Sensor System Research Articles

Detection Capability Analysis of Field of View-Gated Optical Imaging System for All-Time Star Sensor

The field of view (FOV)-gated optical imaging system can relieve the contradiction between a wide FOV and the effective suppression of sky background radiation, making it particularly suitable for all-time star sensors. The detection capability of this novel optical imaging system during daytime differs significantly from that of traditional optical systems. This paper presents the principle of suppressing sky background radiation through FOV-gated imaging. Subsequently, the detection capabilities, including detectable limiting stellar magnitude and the probability of detecting at least three stars, are analyzed for applications on airborne platforms operating at altitudes of no less than 3km. Based on the analysis results, an FOV-gated imaging system operating in the shortwave infrared wavelength band was designed. Additionally, stray light analysis software, ASAP, was employed to simulate the illumination of stellar signals and sky background radiation on the detector. The evaluation of the detection capability of the designed FOV-gated optical system, based on simulation data, aligns with the theoretical analysis value. It demonstrates the system’s ability to detect multiple stars with a high probability during the daytime, thereby providing a theoretical foundation for the practical application of the FOV-gated optical imaging system on airborne platforms.

Assessment of groundwater potential zones for hard rock area of sabi river basin using an integrated approach of remote sensing, GIS and AHP techniques

The study was conducted to assess groundwater potential zones (GWPZs) for hard rock area of Sabi river basin for the sustainable development. Remote Sensing (RS) and Geographic Information System (GIS) have emerged as crucial methods in retrieval, monitoring and conserving groundwater resources were used along with Analytical Hierarchy Process (AHP) to demarcate the GWPZs. In order to assess GWP in the study area, eight thematic maps: drainage density, geomorphology, geology, rainfall, land use/land cover, lineament density, slope and soil were prepared using RS and conventional data. The AHP was applied to assign the weightages for each thematic map and its sub classes. Then, the thematic layers were overlaid using the weighted overlay analysis means of ArcGIS 10.4 to assess the GWPZ map of Sabi basin. The Sabi basin was categorized into five classes of GWPZ as very good (0.01%), good (24.72%), moderate (56.54%), poor (10.45%) and very poor (8.28%). The accuracy of produced GWPZ map was assessed with well yield data by agreement scheme using ground water prospects map and by using ROC curve analysis through R-studio software which indicates the satisfactory GWPZ map prediction accuracy. This study provides valuable database in planning and development of groundwater recharging and to delineate locations of potential artificial recharge sites in the hard rock regions and semi -arid areas so that appropriate structures could be planned and constructed to manage aquifer recharge for sustainable groundwater management in the future.

Differences in gait parameters between supervised laboratory and unsupervised daily assessments of healthy adults measured with an in-shoe motion sensor system

This cross-sectional study compared the gait parameters between supervised laboratory and unsupervised daily life assessments in healthy adults. Gait was evaluated in 24 healthy young adults during 72 h of daily life and a 6-min laboratory gait at a comfortable speed. An in-shoe motion sensor system recorded gait data every 2 min, automatically detected stable gait segments by identifying repetitive movement patterns, and calculated the average of three consecutive valid gait cycles during each measurement period. Significant differences were found in walking speed (stride length divided by stride time; laboratory: 4.60 km/h vs. daily-life: 4.38 km/h), maximum (peak) dorsiflexion angle (laboratory: 29.71° vs. daily-life: 26.65°), maximum (peak) plantar flexion angle (laboratory: 74.54° vs. daily-life: 71.91°), roll angle of heel contact (laboratory: 7.46° vs. daily-life: 6.70°), maximum speed during the swing phase (laboratory: 14.49 km/h vs. daily-life: 12.68 km/h), circumduction (lateral displacement during the swing phase; laboratory: 2.68 cm vs. daily-life: 3.69 cm), toe-in/out angle (laboratory: 13.87° vs. daily-life: 15.32°), stance time (laboratory: 0.62 s vs. daily-life: 0.65 s), and pushing time (time between heel leaving and toe leaving the ground; laboratory: 0.20 s vs. daily-life: 0.21 s). The innovative aspect of this study is the comprehensive evaluation of foot-related gait parameters in real-world environments using an in-shoe motion sensor system. This approach provides ecologically valid insights into gait dynamics during daily activities, emphasizing the importance of real-world assessments for accurately evaluating gait performance and predicting adverse events such as falls. Keywords: Gait, Foot, Laboratory, Daily Life, Unsupervised Assessment, Shoe Motion Sensors.

