Interference Conditions Research Articles

2D MOF‐Based Filtration‐Sensing Strategy for Trace Gas Sensing Under Intense F‐Gas Interference at Room Temperature

AbstractThe detection of trace impurity gases in fluorinated gas (F‐gas) that are widely used in the industry offers a significant avenue for equipment status monitoring and mitigating unnecessary emissions. However, the formidable electron affinity (EA) and adsorption propensity of F‐gas molecules render the identification of trace impurities within a high‐concentration F‐gas atmosphere exceptionally challenging. Herein, the filtration‐sensing strategy is proposed to realize highly sensitive and selective Room Temperature (RT) sensing of trace gases in the F‐gas environment. Through the innovative construction of a bilayer structure, comprising Co3(HITP)2 as the overlayer and SnO2 nanofibers (NFs) as the sensing layer, remarkably sensitive detection of trace impurity gases under intense F‐gas interference conditions is achieved. The efficacy of the Co3(HITP)2 overlayer is further corroborated through the incorporation of Pd‐SnO2 and MoS2‐SnO2 sensors, concurrently facilitating targeted quantitative identification within a complex gas mixture environment. The underlying sensing mechanism is predominantly attributed to interatomic adsorption interactions and the modulation of gas diffusion by microporous structures. This work provides pioneering insights into trace impurity detection within high‐concentration F‐gas atmosphere while presenting a potentially viable solution for the operational maintenance of F‐gas‐based industrial equipment (F‐equipment) in industrial applications.

Research on the Technology and Application of Remote Wireless Transmission of Digital Image Signal

Abstract. With the increasing demand for remote real-time high-quality video transmission, especially in the context of large area applications of unmanned aerial vehicle (UAV) and remote location instant live broadcast, traditional wired transmission methods have been surpassed by digital transmission methods due to their superior performance in image quality, transmission distance and delay. Therefore, this study focuses on the application of digital image wireless transmission in remote scenarios such as UAVs, including key digital transmission technologies such as Orthogonal Frequency Division Multiplexing (OFDM) and Coded Orthogonal Frequency Division Multiplexing (COFDM), as well as public network link communication technologies such as 4G and 5G, by means of a literature study and a case study. It also analyzes the development of WiFi, Lightbridge and OcuSync graphics transmission systems using DJI brand products as case studies. The analysis shows that digital image wireless transmission, especially COFDM and advanced proprietary systems such as DJI's OcuSync, has significant advantages in terms of transmission distance, interference immunity, encryption techniques, and reusability. However, factors such as building obstruction, frequency interference, and weather conditions can affect the transmission quality. To mitigate these challenges, solutions are proposed, such as auxiliary signal enhancement systems, adaptive frequency hopping and software-defined radio technologies.

Research on a signal demodulation algorithm for fiber optic acoustic sensors based on the amplitude ratio of harmonic components

Fiber optic sensing technology's performance is crucially influenced by the choice of demodulation algorithms. Traditional intensity demodulation methods rely on precisely controlling the working point to the quadrature point (Q point) by adjusting the laser wavelength. However, issues like laser wavelength adjustment accuracy and laser power variations with wavelength affect demodulation accuracy, limiting its application in complex scenarios. Additionally, this method requires sensitivity calibration and has a limited dynamic range. To address these limitations, this paper proposes an amplitude ratio of harmonic components (ARHC) demodulation algorithm. It analyzes the harmonic components of the interference light intensity output by the sensor to calculate Fabry-Perot cavity length changes, achieving real-time demodulation of dynamic signals. Compared to traditional methods, the ARHC algorithm simplifies system adjustment, has a larger demodulation range and higher accuracy, and is more adaptable to environmental changes. Simulations and experiments demonstrate its effectiveness and high-precision, large-dynamic-range signal demodulation capabilities under different interference conditions. The EFPI ultrasonic sensitivity based on the ARHC algorithm is 2.5 nm/Pa@23kHz, and it can linearly demodulate up to a vibration amplitude of 372.8 nm for the sensitive diaphragm, with a demodulation error of less than or equal to 0.8 nm.

