Smart Card Research Articles

Design of Fault Diagnosis Method Based on Circuit Frequency Domain Characteristics Fault Dictionary Method

In recent years, with the rapid development of integrated circuits and the widespread application of smart chips, the internal structure of complex electronic systems have become increasingly complex, which greatly increases the difficulty of board-level circuit testing and fault diagnosis of complex electronic systems. At present, traditional manual testing methods are no longer able to meet the maintenance requirements of modern complex electronic system board-level circuit. Therefore, the development of intelligent and universal automatic testing systems has become an important issue in fault detection of complex electronic systems. Based on the testing requirements of the automatic test system(ATS), this paper designs the corresponding overall scheme, software scheme and hardware circuit, and studies the fault diagnosis and test of board-level circuit of complex electronic system, and proposes to apply the traditional Bode diagram to the engineering practice of ATS. Fault detection is realized by utilizing the frequency domain characteristics of the object under test.

Multi-Modal Urban Traffic Transfer Schedule Timetable Bi-Objective Optimization: Model, Algorithm, Comparison, and Case Study

The optimization of the connection between urban rail transit and the bus is an essential issue that benefits passenger travel and the urban structure and has social benefits, which can be realized by reasonably adjusting the bus departure schedule. This study is necessary because the development status quo of China’s urban transportation network planning is unreasonable, travel efficiency is not high, and operating costs are high. This paper sets up the decision variables of bus departure time and departure interval at each station, establishes a dual-objective optimization model with the minimum schedule change and the minimum transfer time, and studies the application of the augmented Chebyshev algorithm in the dual-objective optimization model. Secondly, based on the Shenzhen metro and public transportation integrated circuit card data, the case analysis uses the generalized Chebyshev algorithm and the non-dominated sorting genetic algorithm, respectively. The optimization results show that using the improved augmented and generalized Chebyshev algorithm in the bus schedule alteration time within a reasonable range can maximize the total transfer time, which compared with the original scheme is shortened by 68.06%. In contrast, genetic algorithms will make the complete bus schedule alteration prominent, and the whole transfer time is substantially increased. The results show that the improved augmented generalized Chebyshev algorithm is more suitable for solving the dual-objective rail transit connection problem.

Smart Card Stunting Media Influences Knowledge and Attitudes of Pregnant Women about Stunting Prevention

Stunting is a condition when a child's height is less than the standard height for his age and is a problem of chronic malnutrition caused by insufficient nutritional intake for quite a long time. If pregnant women do not know the causes of stunting, especially nutritional adequacy in early pregnancy, it will have an impact on the attitude of pregnant women who do not apply good nutritional patterns during pregnancy. This can happen because when a child is stunted it disrupts his physical growth, immunity, and cognitive function. The purpose of this study was to determine the effect of education using the stunting smart card media on the knowledge and attitudes of pregnant women regarding stunting prevention in the work area of the Telaga Dewa Health Center, Bengkulu City. This research is a quantitative type of Pre-Experiment design with a one-group pre-test and post-test design. The sampling technique in this study was purposive sampling. The number of samples to be examined in this study were 30 first trimester pregnant women. The results showed that the average attitude score before intervention was negative, namely 28.63 and after education using a stunting smart card, a positive attitude was 37.70. The p value was obtained = 0.000 ≤ 0.05 using a confidence level of 95%, which means that there is an effect of the stunting smart card media on the knowledge and attitudes of pregnant women about stunting prevention in the work area of the Telaga Dewa Health Center, Bengkulu city.

Research on the application of Artificial Intelligence

Currently, the field of artificial intelligence mainly includes key technologies such as computer vision, natural language processing, cross-media analytical reasoning, intellectually adaptive learning, group intelligence, autonomous unmanned systems, smart chips and brain-computer interfaces. These operations that can be completed with the assistance of artificial intelligence, with the help of artificial intelligence, life efficiency is greatly improved and the cost is greatly reduced. However, at present, Artificial Intelligence is only effective in the field of pointed and high-end research and learning, and there are still some thresholds in the application of daily life. In recent days, there have been a series of new technological inventions such as ChatGPT and a series of civilian Artificial Intelligence. Most of these new programs cannot be reasonably applied. This paper takes the application of Artificial Intelligence in the professional field as the background, and deeply explores the application of Artificial Intelligence technology in the non-professional field as exemplified by ChatGPT. This paper finds that in the medical field, Artificial Intelligence has played a considerable role, and Artificial Intelligence research on medical care is also in progress, so this paper makes predictions about the Artificial Intelligence that will appear in the medical industry based on the current situation, and investigates the understanding and use of Artificial Intelligence among teenagers through questionnaires, and analyzes the collected data.

