Ambient Interference Research Articles

Design of performance-weighted blended mode filters for underwater experiments

Normal mode signals are valuable in ocean acoustic tomography and source localization, assuming that they can be separated using spatial or temporal filtering. Common spatial mode filters include the matched filter (MF) [Ferris, JASA, 1972] and the pseudo-inverse (PI) filter [Tindle et al., JASA, 1978]. Choosing a filter and its parameters, e.g., the PI filter rank, requires knowledge that is not readily available. For instance, it is unlikely that the levels of ambient noise or interference from other modes is known a priori. To address this problem, Chakrabarti and Wage [IEEE Oceans, 2021, 2022] developed a Performance-Weighted Blended mode filtering algorithm that adapts quickly to changing conditions and achieves better performance than a single fixed mode filter. The PWB filter blends a set of PI filters of varying ranks, including a rank-1 PI filter (MF). A PI filter can suppress interference from neighboring modes but is more vulnerable to noise than the MF, which has the maximum white noise gain. This talk explores practical aspects of the PWB implementation, including how to select the set of filters to blend given realistic underwater noise conditions. We will show experimental and simulated data to illustrate performance in different environments. [Work supported by ONR.]

Imaging through a Scattering Medium under Different Intensities of Ambient Light Interference

Many solutions for imaging through a scattering medium are sensitive to noise, which can lead to degradation or even to a failure of the image quality. This is especially the case in practical application scenarios, which are always filled with changing ambient light interference; as such, the traditional methods are difficult to practically apply. Therefore, in this paper, a spatial-frequency dual-domain learning neural network is designed for reconstructing the target of a speckle pattern under different intensities of ambient light interference. The network is mainly based on two modules. One module is designed from two perspectives, frequency domain denoising and the spatial-frequency spectrum of the speckle pattern. Another module is a dual-feature fusion attention module, which is used to improve the accuracy of the network. The experimental results demonstrate that the network is capable of reconstructing complex targets with high quality under varying intensities of interfering light. Furthermore, it is not constrained by the optical memory effect, exhibiting remarkable robustness and generalizability. The research based on this paper provides a feasible path for the practical application of scattering imaging methods.

Suppress ambient temperature interference strategy based on SnO2 gas semiconductor sensor using dynamic temperature modulation mode and principal component analysis algorithm

Semiconductor gas sensors have been widely used in environmental monitoring, toxic gas monitoring, respiratory marker monitoring and other fields due to their low price and high sensitivity etc. An easily overlooked parameter, ambient temperature, is a major factor affecting the accuracy of the sensor. In this paper, a new idea to suppress ambient temperature interference is proposed. Dynamic temperature modulation method is used to extend the dimension of output information, so that both ambient temperature and target gas concentration can be expressed simultaneously. The ambient temperature component is separated by coordinate transformation and component elimination. The data inverse transformation is carried out to achieve the purpose of suppressing the ambient temperature interference. Compared with the performance optimization of gas-sensing materials and additional temperature sensors, the first advantage of this method is to reduce the complexity and power consumption of sensor devices. In addition, the use of PCA algorithm also reduces the complexity of the recognition algorithm. At the same time, more intuitive data reduces the cost of parameter optimization.

Imaging through scattering media under strong ambient light interference via the lock-in process.

Scattered light imaging techniques leveraging memory effects have been extensively investigated, yet most approaches are limited to operating in predominantly dark environments. The introduction of additional optical noise disrupts the fine structure of the original speckle pattern, undermining spatial correlation and resulting in imaging failure. In this study, we present a high-performance imaging method that integrates a lock-in process to overcome this limitation. Our experimental results demonstrate that the proposed technique enables successful imaging of targets in low signal-to-background ratio (SBR) environments, even at SBR levels as low as -28.0 dB. Furthermore, the method allows for the directional separation of targets with distinct modulation frequencies. This innovative approach has the potential to significantly expand the applicability of scattering imaging techniques by eliminating the constraints of dark field environments, thereby enhancing the convenience of in vivo microscopy and daytime astronomical observations.

