Intelligent Sensor System Research Articles

Polymer-metal film induced ultrahigh output TVNG and self-powered intelligent sensor system assisted by EEMD

Tribovoltaic nanogenerators (TVNGs) based on tribovoltaic effect can output DC current directly, and has the advantages of high output current density and low internal resistance. While TVNGs have been restricted by its low output voltage and power. Herein, we have developed a metal-polymer film with a micro-uneven structure on the surface, much higher corrosion resistance and wear resistance than metal. On this basis, a centimeter-level sliding structure metal-EP/GaN-TVNG has been fabricated, which shows a high and stable long-term DC electrical output (a maximized power output of 4.19 W/m2 when matched with a 0.07 MΩ resistor, a stable output current of 2.25 A/m2 and a peak Voc of 27 V). Further, a TVNG vehicle travelling intelligent sensor and system which can integrate electrical signals collected by TVNG into digital signal processing algorithms and then feed backing to the driving system, has been designed and manufactured. As an effective method for deep analysis of signals, Ensemble Empirical Mode Decomposition (EEMD) has been used for the first time for deep processing of nanogenerator signals in this work. This work provides a new model of further development of high output TVNG and a new deep signal processing methods of TVNG/TENG signal.

Enhancing Road Safety: Fast and Accurate Noncontact Driver HRV Detection Based on Huber-Kalman and Autocorrelation Algorithms.

Enhancing road safety by monitoring a driver's physical condition is critical in both conventional and autonomous driving contexts. Our research focuses on a wireless intelligent sensor system that utilizes millimeter-wave (mmWave) radar to monitor heart rate variability (HRV) in drivers. By assessing HRV, the system can detect early signs of drowsiness and sudden medical emergencies, such as heart attacks, thereby preventing accidents. This is particularly vital for fully self-driving (FSD) systems, as it ensures control is not transferred to an impaired driver. The proposed system employs a 60 GHz frequency-modulated continuous wave (FMCW) radar placed behind the driver's seat. This article mainly describes how advanced signal processing methods, including the Huber-Kalman filtering algorithm, are applied to mitigate the impact of respiration on heart rate detection. Additionally, the autocorrelation algorithm enables fast detection of vital signs. Intensive experiments demonstrate the system's effectiveness in accurately monitoring HRV, highlighting its potential to enhance safety and reliability in both traditional and autonomous driving environments.

Tunnel silicon nitride manipulated reconfigurable bi-mode nociceptor analog

Neuromorphic applications have shown great promise not only for efficient parallel computing mode to hold certain computational tasks, such as perception and recognition, but also as key biomimetic elements for the intelligent sensory system of next-generation robotics. However, achieving such a biomimetic nociceptor that can adaptively switch operation mode with a stimulation threshold remains a challenge. Through rational design of material properties and device structures, we realized an easily-fabricated, low-energy, and reconfigurable nociceptor. It is capable of threshold-triggered adaptive bi-mode jump that resembles the biological alarm system. With a tunnel silicon nitride (Si3N4) we mimicked the intensity- and rehearsal-triggered jump by means of the tunneling mode transition of Si3N4 dielectric. Under threshold signals the device can also express some common synaptic functions with an extremely low energy density of 33.5 fJ/μm2. In addition, through the modulation of Si3N4 thickness it is relatively easy to fabricate the device with differing pain degree. Our nociceptor analog based on a tunneling layer provides an opportunity for the analog pain alarm system and opens up a new path toward threshold-related novel applications.

Development of an Intelligent Road-Crossing Sensor System for Enhanced Pedestrian Safety

Pedestrian safety at road crossings in urban environments is a critical issue due to the increasing vehicular traffic and high pedestrian density. Traditional traffic control measures often fail to adequately address the dynamic interactions between vehicles and pedestrians, leading to significant risks of accidents. This study proposes the development of an intelligent road-crossing sensor system leveraging advanced technologies such as sensors, artificial intelligence (AI), and the Internet of Things (IoT). The system aims to enhance pedestrian safety by providing real-time alerts and adaptive traffic control, thereby reducing the risk of pedestrian-vehicle collisions. The methodology includes sensor selection, data collection, machine learning model development, and system implementation and testing. The potential benefits and challenges of deploying such systems are also discussed, offering insights into creating smarter and safer urban environments. Keywords: Pedestrian safety, intelligent sensor systems, road crossing, vehicle detection, traffic signals

