Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Filtering PPG Signal on Pulse Arrival Time-Systolic Blood Pressure Correlation

Pulse arrival time (PAT), evaluated from electro-cardiogram (ECG) and photoplethysmogram (PPG) signals, has been widely used for cuff-less blood pressure (BP) estimation due to its high correlation with BP. However, the question of whether filtering the PPG signal impacts the extracted PAT values and consequently, the correlation between PAT and BP, has not been investigated before. In this paper, using data from 18 subjects, changes in the PAT values, and in the subject-specific PAT-systolic BP (SBP) correlation caused by filtering the PPG signal with variable cutoff frequencies in the range of 2 to 15 Hz are studied. For PAT extraction, three PPG characteristic points (foot, maximum slope and systolic peak) are considered. Results show that differences in the cutoff frequency can shift the PAT values and introduce a worst-case error of over 8.2 mmHg for SBP estimation, indicating that PPG signal filter settings can impact PAT-based BP estimations. Our study suggests that extracting the PAT from the maximum slope point of PPG signal filtered at 10 Hz provides the most stable correlation with SBP.
Deep-Learning for High Quality and High Quantitative Ultrasonic Echo Imaging

This paper performs in simulations deep learning (DL) for high quality and high quantitative ultrasonic (US) echo imaging: (i) reduction of multiple echoes (multiple reverberations) and (ii) grading lobe echoes, (iii) separation of multiply crossed waves in US echo images, (iv) US attenuation correction imaging and (v) superresolutioned reflection and scattering imaging. In addition, (vi) segmentations of benign and malignant (cancerous) tumors in breast tissues are also performed. Clinical Relevance- This study about DL suggests the possibility of DL US segmentation for the automatic differential diagnosis about the human in vivo breast tumors in conjunction with the surrounding DL models.
A Stochastic Resonance P- and T-wave Detection Algorithm

An algorithm to detect P- and T-waves in an electrocardiogram (ECG) signal is presented. The algorithm has physical origins inspired by weak signal detection by leveraging stochastic resonance (SR) in a well potential. Specifically, a particle inside an underdamped monostable well is introduced with the ECG signal. The parameters defining the well and system characteristics are optimized towards enhancing the P-, R-, and T -waves while suppressing the other portions including noise-only sections. The enhanced features are detected by thresholding. Based on the performance obtained from the QT database, the algorithm achieves an average sensitivity of 99.97% for P-waves and an average sensitivity of 99.35% for T-waves, better than most P- and T-wave detection algorithms reported. Clinical Relevance— The proposed SR algorithm achieves high P- and T-wave detection performance and can potentially be integrated with implantable long-term cardiac monitors for patients experiencing rare symptoms without deteriorating the battery life.
Ultrafast field control of symmetry, reciprocity, and reversibility in buckled graphene-like materials

Center for Nano-Optics (CeNO) and Department of Physics and Astronomy,Georgia State University, Atlanta, Georgia 30303, USA(Dated: May 4, 2015)We theoretically show that buckled two-dimensional graphene-like materials (silicene and ger-manene) subjected to a femtosecond strong optical pulse can be controlled by the optical eldcomponent normal to their plane. In such strong elds, these materials are predicted to exhibitnon-reciprocal reection, optical recti cation and generation of electric currents both parallel andnormal to the in-plane eld direction. Reversibility of the conduction band population is also eld-and carrier-envelope phase controllable. There is a net charge transfer along the material planethat is also dependent on the normal eld component. Thus a graphene-like buckled material be-haves analogously to a eld-e ect transistor controlled and driven by the electric eld of light withsubcycle (femtosecond) speed.I. INTRODUCTION
A Hardware-based Lightweight ANN for Real-time Wearable Blood Pressure Estimation

