Detected Research Articles

Design and Function of Lower-Limb Prosthetics Based on Biomechanics and Bionic Simulation Using Deep Learning

By mimicking the biomechanics of a missing biological leg, robotic prosthetic legs hold the potential to significantly enhance mobility and quality of life for millions of lower-limb amputees. Unfortunately, compared to traditional passive prosthetics, existing powered prosthetics are heavier, bulkier, and have shorter battery life, which severely limits their clinical feasibility and hinders their practical use in the daily lives of amputees. This paper reviews and explores optimization strategies for the design of bionic robotic prosthetic legs, focusing on challenges related to the stability of biomechanical bionic prosthetics in complex terrain. It discusses how advancements in materials, structural design, and sensor technology can improve stability and adaptability. In the future, biomechanical bionic prosthetics will become more intelligent and personalized. They will possess features like automatic adjustment, 3D - printed customization, and the capacity to modify control parameters and movement patterns in accordance with individual requirements. The research also highlights the potential of neural interface technology to bring new opportunities for biomechanical bionic prosthetics, enabling natural control, efficient signal transmission, and the integration of virtual technologies to assist in rehabilitation.

Research on Low Power Methods for Wireless Devices in Intelligent Industrial Communication Systems

Industrial communication devices achieve low power consumption and high efficiency in WirelessHART networks through hardware and software optimization. Hardware advancements include advanced manufacturing processes, highly integrated chips, low-power components, dynamic power management, energy harvesting, and intelligent sensor technologies. On the software side, the network manager optimizes routing schedules, the data link layer adopts TDMA technology, and the protocol stack is tailored for specific scenarios. These measures ensure the network's efficient operation. Furthermore, the integration of smart devices enhances monitoring, control, and energy-saving capabilities, enabling real-time data analysis and decision-making. This fosters innovation and sustainability in industrial automation, paving the way for advanced analytics and seamless connectivity among different devices and systems in industrial environments.

Artificial Neural Networks as Digital Twins for Whispering Gallery Mode Optical Sensors in Robotics Applications

This paper investigates the use of artificial neural networks (ANNs) as a viable digital twin or alternative to the typical whispering gallery mode (WGM) optical sensors in engineering systems, especially in dynamic environments like robotics. Because of its fragility and limited endurance, the WGM sensor which is based on micro-optical resonators is inappropriate in these kinds of situations. In order to address these issues, the paper suggests an ANN that is specifically designed for the system and makes use of the WGM sensor’s high-quality factor (Q-factor). By extending the applicability and endurance to dynamic contexts and reducing fragility problems, the ANN seeks to give high-resolution measurement. In order to minimize post-processing requirements and maintain system robustness, the study goal is for the ANN to function as a representative predictor of the WGM sensor output. The GUCnoid 1.0 humanoid robot is used in the paper as an example to show how the WGM optical sensors may improve humanoid robot performance for a variety of applications. The results of the experiments demonstrate that the sensitivity, precision, and resolution of ANN outputs and actual WGM shifts are equivalent. As a consequence, current obstacles to the widespread use of high-precision sensing in the robotics industry are removed, and the potential of ANNs as virtual substitutes or the digital twin for genuine WGM sensors in robotics systems is validated. So, this paper can be very beneficial not only to the sensing technologies that are used in robotics, which are subjected to the dynamic environments, but also to the industrial automation and human-machine interface.

High-Sensitivity and Flexible Motion Sensing Enabled by Robust, Self-Healing Wood-Based Anisotropic Hydrogel Composites.

By integrating polyvinyl alcohol (PVA)-borate-tannic acid (TA)-sodium sulfate into cellulosic wood matrices, a novel wood-basedPVA-borate-TA-sodium sulfate (WPBTS) hydrogel is successfully synthesized. Through a multicomponent synergistic design combining natural lignocellulose, PVA, borax, TA, and sodium sulfate, multiple dynamic cross-linking mechanisms-dynamic borate bonding, hydrogen bonding, and metal-ligand interactions-are established, resulting in WPBTS hydrogels with exceptional mechanical properties and self-healing capabilities. The mechanical strength of the WPBTS hydrogel reached an impressive 19.8MPa, a 45-fold increase compared to PVA-borax-tannic acid (PBTS) hydrogels. Furthermore, the assembled WPBTS hydrogel-based flexible sensor demonstrates a remarkably fast response time of just 20ms and maintains excellent performance in challenging simulated saline environments. This innovation represents a significant advancement in sensor technology and highlights the potential for transformative applications in complex and demanding scenarios.

