Accurate Detection Research Articles

Automating cancer diagnosis using advanced deep learning techniques for multi-cancer image classification

Cancer detection poses a significant challenge for researchers and clinical experts due to its status as the leading cause of global mortality. Early detection is crucial, but traditional cancer detection methods often rely on invasive procedures and time-consuming analyses, creating a demand for more efficient and accurate solutions. This paper addresses these challenges by utilizing automated cancer detection through AI-based techniques, specifically focusing on deep learning models. Convolutional Neural Networks (CNNs), including DenseNet121, DenseNet201, Xception, InceptionV3, MobileNetV2, NASNetLarge, NASNetMobile, InceptionResNetV2, VGG19, and ResNet152V2, are evaluated on image datasets for seven types of cancer: brain, oral, breast, kidney, Acute Lymphocytic Leukemia, lung and colon, and cervical cancer. Initially, images undergo segmentation techniques, proceeded by contour feature extraction where parameters such as perimeter, area, and epsilon are computed. The models are rigorously evaluated, with DenseNet121 achieving the highest validation accuracy as 99.94%, 0.0017 as loss, and the lowest Root Mean Square Error (RMSE) values as 0.036056 for training and 0.045826 for validation. These results revealed the capability of AI-based techniques in improving cancer detection accuracy, with DenseNet121 emerging as the most effective model in this study.

Research on power battery anomaly detection method based on improved TimesNet

Health monitoring and abnormality detection of power batteries for new energy vehicles has been one of the hot topics in recent years. Accurate and efficient power battery anomaly detection is crucial to ensure stable operation of the battery system and energy saving. However, power battery data are often non-linear and unstable due to external factors, such as temperature conditions, which pose challenges for anomaly detection. Existing methods for collecting battery data’s temporal and spatial features are available, but many of them yield suboptimal detection accuracy and feature extraction efficiency due to their disregard for the multi-periodicity of the data. This paper proposes a novel network structure for power battery anomaly detection based on an improved TimesNet. Firstly, the original battery data undergo preprocessing, and the feature correlation coefficient matrix is established using the MIC algorithm. Secondly, the improved TimesNet network is employed to convert the one-dimensional time feature sequence into a two-dimensional tensor, enabling the extraction of temporal features and dependencies at various cycle scales. Finally, the two-dimensional tensor is transformed into a one-dimensional time series, which undergoes information-weighted aggregation to obtain the final anomaly detection results. To assess the effectiveness and generalization of the proposed model, experiments are conducted using Battery and four public datasets for anomaly detection. The experimental results demonstrate that the proposed model outperforms the transformer, autoformer, TimesNet, and Dlinear models, achieving an improvement of 1%–19% in the F1 value and 1%–3% in the ACC compared to the other models.

Simulation and Optimization of Transmitting Transducers for Well Logging

Piezoelectric transducers are commonly used in acoustic well logging. However, the low frequency and narrow range of the acoustic waves limit the achievable detection accuracy. In addition, the low amplitude of the waves causes useful information to be easily masked by noise during detection, which affects the accuracy of geological identification and makes it difficult to detect formations tens of meters away. This paper proposes a microporous liquid–electric transmission transducer, in which the microporous electrode structure generates a powerful shock wave through a high-energy instantaneous discharge. First, a model of the liquid–electric microporous transmitting transducer was constructed by combining simulations with numerical calculations, and its electro-acoustic characteristics were analyzed. Then, based on the survey requirements, two innovative optimization schemes for the microporous electrode structure were proposed, namely a triangular pyramid microporous electrode structure and a rectangular microporous electrode structure, and their performances were compared. The results show that the newly optimized triangular pyramid microporous electrode liquid–electric transducer generates acoustic waves with higher amplitude and a wider frequency range than conventional piezoelectric transducers and other microporous structures. It maintains high energy while achieving high frequencies, enabling detection at distances of up to hundreds of meters and the precise characterization of small geological bodies. This has significant implications for applications in marine exploration, land exploration, clean energy, and new energy fields.

