Abstract
Late diagnoses of patients affected by pulmonary artery hypertension (PH) have a poor outcome. This observation has led to a call for earlier, non-invasive PH detection. Cardiac auscultation offers a non-invasive and cost-effective alternative to both right heart catheterization and doppler analysis in analysis of PH. We propose to detect PH via analysis of digital heart sound recordings with over-parameterized deep neural networks. In contrast with previous approaches in the literature, we assess the impact of a pre-processing step aiming to separate S2 sound into the aortic (A2) and pulmonary (P2) components. We obtain an area under the ROC curve of. 95, improving over our adaptation of a state-of-the-art Gaussian mixture model PH detector by +.17. Post-hoc explanations and analysis show that the availability of separated A2 and P2 components contributes significantly to prediction. Analysis of stethoscope heart sound recordings with deep networks is an effective, low-cost and non-invasive solution for the detection of pulmonary hypertension. © 2022 Creative Commons.

Abstract
This study aimed to build convolutional neural network (CNN) models capable of classifying upper endoscopy images, to determine the stage of infection in the development of a gastric cancer. Two different problems were covered. A first one with a smaller number of categorical classes and a lower degree of detail. A second one, consisting of a larger number of classes, corresponding to each stage of precancerous conditions in the Correa's cascade. Three public datasets were used to build the dataset that served as input for the classification tasks. The CNN models built for this study are capable of identifying the stage of precancerous conditions/lesions in the moment of an upper endoscopy. A model based on the DenseNet169 architecture achieved an average accuracy of 0.72 in discriminating among the different stages of infection. The trade-off between detail in the definition of lesion classes and classification performance has been explored. Results from the application of Grad CAMs to the trained models show that the proposed CNN architectures base their classification output on the extraction of physiologically relevant image features. Clinical relevance - This research could improve the accuracy of upper endoscopy exams, which have margin for improvement, by assisting doctors when analysing the lesions seen in patient's images.

Abstract
This paper presents a frequency offset estimation approach to ultra wide band Impulse Radio (UWB-IR) systems based upon the classical idea by Schmidl and Cox. The approach secures low complexity by exploiting the low duty cycle of the time hopping access, assures estimation robustness for all UWB-IR bands by working in the frequency domain, attains quasi optimal performances (at 0.5 dB from the Cramer Rao lower bound) by a careful settings choice, and accomplishes robustness at low signal to noise ratio by a suitable combining algorithm. Performance in a realistic IEEE 802.15.4a scenario are also provided. © 2009 IEEE.

Abstract
Ultra Wide Band (UWB) systems based on Multiband Orthogonal Frequency Division Multiplexing (MB-OFDM) modulation, similarly to traditional OFDM systems, are particularly sensitive to oscillators instability, as carrier and sampling frequency offsets between the transmitter and the receiver destroy the subchannel orthogonality. Precise carrier and sampling frequency offsets estimation and compensation at the receiver side are therefore necessary, and should use methods with moderate complexity due to the high system transmission rates that require fast acquisition times. We formulate algorithms that are based upon the received frequency domain symbols, where the effect of both offsets can be observed, and jointly estimate them with either a linear least squares or maximum likelihood approach. The performance of the algorithms is assessed through simulation in a realistic UWB channel scenario and compared with previous literature results.

Abstract
A novel analytical Gaussian approximation is developed for the evaluation of the distortion spectrum introduced by a nonlinear amplifier. This method allows to consider high-order distortion contributions when the device is driven by a broadband signal with Gaussian amplitude distribution. The results are applied to a ninth-order power series model based on well known single-tone and two-tone analysis parameters. The cascade of two or more amplifiers is investigated as well providing a complete set of tools for the early system specification of broadband transceivers. Simulations shows excellent agreement even for high input power levels, when former third-order and fifth-order approximations fail to yield accurate predictions. © 2008 IEEE.

Abstract
A Simulink blockset for the fast evaluation of the non linear distortion caused by RF amplifiers, mixers and BaseBand amplifiers in broadband transmission systems such as the orthogonal frequency division multiplexing (OFDM) adopted in the WLAN or more recently in the UWB standard is presented. Each amplifier block within the tool is described by a ninth-order power series extracted from classical one-tone and two-tones parameters. The distortion description is directly deduced from the spectral analysis of the system, thus avoiding the need for time consuming, time domain simulations and providing a powerful tool for the fast evaluation of the design requirements. A Gaussian approximation is adopted to simplify calculations for high order distortion terms spectra. The blocks within the tool can be cascaded providing analytical predictions for the chain of two or more amplifiers as well. A simulation comparison to a time domain simulator proves that the model keeps reliable up to very high input power levels, outperforming former third-order and fifth-order power series approximations in the literature. ©2008 IEEE.