Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
This study presents a deterministic geospatial methodology for the alignment of directional television receiving antennas using publicly available broadcast-sector parameters. The proposed approach relies exclusively on geometric computations derived from user geolocation (WGS84 coordinates) and transmitter site information, including sector azimuth and beamwidth characteristics. By computing the geodesic bearing between receiver and transmitter locations, the method evaluates angular deviation relative to sector orientation and provides an interpretable alignment assessment framework. The methodology operates without requiring empirical signal measurements, propagation modeling, or machine-learning techniques, thereby ensuring transparency, reproducibility, and low computational complexity. The approach is particularly suitable for scenarios where lineof- sight conditions dominate signal propagation. Under such assumptions, the proposed framework offers a lightweight and explainable solution for antenna pointing and orientation guidance while explicitly acknowledging the limitations imposed by simplified geometric modeling....
This paper proposes a design method for broadband power amplifiers based on bandpass filter matching networks. The approach incorporates transistor complex impedance transformation into the filter matching network design using a low-pass filter design model. By integrating CRLH and D-CRLH structural elements, it forms LC matching structures with a bandpass filter response. This structure achieves wide-band impedance transformation while also providing excellent frequency-selective capabilities. To validate this approach, a 0.7–1.3 GHz bandpass filtering power amplifier was designed and fabricated. It achieves in-band saturated output power of 38.4–41 dBm, drain efficiency of 41–58%, and power gain exceeding 12 dB. The gain flatness is limited to within ±2 dB. Experimental measurements validate the proposed design methodology. This approach imparts exceptional frequency selectivity and superior filtering performance to the system while enabling effective circuit miniaturization. Moreover, it exhibits considerable engineering significance and promising application potential in key fields such as satellite communications, radar monitoring, and digital broadcasting....
Extreme climate events in Australia are increasing. Since 2019, fires and floods have devastated all states and territories in Australia, leading to a reckoning via several government inquiries, including a Royal Commission, on how governments, emergency services, communities, and individuals prepare for, respond to, and recover from such catastrophic events. It also raises the question of how the media reports and reacts to these events; in Australia, the national broadcaster, the Australian Broadcasting Corporation (ABC), has taken on the role of emergency broadcaster. This paper employs a cross-sectional design to examine how media practitioners from ABC Canberra navigate their role as emergency broadcasters, how they prepare for and respond to emergencies, and how they interact with the community during those events. This examination includes reflections and memories from a series of interviews we conducted with these practitioners about the catastrophic bushfires in 2019/2020 in the Australian Capital Territory (ACT) region. Using this design and a Bourdieusian lens, the study examined the practices of media practitioners during a catastrophic emergency and their perceptions of preparedness for future disasters. We examined how training (cultural capital), networks (social capital), online expertise (digital capital), and experience (habitus) contribute to preparedness in emergency broadcasting. The study has both a theoretical and practical contribution: theoretically, it expands Bourdieu’s cultural production model by applying it to a form of broadcasting that has not been examined in this way; practically, it contributes to our understanding of media practitioners and how they practice during emergency broadcasting....
Recent works have combined random linear network coding (RLNC) with guessing random additive noise decoding (GRAND) to leverage RLNC packets to partially correct bit errors prior to RLNC decoding, so as to reduce the packet erasure rates in wireless broadcast networks. However, existing schemes are restricted to scalar RLNC over the finite field GF(2L). In this paper, we first formulate a general GRAND-assisted decoding framework for vector RLNC over the vector space GF(2)L, and further propose a design rule for vector RLNC schemes such that estimated error vectors can be efficiently obtained without incurring any additional computational overhead. Necessary and sufficient conditions for the correctness of every efficiently obtained estimated error vector are characterized. Two explicit vector RLNC schemes satisfying the proposed design rule are constructed. The first scheme is designed based on the matrix representation of GF(2L), and analytical results show that it achieves the same completion delay performance as the counterpart scalar RLNC scheme over GF(2L), while achieving up to a 37.3% reduction in coding computational complexity compared with the scalar one. The second scheme is designed based on sparse coding coefficient matrices. It further reduces computational complexity by up to 33.6% compared with the first scheme, at the cost of a slight degradation in completion delay performance....
The growing adoption of wearable devices creates a critical need for robust and secure Internet of Things solutions to manage biometric data streams. Current architectures often lack emphasis on seamless data capture, secure cloud storage and integrated dashboard visualization. This research addresses these gaps by investigating and evaluating an IoT framework leveraging lightweight communication and real-time visualization for improved healthcare monitoring. Drawing primarily on recent peer-reviewed journals and reputable conference proceedings, we evaluate an IoT architecture that securely integrates wearable biometric data into a cloud-based dashboard. The system utilizes encrypted advertising packets (e.g., AES-128-CCM) to broadcast biometric signals, eliminating the need for permanent device pairing and minimizing energy consumption. These packets are captured by our prototype ESP32-based (Espressif Systems, Shanghai, China) gateway node, decrypted and forwarded to a secure cloud environment that ensures persistent storage and accessibility. The cloud-based dashboard provides medical staff and end-users with real-time insights and long-term data tracking. Emphasis was placed on evaluating the system’s low latency performance, energy efficiency and data confidentiality. System evaluation demonstrates that encrypted advertising packets can securely transmit biometric signals, while drastically reducing energy consumption and latency. System evaluation demonstrates that encrypted BLE advertising serves as a superior alternative to traditional pairing-based methods for long-term medical monitoring. By implementing a dual-optimization strategy that balances data confidentiality with power efficiency, the proposed system achieved a 33-fold increase in operational autonomy compared with standard permanent BLE connections. These results represent a significant advancement in battery longevity for the IoMT ecosystem, providing a scalable solution for continuous, secure biometric signal transmission with minimal energy overhead....
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