The complexity of a turbo equalizer based on the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm is manageable only for mildly dispersive channels having a small amount of memory. To enable turbo equalization of highly dispersive channels, we propose the soft-feedback equalizer(SFE). The SFE combines linear equalization and soft intersymbol-interference cancellation. Its coefficients are chosen to minimize the mean-squared error(MSE) between the equalizer output and the transmitted sequence, under a Gaussian approximation to the a priori information and the SFE output. The resulting complexity grows only linearly with the number of coefficients, as opposed to the quadratic complexity of previously reported minimum-MSE structures. We will see that an SFE-based turbo equalizer consistently outperforms another structure of similar complexity, and can outperform a BCJR-based scheme when complexity is taken into account.

Purpose: To study whether polypharmacy or drug–drug interactions have differential effect on safety and efficacy in patients treated with direct oral anticoagulants (DOACs) versus warfarin. Methods: We performed a systematic review and meta-analysis of studies that randomized patients with atrial fibrillation to DOACs or warfarin stratified by the number of concomitant drugs. Outcomes included stroke or systemic embolism (SE), all-cause mortality, major bleeding, and intracranial hemorrhage. Risk ratios (RR) were calculated and Mantel-Haenszel random effects were applied. Results: Two high-quality studies were eligible, including 32,465 participants who received apixaban, rivaroxaban, or warfarin, with a median follow-up of 1.9 years. Of participants, 29% used < 5 drugs, 55% used 5–9 drugs, and 16% used ≥ 10 drugs. Drugs interacting with DOACs (P-glycoprotein/CYP3A4) were used by 6460 (20%) of patients. Patients with higher number of drugs (0–4 vs 5–9 vs ≥ 10) had higher rates of mortality (5.8%, 7.9%, 10.0%) and major bleeding (3.4%, 4.8%, 7.7%). Comparative efficacy or safety of DOACs versus warfarin was not affected by polypharmacy status or P-glycoprotein/CYP3A4 inhibitor use. However, the presence of polypharmacy (p = 0.001) or glycoprotein/CYP3A4-modulating drugs (p = 0.03) was correlated with increased risk of major bleeding when compared with warfarin. Overall, DOAC use was associated with a lower risk of stroke/SE (RR, 0.84; 95%CI, 0.74–0.94), all-cause mortality (RR, 0.91; 95%CI, 0.84–0.98), and intracranial hemorrhage (RR, 0.51; 95%CI, 0.38–0.70) compared with warfarin. Conclusions: DOACs were more effective than warfarin, and at least as safe. Polypharmacy was associated with adverse outcomes and attenuated the advantage in risk of major bleeding among rivaroxaban users, particularly in the presence of P-glycoprotein/CYP3A4-modulating drugs.

This article reviews some key aspects of two important branches in unsupervised signal processing: blind deconvolution and blind source separation (BSS). It also gives an overview of their potential application in seismic processing, with an emphasis on seismic deconvolution. Finally, it presents illustrative results of the application, on both synthetic and real data, of a method for seismic deconvolution that combines techniques of blind deconvolution and blind source separation. Our implementation of this method contains some improvements overthe original method in the literature described.

The choice of an appropriate frame, or dictionary, is a crucial step in the sparse representation of a given class of signals. Traditional dictionary learning techniques generally lead to unstructured dictionaries that are costly to deploy and train, and do not scale well to higher dimensional signals. In order to overcome such limitation, we propose a learning algorithm that constrains the dictionary to be a sum of Kronecker products of smaller subdictionaries. This approach, named sum of Kronecker products, is demonstrated experimentally in an image denoising application.

We have addressed blind deconvolution in a multichannel framework. Recently, a robust solution to this problem based on a Bayesian approach called sparse multichannel blind deconvolution (SMBD) was proposed in the literature with interesting results. However, its computational complexity can be high. We have proposed a fast algorithm based on the minimum entropy deconvolution, which is considerably less expensive. We designed the deconvolution filter to minimize a normalized version of the hybrid l1/l2-norm loss function. This is in contrast to the SMBD, in which the hybrid l1/l2-norm function is used as a regularization term to directly determine the deconvolved signal. Results with synthetic data determined that the performance of the obtained deconvolution filter was similar to the one obtained in a supervised framework. Similar results were also obtained in a real marine data set for both techniques.

Online monitoring of Partial Discharges (PDs) in rotating electrical machines is an useful tool for machine prognosis, as it presents reduced costs compared to intrusive inspections and is associated with relevant problems. Although this monitoring method has been developed for almost 50 years, the recent advancements in processes automation and signal processing techniques allow improvements that are still being studied by academic and industrial researchers. To analyze the current context of PDs monitoring, this article presents a literature review based on concepts of PDs in rotating machines, data acquisition techniques, state-of-the art commercial equipment, and recent methodologies for detection and pattern recognition of PDs. The challenges identified in the literature that motivate the development of more reliable and robust PD monitoring systems are presented and discussed.