Ambient Signals Research Articles

Enhanced RF Energy Harvesting System Utilizing Piezoelectric Transformer

RF energy harvesting converts ambient signals into electrical power, providing a sustainable energy source. This study demonstrates the use of a piezoelectric transformer for efficient RF energy harvesting. In this work, a piezoelectric transformer (PT) is employed as a high-gain, efficient inverting amplifier to enhance RF wireless energy harvesting. The PT, composed of lead zirconate titanate (PZT), is placed after the receiving loop antenna, with its output connected to an AC-to-DC converter circuit. Maximum harvested power was observed at the PT’s resonance frequency of 50 kHz, with an optimal load of 40 kΩ. The system, comprising the antenna, transformer, and rectifier circuit, continues to resonate at 50 kHz, as confirmed by input impedance measurements, demonstrating stable and effective performance. The overall system efficiency was characterized to be 88%.

Improving ambient noise crosscorrelations using a polarization-based azimuth filter

Seismic interferometry is widely used to image and monitor earth structures at different scales by extracting an empirical Green’s function (EGF) from the crosscorrelation of ambient noise under the assumption of diffuse wavefields or energy equipartitioning. However, EGFs may be incorrectly estimated and lead to a misunderstanding of subsurface structures because the distribution of ambient noise sources is neither isotropic nor stationary. To extract reliable EGFs from the nonideal ambient noise data, we develop a polarization-based azimuth filter (PAF) to attenuate the ambient noise energy of nonstationary sources to improve the quality of crosscorrelation functions. The PAF is a time-frequency domain azimuth filter for 3C seismic data, which uses time-frequency polarization analysis to measure the azimuths of ambient noise signals and then filters the 3C data to obtain the signals from desired directions. Based on the stationary-phase theory, we use the PAF to extract the ambient noise signals from stationary-phase zones, which improves the quality of the crosscorrelation function and eliminates the requirement for long observations. A synthetic test and two short-time engineering examples demonstrate that uneven source distributions might cause serious spurious signals in EGFs, and we demonstrate the improvement in EGFs using our method. In addition, the PAF may allow for the complete separation of the fundamental and higher modes of Rayleigh waves, which uncovers more information implicit in the ambient noise data.

Efficient multichannel energy harvesting with dedicated energy transmitters in CR-IoT networks

Radio Frequency (RF) energy harvesting is strongly believed to be a sustainable solution to the power depletion problem in battery powered IoT devices. In addition to harvesting energy from ambient RF signals, the use of dedicated energy transmitters (ETs) that transmit energy to nearby IoT devices via RF signals has recently been proposed. In this paper, we study the problem of designing an energy harvesting policy for a group of cognitive radio-enabled IoT (CR-IoT) devices served by a number of ETs to maximize the minimum of their charging rates. With the help of cognitive radios, a CR-IoT node is capable of changing its frequency channel of operation allowing for multi-channel energy harvesting. Frequency channels are assumed to be opportunistically accessible depending on the activity of wireless users that are licensed to use those channels. The problem entails the design of the ET’s transmission policy (to what CR-IoT device, and over what channel) and the design of an ambient harvesting policy for every CR-IoT device (when it is not served by ETs). The problem is formulated as a mixed integer linear program (MILP). The objective is to maximize a lower bound on the total harvested energy in a given time frame per CR-IoT node. This optimization is subject to scheduling, total energy budget, and maximum transmit power constraints. Given the intractability of MILP formulations, a sub-optimal algorithm is proposed. Extensive experimentation is carried out to assess the effectiveness of the proposed sub-optimal algorithm by comparing it to the MILP’s solution obtained using IBM CPLEX solver with a limit on the execution time. We also combine our sub-optimal algorithm withe the CPLEX solver to produce a new two-stages algorithm that improves the original one by around 47%. Finally, we investigate the effect of multiple parameters including number of ETs, number of channels, and channel availability probability on the minimum charging rate.

