Effects of insect pursuit on the Doppler shift compensation in a hipposiderid bat.

Doppler shift compensation (DSC) is a unique feature observed in certain species of echolocating bats and is hypothesized to be an adaptation to detecting fluttering insects. However, current research on DSC has primarily focused on bats that are not engaged in foraging activities. In this study, we investigated the DSC performance of Pratt's roundleaf bat, Hipposideros pratti, which was trained to pursue insects in various motion states within a laboratory setting. Our study yielded three main results. First, H. pratti demonstrated highly precise DSC during insect pursuit, aligning with previous findings of other flutter-detecting foragers during orientation or landing tasks. Second, we found that the motion state of the insect prey had little effect on the DSC performance of H. pratti. Third, we observed variations in the DSC performance of H. pratti throughout the course of insect pursuit. The bats exhibited the highest DSC performance during the phase of maximum flight speed but decreased performance during the phase of insect capture. These findings of high precision overall and the time-dependent performance of DSC during insect pursuit support the hypothesis that DSC is an adaptation to detecting fluttering insects.

CSI-F: A Human Motion Recognition Method Based on Channel-State-Information Signal Feature Fusion.

The recognition of human activity is crucial as the Internet of Things (IoT) progresses toward future smart homes. Wi-Fi-based motion-recognition stands out due to its non-contact nature and widespread applicability. However, the channel state information (CSI) related to human movement in indoor environments changes with the direction of movement, which poses challenges for existing Wi-Fi movement-recognition methods. These challenges include limited directions of movement that can be detected, short detection distances, and inaccurate feature extraction, all of which significantly constrain the wide-scale application of Wi-Fi action-recognition. To address this issue, we propose a direction-independent CSI fusion and sharing model named CSI-F, one which combines Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). Specifically, we have introduced a series of signal-processing techniques that utilize antenna diversity to eliminate random phase shifts, thereby removing noise influences unrelated to motion information. Later, by amplifying the Doppler frequency shift effect through cyclic actions and generating a spectrogram, we further enhance the impact of actions on CSI. To demonstrate the effectiveness of this method, we conducted experiments on datasets collected in natural environments. We confirmed that the superposition of periodic actions on CSI can improve the accuracy of the process. CSI-F can achieve higher recognition accuracy compared with other methods and a monitoring coverage of up to 6 m.

Orientation-Independent Human Activity Recognition Using Complementary Radio Frequency Sensing.

RF sensing offers an unobtrusive, user-friendly, and privacy-preserving method for detecting accidental falls and recognizing human activities. Contemporary RF-based HAR systems generally employ a single monostatic radar to recognize human activities. However, a single monostatic radar cannot detect the motion of a target, e.g., a moving person, orthogonal to the boresight axis of the radar. Owing to this inherent physical limitation, a single monostatic radar fails to efficiently recognize orientation-independent human activities. In this work, we present a complementary RF sensing approach that overcomes the limitation of existing single monostatic radar-based HAR systems to robustly recognize orientation-independent human activities and falls. Our approach used a distributed mmWave MIMO radar system that was set up as two separate monostatic radars placed orthogonal to each other in an indoor environment. These two radars illuminated the moving person from two different aspect angles and consequently produced two time-variant micro-Doppler signatures. We first computed the mean Doppler shifts (MDSs) from the micro-Doppler signatures and then extracted statistical and time- and frequency-domain features. We adopted feature-level fusion techniques to fuse the extracted features and a support vector machine to classify orientation-independent human activities. To evaluate our approach, we used an orientation-independent human activity dataset, which was collected from six volunteers. The dataset consisted of more than 1350 activity trials of five different activities that were performed in different orientations. The proposed complementary RF sensing approach achieved an overall classification accuracy ranging from 98.31 to 98.54%. It overcame the inherent limitations of a conventional single monostatic radar-based HAR and outperformed it by 6%.

An extensive study on the neutron-gamma shielding and mass stopping power of (70-x) CRT-30K2O-xBaO glass system for 252Cf neutron source.

