techniques are two of the effective ways to reduce energy consumption. As it is known that the radio transceiver consumes energy which is proportional to the number of bits transmitted on the network; hence sending fewer bits on the communication channel implies lesser energy consumption. This paper works on compressing the index of secure index on distributed data (SIDD) technique; to reduce the number of bits of an index that is transmitted on the communication channel. The objective being to reduce energy consumption of SIDD. In this paper, we have worked to reduce the number of bits of the index sent on the communication channel, deploying difference encoding. The compression mechanism has established an upper bound on the energy consumption whilst all data items were unique. The scheme is scalable and can be deployed for saving energy consumption.

The application of cryogenic refrigeration in various applications of superconductivity such as HTS cables, HTS motors, SMES and so on, requires a controlled flow of the cryogens to the superconducting mass. The flow control of the cryogens necessitates the use of a pre-calibrated flow meter to monitor the flow rates and hence maintaining the necessary refrigeration. This paper describes a method to calibrate a Cryogenic Turbine based Volumetric Flow Meter (CTVFM) using sub-cooled liquid nitrogen. The experimental setup along with the procedure has been explained. The paper also discusses the practical issues faced during experiments and remedies adopted to overcome them. Further, the data acquisition (DAQ) system used during the experiment for precise logging of values is discussed. The numerical analysis of the calibration data is carried out and a suitable curve fit along with an equation is obtained to calculate the flow rate from output current of the flow meter transmitter.

3D optical sensors are becoming more and more popular in vision guidance of industrial robots and other industrial automation applications. Although research on high-speed 3D shape measurement (or imaging) has experienced tremendous growth over the past decades, simultaneously achieving high-speed and high-accuracy performance remains a major challenge in industrial practice. This paper presents a new FPGA architecture for the PMP algorithm and designs an high-speed embedded binocular structured light 3D measurement sensor. The sensor mainly contains a DLP projector, two cameras and a Xilinx Zynq-7000 UltraScale which make real-time computation possible. Experiments showed the time consuming of processing a set of ${9} \times {2}$ images with a resolution of ${2048} \times {1536}$ using the sensor proposed in this paper is 31.47 ms that has a better performance than 255ms on GPU. To the best of our knowledge, this is the first high-speed FPGA-based embedded binocular structured light 3D measurement sensor. This proposed 3D sensor can obtain ultra high quality 3D information in real time, it can be widely applied to robot random pin-picking and other industrial applications.

Mobile omnidirectional structured light vision is increasingly used in scene perception and robot navigation. A wide range of information is obtained by means of the vision system by only one image and laser image features are detected and extracted easily and quickly. In this paper a novel calibration method for mobile omnidirectional camera based on structured light is presented. Firstly, a set of parallel laser planes is emitted on the walls of corridor as auxiliary targets by structured light and intersects with wall orthogonally. Secondly, the constraint relationship is analyzed between the vanishing points in fisheye images and intrinsic parameters of imaging model. Finally, effects of the laser stripes’ interval and the angle between the wall which contains laser stripes and ground on calibration results are evaluated. Compared to Scaramuzza method, the calibration method shows its superiority in terms of both feasibility and efficiency. The method with the characteristic of self-calibration since the planar target is replaced by actively projected laser stripes. The result illustrates that our method has the advantages of simple and feasible operation, but result is effective and accurate. The calibration parameters are independent of the laser stripes’ interval and the angle between the wall and ground. Therefore, the method of the mobile omnidirectional structured light vision presented in this paper can be applied to many areas.

Through-wall mapping is an emerging field of research with promising applications varying from search and rescue, to health care and to security. Radar is a valuable tool in this process, mainly due to its long wavelength, which can pass through construction materials. However, this capability comes at a price, namely multipath reflections caused by the environment, which can reduce the usability of the produced map considerably. This paper proposes methods to detect and isolate these multipath reflections, eventually leaving out a usable map of the enclosed environment. For this purpose, two different radars, one operating at 24GHz and the other at 76GHz, were evaluated in various wall configurations constructed inside an enclosed space using portable wall segments. The testing arena was probed from the outside with the radars mounted on the top of a differential drive mobile robot. The paper shows that the proposed methods are effective in eliminating multipath reflections and in building an accurate representation of the environment using the radars.

