Conventional Detection Methods Research Articles

Metal ion-complexed DNA probe coupled with CRISPR/Cas12a amplification and AuNPs for sensitive colorimetric assay of metallothionein in fish

The accurate and sensitive detection of metallothionein (MT) is of great significance in the fields of biomedical, toxicological and environmental sciences. In this work, based on the high affinity interaction between MT and the heavy metal ions of Hg2+ and the significant signal amplification capability of Cas12a/crRNA enzyme as well, we report a simple and highly sensitive method for visual detection of MT, a biomarker in fish for heavy metal ion-induced water bio-pollution. The target MT molecules bind Hg2+ in the Hg2+- complexed hairpin DNA probes to unfold the hairpin structure into ssDNAs, which hybridize with the partial dsDNA duplexes via strand displacement to yield specific sequence-containing dsDNAs. Cas12a/crRNA recognizes these specific sequences to activate its enzyme activity to cyclically cleave the ssDNA linkers in the blue colored gold nanoparticle aggregates to transit their color into red to realize visual detection of MT. Owing to the signal amplification by Cas12a/crRNA, as low as 25 nM of MT can be visually detected with naked eye. In addition, our colorimetric detection method has high selectivity for MT against other interference proteins and can detect MT in the livers and kidneys of crucian carps bought from a local supermarket. Moreover, the developed assay overcomes the limitations of conventional MT detection methods in terms of complexity, high cost and low sensitivity and can therefore offer new methods for monitoring water bio-pollutions.

Markovian noise-induced delta synchronization approach for Hindmarsh–Rose model

The paper explores noise-induced synchronization in uncoupled Hindmarsh–Rose neurons, introducing two distinctive elements: the application of Markovian noise and an analysis of synchronization via unpredictability. The noise is defined as an unpredictable and continuous process with characteristics proper for stochasticity. While identical synchronization is also investigated, the primary focus is to reveal synchronization in noise intensity domains that elude conventional detection methods, through delta synchronization within the neural system. Furthermore, a stronger form of synchronization, namely complete synchronization of unpredictability, is found to emerge in the domain with identical synchronization. The research findings are substantiated by numerical outcomes assessing unpredictability and synchronization, alongside comprehensive tables displaying characteristic time sequences for synchronization.

Amplification-free and label-free rapid detection of Staphylococcus aureus using solution-gated graphene transistor-based DNA biosensor with hybridization enhancement by interface engineering

Staphylococcus aureus (S. aureus) is one of the leading causes of foodborne illnesses worldwide, posing a significant risk to food quality and safety. However, conventional detection methods often require complex amplification and labeling procedures, which are time consuming and expensive. In this study, an extremely sensitive electrochemical biosensor using a solution-gated graphene transistor (SGGT) was constructed for the amplification-free and label-free rapid detection of S. aureus by utilizing specific single-stranded DNA (ssDNA) to modify the gold (Au) sensing gate. This modified ssDNA was designed to hybridize with the target S. aureus DNA sequence. Interface engineering of the ssDNA-modified sensing gate via optimization of specific target and probe sequences was proposed to improve its hybridization efficiency. This enabled the sensitive quantitative detection of S. aureus DNA. The resulting biosensor exhibited excellent specificity in distinguishing non-complementary, 3-base/9-base DNA mismatch targets of S. aureus DNA and the specific DNA sequences of other common pathogenic bacteria. It also enabled the rapid detection of extracted target DNA sequences in solution (within 27 min) and had a low limit of detection (LoD) of 10−17 M over a wide linear detection range (10−17–10−8 M). Furthermore, the newly-developed biosensor facilitated the real-time and on-site detection of the S. aureus genome (ATCC 6538) with LoD of 10−17 M (103 CFU/mL) in a portable smart-sensing system. This suggests a promising future for the development of rapid, label-free, and amplification-free detection of foodborne illnesses using low-cost, highly sensitive SGGT DNA biosensors with appropriate functionalization and interface engineering of the sensing electrode.