Measurement of high quality development of manufacturing industry empowered by big data based on intelligent sensor systems

Manufacturing industry is an important field of national economic development, but it is difficult to realize intelligent management and optimize production. Therefore, this study aims to explore the role of big data authorization based on intelligent sensor systems for the high-quality development of manufacturing. Through reviewing the relevant literature and field research, the technical characteristics of intelligent sensor system and its advantages and application scenarios in the manufacturing industry are sorted out. Based on the deep research of sensor technology and big data analysis, the paper analyzes sensor selection and configuration, data acquisition and processing, data storage and transmission, data analysis and application. The feasibility and effectiveness of the proposed measurement method are verified through the verification and evaluation of actual cases. This paper summarizes the importance of measurement of intelligent sensor system, and points out the direction and challenge of further research, which provides a reliable measurement scheme for manufacturing industry and provides important data support for high-quality development.

Near-Infrared Off-Axis Cavity-Enhanced Optical Frequency Comb Spectroscopy for CO2/CO Dual-Gas Detection Assisted by Machine Learning.

Cavity-enhanced direct frequency comb spectroscopy (CE-DFCS) is widely used as a highly sensitive gas sensing technology in various gas detection fields. For the on-axis coupling incidence scheme, the detection accuracy and stability are seriously affected by the cavity-mode noise, and therefore, stable operation inevitably requires external electronic mode-locking and sweeping devices, substantially increasing system complexity. To address this issue, we propose off-axis cavity-enhanced optical frequency comb spectroscopy from both theoretical and experimental aspects, which is applied to the detection of single- and dual-gas of carbon monoxide (CO) and carbon dioxide (CO2) in the near-infrared. An erbium-doped fiber frequency comb with a repetition frequency of ∼41.709 MHz is coupled into a resonant cavity with a length of ∼360 mm in an off-axis manner, exciting numerous high-order modes to effectively suppress cavity-mode noise. The performance of multiple machine learning models is compared for the inversion of a single/dual gas concentration. A few absorbance spectra are collected to build a sample data set, which is then utilized for model training and learning. The results demonstrate that the Particle Swarm Optimization Support Vector Machine (PSO-SVM) model achieves the highest predictive accuracy for gas concentration and is ultimately applied to the detection system. Based on Allan deviation, the detection limit for CO in single-gas detection can reach 8.247 parts per million by volume (ppmv) by averaging 87 spectra. Meanwhile, for simultaneous CO2/CO measurement with highly overlapping absorbance spectra, the LoD can be reduced to 13.196 and 4.658 ppmv, respectively. The proposed optical gas sensing technique indicates the potential for the development of a field-deployable and intelligent sensor system capable of simultaneous detection of multiple gases.

A POM/viologen-based supramolecular fluorescent probe for Ag+ detection and application of visible hydrogel based intelligent sensing system

Silver (Ag+) as a typical heavy metal ion has significant impacts on human health, so monitoring Ag+ level is an important issue. In this work, a new POM (polyoxometalates)/viologen-based supramolecular compound, [(HMSBP)2(γ-Mo8O26)]·4H2O (1) has been synthesized by hydrothermal method (MSBP = 1-(4-Methanesulfonyl-benzyl)-[4,4']bipyridinyl-1-ium). This POM/viologen-based supramolecular compound exhibits the unique supramolecular structure, excellent optical stability and superior fluorescence intensity, which is expected to become a promising fluorescent sensor. The coordination reaction between SO, pyridine N atom and Ag+ in compound 1 makes the detection of Ag+ achieved and the limit of detection (LOD) is 0.0411 μM in the range of 0.1–3 μM. Moreover, this supramolecular fluorescent probe has successfully realized the quantitative monitoring of Ag+ concentration in environmental water samples and commercially available disinfectant spray. Viologen is regarded as a good external stimulus responsive material, while its discoloration behavior can be exploited as an intelligent sensor system. The platform based on hydrogel and smartphones realizes the visualization and real-time quantification of Ag+, which provides an effective way for the rapid and convenient determination of Ag+. This provides a feasible way to promote the qualitative/semi quantitative detection of visual fluorescence POM/viologen sensors.