Blood pressure (BP) is an important indicator of the state of cardiovascular health. BP estimation is an essential method to prevent the occurrence of hypertension. Currently, there is a strong focus on low power design for a wearable BP estimation device. This paper proposes a lightweight artificial neural network (ANN) for BP estimation and implements it on an ultra-low-power application-specific integrated circuit (ASIC). On the test set, the mean absolute error (MAE) and standard deviation (SD) of the estimated systolic BP and diastolic BP are 2.47 ± 3.48 mmHg and 1.45 ± 1.88 mmHg. Besides, in the case of 8-bit quantization, the MAE ± SD of the estimated systolic BP and diastolic BP are 12.41 ± 5.32 mmHg and 6.29 ± 3.03 mmHg respectively. The regression result R2 of overall SBP and DBP is 0.9702. This ASIC whose power is 19.72 µW is validated via the 0.18 µm CMOS process, occupying an area of 730 µmx 730 µm.
Time Series Analysis Using Stacked LSTM Model for Indian Stock Market

Stock market is a well-known option for investments and has impact in today’s economy. Due to high returns and losses, it will be an investor’s dream to predict the rise and fall accurately. But future price is very unpredictable. Although there are different analysis present for years like fundamental analysis, technical analysis etc. Now a days different algorithms are used to forecast the future price. Stock price prediction is very important as it can be seen as the guideline used by the traders and investors about the future trend of the market. So, it will help them to take entry or exit in the market in right time. In this paper, we have predicted the stock price of five NIFTY 50 shares and then compared it with the actual value to analyze the accuracy of price prediction. Here Long-Short-Term Memory, a deep learning (DL) model is used. It is found that the DL model performed very well in predicting the price of required stock. Quantification of the performance of the model is done using mean absolute percentage error.
Millimeter-Wave Fully Integrated Dielectric Resonator Antenna and Its Multi-beam Application

To address the issues of the inconvenient fabrication and integration for millimeter-wave dielectric resonator antennas, a new configuration is proposed. Firstly, a dielectric resonator with artificial electromagnetic boundaries is implemented by introducing the electromagnetic band-gap structure along the four side-wall boundaries of a certain dielectric region on a printed circuit board. The EBG structure is constructed using a printed array of periodic upside-down mushroom-type unit cells. The resonant-mode analysis reveals that the proposed DR can support conventional dielectric resonator modes and dense dielectric patch cavity modes simultaneously. To excite the DR, a substrate integrated gap waveguide transmission line is embedded in the proposed structure for implementing a fully integrated dielectric resonator antenna. For demonstration, a FIDRA operating at 31 GHz is designed. Simulated results show that the antenna offers an 11.5 % -10-dB impedance bandwidth (29.6 GHz to 33.2 GHz), in which a peak gain of 7.85 dBi is obtained. As an extension, a multi-beam antenna array composed of a 1 × 4 FIDRA antenna array and a SIGW 4 × 4 Butler matrix is designed and fabricated. The experimental results verified the effectiveness of the proposed configuration in integrating the DRA and feeding network.
Automated Deep Learning-based Single-Step Diameter Estimation of Carotid Arteries in B-mode Ultrasound

Accurate measurement of blood vessel diameter on ultrasonic images is important in many vascular exams. In one of them, volumetric blood flow measurements, the volume flow rate is calculated by multiplying the time-averaged velocity with the cross-sectional area of the vessel (using diameter measured from B-mode images). Computation of lumen diameter is also vital for planning surgical procedures like carotid artery stenting and endarterectomy. More recently, several automated vessel diameter estimation methods employing deep learning have been proposed. In this paper, we propose a novel single-step automated deep learning-based vessel diameter estimation technique developed on B-mode images. Longitudinal images of the human common carotid artery were acquired by trained vascular sonographers in human subjects using a linear array probe. Ground truth measurements were obtained by a human expert to validate the proposed technique. 504 images (with augmentation) were divided into training, validation, and test sets. Three pre-trained deep learning networks were used for training, and the lumen diameter was predicted in a hold-out test set. The Mean Absolute Deviation (MAD) and Root Mean Square Error (RMSE) ranged from 0.22-0.65 mm and 0.32-0.82 mm, respectively, for the three networks. Furthermore, 5-fold cross-validation resulted in MAD and RMSE of 0.36±0.1 mm and 0.513±0.15 mm, respectively. Clinical Relevance- The results demonstrate that the technology can potentially be embedded in commercial scanners to make the workflow in vascular ultrasound more efficient.
University Microgrid Testbeds: A Literature Survey