Ionization by XFEL radiation produces distinct structure in liquid water

In the warm dense matter (WDM) regime, where condensed, gas, and plasma phases coexist, matter frequently exhibits unusual properties that cannot be described by contemporary theory. Experiments reporting phenomena in WDM are therefore of interest to advance our physical understanding of this regime, which is found in dwarf stars, giant planets, and fusion ignition experiments. Using 7.1 keV X-ray free electron laser radiation (nominally 5×105 J/cm2), we produced and probed transient WDM in liquid water. Wide-angle X-ray scattering (WAXS) from the probe reveals a new ~9 Å structure that forms within 75 fs. By 100 fs, the WAXS peak corresponding to this new structure is of comparable magnitude to the ambient water peak, which is attenuated. Simulations suggest that the experiment probes a superposition of two regimes. In the first, fluences expected at the focus severely ionize the water, which becomes effectively transparent to the probe. In the second, out-of-focus pump radiation produces O1+ and O2+ ions, which rearrange due to Coulombic repulsion over 10 s of fs. Our simulations account for a decrease in ambient water signal and an increase in low-angle X-ray scattering but not the experimentally observed 9 Å feature, presenting a new challenge for theory.

Multi-source wavefield reconstruction of distributed acoustic sensing data using compressive sensing and seismic interferometry.

Seismic data recorded by distributed acoustic sensing (DAS) interrogator units on deployed optical fiber are being used for a variety of subsurface imaging and monitoring investigations. To reduce the costs of active-source DAS surveying applications, seismic interferometry can be applied to estimate inter-sensor wavefields from DAS records. However, recording long-term records for ambient interferometry requires considerable data storage and sections of DAS optical fibers may be unusable because of broadside sensitivity considerations from the DAS fiber orientation and due to localized coherent energy sources with amplitudes significantly larger than the ambient signal of interest. Compressive sensing, a wavefield reconstruction technique, can mitigate the problems of large data storage and unusable data. We apply compressive sensing-based multi-source wavefield reconstruction to estimate correlograms of ambient DAS records from a fiber array in Perth, Australia. The multi-source method uses all available virtual-source gathers for simultaneous wavefield reconstruction and is different from the conventional single-source method that separately reconstructs individual virtual-source gathers. Using the Fourier and curvelet transforms to sparsify interferometric wavefields, we show that multi-source reconstruction is applicable to the DAS data and that the Fourier multi-source reconstruction can improve the recovered wavefields by approximately 5-10 dB, compared to the Fourier and curvelet single-source wavefield reconstructions.

Ambient Signals based Load Modeling with Two Three-phase Induction Motors

Abstract The traditional electric power system simulation and calculation are based on the simple load model including one motor and one static load. On the one hand, the power system load is getting more and more complex. On the other hand, it’s proved that two kinds of three-phase induction motors will show a little difference. As a result, in this paper, a new model load with two three-phase induction motors is proposed. Besides, the dynamic of the power system load is taken into account. The torque of those two three-phase motors will show different characteristics, that one is constant and the other is variable. Formerly, the dynamic of the load is regarded as the result of an induction motor. However, the single-phase motor and electronic load both lead to the complexity of the load. To get a more accurate calculation, we need a more proper load model. One of the ways to improve the load model is to pay more attention to the ingredients ignored. The single-phase motor and power electronic load make a great difference. Besides, it’s proved that the composited load model with two three-phase motors will be better in the calculation.

