Recently, we experience the increasing prevalence of wearable cameras, some of which feature Wireless Local Area Network (WLAN) connectivity, and the abundance of mobile devices equipped with on-board camera and WLAN modules. Motivated by this fact, this work presents an indoor localization system that leverages both imagery and WLAN data for enabling and supporting a wide variety of envisaged location-aware applications ranging from ambient and assisted living to indoor mobile gaming and retail analytics. The proposed solution integrates two complementary localization approaches, i.e., one based on WLAN and another one based on image location-dependent data, using a fusion engine. Two fusion strategies are developed and investigated to meet different requirements in terms of accuracy, run time, and power consumption. The one is a light-weight threshold-based approach that combines the location outputs of two localization algorithms, namely a WLAN-based algorithm that processes signal strength readings from the surrounding wireless infrastructure using an extended Naive Bayes approach and an image-based algorithm that follows a novel approach based on hierarchical vocabulary tree of SURF (Speeded Up Robust Features) descriptors. The second fusion strategy employs a particle filter algorithm that operates directly on the WLAN and image readings and also includes prior position estimation information in the localization process. Extensive experimental results using real-life data from an indoor office environment indicate that the proposed fusion strategies perform well and are competitive against standalone WLAN and imaged-based algorithms, as well as alternative fusion localization solutions.

Compressive sensing and processing handles high-resolution delay-Doppler radar measurements using a low sampling rate, simple receiver design, and inexpensive processing when Nyquist rate sensing and processing becomes impractical. The benefits of compressive sensing and processing are, however, offset by an increase in estimation error that is introduced when processing compressed measurements versus measurements sampled at the Nyquist rate. In this work, an adaptive compressive sensing and processing method is proposed for the radar tracking problem. The adaptive scheme naturally incorporates information on target state that is readily available from a particle filter based tracker. The proposed method is shown to improve tracking performance over nonadaptive compressive sensing and processing, while maintaining low-sampling rates, a computationally inexpensive operation, and a simple receiver design.

Cyprus experiences the highest level of water stress among European Union countries due to several interplaying factors such as rainfall variability and increasing water demand. These instigate the nation to build dams on almost all rivers of the island to satisfy the requirements for drinking water and irrigation. Many studies have been primarily conducted on assessing water availability for various uses, particularly for drinking water supply and irrigation. However, there is still a gap/less explored area in terms of a better understanding of changes in surface water over time. Thus, this study aims to evaluate the water surface area variation in Cyprus over the past four decades based on remote sensing products, timeseries analysis and trend detection. The result reveals a statistically significant increasing trend (p < 0.05) in water surface area between 1984–2021. However, following the completion of the final reservoir in 2010, a statistically significant decreasing trend (p < 0.05) was observed in the permanent water surface area. This decline is related to both climatic variability and increased water demands. We observed cycles of 6, 8, and 11 years in permanent water. These cycles indicate a recurring pattern of water scarcity, with severe implication already observed on both economic activity and agriculture. The recent decade has witnessed a decline in rainfall, and this is evident through the decrease in vegetation greenness in rainfed agricultural regions, highlighting its impact. Therefore, the findings of this study underscore not only the necessity for the development of infrastructure aimed at conserving water, but also reinforces the need to discuss water use priorities in Cyprus.

One preliminary key step for developing an offshore wind farm is identifying favorable sites. The process of sitting involves multiple requirements and constraints, and therefore, its feasible implementation requires either approximating assumptions or an optimization method that is capable of handling non-linear relationships and heterogeneous factors. A new optimization method is proposed to address this problem that efficiently and accurately combines essential technical criteria, such as wind speed, water depth, and distance from shore, to identify favorable areas for offshore wind farm development through a user-friendly data-driven tool. Appropriate ranks and weighting factors are carefully selected to obtain realistic results. The proposed methodology is applied in the central Aegean Sea, which has a high offshore wind energy potential. The application of the proposed optimization method reveals large areas suitable for developing floating wind energy structures. The algorithm matches the accuracy of the exhaustive search method. It, therefore, produces the optimum outcome, however, at a lower computational expense demonstrating the proposed method’s potential for larger spatial-scale analysis and use as a decision support tool.

Eutrophication is a major environmental issue with many negative consequences, such as hypoxia and harmful cyanotoxin production. Monitoring coastal eutrophication is crucial, especially for island countries like the Republic of Cyprus, which are economically dependent on the tourist sector. Additionally, the open-sea aquaculture industry in Cyprus has been exhibiting an increase in recent decades and environmental monitoring to identify possible signs of eutrophication is mandatory according to the legislation. Therefore, in this modeling study, two different types of artificial neural networks (ANNs) are developed based on in situ data collected from stations located in the coastal waters of Cyprus. These ANNs aim to model the eutrophication phenomenon based on two different data-driven modeling procedures. Firstly, the self-organizing map (SOM) ANN examines several water quality parameters’ (specifically water temperature, salinity, nitrogen species, ortho-phosphates, dissolved oxygen, and electrical conductivity) interactions with the Chlorophyll-a (Chl-a) parameter. The SOM model enables us to visualize the monitored parameters’ relationships and to comprehend complex biological mechanisms related to Chl-a production. A second feed-forward ANN model is also developed for predicting the Chl-a levels. The feed-forward ANN managed to predict the Chl-a levels with great accuracy (MAE = 0.0124; R = 0.97). The sensitivity analysis results revealed that salinity and water temperature are the most influential parameters on Chl-a production. Moreover, the sensitivity analysis results of the feed-forward ANN captured the winter upwelling phenomenon that is observed in Cypriot coastal waters. Regarding the SOM results, the clustering verified the oligotrophic nature of Cypriot coastal waters and the good water quality status (only 1.4% of the data samples were classified as not good). The created ANNs allowed us to comprehend the mechanisms related to eutrophication regarding the coastal waters of Cyprus and can act as useful management tools regarding eutrophication control.