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.

Evaluating climate change impacts on reef environments via multibeam echosounder and Acoustic Doppler Current profiler technology.

Crucial to the Earth's oceans, ocean currents dynamically react to various factors, including rotation, wind patterns, temperature fluctuations, alterations in salinity and the gravitational pull of the moon. Climate change impacts coastal ecosystems, emphasizing the need for understanding these currents. This study explores multibeam echosounder (MBES), specifically R2-Sonic 2020 instrument, offering detailed seabed information. Investigating coral reefs, rocky reefs and artificial reefs aimed to map seafloor currents movement and their climate change responses. MBES data viz. Bathymetry and backscatter were classified and acoustic doppler current profiler (ADCP) ground data were validated using random forest regression. Results indicated high precision in currents speed measurement i.e. coral reefs with 0.96, artificial reefs with 0.94 and rocky reefs with 0.97. Currents direction accuracy was notable in coral reefs with 0.85, slightly lower in rocky reefs with 0.72 and artificial reefs with 0.60. Random forest identified sediment and backscatter as key for speed prediction while direction relies on bathymetry, slope and aspect. The study emphasizes integrating sediment size, backscatter, bathymetry and ADCP data for seafloor current analysis. This multibeam data on sediments and currents support better marine spatial planning and determine biodiversity patterns planning in the reef area.

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%.

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.

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.

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.

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%.

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%.

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.

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.

High-resolution trace gas detection by sub-Doppler noise-immune cavity-enhanced optical heterodyne molecular spectrometry: application to detection of acetylene in human breath.

A sensitive high-resolution sub-Doppler detecting spectrometer, based on noise-immune cavity-enhanced optical heterodyne molecular spectrometry (NICE-OHMS), for trace gas detection of species whose transitions have severe spectral overlap with abundant concomitant species is presented. It is designed around a NICE-OHMS instrumentation utilizing balanced detection that provides shot-noise limited Doppler-broadened (Db) detection. By synchronous dithering the positions of the two cavity mirrors, the effect of residual etalons between the cavity and other surfaces in the system could be reduced. An Allan deviation of the absorption coefficient of 2.2 × 10-13 cm-1 at 60 s, which, for the targeted transition in C2H2, corresponds to a 3σ detection sensitivity of 130 ppt, is demonstrated. It is shown that despite significant spectral interference from CO2 at the targeted transition, which precludes Db detection of C2H2, acetylene could be detected in exhaled breath of healthy smokers.

K-band Doppler radar for contact-less overnight sleep marker assessment: a pilot validation study.

An estimated 45 million persons in Europe are annually subjected to sleep-wake disorders. State-of-the-art polysomnography provides sophisticated insights into sleep (patho)physiology. A drawback of the method, however, is the obtrusive setting dependent on a clinical-based sleep laboratory with high operational costs. A contact-less prototype was developed to monitor limb movements and vital signs during sleep. A dual channel K-band Doppler radar transceiver captured limb movements and periodic chest wall motion due to respiration and heart activity. A wavelet transform based multi-resolution analysis (MRA) approach isolated limb movements, respiration, and heart rate from the demodulated signal. A test bench setup characterized the prototype simulating near physiological chest wall motions caused by periodic respiration and heartbeats in humans. Single- and multi-tone test bench simulations showed extremely low relative percentage errors of the prototype for respiratory and heart rate within -2 and 1%. The performance of the prototype was validated in overnight comparative studies, involving two healthy volunteers, with polysomnography as the reference. The prototype has successfully classified limb movements, with a sensitivity and specificity of 88.9 and 76.8% respectively, and has achieved accurate respiratory and heart rate measurement performance with overall absolute errors of 1 breath per minute for respiration and 3 beats per minute for heart rate. This pilot study shows that K-band Doppler radar and wavelet transform MRA seem to be valid for overnight sleep marker assessment. The contact-less approach might offer a promising solution for home-based sleep monitoring and assessment.

Temporal Doppler Effect and Future Orientation: Adaptive Function and Moderating Conditions.

The objectives of this study were to examine whether the temporal Doppler effect exists in different time intervals and whether certain individual and environmental factors act as moderators of the effect. Using hierarchical linear modeling, we examined the existence of the temporal Doppler effect and the moderating effect of future orientation among 139 university students (Study 1), and then the moderating conditions of the temporal Doppler effect using two independent samples of 143 and 147 university students (Studies 2 and 3). Results indicated that the temporal Doppler effect existed in all of our studies, and that future orientation moderated the temporal Doppler effect. Further, time interval perception mediated the relationship between future orientation and the motivation to cope at long time intervals. Finally, positive affect was found to enhance the temporal Doppler effect, whereas control deprivation did not influence the effect. The temporal Doppler effect is moderated by the personality trait of future orientation and by the situational variable of experimentally manipulated positive affect. We have identified personality and environmental processes that could enhance the temporal Doppler effect, which could be valuable in cases where attention to a future task is necessary.

Current Techniques for Postoperative Monitoring of Microvascular Free Flaps.

