Abstract
The Sub-THz and millimeter-wave bands have gained popularity, with the expectation that they will host the next generation of wireless communication systems. Furthermore, research on beam-steering characteristics provided by Programmable Electromagnetic Surfaces, such as Reflective Intelligent Surfaces (RISs), has garnered considerable attention as an enabling technology for 6G communications. Due to size limitations, RISs face challenges related to power consumption in the reconfigurable elements and their integration with unit cells operating at high frequencies. This paper discusses the design of a 1-bit reconfigurable unit cell at the D-band using non-volatile technology to minimize static power consumption. Simulation results show that the proposed unit cell performs well with a reflection loss of less than 1.3 dB in both reconfigurable states across a frequency band from 120 to 170 GHz. Moreover, the phase difference between the two states is maintained at 180? ± 20?, with an operational bandwidth of approximately 16 GHz. The beamforming capabilities, with steering angles from -60? to 60?, of the 12×12 RIS, utilizing the proposed unit cell, have been demonstrated in terms of controlling the main beam radiation precisely to various angles with consistent performance at frequencies of 147 GHz, 152 GHz, and 152.5 GHz. © 2024 IEEE.

Abstract
It is well known that the performance of Machine Learning techniques, notably when applied to Computer Vision (CV), depends heavily on the amount and quality of the training data set. However, large data sets lead to time-consuming training loops and, in many situations, are difficult or even impossible to create. Therefore, there is a need for solutions to reduce their size while ensuring good levels of performance, i.e., solutions that obtain the best tradeoff between the amount/quality of training data and the model's performance. This paper proposes a dataset reduction approach for training data used in Deep Learning methods in Facial Recognition (FR) problems. We focus on maximizing the variability of representations for each subject (person) in the training data, thus favoring quality instead of size. The main research questions are: 1) Which facial features better discriminate different identities? 2) Will it be possible to significantly reduce the training time without compromising performance? 3) Should we favor quality over quantity for very large datasets in FR? This analysis uses a pipeline to discriminate a set of features suitable for capturing the diversity and a cluster-based sampling to select the best images for each training subject, i.e., person. Results were obtained using VGGFace2 and Labeled Faces in the Wild (for benchmarking) and show that, with the proposed approach, a data reduction is possible while ensuring similar levels of accuracy.

Abstract
This paper proposes a method to avoid using a CORDIC or external memory to process the steering vectors to calculate the pseudospectrum of correlation-based beamforming algorithms. We show that if we decompose the steering vector equation, the size of the matrix to be saved in memory becomes independent of the antenna array size. Besides, the amount of data needed is small enough to be saved in the internal block RAMs of the FPGA SoC. Besides, this method greatly reduces the number of memory accesses, by offloading some processing to hardware, while keeping the frequency at 300MHz with a precision of 0.25 degrees. Finally, we show that this approach is scalable since the complexity grows logarithmically for bigger arrays, and the symmetry in the matrices obtained allows even more compact data.

Abstract
Many natural signals exhibit a quasi-periodic behavior and are conveniently modeled as a combination of several harmonic sinusoids whose relative frequencies, magnitudes and phases vary with time. The waveform shape of those signals reflects important physical phenomena underlying their generation, which requires that those parameters be accurately estimated and modeled. In the literature, accurate phase estimation and modeling has received much less research effort than frequency estimation, or magnitude estimation. First, this paper addresses accurate DFT-based phase estimation of individual sinusoids in six scenarios involving two DFT-based filter banks and three different windows. It is shown that bias in phase estimation is less than 1E-3 radians when the SNR is equal to or larger than 2.5 dB. Taking as a reference the Cramér-Rao Lower Bound, it is shown that one particular window offers a performance of practical interest by approximating better the CRLB when signal conditions are favorable, and by minimizing the performance deviation when signal conditions are adverse. Second, this paper explains how a shift-invariant phase-related feature can be devised that characterizes harmonic phase structure, which motivates a signal processing paradigm that greatly simplifies parametric modeling, transformation and synthesis of harmonics signals, in addition to facilitating the understanding and reverse engineering of the phasegram. Theory and results are discussed in a reproducible perspective using dedicated experiments that are supported with code allowing not only to replicate figures and results in this paper, but also to expand research.

Abstract
Many natural signals exhibit quasi-periodic behaviors and are conveniently modeled as combinations of several harmonic sinusoids whose relative frequencies, magnitudes, and phases vary with time. The waveform shapes of those signals reflect important physical phenomena underlying their generation, requiring those parameters to be accurately estimated and modeled. In the literature, accurate phase estimation and modeling have received significantly less attention than frequency or magnitude estimation. This paper first addresses accurate DFT-based phase estimation of individual sinusoids across six scenarios involving two DFT-based filter banks and three different windows. It has been shown that bias in phase estimation is less than 0.001 radians when the SNR is equal to or larger than 2.5 dB. Using the Cram & eacute;r-Rao lower bound as a reference, it has been demonstrated that one particular window offers performance of practical interest by better approximating the CRLB under favorable signal conditions and minimizing performance deviation under adverse conditions. This paper describes the development of a shift-invariant phase-related feature that characterizes the harmonic phase structure. This feature motivates a new signal processing paradigm that greatly simplifies the parametric modeling, transformation, and synthesis of harmonic signals. It also aids in understanding and reverse engineering the phasegram. The theory and results are discussed from a reproducible perspective, with dedicated experiments supported by code, allowing for the replication of figures and results presented in this paper and facilitating further research.

Abstract