Effect of ambient interferences on the effectiveness of a wearable ventilation for reducing pollutant exposure

This paper explores a wearable ventilation for reducing pollutant exposure in industrial environments. The effectiveness of a HAS (Head-mounted air supply) method while the wearer was on the move and in ambient airflows were separately investigated. Dynamic meshing technique combined with numerical simulation were adopted in this study. Results showed that the effectiveness variation while wearer moving was divided into interference, recovery, and stabilization periods according to the dynamic characteristics. A novel indicator SE was proposed to evaluate the stability of the effectiveness for pollutant exposure reduction. When the wearer walking and turning at a typical speed (1 m/s and π rad/s, separately), the HAS must maintain a flow rate of at least 12 L/s to achieve an optimal stability of 1 and 0.9, which reached a time-weighted average effectiveness near 98% and 88% during the moving process, respectively. When the wearer was standing in an ambient air flow (0.5 m/s), the ambient airflow from the side was found to interfere more significantly with the personalized air than from the front and back. During the back and forth moving in the ambient airflow, the pollutant inhalation exposure of the wearer significantly increased, especially during the turnaround phase. This scenario might require a larger flow rate (above 20 L/s) to achieve an optimal stability. These findings offer valuable insights for the application of the HAS in practical industrial environments.

Study on Compensation of Blood Oxygen Sensor’s Ambient Light Based on GA-BP Neural Network

In order to reduce the interference of ambient light on near-infrared blood oxygen sensors (used to measure oxygen saturation of human tissue or oxygen saturation of circulating blood during Extracorporeal membrane oxygenation treatment) and improve the measurement accuracy of a blood oxygen sensor, a compensation method of ambient light interference is proposed. This method does not need an ambient light sensor or an additional circuit. The interference intensity of ambient light is calculated by changing the light intensity value emitted by the LED in the sensor and combining the light intensity value received by the sensor. The Genetic Algorithms-Back Propagation(GA-BP) neural network is modeled, the detection light intensity value and the ambient light interference evaluation values are taken as the input characteristics, the blood gas analysis true value is taken as the expected output, and the network model is used for prediction. The results show that the accuracy of this method is higher than the traditional fitting results, and the error between the network prediction value and the true value of blood gas analysis is basically within 5%.

Sensing mechanisms of CO and H2 with NiO material – DRIFTS investigations

The response of nickel oxide gas sensors towards CO and H2 and the underlying gas sensing mechanisms were investigated with special focus on the influence of ambient humidity interference. Surface reactions were tracked by using diffuse reflectance infrared Fourier transformation spectroscopy with simultaneous resistance measurements. The sensor response to both gases is barely influenced by the background humidity. Spectroscopic results reveal that the underlying processes at the surface are almost identical for CO and H2 reception and similar to the effect of the removal of oxygen. Accordingly, the detection of the analytes is based purely on the reduction and oxidation of the oxide material instead of the formation of analyte specific surface species.

Target-free recognition of cable vibration in complex backgrounds based on computer vision

The cable system is a critical load-bearing member and a vital factor of cable-stayed bridges; its health condition indicates the operation safety of these structures. The cable force estimation based on vibration is a major indicator for assessing the safety of cable-bearing bridges. Contact sensors are used to collect vibration in traditional cable systems. Recently, computer vision-based non-contact remote monitoring has gained popularity due to its cost, efficiency, and safety advantages. However, the cable is a unique structure with poor vision imaging, a low pixel share, and susceptibility to ambient interference. To address these issues, this study proposes a deep learning-based cable vibration recognition system. In complex environments, this system provides reliable recognition of cable vibration without the necessity of markers. The system consists of a composite model based on Resnet-34 (Residual Neural Network of 34 layers) and Swin-B (the base model in Swin Transformer), a linear rigid body motion recognizer based on Hough linear detection, and data processing. The pre-trained composite model performs cable segmentation on the video captured by the camera; this new video contains only cable data and is then transmitted to the linear rigid body recognizer, which recognizes cable vibration in the new video, and the cable force is determined by processing vibration data. As a result of the model experiments conducted in the laboratory and the cable test of cable-stayed bridges under complex outdoor conditions, the effectiveness and robustness of the system have been verified. The error of the proposed method in this study is found to be less than 2.0% comparing the results with that calculated from conventional sensors employed in the tests. The test results indicate that the system proposed in this paper is capable of conducting cable detection in complex environments.