Microgrid technology has been one of the main research topics in the recent energy industry. An interesting research direction is the microgrid application for universities, as there has been an emphasis on sustainability and renewable energy on university campuses everywhere. Many universities have developed microgrid testbeds (MGTB) to meet their research and educational requirements. This paper surveys some representative university MGTB examples in the U.S. and worldwide. The three main types of the university MGTB: simulation-based, hardware-in-the-loop(HIL)-based, and physical testbeds, are first illustrated. Then the relevant information on the three MGTB types, including the most adopted MGTB software simulation platforms, the microgrid control levels and functions, and associated monitoring and measurement aspects along with real-world university examples, are summarized. This work intends to provide a general reference for universities interested in designing and implementing their campus microgrid testbeds.
Recursive Parameter Estimation of Beta-Liouville Hidden Markov Models

This paper proposes a novel approach to recursively estimate the parameters of Beta-Liouville Hidden Markov Model (HMM). With the rapidly increasing volume of data nowadays, the need for analyzing such data is becoming more urgent. Classification is a a machine learning technique for analyzing data. Therefore, in this paper, we assume that a given data set can be described as HMM sequences, then apply Beta-Liouville distribution as an emission probability of the HMM. By estimating the parameters of the considered Beta-Liouville HMM using expectation maximization algorithm, we build a machine learning model using Bayes rule to perform classification. Noting that classical learning methods are computationally extensive, we propose an online learning framework for real-time analysis using recursive parameter estimation approach. Both Dirichlet and Beta-Liouville distributions are studied and compared in this research. The Beta-Liouville distribution has proven to be more flexible in terms of data modeling. The effectiveness of the developed model is shown by evaluating it on real data that concern occupancy estimation in smart buildings.
Improving Angular Estimation Using a Deep CNN network in 6D pose estimation

We investigate a deep learning-based method to estimate 6D pose information of target objects from one or multiple images. We use a modified YOLO2 as the backbone network for feature extraction and a detection network to detect the target's 3D bounding box. The network is trained using 3D errors in addition to the usual 2D pixel errors. Our method also uses a perspective-aware method to select the best keypoints to estimate the 6D pose. Synthesis data experiments show significant angular accuracy improvement in the estimated 6D pose.
Integrated low-profile antenna covering 29-50 GHz band for 5G millimeter-wave application

For the application of 5G millimeter-wave communication, an integrated low-profile antenna covering 29-50 GHz band is proposed. Four metallized layers and four dielectric layers are used to constitute the integrated antenna. The integrated antenna is manufactured by printed circuit board (PCB) technology due to the simple process. A reference antenna with identical dielectric substrates and similar metallic structures is developed for comparison. The profile of the integrated antenna is 0. 07λ0 (At 39 GHz, the free space wavelength is λ0). The integrated antenna generates four resonances in a continuous frequency band, and the impedance bandwidth ranges from 28.3 to 50.1 GHz (about 56% relative bandwidth), where |S11| is lower than -10 dB. The integrated antenna reveals the broadband characteristic and has stable broadside radiation patterns within the band. At 45 GHz, the integrated antenna achieves the maximum gain of 10.8 dBi. The integrated broadband antenna with a low profile is suitable for the antenna-in-package application in 5G millimeter-wave band.
Mitigation of Power Fluctuations in Large-Scale Solar Photovoltaic Plant