The Use of Wearable Systems for Assessing Work-Related Risks Related to the Musculoskeletal System—A Systematic Review

Work-related musculoskeletal disorders (WRMSDs) are a leading cause of chronic conditions among working-age adults. Preventing these disorders is crucial to reducing their impact, and quantitative analysis through sensors can help identify their causes and guide ergonomic solutions. This systematic review aims to compile research from 2000 to 2023 published in English and sourced from Web of Science, Scopus, or PubMed that examines workers’ movements during tasks using wearable sensor systems that are applicable in workplace settings. The goal is to identify the job sectors that have been studied and highlight tasks lacking ergonomic risk research. A total of 111 papers were selected through a screening process across three databases, assessed using the McMaster risk of bias tool. The studies span various job sectors and report on the use of different technologies for data collection and study population sizes. The review identifies existing research on WRMSD risks utilizing wearable systems in different job sectors, drawing attention to under-researched areas that warrant further study. It serves as a foundation for future research aimed at understanding the causes of WRMSDs and developing solutions supported by wearable technologies to mitigate these risks.

Flat-Knitted Double-Tube Structure Capacitive Pressure Sensors Integrated into Fingertips of Fully Fashioned Glove Intended for Therapeutic Use

A therapeutic glove, which enables medical non-professionals to perform physiotherapeutic gripping and holding movements on patients, would significantly improve the healthcare situation in physiotherapy. The glove aims to detect the orthogonal pressure load and provide feedback to the user. The use of textile materials for the glove assures comfort and a good fit for the user. This, in turn, implies a textile realization of the sensor system in order to manufacture both the glove and the sensor system in as few process steps as possible, using only one textile manufacturing technique. The flat knitting technology is an obvious choice here. The aim of the study is to develop a textile capacitive pressure sensor that can be integrated into the fingertips of a glove using flat knitting technology and to evaluate its sensor properties with regard to transmission behavior, hysteresis and drift. It was shown that the proposed method of a flat knitting sensor fabrication is suitable for producing both the sensors and the glove in one single process step. In addition, the implementation of an entire glove with integrated pressure sensors, including the necessary electrical connection of the sensor electrodes via knitted conductive paths in three fingers, was successfully demonstrated.

Advanced self-sensing road stud: Integrating deep learning-based speed detection and energy harvesting

Abstract This study introduces a self-powered and self-sensing vehicle speed detection sensor, representing a significant advancement in transportation. The system employs mechanical components like a slider crank, bevel gears, and one-way bearings for unidirectional rotation, converting translational motion into electrical energy upon the impact of vehicle tyres on road studs. The electrical power generation module, including a DC generator, rectifier, and battery circuit, captures and stores this energy. In addition to energy harvesting, the system integrates a deep learning model using Long Short-Term Memory (LSTM) networks to precisely calculate vehicle speed from the displacement signals of the road studs. Displacement data from an SR-04 sensor is processed and fed into the LSTM network, achieving a classification accuracy of 99.45% for vehicle speeds of 5, 10, 15, and 20 km/h. A mathematical model and MATLAB Simscape simulations were developed, followed by experimental validation using a Mechanical Testing and Sensing System (MTS) under laboratory conditions. Lab-scale testing, a maximum output power of 3.72W and an efficiency of 62.7% were recorded at 8 Hz. Field tests were performed at various vehicle speeds. A peak voltage output of 10V was recorded for a single phase of a three-phase DC generator at 15 km/h. The displacement sensor beneath the road stud was used to record the relative time signal between adjacent peaks to calculate vehicle speed. The sensor is sustainable in energy and easily installable without infrastructure changes, enhances transportation efficiency, and is useful for traffic management, road safety, and smart transportation networks.

AI-Driven Predictive Maintenance in Data Infrastructure: A Multi-Modal Framework for Enhanced System Reliability

This article presents a comprehensive framework for implementing artificial intelligence-driven predictive maintenance in modern data infrastructure environments. While traditional maintenance approaches have relied on reactive or scheduled interventions, the proposed framework leverages multiple AI technologies, including machine learning, natural language processing, and reinforcement learning, to create a proactive maintenance ecosystem. The methodology integrates diverse data streams from infrastructure components, including sensor data, system logs, and historical maintenance records, to predict potential failures and optimize maintenance schedules. The approach combines time series analysis for trend identification, natural language processing for unstructured data analysis, and reinforcement learning for dynamic schedule optimization. Implementation across multiple case studies, including cloud service providers and manufacturing environments, demonstrates significant improvements in system reliability, reduction in unplanned downtime, and optimization of maintenance resource allocation. The results indicate that AI-driven predictive maintenance substantially outperforms traditional approaches in both accuracy and cost-effectiveness. This article contributes to the growing field of intelligent infrastructure management and provides practical guidelines for organizations seeking to enhance their data infrastructure reliability through advanced predictive maintenance strategies.

