Fingerprint Technology Research Articles

A Room-Level Indoor Localization Using an Energy-Harvesting BLE Tag

Energy-efficient and cost-effective localization systems are attractive for large-scale tracking and localization of goods. In this paper, we propose a room-level localization system using energy-harvesting BLE tags to track the targets. We introduce the Dempster–Shafer (D–S) evidence theory combined with fingerprinting technology for location estimation. To reduce the estimation complexity, we divide the indoor environment into clear areas and fuzzy areas. The D–S algorithm is employed to locate the target in the clear areas when the targets are only detected by the anchor nodes within a single room. Conversely, fuzzy areas are characterized by RSSI signals detected by anchor nodes across multiple rooms. Then, the system integrates fingerprint matching to ensure superior positioning accuracy across the deployment. Extensive experiments demonstrate that the proposed system maintains a room-level positioning accuracy above 99% under standard test conditions within an area of approximately 2000 m2 with lots of rooms.

Analysis of Inter-Primer Binding Site Retrotransposon Length Polymorphism in Selected Group of Vaccinium corymbosum

Abstract The genetic variability in 13 selected genotypes of Vaccinium corymbosum using re-trotransposon-based iPBS markers system was characterized in this study. The four selected iPBS primers amplified 232 fragments, average 58 fragments per primer, approximately 53.16% of which were polymorphic. The polymorphism information content of iPBS marker was 0.238. The hierarchical cluster using unweighted pair group method with arithmetic mean analysis divided selected genotypes into two main groups and several subgroups. The values of the Jaccard coefficient of genetic relatedness were between 0.036–0.243. The similarity between two sample sets was 3.6%–24.3%. This study confirmed inter-PBS amplification technique as a reproducible and trustable marker system, but has proved to be an effective DNA fingerprinting technology to generate polymorphism in Vaccinium corymbosum.

Enhancing Cashless Transactions: The Role and Benefits of Fingerprint Technology

In the modern times cashless transactions have become increasingly prevalent in today's digital economy and strong security measures are important to safeguard all the sensitive data related to finances. Fingerprint recognition emerges as a safe solution to upgrade the security and convenience of existing cashless systems. This research paper explores the utilization and benefits of fingerprint recognition./ The paper begins by understanding the basics of this technology, its advancement and uses. It then dives into the integration of cashless transactions with fingerprint recognition systems highlighting the potential benefits. Moreover, this paper also analyzes real- word scenarios and implementation across various industries and geographies. Furthermore, the technical considerations and challenges associated with implementation are also discussed which includes scalability, accuracy, accessibility of the technology. The strategies to make this technology more efficient and compliant are also discussed. In conclusion, this research accentuates the potential to revolutionize the landscape of cashless transactions using fingerprint recognition technology. By addressing and solving the challenges the full potential of biometric technology to upgrade digital payments can be unlocked.

Secure and Transparent Craftwork Authentication and Transaction System: Integrating Digital Fingerprinting and Blockchain Technologies

This study proposes a method that enables craftsmen to define and apply the unique characteristics of their craftworks to distinguish between originals and imitations and to protect and trade their intellectual property rights. In the first step, a digital fingerprint that enables the authentication of the original craftworks was generated by applying hash functions that can digitize various attributes of the craftworks and create a unique ID. In the second step, a blockchain transaction system for the original authentication of the craftwork was developed by applying consortium blockchain technology. This system allows multiple craft-related organizations to participate together, and when a transaction occurs, a smart contract is created and stored on the blockchain, thereby enabling the tracking and management of transaction histories. Furthermore, a DApp was developed that enables buyers to verify the craftwork authentication and access detailed information by scanning the digital fingerprint (QR code) of the craftwork, which is integrated with the blockchain system. In the third step, the research results were evaluated through a satisfaction survey conducted with 121 participants and a usability evaluation with 10 craftsmen, both of which yielded positive feedback. This study successfully realizes a secure and transparent craftwork transaction system that guarantees both security and efficiency through the integration of digital fingerprinting and blockchain technologies.

Utilization of Internet of Things (IoT) for Biometrics Attendance and Web-Based Student Monitoring

SMPN 31 Tangerang aims to enhance the accuracy and efficiency of its student attendance system. The current problem is that the existing attendance system is easy to manipulate and lacks accuracy. This research aims to develop a biometric attendance system based on fingerprint technology to address these issues, with output presented in a web page format and without using paper. The methods used in this study include literature review, interviews, and observations for data collection. Development methods involve system design and the waterfall model. The developed attendance system utilizes IoT (Internet of Things) technology and electronic circuits to facilitate implementation and use. The research results show that the biometric fingerprint attendance system can improve student discipline. The system allows for attendance monitoring through a web interface, with data that can be backed up to Microsoft Excel. This device offers high accuracy, good attendance speed, and ease of use for students. In conclusion, the implementation of the biometric attendance system and web-based monitoring meets the school's needs for efficient and effective attendance management and monitoring.

