As a possible implementation of a low-power wide-area network (LPWAN), Long Range (LoRa) technology is considered to be the future wireless communication standard for the Internet of Things (IoT) as it offers competitive features, such as a long communication range, low cost, and reduced power consumption, which make it an optimum alternative to the current wireless sensor networks and conventional cellular technologies. However, the limited bandwidth available for physical layer modulation in LoRa makes it unsuitable for high bit rate data transfer from devices like image sensors. In this paper, we propose a new method for mangrove forest monitoring in Malaysia, wherein we transfer image sensor data over the LoRa physical layer (PHY) in a node-to-node network model. In implementing this method, we produce a novel scheme for overcoming the bandwidth limitation of LoRa. With this scheme the images, which requires high data rate to transfer, collected by the sensor are encrypted as hexadecimal data and then split into packets for transfer via the LoRa physical layer (PHY). To assess the quality of images transferred using this scheme, we measured the packet loss rate, peak signal-to-noise ratio (), and structural similarity (SSIM) index of each image. These measurements verify the proposed scheme for image transmission, and support the industrial and academic trend which promotes LoRa as the future solution for IoT infrastructure.
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http://dx.doi.org/10.3390/s18103257 | DOI Listing |
ACS Appl Mater Interfaces
January 2025
Department of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210093, China.
Developing damping materials that are both optically transparent and mechanically robust, while offering broad frequency damping capacity, is a significant challenge─particularly for devices that require protection without compromising visual clarity. Conventional methods often either fail to maintain transparency or involve complex designs that are difficult to implement. Here, we present an ionogel system that integrates a physically cross-linked elastic copolymer network with a viscous ionic liquid.
View Article and Find Full Text PDFJ Comput Chem
January 2025
Institute of Soil Science and Soil Conservation, Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, Giessen, Germany.
Halogenated benzenes (HBs) are hydrophobic organic chemicals belonging to persistent organic pollutants. Owing to their persistence, they represent a serious problem in environmental contamination, specifically of soils and sediments. One of the most important physical processes determining the fate of HBs in soils is adsorption to main soil components such as soil organic matter and soil minerals.
View Article and Find Full Text PDFClin Sci (Lond)
January 2025
Department of Medicine, Division of Clinical Pharmacology, Vanderbilt University Medical Center, Nashville, TN, U.S.A.
Salt sensitivity of blood pressure (SSBP) is a complex physiological trait characterized by changes in blood pressure in response to dietary salt intake. Aging introduces an additional layer of complexity to the pathophysiology of SSBP, with mitochondrial dysfunction, epigenetic modifications, and alterations in gut microbiota emerging as critical factors. Despite advancements in understanding these mechanisms, the processes driving increased salt sensitivity with age and their differential impacts across sexes remain unclear.
View Article and Find Full Text PDFACS Appl Bio Mater
January 2025
Laboratório de Processos Eletroquímicos e Corrosão-ELETROCORR, Departamento de Metalurgia, Universidade Federal do Rio Grande do Sul, Avenida Bento Gonçalves 9500, 91501-970 Porto Alegre, Brazil.
This study presents an innovative approach to creating antibacterial aluminum surfaces by combining the antibacterial properties of silver nanoparticles (Ag NPs) with the nanoarchitecture of anodized aluminum oxide in one step. An Al-Ag alloy containing 10 wt % Ag was synthesized and anodized in 0.3 M oxalic acid.
View Article and Find Full Text PDFSmall
January 2025
College of Physical Science and Technology, Xiamen University, Xiamen, 361005, P. R. China.
Twisted bilayer graphene (TBG) has drawn considerable attention due to its angle-dependent electrical, optical, and mechanical properties, yet preparing and identifying samples at specific angles on a large scale remains challenging and labor-intensive. Here, a data-driven strategy that leverages Raman spectroscopy is proposed in combination with deep learning to rapidly and non-destructively decode and predict the twist angle of TBG across the full angular range. By processing high-dimensional Raman data, the deep learning model extracts hidden information to achieve precise twist angle identification.
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