Design and implementation of RF power levels measurement system from indoor to outdoor in Isparta province

This paper presents a novel, low-cost, and portable RF power measurement system, operating in GSM 900 and GSM 1800 frequency bands. This measurement model consists of four basic circuits; microstrip folded dipole antenna (MFDA) that received the ambient RF signals, a power meter that generates DC output voltage according to received RF signal power level, a microprocessor that converts the data's analog to digital, and a computer to record and screen the time-varying data. Seventeen measurements, each for six hours, were performed in nine locations in Isparta for the indoor-to-outdoor scenario. The range between the minimum and maximum power values of the average RF power levels obtained in time-dependent measurements was calculated as approximately 15 dB, 18 dB, and 33 dB for indoor, semi-indoor, and outdoor ambients, respectively. The indoor/semi-indoor/outdoor ambient RF power levels were respectively varied between −55.1 dBm to −4.13 dBm, −43.92 dBm to −6.12 dBm, −44.89 dBm to 0.1 dBm in all measurements.

The SPHERE view of the Taurus star-forming region

The sample of planet-forming disks observed by high-contrast imaging campaigns over the last decade is mature enough to enable the demographical analysis of individual star-forming regions. We present the full census of Taurus sources with VLT/SPHERE polarimetric images available. The whole sample sums up to 43 targets (of which 31 have not been previously published) corresponding to one-fifth of the Class II population in Taurus and about half of such objects that are observable. A large fraction of the sample is apparently made up of isolated faint disks (equally divided between small and large self-shadowed disks). Ambient signal is visible in about one-third of the sample. This probes the interaction with the environment and with companions or the outflow activity of the system. The central portion of the Taurus region almost exclusively hosts faint disks, while the periphery also hosts bright disks interacting with their surroundings. The few bright disks are found around apparently older stars. The overall picture is that the Taurus region is in an early evolutionary stage of planet formation. Yet, some objects are discussed individually, as in an intermediate or exceptional stage of the disk evolution. This census provides a first benchmark for the comparison of the disk populations in different star forming regions.

An Improved Identification Method of Pipeline Leak Using Acoustic Emission Signal

Pipelines constitute a vital component in offshore oil and gas operations, subjected to prolonged exposure to a range of alternating loads. Safeguarding their integrity, particularly through meticulous leak detection, is essential for ensuring safe and reliable operation. Acoustic emission detection emerges as an effective approach for monitoring pipeline leaks, demanding subsequent rigorous data analysis. Traditional analysis techniques like wavelet analysis, empirical mode decomposition (EMD), variational mode decomposition (VMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) often yield results with considerable randomness, adversely affecting leak detection accuracy. This study introduces an enhanced damage recognition methodology, integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and probabilistic neural networks (PNN) for more accurate pipeline leak identification. This novel approach combines laboratory-acquired acoustic emission signals from leaks with ambient noise signals. Application of ICEEMDAN to these composite signals isolates eight intrinsic mode functions (IMFs), with subsequent time–frequency analysis providing insight into their frequency structures and feature vectors. These vectors are then employed to train a PNN, culminating in a robust neural network model tailored for leak detection. Conduct experimental research on pipeline leakage identification, focusing on the local structure of offshore platforms, experimental research validates the superiority of the ICEEMDAN–PNN model over existing methods like EMD, VMD, and CEEMDAN paired with PNN, particularly in terms of stability, anti-interference capabilities, and detection precision. Notably, even amidst integrated noise, the ICEEMDAN–PNN model maintains a remarkable 98% accuracy rate in identifying pipeline leaks.

In‐panel ambient light eliminating differential circuit applied to active pixel fingerprint sensor

AbstractIn this paper, we present a novel approach to counter the influence of ambient light on photodetectors used in applications like biometric recognition and environmental sensing. The proposed solution introduces a circuit‐based technique that utilizes signal differencing to subtract ambient light signals before they reach the integrated circuit (IC). The process involves row and column differential signals, akin to analog circuit differential amplifiers. Simulations validate the circuit's functionality, showing its effectiveness in reducing ambient light impact. However, image reconstruction after differencing introduces blurriness due to the accumulation of noise. An alternative bidirectional fusion method is suggested, resulting in a clearer representation of features without noise accumulation. This innovative approach promises to enhance photodetector performance in challenging lighting conditions for various applications.