Moth‐flame‐optimisation based parameter estimation for model‐predictive‐controlled superconducting magnetic energy storage‐battery hybrid energy storage system

AbstractSuperconducting magnetic energy storage‐battery hybrid energy storage system (HESS) has a broad application prospect in balancing direct current (DC) power grid voltage due to its fast dynamic response ability under low‐frequency/high‐frequency disturbances. Model‐predictive‐control (MPC) with characteristics such as high sampling rate and wide applicability could be applied to HESS. However, considering that the relevant circuit parameters would change with ambient temperature, interference and ageing, the effect of MPC may deteriorate inevitably. This article proposes an improved MPC strategy for SMES‐Battery HESS, taking moth‐flame‐optimisation (MFO) algorithm to calculate the circuit parameters in real time. The actual parameters are updated by MFO and then sent to model predictive controller to minimise the model mismatches. The advantages of high accuracy and fast convergence speed is verified by comparison with grey wolf optimisation and particle swarm optimisation. The simulation shows that by taking the proposed scheme, DC bus voltage are more stable and the superconducting magnetic energy storage can maintain more than 95% capacity utilisation and avoid over‐discharge even if the model parameters are inconsistent with the actual ones under circumstances of alternating current grid fault and fluctuation of new energy output.

NIR-II Imaging and Sandwiched Plasmonic Biosensor for Ultrasensitive Intraoperative Definition of Tumor-Invaded Lymph Nodes.

Radical lymphadenectomy remains the cornerstone of preventing tumor metastasis through the lymphatic system. Current surgical resection of lymph nodes (LNs) based on fluorescence-guided surgery (FGS) suffers from low sensitivity/selectivity with only qualitative information, hampering accurate intraoperative decision-making. Herein, we develop a modularized theranostic system including NIR-II FGS and a sandwiched plasmonic chip (SPC). Intraoperative NIR-II FGS and detection of tumor-positive lymph nodes were performed on the gastric tumor to determine the feasibility of the modularized theranostic system in defining LN metastasis. Under the NIR-II imaging window, the orthotopic tumor and sentinel lymph nodes (SLNs) were successfully excised without ambient light interference in the operating room. Importantly, the SPC biosensor achieved 100% sensitivity and 100% specificity for tumor markers and realized rapid and high-throughput intraoperative SLN detection. We propose the synergetic design of combining the NIR-II FGS and suitable biosensor will substantially improve the efficiency of cancer diagnosis and therapy follow-up.

Plug-and-play algorithm for imaging through scattering media under ambient light interference.

Imaging through scattering media is a fascinating subject in the computational imaging domain. The methods based on speckle correlation imaging have found tremendous versatility. However, a darkroom condition without any stray light is required because the speckle contrast is easily disturbed by ambient light, which can lead to the reduction in object reconstruction quality. Here, we report a plug-and-play (PnP) algorithm to restore the object through scattering media under the non-darkroom environment. Specifically, the PnPGAP-FPR method is established via the generalized alternating projection (GAP) optimization framework, Fienup phase retrieval (FPR) method, and FFDNeT. The proposed algorithm is demonstrated experimentally and shows significant effectiveness and flexible scalability, which describe the potential for its practical applications.

Catalyst-integrated dual-fluorescent sensor array for highly efficient detection of triacetone triperoxide via simplified quadrantal pattern recognition

A catalyst-integrated dual-fluorescent sensor array for efficient vapor detection of triacetone triperoxide (TATP) was designed and realized. The selected loading fluorescence sensors were catalog compounds of substituted 1,8-naphthalimides (C6NI), where boronate substituted C6NIB as H2O2 sensors and azetidine substituted C6NIN as polarity quenching sensors for TATP/acetone sensing. These two sensors were well characterized. The catalyst was integrated into middle of the array, helping detect TATP in site directly by before-catalyst sensors and indirectly by post-catalyst sensors. Different catalysts’ decomposition performance was verified. The array’s accurate selectivity for TATP was confirmed to be unaffected by TATP signature compounds, humidity change or ambient interference via simplified quadrantal pattern recognition (QPR) method, making TATP data points conspicuous without excessive data processing. The array also performed comparable low detection limit and high sensitivity. The array's ability to detect both directly and indirectly, along with the design of proper sensing materials and catalyst setup, allow for the sensitive and selective identification of TATP in a variety of gaseous, in-situ, and real-time scenarios. With fewer sensing materials and simplified data processing, the array offers a promising future for numerous applications with low false positives.