The incident solar irradiance on the solar photovoltaic (PV) modules fluctuates due to moving clouds in the sky, resulting in rapid changes in the solar PV plant generated power. The current work presents a solar PV plant power smoothening algorithm that is suitable for both cloudy and sunny weather conditions. The smoothening algorithm optimizes the battery usage, keeps the battery state-of-charge (SOC) within a predefined reference operating range, and reduces BES charge-discharge losses. In the power conditioning system (PCS) of BES, a thirteen-level cascaded H-bridge voltage source converter (VSC) with fundamental switching of MW size is used. The simulated steady-state, harmonics and dynamic response of 20MW solar PV plant with 10MW/5MWh BES configuration are presented in detail.
Analysis of RIS-Assisted FSO Systems Over F Turbulence Channel With Pointing Errors and Imperfect CSI

Reconfigurable intelligent surface (RIS) is an enabling solution to enhance the wireless communication system performance by smartly tunning the signal reflection via a large number of reconfigurable passive elements. In this letter, we present the performance analysis of RIS-assisted FSO communication systems over Fisher-Snedecor $\mathcal {F}$ distribution channel considering imperfect channel state information (CSI) and pointing errors under two types of detection schemes. The probability density function (PDF) and cumulative distribution function (CDF) of signal-to-noise ratio (SNR) are deduced in terms of multivariable Fox’s H function. Utilizing these results, the novel expressions for outage probability, average BER and ergodic capacity are derived. All presented results are verified by Monte Carlo simulations.
An Advance Angle Control-based SRM Drive for SWPS using Artificial Neural Networks for Rotor Position Estimation

This paper presents a low-cost solution of switched reluctance (SR) drive employed for water source application using an artificial neural network for rotor position estimation. The drive eliminates the use of expensive mechanical position sensor, and instead uses a sensorless scheme to obtain a rotor position information. Because of its highly non-linear magnetic characteristics, SR motor has a challenging task in its mathematical modelling for the control of drive. The neural network modelled in this paper uses a pre-collected magnetic characteristics and is trained offline using a Levenberg–Marquardt (LM) algorithm in a MATLAB/Simulink environment. Since, the number of neurons plays a prominent role in training the network and the fitting level, the model is optimized in using a number of neurons in the hidden layers of the network. Besides using a sensorless scheme, the drive employs an advance angle control for the speed control of SR drive which eliminates the switching losses induced by the conventional hysteresis controller. Overall performance of the drive is evaluated and presented for accounting its suitableness for the solar photovoltaic water pumping system (SWPS).
Self-Organizing Maps for Contrastive Embeddings of Sleep Recordings

Nowadays, high amounts of data can be acquired in various applications, spurring the need for interpretable data representations that provide actionable insights. Algorithms that yield such representations ideally require as little a priori knowledge about the data or corresponding annotations as possible. To this end, we here investigate the use of Kohonen's Self-Organizing Map (SOM) in combination with data-driven low-dimensional embeddings obtained through self-supervised Contrastive Predictive Coding. We compare our approach to embeddings found with an auto-encoder and, moreover, investigate three ways to deal with node selection during SOM optimization. As a challenging experiment we analyze nocturnal sleep recordings of healthy subjects, and conclude that - for this noisy real-life data - contrastive learning yields a better low-dimensional embedding for the purpose of SOM training, compared to an auto-encoder. In addition, we show that a stochastic temperature-annealed SOM-training outperforms both a deterministic and a non-temperature-annealed stochastic approach. Clinical relevance – The hypnogram has for decades been the clinical standard in sleep medicine despite the fact that it is a highly simplified representation of a polysomnography recording. We propose a sensor-agnostic algorithm that is able to reveal more intricate patterns in sleep recordings which might teach us about sleep structure and sleep disorders.
Two-Phase IPOS DC-DC Step-up Converter for PV Interface

As solar modules grow increasingly prevalent in power generation, more efficient and effective power conversion technologies are required. Traditional power converters that convert power from several solar modules in series face several challenges, including partial shading and tracking accurate maximum power points. This paper presents a doubler step-up converter suitable for increasing the voltage gain, processing photovoltaic power, and extracting highest possible solar energy from solar modules. The converter consists of two phases of boost converters connected as input parallel and output series, reducing the input current ripples and allowing for electromagnetic interference reduction and better current measurements. The theoretical background of the converter is illustrated and supported with simulation results.
Les tests de causalité en variance entre deux séries chronologiques multivariées