Research on Anomaly Detection of Ship Behavior Based on Data Mining

With the increase in the number of ships and the intensifying maritime traffic, maritime accidents such as ship collisions and grounding occur frequently, posing serious threats to people's lives and property. Therefore, timely detection and correction of abnormal ship behaviors are of great significance for improving navigation safety. The Automatic Identification System (AIS) can provide real-time information on ships' positions, speeds, headings, and more, offering a rich data resource for data mining. Through the analysis of these data, it is possible to deeply explore the navigation patterns and characteristics of abnormal behaviors of ships. This paper applies data mining technology to the detection of abnormal ship behaviors, which not only improves the accuracy and efficiency of detection but also provides maritime regulatory authorities with richer decision-making support.

Research on Software Vulnerability Detection Methods Based on Deep Learning

This paper aims to investigate software vulnerability detection methods based on deep learning to address the ever-growing challenges in software security. With the rapid development of information technology, software vulnerabilities have become the primary targets of cyberattacks, posing severe threats to economic, military, and social security. By analyzing the limitations of existing software vulnerability detection methods, this paper explores the potential applications of deep learning technology in this field. Through a literature review, relevant theories of software vulnerabilities are introduced, including the concept, types, and impacts of vulnerabilities. Subsequently, detailed descriptions of deep learning-based vulnerability detection methods are provided, encompassing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and their variant LSTM, as well as the application of program slicing techniques in vulnerability detection. By evaluating and improving existing deep learning models, a novel deep learning-based vulnerability detection framework is proposed, with its workflow and technical principles elaborated. The proposed deep learning-based vulnerability detection method can effectively enhance the accuracy and efficiency of software vulnerability detection, reduce reliance on expert knowledge, and lower human resource costs. Experimental results demonstrate that this method performs exceptionally well on multiple datasets, indicating broad application prospects and significant research value. The deep learning-based software vulnerability detection method offers new insights and tools for addressing current software security challenges. In the future, with the continuous development and improvement of deep learning technology, this method will play an even more crucial role in the field of software vulnerability detection.

Evaluating cardiac electrophysiological markers for predicting arrhythmic risk in hypothyroid patients.

This study aimed to evaluate the impact of hypothyroidism and levothyroxine (LT4) treatment on arrhythmic risk by concurrently analyzing multiple electrocardiogram (ECG) parameters such as the Index of Cardio-Electrophysiological Balance (iCEB), frontal QRS-T angle, Tpeak-Tend (Tp-e) interval/QT interval ratio, and QT dispersion (QTd). This cross-sectional study included 132 adult patients with primary hypothyroidism who had been receiving LT4 treatment, and 132 demographically matched healthy controls. The hypothyroid group was also stratified by thyroid-stimulating hormone (TSH) levels (subclinical <4.5 and overt ≥ 4.5). Participants underwent a series of thyroid function and ECG measurements. The hypothyroid and healthy control groups were matched for age and gender (p = 0.080; p = 0.176). Participants with hypothyroidism had higher Tp-e/QT ratios, iCEB, median frontal QRS-T angle, and corrected QT dispersion (cQTd) than healthy controls (p = 0.004; p = 0.025; p = 0.004; p = 0.004, respectively). In the overt group, the Tp-e/QT ratio, iCEB, and median frontal QRS-T angles were all higher (p = 0.012, p = 0.037, and p = 0.016, respectively). Logistic regression analysis indicated that a higher iCEB score (β = 0.60, p = 0.003) was significant for the detection of arrhythmia risk. ROC analysis showed that iCEB had the highest sensitivity (0.80), moderate specificity (0.60), and AUC 0.70. Patients with hypothyroidism have a higher risk of arrhythmia. To assess this risk, it is important to analyze the Tp-e interval, iCEB, frontal QRS-T angle, and QTd. Differentiating between patients with subclinical and overt hypothyroidism can help minimize the risk of arrhythmia. iCEB is the most effective method for identifying arrhythmic risk. Using all these parameters can improve the accuracy of arrhythmic risk detection in patients with hypothyroidism.

