Multiple Interference Research Articles

Deep learning based signal processing and detection for multiple medical devices OFDM systems

In general multiple medical devices orthogonal frequency-division multiplexing (OFDM) communication systems, all the interfering medical users are legitimate but will cause disturbance to the desired user. In this work, we evaluate three deep learning (DL) algorithms: fully connected deep neural networks, convolutional neural networks, and long short-term memory neural networks for signal processing and detection in uncoded multiple medical devices OFDM communications systems. The bit error rates (BER) of these DL methods are compared with the conventional linear minimum mean squared error (LMMSE) detector. Additionally, the relationships between the BER and signal-to-interference ratio, signal-to-noise ratio, the number of interferences, and modulation type are investigated. Numerical results show that DL methods outperform LMMSE under different multiple medical device interference situations and are robust when the wireless channel has high variability. Also, DL methods are proven to have strong anti-interference ability and are useful in multiple medical devices OFDM systems.

Secondary causal mediation analysis of a pragmatic clinical trial to evaluate the effect of chiropractic care for US active-duty military on biopsychosocial outcomes occurring through effects on low back pain interference and intensity

ObjectiveWe evaluate change in low back pain (LBP) intensity and interference as the mechanism by which chiropractic care affects other biopsychosocial factors in US active-duty military members.DesignWe conducted secondary, exploratory...

Structurally Colored Photonic Crystal Biomimetic Microstructures for Daytime Radiative Cooling.

Daytime radiative cooling offers a novel solution to the energy crisis, enabling green and efficient thermal management in space. High reflectance in the solar spectrum is essential for passive radiative cooling, rendering dye coloring and similar methods unsuitable for colored coolers. This paper presents a structurally colored photonic crystal biomimetic microstructured radiative cooler. Inspired by natural biological systems, this cooler features a dual-layered microtruncated-cone array structure on the surface and bottom membrane layers. The silver reflector and 3D micrograting surface structure produce continuous iridescent colors through multiple interference effects. Optimized lithographic process enable the fabrication of the ordered dual-layer surface microstructure arrays with precise angular combinations. The dual-layered microtruncated-cone introduces a gradient refractive index, reducing impedance mismatch at the interface. As a result, the radiative cooler achieves high solar spectral reflectance (0.95) and high mid-infrared emissivity (0.95). Notably, the net theoretical cooling power and the subambient temperature drop are 106.9 W m-2 and 7.4 °C, respectively, at an ambient temperature of 40 °C, with a measured average temperature reduction of 6.1 °C under direct sunlight. This performance matches that of advanced radiative coolers, striking a balance between aesthetics and radiative cooling capability.

Programmed DNA tile response system enabled accurate detection of ATP for clinical diagnosis

Adenosine triphosphate (ATP), as a crucial signaling molecule, is of high potential diagnosis value for multiple diseases. However, current technologies for ATP detection struggle to satisfy the harsh conditions required for biospecimen tests, existing several limitations for clinical practical application. In this study, the DNA tile response system was designed and constructed by integrating nucleic acid aptamer, hybrid chain reaction (HCR), and DNA tile self-assembly technology to achieve accurate detection of ATP in body fluid specimens. The DNA tile response system, possessing high-order structure characteristic, could remove HCR false-positive products and conquer the high noise background to enhance the detection sensitivity. Under the optimal reaction parameters, this system achieved the low limit of detection (LOD) of 189.23 nM over a wide linear range and splendid detection specificity faced with multiple interference terms. Furthermore, this system, possessing simple storage conditions, long-term detection stability, and superior anti-interference performance, was sufficient to satisfy the detection requirements for complicated biological specimens in multi-scenarios. Ultimately, the DNA tile response system showed excellent diagnostic efficiency that could easily distinguish urinary tract infection patients and healthy controls through ATP quantification, as well as hypertension patients and healthy ones. This system provides a high-potential rapid detection tool for early diagnosis and dynamic monitoring of urinary tract infections and hypertension, holding enormous potential for clinical translation due to the superiorities of simple operation, general applicability, and low instrument requirements.

