Purpose In acoustic detection technology, optical microcavities offer higher detection bandwidth and sensitivity than traditional acoustic sensors. However, research on acoustic detection technologies involving optical microcavities has not yet been reported. Therefore, this paper aims to design and construct an underwater acoustic detection system based on optical microcavities and study its acoustic detection technology to improve its performance. Design/methodology/approach Based on the principles of optical microcavity acoustic sensors, a signal-detection circuit was designed to form a detection system in conjunction with a laser, an optical waveguide resonator and an oscilloscope. This circuit consists of two modules: a photodetection module and a filter amplification module. Findings The photodetection module features a baseline noise of −106.499 dBm and can detect device spectral line depths of up to 2410 mV. The gain stability of the filter amplification module was 58 dB ± 1 dB with a noise gain of −107.626 dBm. This design allows the acoustic detection system to detect signals with high sensitivity within the 10 Hz−1.2 MHz frequency band, achieving a maximum sensitivity of −126 dB re 1 V/µPa at 800 Hz and a minimum detectable pressure (MDP) of 0.37 mPa/Hz1/2, corresponding to a noise equivalent pressure (NEP) of 51.36 dB re 1 V/µPa. Originality/value This study designs and constructs a broadband underwater acoustic detection system specifically for optical waveguide resonators based on the sensing principles of silicon dioxide optical waveguide resonators. Experiments demonstrated that the signal detection module improves the sensitivity of underwater acoustic detection based on optical waveguides.

Purpose This study aims to present an innovative approach to detect and monitor ethylene gas during fruit ripening. Design/methodology/approach It uses a specialized composite membrane in conjunction with a solid-state electrochemical method. This unique electroactive membrane, composed of polyvinyl alcohol (PVA), chitosan (CHT), lithium chloride (LiCl) and ammonium molybdate (AMO), exhibits synergistic behavior when applied to a microelectrode chip surface. This composite enhances the sensitivity of electrochemical ethylene detection. Empirical experiments were conducted to elucidate the ripening kinetics in various fruit specimens, including apples, pears and mangoes. These fruits released ethylene, which was analyzed using the molybdenum-permeated electroactive biopolymer composite membrane, a critical determinant of ethylene levels. Findings Characterization of the synthesized composite through techniques such as X-ray diffraction and Fourier-transform infrared spectroscopy revealed reduced crystallinity and decreased hydrogen bond interactions upon activation with Mo ions. Field emission scanning electron microscopy images exhibited a distinctive porous surface morphology with spherical microgranules. Energy dispersive X-ray analysis indicated a significant change in the mass or atomic composition of Mo in the composite membrane after Mo ion activation. Electrochemical measurements, including cyclic voltammetry and potentiostatic electrochemical impedance spectroscopy, validated the efficiency of the Mo-activated PVA-CHT-LiCl-AMO membrane, manifesting an impressive 87.79% increase in sensitivity compared to the nonactivated membrane. Practical implications This research work represents a significant advancement in the field of ethylene detection and fruit ripening monitoring. The Mo-activated PVA-CHT-LiCl-AMO membrane offers a reliable and effective solution for real-time ethylene detection, providing an invaluable tool for the horticultural industry to optimize fruit ripening processes, extend shelf life and ensure the delivery of high-quality produce to consumers. Social implications The findings of this study hold great promise for fostering sustainability and efficiency within the global fruit supply chain, ultimately benefiting both producers and consumers alike. Originality/value The implications of this research extend to the fabrication of a sensor based on a solid-state electroactive PVA-CHT-LiCl-AMO composite membrane, which upon Mo-activation exhibits robust electrochemical fruit ethylene detection when exposed to different fruits.

