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According to their female-absence hypothesis, an intense chorus will happen whenever two conditions are met. Schlicht and colleagues first tagged the birds with radio-frequency-identification (RFID) tags to enable the location of the animals to be determined. [...]the change in male singing activity in relation to the presence of a fertile female about to mate makes sense from a mate-guarding perspective.

Temperature sensors are one of the most fundamental sensors and are found in industrial, environmental, and biomedical applications. The traditional approach of reading the resistive response of Positive Temperature Coefficient thermistors at DC hindered their adoption as wide-range temperature sensors. Here, we present a large-area thermistor, based on a flexible and stretchable short carbon fibre incorporated Polydimethylsiloxane composite, enabled by a radio frequency sensing interface. The radio frequency readout overcomes the decades-old sensing range limit of thermistors. The composite exhibits a resistance sensitivity over 1000 °C −1 , while maintaining stability against bending (20,000 cycles) and stretching (1000 cycles). Leveraging its large-area processing, the anisotropic composite is used as a substrate for sub-6 GHz radio frequency components, where the thermistor-based microwave resonators achieve a wide temperature sensing range (30 to 205 °C) compared to reported flexible temperature sensors, and high sensitivity (3.2 MHz/°C) compared to radio frequency temperature sensors. Wireless sensing is demonstrated using a microstrip patch antenna based on a thermistor substrate, and a battery-less radio frequency identification tag. This radio frequency-based sensor readout technique could enable functional materials to be directly integrated in wireless sensing applications. Thermistors typically read at DC or low frequencies have limited temperature-sensing ranges. Here, authors show how a radio frequency readout can overcome the decades-old range limitation of thermistors, while creating a wireless interface for a soft thermistor composite through antennas and RFID.

The rise in the demand for animal products due to demographic and dietary changes has exacerbated difficulties in addressing societal concerns related to the environment, human health, and animal welfare. As a response to this challenge, Precision Livestock Farming (PLF) technologies are being developed to monitor animal health and welfare parameters in a continuous and automated way, offering the opportunity to improve productivity and detect health issues at an early stage. However, ethical concerns have been raised regarding their potential to facilitate the management of production systems that are potentially harmful to animal welfare, or to impact the human-animal relationship and farmers' duty of care. Using the Five Domains Model (FDM) as a framework, the aim is to explore the potential of PLF to help address animal welfare and to discuss potential welfare benefits and risks of using such technology. A variety of technologies are identified and classified according to their type [sensors, bolus, image or sound based, Radio Frequency Identification (RFID)], their development stage, the species they apply to, and their potential impact on welfare. While PLF technologies have promising potential to reduce the occurrence of diseases and injuries in livestock farming systems, their current ability to help promote positive welfare states remains limited, as technologies with such potential generally remain at earlier development stages. This is likely due to the lack of evidence related to the validity of positive welfare indicators as well as challenges in technology adoption and development. Finally, the extent to which welfare can be improved will also strongly depend on whether management practices will be adapted to minimize negative consequences and maximize benefits to welfare.

This work presents a radio frequency identification (RFID)-based technique to detect falls in the elderly. The proposed RFID-based approach offers a practical and efficient alternative to wearables, which can be uncomfortable to wear and may negatively impact user experience. The system utilises strategically positioned passive ultra-high frequency (UHF) tag array, enabling unobtrusive monitoring of elderly individuals. This contactless solution queries battery-less tag and processes the received signal strength indicator (RSSI) and phase data. Leveraging the powerful data-fitting capabilities of a transformer model to take raw RSSI and phase data as input with minimal preprocessing, combined with data fusion, it significantly improves activity recognition and fall detection accuracy, achieving an average rate exceeding 96.5 % . This performance surpasses existing methods such as convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM), demonstrating its reliability and potential for practical implementation. Additionally, the system maintains good accuracy beyond a 3-m range using minimal battery-less UHF tags and a single antenna, enhancing its practicality and cost-effectiveness.

Developing a compact circularly polarized (CP) antenna with good radiation characteristics for handheld radio frequency identification (RFID) readers is a very challenging task. Many compact CP antennas have been reported in the open literature, but most suffer from critical drawbacks of low gain and/or high back radiation. This paper presents a metasurface (MS) based CP antenna with compact size, high gain, and high front-to-back ratio characteristics. The compact size of the proposed design is achieved by using a 2 × 2 unit-cell MS, while the CP realization is accomplished through a coupling between the MS and a Y-shaped patch as a primary CP source. The final antenna has compact overall dimensions of 0.45λ × 0.45λ × 0.02λ, where λ is the guided wavelength at the center frequency. The operating bandwidth is about 2.0% (2.43–2.48 GHz) and the broadside gain is about 6.3 dBi. Besides, the front-to-back ratio (FBR) defined by the difference gain levels between the forward and backward directions is about 18 dB. Compared with the related compact CP antennas in the literature, the proposed design has the advantages of high gain and high FBR, making it suitable for compact RFID readers.

