This letter proposes two novel algorithms for the identification of quadrature amplitude modulation (QAM) signals. The cyclostationarity-based features used by these are robust with respect to timing, phase, and frequency offsets, phase noise. Based on theoretical analysis simulations, performance proposed compares favorably that alternative approaches.

The effect of directional antenna elements in uniform circular arrays (UCAs) for direction arrival (DOA) estimation is studied this paper. While the vast majority previous work assumes isotropic or omnidirectional dipoles, demonstrates that improved DOA accuracy and increased bandwidth achievable with appropriately-designed antennas. Cramer-Rao Lower Bound (CRLB) derived UCAs antennas compared to 4- 8-element using a theoretical radiation pattern. directivity minimizes CRLB identified...

Automatic detection of a falling person based on noncontact sensing is challenging problem with applications in smart homes for elderly care. In this article, we propose radar-based fall technique time-frequency analysis and convolutional neural networks. The performed by applying the short-time Fourier transform to each radar return signal. resulting spectrograms are converted into binary images, which fed network. network trained using labeled examples nonfall activities. Our method...

Automatic fall detection using radar aids in better assisted living and smarter health care. In this brief, a novel time series-based method for detecting incidents human daily activities is proposed. A series the slow-time obtained by summing all range bins corresponding to fast-time of ultra wideband return signals. This used as input proposed deep convolutional neural network automatic feature extraction. contrast other existing methods, relies on multi-level learning directly from...

Previous studies on the cyclostationarity aspect of orthogonal frequency division multiplexing (OFDM) and single carrier linearly digitally modulated (SCLD) signals assumed simplified signal channel models or considered only second-order cyclostationarity. This paper presents new results concerning these under more general conditions, including time dispersive channels, additive Gaussian noise, phase, frequency, timing offsets. Analytical closed-form expressions are derived for time-...

Continuous measurement of electrocardiogram (ECG) signal is required for detecting various cardiac abnormalities, such as arrhythmia. Wearable devices have become ubiquitous continuous monitoring devices. Due to power and memory restrictions in wearable devices, acquisition may need be done smaller segments using compressive sensing (CS) techniques. However acquisitions lead poor recovery compressed measurements. A Kronecker-based novel technique has been recently proposed improve the...

This paper provides several efficient approximations for the arctangent function using Lagrange interpolation and minimax optimization techniques. These are particularly useful when processing power, memory, power consumption important issues. In addition to comparing errors computational workload of these approximations, we also extend them all four quadrants.

In this paper, we present a novel feature-based neural network (NN) approach for estimation of blood pressure (BP) from wrist oscillometric measurements. Unlike previous methods that use the raw waveform envelope (OMWE) as input to NN, in propose features extracted envelope. The OMWE is mathematically modeled sum two Gaussian functions. optimum parameters model are found by minimizing least squares error between and using Levenberg-Marquardt algorithm used features. Two separate feed-forward...

Radar technology for at home health-care has many advantages such as safety, reliability, privacy-preserving, and contact-less sensing nature. Detecting falls using radar recently gained attention in smart health care. In this paper, CapsFall, a new method fall detection an ultra-wideband that leverages the recent deep learning advances is proposed. To end, time series derived from back-scattered matrix its time-frequency representation obtained used input to capsule network automatic...

Many quantum radars currently studied in the literature use a phenomenon called entanglement to address problem of distinguishing signal from noise, which is one most important problems faced by any radar. Until recently, entanglement-based at radio frequencies existed only theory; their practicality was very much doubt. The situation has changed with recent experimental implementation all necessary components two-mode squeezing (QTMS) radar, operates microwave and can be described as range...

The use of remote sensing technologies such as radar is gaining popularity a technique for contactless detection physiological signals and analysis human motion. This paper presents methodology classifying different events in collection phase modulated continuous wave returns. primary application interest to monitor inmates where the presence vital signs amidst different, interferences needs be identified.A comprehensive set features derived through time frequency domain analyses...

Quantum two-mode squeezing (QTMS) radars and noise detect targets by correlating the received signal with an internally stored recording. A covariance matrix can be calculated between two which, in theory, is a function of single correlation coefficient. This coefficient used to decide whether target present or absent. We estimate minimizing Frobenius norm sample theoretically expected form matrix. Using simulated data, we show that estimates follow Rice distribution whose parameters are...