Electromagnetic wave absorption performance of rapid emergency magnesium phosphate cement (MPC) canvas

This study leverages the rapid emergency protection advantages of magnesium phosphate cement (MPC) canvas to develop a novel electromagnetic wave-absorbing material based on MPC canvas. The composite is composed of an MPC matrix with magnetite and iron powder as absorbers, arranged in a gradient structure. The research investigates the effects of absorbers and gradient contents on the electromagnetic wave absorption performance (EWAP) of MPC canvas. Experimental results show a maximum RL of −37.2 dB in the 2–18 GHz range, with a 6.3 GHz absorption bandwidth below −10 dB. Simulations further confirm that the gradient MPC canvas exhibits excellent EWAP across different frequency bands. The gradient design effectively adjusts the electromagnetic parameters through multiple reflection, scattering, and interference attenuation effects within the multilayer structure, enabling broadband absorption of electromagnetic waves.

Fatigue life prediction for Q420qFNH weathering steel welded joints considering the effect of multiple cracks

Multiple semi-elliptical cracks frequently develop at the weld toes of welded joints. These interfering multiple cracks significantly accelerate crack growth rates and reduce the fatigue life of a welded joint compared to a single crack. This study proposes a method for predicting the fatigue life of a welded joint with multiple cracks, considering weld toe amplification effect, multiple crack interference amplification effect, and crack closure effect. The method is validated through fatigue tests on Q420qFNH weathering steel welded joints. The main factors influencing the interference amplification effect are investigated, and theoretical formulas for the interference amplification factor of the stress intensity factor are derived using the finite element method and the superposition principle. The initiation, aggregation, and morphological evolution during the propagation process of multiple cracks is accurately simulated, and the fatigue life of both cruciform-welded and butt-welded joints is predicted. Additionally, a quantitative analysis is conducted to assess the impact of the number of fatigue cracks on fatigue strength. The results indicate that the interference amplification effect of cracks depends on their distance and mainly affects the surface point where the crack front is closest. The proposed fatigue life prediction method can accurately estimate the fatigue life of Q420qFNH steel welded joints considering the effect of multi-cracks. The interference and coalescence of multiple cracks significantly reduce the fatigue life of welded joints. An increase in the number of fatigue cracks notably decreases the fatigue strength.

Electrochemical sensing of gatifloxacin using Ag2S/RGO nanocomposite

As a widely-used fluoroquinolone antibiotics, 1-Cyclopropyl-6-fluoro-8-methoxy-7-(3-methyl-1-piperazinyl)-4-oxo-1,4-dihydro-3-quinolinecarboxylic acid (gatifloxacin, GAT) has aroused much concern recently and it is of great importance to realize the accurate and efficient detection. Herein, the silver sulfide/reduced graphene oxide composite (Ag2S/RGO) was synthesized with one-pot method and was coated on the glassy carbon electrode to develop an effective electrochemical sensor for GAT. After the optimization of the size of Ag2S, the pH of buffer solution, and the scanning rate, the Ag2S/RGO modified electrode exhibits good linear relationship to GAT within wide ranges of 0.2 µM − 20 µM and 20 µM − 250 µM with a detection limit of 0.0667 µM. Besides, the proposed sensor shows good selectivity to GAT versus multiple interferences, including organic compounds, ions, and other antibiotics. At last, the proposed sensor was successfully applied in the GAT analysis in various real samples (including shrimp, fish, and chicken) with satisfying recoveries of 91.8 % − 102.4 %. In general, the proposed Ag2S/RGO-based electrochemical sensor provides a novel strategy for the GAT analysis, which is of significance for the development of efficient analytical techniques for antibiotics.

Radio Frequency Interference Detection and Automatic Modulation Recognition Based on Mask RCNN

The increasing complexity of wireless communication conditions presents noteworthy obstacles to the adaptability and flexibility of cognitive radio systems. The latter can alleviate the problem of spectrum shortages through proper spectrum management. However, cognitive radio has difficulty distinguishing between signals of interest and interference, which can be a general enemy since it is undesirable because it influences the signal's quality. Efficient detection and characterization of interference in wireless communication networks is critical for ensuring strong security. However, in this research, we use the Mask R-CNN methodology to present a new concept for automatic modulation recognition and radio frequency interference detection. Moreover, this algorithm can segment, detect, and recognize several types of interference that can affect wireless communication systems, such as chirp interference (CI), continuous wave interference (CWI), and multiple continuous wave interference (MCWI) within the signal of the interest (SOI), as well as the modulation kind present in the SoI. Overall, the combination of these distinct techniques can be very valuable in the field of signal processing, especially in anti-jamming strategies in wireless networks. Moreover, the proposed approach showcases exceptional performance in the validation dataset. For radio frequency interference detection, it achieves a mean average precision (mAP) of 0.946 and a mean average recall (mAR) of 0.954. For automatic modulation recognition, it attains a mAP of 0.898 and a mAR of 0.916.