Free tissue transfer (FTT) is used in patients with complicated reconstructive needs; it can provide stable wound coverage, improved aesthetic appearance, and restore functional deficits. Despite the high success rates of free flaps, vascular occlusion is a significant risk leading to flap failure. Many studies have demonstrated that the salvage rate for flaps is inversely related to the time between onset of a vascular problem and its surgical correction. As a result, ongoing postoperative monitoring of free flaps for adequate perfusion is imperative to allow timely and accurate diagnosis of vascular compromise. Close monitoring and prompt notification of the physician if vascular compromise occurs are typically undertaken by first-line nurses. We conducted an integrative literature to identify and evaluate commonly used techniques for monitoring vascular free flaps during the postoperative period. We searched PubMed and Science Direct electronic databases, using the key words: "free-flap" and "monitoring." This article discusses commonly monitoring modalities, along with their advantages and limitations. Whereas large academic institutions may have an experienced nursing staff specifically trained in effective methods for monitoring free flap patients, this situation may not exist in all hospitals where free flap surgeries are performed. We describe techniques that allow easy and timely detection of flap compromise by nursing staff while reducing interuser variability.

Contribution to the Understanding of Particle Motion Perception in Marine Invertebrates.

Marine invertebrates potentially represent a group of species whose ecology may be influenced by artificial noise. Exposure to anthropogenic sound sources could have a direct consequence on the functionality and sensitivity of their sensory organs, the statocysts, which are responsible for their equilibrium and movements in the water column. The availability of novel laser Doppler vibrometer techniques has recently opened the possibility of measuring whole body (distance, velocity, and acceleration) vibration as a direct stimulus eliciting statocyst response, offering the scientific community a new level of understanding of the marine invertebrate hearing mechanism.

Through Wall Radar Classification of Human Micro-Doppler Using Singular Value Decomposition Analysis.

The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated using a through-the-wall (TTW) Frequency Modulated Continuous Wave (FMCW) C-Band radar system named Soprano. The objective of this analysis was to classify whether an individual was carrying an item in both hands or not using micro-Doppler information from a FMCW sensor. The radar was deployed at a standoff distance, of approximately 0.5 m, outside a residential building and used to detect multiple people walking within a room. Through the application of digital filtering, it was shown that significant suppression of the primary wall reflection is possible, significantly enhancing the target signal to clutter ratio. Singular Value Decomposition (SVD) signal processing techniques were then applied to the micro-Doppler signatures from different individuals. Features from the SVD information have been used to classify whether the person was carrying an item or walking free handed. Excellent performance of the classifier was achieved in this challenging scenario with accuracies up to 94%, suggesting that future through wall radar sensors may have the ability to reliably recognize many different types of activities in TTW scenarios using these techniques.

A Pedestrian Detection Scheme Using a Coherent Phase Difference Method Based on 2D Range-Doppler FMCW Radar.

For an automotive pedestrian detection radar system, fast-ramp based 2D range-Doppler Frequency Modulated Continuous Wave (FMCW) radar is effective for distinguishing between moving targets and unwanted clutter. However, when a weak moving target such as a pedestrian exists together with strong clutter, the pedestrian may be masked by the side-lobe of the clutter even though they are notably separated in the Doppler dimension. To prevent this problem, one popular solution is the use of a windowing scheme with a weighting function. However, this method leads to a spread spectrum, so the pedestrian with weak signal power and slow Doppler may also be masked by the main-lobe of clutter. With a fast-ramp based FMCW radar, if the target is moving, the complex spectrum of the range- Fast Fourier Transform (FFT) is changed with a constant phase difference over ramps. In contrast, the clutter exhibits constant phase irrespective of the ramps. Based on this fact, in this paper we propose a pedestrian detection for highly cluttered environments using a coherent phase difference method. By detecting the coherent phase difference from the complex spectrum of the range-FFT, we first extract the range profile of the moving pedestrians. Then, through the Doppler FFT, we obtain the 2D range-Doppler map for only the pedestrian. To test the proposed detection scheme, we have developed a real-time data logging system with a 24 GHz FMCW transceiver. In laboratory tests, we verified that the signal processing results from the proposed method were much better than those expected from the conventional 2D FFT-based detection method.

Wideband Doppler frequency shift measurement and direction ambiguity resolution using optical frequency shift and optical heterodyning.

A photonic approach for both wideband Doppler frequency shift (DFS) measurement and direction ambiguity resolution is proposed and experimentally demonstrated. In the proposed approach, a light wave from a laser diode is split into two paths. In one path, the DFS information is converted into an optical sideband close to the optical carrier by using two cascaded electro-optic modulators, while in the other path, the optical carrier is up-shifted by a specific value (e.g., from several MHz to hundreds of MHz) using an optical-frequency shift module. Then the optical signals from the two paths are combined and detected by a low-speed photodetector (PD), generating a low-frequency electronic signal. Through a subtraction between the specific optical frequency shift and the measured frequency of the low-frequency signal, the value of DFS is estimated from the derived absolute value, and the direction ambiguity is resolved from the derived sign (i.e., + or -). In the proof-of-concept experiments, DFSs from -90 to 90 kHz are successfully estimated for microwave signals at 10, 15, and 20 GHz, where the estimation errors are lower than ±60 Hz. The estimation errors can be further reduced via the use of a more stable optical frequency shift module.