Contribution of Biometric Fingerprint in Reducing Prevalence of Unauthorized Absenteeism among Employees in Public Organizations in Moshi Municipality

This study investigated the contribution of biometric fingerprint technology in reducing unauthorized absences among employees in public organizations within Moshi Municipality, Tanzania. Grounded in the Social Control Theory, the research utilized a convergent parallel mixed-methods approach. The target population consisted of 3,218 public employees from 45 organizations, with a sample of 356 respondents. The study collected qualitative and quantitative data, ensuring validity and reliability through content, face validity, and Cronbach's alpha. Quantitative data was analyzed using descriptive statistics in SPSS, while qualitative data underwent context analysis. Ethical guidelines were strictly adhered to. The findings revealed that public servants in Moshi Municipality perceived biometric fingerprint technology as a reliable solution for addressing unauthorized absences in the workplace. Implementation of this biometric has created a secure and accountable work environment. The study concludes that the use of biometric fingerprint technology has positive contributions to attendance management, accountability, and productivity of public organization employees. Further, it was recommended that public organizations prioritize clear communication with employees about the purpose, benefits, and safeguards of biometric fingerprint technology. Additionally, further research is suggested to explore the challenges faced by employees in utilizing this technology, with the aim of contributing to the existing knowledge base on biometric fingerprints in public organizations.

Penerapan Teknologi Sidik Jari dalam Sistem Hukum: Tinjauan terhadap Prinsip – Prinsip Pembuktian

The application of fingerprint technology in the legal system has brought significant changes to the evidentiary process. This technology enables the identification of individuals with high accuracy, which is an important element in law enforcement and dispute resolution. This study explores how fingerprint technology is applied in the Indonesian legal context, reviews relevant evidentiary principles, and analyzes the implications of its use in various legal cases. This research is descriptive analytical, meaning that this research aims to describe certain circumstances or situations of the objects or events being studied without intending to draw conclusions that can be applied generally. The approach used in this research is the normative juridical method. The results show that, although fingerprint technology makes a significant contribution to the effectiveness of evidentiary systems, fingerprint technology has long been used in the legal system as a tool for individual identification. Fingerprints, with their unique characteristics in each person, provide a guarantee of highly accurate identification. This technology is not only used in crime but also in various aspects of government and public services, such as population registration and securing access. The application of fingerprint technology in a modern legal context is becoming increasingly relevant with advances in digital technology that enable fast and efficient data processing. There are challenges related to privacy, data reliability and the integrity of legal processes. This research also proposes recommendations to increase the integration of fingerprint technology in the legal system in accordance with the principles of justice and human rights.

A Study of New Indexing Techniques for Multimodal Identification Using Iris, Fingerprint, and Face Biometrics

This paper explores innovative indexing techniques for multimodal biometric identification systems, focusing on iris, fingerprint, and facial recognition technologies. With the proliferation of digital identity verification needs, traditional single-modal biometric systems often fall short in terms of accuracy, speed, and security. Multimodal biometric systems, which integrate multiple biological characteristics, are becoming crucial for enhancing identification performance. This study aims to present a comprehensive analysis of new indexing methods that can significantly improve the efficiency, accuracy, and security of multimodal biometric identification systems.

An Efficient Prediction Model on the Operation Quality of Medical Equipment Based on Improved Sparrow Search Algorithm-Temporal Convolutional Network-BiLSTM.

Combining medical IoT and artificial intelligence technology is an effective approach to achieve the intelligence of medical equipment. This integration can address issues such as low image quality caused by fluctuations in power quality and potential equipment damage, and this study proposes a predictive model, ISSA-TCN-BiLSTM, based on a bi-directional long short-term memory network (BiLSTM). Firstly, power quality data and other data from MRI and CT equipment within a 6-month period are collected using current fingerprint technology. The key factors affecting the active power of medical equipment are explored using the Pearson coefficient method. Subsequently, a Temporal Convolutional Network (TCN) is employed to conduct multi-layer convolution operations on the input temporal feature sequences, enabling the learning of global temporal feature information while minimizing the interference of redundant data. Additionally, bidirectional long short-term memory (BiLSTM) is integrated to model the intermediate active power features, facilitating accurate prediction of medical equipment power quality. Additionally, an improved Sparrow Search Algorithm (ISSA) is utilized for hyperparameter optimization of the TCN-BiLSTM model, enabling optimization of the active power of different medical equipment. Experimental results demonstrate that the ISSA-TCN-BiLSTM model outperforms other comparative models in terms of RMSE, MSE, and R2, with values of 0.1143, 0.1157, 0.0873, 0.0817, 0.95, and 0.96, respectively, for MRI and CT equipment. This model exhibits both prediction speed and accuracy in power prediction for medical equipment, providing valuable guidance for equipment maintenance and diagnostic efficiency enhancement.

