# Continuous-time signal processing

Subjects are categorized into three groups based on office blood pressure threshold: Normal (N), Prehypertension (P), and Stage 1 Hypertension (S). Each group contains 100 subjects, and all records have duration of at least 8 minutes. This study uses sliding window with length of 1 second and step size of 1 second to segment records. PPG, ECG and BP yield 167432 segments, respectively. MAP, DBP, and SBP are defined as average, minimum, and maximum of each BP segment, respectively. Max-Min normalization is applied to PPG and ECG segments.

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The source code includes the modified version of QuaDRiGa source code, the scritps that we developed to generate the layout and the channel and delay coefficients for the dual mobile space-ground links. We integrate a practical phenomenon in signal reception by employing both a LOS probability model and a state transition model which follows the semi-Markov approach.

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In this paper, we propose a dual-loop control strategy to address the problems of the interference by the human-machine interaction of the lower limb exoskeleton movement. The outer ring adopts admittance control and the human-machine interaction torque is estimated by the generalized momentum observer based on Kalman filter. The inner ring adopts PID control based on DDPG.

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To achieve improved multi-node temperature estimation with limited training data in Permanent Magnet Synchronous Motors (PMSMs), a novel approach of a Lumped-Parameter Thermal Network (LPTN)-informed neural network is proposed in this paper. Firstly, the parameter and model uncertainties of third or higher-order LPTNs with global parameter identification for temperature estimation are systematically stated based on numerical analysis.

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This dataset comprises data from six experimental participants, each undergoing nine walking trials. Each participant engaged in three trials of low-speed walking, three trials of medium-speed walking, and three trials of high-speed walking. The dataset includes multi-channel electromyography (EMG) data and center of pressure/ground reaction force (COP/GRF) data. Specifically, EMG data is utilized to extract muscle coordination activation time coefficients during human walking, and a deep learning model is established based on these coefficients to predict COP/GRF parameters.

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The uploaded .ZIP file contains the MATLAB codes used in Examples 1 and 2 of the following paper, which the authors have submitted to an IEEE Journal: Data-Driven Saturated State Feedback Design for Polynomial Systems Using Noisy Data. This is the abstract of the paper: "In this note, the problem of data-driven saturated state feedback design for polynomial nonlinear systems is solved by means of a sum-of-squares (SOS) approach. This new strategy combines recent results in dissipativity theory and data-driven feedback control using noisy input-state data.

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This is a PART of the dataset used in our paper titled "Detecting Anomalous Robot Motion in Collaborative Robotic Manufacturing Systems".

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This is a PART of the dataset used in our paper titled "Detecting Anomalous Robot Motion in Collaborative Robotic Manufacturing Systems".

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**ARImulti-mic: real-world speech recordings on a humanoid robot (ARI)**

This dataset includes “real-world” experiments. A recording campaign was held in the acoustic laboratory at Bar-Ilan University. This lab is a [6×6×2.4]m room with a reverberation time controlled by 60 interchangeable panels covering the room facets.

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