Ambient Interference Research Articles

Thermo-hydro-mechanical response of cementitious tailings fill to low humidity and ambient temperature interference

Thermo-hydro-mechanical response of cementitious tailings fill to low humidity and ambient temperature interference

Toward a fast and non-darkroom solution for speckle correlation based scattering imaging

Speckle correlation-based scattering imaging is an ingenious field, as it allows for the efficient reconstruction of object images using computational techniques in a simple setup. However, this method typically necessitates high-contrast speckle images captured in a darkroom environment, restricting its applicability to specific scenarios. Here, we present a fast and non-darkroom imaging framework, namely FNDI, for reconstructing objects through scattering media under ambient light interference. Specifically, a speckle illumination model is established guided by the total variational Retinex (TV-Retinex) theory, and the speckle illumination component is adjusted to obtain an enhanced speckle with significantly improved contrast. Then, a modified Fienup algorithm with the iteration-driven shrinkwrap (IDS) strategy is employed to rapidly reconstruct the object image through tens of iterations. Extensive experiments are conducted under different lighting conditions to evaluate FNDI in comparison with existing non-darkroom methods and the classical speckle correlation method. The results demonstrate that FNDI is effective and efficient, making it highly attractive for practical scattering imaging applications.

CMOS Pulse Oximeter

Abstract: This paper presents the design and implementation of a non-invasive pulse oximeter based on Complementary MetalOxide-Semiconductor (CMOS) technology, aimed at enhancing the accuracy and accessibility of blood oxygen saturation measurements. Pulse oximetry is a critical tool in clinical and personal health monitoring, providing vital information about respiratory function. The integration of CMOS technology allows for miniaturization and energy efficiency, facilitating the development of compact, wearable devices capable of continuous monitoring. The proposed system utilizes advanced signal processing techniques, including lock-in detection and dual-wavelength illumination, to improve the reliability of photoplethysmographic (PPG) signal acquisition under various conditions. Experimental validation demonstrates the device's capability to deliver accurate SpO2 readings even in the presence of motion artifacts and ambient light interference. Furthermore, the calibration methods employed ensure that measurements are consistent and precise across different users. By leveraging the advantages of CMOS technology, this study not only highlights the potential for improved patient comfort and compliance but also opens new avenues for the application of pulse oximetry in diverse environments. The findings underscore the effectiveness of CMOS-based pulse oximeters in advancing non-invasive health monitoring solutions, ultimately contributing to enhanced patient care and outcomes.

A portable dual-mode colorimetric and fluorescence sensing platform for RGB detection in liquid solutions

The color of liquids is crucial in food safety, pharmaceuticals, cosmetics, and environmental monitoring. Traditional color measurement methods typically require expensive spectrophotometers, limiting their utility in rapid testing and cost-effective applications. This study developed a cost-effective, portable dual-mode colorimetric and fluorescence sensing platform with using the TCS230 sensor and STM32F103ZET6 microcontroller for real-time RGB color detection. Equipped with built-in white and ultraviolet light sources, the platform operates reliably across different environmental conditions, aided by its darkroom design and white balance calibration strategy to minimize ambient light interference. Tests with fluorescent and dye solutions show stability and reliability of the platform. The relationship between color saturation and concentration under different concentrations showed the excellent correlation coefficients above 0.99 for CuSO4 and KMnO4 solutions. The measurement deviations of 1.93% to 3.41%, render it an efficient tool for on-site and multiple-mode color testing.

Deep-Learning-Based Real-Time Passive Non-Line-of-Sight Imaging for Room-Scale Scenes.

Non-line-of-sight imaging is a technique for reconstructing scenes behind obstacles. We report a real-time passive non-line-of-sight (NLOS) imaging method for room-scale hidden scenes, which can be applied to smart home security monitoring sensing systems and indoor fast fuzzy navigation and positioning under the premise of protecting privacy. An unseen scene encoding enhancement network (USEEN) for hidden scene reconstruction is proposed, which is a convolutional neural network designed for NLOS imaging. The network is robust to ambient light interference conditions on diffuse reflective surfaces and maintains a fast reconstruction speed of 12.2 milliseconds per estimation. The consistency of the mean square error (MSE) is verified, and the peak signal-to-noise ratio (PSNR) values of 19.21 dB, 15.86 dB, and 13.62 dB are obtained for the training, validation, and test datasets, respectively. The average values of the structural similarity index (SSIM) are 0.83, 0.68, and 0.59, respectively, and are compared and discussed with the corresponding indicators of the other two models. The sensing system built using this method will show application potential in many fields that require accurate and real-time NLOS imaging, especially smart home security systems in room-scale scenes.