The present study focuses on the charged-uncharged particles shielding performance of the addition of a mixed type of cathode ray tube (CRT) in a glass system that is irradiated by the 252Cf neutron source via the MCNPX simulation and analytical calculations, as well as Phy-X: PSD and SRIM software. The CRT waste glass is inserted into the glass system with (70-x) CRT-30K2O-xBaO general formula for x = 0, 10, 20 mol% that produces CG1, CG2, and CG3 glass shielding materials. Using Watt Fission Distribution (WFD) and Doppler Effect (DE) the neutron-gamma photon spectra were extracted for shielded (in the presence of the glass materials) and unshielded (in air) cases. Some calculated attenuation parameters related to the neutron deduced that CG1 is the best neutron attenuator among the selected glass samples. Moreover, by increasing the density of the glass from CG1 to CG3, the ascending trend is observed for the linear attenuation coefficient (LAC, cm-1) of the studied glass, and the best shielding competence is monitored for CG3. Furthermore, two sharp peaks are found in Zeff graphs which may be due to K-edge absorption of Ba and Pb elements and by decreasing the Pb element from CG1 to CG3 the second peak gradually becomes smooth. In addition, Mass Stopping Power/ Projected Ranges of the proton (H1) and alpha particles (He+2) are also estimated by SRIM code and findings show that CG1 can better stop proton and alpha particles in comparison with the other chosen glass structures.

Time-Differenced Carrier Phase Technique for Precise Velocity Estimation on an Android Smartphone.

GNSS (Global Navigation Satellite System) receivers are not only able to accurately determine position, but also velocity, knowledge of which could be important in several applications. The most adopted technique for velocity estimation exploits the Doppler shift due to the relative motion between the signal source and the receiver. Alternatively, the TDCP (Time-Differenced Carrier Phase) technique, based on the differences between consecutive carrier-phase measurements, can be used. TDCP is theoretically able to achieve better performance compared with the Doppler-based approach, exploiting the high precision of a carrier-phase observable, and without suffering the ambiguity issue. The main objective of this study is to analyze TDCP performance on a smartphone GNSS chip. Smartphones GNSS receivers are usually characterized by noisy observables owing to the low quality of the antenna used; it is, therefore, interesting to compare the smartphone TDCP performance with that of the Doppler-based technique. To evaluate the benefits that TDCP can provide, especially in terms of the smartphone chip, these two approaches to velocity determination are compared using three different devices: a Novatel geodetic receiver, a u-blox multi-frequency receiver, and a Xiaomi Mi8 smartphone. The results demonstrate a performance degradation in the smartphone GNSS chip when TDCP is used, compared with the performance of higher-grade receivers. In fact, the Xiaomi Mi8 maximum errors are greater than those of the Novatel geodetic receiver, but they are still acceptable as they do not exceed 6 cm/s, making the TDCP technique a valid approach for advanced algorithms; indeed, TDCP velocity demonstrates a few mm/s accuracy with a smartphone. The application of a RAIM algorithm enables error reduction and the achievement of reliable information; the obtained solution reliability is about 89%.

Real-Time Non-Contact Millimeter Wave Radar-Based Vital Sign Detection.

In this paper, the extraction of the life activity spectrum based on the millimeter (mm) wave radar is designed to realize the detection of target objects and the threshold trigger module. The maximum likelihood estimation method is selected to complete the design of the average early warning probability trigger function. The threshold trigger module is designed for the echo signal of static objects in the echo signal. It will interfere with the extraction of Doppler frequency shift results. The moving target detection method is selected, and the filter is designed. The static clutter interference is filtered without affecting the phase difference between the detection sequences, and the highlight target signal is improved. The frequency and displacement of thoracic movement are used as the detection data. Through the Fourier transform calculation of the sequence, the spectrum value is extracted within the estimated range of the heartbeat and respiration spectrum, and the heartbeat and respiration signals are picked up. The proposed design uses Modelsim and Quartus for CO-simulation to complete the simulation verification of the function, extract the number of logical units occupied by computing resources, and verify the algorithm with the vital signs experiment. The heartbeat and respiration were detected using the sports bracelet; the relative errors of heartbeat detection were 0-6.3%, the respiration detection was 0-9.5%, and the relative errors of heartbeat detection were overwhelmingly less than 5%.

Frequency Error Compensation of Unsynchronized Bistatic CW- MIMO Radar for Multiple Human-Body Localization.