Сoncentration Polarization in Membrane Systems

Concentration polarization (CP) in membrane systems is understood as the phenomenon of the emergence of concentration gradients in a solution near the membrane surface, which is a result of the selective transfer of certain components of the solution through the membrane under the influence of transmembrane driving forces. CP accompanies all types of membrane processes. It affects transfer conditions and reduces the efficiency of separation processes: in most cases, there is a decrease in the overall transfer rate and an increase in energy consumption, as well as a loss of permselectivity. This review examines the general patterns and features of the CP phenomenon in the processes of electrodialysis, reverse osmosis, nanofiltration, ultrafiltration, pervaporation, as well as in membrane sensor systems and fuel cells. The fundamental principles of the CP phenomenon and experimental methods for its study are considered.

Design and test of the electrostatic actuation system of the inertial sensor for space gravitational wave detection

Design and test of the electrostatic actuation system of the inertial sensor for space gravitational wave detection

Luciferase-Based Reporter System for Investigating GPx4-Mediated Ferroptosis and Its Therapeutic Implications in Diabetes.

Ferroptosis, a distinct form of regulated cell death, is characterized by iron-dependent lipid peroxide accumulation in cell membranes from dysregulated cellular iron homeostasis and compromised antioxidant defense mechanisms. Glutathione peroxidase 4 (GPx4) is crucial in the regulation of ferroptosis by controlling lipid peroxide accumulation. Recent research established the association of ferroptosis with several diseases, prompting investigation toward ferroptosis-targeted therapeutic approaches. However, there is a lack of sensor systems designed to evaluate ferroptosis modulation in intact cells. In this study, we developed a highly sensitive luciferase-based reporter system to study GPx4-mediated ferroptosis in cells. We constructed a novel vector flanking the GPx4 promoter driving luciferase gene expression, demonstrating ferroptosis-specific luciferase activity in transfected HEK293T cells. We established stable cells expressing the construct and optimized its suitability for high-throughput screening using well-established ferroptosis modulators. We identified eugenol, a phenolic compound, as a potent ferroptosis inhibitor using the developed reporter system. Eugenol demonstrated dose-dependent protection against ferroptosis-induced damage in pancreatic beta cells, as assessed by the expression of the key markers such as GPx4, SLC7A11, NRF2, and HO1. Further, we showed the regulation of iron levels and total iron-binding capacity of beta cells by eugenol in streptozotocin (STZ) -induced diabetic mice. Additionally, the diabetes-induced downregulation of GPx4 and antioxidant Nrf2 in pancreatic tissue was significantly mitigated by eugenol, as evidenced by both immunohistochemistry and gene expression analysis. This research validates the functionality of the ferroptosis sensor and offers an approach to develop antidiabetic therapy by targeting ferroptosis to protect beta-cell viability and function.

Enhancing the erythritol production of Yarrowia lipolytica by high-throughput screening based on highly sensitive artificial sensor and anchor protein cwp2.

Yarrowia lipolytica is widely used for the industrial production of the natural sweetener erythritol. Despite improvements in fermentation process control and metabolic pathway regulation, bottlenecks still exist in terms of yield and screening technology. Therefore, we constructed an artificial sensor system for effective erythritol detection, established a single-cell droplet-based high-throughput screening system based on fluorescence-activated cell sorting, and obtained Y. lipolytica with improved erythritol production through mutagenesis and high-throughput screening. We used a droplet generator to co-cultivate Y. lipolytica 5-14 with Escherichia coli and used the E. coli fluorescent signal to detect the concentration of erythritol synthesized by Y. lipolytica 5-14 for high-throughput screening. Strains were subjected to UV mutagenesis for 120 s. Under optimized fermentation conditions using Y. lipolytica mutants in 96-well plates, the screening efficiency reached 16.7%. Y. lipolytica 5-14-E6 showed a 21% increase in erythritol to 109.84 g/L. After fermentation at 30°C in a 100 m3 fermenter for 75 h, the mutant Y. lipolytica 5-14-E6 erythritol yield reached 178 g/L.

