RFID Research Articles

Digital Twin Modeling for Hydropower System Based on Radio Frequency Identification Data Collection

The safe and steady operation of hydropower generation systems is crucial for electricity output in the grid. However, hydropower stations have complicated interior structures, making defect detection difficult without disassembly inspections. The application of digital modeling to hydropower stations will effectively promote the intelligent transformation of hydropower stations as well as reduce the maintenance costs of the system. This study provides a model of the power generating and transmission system for hydropower plants, with an emphasis on primary equipment and measured data. The model utilizes PSCAD to digitalize state response in hydropower plants with various short-circuit faults. The fault information is identified and learned using the Adaptive Time–Frequency Memory (AD-TFM) deep learning model. It is demonstrated that our proposed method can effectively obtain the fault information through radio frequency identification (RFID). The accuracy of the traditional method is 0.90, while the results for AD-TFM show a fault classification accuracy of 0.92, which is more than enough to identify multiple fault types compared to the existing methods.

Rancang Bangun Purwarupa Simulasi Sistem Keamanan Rumah Berbasis Internet of Things

The use of information technology has spread to all fields, for example in the field of security using the Internet of Things technology. One of the places that need to be given security is the home, so that in this study prototypes of home security systems using the Internet of Things have been made with the aim of providing information to homeowners in the event of infiltration through windows or doors, gas leaks and fires. The tools used are Arduino Mega 2560 as a microcontroller, gas sensor, fire sensor, magnetic switch sensor, RFID, keypad, and selenoid. The home security system is made so that homeowners can control the condition of the home. The results of this study are that the home security systems that are made can run well and can be integrated with home security information systems. Sensors can detect gas leaks, fires, and doors or windows open when the security system is active. Homeowners can only turn off the home security system and unlock the door by using RFID tags that have registered IDs and correct passwords. Home security systems can also turn on alarms and send notifications to homeowners, fire stations and police stations in the event of a fire, gas leak or window or door that is opened when the security system is active.

RFID-GSM Automatic Gate Control for Highly Secure Organizations

Abstract: In the current dynamic global landscape, security is paramount for organizations, particularly those entrusted with safeguarding sensitive data and valuable assets. Automatic gate control systems are pivotal in fortifying security and optimizing the operational efficiency of access points for highly secure organizations. By integrating Radio-Frequency Identification (RFID) and Global System for Mobile Communications (GSM) technologies, these systems provide sophisticated features that bolster security measures while enabling seamless access for authorized personnel. These automatic gate control systems provide a multi-layered approach to security, addressing various vulnerabilities and potential threats highly secure organizations face. By leveraging RFID technology, these systems enable efficient and reliable authentication of authorized personnel, minimizing the risk of unauthorized access or breaches. Additionally, the integration of GSM technology enhances the system's responsiveness and remote management capabilities, allowing administrators to monitor gate activities in real time and respond promptly to any security incidents or anomalies.

Student Face Recognition and Alert Rule

Abstract: Attendance marking in a classroom during a lecture is not only an onerous task but also a time-consuming one at. Due to an unusually high number of students present during the lecture, there will always be a probability of proxy attendance(s). Attendance marking with conventional methods has been an area of challenge. The growing need for efficient and automatic techniques for marking attendance is a growing challenge in the area of face recognition. In recent years, the problem of automatic attendance marking has been widely addressed through the use of standard biometrics like fingerprint and Radiofrequency Identification tags, etc., However, these techniques lack the element of reliability. In this proposed project an automated attendance marking and management system is proposed by making use of face detection and recognition algorithms. Instead of using conventional methods, this proposed system aims to develop an automated system that records the student’s attendance by using facial recognition technology. The main objective of this work is to make the attendance marking and management system efficient, time-saving, simple, and easy. Here faces will be recognized using face recognition algorithms. The processed image will then be compared against the existing stored record and then attendance is marked in the database accordingly. Compared to the existing system traditional attendance marking system, this system reduces the workload of people. This proposed system will be implemented with 4 phases Image Capturing, Segmentation of group image and Face Detection, Face comparison and Recognition, and Updating of Attendance in the database. Index Term – Mongo DB, React JS, Node JS, Express JS, Attendance, Facial recognition, and detection, Haar cascade, Alert, Student Face.