A Multi‐Module Sensing and Bi‐Directional HMI Integrating Interaction, Recognition, and Feedback for Intelligent Robots

AbstractRapid advances in robotics have placed urgent demands on more intelligent human‐machine interaction technologies. Specifically, the way of establishing dual‐way intuitive communication with a consistent sensory system can greatly enhance efficiency and reliability. Here, a bi‐directional human‐machine interface (HMI) is designed by applying starch‐based hydrogel sensors. The whole system consists of a multi‐modal wearable sensory exoskeleton with a haptic feedback module and sensory robotic hand. The sensory exoskeleton with strain‐sensing glove and rotation‐sensing arm can capture and project the motion of the entire upper limb. The system offers object recognition functions by utilizing a sensing array on the robotic hands and machine learning algorithms, which can identify the shape and hardness information. The recognized results can be delivered back to the operator via vision and vibrational haptic feedback, respectively. This dual‐way intelligent sensory system shows potential application in many key fields such as the Internet of Things, teleoperation, and medical robotics.

Analysis of MIMO methods for 5G and subsequent technologies: advantages and disadvantages

Formulation of the problem. The global bandwidth shortage in the wireless communications sector has prompted the study and research of wireless access technology, known as mass Multiple Input/output (MIMO) technology. Mass MIMO is one of the key technologies for next-generation networks, which combines antennas in both the transmitter and receiver to provide high spectral and energy efficiency using relatively simple processing. Gaining a better understanding of the massive MIMO system to overcome the fundamental challenges of this technology is vital for the successful deployment of 5G and higher networks to implement various intelligent sensor system applications. Thus, in this paper, fully digital, analog and hybrid structures are analyzed and the method of multilevel massive MIMO transmission is described in detail. Purpose. Analysis of the main technologies needed to meet the demand for data transmission expected for 5G and 6G networks, with the allocation of transmission methods necessary for the mass implementation of MIMO. Practical significance. Evaluation of the MIMO system bandwidth, hardware and computational complexity, as well as energy efficiency, it is shown that massive MIMO circuits can rely on asymptotic linear independence when designing signal processing, given the large number of antennas used. Classical MIMO methods are not able to meet the technical requirements, so hybrid architectures use a combination of analog and digital areas to use the spatial resolution provided by a large number of antenna elements, but at the same time the number of energy-consuming and expensive radio frequency circuits remains within reasonable limits, and computational complexity is controlled. It becomes clear that there is no single structure/algorithm that would provide the best compromise between complexity and performance in all possible scenarios, rather, there is a need to adapt them to the characteristics of the application and channel when designing the system. As for optimizing the energy efficiency of the RF power supply, this is achievable using solutions such as multilayer massive MIMO.

An IoT-Enabled E-Nose for Remote Detection and Monitoring of Airborne Pollution Hazards Using LoRa Network Protocol.

Detection and monitoring of airborne hazards using e-noses has been lifesaving and prevented accidents in real-world scenarios. E-noses generate unique signature patterns for various volatile organic compounds (VOCs) and, by leveraging artificial intelligence, detect the presence of various VOCs, gases, and smokes onsite. Widespread monitoring of airborne hazards across many remote locations is possible by creating a network of gas sensors using Internet connectivity, which consumes significant power. Long-range (LoRa)-based wireless networks do not require Internet connectivity while operating independently. Therefore, we propose a networked intelligent gas sensor system (N-IGSS) which uses a LoRa low-power wide-area networking protocol for real-time airborne pollution hazard detection and monitoring. We developed a gas sensor node by using an array of seven cross-selective tin-oxide-based metal-oxide semiconductor (MOX) gas sensor elements interfaced with a low-power microcontroller and a LoRa module. Experimentally, we exposed the sensor node to six classes i.e., five VOCs plus ambient air and as released by burning samples of tobacco, paints, carpets, alcohol, and incense sticks. Using the proposed two-stage analysis space transformation approach, the captured dataset was first preprocessed using the standardized linear discriminant analysis (SLDA) method. Four different classifiers, namely AdaBoost, XGBoost, Random Forest (RF), and Multi-Layer Perceptron (MLP), were then trained and tested in the SLDA transformation space. The proposed N-IGSS achieved "all correct" identification of 30 unknown test samples with a low mean squared error (MSE) of 1.42 × 10-4 over a distance of 590 m.