An Underwater Target Location Algorithm Combined with Schmidt Orthogonalization

Abstract In underwater target localization research, where challenges such as low signal-to-noise ratio and multiple interferences exist in conventional matched field processing, this paper presents a novel approach combining compressed sensing with Schmidt orthogonalization. Initially, an orthogonalization procedure is applied to the observation matrix, yielding a new matrix with reduced column coherence and maximal adherence to the Restricted Isometry Property (RIP) constraint. Subsequently, the Sparse Adaptive Matching Pursuit (SAMP) algorithm from compressed sensing is employed to reconstruct the signal, leading to precise underwater target localization. Simulation experiments substantiate that this combined approach outperforms the conventional matched field algorithm by yielding a higher signal-to-noise ratio for target acoustic source localization and markedly reducing the count of interference virtual sources. Consequently, the performance of localization for targets is substantially enhanced.

Coupled topological edge and corner states in two-dimensional phononic heterostructures with nonsymmorphic symmetries

The exciting discovery of topological phononic states has aroused great interest in the field of acoustic wave control. However, conventional topological edge states and corner states localized at the interface and corner of the two-phase domain wall structures are limited by single channel transmission characteristics, which decreases the flexibility of designing multi-channel acoustic wave devices. Here, we propose a two-dimensional (2D) topological phononic heterostructure with nonsymmorphic symmetries to realize the multiple interface topological multimode interference effect based on the coupling of topological edge and corner states. Topological phase transitions are achieved by altering the rotation angle of the split-ring scatterers in a square lattice. The coupled edge states are generated by the coupling between the edge states of ordinary-topological-ordinary (OTO) interfaces. Moreover, the higher-order topology of the square phononic crystals (PCs) is characterized by nontrivial bulk polarization, the topological and coupled corner states splitting into two pairs appear in the square OTO bend structure owing to the nonsymmorphic PC lack of mirror symmetries. Finally, the topological robustness of the multimode interference effect of coupled edge and corner states against defects is demonstrated. Our results pave the way for guiding and trapping acoustic waves in topological nonsymmorphic heterostructures, whose multi-channel transmission capability can be employed for designing topological phononic filters, couplers and multiplexers.

A novel unified interference management scheme for multicellular MIMO communication with instantaneous relay

SummaryIn the world of emerging wireless networks, interference poses a significant challenge to reliable wireless communication. Additionally, these networks are prone to path loss and blockages, which can be addressed by utilizing the advanced technology of multihop communication with instantaneous relay (IR). However, scenarios involving IR‐assisted networks are considered instances of multihop communications that face potential obstacles caused by interference. As a result, multiple interference management approaches exist to tackle this interference issue, among which aligned interference neutralization (AIN) is a state‐of‐the‐art technology that seamlessly unifies two established interference management strategies: interference alignment (IA) and interference neutralization (IN). Therefore, this paper presents a novel tristaged AIN scheme to mitigate interference in a multicellular multiple‐input multiple‐output (MIMO) interference multiple access channel (IMAC). In the proposed scheme, the initial stage‐1 involves transmitting message signals from individual transmitters or users to the IR and the receiving base stations (BSs). In stage‐2, the IR neutralizes half of the interference signals by performing IN. Finally, in stage‐3, IA is carried out at the receiver BS terminals, aligning the remaining interference signals equally within the available dimensions. Based on this proposed approach, we determined that for an IR‐aided multicellular MIMO IMAC, the achievable degree of freedom (DoF) is 2N. The proposed approach's robustness and effectiveness have been analyzed through extensive simulations, and these simulation results indicated that the proposed approach outperforms other benchmark interference management techniques in terms of DoF and sum rate, thereby improving user performance.