This article presents and experimentally evaluates a frequency error elimination technique suitable for unsynchronized bistatic Multiple-Input Multiple-Output (MIMO) radar for human-body detection. First, a mathematical expression of human-body localization using bistatic MIMO radar is presented. Then the direct path is used to eliminate the phase error created by the frequency difference between the transmitter and receiver. A new Doppler-shifted component of the MIMO channel without phase error is derived, and the locations of the multiple targets are calculated by the 2-dimensional MUltiple SIgnal Classification (MUSIC) method. Next, the results of simulations that examine frequency error versus power ratios are discussed to illustrate the effectiveness of the proposed method. An experiment is carried out in an indoor multipath-rich environment. To emulate the unsynchronized condition, the transmitter and receiver use independent Signal Generators (SGs). One to six targets are tested. The experiments demonstrate that our unsynchronized radar system can identify the locations of multiple targets with high accuracy.

Miniaturization of Laser Doppler Vibrometers-A Review.

Laser Doppler vibrometry (LDV) is a non-contact vibration measurement technique based on the Doppler effect of the reflected laser beam. Thanks to its feature of high resolution and flexibility, LDV has been used in many different fields today. The miniaturization of the LDV systems is one important development direction for the current LDV systems that can enable many new applications. In this paper, we will review the state-of-the-art method on LDV miniaturization. Systems based on three miniaturization techniques will be discussed: photonic integrated circuit (PIC), self-mixing, and micro-electrochemical systems (MEMS). We will explain the basics of these techniques and summarize the reported miniaturized LDV systems. The advantages and disadvantages of these techniques will also be compared and discussed.

Sensing spectrum sharing based massive MIMO radar for drone tracking and interception.

Radar sensors are becoming crucial for environmental perception in a world with the tremendous growth of unmanned aerial vehicles (UAVs) or drones. When public safety is a concern, the localization of drones are of great significance. However, a drone used for a wrong motive can cause a serious problem for the environment and public safety, given the fact that the dynamic movement of a drone's emission signal and location tracking is different from existing positioning. This study proposes a safety zone characterized by the presence of N radars sensors with a goal to track and destabilized rogue drones attending to penetrate safety zones (stadium and school). Specifically, a new joint estimation based on a Gaussian filter has been introduced for spectrum sharing and detection awareness. The profit of this novel sensing method can be clearly seen when the two joint hidden states are taken into consideration. Therefore, the drone's emission state is analyzed by estimating its movement jointly. Considering the drone's unknown states and actual positioning, an algorithm is developed based on dynamic states space model. Where Bernoulli filter model is designed to estimate recursively the unknown stages of the drone and its changing location based on time. Meanwhile a power control acted from the radar to the targeted drones so that rogue drones are optimally tracked and destabilized over time. Furthermore, an expanding mechanism has been generated to accurately track the drone and enhance detection. A thoughtful result of the experimentation shows clearly that, even when the drone is moving, spectral detection can be performed accurately by chasing its positions. Its demonstrates at 90% of credibility that the original signal has a direct effect on the propagated signal. Therefore, the magnitude of the Doppler shift increases with frequency. And the clue of its positioning can be used for cognitive radio optimization.

PCDE-Sync: A Time Synchronization Mechanism Based on Partial Clustering and the Doppler Effect for Underwater Acoustic Networks.

Time synchronization is the basis of coordination and cooperation in underwater acoustic networks. However, because of the propagation delay, node mobility, and Doppler shift, it is impossible to balance the accuracy and energy consumption simply in water. As a promising technology, partial clustering has high convergence and makes breakthroughs in time synchronization. This paper proposes PCDE-Sync, a novel synchronization mechanism with partial clustering and the Doppler effect. Firstly, a clustering method built on the artificial fish swarm algorithm is presented. It models the cluster construction according to fish's preying, swarming, and following behaviors. Secondly, we design a synchronization mechanism to conduct clock correction and compensation by the Doppler effect. Finally, we compare the performance of PCDE-Sync with the most advanced protocols, namely MU-Sync, MM-Sync, and DE-Sync, in terms of the cumulative error after synchronization, the mean square error under different clock skew and that under distinctive node mobility, and energy consumption. The experimental results show that PCDE-Sync makes a trade-off between accuracy and complexity, which does well in solving synchronization issues.

Doppler Measurement of Modulated Light for High Speed Vehicles.