Calibration-free heterodyne phase-sensitive dispersion spectroscopy: quantitative gas sensing and recovery of absorption spectra.

Heterodyne phase-sensitive dispersion spectroscopy (HPSDS) is a quantitative non-intrusive gas sensing technique based on the determination of the refractive index of the target gas in the vicinity of an absorption transition. Since the phase instead of the intensity of the probing laser light is targeted, the technique boasts the advantage of being normalization-free. It is thus largely immune to laser intensity fluctuations due to either system instability or ambient interferences. Previous HPSDS-based sensors typically require calibration using standard mixtures to establish a look-up table between the measured phase signal and gas concentrations, which is both cumbersome and problematic when there are significant compositional variations between the calibration standards and the target gas. In this work, we present a robust and generic technique that addresses this issue with a successful realization of fully calibration-free measurements. Spectral-fitting to the entire dispersion spectra with free variables related to transition linecenter, broadening width, and integrated absorbance were used to eliminate the effects of unknown spectral broadening coefficients. What we believe to be a novel analytical model was proposed to unify both direct injection-current dithering-based HPSDS that includes simultaneous frequency/intensity modulation, and the external electro-optic modulator (EOM) modulation-based HPSDS with a non-ideal linear response of EOM. The proposed technique was first validated via numerical experiment to determine the gas concentration and the recovery of the absorption profiles. Actual experiments were subsequently performed for the measurement of CH4 near 1.65 µm, N2O near 4.46 µm, and NO near 5.26 µm, collectively demonstrating the capability of the technique for both near- and mid-infrared lasers with diverse modulation characteristics. Further demonstrations were performed to measure NH3 concentrations at elevated temperatures through the fitting of the multiple dispersion spectra near 9.06 µm. The robust iterative spectral-fitting strategy and the measurement accuracies confirm the robustness of the proposed calibration-free (CF) HPSDS technique for quantitative gas sensing.

Distribution-free prediction intervals with conformal prediction for acoustical estimation.

Acoustical parameter estimation is a routine task in many domains. The performance of existing estimation methods is affected by external uncertainty, yet the methods provide no measure of confidence in the estimates. Hence, it is crucial to quantify estimate uncertainty before real-world deployment. Conformal prediction (CP) generates statistically valid prediction intervals for any estimation model using calibration data; a limitation is that calibration data needed by CP must come from the same distribution as the test-time data. In this work, we propose to use CP to obtain statistically valid uncertainty intervals for acoustical parameter estimation using a data-driven model or an analytical model without training data. We consider direction-of-arrival estimation and localization of sources. The performance is validated on plane wave data with different sources of uncertainty, including ambient noise, interference, and sensor location uncertainty. The application of CP for data-driven and traditional propagation models is demonstrated. Results show that CP can be used for statistically valid uncertainty quantification with proper calibration data.

Pulse oximetry in pediatric care: Balancing advantages and limitations.

Pulse oximetry has become a cornerstone technology in healthcare, providing non-invasive monitoring of oxygen saturation levels and pulse rate. Despite its widespread use, the technology has inherent limitations and challenges that must be addressed to ensure accurate and reliable patient care. To comprehensively evaluate the advantages, limitations, and challenges of pulse oximetry in clinical practice, as well as to propose recommendations for optimizing its use. A systematic literature review was conducted to identify studies related to pulse oximetry and its applications in various clinical settings. Relevant articles were selected based on predefined inclusion and exclusion criteria, and data were synthesized to provide a comprehensive overview of the topic. Pulse oximetry offers numerous advantages, including non-invasiveness, real-time feedback, portability, and cost-effectiveness. However, several limitations and challenges were identified, including motion artifacts, poor peripheral perfusion, ambient light interference, and patient-specific factors such as skin pigmentation and hemoglobin variants. Recommendations for optimizing pulse oximetry use include technological advancements, education and training initiatives, quality assurance protocols, and interdisciplinary collaboration. Pulse oximetry is crucial in modern healthcare, offering invaluable insights into patients' oxygenation status. Despite its limitations, pulse oximetry remains an indispensable tool for monitoring patients in diverse clinical settings. By implementing the recommendations outlined in this review, healthcare providers can enhance the effectiveness, accessibility, and safety of pulse oximetry monitoring, ultimately improving patient outcomes and quality of care.