Recognition and classification of noise signals by dolphins under conditions of noise interference and spatial uncertainty of their simultaneous presentation

The ability of dolphin`s auditory system to recognize and classify noise signals according to certain invariant features under the influence of noise interference and spatial uncertainty of their simultaneous presentation was studied. The bottlenose dolphins trained to differentiate such signals had to solve this problem under conditions simulating real marine ones, when the perception of a useful signal occurs against the background of similar signals and noise interference. First, the noise signals were sequentially presented to the animal against the background of white masking noise. Further the dolphin had to identify a signal of a positive class from several simultaneously sounding sound sources. The efficiency of the animal was evaluated at several given levels of noise interference. In this case, the actual noise interference was both white noise and simultaneously sounding negative signals. It`s shown that the efficiency and noise immunity of dolphin`s auditory system depend on the degree of alternativeness of the spatial uncertainty of the simultaneous presentation of signals.

Similarity Distractors Increase the Burden of Chinese Character Selection and Updating in Working Memory.

Attentional mechanisms are the primary processes for performing working memory (WM) tasks and can prevent distractors from interfering with the content representations stored in WM. However, our understanding of the mechanisms by which attention affects WM remains limited. As such, we analyzed ERPs of the character n-back task to investigate Chinese character selection, updating, and maintenance in WM. In Experiment 1, we collected electroencephalography data from 27 participants aged 18-25 years to explore the influence of false-character interference and symbol interference on a neural activity in the character n-back task. The results suggest that RT was longer in the false-character interference condition. The N2pc and P300 amplitudes were smaller; however, the slow wave amplitude did not differ significantly. In Experiment 2, we used a single-symbol interference and a multiple-symbol interference to establish whether the number of interferences affected the neural activity in the character n-back task. Thirty participants (aged 19-25 years) took part in the experiment. The findings imply that a longer RT and a larger N2pc amplitude occurred in the multiple-symbol interference condition, but not in the P300 and slow wave conditions. Our findings indicate that distractors that are similar to characters may produce greater interference in character recognition and affect the subsequent updating, whereas the number of distractors may only interfere with early character selection, but not with updating and maintenance phases.

Analysis of methods for reducing the signal PAPR under the influence of the Doppler effect in hybrid communication networks

Background. For further development of communication networks, it is planned to use hybrid satellite networks for traffic transmission. However, satellite communication channels have features such as distortion caused by the Doppler effect and increased energy efficiency requirements. Aim of this study is to analyze variants of orthogonal frequency multiplexing methods and modulation methods in order to choose the most stable technology, taking into account destabilizing factors. Method. Comparing different signal processing technologies and studying their resistance to bit errors is the imitation modeling of the communication channel in the Matlab environment. This approach allows creating a model of the communication network, taking into account the main parameters of communication channels, such as the Doppler effect, energy deficiency and destabilizing factors. Results. The distribution of the bit error coefficient for various signal processing technologies, depending on the signal-to-noise ratio, is compared. The method of frequency multiplexing is defined, providing the minimum peak factor and the most resistant to bit error. It is also noted that the effectiveness of all the studied technologies depends on the spacing and modulation constellations, and that it is necessary to adjust the characteristics of the system for each case. Conclusion. The results of this study can be used to improve the quality of communication in difficult interference conditions of hybrid mobile networks of 5 and 6 generations using the satellite segment.

Development and Evaluation of a Robust UART Communication System with Enhanced Interference Resistance

Interference is a significant issue in UART communication, often causing data jitter and distortion, particularly in industrial environments. This study addresses these challenges by developing an anti-interference UART system that includes the design and implementation of receiving, processing, and sending modules. The system utilizes techniques such as multi-level sampling, synchronization, and cumulative decision-making to enhance data transmission accuracy and reliability. In embedded systems, electromagnetic interference (EMI) poses a major problem, leading to potential data loss and communication failures. Reliable communication is crucial for maintaining the performance and safety of industrial operations. The anti-interference UART system developed in this study aims to improve operational stability by mitigating the effects of EMI. The system’s performance was validated through extensive simulations, demonstrating its ability to maintain stable and accurate data transmission even under various interference conditions. The results show significant improvements in data integrity and communication reliability. This paper presents a comprehensive approach to designing, implementing, and validating a robust UART system that can effectively operate in challenging environments, ensuring reliable data communication and enhancing overall system performance.