Evaluating the impact of ecological factors on the quality and habitat distribution of Lonicera japonica Flos using HPLC and the MaxEnt model.

The quality of traditional Chinese medicine is based on the content of their secondary metabolites, which vary with habitat adaptation and ecological factors. This study focuses on Lonicera japonica Flos (LJF), a key traditional herbal medicine, and aims to evaluate how ecological factors impact its quality. We developed a new evaluation method combining high-performance liquid chromatography (HPLC) fingerprinting technology and MaxEnt models to assess the effects of ecological factors on LJF quality. The MaxEnt model was used to predict suitable habitats for current and future scenarios, while HPLC was employed to analyze the contents of key compounds. We also used ArcGIS for spatial analysis to create a quality zoning map. The analysis identified 21 common chromatographic peaks, with significant variations in the contents of Hyperoside, Rutin, Chlorogenic acid, Cynaroside, and Isochlorogenic acid A across different habitats. Key environmental variables influencing LJF distribution were identified, including temperature, precipitation, and elevation. The current suitable habitats primarily include regions south of the Yangtze River. Under future climate scenarios, suitable areas are expected to shift, with notable expansions in southern Gansu, southeastern Tibet, and southern Liaoning. The spatial distribution maps revealed that high-quality LJF is predominantly found in central and southern Hebei, northern Henan, central Shandong, central Sichuan, southern Guangdong, and Taiwan. The study indicates that suitable growth areas can promote the accumulation of certain secondary metabolites in plants, as the accumulation of these metabolites varies. The results underscore the necessity of optimizing quality based on cultivation practices. The integration of HPLC fingerprinting technology and the MaxEnt model provides valuable insights for the conservation and cultivation of herbal resources, offering a new perspective on evaluating the impact of ecological factors on the quality of traditional Chinese medicines.

Design of the pistol P1 weapon storage system shelf using fingerprint electronic system in the TNI-AD units

One of the most important aspects of preventing illegal usage of firearms is safe storage. An inventive way to guarantee that only authorized owners can access the firearms is to incorporate fingerprint technology into the design of electric firearm storage systems. The use of fingerprint technology to increase the security of gun storage is desperately needed. However, issues with the finger's health, such as stains, cuts, and dampness, can impair the accuracy of the detection. This study employs a quantitative methodology using five fingerprint detection situations to test the accuracy of the system. The Arduino ESP was used as the foundation for the system implementation throughout the nine months that the research was conducted at the Kodiklatad Poltekad Electronics Laboratory. According to the test results, the system has 100% accuracy in identifying five different fingerprint detecting scenarios. But limitations are implied by the system's incapacity to identify damp, damaged, or dusty fingertips. Consequently, further thought must be given to these circumstances in order to improve system reliability. The design of an electric gun storage unit using Arduino ESP-based fingerprint technology has the potential to increase the security of gun storage, but more work is needed to address some issues that limit detection accuracy

Investigation of quality control of Gastrodia elata using electrochemical fingerprint technology

Gastrodia elata, a traditional Chinese medicinal herb, undergoes processing with ginger to enhance its therapeutic properties. This study investigated the impact of ginger processing on the quality control of G. elata using electrochemical fingerprint technology and high-performance liquid chromatography (HPLC). The electrochemical fingerprint conditions were optimized, and the fingerprints of raw and ginger-processed G. elata samples were compared. The HPLC analysis revealed significant changes in the contents of gastrodin, parishin A, and parishin B after processing. The electrochemical fingerprints exhibited distinct differences between raw and ginger-processed samples, with improved batch-to-batch consistency in the latter. Principal component analysis and hierarchical cluster analysis demonstrated the correlation between the electrochemical fingerprints and HPLC results. The findings highlight the potential of electrochemical fingerprint technology as a complementary tool to HPLC for the comprehensive quality assessment of G. elata. This study provides valuable insights into establishing standardized processing protocols and quality control strategies for G. elata, emphasizing the importance of combining multiple analytical techniques for ensuring the consistency, safety, and efficacy of herbal medicines.

