Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Sedentary Breathing Rate from Chest-Worn Accelerometry From Free-Living Data

Breathing rate was estimated from chest-worn accelerometry collected from 1,522 servicemembers during training by a wearable physiological monitor. A total of 29,189 hours of training and sleep data were analyzed. The primary purpose of the monitor was to assess thermal-work strain and avoid heat injuries. The monitor design was thus not optimized to estimate breathing rate. Since breathing rate cannot be accurately estimated during periods of high activity, a qualifier was applied to identify sedentary time periods, totaling 8,867 hours. Breathing rate was estimated for a total of 4,179 hours, or 14% of the total collection and 47% of the sedentary total, primarily during periods of sleep. The breathing rate estimation method was compared to an FDA 510(K)-cleared criterion breathing rate sensor (Zephyr, Annapolis MD, USA) in a controlled laboratory experiment, which showed good agreement between the two techniques. Contributions of this paper are to: 1) provide the first analysis of accelerometry-derived breathing rate on free-living data including periods of high activity as well as sleep, along with a qualifier that effectively identifies sedentary periods appropriate for estimating breathing rate; 2) test breathing rate estimation on a data set with a total duration that is more than 60 times longer than that of the largest previously reported study, 3) test breathing rate estimation on data from a physiological monitor that has not been expressly designed for that purpose.
A Study of Sofa-Type Capacitive Coupling Electrocardiograph System to Measure Stress Relief for Sleeping or Resting with Oxygen Taking

Non-contact electrode type electrocardiograph system was researched in various fields, but few products are commercially available. In this study, we proposed a sofa-type capacitive coupling electrocardiograph which has possibilities to be commercialized. The base system is a commercially available oval shape sofa that provides oxygen. We developed a capacitive coupling electrocardiograph and embedded it into the base system. The system gives feedback by measuring the ECG signal on stress relief during resting with taking oxygen provided by the sofa. In the capacitive coupling electrocardiograph, it is inevitable to develop a suitable active electrode for the target system, so we developed that comprised of a surface electrode, electronics, and metal case. The surface electrode was made of PCB with two layers of copper plate: the top layer is for coupling function (coated with AU), and the bottom layer plays a role as a shield. The fabricated active electrode module is embedded into the sofa. The purpose of the developed electrocardiograph is to measure HRV of sofa users. The measured HRV was compared with that from a reference system by various coupling distances (cotton cloth thickness) to guarantee the quality of measured signals. The comparison result shows that RRI correlation was mostly over 0.99, SDNN variation rate was mostly under 1%, and LF/HF variation rate was less than 1% in the tested thicknesses.
Analysis of the downlink channel estimation methods in communication systems with SCMA

Sparse Code Multiple Access (SCMA) is a nonorthogonal multiple access method that provides high bandwidth, noise immunity and a larger number of connections compared to traditional methods such as Orthogonal Frequency Division Multiple Access (OFDMA). One of the important problems that negatively affects the quality of communication is multipath propagation. To combat it, various equalization techniques are used, which provide for channel estimation by the receiver. In this paper, we consider three methods for estimating the downlink transmission channel for communication systems with SCMA. The first method is an assessment by reference (pilot) signals, which is classical and is widely used in modern telecommunication systems. The second method under consideration is the assessment of the regenerated information symbols. The third is a blind channel estimate. Algorithms are proposed for all three methods, and mathematical modeling is performed. The results of the dependences of the mean square error and bit error on the signal-to-noise ratio are obtained.
Strain-induced Differentiation of Mesenchymal Stem Cells

Directing the fate of human mesenchymal stem/stromal cells (hMSCs) toward bone formation using mechanical strain is a promising approach in regenerative medicine related to bone diseases. Numerous studies have evaluated the effects of vibration or cyclic tensile strain on MSCs towards developing a mechanically-based method for stimulating differentiation. Here, we study the differentiation of hMSCs cultured on elastic polydimethylsiloxane (PDMS) membrane, which is magnetically actuated to induce periodically varying strain. The strain distribution across the membrane was calculated by finite-element modeling and demonstrates three main areas of different strain amplitudes. The strain effect on the hMSCs was evaluated by measuring the mineralization of differentiated hMSCs using Alizarin S red stain. The results indicate a strain-dependent differentiation of hMSCs, where the highest region of strain on the membrane resulted in the most accelerated differentiation. Osteogenic differentiation was achieved as early as two weeks, which is significantly sooner than control hMSCs treated with osteogenic media alone.
A Novel B-MAP Proxy for Greedy Sparse Signal Recovery Algorithms