Nanostructure silver absorbance with polarized light spectroscopy (PLS) and mobile imaging

This study presents a novel interdisciplinary approach for characterizing the optical properties of nanostructured silver films, combining methodologies from polarized light spectroscopy (PLS) and mobile app-based spectroscopic image detection. Silver nanofilms were deposited using electron beam evaporation (EBE) and subsequently analyzed for their absorbance across the visible light spectrum through PLS spectroscopy. The innovative aspect of this study lies in the validation of PLS spectral data using a mobile application developed with MIT App Inventor, which analyzes the intensity of light beams captured in images taken by a smartphone. The findings indicate that the silver nanofilm exhibits peak absorbance at approximately 400 nm within the violet region, corroborated by the mobile app’s data, which shows the lowest light intensity. However, discrepancies were observed from 500 to 600 nm, likely due to ambient light interferences and inherent optical phenomena such as diffraction, reflection, and refraction. Despite these deviations, the app’s readings closely align with the PLS data, supporting the film’s selective absorption properties. This study demonstrates the potential of this multidisciplinary technique in nanomaterial research, highlighting the application of nanotechnology in material science and the transformative potential of integrating software engineering for in-depth material characterization. These findings open avenues for advancements in both academic and practical applications.

Bedside Magnetocardiography with a Scalar Sensor Array.

Decades of research have shown that magnetocardiography (MCG) has the potential to improve cardiac care decisions. However, sensor and system limitations have prevented its widespread adoption in clinical practice. We report an MCG system built around an array of scalar, optically pumped magnetometers (OPMs) that effectively rejects ambient magnetic interference without magnetic shielding. We successfully used this system, in conjunction with custom hardware and noise rejection algorithms, to record magneto-cardiograms and functional magnetic field maps from 30 volunteers in a regular downtown office environment. This demonstrates the technical feasibility of deploying our device architecture at the point-of-care, a key step in making MCG usable in real-world settings.

Research on dynamic positioning of navigation system using K nearest neighbor algorithm and bayesian fusion based on visible light communication

This study presents a novel Bayesian-K Nearest Neighbor (B-KNN) fusion algorithm aimed at mitigating dynamic positioning errors in navigation systems that utilize visible light communication. The proposed fusion methodology leverages a fingerprint database constructed from the received signal strength data acquired by a mobile receiver. Through the selection of the nearest navigation path points for clustering and the utilization of a Bayesian algorithm with Gaussian fitting, the posterior probability of each cluster is computed to facilitate the prediction of the location of navigation path points via fingerprint matching. The results indicate that 89.8% of simulations exhibit a dynamic positioning error below 1 m within a 20 m×5 m×4 m model, achieving a success rate of 89.8%. Comparative analysis reveals that the B-KNN fusion algorithm enhances dynamic positioning accuracy by 68.3% while reducing computational overhead by approximately 48.3% in contrast to conventional KNN algorithms. Experimental assessments demonstrate that in scenarios involving ambient light interference within a 2 m×1 m×1.5 m model, the majority (95%) of dynamic positioning errors exceed 9 cm, with a significant portion surpassing 10 cm. Conversely, under conditions devoid of ambient light interference, only 4% of errors exceed 9 cm, with 14% falling below 5 cm, indicative of a marked 39.81% improvement in accuracy.

A handheld rapid detector of soil total nitrogen based on phase-locked amplification technology

Soil total nitrogen (STN) serves as a critical indicator for agricultural productivity. Rapid STN detection is crucial for nutrient monitoring in farmland and for guiding precision fertilization. Traditional near-infrared spectroscopy-based STN detectors necessitate darkrooms or subterranean settings to negate ambient light interference, restricting their practical use. This study develops a handheld rapid STN detector employing phase-locked amplification technology to address this issue. The device comprises optical, circuit, and control units. The optical unit features a ring-shaped split fiber structure to mitigate soil surface irregularities’ effects on the signal, with a central InGaAs photodiode for signal reception. Eight wavelengths (970 nm, 1050 nm, 1085 nm, 1200 nm, 1300 nm, 1450 nm, 1550 nm, and 1600 nm) are selected for the light sources through an information theory optimized genetic algorithm. The circuit unit employs direct digital synthesis and operational amplifiers for light signal generation and modulation. Reflected soil light signals are demodulated using an InGaAs photodiode and a phase-locked amplifier, effectively filtering ambient light noise. The control unit integrates an STM32F103C8T6 microcontroller and a Raspberry Pi 4B. The STM32 microcontroller manages signal generation, channel selection, frequency control, A/D conversion, and display, while the Raspberry Pi handles data processing, uploading, and transmission. A BP neural network model optimized by genetic algorithms is embedded for real-time STN data acquisition. Cloud-based data storage and visualization are facilitated through the Alibaba Cloud IoT platform. Performance testing reveals the device’s stability and interference resistance, with a standard deviation of electrical signal under varying light conditions below 3 mV. Compared to commercial spectrometers, the correlation coefficient (r) for reflectance data at eight wavebands exceeds 0.9. Accuracy testing shows an R2 above 0.8 and an RMSE of 0.30 g/kg when comparing detector-obtained STN values with standard chemical methods. These results demonstrate the detector’s efficacy in ambient light isolation, stability, and accuracy in agricultural settings, offering technical support for variable fertilization in precision farming.