Battery-free flexible wireless temperature sensing for food storage

The method of monitoring food temperature during food storage needs to be improved to continuously and accurately perceive the temperature of the food in the package to ensure the quality and safety of the food during storage. This paper proposes and develops a battery-free flexible wireless temperature sensing system (BFTS) for food storage. The BFTS consists of a battery-free flexible wireless temperature sensing tag (BFTT), a wireless reader, and a personal computer (PC). The BFTT developed in this paper has good flexibility and can be placed inside the food package to realize the continuous monitoring of temperature changes. The flexible circuits of the BFTT were fabricated by laser engraving laser-induced graphene (LIG) −copper (Cu) plating film made with Cu plating on LIG. The LIG-Cu plating film has good thickness uniformity, electrical conductivity, and laser engraving processability. The antenna of BFTT has good performance. The wireless reader is connected to the PC using a data line, and the BFTT communicates wirelessly with the wireless reader using ultra-high frequency (UHF) radio frequency identification (RFID). The BFTT was realized by the wireless radio frequency (RF) as the supply power from the wireless reader. The BFTS could realize the temperature monitoring of food stored at 0℃ and −18℃, and it has the advantages of low cost, simple manufacturing process, and low energy consumption, which could be used to continuously and accurately monitor the inside temperature of the food packages. Overall, the LIG-Cu plating film developed in this paper could be used in the fabrication of flexible circuits, and the temperature monitoring inside food packages realized by the BFTS has potential applications in actual food storage.

Collaborative Pricing of Green Supply Chain of Prefabricated Construction

In the process of carbon peak and carbon neutrality, prefabricated buildings have developed rapidly, and the concept of green and low carbon has been introduced into the field of prefabricated buildings. This paper establishes an information sharing platform based on BIM (Building Information Modeling), RFID (Radio Frequency IDentification), and GIS (Geographic Information System) technologies from the green supply chain of prefabricated buildings. On the basis of information sharing, the Stackelberg two-stage game is used to analyze and compare the overall profit of the supply chain under the centralized pricing decision and the decentralized pricing decision. Through numerical simulation, this paper analyzes the relationship between pricing and the overall profit of the supply chain, compares the difference of the overall profit of supply chain under two different pricing strategies, centralized pricing and decentralized pricing, and analyzes the influence of information sharing degree on the overall profit of the supply chain. The results show that the overall profit of the supply chain under centralized pricing decisions is significantly higher than that under decentralized pricing decisions. The higher the degree of information sharing, the greater the overall profit of the supply chain.

Digital Transformation in IT Asset Management Process

Information Technology Asset Management (ITAM) is a key process in the Information Technology (IT) sector. Since the late 20th century, maintaining a growing number of IT assets has become a challenging task. It requires technological advancements to streamline ITAM processes efficiently and cost- effectively. The digital transformation of ITAM processes can help organizations overcome many challenges. Digitalizing asset management processes requires a centralized repository for efficiently managing IT assets, such as workstations, laptops, and even software assets. This enhances security and ensures that assets adhere to security compliance. Technologies for digital transformation, alongside techniques such as process automation and the adoption of advanced Radio Frequency Identification (RFID) technology, can be utilized to improve ITAM. These technologies and techniques enable organizations to improve their asset management processes, meeting the organization's needs. This paper addresses the impact of digital transformation on IT Asset Management and explores areas for process improvement. Digital transformation impacts Asset Management by enabling faster asset- related decision-making, optimizing asset utilization, reducing maintenance costs, and providing greater transparency and asset traceability.

Circuits for Printed Electronics for Flexible Displays & Sensors

Printed circuits are crucial components in the field of printed electronics, enabling the creation of flexible, lightweight, and cost-effective electronic devices. This proposal explores the fabrication techniques, materials, and applications of printed circuits in printed electronics. Various printing methods such as inkjet, screen, and flexography are discussed, along with the integration of conductive inks and substrates. The versatility of printed circuits allows for the development of diverse electronic products including wearable sensors, RFID tags, and flexible displays. Moreover, advancements in additive manufacturing technologies continue to drive innovation in printed circuit design and production, promising a future of increasingly sophisticated printed electronic devices.

