Weak Interference Research Articles

Critical Points in the Noiseberg Achievable Region of the Gaussian Z-Interference Channel

The Gaussian signaling strategy with power control for the Gaussian Z-interference channel with weak interference is reviewed in this paper. In particular, we study the various communication strategies that may arise at various points of the capacity region and identify the locations of the phase transitions between the various strategies. The Gaussian Z-interference channel with weak interference is known to have two critical points in its capacity region, where the slope of the region shows a sudden change. They occur at the points of the unconditional maximum rate for one of the users and the maximum rate that can be accommodated by the other user. In this paper, we discuss additional critical points (locations of phase transitions) in the achievable region of this channel. These turn out to be second-order phase transitions, i.e., we do not observe a discontinuous slope in the achievable rate region, but there is a discontinuity in the second derivative of the rate contour of the achievable region. This review paper is mainly based on some of our ITA (Information Theory and Applications Workshop, UCSD, San Diego, CA, USA) papers since 2011.

Performance Analysis of Reconnaissance Coverage for HUAV Swarms under Communication Interference Based on Different Architectures

In environments with unknown communication interference, the mission efficiency of heterogeneous unmanned aerial vehicle (HUAV) swarms is often impacted by communication disruptions due to regions of strong interference encountered when executing reconnaissance and coverage missions. Existing research has rarely focused on communication interference or on the differences in HUAV characteristics under various control architectures; thus, in this paper we explore the performance differences between HUAV swarms based on centralized, distributed, and centralized-distributed architectures when executing reconnaissance and coverage missions in environments with unknown communication interference. First, a communication model in an unknown interference environment is constructed to reflect the real-time communication status of the swarm. Second, in response to the limitations of the traditional artificial potential field (APF) algorithm in this environment, a coverage-oriented artificial potential field (COAPF) algorithm is proposed. Finally, based on the COAPF algorithm, a multi-dimensional comparison of the mission completion efficiency of HUAV swarms with three different architectures is conducted. Our simulation results indicate that the distributed architecture is suitable for large-scale environments with strong interference, while the centralized–distributed architecture performs better in small-scale environments with weak interference. Conversely, the centralized architecture exhibits poor performance in all interference scenarios due to its lack of decision-making capabilities.

Ultrahigh-Reflectivity Circularly Polarized Mirrors Based on the High-Contrast Subwavelength Chiral Metasurface

The circularly polarized laser sources are core components for many optical applications such as biomedicine, quantum technology, and AR/VR. However, conventional techniques make it difficult to further diminish the size of circularly polarized lasers. Thus, the high-contrast subwavelength chiral metasurface (HCCM) with a 980 nm operating wavelength is numerically investigated. The HCCM is composed of chiral metasurfaces modulating the circular dichroism of reflectance and 6 pairs of Distributed Bragg Reflectors (DBR) with 55% reflectivity. The reason that the HCCM has an ultra-high reflectivity (99.9%) at the operating wavelength of 980 nm is the combination of the optical refractive index difference between the GaAs metasurface and the AlOx substrate and weak destructive interference in the AlOx support layer. In addition, the circular dichroism of the chiral metasurfaces (2.1%) is mainly caused by the displacement of two square air holes in opposite directions, thus transforming the unit cell of the metasurface from C2 symmetry to chiral symmetry. The reflector has the advantages of a simple structure and miniaturization, which is expected to greatly reduce the fabrication difficulty and cost of the circular polarization VCSELs.

A dual-mechanism pretreatment idea for improving the derived Raman spectrum for (micro)plastic analysis

ABSTRACT Raman spectroscopy is an effective tool for microplastic analysis, but its performance can be limited by weak signals and surface interference. This study aims to enhance derived Raman spectra through chemical surface treatments. Fresh and biofouled plastic debris samples, including polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET), were treated overnight with various chemical solutions: 30% H2O2, 5% NaOCl, 5% HCl, and a sequential treatment of 5% HCl followed by 5% NaOCl (HCl:NaOCl). Stereo microscope examinations revealed that single-solution treatments removed some stains but left others behind, while complete stain removal was achieved with the HCl:NaOCl treatment. The control samples exhibited a high fluorescence issue, but oxidation pretreatment effectively reduced this, with 5% NaOCl outperforming 30% H2O2. Despite eliminating fluorescence, residual stains still obscured peak signals. In contrast, the sequential HCl:NaOCl treatment removed almost visible stain, resulting in very clean surfaces, minimal background signals, and significantly higher S/N ratios for peak signals. This study demonstrates that dual-mechanism pretreatments – combining acid dissolution and oxidation – are far more effective than single treatments, benefiting applications that require clean plastic samples for subsequent operations.