Improving the performance of speckle correlation imaging by using a speckle refinement method with self-calibrated homomorphic filtering

Scattering imaging based on speckle correlation is an attractive topic as it enables the rapid reconstruction of the object image through computational methods in a compact setup. However, this method typically relies on high-contrast speckle images obtained under narrowband light illumination in a darkroom environment, limiting its application scenarios. Here, we demonstrate a self-calibrated homomorphic filtering (SCHF) method that significantly enhances speckle contrast by modifying the distribution of components within the speckle image, thereby improving imaging performance. The experimental results reveal that our method leverages the physical mechanism of speckle correlation to obtain the optimal filtering solution under the guidance of a self-calibrated strategy. The integration of this method with current mainstream speckle correlation techniques showcases its scalability. Even in scenarios under ambient light interference or broadband light illumination, the SCHF is effective in improving imaging performance, demonstrating its practical application potential.

Design of Fluxgate Current Sensor Based on Magnetization Residence Times and Neural Networks.

This study introduces a novel fluxgate current sensor with a compact, ring-shaped configuration that exhibits improved performance through the integration of magnetization residence times and neural networks. The sensor distinguishes itself with a unique magnetization profile, denoted as M waves, which emerge from the interaction between the target signal and ambient magnetic interference, effectively enhancing interference suppression. These M waves highlight the non-linear coupling between the magnetic field and magnetization residence times. Detection of these residence times is accomplished using full-wave rectification circuits and a Schmitt trigger, with a digital output provided by timing sequence detection. A dual-layer feedforward neural network deciphers the target signal, exploiting this non-linear relationship. The sensor achieves a linearity error of 0.054% within a measurement range of 15 A. When juxtaposed with conventional sensors utilizing the residence-time difference strategy, our sensor reduces linearity error by more than 40-fold and extends the effective measurement range by 150%. Furthermore, it demonstrates a significant decrease in ambient magnetic interference.

Novel application of two- and four-lobed noise-canceling passive integrated transponder antennas for tracking fish in areas of high ambient electromagnetic interference.

Passive integrated transponder (PIT) technology is a leading tool for tracking fish in freshwater systems. PIT is highly applicable for assessing fish passage at anthropogenic infrastructure (e.g., dams and floodgates); however, there are often complications in operating PIT antennas near these structures due to the ambient electromagnetic interference of metal and power-supply equipment. We designed a PIT antenna that is resistant to the effects of ambient electromagnetic interference (AEMI). This design uses lobes with balanced polarity within the antenna to neutralize AEMI within the vicinity of the antenna. This novel PIT antenna provides a more effective and cost-efficient option for researchers tracking fish in environments with high AEMI.

Development and validation of a collaborative robotic platform based on monocular vision for oral surgery: an in vitro study.

Surgical robots effectively improve the accuracy and safety of surgical procedures. Current optical-navigated oral surgical robots are typically developed based on binocular vision positioning systems, which are susceptible to factors including obscured visibility, limited workplace, and ambient light interference. Hence, the purpose of this study was to develop a lightweight robotic platform based on monocular vision for oral surgery that enhances the precision and efficiency of surgical procedures. A monocular optical positioning system (MOPS) was applied to oral surgical robots, and a semi-autonomous robotic platform was developed utilizing monocular vision. A series of vitro experiments were designed to simulate dental implant procedures to evaluate the performance of optical positioning systems and assess the robotic system accuracy. The singular configuration detection and avoidance test, the collision detection and processing test, and the drilling test under slight movement were conducted to validate the safety of the robotic system. The position error and rotation error of MOPS were 0.0906 ± 0.0762mm and 0.0158 ± 0.0069degrees, respectively. The attitude angle of robotic arms calculated by the forward and inverse solutions was accurate. Additionally, the robot's surgical calibration point exhibited an average error of 0.42mm, with a maximum error of 0.57mm. Meanwhile, the robot system was capable of effectively avoiding singularities and demonstrating robust safety measures in the presence of minor patient movements and collisions during vitro experiment procedures. The results of this in vitro study demonstrate that the accuracy of MOPS meets clinical requirements, making it a promising alternative in the field of oral surgical robots. Further studies will be planned to make the monocular vision oral robot suitable for clinical application.