We propose a novel greedy algorithm to recover a sparse signal from a small number of noisy measurements. In the proposed method, a new support index is identified for each iteration, based on bit-wise maximum a posteriori (B-MAP) detection. This approach is an optimal in the sense of detecting one of the remaining support indices, provided that all the indices during the previous iterations are perfectly recovered. Unfortunately, the exact computation of B-MAP detection is not practical since it requires a heavy marginalization of a highdimensional sparse vector to compute a posteriori probability of each remaining support. Our major contribution is to present a good proxy, named B-MAP proxy, on the a posteriori probability. The proposed proxy is easily evaluated only using vector correlations as in popular orthogonal matching pursuit (OMP) and accurate enough to represent a relative ordering on the probabilities. Via simulations, we demonstrate that the proposed greedy algorithm yields a higher recovery accuracy than the existing benchmark methods as OMP and MAP-OMP, having the same computational complexity.A full version of this paper is accessible at: https://arxiv.org/abs/1910.12512/
countries as a crucial factor of the spread of Chikungunya 2 3

247 words 23 Text: 5117 words 24 25 JVI Accepts, published online ahead of print on 26 March 2014 J. Virol. doi:10.1128/JVI.00370-14 Copyright © 2014, American Society for Microbiology. All Rights Reserved.
Predictability of Off-line to On-line Recommender Measures via Scaled Fuzzy Implicators

This paper introduces fuzzy Challenge Response Framework, designed to understand the relationship between the model of a real-world situation and some real observations, based on scaled fuzzy Implicators between them. This general framework is applied to a particular case in recommender systems: the prediction of on-line performance given off-line evaluation results. We perform an empirical evaluation with real data from a Czech travel agency, comparing different recommender algorithms, different metrics for on-line and offline evaluations, and different implication operators.
Deep Learning for Interictal Epileptiform Spike Detection from scalp EEG frequency sub bands

Epilepsy diagnosis through visual examination of interictal epileptiform discharges (IEDs) in scalp electroencephalogram (EEG) signals is a challenging problem. Deep learning methods can be an automated way to perform this task. In this work, we present a new approach based on convolutional neural network (CNN) to detect IEDs from EEGs automatically. The input to CNN is a combination of raw EEG and frequency sub-bands, namely delta, theta, alpha and, beta arranged as a vector for one-dimensional (1D) CNN or matrix for two-dimensional (2D) CNN. The proposed method is evaluated on 554 scalp EEGs. The database consists of 18,164 IEDs marked by two neurologists. Five-fold cross-validation was performed to assess the IED detectors. The resulting 1D CNN based IED detector with multiple sub-bands achieved a false positive rate per minute of 0.23 and a precision of 0.79 at 90% sensitivity. Further, the proposed system is evaluated on datasets from three other clinics, and the features extracted from CNN outputs could significantly discriminate (p-values < 0.05) the EEGs with and without IEDs. We have proposed an optimized method with better performance than the literature that could aid clinicians to diagnose epilepsy expeditiously, and thereby devise proper treatment.
Securing Next Generation Multinodal Leadless Cardiac Pacemaker System: A Proof of Concept in a Single Animal

As the next generation of implanted medical devices for cardiac rhythm management moves towards multi-nodal leadless systems that do without the limitations of transvenous leads, new security threats arise from the wireless communication between the systems’ nodes. Key management and the key distribution problem used in traditional cryptographic methods are considered to be too computationally expensive for small implanted medical devices. Instead, inherent human biometrics could provide a reliable alternative. In this work, we tested the key generation process across different nodes of a mimicked dual-chamber leadless cardiac pacemaker system and a subcutaneous implantable relay (S-relay). The proposed key generation process utilizes the randomness available from inter beat intervals (IBIs). A pre-clinical in-vivo experiment was performed in one dog in order to validate the concept by implanting conventional bipolar cardiac pacemaker leads in the right atrium, the right ventricle and the subcutaneous space. Based on the available randomness and entropy of recorded IBIs, 3-bits were extracted per IBI by approximating a sequence of intervals with a normal distribution. This allowed for the generation of a 128-bit key string across the nodes with an average bit mismatch rate of about 3%. Parity check methods were used to reconciliate the keys across the multiple nodes of a multi-nodal leadless pacemaker and subcutaneous device system. The findings are encouraging and demonstrate that IBIs can be used to generate secure keys for data encryption across different nodes of a leadless pacemaker system and S-relay.
A 10-MHz BW 77.3-dB SNDR 640-MS/s GRO-Based CT MASH ΔΣ Modulator