Design and Build “Smart Key Tracking Systems with RFID and Arduino Mega” for Use in Petrochemical Engineering Department PTSN

The problem concerning key storage at Politeknik Tun Syed Nasir Syed Ismail (PTSN) systems is a lack of systematic administration, leading to uncontrolled and insecure essential management procedures. Consequently, the objective is to create an intelligent key tracking system (SKTS) utilizing radio frequency identification (RFID) and Arduino Mega to establish a secure and properly organized key storage system with efficient data management. This project introduces the monitoring system with automated and real-time data collection. SKTS design uses an Arduino Mega 2560 microcontroller and an ESP8266 Node MCU Wi-Fi module that sends data over the cloud. The project also utilizes the Telegram chat application bot to notify and monitor the keys' retrieval and return. It is also equipped with an RFID reader as a sensor. As the authorized user, once the RFID card is swept, type the selection key number; the solenoid lock for the selected key will open, and the key is free to be picked. The liquid crystal display (LCD) will show the authorized user data and selected room number. A signal will send the information to the Telegram application through a mobile phone to notify the other authorized user. This project successfully achieves its objective by utilizing the Telegram application to notify critical users and authorized individuals when keys are returned. These real-time notifications are visible to all registered group members on the Telegram application. The Likert Scale, which has two points, is established by utilizing data responses from the rubrics form, with evaluators consisting of industrial designers and researchers. The results are subsequently synthesized using qualitative percentages. The responses to the item provided via the rubric assessment form show that the SKTS meets the design, ideal, and functional aspects.

Mobilefacenet-Based Facial Recognition System for Contactless Access Control

Conventional access control systems require physical contact, such as pressing buttons, using fingerprints, or employing radio-frequency identification (RFID). These interactions increase the risk of transmitting diseases like COVID-19 through surface contact. To mitigate this risk, a contactless identification method becomes imperative. Hence, this study proposes an adaptive and seamless dual-mode mobile facial recognition-based access control system. Specifically, the proposed system uses a convolutional neural network (CNN) algorithm (MobileFaceNet) to develop high-precision facial verification on mobile devices with a dual-mode (real-time and offline) facial recognition system. The system is implemented on Raspberry Pi, running an Android Operating System equipped with camera functionality. The system instantaneously operates by detecting human faces, compares the faces with a pre-existing database, and grants access to successful matches. The system underwent preliminary testing on an Android 9 device with an 8-megapixel camera. Faces were detected and recognised in a record time (within seconds), showcasing commendable accuracy. The system achieved a recognition accuracy of 95% under varied lighting conditions and distances. The proposed system can be seamlessly integrated with electronic doors, ensuring optimal access control for smart homes and restricted facilities.Keywords: Access control, convolutional neural network, face recognition, firebase, mobilefacenet

Timber Traceability and Sustainable Transportation Management: A Review of Technologies and Procedures

There has been considerable research on sustainable forest management due to its importance in ensuring the long-term health of the forests and its industries. Traceability is an important tool to ensure that the wood and wood-based products are produced in a legal, sustainable, and ethical manner. Therefore, various technologies were implemented within the supply chain to monitor and trace the wood with the aim of fulfilling the traceability objectives. Using a systematic literature review, this paper provides a comprehensive state-of-the-art on the technologies and procedures used in timber traceability and transportation management. It debates traceability tradition and advanced methods such as smart marking, QR (Quick Response) codes, DNA fingerprinting, smartphone apps, RFID (Radio Frequency Identification), machine learning, and computer vision since these technologies enable the integrity of the supply chain by documenting the source of wood and following up on the wood in all the stages, beginning from the standing tree until the final customer. The paper also reviews the advancements in wood transportation management systems, including spatial databases, GPS (Global Positioning System), and fleet management systems, which ultimately lead to real-time monitoring and optimisation of transportation routes, leading to improved efficiency and minimal environmental impact. The review results acknowledge that financial constraints, infrastructure limitations, data management uncertainties, acceptance and compliance issues, and stakeholder commitment are still challenges to implementing traceability technology in the forestry sector. Moreover, this review not only highlights how traceability systems promote responsible forestry practices, ensure sustainable timber sourcing, and develop supply chain management, but also the advantages of utilising these technological advances at economic, social, and environmental levels.