A multi-stage sub-structural damage localization approach using multi-label radial basis function neural network and auto-regressive model parameters

This paper proposes a Structural Health Monitoring (SHM) approach to detect localized damage by employing a Multi-Label Radial Basis Function Neural Network (ML-RBFNN). The proposed research methodology aims to identify structural damage more efficiently. This involves dividing the main structure into smaller sub-structures known as zones instead of employing a global search for damage across the entire structure. Each zone is further divided into smaller groups, including sub-zones, nodes, or elements. To reduce the complexities associated with feature extraction, auto-regressive (AR) model parameters extracted from raw time series are considered damage-sensitive features. Considering the simultaneous effects of multiple sensors can increase the number of inputs to the networks, making it challenging to reduce the complexity of the dataset. To address this problem, the principal component analysis (PCA) method is utilized to decrease the dimension of feature space. The effectiveness of the proposed approach is evaluated through a comprehensive comparative analysis against conventional single-stage damage detection methods, with a focus on computational costs and accuracy. Furthermore, the noise-sensitivity characteristics of the proposed method are explored, providing insights into their robustness under various conditions. The validation of the approach is carried out through numerical modeling using the ASCE benchmark structure and experimental data collected from the Qatar University Grandstand Simulator. The application of ML-RBFNN with AR parameters as features yielded a remarkable accuracy exceeding 98% in diagnosing damage occurrence and location for both multi-stage and single-stage SHM methods. Moreover, our proposed approach demonstrates enhanced computational efficiency compared to the single-stage method.

Integrated Circuit Bonding Distance Inspection via Hierarchical Measurement Structure.

Bonding distance is defined by the projected distance on a substrate plane between two solder points of a bonding wire, which can directly affect the morphology of the bonding wire and the performance between internal components of the chip. For the inspection of the bonding distance, it is necessary to accurately recognize gold wires and solder points within the complex imagery of the chip. However, bonding wires at arbitrary angles and small-sized solder points are densely distributed across the complex background of bonding images. These characteristics pose challenges for conventional image detection and deep learning methods to effectively recognize and measure the bonding distances. In this paper, we present a novel method to measure bonding distance using a hierarchical measurement structure. First, we employ an image acquisition device to capture surface images of integrated circuits and use multi-layer convolution to coarsely locate the bonding region and remove redundant background. Second, we apply a multi-branch wire bonding inspection network for detecting bonding spots and segmenting gold wire. This network includes a fine location branch that utilizes low-level features to enhance detection accuracy for small bonding spots and a gold wire segmentation branch that incorporates an edge branch to effectively extract edge information. Finally, we use the bonding distance measurement module to develop four types of gold wire distribution models for bonding spot matching. Together, these modules create a fully automated method for measuring bonding distances in integrated circuits. The effectiveness of the proposed modules and overall framework has been validated through comprehensive experiments.

Enhancing gas chromatography-mass spectrometry resolution and pure analyte discovery using intra-chromatogram elution profile matching

Chemometric decomposition methods like multivariate curve resolution-alternating least squares (MCR-ALS) are often employed in gas chromatography-mass spectrometry (GC-MS) to improve analyte identification and quantitation. However, these methods can perform poorly for analytes with a low chromatographic resolution (Rs) and a high degree of spectral contamination from noise and background interferences. Thus, we propose a novel computational algorithm, termed mzCompare, to improve analyte identification and quantitation when coupled to MCR-ALS. The mzCompare method utilizes an underlying requirement that the retention time and peak shape between mass channels (m/z) of the same analyte should be similar. By discovering the selective m/z for a given analyte in a chromatogram, a pure elution profile can be generated and used as an equality constraint in MCR-ALS. The performance of the mzCompare methodology is demonstrated with both experimental and simulated chromatograms. Experimentally, unresolved analytes with a Rs as low as 0.05 could be confidently identified with mzCompare assisted MCR-ALS. Furthermore, application of the mzCompare algorithm to a complex aerospace fuel resulted in the discovery of 335 analytes, a 44 % increase compared to conventional peak detection methods. GC-MS simulations of target-interferent analyte pairs demonstrated that the performance of MCR-ALS deteriorated below a Rs of ∼0.25. However, mzCompare assisted MCR-ALS showed excellent identification and acceptable quantitative accuracy at a Rs of ∼0.02. These results show that the mzCompare algorithm can help analysts overcome modeling ambiguities resulting from the chemometric multiplex disadvantage.