Continual Monitoring of Respiratory Disorders to Enhance Therapy via Real-Time Lung Sound Imaging in Telemedicine

This work presents a configurable Internet of Things architecture for acoustical sensing and analysis for frequent remote respiratory assessments. The proposed system creates a foundation for enabling real-time therapy and patient feedback adjustment in a telemedicine setting. By allowing continuous remote respiratory monitoring, the system has the potential to give clinicians access to assessments from which they could make decisions about modifying therapy in real-time and communicate changes directly to patients. The system comprises a wearable wireless microphone array interfaced with a programmable microcontroller with embedded signal conditioning. Experiments on the phantom model were conducted to demonstrate the feasibility of reconstructing acoustic lung images for detecting obstructions in the airway and provided controlled validation of noise resilience and imaging capabilities. An optimized denoising technique and design innovations provided 7 dB more SNR and 7% more imaging accuracy for the proposed system, benchmarked against digital stethoscopes. While further clinical studies are warranted, initial results suggest potential benefits over single-point digital stethoscopes for internet-enabled remote lung monitoring needing noise immunity and regional specificity. The flexible architecture aims to bridge critical technical gaps in frequent and connected respiratory function at home or in busy clinical settings challenged by ambient noise interference.

Time-difference reassigned transform with application to time difference of arrival for impulsive signal

Time difference of arrival (TDOA) is essential in localization, communication, and navigation. Under ambient noise interference, the impulsive signal is transmitted over a long distance and reaches the sensor with a low signal-to-noise ratio (SNR). Aiming to achieve a precise time delay estimation in scenarios with low SNR, this paper extends the time-reassigned extracting transform (TRET) theory to TDOA and proposes a time difference rearrangement extracting transform (TDRT) algorithm. The implementation process of the proposed algorithm comprises three steps. Initially, a rough two-dimensional time delay estimation is obtained by calculating the partial derivative of the short-time cross-power spectrum with respect to the frequency variable. Secondly, a rearrangement operation separates the signal's time-frequency (TF) points from the noise. Thirdly, a refined TDOA estimation is obtained by inverse Fourier transforming TF points extracted from the time-delay energy ridge. Simulation results show that the TDRT algorithm is effective in time delay estimation. Furthermore, experimental results prove that the performance of the TDRT algorithm outperforms comparable algorithms in low SNR environments.

Uncertainty quantification for acoustical problems

Acoustical parameter estimation is a routine task in many domains and is typically done using signal processing methods. The performance of existing estimation methods is affected due to external uncertainty and yet the methods provide no measure of confidence in the outputs. Hence it is crucial to quantify uncertainty in the estimates before real-world deployment. Conformal prediction is a simple method to obtain statistically valid prediction intervals from an estimation model. In this work, conformal prediction is used for obtaining statistically valid uncertainty intervals for various acoustical parameter estimation tasks. We consider the tasks of DOA estimation and localization of one or more sources in an acoustical environment. The performance is validated on plane wave data with different sources of uncertainty including ambient noise, interference, and sensor location uncertainty, using statistical metrics. Results demonstrate that conformal prediction is a suitable and easy-to-use technique to generate statistically valid uncertainty quantification for acoustical estimation tasks.

An approach to ensure joint illumination & communication performance of a forward error corrected indoor visible light communication (VLC) system in presence of ambient light interference

Abstract In recent time of looming radio frequency (RF) spectrum crisis, visible light communication using lighting infrastructure emerged as a potential alternative at an indoor environment. This paper addresses the setback associated with ambient light interference in an indoor Visible Light Communication (VLC) system to ensure joint communication and illumination performance inside an office room. A novel VLC architecture with suitable white light emitting diode (WLED) luminaire arrangement is presented to minimize the dispersion of signal to interference plus noise ratio (SINR) across the room. Luminaires are categorized in two groups viz. data transmitting illuminants and illuminants for lighting purpose. The first group is dedicated to transmit data as well as serves the purpose of illumination. The other set creates only ambient illumination to achieve quality lighting attributes. The proposed forward error corrected receiver configuration discards the ambient light noise originated by the illuminants that serve the ambient illumination. Tail biting convolutional encoder and viterbi decoder are used at the encoding section of the transmitter and decoding section of the receiver respectively to improve bit error rate. Results obtained through MATLAB simulation shows better average bit error rate (BER) in the order of 10−8 measured at uniformly distributed 25 grid points over the working plane. At the same time achieved average horizontal illuminance with good uniformity comply with ISO recommendation.