Recent advancements in the Internet of Things (IoT) wearable devices such as inertial sensors have increased demand for precise human activity recognition (HAR) with minimal computational resources. The wavelet transform, which offers excellent time-frequency localization characteristics, is well suited HAR systems. Selecting a mother function analysis critical, optimal selection improves performance. time signals data different periodic patterns that can discriminate activities from each...

We apply the maximal overlap discrete wavelet transform (MODWT)-based spectral density estimation method to measure heart rate variability (HRV) from short-duration pulse wave signals produced by an automated oscillometric blood pressure (BP) monitor during routine measurements. To test accuracy of this HRV metric, we study linear correlations that it achieves with chronological age and BP in a healthy population 85 subjects. define as quality regression BP. Results are compared number...

Although estimation of average blood pressure is commonly done with oscillometric measurements, confidence intervals (CIs) for systolic (SBP) and diastolic (DBP) are not usually estimated. This paper adopts bootstrap methodologies to build CI from a small sample set which situation encountered in practice. Three methodologies, namely, nonparametric percentile bootstrap, standard bias-corrected accelerated investigated. A two-step methodology proposed based on pseudomeasurements using...

This paper introduces a novel approach to estimate the systolic and diastolic blood pressure ratios (SBPR DBPR) based on maximum amplitude algorithm (MAA) using Gaussian mixture regression (GMR). The relevant features, which clearly discriminate SBPR DBPR according targeted groups, are selected in feature vector. vector is then represented by model. subsequently obtained with help of GMR mapped back SBP DBP values that more accurate than those conventional MAA method.

Human Activity Recognition (HAR) systems using sensor data have widespread use in many real-life applications, making it an important emerging area of research. As inertial sensors are readily available handheld devices, HAR generally designed based on the obtained from them. In this paper, Logistic Model Trees (LMT) machine learning method for predicting human motion smartphone-based is considered. This study aims to demonstrate capabilities LMT obtaining higher prediction rates even with...

Wearable devices with embedded photoplethysmography (PPG) sensors enable continuous monitoring of cardiovascular activity, allowing for the detection problems, such as arrhythmias. However, quality wrist-based PPG is highly variable, and subject to artifacts from motion other interferences. The goal this paper evaluate signal obtained when used in an ambulatory setting.Ambulatory data were collected over a 24 h period 10 elderly, 16 non-elderly participants. Visual assessment gold standard...

Detecting and identifying drones is of great interest due to the proliferation highly manoeuverable with on-board sensors increasing sensing capabilities. In this paper, we investigate use radars for tackling problem. particular, focus on problem detecting rotary distinguishing between single-propeller multi-propeller using a micro-Doppler analysis. Two different were used, an ultra wideband (UWB) continuous wave (CW) C-band radar automotive frequency modulated (FMCW) W-band radar, collect...

Noise radars, as well certain types of quantum radar, can be understood in terms a correlation coefficient which characterizes their detection performance. Although most results the noise radar literature are stated signal-to-noise ratio (SNR), we show that it is possible to carry out performance prediction coefficient. To this end, derive range dependence under assumption all external additive white Gaussian noise. We then combine our result with previously-derived expression for receiver...

Feed-Forward Neural Network (FFNN) has recently been utilized to estimate blood pressure (BP) from the oscillometric measurements. However, there no study till now that consolidated role played by different neural network (NN) training algorithms in affecting BP estimates. This paper compares estimation errors due ten belonging three classes: steepest descent (with variable learning rate, with rate and momentum, resilient backpropagation), quasi-Newton (Broyden-Fletcher-Goldfarb-Shanno, one...

This paper investigates the second-order cyclostationarity of block transmitted-single carrier linearly digitally modulated (BT-SCLD) signals, and its applications to signal classification blind (non-data aided) parameter estimation. Analytical closed-form expressions are derived for cyclic autocorrelation function (CAF), spectrum (CS), complementary CAF (CCAF), CS (CCS), corresponding cycle frequencies (CFs). Furthermore, conditions avoiding aliasing in spectral frequency domains obtained....