FSN: Feature Shift Network for Load-Domain (LD) Domain Generalization

Conventional deep learning methods for fault detection often assume that the training and the testing sets share the same fault domain spaces. However, some fault patterns are rare, and many real-world faults have not appeared in the training set. As a result, it is hard for the trained model to achieve desirable performance on the testing set. In this paper, we introduce a novel domain generalization, Load-Domain (LD) domain generalization, which is based on the analysis of the Case Western Reserve University (CWRU) bearing dataset and takes advantage of the physical information of this dataset. For this scenario, we propose a feature shift model called Feature Shift Network (FSN). FSN is trained for feature shift on adjacent source domains and finally shifts target domain features into adjacent source domain feature space to achieve the purpose of domain generalization. Furthermore, through the hybrid classification method, the generalization performance of the model on unseen target domains is effectively improved. The results on the CWRU bearing dataset demonstrate that FSN is better than the existing models in the LD domain generalization. Furthermore, we have another test on the rotated MNIST, which also shows FSN can achieve the best performance.

Human-derived Tumor-On-Chip model to study the heterogeneity of breast cancer tissue

One of the leading causes that complicate the treatment of some malignancies, including breast cancer, is tumor heterogeneity. In addition to inter-heterogeneity and intra-heterogeneity of tumors that reflect the differences between cancer cell characteristics, heterogeneity in the tumor microenvironment plays a critical role in tumor progression and could be considered an overlooked and a proper target for the effective selection of therapeutic approaches. Due to the difficulty of completely capturing tumor heterogeneity in conventional detection methods, Tumor-on-Chip (TOC) devices with culturing patient-derived spheroids could be an appropriate alternative. In this research, human-derived spheroids from breast cancer individuals were cultured for 6 days in microfluidic devices. To compare TOC data with conventional detection methods, immunohistochemistry (IHC) and ITRAQ data were employed, and various protein expressions were validated using the transcriptomic databases. The behavior of the spheroids in the collagen matrix and the cell viability were monitored over 6 days of culture. IHC and immunocytochemistry (ICC) results revealed that inter and intra-heterogeneity of tumor spheroids are associated with HER2/ER expression. HER2 expression levels revealed a more important biomarker associated with invasion in the 3D culturing of spheroids. The expression levels of CD163 (as a marker for Ma2 macrophages) and CD44 (a marker for cancer stem cells (CSCs)) were also evaluated. Interestingly, the levels of M2a macrophages and CSCs were higher in triple-negative specimens and samples that showed higher migration and invasion. Cell density and extracellular matrix (ECM) stiffness were also important factors affecting the migration and invasion of the spheroids through the matrix. Among these, rigid ECM revealed a more crucial role than cell density. To sum up, these research findings demonstrated that human-derived spheroids from breast cancer specimens in microfluidic devices provide a dynamic condition for predicting tumor heterogeneity in patients, which can help move the field forward for better and more accurate therapeutic strategies.

Highly-fluorescent extracts from Pterocarpus wood for Fe3+ ion detection

At present, excessive Fe3+ in daily water has become a threat to human health. Among the conventional detection methods for Fe3+, fluorescent probes have been applied on a large scale due to their simplicity and efficiency. However, the currently available fluorescent probes are difficult to synthesize, costly and environmentally unfriendly, limiting their applications. In this work, a fluorescent extract of Pterocarpus wood was successfully obtained, and the structure of some coumarin-based molecules in this extract was determined by 2D-NMR. Subsequently, the intensity of this fluorescence was optimized using response surface methodology (RSM), resulting in a high-intensity fluorescent probe. The probe was sensitive to the concentrations of Fe3+ and MnO4−, and could efficiently detects Fe3+ in the range of 2.7 μM–8.0 μM, with LOD and LOQ reaching 1.06 μM and 3.20 μM, respectively. Moreover, based on the strong complexation property of EDTA on Fe3+, this work designed the "switch-on" fluorescent probes. The experiment shows that both static and dynamic quenching exist in this system. The mechanism of complexation and oxidation of fluorescent molecules by the quencher is interpreted in the quenching reaction. In addition, the fluorescent probe has a high yield and low cost, it also performs well in actual water sample tests. This method is expected to be developed as a new way on Fe3+ detection.