How and Where Did Older People Travel Before and After COVID? Insights from Washington, DC’s Smart Card Data

Using smart card data of subway trips, this paper analyzed travel behaviors of older adults in Washington, DC in three phases of COVID-19 (Pre: 2018-2019, Early: 2020, Late: 2021-2022). The findings show that the impact of COVID-19 on average daily travel patterns was more pronounced on weekday travels, compared to weekend trips. In addition, compared to the general population, older adults’ subway usage showed a slower recovery to normal patterns in both usage levels and trip destinations. The results reveal important insights for transportation planners and transit authorities about older adults’ travel patterns during normal times and unusual events.

Assessing effects of pandemic-related policies on individual public transit travel patterns: A Bayesian online changepoint detection based framework

During a pandemic or natural disaster, people may alter transit usage behavior due to perception of changes in the environment. To effectively respond to these crises, it is important for governments and public transit agencies to understand when these changes occurred and how they were affected by relevant policies and responsive strategies. In this study, we develop a methodological framework based on Bayesian online changepoint detection (BOCD) to identify the occurrence time, direction, and persistency of changes in individual-level transit usage. We demonstrate the effectiveness of this framework in informing government decision-making in the context of COVID-19. Using Jeju Island, South Korea as a case study, we apply the framework over a nearly two-year smart card dataset collected from the beginning of 2019 till nine months into the pandemic. By focusing on frequent transit users, we detect when these users significantly changed their transit usage frequency during the pandemic and identify several types of users who experienced different behavior change patterns. Besides demonstrating the great heterogeneity in individual-level behavior changes, we perform a regression analysis to further understand how these changes were affected by key government policies (e.g., Risk alert, Social distancing, Public transit policy, and Eased social distancing). Our results suggest that only certain sets of policies appear to have significant effects. In particular, introducing Risk alert would cause a 277% to 317% increase in the number of users who reduced transit usage frequency. Policies that eased social distancing, though, would cause a 134% to 155% increase in the number of users with travel frequency increase. The proposed BOCD framework enables a scalable solution to identifying and understanding changes of individual transit behavior. The methodology and findings are beneficial for developing targeted policies and interventions to facilitate daily travel and public transit operations during public health crises.

Automated Petrol Dispensing System with Smart Card and RFID Integration

Today, the majority of petrol stations employ microcontrollers to manage the pump operations, display controls, and flow measurement, but human interaction is still needed for payment collection. This project aims to automate the petrol dispensing process, eliminating the need for workers to handle payments. Users are provided with a rechargeable smart petrol card, which allows them to access petrol. When the card is placed in the reader, the microcontroller reads the data and prompts the user to enter the required liters of petrol on an LCD screen via a keypad. The system checks the card balance, dispenses the petrol if sufficient funds are available, and signals completion with a buzzer. Additionally, the system includes vehicle monitoring using RFID readers to scan vehicle number plates, alerting authorities if a vehicle is linked to a crime via SMS. A smart phone app tracks vehicle locations continuously.

Real-world evidence of heparin and citrate use in extracorporeal photopheresis: A hypothesis-generating data review of device settings and performance.