A Mechanical Fault Identification Method for On-Load Tap Changers Based on Hybrid Time—Frequency Graphs of Vibration Signals and DSCNN-SVM with Small Sample Sizes

In engineering applications, the accuracy of on-load tap changer (OLTC) mechanical fault identification methods based on vibration signals is constrained by the quantity and quality of the samples. Therefore, a novel small-sample-size OLTC mechanical fault identification method incorporating short-time Fourier transform (STFT), synchrosqueezed wavelet transform (SWT), a dual-stream convolutional neural network (DSCNN), and support vector machine (SVM) is proposed. Firstly, the one-dimensional time-series vibration signals are transformed using STFT and SWT to obtain time–frequency graphs. STFT time–frequency graphs capture the global features of the OLTC vibration signals, while SWT time–frequency graphs capture the local features of the OLTC vibration signals. Secondly, these time–frequency graphs are input into the CNN to extract key features. In the fusion layer, the feature vectors from the STFT and SWT graphs are combined to form a fusion vector that encompasses both global and local time–frequency features. Finally, the softmax classifier of the traditional CNN is replaced with an SVM classifier, and the fusion vector is input into this classifier. Compared to the traditional fault identification methods, the proposed method demonstrates higher identification accuracy and stronger generalization ability under the conditions of small sample sizes and noise interference.

A novel dynamic tracking method for coded targets with complex background noise

To address the issues of low tracking efficiency and poor localization accuracy of artificial coded targets under complex background interference conditions, a new method for dynamic tracking of coded targets is proposed. This method includes a lightweight feature tracker (CBAM-Slim-Net) for adaptive localization of coded circles and a large-capacity coded target solver (CSN-BSSCT). The CBAM-Slim-Net feature tracker achieves a detection accuracy of 0.987 with only 6.030 M parameters. In actual measurement environments with complex background interference, CSN-BSSCT can decode quickly and accurately, with a mean error of 0.036 mm in the three-dimensional Euclidean distance between coded circles in static measurement scenarios. Additionally, this method can analyze the motion trajectory of the target and perform dynamic stitching for 3D measurement from multiple perspectives, making it highly significant for applications in robot motion control and large-field-of-view 3D measurement.

Leak-Rate Through Carbon Brush Seals: Experimental Tests Versus Predictions From a Porous Medium Approach

Abstract This study presents a detailed comparative analysis between experimental leakage flow rates and numerical predictions for carbon brush seals with long bristles, utilizing a porous medium model approach. A series of tests were carried out on a static rig (without rotor rotation). The experimental setup allows tests under various interference conditions, revealing significant insights into the flow behavior through the brush seal. A numerical model based on the Darcy-Forchheimer equation is developed to interpret the complex flow dynamics within the brush seal, accounting for viscous, compressible and inertial effects. The study evaluates the impact of brush deformation and porosity on flow resistance, leveraging experimental data to refine the numerical model parameters. This investigation not only deepens the understanding of brush seal flow physics but also improves the predictive accuracy of the numerical model in simulating operational conditions.

The Infralimbic, but not the Prelimbic Cortex is needed for a Complex Olfactory Memory Task.

