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Fast and accurate respiratory rate (RR) estimation from photoplethysmography (PPG) signal is still a challenging problem. In this paper, we propose a real-time algorithm for RR estimation from PPG signal using sparse signal reconstruction (SSR) based on orthogonal matching pursuit (OMP). This algorithm greatly reduces the computational complexity of the original sparse signal reconstruction and respiratory...
A new filter bank structure or channelizer is developed, named the IFIR-FB, as it is the result of combining the concepts of interpolated FIR filters (IFIR) and filter banks (FB). The filter design procedures for the IFIR-FB are developed and explained. The resulting IFIR-FB structure is shown to be competitive with the state-of-the-art non-maximally-decimated filter bank, in terms of the number of...
Massive multi-user (MU) multiple-input multiple-output (MIMO) is widely believed to be a core technology for the upcoming fifth-generation (5G) wireless communication standards. The use of low-precision digital-to-analog converters (DACs) in MU-MIMO base stations is of interest because it reduces the power consumption, system costs, and raw baseband data rates. In this paper, we develop novel algorithms...
We consider the problem of inferring time-varying Granger causal interactions among multiple simultaneously recorded spike trains from a neuronal ensemble. We present a dynamic Granger causality measure with sparsity and adaptivity features for point process observations, and estimate it recursively. We develop a statistical inference framework based on asymptotic analysis of deviance, and perform...
The baseband Volterra series is a general approach to model nonlinear passband systems like radio frequency power amplifiers in equivalent baseband. In the present paper, we review the derivation of the baseband Volterra series using a compact vector notation and show that it only includes odd-order terms. After that, we present a new derivation which shows that by assuming modified basis functionals...
We introduce learned attention models into the radio machine learning domain for the task of modulation recognition by leveraging spatial transformer networks and introducing new radio domain appropriate transformations. This attention model allows the network to learn a localization network capable of synchronizing and normalizing a radio signal blindly with zero knowledge of the signal's structure...
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