Technical details associated with a novel relative motion sensor system are elaborated in the paper. By utilizing the Doppler effect, the optical sensor system estimates the relative motion rates between the sensor and the moving object equipped with modulating light sources and relatively inexpensive electrical components. A transimpedance amplifier (TIA) sensing circuit is employed to measure the Doppler shift exhibited by the amplitude modulated light sources on the moving platform. Implementation details associated with the amplitude modulation and photo-detection processes are discussed using representative hardware elements. A heterodyne mixing process with a reference signal is shown to improve the signal-to-noise ratios of the Doppler shift estimation processing pipeline. Benchtop prototype experiments are used to demonstrate the utility of the proposed technology for relative motion estimation applications.

Full Respiration Rate Monitoring Exploiting Doppler Information with Commodity Wi-Fi Devices.

Respiration rate is an essential indicator of vital signs, which can demonstrate the physiological condition of the human body and provide clues to some diseases. Commercial Wi-Fi devices can provide a non-invasive, cost-effective and long-term respiration rate-monitoring scheme for home scenarios. However, previous studies show that the breathing depth and location may affect the detectability of respiratory signals. In this study, we leverage the variation of the Doppler spectral energy extracted from the channel state information (CSI) collected by Wi-Fi devices to track the chest displacement induced by respiration. First, the random phase is eliminated by phase-fitting method to obtain the complex CSI containing the Doppler shift. Then, the multipath decomposition of CSI is carried out to obtain the channel impulse response, which eliminates the interference phase of the time delay and retains the Doppler shift. The dynamic path units are also separate from the multipath, which overcomes the indoor multipath effect. Finally, we conduct a time-frequency analysis to dynamic units to accumulate Doppler spectral energy. Based on these ideas, we design a complete respiration rate-monitoring system to obtain the respiration rate by using the consistency between the Doppler energy change period and the respiratory cycle. We evaluate our system through extensive experiments in several typical home environments filled with multipath. Experimental results show that the errors of the three scenarios are approximate, the maximum error is less than 0.7 bpm, and the average errors are approximately 0.15 bpm. This result indicates that our scheme can achieve high precision respiration monitoring and has good anti-multipath ability compared with existing methods.

Doppler imaging detects bacterial infection of living tissue.

Living 3D in vitro tissue cultures, grown from immortalized cell lines, act as living sentinels as pathogenic bacteria invade the tissue. The infection is reported through changes in the intracellular dynamics of the sentinel cells caused by the disruption of normal cellular function by the infecting bacteria. Here, the Doppler imaging of infected sentinels shows the dynamic characteristics of infections. Invasive Salmonella enterica serovar Enteritidis and Listeria monocytogenes penetrate through multicellular tumor spheroids, while non-invasive strains of Escherichia coli and Listeria innocua remain isolated outside the cells, generating different Doppler signatures. Phase distributions caused by intracellular transport display Lévy statistics, introducing a Lévy-alpha spectroscopy of bacterial invasion. Antibiotic treatment of infected spheroids, monitored through time-dependent Doppler shifts, can distinguish drug-resistant relative to non-resistant strains. This use of intracellular Doppler spectroscopy of living tissue sentinels opens a new class of microbial assay with potential importance for studying the emergence of antibiotic resistance.

Ultra-broadband local active noise control with remote acoustic sensing.

One enduring challenge for controlling high frequency sound in local active noise control (ANC) systems is to obtain the acoustic signal at the specific location to be controlled. In some applications such as in ANC headrest systems, it is not practical to install error microphones in a person's ears to provide the user a quiet or optimally acoustically controlled environment. Many virtual error sensing approaches have been proposed to estimate the acoustic signal remotely with the current state-of-the-art method using an array of four microphones and a head tracking system to yield sound reduction up to 1 kHz for a single sound source. In the work reported in this paper, a novel approach of incorporating remote acoustic sensing using a laser Doppler vibrometer into an ANC headrest system is investigated. In this "virtual ANC headphone" system, a lightweight retro-reflective membrane pick-up is mounted in each synthetic ear of a head and torso simulator to determine the sound in the ear in real-time with minimal invasiveness. The membrane design and the effects of its location on the system performance are explored, the noise spectra in the ears without and with ANC for a variety of relevant primary sound fields are reported, and the performance of the system during head movements is demonstrated. The test results show that at least 10 dB sound attenuation can be realised in the ears over an extended frequency range (from 500 Hz to 6 kHz) under a complex sound field and for several common types of synthesised environmental noise, even in the presence of head motion.