Implementation of Embedded PID Controller to Water Level Control System

In chemical and industrial processes, the level control of liquids in tanks is significant and widely used, especially in power stations and petroleum industries. The liquid level can be controlled by the most common method, PID control due to its efficiency and performance in achieving the desired set point. In this paper, the water level in tank control using a level sensor and PID controller system will be designed and implemented based on the PID Arduino microcontroller algorithm that controls the change in flow rate by controlling a DC water pump with the help of a MOSFETs interface circuit. Tuning the specific PID level tank controller will be implemented to increase the system accuracy with no error and overshoot when there is a disturbance (drawn or excess water).

Actuation system for inertial sensors in high-precision space missions using a torsion pendulum

Actuation system for inertial sensors in high-precision space missions using a torsion pendulum

Development of Sensor and Decision Support System (DSS)-based Automated Irrigation System for Enhancing Water Productivity of Tomato Crop

Development of Sensor and Decision Support System (DSS)-based Automated Irrigation System for Enhancing Water Productivity of Tomato Crop

Sensorless Design and Analysis of a Brushed DC Motor Speed Regulation System for Branches Sawing

Saw rotational speed critically influences cutting force and surface quality yet is often destabilized by variable cutting resistance. The sensorless detection method for calculating rotational speed based on current ripple can prevent the contact of wood chips and dust with Hall sensors. This paper introduces a speed control system for brushed DC motors that capitalizes on the linear relationship between current ripple frequency and rotational speed. The system achieves speed regulation through indirect speed measurement and PID control. It utilizes an H-bridge circuit controlled by the EG2014S driver chip to regulate the motor direction and braking. Current ripple detection is accomplished through a 0.02 Ω sampling resistor and AMC1200SDUBR signal amplifier, followed by a wavelet transform and Savitzky–Golay filtering for refined signal extraction. Experimental results indicate that the system maintains stable speeds across the 2000–6000 RPM range, with a maximum error of 2.32% at 6000 RPM. The improved ripple detection algorithm effectively preserves critical signals while reducing noise. This enables the motor to quickly regain speed when resistance is encountered, ensuring a smooth cutting surface. Compared to traditional Hall sensor systems, this sensorless design enhances adaptability in agricultural applications.

Sub-Hz fundamental, sub-kHz integral linewidth self-injection locked 780 nm hybrid integrated laser

Today’s precision experiments for timekeeping, inertial sensing, and fundamental science place strict requirements on the spectral distribution of laser frequency noise. Rubidium-based experiments utilize table-top 780 nm laser systems for high-performance clocks, gravity sensors, and quantum gates. Wafer-scale integration of these lasers is critical for enabling systems-on-chip. Despite progress towards chip-scale 780 nm ultra-narrow linewidth lasers, achieving sub-Hz fundamental linewidth and sub-kHz integral linewidth has remained elusive. Here we report a hybrid integrated 780 nm self-injection locked laser with 0.74 Hz fundamental and 864 Hz integral linewidths and thermorefractive-noise-limited 100 Hz2/Hz at 10 kHz. These linewidths are over an order of magnitude lower than previous photonic-integrated 780 nm implementations. The laser consists of a Fabry-Pérot diode edge-coupled to an on-chip splitter and a tunable 90 million Q resonator realized in the CMOS foundry-compatible silicon nitride platform. We achieve 2 mW output power, 36 dB side mode suppression ratio, and a 2.5 GHz mode-hop-free tuning range. To demonstrate the potential for quantum atomic applications, we analyze the laser noise influence on sensitivity limits for atomic clocks, quantum gates, and atom interferometer gravimeters. This technology can be translated to other atomic wavelengths, enabling compact, ultra-low noise lasers for quantum sensing, computing, and metrology.

Artificial mechano-nociceptive system based on transparent ITO/AlN/ITO memristor nociceptor neuron

Artificial nociceptors demonstrate significant potential in emerging fields such as intelligent prosthetics, humanoid robotics, and electronic skin, capable of transducing external noxious stimuli to the central nervous system. Unlike common sensory neurons, nociceptors exhibit unique characteristics, including “no adaptation,” “relaxation,” “threshold firing,” and “sensitization of allodynia/hyperalgesia.” This study presents a forming-free volatile transparent ITO/AlN/ITO memristor that emulates biological nociceptor behaviors. Leveraging this artificial nociceptor, an artificial mechano-nociceptive system is developed by integrating the ITO/AlN/ITO memristor into a piezoelectric force sensor system for pain sensing and noxious stimuli warning. This research contributes to the advancement of human cognitive capability emulation and artificial intelligence systems, particularly in the domain of pain perception and response.

