Multi-person breathing detection with switching antenna array based on WiFi signal

Background: WiFi sensing, an emerging sensing technology, has been widely used in vital sign monitoring. However, most respiration monitoring studies have focused on single-person tasks. Methods: In this paper, we propose a multi-person breathing sensing system based on WiFi signals. Specifically, we use radio frequency (RF) switch to extend the antennas to form switching antenna array. A reference channel is introduced in the receiver, which is connected to the transmitter by cable and attenuator. The phase offset introduced by asynchronous transceiver devices can be eliminated by using the ratio of the channel frequency response (CFR) between the antenna array and the reference channel. In order to realize multi-person breathing perception, we use beamforming technology to conduct two-dimensional scanning of the whole scene. After eliminating static clutter, we combine frequency domain and angle of arrival (AOA) domain analysis to construct the AOA and frequency (AOA-FREQ) spectrogram. Finally, the respiratory frequency and position of each target are obtained by clustering. Results: Experimental results show that the proposed system can not only estimate the direction and respiration rate of multi-person, but also monitor abnormal respiration in multi-person scenarios. Conclusion: The proposed low-cost, non-contact, rapid multi-person respiratory detection technology can meet the requirements of long-term home health monitoring

Multi-timescale measurements of brain responses in visual cortex during functional stimulation using time-resolved spectroscopy

Studies of neurovascular coupling (hemodynamic changes and neuronal activation) in the visual cortex using a timedomain single photon counting system have been undertaken. The system operates in near infrared (NIR) range of spectrum and allows functional brain monitoring to be done non-invasively. The detection system employs a photomultiplier and multi-channel scaler to detect and record emerging photons with sub-microsecond resolution (the effective collection time per curve point is ~ 200 ns). Localisation of the visual evoked potentials in the brain was done using knowledge obtained from electroencephalographic (EEG) studies and previous frequency-domain optical NIR spectroscopic systems. The well-known approach of visual stimulation of the human brain, which consists of an alternating black and white checkerboard pattern used previously for the EEG study of neural responses, is applied here. The checkerboard pattern is synchronized with the multi-channel scaler system and allows the analysis of time variation in back-scattered light, at different stimulation frequencies. Slow hemodynamic changes in the human brain due to Hb- HbO2 changes in the blood flow were observed, which is evidence of the system’s capability to monitor these changes. Monocular visual tests were undertaken and compared with those done with an EEG system. In some subjects a fast optical response on a time scale commensurate with the neural activity associated with the visual cortex was detected. Future work will concentrate on improved experimental protocols and apparatus to confirm the existence of this important physiological signal

Passive detection of moving aerial target based on multiple collaborative GPS satellites

Passive localization is an important part of intelligent surveillance in security and emergency applications. Nowadays, Global Navigation Satellite Systems (GNSSs) have been widely deployed. As a result, the satellite signal receiver may receive multiple GPS signals simultaneously, incurring echo signal detection failure. Therefore, in this paper, a passive method leveraging signals from multiple GPS satellites is proposed for moving aerial target detection. In passive detection, the first challenge is the interference caused by multiple GPS signals transmitted upon the same spectrum resources. To address this issue, successive interference cancellation (SIC) is utilized to separate and reconstruct multiple GPS signals on the reference channel. Moreover, on the monitoring channel, direct wave and multi-path interference are eliminated by extensive cancellation algorithm (ECA). After interference from multiple GPS signals is suppressed, the cycle cross ambiguity function (CCAF) of the signal on the monitoring channel is calculated and coordinate transformation method is adopted to map multiple groups of different time delay-Doppler spectrum into the distance−velocity spectrum. The detection statistics are calculated by the superposition of multiple groups of distance-velocity spectrum. Finally, the echo signal is detected based on a properly defined adaptive detection threshold. Simulation results demonstrate the effectiveness of our proposed method. They show that the detection probability of our proposed method can reach 99%, when the echo signal signal-to-noise ratio (SNR) is only −64 dB. Moreover, our proposed method can achieve 5 dB improvement over the detection method using a single GPS satellite