Wearable Photonic Artificial Throat for Silent Communication and Speech Recognition.

Advocating for the voices of the disabled, particularly through wearable artificial throats, has garnered significant attention recently. Such devices necessitate sensors with stretchability, high sensitivity, and excellent skin conformability. In this study, an intelligent photonic artificial throat has been developed. It features a sandwich-structured optical fiber sensor encapsulated in Dragon Skin 20, which has an elastic modulus similar to human tissue and is integrated with sensitivity-enhancing rings and fabric for enhanced wearability. With ultrafast response (response time: 10 ms, recovery time: 32 ms) and high sensitivity (1.92 μW/mN), it detects throat area vibrations and muscle contractions, accurately identifying tones in Mandarin, vowels, words, and sentences in English, achieving accurate bilingual detection. It also distinguishes animal sounds (horse neighing and cuckoo's call) and pop songs when mounted on speakers. Furthermore, the artificial throat can accurately detect subtle movements of the head and neck, and by combining nodding actions with the MORSE code, silent communication between individuals has been successfully achieved. Integrated with an advanced artificial intelligence (AI) algorithm, it recognizes tones (97.50%), vowel letters (97.00%), common words (98.00%) and sentences (96.52%), opening prospects for biomedical applications, language education, speech recognition, motion monitoring, and more.

Three-Component Accelerometer Based on Distributed Optical Fiber Sensing

The three-component accelerometer array has garnered significant attention in seismic wave detection. In this paper, we designed a three-dimensional optical fiber accelerometer based on a circular cross-section cantilever beam and distributed optical fiber strain interrogator. An externally modulated optical frequency domian reflectometry (OFDR) system with centimeter-level spatial resolution is developed to demodulate the dynamic strain on fiber. An algorithm to reconstruct the three-component acceleration from the strain of the optical fiber was derived, and the factors affecting the errors in reconstruction were also investigated. The developed accelerometer exhibits comparable performance to an electrical accelerometer in the experiment. The correlation coefficient between the reconstructed signal waveforms from the two accelerometers exceeded 0.9, and the angular error was less than 8°. The proposed accelerometer is highly compatible with distributed optical fiber sensing technology, presenting significant potential for long-distance array deployment of three-component seismic wave monitoring.

Pressure and liquid depth sensing using a Fabry–Perot probe based on a silica capillary fiber and an agar membrane

The continuous evolution of fiber optics technology drives the photonics community to explore novel sensing configurations, aiming at developing optical sensors with enhanced capabilities. Among these efforts, Fabry–Perot interferometers (FPIs) emerge as a promising strategy for building fiber sensors due to their simplicity, miniature size, resolution tunability, and capability of acting as platforms for humidity, temperature, and pressure sensors. In addition to traditional materials for constructing FPIs, agar appears as a promising photonic material, offering biocompatibility, flexibility, and ease of preparation. In this manuscript, we present the development of a FPI sensing probe based on a silica capillary and an agar membrane for pressure and liquid depth sensing. Our report encompasses the description of the probe preparation procedure, the FPI spectral characterization, and the sensing measurements. Our efforts allowed us to demonstrate sensors with sensitivities of (− 4.9 ± 0.1) nm/bar and (− 5.9 ± 0.2) pm/mm. We consider that our work expands the application scenario of agar within photonics and offers a new avenue for realizing biocompatible pressure and liquid depth sensors.

Application of artificial intelligence and machine learning in bovine respiratory disease prevention, diagnosis, and classification.