On obtaining the coupled-mode theory using a model of coupled plane waves for symmetric resonant diffraction gratings

We consider two analytical models describing resonant scattering of light by resonant diffraction gratings possessing a horizontal symmetry plane. For the case of oblique incidence, a multiple interference model is formulated, on the basis of which, a new approach for deriving the coupled-mode theory for the considered structures is proposed. Both considered models describe resonances in the reflectance and transmittance spectra of the studied diffraction gratings arising due to the excitation of quasiguided modes and Fabry–Pérot modes. Interaction of these modes leads to the appearance of bound states in the continuum, which are also described by the proposed models.

HTG transformation: an amplitude modulation method and its application in bearing fault diagnosis

Abstract Rolling bearings are critical components in modern mechanical equipment, and the health monitoring and predictive maintenance of bearings are crucial for the normal operation of machinery. Hence, there is a compelling need to delve into advanced methodologies for enhancing the detection of fault characteristics in bearings. Faulty bearings produce periodic impulses during constant-speed rotation, which can typically be detected through envelope analysis. However, in some complex conditions, the relevant fault frequencies may be hidden within interfering components. This paper presents an amplitude modulation technique called the hyperbolic tangent Gaussian (HTG) transformation, designed to extract weak fault components from signals. Firstly, a family of amplitude modulation functions, known as the HTG functions, is constructed. These functions modulate signals with normalized amplitudes to obtain a series of modulated signals. Simultaneously, a frequency domain amplitude ratio metric is used for the automatic selection of the optimal components. Finally, the HTGgram is introduced, a spectral decomposition method based on trend components, aiming to identify the best combination of filtering and modulation components. Simulations with multi-component bearing fault signals and experimental signals with composite bearing faults demonstrate that this method not only highlights fault features and suppresses noise interference but also adaptively selects frequency bands related to faults, enhancing fault information. This approach exhibits excellent adaptability and effectiveness in complex operating conditions with multiple interference components.

Multiple self-mixing interference with the Faraday effect for detecting magnetic fields.

Detection of magnetic fields is widely used in magnetic materials, electronic devices, medical imaging, and navigation systems. We conducted the experiments using a TGG crystal in different magnetic fields to observe multiple laser self-mixing interference in the frequency domain for experimentation. Every spectral line of the multiple laser self-mixing interference contained the peaks exhibiting a decay trend due to the rotation of the light's polarized orientation according to the Faraday effect. The decay rate of the spectral line depended on the magnetic field density. To quantify the decay rate, the decay coefficient was obtained via fitting of the spectral line. The equation between the decay coefficient and the magnetic field density was established from the experimental results, and the magnetic field density was detected using the equation. This work exhibited a potential detection capability for magnetic fields.

Observation of sunlight diffraction through tree twigs and leaves

This work reports the observation of sunlight diffraction through empty spaces in between twigs and leaves in tree crowns. The diffraction patterns are dominated by red wavelength on front followed by the trail of rest of spectral bands of white light rainbow bursts. Tree crowns act as obstacles and apertures causing diffraction/ interference of solar rays. Separation between leaves and twigs (d) is a few inches and distance between crown and camera (D) is in range of a few meters. Empty spaces in tree crown are much longer than red to violet wavelengths in sunlight therefore scattering, dispersion, reflection, diffraction and interference effects cannot be ruled out. Tree crown deflects solar rays into narrow angles all around to enable interference inside camera lens to create grating structure diffracting light into regular patterns. Diffraction pattern look like red petunia flower having 3 × 3 rose petals. Motorola mobile phone camera was used to record the sunlight diffraction patterns. Mobile camera consists of small focal length (≈5 mm) lens, a fixed opening aperture and an image sensor. Lens consists of few plastic or glass elements and sensor consists of charge coupled device (CCD) or CMOS technology. These diffraction/ interference patterns have potential to increase efficiency of Next Gen solar cells. This paper describes possible theoretical background of light diffraction and multiple ray interference through tree twigs and leaves.