Automated monitoring of brush use in dairy cattle.

Access to brushes allows for natural scratching behaviors in cattle, especially in confined indoor settings. Cattle are motivated to use brushes, but brush use varies with multiple factors including social hierarchy and health. Brush use might serve an indicator of cow health or welfare, but practical application of these measures requires accurate and automated monitoring tools. This study describes a machine learning approach to monitor brush use by dairy cattle. We aimed to capture the daily brush use by integrating data on the rotation of a mechanical brush with data on cow identify derived from either 1) low-frequency radio frequency identification or 2) a computer vision system using fiducial markers. We found that the computer vision system outperformed the RFID system in accuracy, and that the machine learning algorithms enhanced the precision of the brush use estimates. This study presents the first description of a fiducial marker-based computer vision system for monitoring individual cattle behavior in a group setting; this approach could be applied to develop automated measures of other behaviors with the potential to better assess welfare and improve the care for farm animals.

Toward designing a lightweight RFID authentication protocol for constrained environments

AbstractIn present times, Radio‐Frequency Identification (RFID) systems have seen a significant rise in their usage. There has been an increasing interest in developing even lighter RFID protocols suitable for resource‐constrained environments. Ensuring security and privacy remain critical challenges in RFID‐based systems. Recently proposed lightweight authentication schemes, namely LRSAS+ and LRARP+, are ideally suited for constrained devices. However, this article investigates these schemes and reveals certain vulnerabilities: LRSAS+ is susceptible to tag impersonation, desynchronization, and traceability attacks, while LRARP+ can fall prey to traceability and secret disclosure attacks. An enhanced version of these authentication systems is proposed that tackles their inherent weaknesses by leveraging the function. To verify the security of the proposed scheme, a formal analysis is conducted using Gong–Needham–Yahalom logic (GNY logic) and an automated security protocol verification tool, ProVerif. The improved scheme's effectiveness is also compared with multiple contemporary lightweight systems. The results indicate that the enhanced scheme not only meets the security requirements for lightweight authentication schemes but also achieves this with minimal computational overhead.

Digital twin technology for enhanced smart grid performance: integrating sustainability, security, and efficiency

This research paper presents the development and analysis of a multifaceted smart grid prototype. It combines various technologies for the smart grid operation. The first technology is environmental analysis of smart grid and solar panel cleaning. Secondly, radio-frequency identification (RFID)-based security and access control system has been integrated for smart grid. The third component is internet of things (IoT)-based energy monitoring and load management. For environmental analysis sensors such as temperature, humidity, light-dependent resistor, and flame sensors are connected to a NodeMCU controller for real time monitoring. Moreover, IoT based solar cleaning system is developed in the form of prototype with the help of Blynk and servo motor. The second component of prototype is smart security system which is developed with the help of Arduino and RFID module to facilitate secure access control. The third part of prototype employs voltage and current sensors with an ESP32 microcontroller and the Blynk application for real-time energy consumption analysis. This setup enables remote monitoring of voltage, power dynamics, and consumption patterns in a smart grid. It also offers an IoT based solution for load management and load shedding within the smart grid. The complete prototype overall demonstrates a comprehensive approach to 1) smart grid management, 2) environmental analysis, 3) security, and 4) energy monitoring.

Definition, detection, and tracking of nanowaste in foods: Challenges and perspectives.