Purpose To address the issues of low localization and mapping accuracy, as well as map ghosting and drift, in indoor degraded environments using light detection and ranging-simultaneous localization and mapping (LiDAR SLAM), a real-time localization and mapping system integrating filtering and graph optimization theory is proposed. By incorporating filtering algorithms, the system effectively reduces localization errors and environmental noise. In addition, leveraging graph optimization theory, it optimizes the poses and positions throughout the SLAM process, further enhancing map accuracy and consistency. The purpose of this study resolves common problems such as map ghosting and drift, thereby achieving more precise real-time localization and mapping results. Design/methodology/approach The system consists of three main components: point cloud data preprocessing, tightly coupled inertial odometry based on filtering and backend pose graph optimization. First, point cloud data preprocessing uses the random sample consensus algorithm to segment the ground and extract ground model parameters, which are then used to construct ground constraint factors in backend optimization. Second, the frontend tightly coupled inertial odometry uses iterative error-state Kalman filtering, where the LiDAR odometry serves as observations and the inertial measurement unit preintegration results as predictions. By constructing a joint function, filtering fusion yields a more accurate LiDAR-inertial odometry. Finally, the backend incorporates graph optimization theory, introducing loop closure factors, ground constraint factors and odometry factors from frame-to-frame matching as constraints. This forms a factor graph that optimizes the map’s poses. The loop closure factor uses an improved scan-text-based loop closure detection algorithm for position recognition, reducing the rate of environmental misidentification. Findings A SLAM system integrating filtering and graph optimization technique has been proposed, demonstrating improvements of 35.3%, 37.6% and 40.8% in localization and mapping accuracy compared to ALOAM, lightweight and ground optimized lidar odometry and mapping and LiDAR inertial odometry via smoothing and mapping, respectively. The system exhibits enhanced robustness in challenging environments. Originality/value This study introduces a frontend laser-inertial odometry tightly coupled filtering method and a backend graph optimization method improved by loop closure detection. This approach demonstrates superior robustness in indoor localization and mapping accuracy.

Purpose This paper aims to apply the spacing effect of capacitive imaging (CI) sensors to inspect and differentiate external flaws of the protective module, internal flaws of the protective module and external flaws of the metallic module in oil and gas pipelines simultaneously. Through experimental verification, a method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors has been demonstrated. Design/methodology/approach A 3Dimensions (3D) model for simulating the inspection of these flaws was established by using COMSOL. A novel CI sensor with adjustable working electrode spacing was designed, and a modular CI system was developed to substantiate the theoretical findings with experimental evidence. A method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors was established. Findings The results indicate that the method can successfully discriminate external flaws of the protective module, internal flaws of the protective module and external flaws of the metallic module using CI sensors. Originality/value The method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors is vital for keeping the transportation safety of oil and gas pipelines.

Purpose This study aims to establish a multi-physics-coupled model for an electrostatic particulate matter (PM) sensor. The focus lies on investigating the deposition patterns of particles within the sensor and the variation in the regeneration temperature field. Design/methodology/approach Computational simulations were initially conducted to analyse the distribution of particles under different temperature and airflow conditions. The study investigates how particles deposit within the sensor and explores methods to expedite the combustion of deposited particles for subsequent measurements. Findings The results indicate that a significant portion of the particles, approximately 61.8% of the total deposited particles, accumulates on the inside of the protective cover. To facilitate rapid combustion of these deposited particles, a ceramic heater was embedded within the metal shielding layer and tightly integrated with the high-voltage electrode. Silicon nitride ceramic, selected for its high strength, elevated temperature stability and excellent thermal conductivity, enables a relatively fast heating rate, ensuring a uniform temperature field distribution. Applying 27 W power to the silicon nitride heater rapidly raises the gas flow region's temperature within the sensor head to achieve a high-temperature regeneration state. Computational results demonstrate that within 200 s of heater operation, the sensor's internal temperature can exceed 600 °C, effectively ensuring thorough combustion of the deposited particles. Originality/value This study presents a novel approach to address the challenges associated with particle deposition in electrostatic PM sensors. By integrating a ceramic heater with specific material properties, the study proposes an effective method to expedite particle combustion for enhanced sensor performance.