Dual-drive strategy-based modulation of the interfacial charge of S-scheme heterojunction and photoelectrochemical ultrasensitive detection of CD44

The efficacy of charge carrier separation plays a crucial role in influencing the practical application of photoelectrochemical (PEC) detection platforms, which can be meticulously engineered to realize highly sensitive detection at the sensing interface. Hence, a novel dual-drive strategy was proposed to integrate thermally-assisted external drive behavior with the internal drive behavior of sulfur vacancies (SVs), realizing the rapid separation of charge at the S-scheme heterojunction sensing interface. On the one hand, unabsorbed solar energy was utilized an external driving force to facilitate efficient photothermoelectric conversion within the In2S3/CdS heterojunction, achieving the photocurrent response intensity 1.5 times higher than conventional PEC detection methods. On the other hand, introducing sulfur vacancies (SVs) as internal driving forces induced ordered migration and directional flow of charge carriers. Moreover, density functional theory (DFT) calculations and experimental results demonstrated that photo-generated carriers achieved rapid separation along the S-scheme heterojunction. Under the optimal conditions, a biosensor constructed based on the dual-drive strategy exhibited high sensitivity for the CD44 (cluster of differentiation-44) cluster, with a low detection limit of 0.132 pg mL−1and a wide linear range of 0.001 ∼ 1000 ng mL−1. The proposed strategy offers new insights into how to effectively harness excess energy in the traditional ultraviolet spectral range for ultrasensitive biosensors.

Value of Metagenomic Next-Generation Sequencing in Clinical Diagnosis and Targeted Therapy of Nontuberculous Mycobacteria Infection

Abstract The purpose of this study was to assess the value of metagenomic next-generation sequencing (mNGS) for rapid diagnosis of diseases caused by nontuberculous mycobacteria (NTM). We retrospectively reviewed four NTM-infected cases diagnosed by smear microscopy, mycobacterial culture and mNGS methods. We found that the mNGS method not only had a shorter detection turnaround time (3–4 days) than Mycobacterium culture (15–20 days) but also had higher sensitivity and specificity to identify NTM compared with conventional detection methods. In addition, mNGS was able to identify coinfections by NTM and other bacteria, fungi or viruses. Overall, diagnosis of NTM by mNGS can provide timely and precise guidance for subsequent clinical treatment of NTM infections.

Disposable electrochemical biosensors for the detection of bacteria in the light of antimicrobial resistance.

Persistent and inappropriate use of antibiotics is causing rife antimicrobial resistance (AMR) worldwide. Common bacterial infections are thus becoming increasingly difficult to treat without the use of last resort antibiotics. This has necessitated a situation where it is imperative to confirm the infection to be bacterial, before treating it with antimicrobial speculatively. Conventional methods of bacteria detection are either culture based which take anywhere between 24 and 96 hor require sophisticated molecular analysis equipment with libraries and trained operators. These are difficult propositions for resource limited community healthcare setups of developing or less developed countries. Customized, inexpensive, point-of-care (PoC) biosensors are thus being researched and developed for rapid detection of bacterial pathogens. The development and optimization of disposable sensor substrates is the first and crucial step in development of such PoC systems. The substrates should facilitate easy charge transfer, a high surface to volume ratio, be tailorable by the various bio-conjugation chemistries, preserve the integrity of the biorecognition element, yet be inexpensive. Such sensor substrates thus need to be thoroughly investigated. Further, if such systems were made disposable, they would attain immunity to biofouling. This article discusses a few potential disposable electrochemical sensor substrates deployed for detection of bacteria for environmental and healthcare applications. The technologies have significant potential in helping reduce bacterial infections and checking AMR. This could help save lives of people succumbing to bacterial infections, as well as improve the overall quality of lives of people in low- and middle-income countries.

Transfer Learning-Based Structural Damage Identification for Building Structures with Limited Measurement Data

Structural damage detection is crucial for ensuring the safety of civil building structures in operational environments. Recently, deep learning-based methods have gained increasing attention from engineers and researchers. The performance of conventional deep learning methods for structural damage detection relies on a large number of labeled training datasets. However, it is difficult or/and impossible to obtain sufficient datasets to cover various damage scenarios for in-service structures. A little research has been conducted to identify both the damage severity and location with limited labeled measurement data. A novel transfer learning-based method for structural damage identification with limited measurements has been proposed utilizing frequency response functions (FRFs) as the input. The real structure is regarded as the target domain and its numerical model is as the source domain. The samples for various damage scenarios are generated using the numerical model, and a designed deep convolutional neural network (CNN) is pre-trained. The knowledge of the pre-trained network is transferred to identify the damage location and severity of the real structure using limited measurement data. Numerical and experimental studies have been conducted on a three-story building structure to verify the performance of the proposed method. To understand transfer learning and model interpretability, the t-SNE feature visualization is adopted to show the feature distribution changes during transfer learning. Numerical and experimental results show that the proposed approach outperforms conventional CNN models, and it is effective and accurate in identifying structural damage location and severity in real structures with limited measurement data.

