A range of optical fibre-based sensors for the measurement of ethanol, primarily in aqueous solution, have been developed and are reviewed here. The sensing approaches can be classified into four ...groups according to the measurement techniques used, namely absorption (or absorbance), external interferometric, internal fibre grating and plasmonic sensing. The sensors within these groupings can be compared in terms of their characteristic performance indicators, which include sensitivity, resolution and measurement range. Here, particular attention is paid to the potential application areas of these sensors as ethanol production is globally viewed as an important industrial activity. Potential industrial applications are highlighted in the context of the emergence of the internet of things (IoT), which is driving widespread utilization of these sensors in the commercially significant industrial and medical sectors. The review concludes with a summary of the current status and future prospects of optical fibre ethanol sensors for industrial use.
A novel long period grating (LPG) inscribed balloon-shaped heterocore-structured plastic optical fibre (POF) sensor is described and experimentally demonstrated for real-time measurement of the ...ultra-low concentrations of ethanol in microalgal bioethanol production applications. The heterocore structure is established by coupling a 250 μm core diameter POF between two 1000 μm diameter POFs, thus representing a large core-small core-large core configuration. Before coupling as a heterocore structure, the sensing region or small core fibre (SCF; i.e., 250 μm POF) is modified by polishing, LPG inscription, and macro bending into a balloon shape to enhance the sensitivity of the sensor. The sensor was characterized for ethanol-water solutions in the ethanol concentration ranges of 20 to 80
, 1 to 10
, 0.1 to 1
, and 0.00633 to 0.0633
demonstrating a maximum sensitivity of 3 × 10
%/RIU, a resolution of 7.9 × 10
RIU, and a limit of detection (LOD) of 9.7 × 10
RIU. The experimental results are included for the intended application of bioethanol production using microalgae. The characterization was performed in the ultra-low-level ethanol concentration range, i.e., 0.00633 to 0.03165
, that is present in real culturing and production conditions, e.g., ethanol-producing blue-green microalgae mixtures. The sensor demonstrated a maximum sensitivity of 210,632.8 %T/
(or 5 × 10
%/RIU as referenced from the RI values of ethanol-water solutions), resolution of 2 × 10
(or 9.4 × 10
RIU), and LOD of 4.9 × 10
(or 2.3 × 10
RIU). Additionally, the response and recovery times of the sensor were investigated in the case of measurement in the air and the ethanol-microalgae mixtures. The experimentally verified, extremely high sensitivity and resolution and very low LOD corresponding to the initial rate of bioethanol production using microalgae of this sensor design, combined with ease of fabrication, low cost, and wide measurement range, makes it a promising candidate to be incorporated into the bioethanol production industry as a real-time sensing solution as well as in other ethanol sensing and/or RI sensing applications.
The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a data-driven infrastructure that highly relies on smart sensing devices (i.e., blood pressure monitors, ...temperature sensors, etc.) for faster response time, treatments, and diagnosis. However, with the evolving cyber threat landscape, IoT devices have become more vulnerable to the broader risk surface (e.g., risks associated with generative AI, 5G-IoT, etc.), which, if exploited, may lead to data breaches, unauthorized access, and lack of command and control and potential harm. This paper reviews the fundamentals of healthcare IoT, its privacy, and data security challenges associated with machine learning and H-IoT devices. The paper further emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application. Detecting and responding to anomalies involves various cyber-attacks and protocols such as Wi-Fi 6, Narrowband Internet of Things (NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). A robust authentication mechanism based on machine learning and deep learning techniques is required to protect and mitigate H-IoT devices from increasing cybersecurity vulnerabilities. Hence, in this review paper, security and privacy challenges and risk mitigation strategies for building resilience in H-IoT are explored and reported.
We report a U-bend plastic optical fiber (POF) sensor for measuring ultralow concentration of ethanol corresponding to the initial bioethanol production rate by cyanobacteria, i.e., 0.1-0.5 ...gL<inline-formula> <tex-math notation="LaTeX">^{\mathrm {\mathbf {-1}}} </tex-math></inline-formula> day<inline-formula> <tex-math notation="LaTeX">^{\mathrm {\mathbf {-1}}} </tex-math></inline-formula>. This production rate corresponds to 0.00499-0.0499 %wt of ethanol in total solution in terms of weight percent concentration. Refractive indices for these minute ethanol concentration values are not available in the literature hence mathematical estimation of the refractive indices for the ethanol-water solutions in this concentration range is demonstrated. The sensing principle is based on optical fiber-based evanescent wave absorption. Parameters affecting the response of an evanescent wave absorption sensor are analyzed for the intended concentration range of ethanol. Experimental results using the U-bend evanescent wave POF sensor are also presented for ethanol-water solutions having refractive indices corresponding to the bioethanol production rate. The excellent repeatability of the measurement is established and these real-time measurements show that sensor has a sensitivity of 817.76 O.D/RIU (O.D refers to optical density, unit of absorbance) with a 99.76% linearity. The limit of detection of the sensor is <inline-formula> <tex-math notation="LaTeX">9.2\times 10^{\mathrm {\mathbf {-7}}} </tex-math></inline-formula> RIU. It is also proved using refractive index calculations of ethanol-water solutions that the sensor exhibits a resolution of <inline-formula> <tex-math notation="LaTeX">10^{\mathrm {\mathbf {-7}}} </tex-math></inline-formula> RIU. The sensor of this investigation, therefore, represents a potential solution for online and real-time monitoring of the production of biofuels even at the very low-level initial concentration of product.