Purpose The study aims to develop an inexpensive metal oxide semiconductor gas sensor with high sensitivity, excellent selectivity for a specific gas and rapid response time. Design/methodology/approach This study synthesized Zn2SnO4 nanostructures using a hydrothermal method with a 1 M concentration of zinc chloride (ZnCl2) as the zinc source and a 0.7 M concentration of tin chloride (SnCl4) as the tin source. Thick films of nanostructured Zn2SnO4 were then produced using screen printing. The structural properties of Zn2SnO4 were confirmed using X-ray diffraction, and the formation of Zn2SnO4 nanoparticles was verified by transmission electron microscopy. Scanning electron microscopy was used to analyse the surface morphology of the fabricated material, while energy dispersive spectroscopy provided insight into the chemical composition of the thick film. These fabricated thick films underwent testing for various hazardous gases, including nitrogen dioxide, ammonia, hydrogen sulphide (H2S), ethanol and methanol. Findings The nanostructured Zn2SnO4 thick film sensor demonstrates a notable sensitivity to H2S gas at a concentration of 500 ppm when operated at 160°C. Its selectivity, response time and recovery time were assessed and documented. Research limitations/implications The primary limitations of this research on metal oxide semiconductor gas sensors include poor selectivity to specific gases, limited durability and challenges in achieving detection at room temperature. Practical implications The nanostructured Zn2SnO4 thick film sensor demonstrates a strong response to H2S gas, making it a promising candidate for commercial production. The detection of H2S is crucial in various sectors, including industries and sewage plants, where monitoring this gas is essential. Social implications Currently, heightened global apprehension about atmospheric pollution stems from the existence of perilous toxic and flammable gases. This underscores the imperative need for monitoring such gases. Toxic and flammable gases are frequently encountered in both residential and industrial environments, posing substantial hazards to human health. Noteworthy accidents involving flammable gases have occurred in recent years. It is crucial to comprehend the presence and composition of these gases in the surroundings for precise detection, measurement and control. Thus, there has been a significant push for extensive research and development in diverse sensor technologies using various materials and methodologies to monitor and regulate these gases effectively. Originality/value In this research, Zn2SnO4 nanostructures were synthesized using a hydrothermal method with ZnCl2 at a concentration of 1 M for zinc and SnCl4 at a concentration of 0.7 M for tin. Thick films of nanostructured Zn2SnO4 were then fabricated via screen printing technique. Following fabrication, all thick films were subjected to testing with various toxic gases, and the results were compared to previously published data. The analysis indicated that the nanostructured Zn2SnO4 thick film sensor demonstrated outstanding performance concerning gas response, gas concentration, selectivity and response time, particularly towards H2S gas.

Purpose This paper aims to explore recent advances in drift compensation algorithms for Electronic Nose (E-nose) technology and addresses sensor drift challenges through offline, online and neural network-based strategies. It offers a comprehensive review and covers causes of drift, compensation methods and future directions. This synthesis provides insights for enhancing the reliability and effectiveness of E-nose systems in drift issues. Design/methodology/approach The article adopts a comprehensive approach and systematically explores the causes of sensor drift in E-nose systems and proposes various compensation strategies. It covers both offline and online compensation methods, as well as neural network-based approaches, and provides a holistic view of the available techniques. Findings The article provides a comprehensive overview of drift compensation algorithms for E-nose technology and consolidates recent research insights. It addresses challenges like sensor calibration and algorithm complexity, while discussing future directions. Readers gain an understanding of the current state-of-the-art and emerging trends in electronic olfaction. Originality/value This article presents a comprehensive review of the latest advancements in drift compensation algorithms for electronic nose technology and covers the causes of drift, offline drift compensation algorithms, online drift compensation algorithms and neural network drift compensation algorithms. The article also summarizes and discusses the current challenges and future directions of drift compensation algorithms in electronic nose systems.

Purpose Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait. Design/methodology/approach The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds. Findings Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning. Originality/value A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.