Exploiting multi-stiffness combination inspired absorbers for simultaneous energy harvesting and vibration mitigation

In scenarios where wide-amplitude shock excitation is encountered, a routine bistable configuration struggles to achieve satisfactory energy transfer, and a sensible combination of multiple nonlinear oscillators with different stiffness mechanisms can be exploited to achieve optimal vibration mitigation performance. In this paper, a nonlinear energy conversion and multi-stiffness combination inspired dynamic vibration absorber (NECMC-DVA) is proposed with a magnetic-spring tunable nonlinear stiffness element as the substructure. Parametric studies are performed for different modes of magnet-spring tunable nonlinear stiffness unit arrays considering transient impulses and harmonic excitation. The NECMC-DVA device, which comprises bistable oscillators, cubic stiffness oscillators, and hybrid stiffness oscillators, is also experimentally demonstrated for its synergistic performance in vibration mitigation and energy harvesting. The performance of the NECMC-DVA is measured under transient impulses and harmonic excitations. It has been found that the NECMC-DVA, which consists of a bistable oscillator, a cubic stiffness oscillator, a hybrid stiffness oscillator, energy-capture elements, and motor sequences, achieves high-efficiency nonlinear energy transfer and significant shock robustness in wide-amplitude impulsive scenarios. The NECMC-DVA with hybrid energy sinks demonstrates more remarkable targeted energy transfer and energy conversion performance than its pure bistable oscillator counterpart. Furthermore, the NECMC-DVA prototype can achieve adaptive vibration control modulation and energy conversion management through motion control boards by monitoring ambient excitation signals. Therefore, the NECMC-DVA enables integrated active and passive mitigation and energy harvesting to simultaneously accommodate multi-source indeterminate vibration and shock environments.

Intelligent interference cancellation and ambient backscatter signal extraction for wireless-powered UAV IoT network

Unmanned aerial vehicles (UAVs) offer a new approach to wireless communication, leveraging their high mobility, flexibility, and visual communication capabilities. Ambient backscatter communication enables Internet of Things devices to transmit data by reflecting and modulating ambient radio waves, eliminating the need for additional wireless channels, and reducing energy consumption and cost for sensors. However, passive ambient backscatter communication has limitations such as limited range and poor communication quality. By utilizing UAVs as communication nodes, these limitations can be overcome, expanding the communication range and improving the quality of communication. Although some research has been conducted on combining UAVs and ambient backscatter, several challenges remain. One key challenge is the strong direct link interference in ambient backscatter under UAV conditions, which significantly affects communication quality. In this paper, we propose an intelligent backward and forward straight link interference cancellation algorithm based on NOMA decoding technique to enhance ambient backscatter communication quality under UAV conditions and extract more ambient energy for UAV energy supply. The paper includes theoretical derivation, algorithm description, and simulation analysis to validate the error bit rate of labeled information bits. The results demonstrate that the forward algorithm reduces the error bit rate by approximately 20% under low signal-to-noise ratio (SNR) conditions, while the backward algorithm reduces the error bit rate under high SNR conditions. The combination of the forward and backward algorithms reduces the error bit rate under both high and low SNR conditions. The proposed method contributes to improving the quality of ambient backscatter communication in UAV settings.

Contactless Respiration Monitoring Using Wi-Fi and Artificial Neural Network Detection Method.

Detecting respiration in a non-intrusive manner is beneficial not only for convenience but also for cases where the traditional ways cannot be applied. This paper presents a novel simple low-cost system where ambient Wi-Fi signals are acquired by a third-party tool (Nexmon) installed in a Raspberry Pi and is able to detect the respiration time domain waveform of a person. This tool was selected as it uses 80 MHz bandwidth of the Wi-Fi signal and supports the latest implementations that are widely used, such as 802.11ac. A neural network is developed to detect the respiration frequency of the waveform. Generated waves emulating respiration waveforms were used for training, validating, and testing the model. The model can be applied to unseen real measurement data and successfully determine the breathing frequency with a very low average error of 4.7% tested in 20 measurement datasets.