Intelligent Sensor System with Transmission Coefficient in X-band Frequency for Determining Sugar Content

Intelligent Sensor System with Transmission Coefficient in X-band Frequency for Determining Sugar Content

Measurement of Moisture in Transformer Insulation Using the Intelligent High-Frequency Sensor System

High-frequency sensor-based system is proposed for evaluating the degradation of the transformer insulation through the measurement of moisture concentration. The proposed system employs an intelligent open-ended coaxial probe microwave technique. An optimized ANN model is designed by selecting appropriate features to obtain accurate dielectric characterisation. Redundant feature elimination reduces the computational burden and also bypasses the repeated calibration refresh required to complete RF measurement in the conventional technique. ANN model is trained in such a way that measured impedance mismatch due to the termination by the sample at the probe end is related to its permittivity. The introduction of an intelligent standardization procedure led to a low error in the measurement of moisture (< ±3%) with a sensitivity of 0.027/ppm. This method is found to be simple and faster compared to other existing techniques. Due to the advantage of high-speed measurement and low error, this sensing technique is very suitable for transformer condition monitoring.

ИНТЕЛЛЕКТУАЛЬНАЯ СЕНСОРНАЯ СИСТЕМА РАНЖИРОВАНИЯ ИОННОГО СОСТАВА ГРУДНОГО МОЛОКА

An intelligent sensor system has been developed and investigated that allows qualitative medical ranking of breast milk and dairy products. Electrochemical sensors with sensitivity to the principal significant components of the biological medium under study were used. It was found that the 'digital images' of the breast milk of women who had undergone disease differed significantly from those of healthy women. The principal component method has been applied to rank breast milk and cow's milk, and groups of biological media similar in their properties have been identified. Analyzing the results of the studies, it is possible to state the effectiveness of the developed method for biomedical research.

Multifunctional Integrated Interdigital Microsupercapacitors and Self-Powered Iontronic Tactile Pressure Sensor for Wearable Electronics.

Multifunctionality and self-powering are key technologies for next-generation wearable electronics. Herein, an interdigitated MXene/TiS2-based self-powered intelligent pseudocapacitive iontronic sensor system is designed, realizing integration of energy storage and pressure-sensitive sensing function into one device. The intercalation of TiS2 nanosheet can effectively prevent self-stacking of MXene and results in mesoporous cross-linked framework, therefore exposing more active sites and broadening the electron/ion transport channels. The pressure sensing performance together with developed all-solid-state microsupercapacitor is explored systematically. When applied in a symmetrical microsupercapacitor, it presents a satisfactory energy density of 31.6 Wh/kg at 400 W/kg and 79.8% capacitance retention after 10 000 cycles. Meanwhile, with MXene/TiS2//MXene/TiS2 interdigitated structure as flexible self-powering pressure sensor, it illustrates outstanding pressure-sensing response toward external pressure, realizing accurate and continuous detection of human body motion signals. It is believed that this work proposes a feasible strategy by integrating pressure-sensing with a self-powering function for the next-generation self-powered E-skin electronics.