An advanced quantum support vector machine for power quality disturbance detection and identification

Quantum algorithms have demonstrated extraordinary potential across numerous fields, offering significant advantages in solving practical problems. Power Quality Disturbances (PQDs) have always been a critical factor affecting the stability and safety of electrical power systems, and accurately detecting and identifying PQDs is crucial for ensuring reliable system operation. This paper explores the application of quantum algorithms in the field of power quality and proposes a novel method using Quantum Support Vector Machines (QSVM) to detect and identify PQDs, which marks the first application of QSVM in PQD analysis. The QSVM model employed involves three main stages: quantum feature mapping, quantum kernel computation, and model training. Quantum feature mapping uses quantum circuits to map classical data into a high-dimensional Hilbert space, enhancing feature separability. Quantum kernel computation calculates the inner products between features for model training. Rigorous theoretical and experimental analyses validate our approach. This method achieves a time complexity of O(N2log(N))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$O(N^{2} \\log (N))$\\end{document}, superior to classical SVM algorithms. Simulation results show high accuracy in PQDs detection, achieving a 100% detection rate and a 96.25% accuracy rate in single PQD identification. Experimental outcomes demonstrate robustness, maintaining over 87% accuracy even with increased noise levels, confirming its effectiveness in PQDs detection and identification.

Road Defect Identification and Location Method Based on an Improved ML-YOLO Algorithm

The conventional method for detecting road defects relies heavily on manual inspections, which are often inefficient and struggle with precise defect localization. This paper introduces a novel approach for identifying and locating road defects based on an enhanced ML-YOLO algorithm. By refining the YOLOv8 object detection framework, we optimize both the traditional convolutional layers and the spatial pyramid pooling network. Additionally, we incorporate the Convolutional Block Attention to effectively capture channel and spatial features, along with the Selective Kernel Networks that dynamically adapt to feature extraction across varying scales. An optimized target localization algorithm is proposed to achieve high-precision identification and accurate positioning of road defects. Experimental results indicate that the detection accuracy of the improved ML-YOLO algorithm reaches 0.841, with a recall rate of 0.745 and an average precision of 0.817. Compared to the baseline YOLOv8 model, there is an increase in accuracy by 0.13, a rise in recall rate by 0.117, and an enhancement in average precision by 0.116. After the high detection accuracy of road defects was confirmed, generalization experiments were carried out on the improved ML-YOLO model in the public data set. The experimental results showed that compared with the original YOLOv8n, the average precision and recall rate of all types of ML-YOLO increased by 0.075, 0.121, and 0.035 respectively, indicating robust generalization capabilities. When applied to real-time road monitoring scenarios, this algorithm facilitates precise detection and localization of defects while significantly mitigating traffic accident risks and extending roadway service life. A high detection accuracy of road defects was achieved.

Unsupervised domain adaptation for drive-by condition monitoring of multiple railway tracks

Monitoring railway tracks through drive-by vibration data collected by in-service trains offers a cost-effective and adaptable solution for inspecting multiple railway lines. However, numerous existing drive-by monitoring methods rely on supervised learning models, necessitating extensive labelled data for each line. In this paper, a novel framework is proposed based on Unsupervised Domain Adaptation (UDA) concept which facilitates the transfer of a geometric defects diagnosis model learned from one line to a new line without the need for any labelled data from the new line. The proposed framework learns the dynamic-based features that are sensitive to damage and also invariant to different railway tracks. It comprises three components: data pre-processing, UDA implementation, and damage diagnosis. The framework uses the data from the source domain, including corresponding labels, as well as the unlabelled data from the target domain as input. The outputs of the framework consist of the predicted labels for the target domain. The performance of the proposed framework is evaluated using a comprehensive dataset of field measurements of a high-speed train passing 4 different lines within the French high-speed rail network. The proposed UDA framework is implemented using four common UDA algorithms including Information-Theoretical Learning (ITL), Geodesic Flow Kernel (GFK), Transfer Component Analysis (TCA), and Subspace Alignment (SA). The results show that the proposed framework has a 14% increase in the anomaly detection accuracy compared to traditional unsupervised learning methods in which UDA is not used. Furthermore, this study investigates the impact of incorporating a percentage of target data labels during training (semi-supervised domain adaptation), along with various sensor layouts and different tuning parameters, on the accuracy of the proposed approach. The results show that the proposed framework can significantly facilitate the monitoring of railway track conditions using the data collected by in-service trains which could be great interest of railway owners.

