A compact broadband ambient RF energy harvester operating from 1800 MHz up to 2.5 GHz is proposed in this paper. This work is motivated by the huge amount of free and continuously available RF energy in the surroundings that can be utilized into useable energy. Harvest performance is investigated using two antennas in this work, a circular polarized and an array antenna. Due to low ambient power densities, a multistage rectifier is utilized to improve the output dc voltage of the proposed system. Measurements indicate the system is capable of harvesting up to 1.8 Vdc output from nondedicated ambient RF energy sources in an urban area, which is significant in the absence of other energy sources.
The current practice of adjusting hearing aids (HA) is tiring and time-consuming for both patients and audiologists. Of hearing-impaired people, 40–50% are not satisfied with their HAs. In addition, good designs of HAs are often avoided since the process of fitting them is exhausting. To improve the fitting process, a machine learning (ML) unsupervised approach is proposed to cluster the pure-tone audiograms (PTA). This work applies the spectral clustering (SP) approach to group audiograms according to their similarity in shape. Different SP approaches are tested for best results and these approaches were evaluated by Silhouette, Calinski-Harabasz, and Davies-Bouldin criteria values. Kutools for Excel add-in is used to generate audiograms’ population, annotated using the results from SP, and different criteria values are used to evaluate population clusters. Finally, these clusters are mapped to a standard set of audiograms used in HA characterization. The results indicated that grouping the data in 8 groups or 10 results in ones with high evaluation criteria. The evaluation for population audiograms clusters shows good performance, as it resulted in a Silhouette coefficient >0.5. This work introduces a new concept to classify audiograms using an ML algorithm according to the audiograms’ similarity in shape.
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