On electromagnetic head digitization in MEG and EEG

In MEG and EEG studies, the accuracy of the head digitization impacts the co-registration between functional and structural data. The co-registration is one of the major factors that affect the spatial accuracy in MEG/EEG source imaging. Precisely digitized head-surface (scalp) points do not only improve the co-registration but can also deform a template MRI. Such an individualized-template MRI can be used for conductivity modeling in MEG/EEG source imaging if the individual’s structural MRI is unavailable. Electromagnetic tracking (EMT) systems (particularly Fastrak, Polhemus Inc., Colchester, VT, USA) have been the most common solution for digitization in MEG and EEG. However, they may occasionally suffer from ambient electromagnetic interference which makes it challenging to achieve (sub-)millimeter digitization accuracy. The current study—(i) evaluated the performance of the Fastrak EMT system under different conditions in MEG/EEG digitization, and (ii) explores the usability of two alternative EMT systems (Aurora, NDI, Waterloo, ON, Canada; Fastrak with a short-range transmitter) for digitization. Tracking fluctuation, digitization accuracy, and robustness of the systems were evaluated in several test cases using test frames and human head models. The performance of the two alternative systems was compared against the Fastrak system. The results showed that the Fastrak system is accurate and robust for MEG/EEG digitization if the recommended operating conditions are met. The Fastrak with the short-range transmitter shows comparatively higher digitization error if digitization is not carried out very close to the transmitter. The study also evinces that the Aurora system can be used for MEG/EEG digitization within a constrained range; however, some modifications would be required to make the system a practical and easy-to-use digitizer. Its real-time error estimation feature can potentially improve digitization accuracy.

Physical-Layer Network Coding Enhanced Visible Light Communications Using RGB LEDs

Compared with traditional radio frequency (RF)-based techniques, the advantages of wider bandwidth, higher security, and stronger electromagnetic immunity (EMI) capability make visible light communications (VLC) a good candidate technology for the 6th generation (6G) wireless communications. Red, green, and blue (RGB) light-emitting diodes (LEDs) based VLC has become an important research branch due to its low price and high reliability. However, the saturation of photodiode (PD) caused by the ambient background light may seriously degrade the bit error rate (BER) performance of an RGB-VLC system's three spatially uncoupled information streams (i.e., red, green, and blue LEDs can transmit different data packets simultaneously) in practical applications. To mitigate the ambient light interference in point-to-point RGB-VLC systems, we propose, PNC-VLC, a network-coded scheme that uses two LEDs with the same color at the transmitter to transmit two different data streams and we make use of the naturally overlapped signals at the receiver to formulate physical-layer network coding (PNC). The adaptivity of PNC-VLC could effectively improve the BER degradation problem caused by the saturation of PD under the influence of ambient light. We conducted simulations based on the parameters of commercial off-the-shelf (COTS) products to prove the superiority of the PNC-VLC under the influence of four typical illuminants. Simulation results show that the PNC-VLC system can maintain a better and more stable system BER performance under different ambient background light conditions. Remarkably, with 2/3 throughput efficiency, PNC-VLC can bring 133.3% gain to the BER performance when compared with the traditional RGB-VLC systems under the Illuminant A interference model, making it a good option for VLC with unpredictable ambient background interferences.