Construction of a Fluorescence/Phase-Change Dual-Mode Sensor Based on Carbon Dots/Poly(acrylic acid) for Highly Selective and Sensitive Detection of Ferric Ions.

Fe3+ is one of the crucial metal ions in biological systems, and its excess or deficiency in the body can trigger various diseases, posing a serious threat to human health. Moreover, improper handling or disposal of Fe3+ can lead to water pollution, thereby harming the environment. Therefore, the development of highly selective and sensitive Fe3+ detection probes is particularly urgent. In this paper, a dual-mode sensor based on sol-gel and fluorescence signal responses was developed for the visual detection of Fe3+. The visual sensing method based on the simultaneous response of Fe3+-triggered dual signals can minimize the interference from false-positive signals and enhance detection accuracy. The dual-mode sensor, denoted as PAA@CDs, was constructed by incorporating high-brightness (high fluorescence emission intensity) green-yellow carbon dots (CDs) into poly(acrylic acid) (PAA), which possesses a large number of carboxyl functional groups. Based on the interaction of Fe3+ with the surface functional groups of CDs, nonfluorescent complexes are formed, leading to nonradiative electron transfer, which induces fluorescence quenching and produces a fluorescence signal visible to the naked eye. Additionally, the interaction of Fe3+ with the carboxyl groups of PAA triggers the cross-linking of PAA, causing a sol-gel phase change signal. Consequently, the PAA@CDs exhibit a dual-response signal in Fe3+ detection. Based on the fluorescence method, the linear detection range of PAA@CDs for Fe3+ is 0.05-2.60 mM with a limit of detection (LOD) of 5.14 μM. Meanwhile, using the sol-gel method, the linear detection range is 0.02-2.20 mM, and the LOD is 42.5 μM. Furthermore, the PAA@CDs probes can be successfully applied to the detection of Fe3+ in real water samples, demonstrating their potential value in the analysis of real samples containing multiple ions.

Vision-Based Localization Method for Picking Points in Tea-Harvesting Robots

To address the issue of accurately recognizing and locating picking points for tea-picking robots in unstructured environments, a visual positioning method based on RGB-D information fusion is proposed. First, an improved T-YOLOv8n model is proposed, which improves detection and segmentation performance across multi-scale scenes through network architecture and loss function optimizations. In the far-view test set, the detection accuracy of tea buds reached 80.8%; for the near-view test set, the mAP0.5 values for tea stem detection in bounding boxes and masks reached 93.6% and 93.7%, respectively, showing improvements of 9.1% and 14.1% over the baseline model. Secondly, a layered visual servoing strategy for near and far views was designed, integrating the RealSense depth sensor with robotic arm cooperation. This strategy identifies the region of interest (ROI) of the tea bud in the far view and fuses the stem mask information with depth data to calculate the three-dimensional coordinates of the picking point. The experiments show that this method achieved a picking point localization success rate of 86.4%, with a mean depth measurement error of 1.43 mm. The proposed method improves the accuracy of picking point recognition and reduces depth information fluctuations, providing technical support for the intelligent and rapid picking of premium tea.