3D Gait Tracking by Acoustic Doppler Effects.

Rehabilitation and physical therapies can recover people suffering from neurological disorder. Due to limited medical personnels, there are not enough medical personnels help patients with their posture diagnosis. In this paper, we propose a 3D gait tracking method to help medical personnels monitor patients. Based on acoustic signals, our approach derives displacement by only one integration of velocity. When one walks, his feet move back and forth, causing relative movements to our acoustic sensors, which we call self-Doppler effect. We utilize three buzzers and one microphone mounted on feet to collect the frequency shifts caused by relative movements and measure 3D trajectories. We validate through simulations that this approach would perform very well. In real experiments, due to the existence of noise and the limitation of hardware, we observe an average error of 0.1669 m in step length estimation and 0.0867 m in step height estimation.

Time-Frequency Spectral Signature of Limb Movements and Height Estimation Using Micro-Doppler Millimeter-Wave Radar.

We present a technique for the identification of human and animal movement and height using a low power millimeter-wave radar. The detection was based on the transmission of a continuous wave and heterodyning the received signal reflected from the target to obtain micro-Doppler shifts associated with the target structure and motion. The algorithm enabled the extraction of target signatures from typical gestures and differentiated between humans, animals, and other 'still' objects. Analytical expressions were derived using a pendulum model to characterize the micro-Doppler frequency shifts due to the periodic motion of the human and animal limbs. The algorithm was demonstrated using millimeter-wave radar operating in the W-band. We employed a time-frequency distribution to analyze the detected signal and classify the type of targets.

Índice de pulsatilidad de la arteria uterina entre las 11 y 14 semanas de gestación, como predictor de preeclampsia / Pulsatility index of the uterine artery between 11 and 14 weeks of gestation, as a predicator of preeclampsia

Introducción: El índice de pulsatilidad de la arteria uterina puede usarse para estimar el riesgo de preeclampsia. En el segundo y tercer trimestre del embarazo. Objetivo: Generar conocimiento sobre el índice de pulsatilidad de las arterias uterinas en la predicción de la preeclampsia en gestantes entre 11 y 14 semanas. Materiales y métodos: Es un estudio descriptivo de búsqueda bibliografía y se ha realizado en Pubmed, Medline, Scielo, bibliotecas de universidades nacionales e internacionales. Resultados: La media del índice de pulsatilidad en las gestantes con preeclampsia encontrado fue variada que va de 1.92 a 2.41, teniendo como puntos de corte de IP > 1.71 (p<0,05), en el doppler color de la arteria uterina a las 11-14 semanas de gestación. La asociación de pre-eclampsia según el índice de pulsatilidad de la arteria uterina entre las 11 y 14 semanas, es un buen método para el cribado de mujeres en riesgo de desarrollar preeclampsia, ya que presenta una sensibilidad, especificidad, VPP y VPN variada, pero suficiente para aceptarlo como método predictor de pre-eclampsia. Conclusiones: Existe suficiente evidencia que concluye que el uso del índice de pulsatilidad por ecografía doppler de la arteria uterina es un método adecuado para la detección a las 11 a 14 semanas de gestación en mujeres para desarrollar pre-eclampsia. (AU)

Introduction: The pulsatility index of the uterine artery can be used to estimate the risk of preeclampsia. In the second and third trimesters of pregnancy. Objective: To generate knowledge about the pulsatility index of the uterine arteries in the prediction of preeclampsia in pregnant women between 11 and 14 weeks. Materials and methods: It is a descriptive study of literature search and has been carried out in Pubmed, Medline, Scielo, libraries of national and international universities. Results: The average pulsatility index in pregnant women with pre-eclampsia was varied ranging from 1.92 to 2.41, having as cut-off points of IP> 1.71 (p <0.05), in the color doppler of the uterine artery at 11- 14 weeks gestation. The association of pre-eclampsia according to the pulsatility index of the uterine artery between 11 and 14 weeks, is a good method for screening women at risk of developing preeclampsia, since it has a varied sensitivity, specificity, PPV and NPV, but enough to accept it as a predictive method of pre-eclampsia. Conclusions: There is sufficient evidence to conclude that the use of the pulsatility index by Doppler ultrasound of the uterine artery is a suitable method for detection at 11 to 14 weeks gestation in women to develop pre-eclampsia. (AU)

Blind Source Separation on Non-Contact Heartbeat Detection by Non-Negative Matrix Factorization Algorithms.

