Sensors

Population ageing is a growing global trend. It was estimated that by 2050, people over 60 years of age will represent 35% of the population in industrialised countries. This context demands strategies that incorporate technologies, such as fall detection systems, to facilitate remote monitoring and the automatic activation of risk alarms, thus improving quality of life. This article presents a scoping review of the leading technological solutions developed over the last decade for detecting falls in older adults, describing their principles of operation, effectiveness, advantages, limitations, and future trends in their development. The review was conducted under the PRISMA^® methodology, including articles indexed in SCOPUS, ScienceDirect, Web of Science, PubMed, IEEE Xplore and Taylor & Francis. There is a predominance in the use of inertial systems that use accelerometers and gyroscopes, valued for their low cost and wide availability. However, those approaches that combine image analysis with artificial intelligence and machine learning algorithms show superiority in terms of accuracy and robustness. Similarly, progress has been made in the development of multisensory solutions based on IoT technologies, capable of integrating information from various sources, which optimises decision-making in real time.

The paper examines the development of forecasting and modeling technologies for environmental processes using classical and quantum data analysis methods. The main focus is on the integration of deep neural networks and classical algorithms, such as AutoARIMA and BATS, with quantum approaches to improve the accuracy of forecasting environmental parameters. The research is aimed at solving key problems in environmental monitoring, particularly insufficient forecast accuracy and the complexity of processing small data with high discretization. We developed the concept of an adaptive system for predicting environmental conditions in urban agglomerations. Hybrid forecasting methods were proposed, which include the integration of quantum layers in LSTM, Transformer, ARIMA, and other models. Approaches to spatial interpolation of environmental data and the creation of an interactive air pollution simulator based on the A* algorithm and the Gaussian kernel were considered. Experimental results confirmed the effectiveness of the proposed methods. The practical significance lies in the possibility of using the developed models for operational monitoring and forecasting of environmental threats. The results of the work can be applied in environmental information systems to increase the accuracy of forecasts and adaptability to changing environmental conditions.

This article presents a miniaturized dual-band frequency selective surface (FSS) based on capacitance-enhancing technique for RF shielding applications. The FSS incorporates two independent corner-modified square loop (CMSL) elements realized on a lossy dielectric, effectively suppressing the WiFi 2.45 GHz and WLAN 5.5 GHz bands simultaneously. The capacitance of FSS element is enhanced through corner truncation without using additional lumped elements. The symmetric geometry enables the FSS shield to manifest angularly stable and polarization-insensitive spectral responses under various oblique incident angles. Moreover, an equivalent circuit model (ECM) of the FSS structure is designed. A finite FSS prototype is fabricated and tested to verify the EM simulations. The measured results are in good agreement with the simulated responses. More importantly, the proposed design is scalable to other frequencies and is capable of selectively mitigating electromagnetic interference or confine the EM fields.

Aiming at the problem that low-quality images and low-precision control points lead to scale differences between the survey area model and the real model in UAV (Unmanned Aerial Vehicle) vision-based 3D deformation monitoring, which impairs the accuracy of deformation monitoring, this paper develops a spatial 3D scale for providing high-precision scale information and proposes a UAV vision-based deformation monitoring method with 3D scale constraints, thereby improving the deformation monitoring accuracy in large-scale survey areas. Experimental results show that compared with the monitoring method using only control points as constraints, the proposed method achieves accuracy (RMSE) improvement rates of 38.6% and 48.1% in the horizontal and elevation directions respectively during four phases of UAV operations, and the 3D deformation accuracy (RMSE) improvement rate remains at approximately 42.3% during seven phases of UAV operations. This verifies the effectiveness and reliability of the UAV vision-based deformation monitoring method with 3D scale constraints.