IEMS: An IoT-Empowered Wearable Multimodal Monitoring System in Neurocritical Care

IoT-empowered wearable multimodal monitoring system (IEMS), an IEMS for neurocritical care is developed to perform simultaneous monitoring of 8-channel electroencephalogram (EEG), 2-channel regional cerebral oxygen saturation (rSO2) based on near-infrared spectrum (NIRS), body surface temperature, electrocardiogram (ECG), photoplethysmography (PPG), and bioimpedance (Bio-Z). The IoT platform and wireless devices enable the patients’ signals available for remote diagnosis. Besides, analysis functions and artificial intelligence (AI) algorithms could be embedded in both the bedside platform and the cloud server to support physicians with clinical decisions. In the multimodal neural monitoring device, the following designs are adopted to face the neurological intensive care unit (NICU) application. Active electrodes and preamplifying free topology provide better signal quality and a larger dynamic range (DR). Dedicated low-power designs ensure the device lasts 10 h of operation. A nonwoven headset improves long-term wearability, which is also quick and easy to install. In the cardiovascular patch, the disposable electrode patch based on elastic materials ensures tight and comfortable contact with skin. Besides, the reusable wireless sensing module is tiny (20 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times16$ </tex-math></inline-formula> mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times9$ </tex-math></inline-formula> mm) but could measure ECG, PPG, and Bio-Z simultaneously. Electrical tests and human subject (healthy volunteers and NICU patients) studies were conducted to examine the performance. The EEG channels show 130.75-dB DR and 0.84- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu {}\text{V}_{\mathrm{ RMS}}$ </tex-math></inline-formula> input-referred noise, which also yields signals with high quality during human EEG monitoring. The NIRS channels exhibit good linearity and are able to operate under severe ambient interference. The temperature sensors show ±0.2 °C accuracy. Moreover, the system complies with mandatory standards for medical equipment. Overall, an IEMS can meet the requirements for NICU applications and could provide better comfort during long-term wearing.

Spin-Antenna: Enhanced 3D Motion Tracking Via Spinning Antenna Based on COTS RFID

With the rising of demands for novel Human-Computer Interaction (HCI) approaches in the 3D space, a number of intelligent approaches have been proposed to achieve the HCI by tracking the translation and rotation of the target devices. In this paper, we propose to realize a light-weight, battery-free, 3D motion tracking solution by leveraging a spinning linearly polarized antenna to track a passive RFID tag array. Instead of using the fixed antennas, which can only receive stable signal in some specific environments due to the unpredictable multipath effect, we propose to mitigate the multipath effect and the ambient interference by continuously spinning a linearly polarized antenna, and then extract the most distinctive features based on the optimal reading conditions of the spinning antenna. In particular, because the phase variation around the matching direction is more stable while the RSSI variation around the mismatching direction is more distinctive, we leverage such matching/mismatching property of the linearly polarized antenna to extract the most distinctive features for motion tracking. To depict the property, we build a theoretical model to explain the RSSI and the phase variation of the RFID tag along with the spinning of the antenna, and further extend the model from a single RFID tag to an RFID tag array. Based on the model, we can extract the distinctive RSSI features for the rotation tracking and the stable phase features for the translation tracking. Moreover, to tackle the low rate of feature extraction due to the spinning of antenna, we further propose to enhance the unstable phase features based on the overall trend of other tags with interpolation, such that the sampling rate can be efficiently improved. Finally, we propose a LSTM (Long Short Term Memory)-based network to track the 3D motion based on the signal features extracted based on the polarization model. The experimental results show that our system can achieve an average error of 10.45 cm in the translation tracking, and an average error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$6.02^\circ$</tex-math></inline-formula> in the rotation tracking in the 3D space.

Complex Magnetic Anomaly Detection Using Structured Low-Rank Approximation With Total Variation Regularization

In the field of magnetic anomaly detection (MAD), the anomaly signal is easily submerged by ambient electromagnetic interference. Though the existing noise suppression methods can effectively improve the signal-to-noise ratio (SNR), there are still some intractable problems, such as signal distortion and boundary blur. To solve these problems, a novel MAD method based on structured low rank (SLR) and total variation (TV) regularization constraints is proposed in this letter. The noise suppression performance is improved by leveraging the structured low rankness of the signal. To preserve clean boundaries of the anomalies, an anisotropic TV regularization constraint is employed in the approach. Comparing the SLR-TV method with four state-of-the-art methods with extensive field tests, the results demonstrate that the proposed SLR-TV method achieves the greatest SNR improvement by about 63.24% and the best structural similarity (SSIM) improvement by about 53.02% over other methods in the range from −40 to 0 dB, showing the utility and high fidelity of the proposed framework in low SNR.

