It can process image data in 3D and has achieved accuracy of 99.72% on MRI brain dataset, which outperformed results achieved by human expert. On lumbar and thoracic vertebrae CT dataset it has achieved the accuracy of 99.80%. The architecture proposed in this paper can also be easily applied to any task already using U-Net network as a segmentation algorithm to enhance its results. Complete source code was released online under open-source license. Technological evolution in the remote sensing domain has allowed the acquisition of large archives of satellite image time series (SITS) for Earth Observation. In this context, the need to interpret Earth Observation image time series is continuously increasing and the extraction of information from these archives has become difficult without adequate tools. In the paper [9], _[PERSON]_ and _[PERSON]_ propose a fast and effective two-step technique for the retrieval of spatio-temporal patterns that are similar to a given query. The method is based on a query-by-example procedure whose inputs are evolution patterns provided by the end-user and outputs are other similar spatio-temporal patterns. The comparison between the temporal sequences and the queries is performed using the Dynamic Time Warping alignment method, whereas the separation between similar and non-similar patterns is determined via Expectation-Maximization. The experiments, which are assessed on both short and long SITS, prove the effectiveness of the proposed SITS retrieval method for different application scenarios. For the short SITS, two application scenarios, namely the construction of two accumulation lakes and flooding caused by heavy rain were considered. For the long SITS, a database formed of 88 Landsat images was used, and authors showed that the proposed method is able to retrieve similar patterns of land cover and land use. In the last paper [10], [PERSON] et al. discuss the time-interleaved analog-to-digital converter (TIADC), which is a good option for high sampling rate applications. However, the inevitable sample-and-hold (S/H) mismatches between channels incur undesirable error and then affect the TIADC's dynamic performance. Several calibration methods have been proposed for S/H mismatches which either need training signals or have less extensive applicability for different input signals and different numbers of channels. This paper proposes a statistics-based calibration algorithm for S/H mismatches in \(M\)-channel TIADCs. Initially, the mismatch coefficients are identified by eliminating the statistical differences between channels. Subsequently, the mismatch-induced error is approximated by employing variable multipliers and differentiators in several Richardson iterations. Finally, the error is subtracted from the original output signal to approximate the expected signal. Simulation results illustrate the effectiveness of the proposed method, the selection of key parameters and the advantage to other methods. In summary, this Special Issue contains a series of excellent research works on telecommunications and signal processing. This collection of 10 papers is highly recommended and believed to be interesting, inspiring, and motivating readers in their further research. We would like to thank all authors, the many dedicated referees, the editor team of _Applied Sciences_, and especially [PERSON] (Managing Editor) for their valuable contributions, making this special issue a success. The authors declare no conflict of interest.