Fall Detection System for Elderly Care

Abstract: This project introduces a revolutionary Fall Detection System for Elderly Care, focusing on swift detection and immediate response to falls, particularly among wheelchair-bound seniors. In response to the escalating challenges posed by an aging population, Especially concerning the well-being of the elderly, the development of innovative solutions has become imperative. Central to these concerns is the prevalence of falls among seniors, which not only jeopardizes their physical health but also threatens their independence. Conventional fall detection methods have proven inadequate in providing timely assistance, underscoring the critical need for more advanced technologies. This project addresses this pressing need through the creation of a comprehensive Fall Detection System, with a specific focus on wheelchair-bound individuals. By seamlessly integrating state-of-the-art hardware and software components, the system aims to revolutionize elderly care by prioritizing safety and autonomy. At its core, the system utilizes the MPU6050 sensor module, renowned for its precision in monitoring body position and movement. Through the implementation of sophisticated algorithms, the software component meticulously analyzes real-time sensor data to swiftly detect falls. Upon detection of a fall event, the system autonomously triggers alerts to nearby healthcare services or designated contacts, ensuring a rapid response and timely provision of assistance to the elderly individual in need.

Harnessing photocurrent enhancement in silver-bacterial cellulose nanocomposite for ultra-sensitive Hg2+ electrochemical detection

Global health and ecosystem concerns over mercury pollution require stringent monitoring. Herein, we showcase a novel approach for detecting trace Hg2+ ions in water using cyclic voltammetry (CV). Our approach involves modifying glassy carbon electrode (GCE) and screen printed electrode (SPE) surfaces with a nanocomposite of ascorbic acid-capped silver nanoparticles (AsAgNPs) embedded in nanocrystalline bacterial cellulose (AsAgNP-NBC). Analytical techniques confirmed the nanocomposite’s stability and morphological characteristics, exhibiting high accuracy within a linear range of 10 nM to 1 µM Hg2+ and a low limit of detection (LOD) of 3.531 nM. Additionally, on irradiation with 455 nm light source, AsAgNP-NBC modified SPE displayed a remarkable 9.6 times enhanced photocurrent, achieving an LOD of 3.95 pM, and enhanced photoresponsivity of 55.2 mA W−1, showcasing its potential for ultra-trace level detection. This cost-effective and biocompatible nanocomposite presents a promising alternative to conventional analytical methods for selective detection of trace Hg2+ ions in environmental samples.

Sparse Wasserstein stationary subspace analysis for fault detection and diagnosis of nonstationary industrial processes

Fault detection and diagnosis of nonstationary processes are crucial for ensuring the safety of industrial production systems. However, the nonstationarity of process data poses multifaceted challenges to them. First, conventional stationary fault detection methods encounter difficulties in discerning evolving trends within nonstationary data. Secondly, the majority of current nonstationary fault detection methods directly extract features from all variables, rendering them susceptible to redundant interference. Moreover, nonstationary trends possess the capacity to conceal and modify the correlations among variables. Coupled with the smearing effect of faults, it is challenging to achieve accurate fault diagnosis. To address these challenges, this paper proposes sparse Wasserstein stationary subspace analysis (SWSSA). Specifically, a ℓ2,p-norm constraint is introduced to endow the stationary subspace model with excellent sparse representation capability. Furthermore, recognizing that fault variables within the sparse stationary subspace influence only a limited subset of stationary sources, this paper proposes a novel contribution analysis method based on local dynamic preserving projection (LDPP), termed LDPPBC, which can effectively mitigate the smearing effect on nonstationary fault diagnosis. LDPPBC establishes a LDPP matrix by extracting the latent positional information of fault variables within the stationary subspace. This allows LDPPBC to selectively analyze the contributions of variables within the latent fault subspace to achieve precise fault diagnosis while avoiding the interference of variable contributions from the fault-free subspace. Finally, the superiority of the proposed method is thoroughly validated through a numerical simulation, a continuous stirred tank reactor, and a real industrial roaster.