The medial prefrontal cortex (mPFC) plays a key role in memory and behavioral flexibility, and a growing body of evidence suggests that the prelimbic (PL) and infralimbic (IL) subregions contribute differently to these processes. Studies of fear conditioning and goal-directed learning suggest that the PL promotes behavioral responses and memory retrieval, while the IL inhibits them. Other studies have shown that the mPFC is engaged under conditions of high interference. This raises the possibility that the PL and IL play differing roles in resolving interference. To examine this, we first used chemogenetics (DREADDs) to suppress mPFC neuronal activity and tested subjects on a conditional discrimination task known to be sensitive to muscimol inactivation. After confirming the effectiveness of the DREADD procedures, we conducted a second experiment to examine the PL and IL roles in a high interference memory task. We trained rats on two consecutive sets of conflicting odor discrimination problems, A and B, followed by test sessions involving a mid-session switch between the problem sets. Controls repeatedly performed worse on Set A, suggesting that learning Set B inhibited the rats' ability to retrieve Set A memories (i.e. retroactive interference). PL inactivation rats performed similarly to controls. However, IL inactivation rats did not show this effect, suggesting that the IL plays a critical role in suppressing the retrieval of previously acquired memories that may interfere with retrieval of more recent memories. These results suggest that the IL plays a critical role in memory control processes needed for resolving interference.

A Matter of Memory? Age-Invariant Relative Clause Disambiguation and Memory Interference in Older Adults.

Past research suggests that Working Memory plays a role in determining relative clause attachment bias. Disambiguation preferences may further depend on Processing Speed and explicit memory demands in linguistic tasks. Given that Working Memory and Processing Speed decline with age, older adults offer a way of investigating the factors underlying disambiguation preferences. Additionally, older adults might be subject to more severe similarity-based memory interference given their larger vocabularies and slower lexical access. Nevertheless, memory interference and sentence disambiguation have not been combined in studies on older adults before. We used a self-paced reading paradigm under memory load interference conditions. Older (n=30) and Younger (n=35) readers took part in the study online; reading times were recorded and measures of comprehension accuracy and load recall were collected. This setup allowed for the implicit measurement of attachment biases and memory interference effects interactively. Results show that similarity-based interference affected both age groups equally, but was more pronounced in NP2-biased structures, which took participants generally longer to read. Attachment preferences did not differ by group and were unaffected by Working Memory span. However, accuracy on recall prompts was predicted by Working Memory span in both groups. Findings of greater interference in syntactically dispreferred structures support unified processing models where parsing constraints naturally interact. The lack of age differences on our measures further aligns with research finding age-invariant implicit language processing.

Effects of video game immersion and task interference on cognitive performance: a study on immediate and delayed recall and recognition accuracy.

This study investigates the cognitive impacts of video game immersion and task interference on immediate and delayed recall as well as recognition tasks. We enrolled 160 subjects aged 18 to 29, who were regular players of "shoot-em-up" video games for at least 3 years. Participants were assigned to one of three experimental groups or a control group. The experimental conditions varied in the timing and type of tasks: the first group performed a video game session between recall tasks, the second group multitasked with video games and recall tasks simultaneously, and the third group engaged in task switching from video games to recall tasks. Using the Rey Auditory Verbal Learning Test, we measured the effects of these conditions on cognitive performance, focusing on error types and recall accuracy. Results indicated that multitasking and task switching significantly affected the subjects' performance, with notable decrements in recall and recognition accuracy in conditions of high task interference. The study highlights the cognitive costs associated with multitasking in immersive digital games and provides insights into how task similarity and interference might increase error rates and affect memory performance.

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.

A novel image inpainting method based on a modified Lengyel–Epstein model

With the increasing popularity of digital images, developing advanced algorithms that can accurately reconstruct damaged images while maintaining high visual quality is crucial. Traditional image restoration algorithms often struggle with complex structures and details, while recent deep learning methods, though effective, face significant challenges related to high data dependency and computational costs. To resolve these challenges, we propose a novel image inpainting model, which is based on a modified Lengyel–Epstein (LE) model. We discretize the modified LE model by using an explicit Euler algorithm. A series of restoration experiments are conducted on various image types, including binary images, grayscale images, index images, and color images. The experimental results demonstrate the effectiveness and robustness of the method, and even under complex conditions of noise interference and local damage, the proposed method can exhibit excellent repair performance. To quantify the fidelity of these restored images, we use the peak signal-to-noise ratio (PSNR), a widely accepted metric in image processing. The calculation results further demonstrate the applicability of our model across different image types. Moreover, by evaluating CPU time, our method can achieve ideal repair results within a remarkably brief duration. The proposed method validates significant potential for real-world applications in diverse domains of image restoration and enhancement.