Optical sensor for fast and accurate lung cancer detection with tissue autofluorescence and diffuse reflectance spectroscopy.

Cancer is a severe threat to human health, and surgery is a major method of cancer treatment. This study aimed to develop an optical sensor for fast cancer tissue. The tissue autofluorescence spectrum and diffuse reflectance spectrum were obtained by using a laboratory-developed optical sensor system. A total of 151 lung tissue samples were used in this ex vivo study. Experimental results demonstrate that tissue autofluorescence spectroscopy with a 365-nm excitation has better performance than diffuse reflectance spectroscopy, and 63 of 64 test samples (98.4% accuracy) were correctly classified with tissue autofluorescence spectroscopy and our developed data analysis method. Our promising ex vivo study results show that the developed optical sensor system has great promise for future clinical translation for intraoperative lung cancer detection and other applications.

Automated image registration of RGB, hyperspectral and chlorophyll fluorescence imaging data

BackgroundThe early and specific detection of abiotic and biotic stresses, particularly their combinations, is a major challenge for maintaining and increasing plant productivity in sustainable agriculture under changing environmental conditions. Optical imaging techniques enable cost-efficient and non-destructive quantification of plant stress states. Monomodal detection of certain stressors is usually based on non-specific/indirect features and therefore is commonly limited in their cross-specificity to other stressors. The fusion of multi-domain sensor systems can provide more potentially discriminative features for machine learning models and potentially provide synergistic information to increase cross-specificity in plant disease detection when image data are fused at the pixel level.ResultsIn this study, we demonstrate successful multi-modal image registration of RGB, hyperspectral (HSI) and chlorophyll fluorescence (ChlF) kinetics data at the pixel level for high-throughput phenotyping of A. thaliana grown in Multi-well plates and an assay with detached leaf discs of Rosa × hybrida inoculated with the black spot disease-inducing fungus Diplocarpon rosae. Here, we showcase the effects of (i) selection of reference image selection, (ii) different registrations methods and (iii) frame selection on the performance of image registration via affine transform. In addition, we developed a combined approach for registration methods through NCC-based selection for each file, resulting in a robust and accurate approach that sacrifices computational time. Since image data encompass multiple objects, the initial coarse image registration using a global transformation matrix exhibited heterogeneity across different image regions. By employing an additional fine registration on the object-separated image data, we achieved a high overlap ratio. Specifically, for the A. thaliana test set, the overlap ratios (ORConvex) were 98.0 ± 2.3% for RGB-to-ChlF and 96.6 ± 4.2% for HSI-to-ChlF. For the Rosa × hybrida test set, the values were 98.9 ± 0.5% for RGB-to-ChlF and 98.3 ± 1.3% for HSI-to-ChlF.ConclusionThe presented multi-modal imaging pipeline enables high-throughput, high-dimensional phenotyping of different plant species with respect to various biotic or abiotic stressors. This paves the way for in-depth studies investigating the correlative relationships of the multi-domain data or the performance enhancement of machine learning models via multi modal image fusion.

Mobile Accelerometer Applications in Core Muscle Rehabilitation and Pre-Operative Assessment.

Individual physiotherapy is crucial in treating patients with various pain and health issues, and significantly impacts abdominal surgical outcomes and further medical problems. Recent technological and artificial intelligent advancements have equipped healthcare professionals with innovative tools, such as sensor systems and telemedicine equipment, offering groundbreaking opportunities to monitor and analyze patients' physical activity. This paper investigates the potential applications of mobile accelerometers in evaluating the symmetry of specific rehabilitation exercises using a dataset of 1280 tests on 16 individuals in the age range between 8 and 75 years. A comprehensive computational methodology is introduced, incorporating traditional digital signal processing, feature extraction in both time and transform domains, and advanced classification techniques. The study employs a range of machine learning methods, including support vector machines, Bayesian analysis, and neural networks, to evaluate the balance of various physical activities. The proposed approach achieved a high classification accuracy of 90.6% in distinguishing between left- and right-side motion patterns by employing features from both the time and frequency domains using a two-layer neural network. These findings demonstrate promising applications of precise monitoring of rehabilitation exercises to increase the probability of successful surgical recovery, highlighting the potential to significantly enhance patient care and treatment outcomes.