Sensor Technique Research Articles

Modeling the Visual Landscape: A Review on Approaches, Methods and Techniques.

Modeling the perception and evaluation of landscapes from the human perspective is a desirable goal for several scientific domains and applications. Human vision is the dominant sense, and human eyes are the sensors for apperceiving the environmental stimuli of our surroundings. Therefore, exploring the experimental recording and measurement of the visual landscape can reveal crucial aspects about human visual perception responses while viewing the natural or man-made landscapes. Landscape evaluation (or assessment) is another dimension that refers mainly to preferences of the visual landscape, involving human cognition as well, in ways that are often unpredictable. Yet, landscape can be approached by both egocentric (i.e., human view) and exocentric (i.e., bird's eye view) perspectives. The overarching approach of this review article lies in systematically presenting the different ways for modeling and quantifying the two 'modalities' of human perception and evaluation, under the two geometric perspectives, suggesting integrative approaches on these two 'diverging' dualities. To this end, several pertinent traditions/approaches, sensor-based experimental methods and techniques (e.g., eye tracking, fMRI, and EEG), and metrics are adduced and described. Essentially, this review article acts as a 'guide-map' for the delineation of the different activities related to landscape experience and/or management and to the valid or potentially suitable types of stimuli, sensors techniques, and metrics for each activity. Throughout our work, two main research directions are identified: (1) one that attempts to transfer the visual landscape experience/management from the one perspective to the other (and vice versa); (2) another one that aims to anticipate the visual perception of different landscapes and establish connections between perceptual processes and landscape preferences. As it appears, the research in the field is rapidly growing. In our opinion, it can be greatly advanced and enriched using integrative, interdisciplinary approaches in order to better understand the concepts and the mechanisms by which the visual landscape, as a complex set of stimuli, influences visual perception, potentially leading to more elaborate outcomes such as the anticipation of landscape preferences. As an effect, such approaches can support a rigorous, evidence-based, and socially just framework towards landscape management, protection, and decision making, based on a wide spectrum of well-suited and advanced sensor-based technologies.

Comparative MIP sensor technique: photopolymerization or thermal polymerization for the sensitive determinationof anticancer drug Regorafenib in different matrixes.

Regorafenib (REG) is a diphenylurea derivative oral multikinase inhibitor. It plays an important role in the treatment of colorectal cancer, metastatic gastrointestinal stromal tumors, and hepatocellular carcinoma. Molecularly imprinted polymer (MIP) based glassy carbon electrodes (GCE) were fabricated using photopolymerization (PP) and thermal polymerization (TP) methods. The characterizations of the proposed sensors were investigated by electrochemical techniques, Fourier transform infrared spectroscopy (FT-IR), and scanning electron microscopy (SEM). Several parameters were studied in detail for the optimum conditions of MIP-based sensors, such as dropping volume, photopolymerization and thermal polymerization durations, removal medium and time, and rebinding time. Both sensors' analytical validation and electroanalytical performance comparison were made in different REG concentrations ranging between 0.1 nM and 2.5 nM in standard solution and commercial human serum samples. The limit of detection (LOD) of PP-REG@MIP/GCE and TP-REG@MIP/GCE were 9.13 × 10-12 M and 1.44 × 10-11 M in standard solutions and 2.04 × 10-11 M and 2.02 × 10-11 M in serum samples, respectively. The applicability of the proposed sensors was tested using commercial human serum samples and pharmaceutical form of REG with high recovery values (PP-REG@MIP/GCE and TP REG@MIP/GCE sensors, 99.56-101.59%, respectively). The selectivity of the sensor for REGwas investigatedin the presence of similar molecules: Sorafenib, Sunitinib, Nilotinib, and Imatinib. The developed techniques and sensors checked the possible biological compounds and ions' effects and storage stability.

Strategic detection of food contaminants using nanoparticle‐based paper sensors

AbstractFood is necessary for survival, and contamination can shorten humans' lives on Earth in all dimensions. Real‐time screening of food pollutants, such as toxins, infectious agents, and chemical contaminants, is in high demand today. Thus, a cost‐effective and ecologically benign technology must be developed to detect food contamination in a rapid fashion. Nanoparticle (NP)‐based paper sensors are one of the most important alternatives to traditional methods because these are simple, inexpensive, highly selective, and sensitive tools. This article provides an overview of NP‐based paper sensor techniques for detecting toxic food contaminants.