Synergistic Hankel Structured Low-Rank Approximation With Total Variation Regularization for Complex Magnetic Anomaly Detection

In the field of magnetic anomaly detection (MAD), the anomaly signal is easy to be submerged by ambient electromagnetic interference. Though the existing noise suppression methods can effectively improve the signal-to-noise ratio (SNR), there are still some intractable problems, such as signal distortion and boundary blur. To solve these problems, a novel MAD method based on structured low-rank (SLR) and total variation (TV) regularization constraints is proposed in this paper. To be specific, a new framework SLR-TV, which mainly contains abnormal signal acquisition, objective function solution, and inverse transform operation is constructed. The noise suppression performance is improved by leveraging the structured low-rankness of the signal. To preserve clean boundaries of the anomalies, an anisotropic TV regularization constraint is employed in the approach. Comparing the SLR-TV with five state-of-the-art methods with extensive synthetic and field tests, the results demonstrate that the proposed SLR-TV method achieves the highest SNR improvement by about 15.62% and the best structural similarity (SSIM) improvement by about 62.95% over other methods in the range from -40 dB to 0 dB, showing the utility and high-fidelity of the proposed framework in low SNR.

Implementation of Digital Filters for Real-Time PPG Signal Processing in VLC

Health care units depend on Wi-Fi or local area networks for biomedical data communication within their premises. The major hazard associated with biomedical data communication in healthcare units is radio frequency interference with medical devices. Visible light communication (VLC) is a developing technology that solves the issue of RF interference in healthcare units. Long-range communication is a great challenge in VLC as the transmitted data get corrupted by noise from ambient light and degrade with distance. Hence it is necessary to upgrade the signal condition and eradicate the noise due to ambient light interference. The proposed work addresses these issues and enables robust communication in healthcare units. The experimental validation of the proposed work has been executed by analyzing the VLC communication performance up to a distance of 5[Formula: see text]m. The proposed work focuses on implementation of digital filters like finite impulse response, infinite impulse response and least mean square adaptive filter for PPG signal processing. The least mean square filter showed a superior performance with a mean square error of 7.23e[Formula: see text] than the other filters, leading to an accurate diagnosis for patients without any RF interference inside healthcare units.

Detecting Atrial Fibrillation in Real Time Based on PPG via Two CNNs for Quality Assessment and Detection

Real-time detection of atrial fibrillation (AFib) is made possible by the quality assessment via a 1-D convolutional neural network (1D-CNN) in a processor of a photoplethysmography (PPG) sensor patch and a 2-D convolutional neural network (2D-CNN) for AFib detection in cloud. The 1D-CNN is able to filter out the unqualified PPG that is contaminated by motion artifacts and/or ambient light interference. The remaining qualified PPG is then inputted to another built 2D-CNN for detecting AFib. This 1D-CNN consists of four layers of convolutions and max pooling, one long short-term memory (LSTM), and an output dense layer. The 2D-CNN is pretrained based on the electrocardiography (ECG) data from multiparameter intelligent monitoring in intensive care (MIMIC) III database, for which the RR-intervals (RRIs) of ECG data are first extracted in Poincaré images and then regarded as input features to the model for training. This 2D-CNN has also four layers of convolutions and max pooling and four output dense layers. The pretrained model is next fine-tuned based on peak-to-peak intervals (PPIs) of PPG measured from wearable devices as input features for detecting AFib effectively. The quality-assessment 1D-CNN model is implemented in the wearable device to transmit only qualified data to the 2D-CNN model in cloud for AFib detection, achieving power efficiency. Both models are trained by the Adam optimizer. To validate the models, the PPIs of PPG were collected to evaluate the performance of the established models in real time. Experimental results show that the fine-tuned 2D-CNN for AFib detection achieves the accuracy, sensitivity, and specificity were 98.08%, 96.82%, and 98.86%, respectively, the most favorable as opposed to other reported works based on PPG. The models are able to not only assist clinicians in AFib detection but also provide a mechanism to detect AFib via wearable devices in real time.