Commercial applications of nanotechnology in the food industry are rapidly increasing. Accordingly, there is a simultaneous increase in the amount and diversity of nanowaste, which arise as byproducts in the production, use, disposal, or recycling processes of nanomaterials utilized in the food industry. The potential risks of this nanowaste to human health and the environment are alarming. It is of crucial significance to establish analytical methods and monitoring systems for nanowaste to ensure food safety. This review provides comprehensive information on nanowaste in foods as well as comparative material on existing and new analytical methods for the detection of nanowaste. The article is specifically focused on nanowaste in food systems. Moreover, the current techniques, challenges as well as potential use of new and progressive methods are underlined, further highlighting advances in technology, collaborative efforts, as well as future perspectives for effective nanowaste detection and tracking. Such detection and tracking of nanowaste are required in order to effectively manage this type ofwasted in foods. Although there are devices that utilize spectroscopy, spectrometry, microscopy/imaging, chromatography, separation/fractionation, light scattering, diffraction, optical, adsorption, diffusion, and centrifugation methods for this purpose, there are challenges to be overcome in relation to nanowaste as well as food matrix and method characteristics. New technologies such as radio-frequency identification, Internet of things, blockchain, data analytics, and machine learning are promising. However, the cooperation of international organizations, food sector, research, and political organizations is needed for effectively managing nanowaste. Future research efforts should be focused on addressing knowledge gaps and potential strategies for optimizing nanowaste detection and tracking processes.

Performance of a UHF RFID Detection System to Assess Activity Levels and Lying Behaviour in Fattening Bulls

Animal welfare strongly influences the health and performance of cattle and is an important factor for consumer acceptance. One parameter for the quantification of health status is the lying duration, which can be deployed for the early detection of possible production-related illnesses. Usually, 3D-accelerometers are the tool to detect lying duration in cattle, but the handling of bulls sometimes has special requirements because frequent manipulation in daily farming routines is often not possible. An ultrahigh-frequency (UHF) radio-frequency identification (RFID) system was installed in a beef cattle barn in Germany to measure the activity and lying time of bulls. Such UHF RFID systems are typically used for estrus detection in dairy cows via activity level, but can also be considered, for instance, as an early detection for lameness or other diseases. The aim of the study was to determine whether the estimations of activity level and lying duration can also be traced in husbandry systems for fattening bulls. Two groups of bulls (Uckermärker cattle, n = 10 and n = 13) of the same age were equipped with passive UHF RFID ear transponders. Three cameras were installed to proof the system and to observe the behaviour of the animals (standing, lying, and moving). Furthermore, accelerometers were attached to the hind legs of the bulls to validate their activity and lying durations measured by the RFID system in the recorded area. Over a period of 20 days, position (UHF RFID) and accelerometer data were recorded. Videos were recorded over a period of five days. The UHF RFID system showed an overall specificity of 95.9%, a sensitivity of 97.05%, and an accuracy of 98.45%. However, the comparison of the RFID and accelerometer data revealed residuals (ԑ) of median lying time (in minutes per day) for each group of ԑGroup1 = 51.78 min/d (p < 0.001), ԑGroup2 = −120.63 min/d (p < 0.001), and ԑGroup1+2 = −34.43 min/d (p < 0.001). In conclusion, UHF RFID systems can provide reliable activity and lying durations in 60 min intervals, but accelerometer data are more accurate.

Predator presence influences survival and behavior of translocated stream fish in ponds

Abstract Small artificial impoundments such as farm ponds have recently been recognized as potential habitat for threatened native fish species. However, factors influencing translocation or colonization success into these environments, including connectivity to stream networks and interactions with existing fish community, are largely unknown. In this study we conducted a controlled experiment to quantify the influence of piscivorous Largemouth Bass Micropterus salmoides on the survival of a translocated native minnow species that we used as a surrogate for federally endangered Topeka Shiner Notropis topeka. We translocated or released 100 Bluntnose Minnow Pimephales notatus into each replicate treatment ponds with and without Largemouth Bass in the summers of 2020 and 2021. Each minnow was implanted with a passive integrated transponder (PIT) tag. Translocated populations were monitored using stationary and mobile passive integrated transponder antennas and estimates of apparent survival and probability of detection for each pond were derived from open population mark-recapture models. Apparent survival was nearly two times higher in ponds without bass suggesting predation by bass leads to higher mortality. Additionally, probability of detection was nearly 10 times higher in ponds without bass, suggesting reduced movement of translocated minnows when bass were present. While the direct effect of mortality impacts translocated populations, the indirect effect of altered behavior may also be impactful on translocation success. These results confirm that Largemouth Bass can limit the success of translocated minnow species.