Two Decades of Arrayed Imaging Reflectometry for Sensitive, High-Throughput Biosensing.

Arrayed imaging reflectometry (AIR), first introduced in 2004, is a thin-film interference sensor technique that optimizes optical properties (angle of incidence, polarization, substrate refractive index, and thickness) to create a condition of total destructive interference at the surface of a silicon substrate. The advantages of AIR are its sensitivity, dynamic range, multiplex capability, and high-throughput compatibility. AIR has been used for the detection of antibodies against coronaviruses, influenza viruses, Staphylococcus aureus, and human autoantigens. It has also shown utility in detection of cytokines, with sensitivity comparable to bead-based and ELISA assays. Not limited to antibodies or antigens, mixed aptamer and protein arrays as well as glycan arrays have been employed in AIR for differentiating influenza strains. Mixed arrays using direct and competitive inhibition assays have enabled simultaneous measurement of cytokines and small molecules. Finally, AIR has also been used to measure affinity constants, kinetic and at equilibrium. In this review, we give an overview of AIR biosensing technologies and present the latest AIR advances.

Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis.

High-quality indoor air is essential in open-kitchen restaurants for ensuring a healthy workplace and comfortable conditions for visitors. In this study, indoor air quality interdependence between the kitchen and the dining zones in open-kitchen restaurants was analyzed. The method was based on measurements of selected air parameters using a sensor technique and mutual information (MI) analysis. A long-term approach (based on a several-hour time series) and a short-term approach (based on a several-minute time series) were applied. This study involved four open-kitchen restaurants. The indoor conditions were represented by the temperature, relative humidity, CO2 concentration, and content of the total volatile organic compounds (TVOC) in the air. The MI analyses showed that the long-term co-dependence of the indoor conditions between the kitchen and the dining zones was smaller during business hours (MI = 0.12 ÷ 0.40) compared to night hours (MI = 0.24 ÷ 0.58). The ranking of the long-term MI values for the individual air parameters was MICO2 (0.34) ≅ MIT (0.34) > MIRH (0.28) > MITVOC (0.23). The short-term interdependencies were smaller during night hours (median MI = 0.01 ÷ 0.56) compared to business hours (MI = 0.23 ÷ 0.61). Additionally, the short-term MI was subject to high temporal variability. The ranking of the short-term MI values for the individual air parameters was MICO2 (0.48) > MIT (0.46) > MIRH (0.37) > MITVOC (0.26). Due to the weak and highly variable co-dependence of the air parameters between the kitchen and dining areas, multi-zone monitoring of air parameters with an emphasis on TVOC measurements is recommended to ensure proper indoor conditions in open-kitchen restaurants. The presented approach may be applied to design indoor air quality monitoring and ventilation systems not only in open-kitchen restaurants but also in other interiors with functionally different zones.

A 12-bit single slope ADC with multi-step structure and ramp calibration technique for image sensors

This paper presents a novel multi-step single slope analog to digital converter (MS SS ADC). The proposed ADC splits the 12-bit conversion into 3 steps, including 1-bit half section decision, 4-bit coarse conversion, and finally 8-bit fine conversion with 1-bit redundancy. The conversion step is substantially reduced from conventional 4096 to 276. The linearity of the ADC is guaranteed by a stable common mode voltage of the comparator varying only within 31.25 mV. Moreover, two high-accuracy ramp calibration techniques are utilized to correct the current variation in the fine ramp and the slope error between the coarse and fine ramps. A hybrid comparator is utilized in the ADC to save power while maintaining the performance. The ADC is designed and simulated in 0.18 μm CMOS process with 7.5 μm width. The differential nonlinearity and integral nonlinearity of the proposed ADC are reduced from +0.3/-1 LSB and +3.1/-2.55 LSB to +0.3/-0.3 LSB and +1/-0.45 LSB at 61.7 kS/s sample rate by the calibrations.