Security Design In Doors And Windows Arduino Based Using Fingerprint And Sms Gateway (Case Study At Pesona Asri Tembalang Housing)

This research discusses security design for Arduino-based doors and windows using fingerprint technology and SMS Gateway. The case study was conducted at Pesona Asri Tembalang Housing. The main goal of this research is to improve household security by combining fingerprint authentication technology and text messaging (SMS) as a safer and more efficient access mechanism.This system development method includes analyzing household security needs, designing an Arduino-based system, integrating fingerprint modules and SMS Gateway, as well as testing and evaluating system performance. The research results show that this system is able to provide a high level of security with accurate fingerprint authentication and direct notification via SMS Gateway when suspicious activity occurs.In conclusion, an Arduino-based door and window security design with fingerprint technology and SMS Gateway is an effective solution for increasing household security. Implementing these systems can help reduce the risk of intrusion and give homeowners better control over access to their property.

Facial Recognition Student Attendance System Web Application

Abstract: In the digital era, facial recognition technologies are indispensable in almost every business. Face is one biometric that is commonly used. Acknowledgement. In addition to being helpful for identification, security, and authentication, it offers a host of other advantages. Despite their increased accuracy, fingerprint and iris recognition technologies are still widely used because of their non-invasive and contactless nature. Facial recognition systems may also be used to show attendance in offices, classrooms, universities, and other contexts. This system's objective is to develop a facial recognition-based class attendance system because the manual method now in use takes a lot of time and work to maintain. Through the use of this structure, it issimple to resolve the issue of proxies and students receiving present grades even when they are not there in person.

Enhance the Security of ATM Machine Access with Face and Fingerprint Recognition

Abstract: The project introduces an innovative approach to enhance the security and accessibility of Automated Teller Machines (ATMs). In this era where financial transactions are increasingly digital and essential, safeguarding ATM access is of paramount importance. This project leverages the power of biometric authentication, combining fingerprint and face recognition technologies to create a robust and reliable access control system. The proposed system allows ATM users to authenticate their identity with a seamless and secure process. Fingerprint recognition provides a high-precision, individualized identification method, while face recognition adds an additional layer of security, ensuring the users identity matches the stored biometric data. This dual-biometric approach significantly reduces the risk of unauthorized access and fraudulent activities at ATMs. Beyond enhancing security, the project contributes to user convenience by streamlining the authentication process, eliminating the need for traditional ATM cards or PINs. Users can access their accounts swiftly and securely, thereby improving the overall ATM experience. Furthermore, the project’s incorporation of state-of-the-art biometric technologies underscores its potential to set a standard for secure ATM access not only in the financial sector but also in various other domains where access control is critical.

Systematic review on fingerprinting development to determine adulteration of Chinese herbal medicines

BackgroundIt has been a current research hospots using fingerprinting technology for quality control of Chinese herbal medicines (CHMs), which provides a scientific basis for establishment of overall quality control in accordance with the characteristics of CHMs. The fingerprinting technology for CHMs is diverse, and the research field covers many disciplines, such as analytical chemistry, pharmacology, pharmaceutics, biochemistry, and molecular biology. PurposeTo effectively understand the key areas and future directions of research regarding the fingerprint and adulteration of CHMs. Methods/resultsthis paper analyzed 879 articles in this field in the Web of Science Core Collection from 2000 to 2023 with CiteSpace and VOSviewer, and systematically assessed the research process, hotspots, topic distribution among disciplines, etc. The most prominent contributors of fingerprint and adulteration of CHMs research are mainly from China, India, the United States, England, and Brazil. The knowledge domains of fingerprint and adulteration of CHMs research focus mainly on the topics of molecular authentication, DNA barcoding, HPLC, near-infrared spectroscopy, manage data, chemometrics, and electrochemical fingerprinting. Most countries have recognized the pharmaceutical potential of natural products, and have paid more attention to the fingerprint and adulteration of CHMs in the past decade. Future the research tends to focus more on molecular identification and authentication, and electrochemical and chromatographic fingerprinting in controlling the adulteration of CHMs. ConclusionThis research provides a valuable reference for scholars in related fields to analyze existing research results, understand the development trend, and explore new research directions.

