arXiv subjects

David Gutierrez

Publications and source records attributed to David Gutierrez.

4 recordsLinked to original sources

arXiv2025

A Dynamic Anti-Equinus Orthosis with Electromyography Sensor for Neuromuscular Rehabilitation

The equinus foot is a neuromuscular condition that affects ankle dorsiflexion, impairing gait and reducing quality of life. This study presents EquiSay, a dynamic anti-equinus orthosis equipped with an anterior elastic tension system and an electromyography (EMG) sensor to quantify muscle activation, particularly of the tibialis anterior. EquiSay provides dynamic support that improves foot posture and natural movement while enabling real-time neuromuscular monitoring. To address the limited availability of EMG data, the system incorporates a U-Net based model for generating synthetic EMG signals and a predictive framework for automatic calibration of minimum activation thresholds. Experimental results show improved dorsiflexion, increased patient satisfaction, and valuable clinical insights for rehabilitation planning. These findings highlight the potential of EquiSay as an assistive tool and as a platform for future AI-enhanced developments.

physics.med-ph↗

arXiv2021

Spin Wave Interference Detection via Inverse Spin Hall Effect

In this letter, we present experimental data demonstrating spin wave interference detection using spin Hall effect (ISHE). Two coherent spin waves are excited in a yttrium-iron garnet (YIG) waveguide by continuous microwave signals. The initial phase difference between the spin waves is controlled by the external phase shifter. The ISHE voltage is detected at a distance of 2 mm and 4 mm away from the spin wave generating antennae by an attached Pt layer. Experimental data show ISHE voltage oscillation as a function of the phase difference between the two interfering spin waves. This experiment demonstrates an intriguing possibility of using ISHE in spin wave logic circuit converting spin wave phase into an electric signal

cond-mat.mes-hall↗

arXiv2020

Quantum Computing without Quantum Computers: Database Search and Data Processing Using Classical Wave Superposition

Quantum computing is an emerging field of science which will eventually lead us to new and powerful logic devices with capabilities far beyond the limits of current transistor-based technology. There are certain types of problems which quantum computers can solve fundamentally faster than the tradition digital computers. There are quantum algorithms which require both superposition and entanglement (e.g. Shor algorithm). But neither the Grover algorithm nor the very first quantum algorithm due to Deutsch and Jozsa need entanglement. Is it possible to utilize classical wave superposition to speedup database search? This interesting question was analyzed by S. Lloyd. It was concluded that classical devices that rely on wave interference may provide the same speedup over classical digital devices as quantum devices. There were several experimental works using optical beam superposition for emulating Grover algorithm. It was concluded that the use of classical wave superposition comes with the cost of exponential increase of the resources. Since then, it is widely believed that the use of classical wave superposition for quantum algorithms is inevitably leading to an exponential resources overhead (number of devices, power consumption, precision requirements). In this work, we describe a classical Oracle machine which utilizes classical wave superposition for database search and data processing. We present experimental data on magnetic database search using spin wave superposition. The data show a fundamental speedup over the digital computers without any exponential resource overhead. We argue that in some cases the classical wave-based approach may provide the same speedup in database search as quantum computers.

quant-ph↗

arXiv2017

MV-PURE Spatial Filters with Application to EEG/MEG Source Reconstruction

In this paper we propose spatial filters for a linear regression model which are based on the minimum-variance pseudo-unbiased reduced-rank estimation (MV-PURE) framework. As a sample application, we consider the problem of reconstruction of brain activity from electroencephalographic (EEG) or magnetoencephalographic (MEG) measurements. The proposed filters come in two versions depending on whether or not the EEG/MEG forward model explicitly considers interfering activity in the way of brain activity originating in regions different to those of main interest, but measured as correlated with signals of interest by the EEG/MEG sensor array. In both cases, the proposed filters are equipped with a rank-selection criterion minimizing the mean-square error (MSE) of the filter output. Therefore, we consider them as novel nontrivial generalizations of well-known linearly constrained minimum variance (LCMV) and nulling filters. In order to facilitate reproducibility of our research, we provide (jointly with this paper) comprehensive simulation framework that allows for estimation of error of signal reconstruction for a number of spatial filters applied to MEG or EEG signals. Based on this framework, chief properties of proposed filters are verified in a set of detailed simulations.

eess.SP↗