Extracorporeal photopheresis (ECP) is widely used for the treatment of cutaneous T-cell lymphoma, graft-vs-host disease, and other immune-related conditions. To avoid clotting during treatment, the ECP system used must be effectively primed with an anticoagulant. Heparin is the recommended anticoagulant for the THERAKOS CELLEX System, but acid citrate dextrose-A (ACDA) is often used. We compared system performance between these two anticoagulants for this ECP system. Deidentified data for ECP device performance were obtained at each treatment session, from automatically logged Smart Cards or labels completed by device operators. We compared the effects of ACDA or heparin on overall treatment duration, buffy coat (leukocyte) collection time, photoactivation time and the number of alarms and warnings. The variability in these parameters was also assessed. Data from 23 334 treat sessions were analyzed; ACDA was used in 34.4% and heparin in 65.6%. Overall, the ECP procedure duration, buffy coat collection time and photoactivation time were numerically similar regardless of whether ACDA or heparin was used, and regardless of needle mode. Photoactivation time variability was lower with ACDA compared with heparin in all needle modes. Among treatments that were completed automatically without any operator intervention, total treatment duration and photoactivation time were significantly reduced with ACDA use in both the double- and single-needle modes. The data presented indicate that, in both double- and single-needle modes, the THERAKOS® CELLEX® integrated ECP system performed similarly with ACDA compared to heparin, although ACDA demonstrated potential benefits in reducing variability in photoactivation time.

A two-step passenger flow anomaly detection scheme based on machine learning methods

Subway is an important transportation means for residents due to its large volume, punctuality and environmental friendliness. However, weather factors, sports events, concerts and some unexpected events can lead to a surge or abnormality in passenger flow, which brings enormous pressure to the management of stations and passenger flow guidance. Inspired by this, this paper formulates the abnormal passenger flows into different categories in terms of the characteristics and periodical trends, and proposes a two-step abnormal detection scheme to identify the anomalies and their type, and locate abnormal positions. First, two abnormal passenger flows recognition methods based on Jensen–Shannon divergence, dynamic time warping, and density-based spatial clustering of applications with noise are established to identify the station-level abnormal passenger flow. Then, a triple standard deviation algorithm based on sliding window is further proposed to identify the abnormal type and position. Real-world smart card data of the Beijing subway in China, and the manual mutation data of the real data are employed to evaluate effectiveness of our framework. The results show that our two-step scheme is superior to the state-of-the-art algorithms, which can detect out and locate abnormal passenger flows with various characteristics. On more mutation data, this paper discusses the performances on various anomalies of different types of stations in depth, which further indicates our framework is robust and effective in practice.

Low complexity smart grid security protocol based on elliptic curve cryptography, biometrics and hamming distance.

The incorporation of information and communication technologies in the power grids has greatly enhanced efficiency in the management of demand-responses. In addition, smart grids have seen considerable minimization in energy consumption and enhancement in power supply quality. However, the transmission of control and consumption information over open public communication channels renders the transmitted messages vulnerable to numerous security and privacy violations. Although many authentication and key agreement protocols have been developed to counter these issues, the achievement of ideal security and privacy levels at optimal performance still remains an uphill task. In this paper, we leverage on Hamming distance, elliptic curve cryptography, smart cards and biometrics to develop an authentication protocol. It is formally analyzed using the Burrows-Abadi-Needham (BAN) logic, which shows strong mutual authentication and session key negotiation. Its semantic security analysis demonstrates its robustness under all the assumptions of the Dolev-Yao (DY) and Canetti- Krawczyk (CK) threat models. From the performance perspective, it is shown to incur communication, storage and computation complexities compared with other related state of the art protocols.

Key Distribution and Authentication Protocols in Wireless Sensor Networks: A Survey

We use sensor technologies in many areas of everyday life. We use sensors to check and study various phenomena and to improve our lives. Hence, the sensors are used in medicine, industry, sports, and many other aspects of everyday life. Interconnected sensors and other wireless devices and servers form Wireless Sensor Networks. During communication between the nodes of such networks, we often send private and sensitive data. These data should be properly secured. Securing communication in a Wireless Sensor Network mainly affects the reconciliation and distribution of session keys and authentication. Specially designed protocols must protect both methods. In turn, the protocols may be exposed to dishonest users of such networks and thus exposed to various attacks (for example, replay attacks and smart card stolen attacks). This article surveys the existing session key negotiation, distribution, and authentication protocols. We will explain the security problems and threats to which the sensor networks are exposed. We will discuss the security levels implemented by the protocols currently used in sensor networks. We will analyze the challenges and requirements faced by the newly designed protocols.