Validation of RANS-Based Turbulence Models Against High-Resolution Experiments and DNS for Buoyancy-Driven Flow with Stratified Fronts

To provide computational fluid dynamics (CFD)–grade experimental data for studying stratification, measurements on the High-Resolution Jet (HiRJet) facility at the University of Michigan have been conducted with density differences of 1.5 % and − 1.5 % , respectively. Fluid with a density different from the fluid initially present in the HiRJet tank was injected, and the propagation of the time-dependent density stratification was captured on a two-dimensional plane with the aid of the wire-mesh sensor technique for Reynolds numbers near 5000 and Richardson numbers near 0.29. Direct numerical simulations (DNSs) of the two cases have also been conducted to expand the multifidelity database. The novel experimental and DNS data were then used to assess the predictive capabilities of the Standard k − ε (SKE) model and the Reynolds Stress Transport (RST) model. In particular, the propagation speed and thickness of the stratification fronts were assessed by comparing the CFD results against the experimental and DNS data. It was found that the general trends of the stratified density fronts were well predicted by the CFD simulations; however, slight overprediction of the thickness of the stratification layer was found with the SKE model while the RST model gave a larger overprediction of the mixing. Examination of the turbulent statistics showed that the turbulent viscosity was largely overpredicted by the RST model compared to the SKE model.

Preliminary characterization on the dynamic mechanism of asphalt mixture under on-site vibratory compaction

Compaction is the final critical step in the construction of asphalt pavements, but the dynamic response of the mixtures at the particle scale and its correlation with the compaction degree have received limited in-depth investigations. Based on the SmartRock sensor technique, this study is aimed for this inadequacy and to further understand the interaction characteristics (e.g., rolling speed, vibration range and contact width) between the steel drums and pavement surface during compaction at the particle scale. Meanwhile, the dynamic responses (i.e., acceleration frequency and amplitude) of the aggregates in the asphalt mixture were comprehensively investigated to establish the correlation between the acceleration amplitude and compaction degree of pavement. According to the results, the vibrating drum shows a higher compaction effectiveness than the driving drum in the z-direction. The acceleration frequency reflects the energy attenuation of vibration waves during vibrational compaction, and the attenuation of vibration acceleration is normally distributed with the distance from the SmartRock. Lastly, an empirical relationship was developed between the maximum acceleration and pavement compaction degree and can be used to provide reasonable estimations for the in-situ density. The findings are expected to shed light on the compaction mechanism of asphalt pavement, and to provide a potential approach/basis for improving the on-site quality control and decision-making during compaction.

Taiwan renal data report system: Framework, function, and development.

The world healthcare system is actively seeking possible solutions for the rapid growth of kidney disease threats. The Taiwan Renal Data System (TWRDS) was central in assisting kidney health and care policymaking to reduce end-stage kidney disease incidence and mortality. This article summarizes the TWRDS framework, recent applications, and developments to provide new insights for some international researchers to promote planetary kidney health. The TWRDS originated in 1987 for the accreditation and quality monitoring of dialysis units and was connected with enriched health claim databases after the implementation of universal national health insurance in Taiwan in 1995. As a healthcare information centre, TWRDS has published annual reports forming indispensable instructions for renal care improvement since 2014. The TWRDS possesses three main functions: (1) kidney disease surveillance; (2) offering rich materials for research purposes; (3) achieving precision prevention and care through complex algorithms. In the new era, TWRDS can help build a more resilient society against communicable disease threats by integrating remote sensor techniques for developing future remote healthcare structures, as well as identifying kidney health inequity populations and promoting healthcare resources distributed equity. The global healthcare system is facing escalating burdens of non-communicable disease care due to the rapidly growing elderly population. Therefore, a considerable-scale data system is an essential decision-supportive tool in promoting an evidence-based, resilient, sustainable, equity care environment. Undoubtedly, TWRDS experience is a practical example of leveraging healthcare providers' decisions, care outcomes, and renovation.