Real-Time Vehicle Sound Detection System Based on Depthwise Separable Convolution Neural Network and Spectrogram Augmentation

This paper proposes a lightweight model combined with data augmentation for vehicle detection in an intelligent sensor system. Vehicle detection can be considered as a binary classification problem, vehicle or non-vehicle. Deep neural networks have shown high accuracy in audio classification, and convolution neural networks are widely used for audio feature extraction and audio classification. However, the performance of deep neural networks is highly dependent on the availability of large quantities of training data. Recordings such as tracked vehicles are limited, and data augmentation techniques can be applied to improve the overall detection accuracy. In our case, spectrogram augmentation is applied on the mel spectrogram before extracting the Mel-scale Frequency Cepstral Coefficients (MFCC) features to improve the robustness of the system. Then depthwise separable convolution is applied to the CNN network for model compression and migrated to the hardware platform of the intelligent sensor system. The proposed approach is evaluated on a dataset recorded in the field using intelligent sensor systems with microphones. The final frame-level accuracy achieved was 94.64% for the test recordings and 34% of the parameters were reduced after compression.

Application of Motion Capture Based on Digital Filtering Algorithm in Sports Dance Teaching

In order to improve the teaching effect of sports dance, this paper analyzes the traditional dance teaching motion capture, uses sensor motion perception algorithms to capture sports dance motion perception, and designs an intelligent sensor system that can be used for sports dance motion capture. Moreover, this paper combines the digital filter algorithm to design the hardware system structure of the sports dance motion capture system and builds a motion capture system for sports dance teaching based on the digital filter algorithm according to actual needs. In addition, this paper combines the simulation test to evaluate the performance of the system designed in this paper. The research results show that the motion capture system for sports dance teaching based on the digital filtering algorithm proposed in this paper can play an important role in sports dance teaching and effectively improve the efficiency of sports dance teaching.

Application of an Intelligent Sensor and Active Packaging System Based on the Bacterial Cellulose of Acetobacter xylinum to Meat Products.

Combining intelligent and active packaging serves the dual purpose of detecting color changes in food that reflect changes in its quality and prolonging its shelf life. This study developed an intelligent and active packaging system made from the cellulose of Acetobacter xylinum and assessed its ability to detect changes in the quality and to increase shelf-life of packaged fresh beef. The properties of the intelligent packaging’s sensor and active packaging films were determined. The application of this system to fresh beef stored at room temperature (28 ± 2 °C) for 24 h was tested. The color of the bromothymol blue (BTB) solution (pH 2.75) in the indicator of the intelligent packaging system changed from orange to dark green to indicate that beef quality changed from fresh to rotten. The meat treated with the active packaging with 10% and 15% garlic extract decayed on the 16th h. In contrast, the meat treated with the active packaging without the garlic extracts rotted on the 12th h. The shift in the indicator’s color was linearly related to the total plate count (TPC), total volatile basic nitrogen (TVBN), and pH of the meat packaged using the active packaging system. Therefore, BTB solution (pH 2.75) can be used as an intelligent packaging indicator that will allow consumers to assess the quality of packaged meat easily. As an antimicrobial agent, the addition of 10–15% garlic extract to the active packaging films can help delay the spoilage of packaged beef.

Tennis Practice on the Control Ability of the Intelligent Sensor System of the Elderly Posture

Tennis Practice on the Control Ability of the Intelligent Sensor System of the Elderly Posture

Design of meteorological intelligent sensor based on POE Technology

An application scheme of Poe based Ethernet technology in meteorological intelligent sensor system is designed. The working principle and implementation method of Ethernet power supply system (POE) based on IEEE802.3af Ethernet power supply industry standard are experimentally analyzed. The power supply part of the meteorological intelligent sensor makes full use of Poe technology to provide current on the network cable transmitting data, which greatly reduces the complexity of the power supply system and improves the reliability of the system power supply design. Through the test of the actual system, the function and performance of the meteorological intelligent transmission system have achieved the expected results.

Design and Implementation of Artificial Intelligence Image Detection System Based on Noise Monitoring

Image quality detection technology can replace manual inspections to automatically detect video quality, accurately analyze, judge and alarm abnormalities in video images in the surveillance system to ensure the normal operation of the expanding network video surveillance system. The thesis designs an intelligent traffic image sensor system to realize fast motion detection and object recognition of the surveillance scene. This system introduces a background model based on region segmentation and a feature-based object recognition algorithm. Experimental results show that the system can perform automatic movement detection and object classification and recognition efficiently in real time.