GCS-YOLOv8: A Lightweight Face Extractor to Assist Deepfake Detection

To address the issues of target feature blurring and increased false detections caused by high compression rates in deepfake videos, as well as the high computational resource requirements of existing face extractors, we propose a lightweight face extractor to assist deepfake detection, GCS-YOLOv8. Firstly, we employ the HGStem module for initial downsampling to address the issue of false detections of small non-face objects in deepfake videos, thereby improving detection accuracy. Secondly, we introduce the C2f-GDConv module to mitigate the low-FLOPs pitfall while reducing the model’s parameters, thereby lightening the network. Additionally, we add a new P6 large target detection layer to expand the receptive field and capture multi-scale features, solving the problem of detecting large-scale faces in low-compression deepfake videos. We also design a cross-scale feature fusion module called CCFG (CNN-based Cross-Scale Feature Fusion with GDConv), which integrates features from different scales to enhance the model’s adaptability to scale variations while reducing network parameters, addressing the high computational resource requirements of traditional face extractors. Furthermore, we improve the detection head by utilizing group normalization and shared convolution, simplifying the process of face detection while maintaining detection performance. The training dataset was also refined by removing low-accuracy and low-resolution labels, which reduced the false detection rate. Experimental results demonstrate that, compared to YOLOv8, this face extractor achieves the AP of 0.942, 0.927, and 0.812 on the WiderFace dataset’s Easy, Medium, and Hard subsets, representing improvements of 1.1%, 1.3%, and 3.7% respectively. The model’s parameters and FLOPs are only 1.68 MB and 3.5 G, reflecting reductions of 44.2% and 56.8%, making it more effective and lightweight in extracting faces from deepfake videos.

Steel Wire Rope Damage Width Identification Method Based on Residual Networks and Multi-Channel Feature Fusion

In order to ensure the safety of steel wire rope in various application scenarios, it is particularly important to quantitatively detect the defects of wire rope. Complex detection conditions affect the detection efficiency of wire rope. Therefore, based on the magnetic flux leakage method, this study proposes a method to identify the damage width of steel wire rope for multi-channel fusion of a Hall sensor array. Firstly, the Hall sensor array is used to capture the magnetic flux leakage data of steel wire rope; then, continuous wavelet transform is used to decompose the original data, and moving average filtering is used to denoise each component; the denoised components are merged and converted into a time spectrum, and the time spectrum is classified by ResNet50 image classification model to realize the detection of wire rope damage width. According to the dataset used in this study, the results show that the proposed method performs best in the mainstream noise reduction model; detection accuracy for the width of damage in steel wire ropes is 97%, which proves that the proposed method is effective and feasible.

Deep Neural Network-Based Cigarette Filter Defect Detection System with FPGA Acceleration for Online Recognition

In the cigarette manufacturing industry, machine vision and artificial intelligence algorithms have been employed to improve production efficiency by detecting product defects. However, achieving both high accuracy and real-time defect detection for cigarettes with complex patterns remains a challenge. To address these issues, this study proposes a model based on RESNET18, combined with a feature enhancement algorithm, to improve detection accuracy. Additionally, a method is designed to deploy the model on a field-programmable gate array (FPGA) with high parallel processing capabilities to achieve high-speed detection. Experimental results demonstrate that the proposed detection model achieves a detection accuracy of 95.88% on a cigarette filter defect dataset with an end-to-end detection speed of only 9.38 ms.

Enhanced Cooperative Compressive Spectrum Sensing in Cognitive Radio Networks

ABSTRACTThe demand for bandwidth in cognitive radio networks (CRNs) is growing as applications become increasingly data intensive. Techniques such as compressed spectrum sensing (CSS) and cooperative spectrum sensing (C‐SS) are employed to address this challenge. C‐SS enhances overall detection accuracy and reliability by enabling multiple nodes to share and combine their local sensing data. Conversely, CSS effectively reduces the required information for spectrum usage decision making, thereby improving bandwidth utilization. Integrating these two methods allows CRNs to utilize the spectrum reliably and efficiently, leading to increased spectral efficiency. To further improve reconstruction performance, we leverage the sparsity concept to transcend hardware constraints and merge restrictions from both real and synthesized channels. This approach involves the virtual synthesis of channels, which linearly enhances the signal‐to‐noise ratio (SNR) within the network's size range. Simulation results demonstrate that our proposed method offers significant advantages over single‐node recovery, as validated by simulations and software defined radio (SDR) Implementation. The integration of spectral estimations from various local CR detectors enhances spatial diversity gain and sensing quality, particularly in fading channels. Compared to traditional approaches, our method achieves superior performance, evidenced by an increase in (from 93.97% to 96.52%) with almost the same .