Preface

The 2023 2nd International Conference on Machine Vision, Automatic Identification and Detection (MVAID 2023) took place in Hangzhou, China from April 14th to 16th, 2023, with the attendance of about 60 people.MVAID 2023 focused on research areas such as Computer Vision, Image Processing, Document Processing and Recognition, Advanced Learning Methods, Sensor Technique & Application, Image Forensics and Identification, etc. The main goal was to bring together basic scientists, researchers and developers engaged in machine vision, automatic identification and detection to bridge the gap between research needs and advanced technological solutions. MVAID 2023 provided an opportunity for researchers and related personnels to present their recent theoretical developments and practical applications as well as to discuss unsolved problems. It also fostered research and development in machine vision, automatic identification and detection.The Program of the Conference was divided into three main topics. First day of the Conference was devoted to registration for all the participants. The second day was focused on keynote speech, oral and poster presentation, as well as discussion on the talks and the whole meeting. Academic investigation took place during the third day of the Conference.We would like to highlight the main developments presented at the Conference and introduce the prominent speakers. The keynote speaker of the Conference Professor Yulin Wang (Wuhan University, China) presented a report on Drone: From Bionic Flight to Brain Like Autonomous Navigation. He mainly works for image and video processing, digital rights management, information security, intelligent system, e-commerce, IoT, code clone and so on. Professor Cairong Zhao (Tongji University, China) demonstrated his speech on Occluded Person Re-Identification, in which he elaborated on the four key issues caused by occlusion, namely positional displacement, scale displacement, noise information, and missing information, and discuss their solutions, and analyzed and discussed the future development trends of occluded person re-identification.Finally, we would like to thank the members of the Technical Program Committee for giving an opportunity to share scientific ideas and new developments at the Conference as well as Hangzhou Dianzi University and Politechnika Koszalinska for their friendly support. Last but not the least, our special acknowledgement also goes to the editors and staff of Journal of Physics: Conference Series, for their kindness and assistance in publishing this volume.The Committee of MVAID 2023List of Committee Member is available in this Pdf.

Attention-Enhanced Generative Adversarial Network for Hyperspectral Imagery Spatial Super-Resolution

Hyperspectral imagery (HSI) with high spectral resolution contributes to better material discrimination, while the spatial resolution limited by the sensor technique prevents it from accurately distinguishing and analyzing targets. Though generative adversarial network-based HSI super-resolution methods have achieved remarkable progress, the problems of treating vital and unessential features equally in feature expression and training instability still exist. To address these issues, an attention-enhanced generative adversarial network (AEGAN) for HSI spatial super-resolution is proposed, which elaborately designs the enhanced spatial attention module (ESAM) and refined spectral attention module (RSAM) in the attention-enhanced generator. Specifically, the devised ESAM equipped with residual spatial attention blocks (RSABs) facilitates the generator that is more focused on the spatial parts of HSI that are difficult to produce and recover, and RSAM with spectral attention refines spectral interdependencies and guarantees the spectral consistency at the respective pixel positions. Additionally, an especial U-Net discriminator with spectral normalization is enclosed to pay more attention to the detailed informations of HSI and yield to stabilize the training. For producing more realistic and detailed super-resolved HSIs, an attention-enhanced generative loss is constructed to train and constrain the AEGAN model and investigate the high correlation of spatial context and spectral information in HSI. Moreover, to better simulate the complicated and authentic degradation, pseudo-real data are also generated with a high-order degradation model to train the overall network. Experiments on three benchmark HSI datasets illustrate the superior performance of the proposed AEGAN method in HSI spatial super-resolution over the compared methods.

Choice and impact of frequency increment in direct digital synthesiser (DDS)‐based linear FMCW radar

AbstractFMCW radar is a popular technique for radar sensors. A Direct Digital Synthesiser (DDS) is commonly used to generate the required linear frequency sweep. DDS devices are widely‐available and easy to configure in terms of the key radar parameters of sweep bandwidth and pulse duration. They generate a step‐approximation to a linear frequency sweep for which the frequency increment parameter also needs to be defined. However, there is little information available in the literature to guide radar designers on the choice of this parameter. As a result, most designs are based on an empirical estimate of the DDS frequency increment. The aim is to provide an analytic basis and design rules for the choice of this parameter. Two principal types of FMCW radar (direct sampling and deramp) are studied to determine the effect on the range profile of the finite DDS frequency increment. It is shown that a set of spurs appears around each target response. Analytic expressions are derived to quantify the amplitude and distribution of these spurs from which simple design rules are presented to allow an informed choice of DDS frequency increment. These analytic results are convincingly validated using numerical simulations and experimental measurements.

Multievent localization for loop-based Sagnac sensing system using machine learning.

In optical sensing applications such as pipeline monitoring and intrusion detection systems, accurate localization of the event is crucial for timely and effective response. This paper experimentally demonstrates multievent localization for long perimeter monitoring using a Sagnac interferometer loop sensor and machine learning techniques. The proposed method considers the multievent localization problem as a multilabel multiclassification problem by dividing the optical fiber into 250 segments. A deep neural network (DNN) model is used to predict the likelihood of event occurrence in each segment and accurately locate the events. The sensing loop comprises 106.245 km of single-mode fiber, equivalent to ∼50 km of effective sensing distance. The training dataset is constructed in simulation using VPItransmissionMaker, and the proposed machine learning model's complexity is reduced by using discrete cosine transform (DCT). The designed DNN is tested for event localization in both simulation and experiment. The simulation results show that the proposed model achieves an accuracy of 99% in predicting the location of one event within one segment error, an accuracy of 95% in predicting the location of one event out of the two within one segment error, and an accuracy of 78% in predicting the location of the two events within one segment error. The experimental results validate the simulation ones, demonstrating the proposed model's effectiveness in accurately localizing events with high precision. In addition, the paper includes a discussion on extending the proposed model to sense more than two events simultaneously.

