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关键词： artificial intelligence   behaviour   machine learning   RFID   wildlife monitoring  

摘要： Automatic monitoring of wildlife is becoming a critical tool in the field of ecology. In particular, Radio-Frequency IDentification (RFID) is now a widespread technology to assess the phenology, breeding and survival of many species. While RFID produces massive datasets, no established fast and accurate methods are yet available for this type of data processing. Deep learning approaches have been used to overcome similar problems in other scientific fields and hence might hold the potential to overcome these analytical challenges and unlock the full potential of RFID *** present a deep learning workflow, coined "RFIDeep", to derive ecological features, such as breeding status and outcome, from RFID mark-recapture data. To demonstrate the performance of RFIDeep with complex datasets, we used a long-term automatic monitoring of a long-lived seabird that breeds in densely packed colonies, hence with many daily entries and *** determine individual breeding status and phenology and for each breeding season, we first developed a one-dimensional convolution neural network (1D-CNN) architecture. Second, to account for variance in breeding phenology and technical limitations of field data acquisition, we built a new data augmentation step mimicking a shift in breeding dates and missing RFID detections, a common issue with RFIDs. Third, to identify the segments of the breeding activity used during classification, we also included a visualisation tool, which allows users to understand what is usually considered a "black box" step of deep learning. With these three steps, we achieved a high accuracy for all breeding parameters: breeding status accuracy = 96.3%;phenological accuracy = 86.9%;and breeding success accuracy = 97.3%.RFIDeep has unfolded the potential of artificial intelligence for tracking changes in animal populations, multiplying the benefit of automated mark-recapture monitoring of undisturbed wildlife populations. RFIDeep is an open source code to fac

关键词： Energy Efficiency   IOT   Reader Collision   RFID   Throughput   WSN  

摘要： Radio-Frequency Identification and Wireless Sensor Networks (WSN) are two basic pillars in the Internet of Things (IOT);they are used to identify and track objects, and collect information from the environment in various applications of the IOT. One of the architectures used for combining these two technologies is the combination of readers with wireless sensor nodes. In the combination of Radio-Frequency Identification and WSN, we face issues concerning reader collision, tag rereading and load imbalance. In the dense environments of this hybrid network, due to congestion and the possible mobility of nodes, controlling access to the shared channel is one of the basic challenges. In addition, owing to the energy limitations of these networks, data redundancy and tag re-reading are critical issues. An effective anti-collision protocol suitable for RFID-WSN networks is proposed to avoid the above-mentioned issues. The Energy-saving and Anti-collision Protocol (EAP) method is a CSMA-based multi-channel protocol that not only solves the issue of readback collisions, but also, through tagging, it prevents the rereading of the tags. To evaluate the performance of the EAP method, this protocol is compared with the most prominent reader anti-collision protocols ever presented. The results indicate that due to the better throughput and higher energy efficiency that the proposed protocol has, when compared to other multi-channel protocols, it will be a better option for use in the IOT platform with dense conditions.

关键词： Electrical insulators   High voltage towers   Radio frequency   Pollution   Tag   UHF RFID  

摘要： Pollution diagnosis is difficult and costly for power grid operators. For the purpose of have an effective, low-cost and user-friendly pollution diagnosis, a Radio Frequency Identification prototype is proposed -an RFID tag for identifying and monitoring pollution in high voltage towers. For that, the following steps were performed: dielectric characterization of artificial pollution;project via simulation of RFID tags for application in metallic towers;manufacture, validation and testing of tags;sensitivity analysis of tag reach;and computational algo-rithm for signal treatment and subsequent identification. Pollution identification was based on variations of the Received Signal Intensity Indicator (RSSI) by the reader as a function of the amount of pollution deposited. The validation and testing of the tags were carried out in an external environment with an emulation of a de-energized high voltage tower. For all levels of deposited pollution analyzed, the average maximum reach of the signal reading, in an external environment, was 18 m. The RSSI data, obtained by reading the RFID tag, were processed and analyzed to identify the pollution deposited on the high voltage transmission towers. The results demonstrated the potential of the tags for identification and monitoring pollution levels in high voltage metal towers.

关键词： Internet of things  

摘要： The increase of the global population, climate change and the scarcity of resources are some of the reasons why the improvement of the agricultural sector has become a crucial issue for the future of our society. Recently, the growth of Internet of Things (IoT) has meant a revolution in many industries, including the agricultural sector, where the employment of this technology is considered the ideal formula to boost crop efficiency. In this work, a complete high performance IoT solution consisting of an innovative printed smart radio frequency identification (RFID) tag and an autonomous measuring system based on a robot is demonstrated for soil monitoring purposes. The smart RFID tags are employed to obtain soil parameters, crucial for the correct development of the crops: humidity, oxygen concentration and temperature. The tag is novelly developed on a humidity dependent and flexible transparent foil as substrate. The former feature is to directly extract the value of the relative humidity (RH) by a capacitive structure defined on the substrate while the latter characteristic is to allow the sensing of the oxygen content via a membrane whose luminescence intensity changes with the influence of this parameter. Effects on the antenna response are measured when the tag is placed in soil environment at root depth to validate the excellent sensing behaviour of the tag. Finally, to take the work a step further and in order to show the promising performance of the tags as part of a monitoring system in the field of smart agriculture, we design a robot equipped with an UHF reader and IoT features, capable of automating the finding and reading of the designed RFID tags. This robot sends the information via internet for its further processing, thus constituting a low-cost, large-scale and environmentally friendly solution, ideal for enhancing crops performance. © 2023, The Authors. All rights reserved.