A-148 Performance Evaluation ofthe Zybio EXT 4800: A Novel Optical-Magnetic Coagulation Analyzer

Abstract Background Coagulation analysis, crucial in clinical diagnostics, predominantly employs optical and Mechanical methods. Optical methods are preferred for their simplicity and cost-effectiveness but are susceptible to hemolysis, icterus, and lipemia interferences. Mechanical methods, considered more accurate, are hindered by higher costs and operational complexity. This study evaluates the Zybio EXT 4800, a novel analyzer integrating both technologies(U.S. Patent:US11022558 B2), to harness their collective benefits. Methods The study compared optical, mechanical, and integrated optical-mechanical detection methods for Prothrombin Time (PT) testing, particularly under interference conditions such as chyle. To ensure reagent reliability, a comprehensive open-vial stability assessment was conducted for Zybio's reagents. Additionally, a clinical correlation study compared Zybio EXT 4800 with Siemens CS-2000i Systems, covering multiple parameters. Through this rigorous methodology, the study sought to establish the accuracy, reliability, and clinical applicability of these advanced coagulation analysis technologies. Results In the presence of lipemia interferents during PT testing, notable fluctuations were observed with the optical method, resulting in deviations ranging from 80% to 104% from the set values. Conversely, when utilizing the integrated optical-mechanical approach for analysis, the instrument automatically identifies interferences in the sample, intelligently switching the detection method from optical to mechanical.This intelligent adaptation to interference exhibits strong anti-interference capabilities. The experimental results of mechanical testing show deviations ranging from 0.8% to 1.7%, while the experimental results of optical retesting after high-speed centrifugation of samples show deviations ranging from 1.1% to 2.3%. Regarding reagent stability, Zybio's reagents showcased exceptional durability. Following an accelerated aging process of 50 days at 37°C, Zybio reagents exhibited smaller relative deviations compared to the comparator reagents, underscoring the remarkable stability and reliability offered by Zybio's reagents. Furthermore, in a clinical correlation analysis between Zybio's “EXT 4800 Automated Coagulation Analyzer” and Siemens' “CS-2000i Systems,” strong correlations were observed. The R-squared values perform well, with determinations of exemplary quantitative parameters: FIB for 0.985, D-Dimer for 0.983, and AT for 0.980. These results not only validate the performance equivalence between the two systems but also highlight the advanced stability and anti-interference capabilities of the EXT 4800, positioning it as a formidable alternative to existing solutions. Conclusions The EXT 4800 coagulation analyzer effectively merges optical and mechanical methodologies, offering high sensitivity, accuracy, and reduced susceptibility to interferences. It presents as a cost-effective, reliable alternative in coagulation analysis, with strong performance correlations to established systems and superior reagent stability, making it a valuable addition to clinical laboratories.

Finite element analysis and detection of melanoma cells under dual-cell photoacoustic interference conditions

In order to detect the presence of melanoma cells in two cells under the condition of cell photoacoustic wave interference, this paper conducted a finite element analysis of the photoacoustic wave interference field of two cells. First, the wavelength corresponding to the dominant frequency of the signal from a single red blood cell (mean diameter) was calculated. Then, the distance between two identical red blood cells (mean diameter) was set as a multiple of the wavelength to identify the optimal interference distance and the position of the enhanced zone detection point. Next, under the optimal distance, the signal curves of two cells as red blood cells and when melanoma cells exist in two cells were calculated in sequence. Finally, the frequency domain sound pressure level curve of the detection point under the two states was compared with the single-cell signal to obtain the Frechet distance. The results show that when both cells are red blood cells, the Frechet value is less than 48; when melanoma cells exist in both cells, the Frechet value is greater than 52. This study shows that the presence of melanoma cells in two cells can be determined by adjusting the distance between the cells, arranging the positions of the detection points, and employing the Frechet distance metric curve difference under the condition that the two-cell photoacoustic waves interfere with each other.