An envelope-filtered method of enhancement of non-invasive Hemoglobin (Hb) monitoring is described. Results using a four-wavelength two-wavelength Photoplethysmography (PPG) probe gathered from five ...randomly selected healthy subjects demonstrate that the measurement of Hb coefficient, based on the novel envelope method filtered PPG, has a superior performance on minimization of the motion artefact than that from band-pass filtered PPG data in both stationary and non-stationary scenarios. Hemodynamic pressure variation near the sensor probe is also induced from a vertical height difference (VHD) between the finger sensor probe and the heart level. Results of this study show that the Hb coefficients determined using the proposed filtering method based on the envelope algorithm are also capable of minimization of VHD variations similar to the traditional band-pass filter method.
In this paper, the surface roughness characteristic of D-shaped optical fibre sensors with its effects on the sensitivity has been studied. The ULTRAPOL end and edge polishing system was used with ...some modifications to fabricate the D-shaped sensors with planar sensing zone from the single-mode optical fibres. The mean surface roughness of 343, 96, 25 and 9 nm was estimated at the sensing zone of the D-shaped sensors which were sequentially polished with 30, 9, 3 and 0.5 µm grit size polishing films, respectively. From the experimental results, it has been observed that surface roughness of the sensing zone does not exhibit the significant effects on the output signal strength, whereas the sensitivity of the D-shaped sensors nonlinearly related with the surface roughness of the sensing zone. The designed D-shaped optical fibre sensors have potential applications in biomedical and chemical industries.
IEEE Access, vol. 11, pp. 145869-145896, 2023 The Healthcare Internet-of-Things (H-IoT), commonly known as Digital
Healthcare, is a data-driven infrastructure that highly relies on smart sensing
...devices (i.e., blood pressure monitors, temperature sensors, etc.) for faster
response time, treatments, and diagnosis. However, with the evolving cyber
threat landscape, IoT devices have become more vulnerable to the broader risk
surface (e.g., risks associated with generative AI, 5G-IoT, etc.), which, if
exploited, may lead to data breaches, unauthorized access, and lack of command
and control and potential harm. This paper reviews the fundamentals of
healthcare IoT, its privacy, and data security challenges associated with
machine learning and H-IoT devices. The paper further emphasizes the importance
of monitoring healthcare IoT layers such as perception, network, cloud, and
application. Detecting and responding to anomalies involves various
cyber-attacks and protocols such as Wi-Fi 6, Narrowband Internet of Things
(NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). A robust
authentication mechanism based on machine learning and deep learning techniques
is required to protect and mitigate H-IoT devices from increasing cybersecurity
vulnerabilities. Hence, in this review paper, security and privacy challenges
and risk mitigation strategies for building resilience in H-IoT are explored
and reported.
Cataclysmic circumstances around the globe are increasing day by day either because of some severe natural disasters or human faults. Fire occurrences have a place with the latter kind of fiasco, ...which typically need to do with human mistake. The biggest casualties of fire risk are generally the commercial ventures, processing plants, oil refineries, industries and comparative locales, which essentially work with combustible or volatile materials. Surveillance of such sites is the requirement to build security for the human work power and property. This work displays a model for the observation of an imitated fire site utilizing multiple fire identifying robots. This model addresses localization of two fire detecting mobile robots on a round lattice in a dynamic domain. The robots are assembled with the LEGO MINDSTORMS NXT 2.0 kits and are customized via LabVIEW Module of LEGO MINDSTORMS (LVLM). The aforementioned mobile robots - revolving in circular movement - screen the imitated fire site and when any of the two robots identify a flame, they perform certain characterized tasks. The Bluetooth protocol feature is used to establish communication between the two robots. Each robot is equipped with a thermal infrared sensor (TIR) by Dexter Industries for detecting fire and an ultrasonic sensor to avoid collision with obstacles being presented with a dynamic environment.
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