Multivariate conditional heteroscedasticity models have been found useful for modelling financial data. In many applications, to verify the existence of a relationship between two multivariate time series represents an important consideration. In this article, the procedure developed by Cheung and Ng (1996) designed to test causality in variance for univariate time series is generalized in several directions. Relying on the work of El Himdi and Roy (1997) and Duchesne (2004), a first approach proposes test statistics based on residual cross-covariance matrices of squared (standardized) residuals and cross products of (standardized) residuals. The new test statistics converge in distribution toward convenient chi-square distributions under the null hypothesis of absence of causality in variance. In a second approach, transformed residuals are defined as in Ling and Li (1997) for each residual vector time series, and test statistics are constructed based on the cross-correlations of these transformed residuals. In both approaches, test statistics at individual lags are developed, and also portmanteau type test statistics; both procedures reduce to Cheung and Ng’s test statistics in the case of two univariate time series. The proposed methodology can be used to determine the directions of causality in variance, and appropriate test statistics are presented. Monte Carlo ∗Abbreviated title: "Causality in variance between two multivariate time series". Corresponding author: Pierre Duchesne, Université de Montréal; Département de mathématiques et de statistique; C.P. 6128 Succursale Centre-Ville; Montréal, Québec H3C 3J7; Canada. †This work was supported by a grant from the Natural Science and Engineering Research Council of Canada. 17 simulation results show that the new test statistics offer satisfactory empirical properties. An application with real data illustrates the methods.
Post-Reconstruction PET Resolution Modelling by Synthesised Image Reconstruction

Resolution recovery techniques in PET aim to improve spatial resolution, signal-to-noise ratio and quantitative accuracy. Whilst several factors are responsible for the degradation of spatial resolution, image-space point spread function (PSF) modelling has been developed to compensate for resolution losses. For example, the widely used Richardson-Lucy (RL) algorithm can be used to model the PSF and improve spatial resolution. However, when the RL algorithm is applied to post-reconstruction resolution recovery, noise rapidly develops even in early iterations. On the other hand, incorporation of the PSF directly into iterative reconstruction algorithms has shown potential benefits (depending on the task), with less rapid noise accumulation compared to RL, and with resolution improvements compared to conventional reconstruction with no PSF modelling. However, PSF-based reconstruction requires raw projection data and knowledge of the forward/back-projectors relating to the scanner's geometry, and if these are unavailable the technique becomes infeasible. In this proof-of-concept work, we propose a novel post-reconstruction resolution recovery technique based on synthesising an image reconstruction problem. Tomographic data are synthesised from the supplied image and a new inverse problem with embedded PSF resolution modelling is then solved. This work evaluates the proposed method in 2D using a simulated phantom at various count levels. We compare the performance of the proposed method with the RL algorithm and PSF-based maximum likelihood expectation maximisation (MLEM) reconstruction. In conditions which match typical clinical scans (e.g. low iteration numbers and counts), the proposed method achieves a substantially lower root mean square error than the RL algorithm. The performance of the proposed method is comparable to PSF-based reconstruction despite the proposed method having no access to the original sinogram data.
A sliding mode observer for monitoring and fault estimation in a network of dynamical systems

In this paper, a novel fault estimation strategy is proposed for a network of dynamical systems at a supervisory monitoring level. The network nodes include linear and Lipschitz nonlinear dynamics and time‐varying coupling strength. The aim is to enhance the autonomy level of this class of systems by means of this inherently robust, nonlinear strategy based on sliding mode ideas. The faults are reconstructed from the equivalent output error injection signal which is used to maintain sliding. A key facet of the strategy is that the synthesis of the sliding mode observer for the network depends solely on the dynamics of an individual node of the network. The theoretical results developed in the paper are demonstrated with an example consisting of a network of pendulums. Copyright © 2013 John Wiley & Sons, Ltd.