Overcoming the challenge of potent endogenous interferences in limaprost quantification: An innovative methodology combining differential mobility spectrometry with LC-MS/MS for ultra-high sensitivity, selectivity and significantly enhanced throughput

Limaprost, an orally administered analogue of prostaglandin E1, possesses potent vasodilatory, antiplatelet, and cytoprotective properties. Due to its extremely low therapeutic doses and exceedingly low plasma concentrations, the pharmacokinetic and bioequivalence studies of limaprost necessitate a highly sensitive quantitative method with a sub-pg/mL level of lower limit of quantification. Moreover, the intensity of endogenous interferences can even exceed the maximum concentration level of limaprost in human plasma, presenting further challenge to the quantification of limaprost. As a result, existing methods have not yet met the necessary level of sensitivity, selectivity, and throughput needed for the quantitative analysis of limaprost in pharmacokinetic and bioequivalence investigations. This study presents a new methodology that combines differential mobility spectrometry (DMS) with liquid chromatography-tandem mass spectrometry (LC-MS/MS) and utilizes a distinctive strategy to achieve more accurate DMS conditions. This integration yields a method that is currently the most sensitive and features the shortest analytical time, making it the sole technique capable of meeting the requirements for limaprost pharmacokinetic and bioequivalence investigations. This method demonstrates robustness and is successfully employed in a pharmacokinetic investigation of limaprost in human subjects, underscoring that the combination of DMS with LC-MS/MS serves as an efficacious strategy for overcoming the challenges inherent in analyzing biological samples afflicted by multiple interferences.

Internal-multiple-elimination with application to migration using two-way wave equation depth-extrapolation scheme

Internal multiple interference, affecting both seismic data processing and interpretation, has been observed for long time. Although great progress has been achieved in developing a variety of internal-multiple-elimination (IME) methods, how to increase accuracy and reduce cost of IME still poses a significant challenge. A new method is proposed to effectively and efficiently eliminate internal multiples, along with its application in internal-multiple-eliminated-migration (IMEM), addressing this issue. This method stems from two-way wave equation depth-extrapolation scheme and associated up/down wavefield separation, which can accomplish depth-extrapolation of both up-going and down-going wavefields simultaneously, and complete internal-multiple-elimination processing, adaptively and efficiently. The proposed method has several features: (1) input data is same as that for conventional migration: source signature (used for migration only), macro velocity model, and receiver data, without additional requirements for source/receiver sampling; (2) method is efficient, without need of iterative calculations (which are typically needed for most of IME algorithms); and (3) method is cost effective: IME is completed in the same depth-extrapolation scheme of IMEM, without need of a separate processing and additional cost. Several synthesized data models are used to test the proposed method: one-dimensional model, horizontal layered model, multi-layer model with one curved layer, and SEG/EAGE Salt model. Additionally, we perform a sensitivity analysis of velocity using smoothed models. This analysis reveals that although the accuracy of velocity measurements impacts our proposed method, it significantly reduces internal multiple false imaging compared to traditional RTM techniques. When applied to actual seismic data from a carbonate reservoir zone, our method demonstrates superior clarity in imaging results, even in the presence of high-velocity carbonate formations, outperforming conventional migration methods in deep strata.

Research on Improved Road Visual Navigation Recognition Method Based on DeepLabV3+ in Pitaya Orchard

Traditional DeepLabV3+ image semantic segmentation methods face challenges in pitaya orchard environments characterized by multiple interference factors, complex image backgrounds, high computational complexity, and extensive memory consumption. This paper introduces an improved visual navigation path recognition method for pitaya orchards. Initially, DeepLabV3+ utilizes a lightweight MobileNetV2 as its primary feature extraction backbone, which is augmented with a Pyramid Split Attention (PSA) module placed after the Atrous Spatial Pyramid Pooling (ASPP) module. This improvement enhances the spatial feature representation of feature maps, thereby sharpening the segmentation boundaries. Additionally, an Efficient Channel Attention Network (ECANet) mechanism is integrated with the lower-level features of MobileNetV2 to reduce computational complexity and refine the clarity of target boundaries. The paper also designs a navigation path extraction algorithm, which fits the road mask regions segmented by the model to achieve precise navigation path recognition. Experimental findings show that the enhanced DeepLabV3+ model achieved a Mean Intersection over Union (MIoU) and average pixel accuracy of 95.79% and 97.81%, respectively. These figures represent increases of 0.59 and 0.41 percentage points when contrasted with the original model. Furthermore, the model’s memory consumption is reduced by 85.64%, 84.70%, and 85.06% when contrasted with the Pyramid Scene Parsing Network (PSPNet), U-Net, and Fully Convolutional Network (FCN) models, respectively. This reduction makes the proposed model more efficient while maintaining high segmentation accuracy, thus supporting enhanced operational efficiency in practical applications. The test results for navigation path recognition accuracy reveal that the angle error between the navigation centerline extracted using the least squares method and the manually fitted centerline is less than 5°. Additionally, the average deviation between the road centerlines extracted under three different lighting conditions and the actual road centerline is only 2.66 pixels, with an average image recognition time of 0.10 s. This performance suggests that the study can provide an effective reference for visual navigation in smart agriculture.