How does multi‐modal travel enhance tourist attraction accessibility? A refined two‐step floating catchment area method using multi‐source data

AbstractIn the post‐pandemic era, tourism has emerged as the most popular form of recreation in densely populated areas, driven by the pursuit of improved quality of life and the younger generation's growing enthusiasm for connecting with nature or history. Accurate measurement of the accessibility of tourist attractions has become crucial due to this trend. However, existing methods for measuring accessibility, such as the widely used two‐step floating catchment area (2SFCA) method, show limitations in multi‐modal transport networks as it overlooks multi‐modal travels and assumes uniform access for each transport mode within the catchment area. In this article, we present a novel multi‐modal network‐based two‐step floating catchment area (MMN‐2SFCA) method, which incorporates a super‐network to explicitly model the multi‐modal travels. The proposed method emphasizes the modeling of transfer behavior, resulting in a more comprehensive and accurate measurement of tourist attraction accessibility compared to the conventional 2SFCA method. The proposed method emphasizes the modeling of transfer behavior, travel mode choice, and travel demand estimation by multi‐source big data (including mobile phone signaling data, travel survey data, subway smart card data, and bike‐sharing data), resulting in a more comprehensive and accurate measurement of tourist attraction accessibility compared to the conventional 2SFCA method. The results of the case study in Suzhou (a famous tourism city in China) demonstrate the effectiveness of the proposed MMN‐2SFCA method. The method can rectify the imbalance in accessibility distribution caused by considering only single‐modal trips, and avoid overestimation of accessibility by accounting for transfer behavior. This study contributes to advancing accessibility measurement for multi‐modal transport networks. Moreover, the MMN‐2SFCA method offers excellent extensibility, enabling authorities to optimize and coordinate multi‐modal transport networks for improving the accessibility of tourist attractions and other facilities.

Mining smart card data to estimate transfer passenger flow in a metro network

AbstractMetro systems play an important role in reducing urban traffic congestion and promoting the sustainable development of urban transport in megacities. With the expansion of a metro network, transfer stations are necessary for increasing the service connectivity of a metro network. An accurate estimation of transfer passenger flow can help improve the operations management of a metro system. This study proposes a data‐driven methodology for estimating the transfer passenger flow volume of each transfer station in a metro network by mining smart card data. The estimated transfer passenger flow data are visualized to show the spatial‐temporal distribution characteristics of metro transfer passenger flow. The case study results of the Shenzhen Metro network demonstrate that the proposed data‐driven methodological framework is very effective in estimating different types of transfer passenger flows, such as total transfer passenger flow, hourly transfer passenger flow, and inbound and outbound transfer flows at each transfer station. The spatial‐temporal distribution characteristics of transfer passenger flow can be very useful for designing effective and efficient passenger flow management measures to ensure the safe and efficient operation of a metro system.

ECC-based three-factor authentication and key agreement scheme for wireless sensor networks

In wireless sensor networks (WSNs), protocols with authentication and key agreement functions can enhance the security of the interaction between users and sensor nodes, guaranteeing the security of user access and sensor node information. Existing schemes have various security vulnerabilities and are susceptible to security attacks (e.g., masquerading user, password guessing, internal privilege, and MITT attacks), so they cannot meet the anonymity requirements or achieve forward security. To effectively improve the security performance of WSNs, an elliptic curve cryptography (ECC)-based three-factor authentication and key agreement scheme for WSNs is proposed. The scheme is based on the ECC protocol and combines biometrics, smart card and password authentication technology; uses a challenge/response mechanism to complete the authentication between users, gateways, and sensors; and negotiates a secure session key. The Burrows, Abadi and Needham logic for formal security analysis proves the correctness and security of the scheme, and the informal analysis of multiple known attacks proves that the scheme can resist various attacks and has high security characteristics. The feasibility of the scheme has been analysed and verified with the ProVerif tool. The efficiency analysis results show that the scheme is suitable for resource-constrained WSNs.