Fabrication of molecularly imprinted electrochemical sensors for sensitive codeine detection

Codeine (COD) is a natural opiate alkaloid obtained from hashish or prepared by methylation from morphine, widely used as an analgesic, antitussive, and antidiarrheal drug. A sensitive and selective electrochemical sensor based on molecular imprinting polymer (MIP) was developed to detect COD in tablet form and biological fluid samples. The polymeric thin film on the glassy carbon electrode (GCE) surface was formed using template molecule COD, a functional monomer based on amino acid N-methacryloyl-L-tryptophan (MA-Trp), basic monomer 2-hydroxyethyl methacrylate (HEMA), cross-linker ethylene glycol dimethacrylate (EGDMA), and the pore-making sacrificial material zinc oxide (ZnO). The developed MIP-based electrochemical sensor (MA-Trp@MIP/GCE) was characterized by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FT-IR), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) methods. The electrochemical properties of the sensor were examined using differential pulse voltammetry (DPV). Under optimum experimental conditions, the dynamic linear range of the developed sensor was calculated as 0.1–1.0 pM and the detection limit of 0.0150 pM. The current electrochemical sensor technique also demonstrated outstanding recovery in human urine, synthetic serum samples, and tablet form, with relatively high recoveries of 98.5%, 101.4%, and 101.5%, respectively. Moreover, the MA-Trp@MIP/GCE sensor exhibited good reproducibility, repeatability, stability, and fast response time for the electrochemical analysis of COD. The presented sensor successfully determined COD in tablet and biological fluid samples.

First in Human Subjects Testing of the UroMonitor: A Catheter-free Wireless Ambulatory Bladder Pressure Monitor.

Urodynamics is the standard method of diagnosing bladder dysfunction, but involves catheters and retrograde bladder filling. With these artificial conditions, urodynamics cannot always reproduce patient complaints. We have developed a wireless, catheter-free intravesical pressure sensor, the UroMonitor, which enables catheter-free telemetric ambulatory bladder monitoring. The purpose of this study was twofold: to evaluate accuracy of UroMonitor pressure data, and assess safety and feasibility of use in humans. Eleven adult female patients undergoing urodynamics for overactive bladder symptoms were enrolled. After baseline urodynamics, the UroMonitor was transurethrally inserted into the bladder and position was confirmed cystoscopically. A second urodynamics was then performed with the UroMonitor simultaneously transmitting bladder pressure. Following removal of urodynamics catheters, the UroMonitor transmitted bladder pressure during ambulation and voiding in private. Visual analogue pain scales (0-5) were used to assess patient discomfort. The UroMonitor did not significantly alter capacity, sensation, or flow during urodynamics. The UroMonitor was also easily inserted and removed in all subjects. The UroMonitor reproduced bladder pressure, capturing 98% (85/87) of voiding and nonvoiding urodynamic events. All subjects voided with only the UroMonitor in place with low post-void residual volume. Median ambulatory pain score with the UroMonitor was rated 0 (0-2). There were no post-procedural infections or changes to voiding behavior. The UroMonitor is the first device to enable catheter-free telemetric ambulatory bladder pressure monitoring in humans. The UroMonitor appears safe and well tolerated, does not impede lower urinary tract function, and can reliably identify bladder events compared to urodynamics.

Review on Drone application methodologies in agriculture precision

In recent times, Drone use has increased significantly in the field of precise agriculture. These aircraft, which are equipped with thermal and multi-spectrum cameras, allow us to increase agricultural production in proportion to the increasing demand of the population and monitor crops grown in large areas, which is a very difficult task given the large areas of fields and the standards required for the successful cultivation of the crop in terms of soil moisture and fertility, proximity to water sources, and the possibility of its delivery In a way that reduces the waste of this water. Fields are at risk of crop damage due to diseases to crops caused by insects and pests, as drones can be used to spray pesticides and fertilizers in a way that reduces the health risks to people when spreading them manually. In this research paper, we will address several previous research related to the use of sensor and drone techniques in the field of precision agriculture and their efficiency in helping to sustain production and increase crop yields.