The use of in situ infrasound calibration to correct wave parameter estimation

As part of the Comprehensive Nuclear-Test-Ban Treaty (CTBT), the International Monitoring System (IMS), which includes infrasound stations, has been established to monitor nuclear testing, as well as many other infrasound sources of interest to the scientific community. To minimize the effects of wind-noise, wind-noise reduction systems (WNRS) are installed as part of each sensor. In order to provide the best wave parameter (azimuth, trace velocity, amplitude) estimates, the response of the WNRS must be properly taken into account. Therefore, it is important to perform an in-situ calibration of the sensor using a co-located reference sensors and ambient signals to determine this response. This in-situ calibration can be used to monitor the status of the sensors, and provide feedback to the station operators. Experiments were performed at the IS26 site in Germany using a temporary WNRS to provide quantitative measurements of the effects of the WNRS and its calibration on the wave parameter estimation. These calibration results were used to provide corrections to the raw signal data for retrieval of the corrected wave parameters and were compared to the IS26 measurements, demonstrating that accurate measurements can be retrieved using the in-situ calibration.

The use of passive seismic interferometry for the monitoring of subsurface fluids – from shallow groundwater to native or storage gas reservoirs

Passive (ambient noise) seismic interferometry provides multiple ways to gather information about the subsurface seismic properties using recordings of the seismic ambient noise signal. While the first developments and applications of this method showed a useful capacity to either image geological contrasts or monitor the structural properties of the soil, an increasing momentum is observed toward applications related to fluid monitoring of different types (liquid, gas), at all the scales of the subsurface (from meters to kilometers). In this paper we summarize the existing possibilities and technics of seismic interferometry analysis for subsurface fluid detection and characterization and elaborate on their respective deployment in different contexts. We also present a new approach based on estimating and continuously measuring seismic attenuation proxy within interferometric-based surface wavefields, which show a high sensitivity to fluid dynamics and the associated petrophysical variations. The method is illustrated through a field case study related to geological gas storage monitoring, and we elaborate on its potential respective deployment at the industrial scale and for different applications.

Giant Stark effect assisted radio frequency energy harvesting using atomically thin earth-abundant iron sulphide (FeS2)

2D FeS2 has the potential to convert ambient radiofrequency electromagnetic radiation signals into usable energy, which can be utilized to power portable and wearable electronic devices.

Dual-Polarization Ambient Backscatter Communications and Signal Detection.

Ambient backscatter communication (AmBC), an emerging mechanism for batteryless communications that can utilize ambient radio-frequency signals to modulate information and thus reduce power consumption, has attracted considerable attention and has been considered as a critical technology in green "Internet of Things" sensor networks due to its ultra-low power consumption. This paper presents the first a complete dual-polarization AmBC (DPAm) system model, which can extend AmBC into polarization diversity and improve the data-transmission rate of backscatter symbols. We proposed two scenarios: direct dual-polarization-based DPAm node structures and polarization-conversion-based DPAm node structures. In addition, we consider a parallel backscatter mode with differential coding and develop corresponding detectors, which also give the analytical detection thresholds. Moreover, we consider a simultaneous backscatter mode with Manchester coding in order to avoid complex-parameter estimation. To address the power imbalance problem of the DPAm system that arises because the polarization deflection angle would cause the power level to change with different polarization patterns, we also develop a power-average detector and a clustering detector. Simulation results show the throughput performance on each DPAm node structure with each detector, demonstrating the feasibility and efficiency of the proposed DPAm nodes and detectors. Compared with single-polarization AmBC (SPAm), the proposed DPAm node can achieve higher throughput in most cases. Finally, the clustering detector is shown to be more robust to short training sequences and complex environments.