Surface‐enhanced Raman scattering spatial fingerprinting decodes the digestion behavior of lysosomes in live single cells

AbstractLysosome, the digestive organelle in eukaryotic cells, plays an important role in the degradation and recirculation of cellular products as well as in maintaining the stability of cellular metabolic microenvironment. Surface‐enhanced Raman scattering (SERS) is a molecular fingerprint technology with high detection sensitivity and photostability, suited for revealing various intracellular molecular information by inducing endocytosis of SERS‐active nanoparticles. However, it remains challenging to selectively extract the molecular information of specific organelles (e.g., lysosomes) from a high‐dimensional spectral set. Herein, we proposed a novel paradigm by combining label‐free SERS spectroscopy with confocal fluorescence imaging to investigate the digestion behavior of lysosomes in cells. The structural similarity algorithm was innovatively introduced and exhibited its effectiveness in screening out the wavenumbers in the SERS spectral set with high correlation with the metabolic behaviors of lysosomes. With comprehensive experiments on HeLa single cells, we captured the intracellular macromolecular digestion phenomenon and discovered the changing pattern of cellular SERS spectra after starvation‐induced autophagy, and analyzed the molecular information within the lysosomes in three‐dimensional space.

Insights into growth stages and genotypes in airborne Pb accumulation in Oryza sativa L. grains: Utilizing isotope fingerprinting alongside a model study

Atmospheric exposure is an important pathway of accumulation of lead (Pb) in Oryza sativa L. grains. In this study, source contributions of soil, early atmospheric exposure, and late atmospheric exposure, along with their bioaccumulation ratios were examined both in the pot and field experiments using stable Pb isotope fingerprinting technology combined with a three-compartment accumulation model. Furthermore, genotype differences in airborne Pb accumulation among four field-grown rice cultivars were investigated using the partial least squares path model (PLS-PM) linking rice Pb accumulation to agronomic traits. The findings revealed that during the late growth period, the air-foliar-grain transfer of Pb was crucial for rice Pb accumulation. Approximately 69–82% of the Pb found in polished rice was contributed by atmospheric source, with more than 80% accumulating during the late growth stage. The air accumulation ratios of rice grains were genotype-specific and estimated to be 0.364–1.062 m3/g during the late growth. Notably, grain size exhibited the highest standardized total effects on the airborne Pb concentrations in the polished rice, followed by leaf Pb and the upward translocation efficiency of Pb. The present study indicates that mitigating the health risks associated with Pb in rice can be achieved by controlling atmospheric Pb levels during the late growth stage and choosing Japonica inbred varieties characterized by large grain size.

Biometric door lock system with automated fire detection alert

The reconciliation of biometric advancements and fire recognition components in shrewd homes is critical for access control and crisis readiness. The Blynk mobile app is used to access this project's biometric door lock and automated fire detection system. For access authorization, the biometric system uses cutting-edge fingerprint or facial recognition technology. The mechanized fire location framework screens the climate for potential fire dangers and triggers an alert when distinguished. The Blynk application gives controller and checking abilities, permitting inhabitants to lock or open the entryway, get ongoing warnings, and remotely screen the fire discovery framework's status. The microcontroller coordinates communications between the frameworks, outfitted with Wi-Fi availability. The Blynk application point of interaction is planned with easy-to-understand gadgets, permitting inhabitants to control and screen the coordinated frameworks. Safety efforts, for example, encoded correspondence conventions and secure stockpiling of biometric information, shield client protection. This all-encompassing approach to residential security ensures that their homes can effectively handle access control and emergencies, providing residents with security and peace of mind.

Development of Machine Learning Models for Ion-Selective Electrode Cation Sensor Design.

Polyvinyl chloride (PVC) membrane-based ion-selective electrode (ISE) sensors are common tools for water assessments, but their development relies on time-consuming and costly experimental investigations. To address this challenge, this study combines machine learning (ML), Morgan fingerprint, and Bayesian optimization technologies with experimental results to develop high-performance PVC-based ISE cation sensors. By using 1745 data sets collected from 20 years of literature, appropriate ML models are trained to enable accurate prediction and a deep understanding of the relationship between ISE components and sensor performance (R 2 = 0.75). Rapid ionophore screening is achieved using the Morgan fingerprint based on atomic groups derived from ML model interpretation. Bayesian optimization is then applied to identify optimal combinations of ISE materials with the potential to deliver desirable ISE sensor performance. Na+, Mg2+, and Al3+ sensors fabricated from Bayesian optimization results exhibit excellent Nernst slopes with less than 8.2% deviation from the ideal value and superb detection limits at 10-7 M level based on experimental validation results. This approach can potentially transform sensor development into a more time-efficient, cost-effective, and rational design process, guided by ML-based techniques.