Bovine respiratory disease (BRD) is the leading infectious disease in cattle, resulting in significant economic losses and welfare concerns in beef and dairy production systems. Traditional diagnostic methods for BRD typically rely on clinical observations and diagnostic laboratory tests, which can be time consuming with moderate diagnostic sensitivity. In recent years, machine learning (ML) and AI have emerged as powerful tools in animal health research, offering opportunities for improving BRD diagnostics and management. This review explores the current landscape of published literature on the use of ML and AI in BRD prevention, diagnostics, and classification. First, disease classification and pathogen identification models leveraging supervised models and metagenomic sequencing have identified specific community structure information in classifying specific BRD cases. From epidemiological datasets tracking disease outbreaks and risk factors, user-friendly platforms for producers and veterinarians are capable of being generated and deployed, providing customized scenarios, potential economic impacts, and pathogenic effects as a decision-support tool. Veterinarian-operated technologies, such as computer-aided lung auscultation stethoscopes, can automatically calculate lung scores and associated BRD severity likelihoods. Prediction and detection models used to leverage physical characteristics and feed consumption data provide novel methods of categorizing BRD risk. Finally, sensor technology monitoring behavioral or motion-based information provides continuous data on animal health and can enable early automated detection of BRD symptoms. Through synthesizing research in these key areas, this narrative review highlights the transformative potential of AI and ML in improving the accuracy, speed, and efficiency of BRD diagnostics, enhancing disease control and cattle welfare.

Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review

Landslides present a significant global hazard, resulting in substantial socioeconomic losses and casualties each year. Traditional monitoring approaches, such as geodetic, geotechnical, and geophysical methods, have limitations in providing early warning capabilities due to their inability to detect precursory subsurface deformations. In contrast, the acoustic emission (AE) technique emerges as a promising alternative, capable of capturing the elastic wave signals generated by stress-induced deformation and micro-damage within soil and rock masses during the early stages of slope instability. This paper provides a comprehensive review of the fundamental principles, instrumentation, and field applications of the AE method for landslide monitoring and early warning. Comparative analyses demonstrate that AE outperforms conventional techniques, with laboratory studies establishing clear linear relationships between cumulative AE event rates and slope displacement velocities. These relationships have enabled the classification of stability conditions into “essentially stable”, “marginally stable”, “unstable”, and “rapidly deforming” categories with high accuracy. Field implementations using embedded waveguides have successfully monitored active landslides, with AE event rates linearly correlating with real-time displacement measurements. Furthermore, the integration of AE with other techniques, such as synthetic aperture radar (SAR) and pore pressure monitoring, has enhanced the comprehensive characterization of subsurface failure mechanisms. Despite the challenges posed by high attenuation in geological materials, ongoing advancements in sensor technologies, data acquisition systems, and signal processing techniques are addressing these limitations, paving the way for the widespread adoption of AE-based early warning systems. This review highlights the significant potential of the AE technique in revolutionizing landslide monitoring and forecasting capabilities to mitigate the devastating impacts of these natural disasters.

Complex impedance measurement and saturation energy analysis of optical transition-edge sensors

Abstract Transition Edge Sensors (TESs) are highly sensitive thermal detectors that operate across a wide frequency range, from millimeter waves to gamma rays. For optical single-photon TES detectors, a high energy resolution and large saturation energy are required, primarily limited by factors such as heat capacitance and temperature sensitivity, αI. In this study, we evaluated TES devices of varying sizes by conducting complex impedance measurements using transfer function calibration, revealing that our titanium-based TES exhibits higher heat capacities compared to theoretical values. We obtained the measured and theoretical time constants, energy resolutions, and saturation energies for the different-sized TESs, notably constructing histograms from pulse area integral analysis. Our devices achieved an optimal energy resolution of 0.2 eV, with the largest TES demonstrating an energy resolution of 0.34 eV, a saturation energy of 35 eV and the ability to discriminate up to 20 photons at 438 nm. These results provide valuable insights for optimizing TES performance in precise photon detection.

Highly sensitive temperature and pressure fiber optic sensor based on harmonic Vernier effect

Highly sensitive temperature and pressure fiber optic sensor based on harmonic Vernier effect

IoT-Integrated Smart Energy Meter for Industrial Energy Monitoring

- Electric energy monitoring in industrial loads like pressure die casting is extremely vital especially due to their energy handling requirements. This work details the conception and implementation of an Internet of Things based Smart Energy Meter designed specifically for Industrial purposes. The system can measure three-phase voltage, current (up to 100A) and furnace temperatures with the aid of Resistance Temperature detectors. Using the ESP32 microcontroller and the Blynk Internet of things platform, the meter enables the collection, storage, and displaying of the data on the Cloud in real time. In order to enhance accuracy, the hardware architecture combines ZMPT101B voltage sensors, SCT-013-000 current sensors and a MAX31865 Resistance Temperature detector module. The software part of the system uses the Open-Energy Monitor library which contains various algorithms for energy calculations and transfers data over Serial peripheral interface/Wi-Fi protocols. The system has been tested in an industrial setting and the results have shown an accuracy of measurement within a tolerance of 1% for electrical and thermal characteristics plus a normal operation for variable conditions. The presented results prove the possibility of intra- system energy optimization and the industrial prospects of its application. Promising research directions include the addition of predictive maintenance features based on machine learning as well as more efficient scaling to support many different industrial applications. Key Words: Smart Energy Meter, IoT, ESP32, RTD Sensors, Energy Monitoring, Industrial Automation

Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection

This research proposes a ground-breaking technique for protecting agricultural fields against animal invasion, addressing a key challenge in the agriculture industry. The suggested system guarantees real-time intrusion detection and quick reactions by combining cutting-edge sensor technologies, image processing capabilities, and the Internet of Things (IoT), successfully safeguarding crops and reducing agricultural losses. This study involves a thorough examination of five models—Inception, Xception, VGG16, AlexNet, and YoloV8—against three different datasets. The YoloV8 model emerged as the most promising, with exceptional accuracy and precision, exceeding 99% in both categories. Following that, the YoloV8 model’s performance was compared to previous study findings, confirming its excellent capabilities in terms of intrusion detection in agricultural settings. Using the capabilities of the YoloV8 model, an IoT device was designed to provide real-time intrusion alarms on farms. The ESP32cam module was used to build this gadget, which smoothly integrated this cutting-edge model to enable efficient farm security measures. The incorporation of this technology has the potential to transform farm monitoring by providing farmers with timely, actionable knowledge to prevent possible threats and protect agricultural production.

Innovative Approaches to Gas Sensing: Bridging Theory and Human Applications

In this study, a comprehensive analysis of gas sensor technology was conducted, focusing on various types and their applications in industrial instrumentation. The technical characteristics of different gas sensors, both static and dynamic, are explored, along with an examination of working principles. Connection methods with other devices were demonstrated using simulation software for wiring diagrams and microcontroller programming. An analysis of circuit characteristics was presented, with a focus on input-output relations such as amplitude and sensitivity. Additionally, a conceptual design for a new type of gas sensor was proposed, utilizing existing components. This study not only contributes to the understanding of current gas sensor technologies but also outlines key directions for future innovations, specifically focusing on optimizing sensor efficiency for human-based technology applications.

Construction of Nonenzymatic Flexible Electrochemical Sensor for Glucose Using Bimetallic Copper Ferrite/Sulfur-Doped Graphene Oxide Water-Based Conductive Ink by Noninvasive Method.

Diabetes is a chronic disease that results in elevated blood glucose levels due to insufficient insulin production by the pancreas or impaired insulin utilization by the body. The development of effective tools for the in vitro detection of blood glucose is of paramount importance. Flexible electrodes serve as indispensable components in point-of-care systems, thus increasing accessibility and personalization in health monitoring. We present the preparation of handmade screen-printed electrode with water-based conductive ink for in vitro nonenzymatic glucose (Glu) determination at physiological pH values. The investigation aimed to identify the optimal conditions for formulating and composing the conductive ink used to create a copper ferrite/sulfur-doped graphene oxide/graphite/screen-printed electrode (CuFe2O4/S-GO/G/SPE). The resulting CuFe2O4/S-GO/G/SPE shows excellent glucose sensing ability with a limit of detection (LOD) of 2.93 μM. The superior determination at physiological pH is attributed to the complex structure formed by CuFe2O4 nanoparticles with glucose molecules in the basic pH conductive ink structure. Additionally, the excellent delocalization and conductivity of the S-GO particles in this complex structure contribute to improved performance. The study on artificial sweat samples resulted in achieving recovery values of 96.60% to 104.97%. In conclusion, the nonenzymatic and noninvasive Glu sensor printed with conductive ink containing CuFe2O4/S-GO/G on a flexible paper substrate surface demonstrated remarkable capabilities for determining Glu levels in artificial sweat samples. SPEs prepared with conductive ink produced by using these materials are promising candidates for use as electrodes in flexible and wearable sensor technology.

Machine Learning-Integrated Numerical Simulation for Predicting Photothermal Conversion Performance of Metallic Nanofluids.