Advancements in Chemical and Biosensors for Point-of-Care Detection of Acrylamide.

Acrylamide (AA), an odorless and colorless organic small-molecule compound found generally in thermally processed foods, possesses potential carcinogenic, neurotoxic, reproductive, and developmental toxicity. Compared with conventional methods for AA detection, bio/chemical sensors have attracted much interest in recent years owing to their reliability, sensitivity, selectivity, convenience, and low cost. This paper provides a comprehensive review of bio/chemical sensors utilized for the detection of AA over the past decade. Specifically, the content is concluded and systematically organized from the perspective of the sensing mechanism, state of selectivity, linear range, detection limits, and robustness. Subsequently, an analysis of the strengths and limitations of diverse analytical technologies ensues, contributing to a thorough discussion about the potential developments in point-of-care (POC) for AA detection in thermally processed foods at the conclusion of this review.

DETECTION OF ANDROID BOTNETS

In the dynamic landscape of cybersecurity, the persistent proliferation of botnets remains a formidable challenge. Conventional detection methods often grapple with elevated false positive rates and struggle to keep pace with the evolving tactics employed by malicious actors. This study delves into the domain of botnet detection, with a primary focus on refining the identification process for HTTP-based botnets. Our approach harnesses the power of K-Nearest Neighbors (KNN) and Logistic Regression algorithms, strategically amalgamating their capabilities to navigate the intricate digital terrain. Departing from prior studies, we directly confront the botnet detection challenge by synergizing two robust machine learning techniques. Through the fusion of KNN and Logistic Regression, our system achieves unparalleled accuracy and efficiency in discerning HTTP botnet activities. Furthermore, this research pioneers a groundbreaking methodology for botnet detection, centering around the innovative fusion of TFIDF and Textrank algorithms. This hybrid approach significantly bolsters the precision of information extraction and summarization. In an age inundated with vast digital datasets, our method adeptly sifts through information while furnishing concise and relevant summaries, thereby conserving invaluable time and resources. The distinctive aspect of our approach lies in its ability to swiftly adapt to emerging botnet strategies. Through extensive testing and comparative analysis against existing models, our system surpasses prior methodologies, demonstrating its proficiency in accurately identifying botnet activities while minimizing false positives. Furthermore, our solution serves as an exemplar of efficiency, facilitating prompt and effective identification of pernicious botnets that imperil data security Key Words: Botnet Detection, Cybersecurity, K-Nearest Neighbors (KNN), Logistic Regression, TFIDF, Textrank, Information Summarization, Digital Security, Malware Detection.

Advances in Microfluidic Paper-Based Analytical Devices (µPADs): Design, Fabrication, and Applications.

Microfluidic Paper-based Analytical Devices (µPADs) have emerged as a new class of microfluidic systems, offering numerous advantages over traditional microfluidic chips. These advantages include simplicity, cost-effectiveness, stability, storability, disposability, and portability. As a result, various designs for different types of assays are developed and investigated. In recent years, µPADs are combined with conventional detection methods to enable rapid on-site detection, providing results comparable to expensive and sophisticated large-scale testing methods that require more time and skilled personnel. The application of µPAD techniques is extensive in environmental quality control/analysis, clinical diagnosis, and food safety testing, paving the way for on-site real-time diagnosis as a promising future development. This review focuses on the recent research advancements in the design, fabrication, material selection, and detection methods of µPADs. It provides a comprehensive understanding of their principles of operation, applications, and future development prospects.

Research and Implementation of Pedestrian Attribute Recognition Algorithm Based on Deep Learning

In the realm of computer vision, recognizing pedestrian attributes is a crucial task, with the objective of deducing the identity characteristics of individuals on foot. This differs from conventional pedestrian detection methods in that it not only identifies the presence of pedestrians but also examines their attributes such as gender, age, and attire by scrutinizing their visual features. Not only can recognition identify the existence of pedestrians, but it can also delve into the analysis of pedestrian attributes. This paper proposes, explores, and implements a pedestrian attribute recognition algorithm grounded in deep convolutional neural networks. First, several datasets containing large-scale pedestrian images and their corresponding data are used for the recognition of pedestrian attributes. containing large-scale pedestrian images and their corresponding attribute labels are utilized. Through data pre-processing and enhancement techniques, the noise and inconsistency of pedestrian images are reduced. techniques, the noise and inconsistency of the data are.