This paper presents a block-chain enabled inkjet-printed ultrahigh frequency radiofrequency identification (UHF RFID) system for the supply chain management, traceability and authentication of hard to tag bottled consumer products containing fluids such as water, oil, juice, and wine. In this context, we propose a novel low-cost, compact inkjet-printed UHF RFID tag antenna design for liquid bottles, with 2.5 m read range improvement over existing designs along with robust performance on different liquid bottle products. The tag antenna is based on a nested slot-based configuration that achieves good impedance matching around high permittivity surfaces. The tag was designed and optimized using the characteristic mode analysis. Moreover, the proposed RFID tag was commercially tested for tagging and billing of liquid bottle products in a conveyer belt and smart refrigerator for automatic billing applications. With the help of block-chain based product tracking and a mobile application, we demonstrate a real-time, secure and smart supply chain process in which items can be monitored using the proposed RFID technology. We believe the standalone system presented in this paper can be deployed to create smart contracts that benefit both the suppliers and consumers through the development of trust. Furthermore, the proposed system will paves the way towards authentic and contact-less delivery of food, drinks and medicine in recent Corona virus pandemic.

Molecular tagging is an approach to labeling physical objects using DNA or other molecules that can be used when methods such as RFID tags and QR codes are unsuitable. No molecular tagging method exists that is inexpensive, fast and reliable to decode, and usable in minimal resource environments to create or read tags. To address this, we present Porcupine, an end-user molecular tagging system featuring DNA-based tags readable within seconds using a portable nanopore device. Porcupine’s digital bits are represented by the presence or absence of distinct DNA strands, called molecular bits (molbits). We classify molbits directly from raw nanopore signal, avoiding basecalling. To extend shelf life, decrease readout time, and make tags robust to environmental contamination, molbits are prepared for readout during tag assembly and can be stabilized by dehydration. The result is an extensible, real-time, high accuracy tagging system that includes an approach to developing highly separable barcodes. Molecular tagging using DNA is an attractive option in cases that are not suitable for RFID tags or QR codes. Here, the authors present Porcupine, DNA tags directly classifiable from raw nanopore signals.

As the construction industry embraces sustainability, circular economy reshapes how we manage resources, prioritizing reuse over disposal. To unlock this potential, organizing and managing building component data must become more systematic. Platforms and stakeholders adopt different strategies, like digital twins, to archive building data, but perspectives on the selection of included information and building components vary significantly. Currently, tracking components for future circular use is rare, and a lack of standardization causes uncertainty in adopting forward-thinking practices and systems. Understanding the building structure—systems, components, and materials—as well as minimizing essential stored is crucial in ensuring long-term accessibility and usability. Enhanced by technologies like QR codes for basic, and RFID chips for detailed, cloud-linked data, this research proposes a streamlined, transdisciplinary approach to managing essential building metadata to facilitate reuse, dismantling, and storage.Built on a basis of an overview of existing circular economy frameworks in the context of ongoing digitalization, it presents findings concerning the data for building elements, systems and components tied to user-cases. This analysis informs a conceptual model that integrates construction processes (installation, use, removal) across physical, digital and electronic dimensions. Practical examples illustrate the approach, aligning physical construction, electronic tagging, and digital systems.

Current approaches to activity-assisted living (AAL) are complex, expensive, and intrusive, which reduces their practicality and end user acceptance. However, emerging technologies such as artificial intelligence and wireless communications offer new opportunities to enhance AAL systems. These improvements could potentially lower healthcare costs and reduce hospitalisations by enabling more effective identification, monitoring, and localisation of hazardous activities, ensuring rapid response to emergencies. In response to these challenges, this paper introduces the T ransparent R FID T ag Wall (TRT-Wall) , a novel system taht utilises a passive ultra-high frequency (UHF) radio-frequency identification (RFID) tag array combined with deep learning for contactless human activity monitoring. The TRT-Wall is tested on five distinct activities: sitting, standing, walking (in both directions), and no-activity. Experimental results demonstrate that the TRT-Wall distinguishes these activities with an impressive average accuracy of 95.6 % under four distinct distances (2, 2.5, 3.5 and 4.5 m) by capturing the RSSI and phase information. This suggests that our proposed contactless AAL system possesses significant potential to enhance elderly patient-assisted living.

The article proposes a novel approach to assess rotor angle stability in microgrids by enhancing the Modified Galerkin Method (MGM), which is based on the Polynomial Approximation, using real-time RFID data acquisition. Due to their reliance on assumptions, traditional rotor angle stability methodologies frequently fail in online transient stability testing. MGM successfully captures the dynamic behavior of microgrids by approximating state variables using a sequence of polynomials and coefficients. Redundant data, like as vibrations or noise signals, can cause delays in defect diagnosis and decrease diagnostic accuracy. This problem is addressed by integrating RFID technology. RFID technology could potentially be used with a hybrid CNN-LSTM model to develop a sophisticated fault diagnostic system. This entails identifying fault characteristics through the use of signal processing techniques and feature extraction methods, such as the Fourier transform and time-domain statistical features. In addition, we use Total Harmonic Distortion (THD) to reduce superfluous data. The suggested techniques significantly increase fault detection efficiency and precision, outperforming existing techniques with a 0.94 classification accuracy. An extensive case study on an IEEE 3-machine 9-bus system is used to illustrate its efficacy, showing observable improvements in fault detection speed and accuracy that make microgrid operations safer and more dependable.