AtomGID: An Atomic Gesture Identifier for Qualitative Spatial Reasoning

In this paper, we present a novel non-deep-learning-based approach for real-time object tracking and activity recognition within smart homes, aiming to minimize human intervention and dataset requirements. Our method utilizes discreet, easily concealable sensors and passive RFID technology to track objects in real-time, enabling precise activity recognition without the need for extensive datasets typically associated with deep learning techniques. Central to our approach is AtomGID, an algorithm tailored to extract highly generalizable spatial features from RFID data. Notably, AtomGID’s adaptability extends beyond RFID to other imprecise tracking technologies like Bluetooth beacons and radars. We validate AtomGID through simulation and real-world RFID data collection within a functioning smart home environment. To enhance recognition accuracy, we employ a clustering adaptation of the flocking algorithm, leveraging previously published Activities of Daily Living (ADLs) data. Our classifier achieves a robust classification rate ranging from 85% to 93%, underscoring the efficacy of our approach in accurately identifying activities. By prioritizing non-deep-learning techniques and harnessing the strengths of passive RFID technology, our method offers a pragmatic and scalable solution for activity recognition in smart homes, significantly reducing dataset dependencies and human intervention requirements.

Characterising a potential nearshore nursery ground for the blackchin guitarfish (Glaucostegus cemiculus) in Ma’agan Michael, Israel

The blackchin guitarfish Glaucostegus cemiculus has suffered severe declines and regional extirpation throughout its known distributions. While this species and its relative, the common guitarfish Rhinobatos rhinobatos, have been described in the Mediterranean Sea with co-occurring habitat ranges, no research has recorded the existence or extent of these two separate populations along the Israeli coastal waters. Along a particular coast in Israel, Ma’agan Michael, fishermen have reported annual observations of juvenile guitarfish between June to November for the last forty years. Based on these citizen-based observations the main research objective is to establish whether Ma’agan Michael fulfils all three criteria from the literature by Dr Michelle Heupel, allowing it to be acknowledged as a nursery ground for G. cemiculus. The methodology built for this objective integrates biological characteristics data with the identification of a recurrent seasonal distribution. Visual surveys exhibited a significantly higher abundance in Ma’agan Michael when compared to an adjacent area (Caesarea), with 2,096 recorded observations overall. Additionally, using a species-specific modified Catch and Release protocol, a total of 492 juveniles were captured with a beach seine net. During these capturing events, individuals were morphometrically measured and sampled for future genetic analyses. Out of these, 327 specimens were also fitted for PIT tags to track recaptures in subsequent captures. The highest abundance of neonates was caught from August to September each year (2017–2019), and all individuals captured during this study were identified in the field as G. cemiculus, ranging from 20–35 cm in length (85% of captures). Many specimens had an umbilical cord scar (n = 88), with a large percentage possessing visual remains of the yolk sac. For the first time, this study provides an inter-year description of the species Glaucostegus cemiculus present along the Israeli shoreline.

An Innovative Method Based on Wavelet Analysis for Chipless RFID Tag Detection

Chipless RFID tags have attractive low-cost advantages. However, traditional RFID anti-collision algorithms cannot be applied due to a lack of computing and processing capabilities. Problems with multitag detection must be solved to commercialize chipless RFID tags. In this paper, an innovative method for frequency-domain chipless RFID tag detection is proposed. The tags’ scattered signals are processed via wavelet analysis, and a time–frequency plot that can read the code is obtained. When the distance between tags is too close to distinguish in the time–frequency plot, independent component analysis is used to separate individual scattered signals from mixed echo signals; then, the code is read by means of wavelet analysis. To validate the proposed method, C-shaped frequency-domain chipless RFID tag models and a multitag detection simulation scenario were constructed in selected software. The short-time matrix pencil method (STMPM), short-time Fourier transform (STFT), and the proposed method were compared. When the tag spacing is 0.05 m, the code can be read successfully. Compared with the STMPM, the proposed method greatly reduces the computational quantity and shortens the reading time. Furthermore, adjustment of the window width and search step parameters is avoided.