"Seeing" ENF From Neuromorphic Events: Modeling and Robust Estimation.

Most artificial lights exhibit subtle fluctuations in intensity and frequency in response to the influence of the grid's alternating current, providing the potential to estimate the Electric Network Frequency (ENF) from conventional frame-based videos. Nevertheless, the performance of Video-based ENF (V-ENF) estimation largely relies on the imaging quality and thus may suffer from significant interference caused by non-ideal sampling, scene diversity, motion interference, and extreme lighting conditions. In this paper, we show that the ENF can be extracted without the above limitations from a new modality provided by the so-called event camera, a neuromorphic sensor that encodes the light intensity variations and asynchronously emits events with extremely high temporal resolution and high dynamic range. Specifically, we formulate and validate the physical mechanism for the ENF captured in events and then propose a simple yet robust Event-based ENF (E-ENF) estimation method through mode filtering and harmonic enhancement. To validate the effectiveness, we build the first Event-Video ENF Dataset (EV-ENFD) and its extension EV-ENFD+ with diverse scenarios, including static, dynamic, and extreme lighting scenes. Comprehensive experiments have been conducted on our proposed datasets, showcasing that our proposed E-ENF significantly outperforms the V-ENF in extracting accurate ENF traces, especially in challenging environments.

Fault Diagnosis of Pumped Storage Units—A Novel Data-Model Hybrid-Driven Strategy

Pumped storage units serve as a crucial support for power systems to adapt to large-scale and high-proportion renewable energy sources by providing a stable and flexible energy supply. However, due to the coupling effects of electric power load demands and the complex multi-source factors within the water–mechanical–electrical system, the interrelationship between unit parameters becomes more intricate, posing significant threats to the operational reliability and health status of the units. The complexity of fault diagnosis is further aggravated by the intricate and varied nature of fault characteristics, as well as the challenges in signal extraction under conditions of strong electromagnetic interference and high noise levels. To address these issues, this paper proposes a novel data-model hybrid-driven strategy that analyzes vibration signals to achieve rapid and accurate fault diagnosis of the units. Firstly, the spectral kurtosis theory is employed to enhance the traditional empirical mode decomposition, achieving optimal decomposition and noise reduction effects for vibration signals. Secondly, the intrinsic mode functions (IMFs) obtained from the decomposition are reconstructed, and the entropy values of effective IMFs are calculated as fault feature vectors. Subsequently, the CNN-LSTM model is utilized for fault diagnosis. The effectiveness and feasibility of the proposed method are verified through actual operational data from pumped storage units in a specific region. Through analysis, the fault diagnosis accuracy of the method proposed in this paper can be maintained above 95%, demonstrating robustness in complex engineering environments and effectively ensuring the safe and stable operation of pumped storage units.

Efficient Monte Carlo Algorithms based on Stochastic Geometry for Simulating Downlink and Uplink Cellular Communications

Stochastic geometry (SG) is increasingly utilized in modeling cellular communications systems, as it yields accurate and realistic analytical results. The most common aim of such modeling is to characterize the coverage probability in both the downlink (DL) and uplink (UL) directions. To validate these results, however, researchers need to use simulation, for which efficient algorithms are by and large not available, and that is where the present article comes in. We introduce here two SG based Monte Carlo algorithms to simulate DL and UL cellular communications efficiently and accurately. The algorithms are so comprehensive that they take the parameters of the system as input and deliver its coverage probability as output, considering in the process such details as equipment deployment, signal power, interference, and channel conditions. To demonstrate their superior efficiency, the algorithms have been analyzed rigorously. To test their accuracy, they have been coded and applied to some sample systems, producing results that match those produced analytically to within 4% in DL and 5% in UL. Although they are designed for the field of communications, the algorithms can be easily adapted for other fields where SG is also applicable, such as biology, astronomy and forestry.