A phase-variation microwave sensor operating in transmission and implemented by means of a high-impedance half-wavelength sensing line is reported in this paper. The sensor is useful for dielectric constant measurements and dielectric characterization of materials. By forcing the electrical length of the sensing line to be a half-wavelength when it is loaded with the so-called reference (REF) material, perfect matching is obtained regardless of the characteristic impedance of the line. This fact can be used to enhance the sensitivity for small perturbations, by merely increasing the characteristic impedance of the sensing line. An exhaustive analysis that supports such conclusion is reported in the paper. Then, two prototype sensors are designed and fabricated for validation purposes. As compared to the ordinary phase-variation permittivity sensor implemented by means of a matched ( $50-\Omega $ ) line with identical length, the sensitivity for small perturbations in the proposed sensor is 2.1 times larger. Further advantages of these sensors are low-cost, small size, implementation in planar technology, and very simple design and fabrication, derived from the fact that the sensing region is a half-wavelength transmission line.

The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication. The millimeter wave spectrum is part of 5G and covers frequencies between 30 and 300 GHz corresponding to wavelengths ranging from 10 to 1 mm. Although millimeter wave is often considered as a communication medium, it has also proved to be an excellent 'sensor', thanks to its narrow beams, operation across a wide bandwidth, and interaction with atmospheric constituents. In this paper, which is to the best of our knowledge the first review that completely covers millimeter wave sensing application pipelines, we provide a comprehensive overview and analysis of different basic application pipeline building blocks, including hardware, algorithms, analytical models, and model evaluation techniques. The review also provides a taxonomy that highlights different millimeter wave sensing application domains. By performing a thorough analysis, complying with the systematic literature review methodology and reviewing 165 papers, we not only extend previous investigations focused only on communication aspects of the millimeter wave technology and using millimeter wave technology for active imaging, but also highlight scientific and technological challenges and trends, and provide a future perspective for applications of millimeter wave as a sensing technology., 36 pages, submitted to IEEE Sensors Journal

This paper proposes a novel two-stage method for imputing missing SCADA data of wind turbines with high accuracy based on deep nonparametric models, sparse autoencoders (SAE), and a gradient-based optimization algorithm, coordinate descent (CD). A complex pattern of missing data, namely, data loss in correlated attributes (DLCA) that occurs simultaneously, is focused on and studied. In this paper, the missing data imputation is formulated as a two-stage optimization problem. In the first stage, the reconstruction error (RE) of SAE is regarded as the loss for training nonparametric attribute reconstruction models via a complete dataset to learn a low-dimensional manifold, in which data are densely distributed. At the second stage, RE serves as an objective function for optimizing the missing data imputation of a similar but incomplete dataset based on the developed SAE. According to the potential convexity of REs with respect to the imputation of missing attributes, which is empirically discovered through preliminary experiments, the CD algorithm is applied to efficiently solve the optimization problem. The efficacy of the proposed method is validated by using a large real wind turbine dataset. The results of the computational experiments demonstrate that the proposed method performs well on considered benchmarks that are well known for imputing missing data.

In process industries, air flow rate measurement and transmission are usually performed by various passive conventional sensors. Here, air flow rate for moderate flow applications is sensed by resistive type flexible bend sensor having the characteristics of light weight, tiny size, cost-effective, fine linearity and also good repeatability. The flexible resistive type bends sensor which acts as a cantilever beam derives the linear relationship between air flow rate and the change in resistance of the sensor in the form of an electrical signal. For transmission purposes, the generated electrical signal passes through the electrical communication system which is not permeable in hazardous and inflammable areas of the process industry. Henceforth, for the data transmission purpose, Internet of Things (IoT) is being introduced in industries for avoiding explosions due to electric spark and also the real-time data is transmitted throughout the globe with the help of internet. The proposed air flow transmitter has been theoretically analyzed in this paper and the relationship of air flow rate with a transmitted voltage signal has been derived. The experiment has been performed on the fabricated prototype air flow measuring unit. The experimental results are presented in the paper which is matched with the theoretical one. The proposed IoT based air flow transmitter unit shows linear characteristic with good repeatability. The higher value of air or any clean fluid flow rate can be measured by this prototype flow transmitter by only changing the dimensions of the cantilever type bend sensor.

Infrared thermal imager is an important means of temperature measurement. However, the measurement results of infrared thermal imagers are easily affected by interference factors, resulting in non-negligible temperature measurement errors, and the infrared thermal imager may be affected by multiple factors at the same time. To research the influence of multiple interference factors on infrared temperature measurement, this paper takes two common interference factors in actual temperature measurement processes, dust and measuring distance, as examples. Firstly, this paper analyzed the influence of dust and measuring distance and carried out the temperature measurement experiments under multi-factor interference and the experiments under single-factor interference respectively. Then, based on nonlinear polynomial regression, a compensation method is proposed to compensate for the measurement errors caused by dust and measuring distance. Results demonstrate that the proposed compensation method can significantly reduce the measurement errors caused by multiple interference factors, which is essential to promote the application of infrared thermal imagers in complex scenes with multiple interferences.