Graphene-infused terracotta acoustic sound amplifiers.

This work introduces a novel approach for enhancing mobile audio performance by incorporating graphene-infused terracotta as an acoustic amplifier. Leveraging the high thermal conductivity and mechanical strength of graphene, this study demonstrates the design, fabrication, and acoustic characterization of terracotta amplifiers. We demonstrate the significant impact of graphene-infused terracotta amplifiers on improving sound amplification in addition to mechanical strength.

A wavelet subband based LSTM model for 12-lead ECG synthesis from reduced lead set.

Synthesis of a 12-lead electrocardiogram from a reduced lead set has previously been extensively studied in order to meet patient comfort, minimise complexity, and enable telemonitoring. Traditional methods relied solely on the inter-lead correlation between the standard twelve leads for learning the models. The 12-lead ECG possesses not only inter-lead correlation but also intra-lead correlation.

Adaptive continuation based smooth -norm approximation for compressed sensing MR image reconstruction.

Purpose: There are a number of algorithms for smooth -norm (SL0) approximation. In most of the cases, sparsity level of the reconstructed signal is controlled by using a decreasing sequence of the modulation parameter values. However, predefined decreasing sequences of the modulation parameter values cannot produce optimal sparsity or best reconstruction performance, because the best choice of the parameter values is often data-dependent and dynamically changes in each iteration.

Generalised Analog LSTMs Recurrent Modules for Neural Computing.

The human brain can be considered as a complex dynamic and recurrent neural network. There are several models for neural networks of the human brain, that cover sensory to cortical information processing. Large majority models include feedback mechanisms that are hard to formalise to realistic applications.

Multi-echo gradient-recalled-echo phase unwrapping using a Nyquist sampled virtual echo train in the presence of high-field gradients.

Purpose: To develop a spatio-temporal approach to accurately unwrap multi-echo gradient-recalled echo phase in the presence of high-field gradients.

Theory And Methods: Using the virtual echo-based Nyquist sampled (VENyS) algorithm, the temporal unwrapping procedure is modified by introduction of one or more virtual echoes between the first lower and the immediate higher echo, so as to reinstate the Nyquist condition at locations with high-field gradients. An iterative extension of the VENyS algorithm maintains spatial continuity by adjusting the phase rotations to make the neighborhood phase differences less than π.