Photothermal conversion in metallic nanofluids, driven by localized surface plasmon resonances, is essential for applications in biomedicine, such as cancer treatment and biosensing. However, accurately predicting photothermal conversion performance, particularly the spatial temperature distribution, remains challenging due to the complex interplay of nanoparticle properties. Existing experimental methods are labor-intensive and often insufficient in providing detailed thermal profiles. Here, a novel approach that integrates machine learning is presented with numerical simulations to predict the photothermal conversion efficiency and spatial temperature distribution in gold nanorod nanofluid. The method employs Discrete Dipole Approximation for optical property calculations, Monte Carlo simulations for light transport, and finite element methods for temperature distribution modeling. The machine learning model, trained on 1,024 cases of photothermal conversion efficiency and 2,016 cases of temperature fields, achieves rapid and accurate predictions with a high correlation coefficient (R2 = 0.972) to simulation results. This approach not only streamlines the prediction process but also provides an accessible tool for optimizing nanoparticle design, with significant implications for advancing biomedicine, energy, and sensor technologies.

Upconversion Optical Fiber Microsphere Sensor Designed for Precise Measurement of FPGA Temperature

Upconversion Optical Fiber Microsphere Sensor Designed for Precise Measurement of FPGA Temperature

Experimental Investigation of the Evaluation of the Cement Hydration Process in the Annular Space Using Distributed Fiber Optic Temperature Sensing

This study employed a full-scale cement sheath quality evaluation apparatus, along with a high-precision distributed fiber optic temperature sensing system, to perform real-time, continuous monitoring of the temperature change throughout the cement hydration process. The results of the cement annulus and cement bond defect monitoring during the hydration process indicated that the distributed fiber optic temperature data enabled centimeter-level resolution in defect identification. Defective regions exhibited significantly reduced temperature fluctuation amplitudes, and an inversion in temperature change at the early hydration stage, detected at the cement–defect boundary, facilitated the early detection of defect locations. The distributed fiber optic system was capable of conducting continuous and comprehensive monitoring of the sequential hydration temperature peaks of cement stages injected into the annulus. The results revealed the interdependence among different cement stages, as well as a phenomenon whereby an elevated annular temperature accelerates the progression of cement hydration. The experimental findings provide a reference for identifying the characteristic signals in distributed fiber optic monitoring of well-cementing operations, thereby establishing a foundation for the optimal and effective use of distributed fiber optics in assessing well-cementing quality.

Design and analysis of a linear astigmatism-free confocal off-axis reflective collimator for aerial applications

This paper presents the design and analysis of a linear astigmatism-free confocal off-axis reflective collimator for line-of-sight alignment in multi-wavelength/multi-field-of-view optical sensor modules. Comprising two off-axis mirrors and one flat mirror, the collimator has a 73 mm entrance pupil diameter and a 1200 mm focal length. Sensitivity analysis and Monte Carlo simulations using Optic Studio (ZEMAX) confirmed that the design meets all specified requirements. Stray light analysis shows that stray light is effectively blocked by the optomechanical structures only, eliminating the need for additional baffle structures. A semi-kinematic mount ensures assembly precision, while modal analysis indicates a fundamental frequency of 232.55 Hz, ensuring durability in aerial operations.

Micropatterned Liquid Crystalline Networks for Multipurpose Color Pixels.

Materials that can visually report changes in the surrounding environments are essential for future portable sensors that monitor temperature and detect hazardous chemicals. Ideal responsive materials for optical sensors are defined by a rapid response and readout, high selectivity, the ability to operate at room temperature, and simple microfabrication. However, because of the lack of viable materials and approaches, compact, passive, and multipurpose practical devices are still beyond reach. To address this challenge, we develop a methodology to fabricate colored and responsive micropixels printed by digital light projection lithography on gold substrates. These structures are made by polymeric Liquid Crystalline Networks (LCNs) whose birefringence and external stimuli responsiveness allow for micrometric devices with visual and fast response that we here apply to a few applications. First, we show how varying the projected geometrical shape can become an effective tool to engineer symmetric disclination lines in the liquid crystal order. Depending on the thickness of the micropixels, LCNs give rise to a birefringence color under polarized light or a structural color under white light due to thin-film interference. By exposing the micropatterns to temperature variation and solvents, we demonstrate a real-time optical temperature detection and differentiation between selected organic chemicals. The proposed materials and fabrication method could be scaled up and extended to roll-to-roll printing, enabling future real-life applications of liquid crystalline polymers in affordable microdevices and optical sensors with a net advantage with respect to traditional lithographic techniques in terms of fabrication speeds and costs.