关键词： Graphene  

摘要： Graphene-based printable inks are advanced and promising candidates for flexible printed electronic devices. To develop large-scale graphene-based highly conductive electronics on the basis of existing printing approaches of the material, it is imperative to scale up its ink production methods and optimize the electrical properties of the printed films. Here, a sustainable approach for preparing high-conductive screen-printing graphene ink by recycling cellulose solvent is reported. The ink uses Cyrene solvent in circulation and produces highly concentrated and conductive graphene ink to conquer the limitation of graphene inks in massive industrial production brought by the high cost of the cellulose solvents. A systematic study of the sheet resistance variation with the number of cycles has been carried out. The average conductivity of the ink can exceed 3×104 S m−1, with only 2-hour shear exfoliation process, which exhibits about the same conductivity of recent works (using toxic NMP solvent, complex centrifugation, ultrasonication, etc.), but with much simpler and cost- effective fabrication process. The formulation of graphene inks allows flexible metal-free far- field tag antenna to be screen printed and integrated with off-the-shelf sensory chip to achieve battery-free wireless UHF RFID far field temperature sensing. The high responsiveness, accuracy and sensitivity of the RFID sensing tag were observed with instantaneous detection, accurate and reliable wireless data transmission. © 2023, The Authors. All rights reserved.

关键词： Fingerprint   Token   GSM   RFID and Module  

摘要： The 3-factor authentication control system is aim at providing maximum security in company's stores, special utilities and premises where valuables are stored. A developed security system with automatic sensing was introduced using the integration of Radio Frequency Identification (RFID) card tagging system, fingerprint sensing biometric security system and Global System for Mobile communication (GSM) to send token generated by the system to the authorized user's access cell-phone. The accuracy of the system was measured virtually in a simulation screen. Results and performance evaluation test shows over 98% accuracy in access granted to access denial, for both right and wrong RFID card and correct to incorrect fingerprint scanning respectively. This consequently, shows satisfaction in the performance of the system test, which proves the validity and efficiency of the system by ensuring full integrity of the door lock in any premises as the case may be.

关键词： Sensors   RFID tags   Optical fiber sensors   Manufacturing   Acoustics   Plastics   Sensor arrays   Carbon-fiber reinforced polymer (CFRP)   deep learning   glass-fiber-reinforced polymer (GFRP)   non-destructive testing (NDT)   radio frequency identification (RFID)  

摘要： Fiber-reinforced polymers (FRPs) are used in various applications and industries such as aerospace, automotive, marine, and energy. Therefore, monitoring their integrity is critical from both safety and economic viewpoints. Non-destructive testing (NDT) methods can help detect defects or damage in FRPs during manufacturing or while in use. In this work, we develop a new NDT approach for FRPs to detect and identify common defects that may occur during manufacturing. The proposed approach is based on scanning FRP plates using a 3-D-printed radio frequency identification (RFID) tag array mounted on a conveyor belt and processing the sensed data via appropriate deep-learning algorithms. Using data collected from real experiments, we verify that our novel RFID-tag-array-based NDT approach can identify defects such as voids and misalignments in carbon FRPs (CFRP) and glass-FRP (GFRP) with high accuracy. The proposed approach is cost-effective and scalable and can be used instead of or in addition to existing NDT techniques that use alternative modes of sensing such as ultrasound, X-ray, or vision.

关键词： RFID technology   RFID middleware   RBAC   blowfish algorithm   CEP  

摘要： Radio Frequency Identification (RFID) is a contactless technology that has developed over the 90s and 20th centuries. It employs electromagnetic or electrostatic coupling in the radio frequency part of the electromagnetic spectrum to uniquely identify traceable objects, and is widely used in various sectors (e.g., medical, Supply Chain Management, transportation, and IoT applications.). Through the supply of real-world monitoring and context information about things, the integration of this technology in such areas delivers various benefits in the future of ubiquitous computing. However, one of the primary challenges will be the capacity to manage data since RFID events have specific characteristics and requires special treatment, such as the large volume of data flow, inaccuracy, temporal and spatial data, are typical examples of RFID event data. The goal of this research is to first highlight the concerns and limitations of existing middleware architectures before introducing and implementing a new Middleware architecture to address the identified issues, specifically real-time processing of massive volumes of data coming from physical RFID infrastructure. This middleware combines role-based access control with an encryption algorithm to increase security, a NoSQL database for storing large amounts of data, complex event processing (CEP) to provide high-volume data stream processing, and improved interoperability via the Data Transformation Module. Finally, our architecture is evaluated and compared to several middleware architectures based on standard ISO/IEC 9126 metrics.