Challenges for structural health monitoring of concrete curing using piezoelectric sensor and electromechanical impedance (EMI) technique: A critical review

Monitoring the strength of early-age concrete is crucial by assessing an accurate time of removing construction formwork and loads on the structural elements, as well as the high technology strength identifications. This high technology is based on electromechanical impedance (EMI). EMI technique might also be monitored for efficient and verifiable effects, such as the curing phenomenon in situ investigation level. The curing effect on concrete strength elements has been significantly detected using piezoelectric patches (PZT) as smart material devices in electromechanical impedance investigation. This EMI approach is also becoming a sustainable solution for diagnostic demands in order to determine the signature characteristics at earlier phases of the concrete strength growth. In 2005, the first investigation on the curing problem was published, then became increasingly well-known in many relevant studies. Consequently, this paper presents the most recent state-of the- art curing studies by EMI technology. The literature reviews categorized relevant information into vital concrete curing challenges to be highlighted in future research. State-of-art listed the critical outputs and findings as installation techniques, computing technology integration, modelling, analysis techniques, signal processing, and environmental factors. In conclusion, EMI technology might potentially be transformed into a self-contained monitoring device to detect early damage in concrete. Similarly, the appropriate research gaps might examine more experiments and simultaneously provides an explanation for more research problems in the field of structural health monitoring.

Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques.

Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data.

Analysis of the opportunities and costs of energy saving in lightning system of library buildings with the aid of building information modelling and Internet of things

The electrical lighting technique is one of the primary culprits behind a library's high energy usage and operating expenses. Unfortunately, lighting systems are often unregulated, resulting in suboptimal lighting conditions, while older buildings consume significant amounts of electricity. To reduce energy waste and usage, it is crucial to evaluate the entire library's performance, including indoor lighting, solar radiation, and interior temperature, from the initial design phase. The paucity of high-quality real-world data and procedural knowledge has hindered the accuracy of artificial intelligence (AI) models used in previous research. Consequently, the purpose of this study is to integrate distributed wireless sensor networks (WSN) and sensor techniques employing Internet of Things (IoT) and Building Information Modelling (BIM) approaches in order to reduce energy consumption by implementing a smart illumination technique. The purpose of this study is to examine the strategies and options involved in prototyping an IoT- and BIM-based intelligent illumination system and its effect on library maintenance. This paper presents several strategies for transforming the inefficient lighting system of a typical library into a sustainable one with a quick repayment period. This technology can substantially improve lighting control and energy efficiency by employing motion sensors to detect human movement and adjusting lighting in particular areas. As a result, this solution provides libraries with the opportunity to establish a more sustainable environment while simultaneously increasing productivity. This study concludes with strategies and solutions for instituting energy-efficient measures in libraries, particularly lighting systems. By integrating distributed wireless sensor networks and sensor techniques with IoT and BIM approaches, this research seeks to address the challenges posed by conventional lighting systems and enhance the energy efficiency of libraries as a whole.

A Review of Hydrogen Sensors for ECLSS: Fundamentals, Recent Advances, and Challenges

The development of hydrogen sensors with high detection accuracy, fast response times, long calibration periods, and good stability has become the focus of the space station environmental control and life support subsystem. We analyze the current research status of different types of hydrogen sensors, including catalyst combustion type, heat conduction type, semiconductor type, fiber optic type, etc. The response signals of most hydrogen sensors are affected by temperature and humidity, resulting in cross-sensitivity. Reducing the cross-sensitivity of temperature, humidity, and other interfering factors to achieve accurate hydrogen measurement in different environments is a challenge that limits the development of hydrogen sensors. Several hydrogen sensors that are currently commercially available have a narrow operating temperature range, most of them can only measure at room temperature, and high-temperature environments require a higher accuracy and lifetime of the sensor than required at room temperature. Many new hydrogen-sensitive materials were developed to improve the performance of the sensors. The excellent performance of fiber-optic hydrogen sensors is beneficial to temperature compensation and distributed multiparameter measurement, as well as to the research and development of intelligent sensing systems, in the context of the Internet of Things. The signal detection and demodulation techniques of fiber-optic sensors are the focus of future hydrogen sensor research.