High-Speed and High-Resolution Photon Counting for Near-Range Lidar

The near-range lidars for hard target and atmosphere detection should follow to the quick motion/activity of the measurement target. The transmitting optical power will be lowered for safety in the human activity space. And the lidar should increase the pulse repetition frequency to get the enough signal-to-noise ratio. The high-speed, high-resolution and high-repetition photon counting is desired for the near-range lidar. It is not a single photon counting at a certain delay time, but a multi-channel scaler with a deep memory for a series of delay times, that is, ranging data acquisition for lidar application. In this chapter, the mini-lidar for near-range observation is discussed. The targets are dust, gas, and atmosphere (aerosols). The activity monitoring of the atmosphere within a few hundred meters is the purpose of this mini-lidar. To follow and visualize the rapid motion of the target, high-speed and high-resolution photon counters (multi-channel scalers) were developed. The observation range can be easily adjustable depending on the lidar setup. The system can visualize the rapid motion of target with the high-resolution of 0.15 m (BIN width of 1 ns) and the minimum summation time of 0.2 s

AUTOMATED ARTIFACT REMOVAL AND DETECTION OF MILD COGNITIVE IMPAIRMENT FROM SINGLE CHANNEL ELECTROENCEPHALOGRAPHY SIGNALS FOR REAL-TIME IMPLEMENTATIONS ON WEARABLES

Electroencephalogram (EEG) is a technique for recording asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. EEG signal is well studied to evaluate the cognitive state, detect brain diseases such as epilepsy, dementia, coma, autism spectral disorder (ASD), etc. In this dissertation, the EEG signal is studied for the early detection of the Mild Cognitive Impairment (MCI). MCI is the preliminary stage of Dementia that may ultimately lead to Alzheimers disease (AD) in the elderly people. Our goal is to develop a minimalistic MCI detection system that could be integrated to the wearable sensors. This contribution has three major aspects: 1) cleaning the EEG signal, 2) detecting MCI, and 3) predicting the severity of the MCI using the data obtained from a single-channel EEG electrode. Artifacts such as eye blink activities can corrupt the EEG signals. We investigate unsupervised and effective removal of ocular artifact (OA) from single-channel streaming raw EEG data. Wavelet transform (WT) decomposition technique was systematically evaluated for effectiveness of OA removal for a single-channel EEG system. Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), is studied with four WT basis functions: haar, coif3, sym3, and bior4.4. The performance of the artifact removal algorithm was evaluated by the correlation coefficients (CC), mutual information (MI), signal to artifact ratio (SAR), normalized mean square error (NMSE), and time-frequency analysis. It is demonstrated that WT can be an effective tool for unsupervised OA removal from single channel EEG data for real-time applications.For the MCI detection from the clean EEG data, we collected the scalp EEG data, while the subjects were stimulated with five auditory speech signals. We extracted 590 features from the Event-Related Potential (ERP) of the collected EEG signals, which included time and spectral domain characteristics of the response. The top 25 features, ranked by the random forest method, were used for classification models to identify subjects with MCI. Robustness of our model was tested using leave-one-out cross-validation while training the classifiers. Best results (leave-one-out cross-validation accuracy 87.9%, sensitivity 84.8%, specificity 95%, and F score 85%) were obtained using support vector machine (SVM) method with Radial Basis Kernel (RBF) (sigma = 10, cost = 102). Similar performances were also observed with logistic regression (LR), further validating the results. Our results suggest that single-channel EEG could provide a robust biomarker for early detection of MCI. We also developed a single channel Electro-encephalography (EEG) based MCI severity monitoring algorithm by generating the Montreal Cognitive Assessment (MoCA) scores from the features extracted from EEG. We performed multi-trial and single-trail analysis for the algorithm development of the MCI severity monitoring. We studied Multivariate Regression (MR), Ensemble Regression (ER), Support Vector Regression (SVR), and Ridge Regression (RR) for multi-trial and deep neural regression for the single-trial analysis. In the case of multi-trial, the best result was obtained from the ER. In our single-trial analysis, we constructed the time-frequency image from each trial and feed it to the convolutional deep neural network (CNN). Performance of the regression models was evaluated by the RMSE and the residual analysis. We obtained the best accuracy with the deep neural regression method

Simultaneous chromatic dispersion, polarization-mode-dispersion and OSNR monitoring at 40Gbit/s

A novel method for independent and simultaneous monitoring of chromatic dispersion ( CD), first-order PMD and OSNR in 40Gbit/s systems is proposed and demonstrated. This is performed using in-band tone monitoring of 5GHz, optically down-converted to a low intermediate-frequency (IF) of 10kHz. The measurement provides a large monitoring range with good accuracies for CD (4742 +/- 100ps/nm), differential group delay (DGD) (200 +/- 4ps) and OSNR (23 +/- 1dB), independently of the bit-rate. In addition, the use of electro-absorption modulators (EAM) for the simultaneous down-conversion of all channels and the use of low-speed detectors makes it cost effective for multi-channel operation. (C) 2008 Optical Society of Americ