We present in this brief a novel multi-stage noise-shaping (MASH) 3–1 continuous-time (CT) delta-sigma modulator (<inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma \text{M}$ </tex-math></inline-formula>) with gated ring oscillator based quantizers (GROQs) in both stages of the cascade. The use of GROQs increases the linearity performance with respect to the conventional voltage controlled oscillator based quantizers (VCOQs) and allows a more robust extraction of the front-end stage quantization error in the time domain, thus making the proposed architecture more suitable to implement high-order expandable scaling-friendly MASH <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula>Ms, in which the back-end stages are implemented by mostly-digital GRO-based time-to-digital converters (TDCs). The circuit has been fabricated in a 65-nm CMOS technology with 1-V supply voltage, and it operates at 640-MHz sampling frequency to digitize 10-MHz signals. To the best of the authors’ knowledge, this is the first reported experimental validation of a GRO-based CT MASH <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma \text{M}$ </tex-math></inline-formula>, featuring a 79.8-dB signal to noise ratio (SNR) at −2.2-dBFS, a 77.3-dB signal to (noise + distortion) ratio (SNDR) at −4-dBFS and a dynamic range (DR) of 81.7 dB, with a power consumption of 12-mW. These metrics demonstrate state-of-the-art performance with a DR-based Schreier FOM of 170.9 dB.
An Effective Zeros-Time Windowing Strategy to Detect Sensorimotor Rhythms Related to Motor Imagery EEG Signals

Brain-computer interface (BCI) acquires, analyzes and transforms human brain activity to control commands allowing as such disabled people to communicate or control external devices. A motor imagery-based BCI enables patients to control artificial peripherals and communicate with the outside world by merely thinking of the task such as, e.g., the imagination of left-hand, right-hand, or foot movement. The mere intention of moving one of the limbs triggers neural activity, which is induced in the primary sensorimotor areas like that observed with real executed movements. Tracking generated sensorimotor rhythms (SMRs) and extracting robust and informative features from electroencephalogram (EEG) signals are challenging due to the time-varying nature of EEG signals and the inter-human variability. In this paper, we proposed an EEG-zeros-time windowing (E2ZTW) approach based on a highly decaying window function to track SMRs and identify the temporal epochs containing useful information without any prior information on the trigger. The proposed approach involves the application of the group-delay function, allowing the improvement of the spectral resolution due to the additive property of the function on individual resonances. Some algorithms were integrated into the proposed approach, such as the common spatial pattern algorithm, which is used to extract features and linear discriminant analysis and a convolutional neural network, which are used for the classification of the features. The effectiveness of the proposed approach in tracking the SMRs rhythms is evaluated in terms of accuracy. Experiments were performed on three public datasets provided by BCI competition for 17 subjects. Following experimental results, it is shown that discrimination between the left- and right-hand movements can be achieved within a few seconds with high classification accuracy. As compared to other state-of-art techniques, the proposed approach achieves an average classification accuracy and standard error values of 82% and 13, respectively, thereby outperforming existing algorithm by an accuracy mean of 2%.
An Event-based Categorization Model Using Spatio-temporal Features in a Spiking Neural Network

This paper introduces an event-based categorization model to explore neuromorphic computing for object recognition. The proposed method makes full use of the precise timing information inherently present in the output of a bio-inspired vision sensor and utilizes event-driven processing to keep the form of address event representation (AER). In this model, a hierarchy of event-based time-surface is used to extract the spatio-temporal features of the AER data, and then these extracted features are classified by spiking neural network (SNN) with event-driven Tempotron rule. To demonstrate the effectiveness of the presented method, we conducted a series of experiments on some accessible AER datasets and compared our method to other developed event-based categorization models. Additional pooling operation in feature extraction can reduce the number of parameters and computing time. Experimental results show that our method has better classification performance and real-time computation. That is, the proposed model can extract useful features and consumes much less simulation time while still maintaining competitive accuracy.
A Hybrid Method for Short-Term Traffic Flow Prediction

We analyze the problem of short-term traffic flow prediction using a hybrid approach composed of regression and optimization. First, the data preprocessing method is described for the scenario of traffic flow prediction. Second, extreme gradient boosting is used to generate boosted trees that are then used to transform the input of each record. Finally, following optimization by particle swarm, support vector regression is selected as the model for prediction. Experimental results show the proposed method is effective.
A Low-Noise, Low-Power, Dynamic Latched Comparator Using Cascoded Structure

In this paper, a design for a low-noise, low-power, dual-tail dynamic latched comparator is introduced. The input referred noise is reduced through the use of a larger gain comparator dynamic amplifier; a cascode structure is adopted for such an amplifier with a direct effect on the power. To fix this problem without degrading the gain or the input referred noise, bulktuning is applied to the amplifier bias transistor. In addition, a nonconventional power-efficient latch structure is used. Designed in a 130nm UMC process, with a supply voltage of 1.2V and at a clock frequency of 1-GHz, the proposed comparator has an input-referred noise of 290μV, and an input offset voltage of 4.1mV while consuming 100μW of power with a power efficiency of 100fJ/conv. The maximum achievable frequency is 1.25GHz for a differential input 292μ V.
Remote Sensing Scene Classification with Dual Attention-Aware Network