Optimization technology of hydroelectric power plant unit speed control based on the constriction coefficient-based particle swarm gravitational search algorithm fusion model

In recent years, the integration of wind power into hydropower networks has become a trend. But after wind power is connected to the grid, its unstable power will have an impact on the stability of the power grid. In order to improve the impact of wind power grid connection on the stability of the power supply network, a water motor speed control fusion model considering wind power interference is proposed. Firstly, analyze the impact of wind power grid connection and construct a speed regulation model for grid connected units. Secondly, the gravity search algorithm is introduced to optimize the unit speed control parameters. Considering that the algorithm is prone to local convergence, the convergence factor particle swarm optimization algorithm is adopted to optimize the gravity search algorithm. In addition, considering the impact of wind power interference on unit regulation, the Hopf bifurcation theory is introduced to analyze the dynamic regulation parameter range of hydropower units, in order to meet the requirements of stable operation of the power grid. The research contribution is mainly reflected in suppressing wind power grid connection interference through the speed regulation of hydroelectric units. At the same time, considering the impact of multiple interferences on grid connection, an intelligent model is introduced to optimize the speed control parameters of the unit, thereby improving the stable impact of wind power grid connection on the water conservancy grid. In the simulation analysis of hydroelectric unit speed regulation, after adding 5 % disturbance signal, the proposed model under rigid water hammer disturbance could stabilize the unit's stable control within 15 s, which is superior to other models. In the simulation analysis of speed regulation under wind power disturbance, the proposed model could achieve the fastest and most stable speed regulation under rigid water hammer step wind power disturbance, with a duration of 60 s. The proposed model could respond quickly to small overshoot and overshoot in elastic water hammer slope type wind power disturbances. Therefore, the proposed speed regulation technology for hydropower units has excellent application effects in practical scenarios, which will provide effective technical references for wind power grid connection and hydropower unit regulation.

A Sparse Recovery Algorithm for Suppressing Multiple Linear Frequency Modulation Interference in the Synthetic Aperture Radar Image Domain.

In synthetic aperture radar (SAR) signal processing, compared with the raw data of level-0, level-1 SAR images are more readily accessible and available in larger quantities. However, an amount of level-1 images are affected by radio frequency interference (RFI), which typically originates from Linear Frequency Modulation (LFM) signals emitted by ground-based radars. Existing research on interference suppression in level-1 data has primarily focused on two methods: transforming SAR images into simulated echo data for interference suppression, or focusing interference in the frequency domain and applying notching filters to reduce interference energy. However, these methods overlook the effective utilization of the interference parameters or are confined to suppressing only one type of LFM interference at a time. In certain SAR images, multiple types of LFM interference manifest bright radiation artifacts that exhibit varying lengths along the range direction while remaining constant in the azimuth direction. It is necessary to suppress multiple LFM interference on SAR images when original echo data are unavailable. This article proposes a joint sparse recovery algorithm for interference suppression in the SAR image domain. In the SAR image domain, two-dimensional LFM interference typically exhibits differences in parameters such as frequency modulation rate and pulse width in the range direction, while maintaining consistency in the azimuth direction. Based on this observation, this article constructs a series of focusing operators for LFM interference in SAR images. These operators enable the sparse representation of dispersed LFM interference. Subsequently, an optimization model is developed that can effectively suppress multi-LFM interference and reduce image loss with the assistance of a regularization term in the image domain. Simulation experiments conducted in various scenarios validate the superior performance of the proposed method.