In non-contact heart rate (HR) monitoring via Doppler radar, the disturbance from respiration and/or body motion is treated as a key problem on the estimation of HR. This paper proposes a blind source separation (BSS) approach to mitigate the noise effect in the received radar signal, and incorporates the sparse spectrum reconstruction to achieve a high-resolution of heartbeat spectrum. The proposed BSS decomposes the spectrogram of mixture signal into original sources, including heartbeat, using non-negative matrix factorization (NMF) algorithms, through learning the complete basis spectra (BS) by a hierarchical clustering. In particular, to exploit the temporal sparsity of heartbeat component, two variants of NMF algorithms with sparseness constraints are applied as well, namely sparse NMF and weighted sparse NMF. Compared with usual BSS, our proposed BSS has three advantages: 1) clustering-induced unsupervised manner; 2) compact demixing architecture; and 3) merely requiring single-channel input data. In addition, the HR estimation method using our proposal delivers more satisfactory precision and robustness over other existing methods, which is demonstrated through the measurements of distinguishing people's activities, gaining both smallest absolute errors of HR estimation for sitting still and typewriting.

Highly Linear Phase-Canceling Self-Injection-Locked Ultrasonic Radar for Non-Contact Monitoring of Respiration and Heartbeat.

A novel phase-canceling demodulation scheme to improve the linearity of a self-injection-locked (SIL) ultrasonic radar is proposed with the goal of solving the null detection problem and accurately sensing large displacements of a moving target. A proportional-integral (PI) controller regulates the phase of the injection signal and cancels the Doppler phase shift by tuning a delay in the received echo signal, and this tunable delay serves as the radar output, which is linearly proportional to the displacement of the target. Without assuming weak injection, the frequency and phase equations for an SIL oscillator are derived, supporting the construction of a plant model and the design of a PI controller. Also, a new ultrasonic radar equation is presented for estimating the radar detection range. The SIL radar with phase regulation is operated in its anti-phase injection mode for better performance. The proposed design is implemented on an FPGA to make a 40 kHz continuous-wave ultrasonic radar. The maximum detectable peak-to-peak motion is up to 120 mm (approximately 14 wavelengths of displacement), with a total harmonic distortion as low as 2.3% for the detection of 1 Hz harmonic motion. The radar is used to detect the human chest movement for non-contact monitoring of the respiratory rate and heart rate. Due to the high linearity and sensitivity, the radar is capable of faithfully detecting the relatively large involuntary body movements and lung movements while still preserving the weak heartbeat rhythm buried in them, with the average error of measured heart rates less than 1 BPM.

Fast-moving bat ears create informative Doppler shifts.

Many animals have evolved adept sensory systems that enable dexterous mobility in complex environments. Echolocating bats hunting in dense vegetation represent an extreme case of this, where all necessary information about the environment must pass through a parsimonious channel of pulsed, 1D echo signals. We have investigated whether certain bats (rhinolophids and hipposiderids) actively create Doppler shifts with their pinnae to encode additional sensory information. Our results show that the bats' active pinna motions are a source of Doppler shifts that have all attributes required for a functional relevance: (i) the Doppler shifts produced were several times larger than the reported perception threshold; (ii) the motions of the fastest moving pinna portions were oriented to maximize the Doppler shifts for echoes returning from the emission direction, indicating a possible evolutionary optimization; (iii) pinna motions coincided with echo reception; (iv) Doppler-shifted signals from the fast-moving pinna portion entered the ear canal of a biomimetic pinna model; and (v) the time-frequency Doppler shift signatures were found to encode target direction in an orderly fashion. These results indicate that instead of avoiding or suppressing all self-produced Doppler shifts, rhinolophid and hipposiderid bats actively create Doppler shifts with their own pinnae. These bats could hence make use of a previously unknown nonlinear mechanism for the encoding of sensory information, based on Doppler signatures. Such a mechanism could be a source for the discovery of sensing principles not only in sensory physiology but also in the engineering of sensory systems.