Remote sensing scene classification is of great importance to remote sensing image analysis. Most existing methods based on Convolutional Neural Network (CNN) fail to discriminate the crucial information from the complex scene content due to the intraclass diversity. In this paper, we propose a dual attention-aware network for remote sensing scene classification. Specifically, we use two kinds of attention modules (i.e. channel and spatial attentions) to explore the contextual dependencies from the channel and spatial dimensions respectively. The channel attention module intends to capture the channel-wise feature dependencies and further exploit the significant semantic attention. On the other hand, the spatial attention module aims to concentrate the attentive spatial locations and thus discover the discriminative parts inside the scene. The outputs of two attention modules are finally integrated as the attention-aware feature representation for improving classification performance. Experimental results on RSSCN7 and AID benchmark datasets show the effectiveness and superiority of the proposed methods for scene classification in remote sensing imagery.
Elapsed Time of IoT Application Protocol for ECG: A Comparative Study Between CoAP and MQTT

Smart devices adopt different IoT application protocols to achieve interoperability across various nodes of the IoT network. CoAP and MQTT are two application protocols among a set of available IoT application protocols, and e-health is a vital IoT domain where the performance is highly considered. This paper studies performance of CoAP and MQTT protocols using elapsed time as a metric and different simulations for ECG devices conducted in the form of three experiments to study the elapsed time of each protocol. CoAP shows better performance when the number of connected sensors is increased. The results of the paper contribute to the selection of the adopted IoT application protocols for ECG devices specifically, and e-health and IoT devices in general.
Target Search on Road Networks With Range-Constrained UAVs and Ground-Based Mobile Recharging Vehicles

We study a range-constrained variant of the multi-UAV target search problem where commercially available UAVs are used for target search in tandem with ground-based mobile recharging vehicles (MRVs) that can travel, via the road network, to meet up with and recharge a UAV. We propose a pipeline for representing the problem on real-world road networks, starting with a map of the road network and yielding a final routing graph that permits UAVs to recharge via rendezvous with MRVs. The problem is then solved using mixed-integer linear programming (MILP) and constraint programming (CP). We conduct a comprehensive simulation of our methods using real-world road network data from Scotland. The assessment investigates accumulated search reward compared to ideal and worst-case scenarios and briefly explores the impact of UAV speeds. Our empirical results indicate that CP is able to provide better solutions than MILP, overall, and that the use of a fleet of MRVs can improve the accumulated reward of the UAV fleet, supporting their inclusion for surveillance tasks.
"How are you?" Estimation of anxiety, sleep quality, and mood using computational voice analysis

We developed a method of estimating impactors of cognitive function (ICF) - such as anxiety, sleep quality, and mood - using computational voice analysis. Clinically validated questionnaires (VQs) were used to score anxiety, sleep and mood while salient voice features were extracted to train regression models with deep neural networks. Experiments with 203 subjects showed promising results with significant concordance correlation coefficients (CCC) between actual VQ scores and the predicted scores (0.46 = anxiety, 0.50 = sleep quality, 0.45 = mood).
Voxelwise optimization of hemodynamic lags to improve regional CVR estimates in breath-hold fMRI*

Cerebrovascular Reactivity (CVR), the responsiveness of blood vessels to a vasodilatory stimulus, is an important indicator of cerebrovascular health. Assessing CVR with fMRI, we can measure the change in the Blood Oxygen Level Dependent (BOLD) response induced by a change in CO2 pressure (%BOLD/mmHg). However, there exists a temporal offset between the recorded CO2 pressure and the local BOLD response, due to both measurement and physiological delays. If this offset is not corrected for, voxel-wise CVR values will not be accurate. In this paper, we propose a framework for mapping hemodynamic lag in breath-hold fMRI data. As breath-hold tasks drive task-correlated head motion artifacts in BOLD fMRI data, our framework for lag estimation fits a model that includes polynomial terms and head motion parameters, as well as a shifted variant of the CO2 regressor (±9 s in 0.3 s increments), and the hemodynamic lag at each voxel is the shift producing the maximum total model R2 within physiological constraints. This approach is evaluated in 8 subjects with multi-echo fMRI data, resulting in robust maps of hemodynamic delay that show consistent regional variation across subjects, and improved contrast-to-noise compared to methods where motion regression is ignored or performed earlier in preprocessing.Clinical Relevance— We map hemodynamic lag using breathhold fMRI, providing insight into vascular transit times and improving the regional accuracy of cerebrovascular reactivity measurements