Empowering robust biometric authentication: The fusion of deep learning and security image analysis

Many societal entities now have more excellent standards for the efficacy and dependability of identification systems due to the ongoing advancement of computer technology. Traditional identification methods, such as keys and smart cards, have been supplanted by biometric systems in highly secure environments. This research presents a smart computational method for automatically authenticating fingerprints for identity (ID) verification and personal identification. Compared to more traditional machine learning algorithms, the results from applying Deep learning (DL) in areas like computer vision, image identification, robotics, and voice processing have generally been positive. Due to their capacity to analyse big data size and deal with fluctuations in biometric data (such as ageing or expression problems), DL has been heavily used by the artificial intelligence research community. Several biometric systems have succeeded with automatic feature extraction employing deep learning approaches like Convolutional Neural Networks (CNNs). In this research, we provide a biometric process that uses convolutional neural networks. This work introduces a deep learning-based biometric identification system that uses Monte Carlo Dropout (MC Dropout). Combining these two systems makes the authentication process more secure and dependable. Fingerprint image enhancement techniques involve the application of Gabor filters and structure-adaptive anisotropic filters, which have proven to be effective in enhancing the clarity and distinctiveness of fingerprint patterns. To improve the efficiency of deep learning models, this work proposes the Inception-Augmentation GAN (IAGAN) model for data augmentation. The study adds to security development by integrating novel biometric identification and authentication approaches with cutting-edge neural network technology. In this research, we provide a new activation function to speed up the convergence of deep neural networks. The results of 99.6% on Gabor filters and 99.8% on the structure-adaptive anisotropic filter with GACNN with MCD show that deep neural networks can excel over competing approaches with enough training data.

Activity-based individual travel regularity exploring with entropy-space K-means clustering using smart card data

Travel activities influence individual travel location, time, and frequencies. Understanding individual travel regularity under different activities is crucial for individual mobility prediction and urban facilities planning. Existing studies need further improvement as they have not adequately taken into account the impact of activity factors on individual travel patterns. In this paper, we propose an innovative framework for exploring travel regularity and the correlation with urban region attributes at the individual level. The framework’s novelty and uniqueness lie in its three layers: (i) A trip pre-processing layer for extracting every complete public transit trip and trip purposes (e.g. commuting activity and non-commuting activity) by combining individual’s travel characteristics and public transport network. (ii) An entropy-based travel regularity measurement and K-means based multiple-view clustering layer to assess the extent of individual travel repetition over time for various travel activity and investigate the similarities and differences among users. (iii) A region dependence correlation analysis layer for exploring the correlation between individual travel regularity and regions attributes of two key locations: home and workplace More importantly, by employing the framework, we gained empirical insights based on a large-scale dataset (covering 0.64 million users) collected from public traffic smart cards. For instance, commuters can be categorized into four groups based on the regularity of their commuting activities: Regular Workers (24%), Flex-time Workers (21%), Overtime Workers (35%), and Other Workers (20%). The distribution of these four groups is associated with workplace’s administrative characteristics, with a Pearson correlation of 0.503. In addition, individuals can also be classified into two groups based on their travel regularity of non-commuting activities: Limited Visitors (37%) and Active Explorers (63%). The distribution of these individuals is correlated with the coverage ratio of public transit facility in their home locations, showing a Pearson correlation of 0.604.

OSL at elevated temperature of smart chip cards for retrospective dosimetry

Smart chip cards are a promising material in retrospective dosimetry due to their high collectability with low replacement cost. In this study, dose reconstruction using OSL at elevated temperatures for various chip cards was investigated. The approximate spectral emission of the chip cards was determined through a comparison of radiation-induced and intrinsic background TL signals using different detection filters. In the OSL decay curve, a fast component (0.0–0.4 s) was integrated to avoid high intrinsic background signals. To minimize sensitivity changes due to heat treatment, a protocol to measure OSL at 100 °C was developed and the data were compared with those from a similar protocol for OSL measured at room temperature. In the 0.1 - 5 Gy range, linear and power functions were fitted for room temperature and 100 °C OSL protocols, respectively. The minimum detectable dose was evaluated immediately after exposure, with values of 25 mGy for room temperature and 240 mGy for 100 °C OSL being determined. Signal fading was measured up to 30 days after irradiation for the investigated protocols. A dose recovery test was made 10 days after irradiation using various chip cards, and the 100 °C OSL protocol produced more reliable results than the room temperature OSL protocol. As a result, the study emphasizes the necessity of further investigation of OSL at high temperatures using chip cards.