The Role of 70-MHz Ultrahigh-Frequency Ultrasound in the Peripheral Nerve Injury.

High-frequency ultrasound with an 18-MHz probe (18 MHz-HFUS) plays a relevant role in the evaluation of peripheral nerve injury (PNI). Ultrahigh-frequency ultrasound with a 70-MHz probe (70 MHz-UHFUS) offers higher spatial resolution and could allow a better detection of PNI. This study aimed to compare the diagnostic performance of HFUS and UHFUS in PNI detection. In this retrospective study, were selected, between July 2022 and April 2024, 61 patients underwent HFUS, UHFUS, and nerve conduction study (NCS) for clinical suspicion of traumatic forearm PNI. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of HFUS and UHFUS in PNI detection were calculated and compared. NCS was used as the reference standard. Nonparametric statistical tests were used. A p value of < 0.05 was considered statistically significant. Comparing the diagnostic performance in PNI detection, the 70 MHz-UHFUS showed a sensitivity and diagnostic accuracy significantly higher than 18 MHz-HFUS, respectively, 98.0% versus 82.4% (p = 0.0205) and 95.1% versus 82.0% (p = 0.0468). Otherwise, not significantly difference were in specificity, PPV, and NPV. UHFUS compared to HFUS demonstrated a higher sensitivity and diagnostic accuracy in PNI detection.

Combining RNA tape stripping with dermoscopic features for melanoma identification

AbstractBackgroundA noninvasive, accurate diagnostic method for melanoma is needed to improve early detection and decrease number of excisions. Gene expression as measured by RNA levels uses tape strips to directly assesses genetic markers from skin lesions. Commercial RNA assays show varying diagnostic accuracy in melanoma detection. Combining dermoscopic features with RNA tape strips could reduce unnecessary removal of benign lesions by increasing specificity.ObjectivesTo test an RNA‐based rule‐out test and integrate dermoscopic features with RNA analysis to improve diagnostic accuracy of malignant melanoma (MM). Furthermore, we explore the association between RNA profiles and dermoscopic features.MethodsSeventy patients with pigmented skin lesions suspected of being melanoma were imaged with dermoscopy and tape stripped for RNA analysis before surgical excision. The images were evaluated for seven dermoscopic features, and RNA levels of 11 genes were analyzed. A combined test using both gene expression and dermoscopic features was developed. Associations between RNA profiles and dermoscopic features were explored.ResultsHistopathology revealed 19 malignant lesions (17 MM and two basal cell carcinomas) and 51 benign lesions. The combined dermoscopy and RNA test identified all malignant lesions (100% sensitivity) based on PRAME expression, blotch and regression structures and patient age. This combined model increased specificity to 35%, compared to 24% with the original RNA rule‐out test, without missing any malignant lesions. Significant differences in RNA profiles were observed for lesions expressing atypical network and regression structures.ConclusionsCombining RNA tape stripping with dermoscopic features can reduce the removal of benign lesions by over one‐third while maintaining 100% sensitivity. We found specific RNA profiles to be strongly associated with dermoscopic features, presenting a promising opportunity to integrate molecular and morphological information and provide valuable guidance for dermatologists managing atypical pigmented lesions.