Multi-BD Symbiotic Radio-Aided 6G IoT Network: Energy Consumption Optimization With QoS Constraint Approach

The commensal symbiotic radio (CSR) system is proposed as a novel solution for connecting systems through green communication networks. This system enables us to establish secure, ubiquitous, and unlimited connectivity, which is a goal of 6G. The base station uses MIMO antennas to transmit its signal. Passive IoT devices, called symbiotic backscatter devices (SBDs), receive the signal and use it to charge their power supply. When the SBDs have data to transmit, they modulate the information onto the received ambient RF signal and send it to the symbiotic user equipment, which is a typical active device. The main purpose is to enhance energy efficiency in this network by minimizing energy consumption (EC) while ensuring the minimum required throughput for SBDs. To achieve this, we propose a new scheduling scheme called Timing-SR that optimally allocates resources to SBDs. The main optimization problem involves non-convex objective functions and constraints. To solve this, we use mathematical techniques and introduce a new approach called sequential quadratic and conic quadratic representation to relax and discipline the problem, leading to reducing its complexity and convergence time. The simulation results demonstrate that the proposed approach outperforms other outlined schemes in reducing EC.

FastCAR: fast correction for ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets

Cell type-specific differential gene expression analyses based on single-cell transcriptome datasets are sensitive to the presence of cell-free mRNA in the droplets containing single cells. This so-called ambient RNA contamination may differ between samples obtained from patients and healthy controls. Current ambient RNA correction methods were not developed specifically for single-cell differential gene expression (sc-DGE) analyses and might therefore not sufficiently correct for ambient RNA-derived signals. Here, we show that ambient RNA levels are highly sample-specific. We found that without ambient RNA correction, sc-DGE analyses erroneously identify transcripts originating from ambient RNA as cell type-specific disease-associated genes. We therefore developed a computationally lean and intuitive correction method, Fast Correction for Ambient RNA (FastCAR), optimized for sc-DGE analysis of scRNA-Seq datasets generated by droplet-based methods including the 10XGenomics Chromium platform. FastCAR uses the profile of transcripts observed in libraries that likely represent empty droplets to determine the level of ambient RNA in each individual sample, and then corrects for these ambient RNA gene expression values. FastCAR can be applied as part of the data pre-processing and QC in sc-DGE workflows comparing scRNA-Seq data in a health versus disease experimental design. We compared FastCAR with two methods previously developed to remove ambient RNA, SoupX and CellBender. All three methods identified additional genes in sc-DGE analyses that were not identified in the absence of ambient RNA correction. However, we show that FastCAR performs better at correcting gene expression values attributed to ambient RNA, resulting in a lower frequency of false-positive observations. Moreover, the use of FastCAR in a sc-DGE workflow increases the cell-type specificity of sc-DGE analyses across disease conditions.

Dances with Blues: Harnessing Multi-Frequency Carriers for Commodity Bluetooth Backscatter

We present DanBlue, a commodity Bluetooth backscatter system that can take multi-frequency signals as excitations. Unlike all prior systems, DanBlue leverages ambient Bluetooth signals of various frequencies to backscatter in the standard Bluetooth-hopping way. To do so, we first introduce an edge proxy to identify uncontrolled ambient Bluetooth signals. Then, we design a wideband channel hopping to enable fast frequency shifts for low-power tags, empowering backscatter hopping much like active Bluetooth hopping. We prototype the DanBlue tag using off-the-shelf FPGAs and the DanBlue edge with commercial off-the-shelf (COTS) chips. Through comprehensive field studies, we show that DanBlue supports hopping from any-frequency Bluetooth excitations to any-frequency Bluetooth channels. Furthermore, the accuracy of frequency identification is as high as 99% with less than 7.1 ms latency. For the first time, we demonstrate DanBlue can emulate the Bluetooth protocol stack and seamlessly build connectivity with multiple active Bluetooth radios. We believe DanBlue is taking a crucial step forward on fully functioning battery-free Bluetooth.