Secure Transactions in a Chip: A Contemporary Review of Smart Card Innovations

Smart card technology has emerged as a powerful tool in the field of secure identification, authentication, and transaction processing. This abstract provides a comprehensive overview of smart card technology, highlighting its key features, applications, and benefits. Smart cards, also known as integrated circuit cards, are portable devices that incorporate a microprocessor and memory to securely store and process information. These cards have revolutionized various industries by enabling secure access control, secure payment transactions, and secure storage of sensitive data. The abstract begins by exploring the fundamental components and architecture of smart cards. It delves into the different types of smart cards, such as contact-based and contactless cards, and explains the communication protocols employed in their operation. Furthermore, the abstract discusses the extensive range of applications where smart cards have found widespread adoption. These applications include identification cards, payment cards, healthcare cards, transportation cards, and more. The abstract highlights the advantages of using smart cards in each of these domains, such as enhanced security, convenience, and interoperability.

Investigating the potential of aggregated mobility indices for inferring public transport ridership changes.

Aggregated mobility indices (AMIs) derived from information and communications technologies have recently emerged as a new data source for transport planners, with particular value during periods of major disturbances or when other sources of mobility data are scarce. Particularly, indices estimated on the aggregate user concentration in public transport (PT) hubs based on GPS of smartphones, or the number of PT navigation queries in smartphone applications have been used as proxies for the temporal changes in PT aggregate demand levels. Despite the popularity of these indices, it remains largely untested whether they can provide a reasonable characterisation of actual PT ridership changes. This study aims to address this research gap by investigating the reliability of using AMIs for inferring PT ridership changes by offering the first rigorous benchmarking between them and ridership data derived from smart card validations and tickets. For the comparison, we use monthly and daily ridership data from 12 cities worldwide and two AMIs shared globally by Google and Apple during periods of major change in 2020-22. We also explore the complementary role of AMIs on traditional ridership data. The comparative analysis revealed that the index based on human mobility (Google) exhibited a notable alignment with the trends reported by ridership data and performed better than the one based on PT queries (Apple). Our results differ from previous studies by showing that AMIs performed considerably better for similar periods. This finding highlights the huge relevance of dealing with methodological differences in datasets before comparing. Moreover, we demonstrated that AMIs can also complement data from smart card records when ticketing is missing or of doubtful quality. The outcomes of this study are particularly relevant for cities of developing countries, which usually have limited data to analyse their PT ridership, and AMIs may offer an attractive alternative.

Anonymous and robust biometric authentication scheme for secure social IoT healthcare applications

In the era of rapid technological advancement, the Internet of Things (IoT) has revolutionised healthcare through systems like the Telecare Medicine Information System (TMIS), designed to streamline patient-doctor interactions and enhance medical treatment. However, the transmission of sensitive patient data over inherently insecure Internet channels exposes it to a spectrum of security risks. Protecting patient medical privacy and ensuring system reliability necessitate mutual authentication between both patients and medical servers. TMIS relies on robust authentication mechanisms, and combining passwords and smart cards has been a recognised approach for mutual authentication. This research introduces an innovative three-factor authentication technique with perfect forward secrecy by leveraging the power of Elliptic Curve Cryptography (ECC) in tandem with smart cards. Additionally, we have incorporated biometric authentication with a Fuzzy Extractor technology to enhance the security and reliability of the system, setting a new standard for user authentication within the realm of Social IoT healthcare. The use of ECC in the method is justified due to its compact key size and robust security measures, making the solution both efficient and secure. The proposed method safeguards user privacy by permitting registered users to change their passwords without divulging their identity to the server. The Burrows–Abadi–Needham logic (also known as the BAN logic) serves as a proof-of-concept for the proposed scheme’s security. Our system provides privacy protection along with mutual authentication and session key negotiation at a considerably low computation cost and communication cost of up to 71.03% compared to the other four relevant techniques, making it more useful in real-world scenarios.