High-precision 3D Modeling of Construction Waste Pile Bodies by Integrating Multi-source Data

Abstract. The instability of construction waste pile bodies, as an increasingly concerning disaster, poses significant risks to the safety of people's lives and property. Currently, there is limited research on high-precision 3D modeling techniques for construction waste pile bodies, which significantly hinders the accuracy and reliability of early detection and risk assessment of pile body instability. Therefore, constructing high-precision 3D models of construction waste pile bodies using multi-source data plays a crucial role in improving the accuracy and timeliness of early warning systems for pile body instability, offering significant theoretical research and practical application value. Building 3D models of construction waste pile bodies solely based on unmanned aerial vehicle (UAV) oblique photography data faces challenges, such as various degrees of data voids and insufficient model completeness, and few methods currently address the construction of 3D models of construction waste pile bodies through the fusion of multi-source data. This paper attempts to use the Iterative Closest Point (ICP) algorithm, combining SLAM laser point cloud data with UAV oblique photography data to fill data voids, and utilizes the fused point cloud to reconstruct the triangulation network, achieving the transformation from 3D point cloud models to 3D surface models. On the basis of texture mapping, a high-precision 3D model of the construction waste pile body is constructed. The research results show that the 3D model of the construction waste pile body, integrated with multi-source data, has a planar error of 0.0187m and an elevation error of 0.0368m, meeting the corresponding model accuracy requirements. It can more realistically restore the fine 3D features of the construction waste pile body, effectively compensating for the shortcomings of single-source data in 3D modeling of construction waste pile bodies, providing a new method for the 3D model reconstruction of construction waste pile bodies, and offering effective data support for construction waste research.

Harmonic analysis of phase-sensitive optical time-domain reflectometer

This study innovatively proposes a method of using harmonics to mitigate the influence of fading noise in Phase-Sensitive Optical Time-Domain Reflectometer (Φ-OTDR). Numerical modeling was conducted to investigate the behavior of the Φ-OTDR. A section optical fiber is coiled around a PZT (piezoelectric transducer). By applying a signal to the PZT, it emulates an external vibration. The response of the simulated vibration was extracted using phase demodulation algorithm. In the response spectrum of the vibration, the fundamental frequency may be submerged because of the fading noise, causing the useful information to be obscured. However, the region impacted by fading noise in the fundamental frequency is unaffected in certain harmonic components. Thus, information from the harmonics can be used to compensate for the missing information in the fundamental frequency. By analyzing multiple harmonic components, the accuracy of vibration detection and localization can be improved. Experiment was performed to corroborate the findings, and the results demonstrated good consistency with the simulated data. Both the simulation and the experiment proved that considering harmonics can enhance the measurement accuracy of Φ-OTDR. This discovery provides new insights and methods for the application of Φ-OTDR.

Vital signs detection of moving targets using FMCW radar

Abstract Respiratory and heartbeat rates are crucial indicators for human health assessment. Compared to contact-based measurements, millimeter-wave radar detection of these vital signs avoids the discomfort caused by physical contact and better protects personal privacy, making it highly valuable for home health monitoring. The use of millimeter-wave radar for vital sign detection of the human body is mostly focused on targets in a stationary state at present. However, the human body may sway or even move during actual detection. This article proposes a non-contact vital sign detection method for moving targets. Compared with methods for detecting vital signs of stationary targets, detecting vital signs of moving targets requires determining the range bin where the targets are located continuously and extracting target phase information. The noise components such as movement and sway contained in the phase signal need to be removed. In this paper, moving target indication is used to remove static components, an adaptive range bin selection method is proposed to determine the range bin where the targets are located, and range bin selection fluctuation is smoothed using a moving average filter. The wavelet transform is used to decompose the phase signal, remove swaying noise based on autocorrelation function, and reconstruct the life signal for different scale factors. A bandpass filter is used to separate the respiratory and heartbeat signals, and a notch filter is designed to suppress respiratory harmonic signals. The experimental results show that the proposed method can separate vital signs signals from the phase signals of moving targets, achieving detection of respiration and heartbeat rate. The average accuracy of respiration and heartbeat rate detection is 94.7% and 95.5%, respectively.