Experimental Investigation Using Robust Deep VMD-ICA and 1D-CNN for Condition Monitoring of Roller Element Bearing

Abstract A rotor-bearing system experiences numerous vibrations during the operation that frequently degrade performance and endanger operational safety. Roller bearing failure has significant consequences, leading to downtime or even a complete outage of rotating machinery. It is crucial to detect and diagnose incipient bearing defects promptly to ensure optimal operation of the machinery and minimize potential disruptions to the process. Deep Independent Component Analysis is a necessity used in modern condition monitoring to detect bearing failure earlier than it occurs. To address this issue the feasibility of utilizing Deep Independent Component Analysis (ICA) method based on Variational Modal Decomposition (VMD) with one-dimensional convolutional neural network (1D-CNN) to diagnose incipient bearing defect. Fast Fourier Techniques are utilized to extract the vibration signatures of artificially damaged bearings on a newly built test bed. VMD addresses to minimize data noise, allowing data to decompose into various sub-datasets for extraction of incipient defect features. With weak defect characteristic signal and noise interference, the Deep VMD-ICA model and 1D-CNN simplicity improved the accuracy of diagnosis corresponding to the experimental results. Moreover, Deep VMD-ICA with 1D-CNN has additionally demonstrated strong performance in comparison to experimental results and is useful for monitoring the condition of industrial machinery. The results reveal that this fault diagnosis approach is reliable, with a diagnostic accuracy of 98.93% for bearing faults.

Optimal acquisition time estimation method for CSEM with high-order pseudo-random signal

High-order pseudo-random signal is gradually being applied in controlled-source electromagnetic (CSEM) exploration. In contrast to the conventional single-frequency sweep mode, the high-order pseudo-random signal enables simultaneous transmission of multiple frequencies. However, estimating a fixed acquisition time based on observed noise levels often results in poor adaptability for high-order pseudo-random signal, which only require reception of one set of waveform. In this study, we presented an estimation method for acquisition time for CSEM with high-order pseudo-random signal using an improved logistic function. The improved logistic function was proposed to introduce a time-decay factor into the governing equation for the first time. By considering the transformation rule of noise statistical characteristics with time, the specific parameters of the function have been determined to better describe the dynamic evolution process of the signal quality. The effective frequencies were extracted at various acquisition times based on the noise evaluation number, and the resulting quantity of effective frequencies was used as the fitting target. Guidance for the fieldwork was determined based on the average time of the saturation period, in accordance with the properties of the function. The reliability of the improved logistic function was validated through a transmission current data simulation. The proposed method was demonstrated through the measured data from both strong and weak interference areas.

Reprogramming Yarrowia lipolytica metabolism for efficient synthesis of itaconic acid from flask to semipilot scale.

Itaconic acid is an emerging platform chemical with extensive applications. Itaconic acid is currently produced by Aspergillus terreus through biological fermentation. However, A. terreus is a fungal pathogen that needs additional morphology controls, making itaconic acid production on industrial scale problematic. Here, we reprogrammed the Generally Recognized As Safe (GRAS) yeast Yarrowia lipolytica for competitive itaconic acid production. After preventing carbon sink into lipid accumulation, we evaluated itaconic acid production both inside and outside the mitochondria while fine-tuning its biosynthetic pathway. We then mimicked the regulation of nitrogen limitation in nitrogen-replete conditions by down-regulating NAD+-dependent isocitrate dehydrogenase through weak promoters, RNA interference, or CRISPR interference. Ultimately, we optimized fermentation parameters for fed-batch cultivations and produced itaconic acid titers of 130.1 grams per liter in 1-liter bioreactors and 94.8 grams per liter in a 50-liter bioreactor on semipilot scale. Our findings provide effective approaches to harness the GRAS microorganism Y. lipolytica for competitive industrial-scale production of itaconic acid.

A Multi-Format, Multi-Wavelength Erbium-Doped Fiber Ring Laser Using a Tunable Delay Line Interferometer

This work demonstrates the use of an erbium-doped fiber amplifier (EDFA), a tunable bandpass filter (TBF), and a tunable delay line interferometer (TDLI) to form a ring laser that produces multi-format, multi-wavelength laser beams. The TDLI serves as the core of the proposed laser generation system. TDLI harnesses the weak Fabry–Pérot (FP) interferences generated by its built-in 50/50 beamsplitter (BS) with unalterable filtering characteristics and the interferences with free spectral range (FSR) adjustable from each of its two outputs with nearly complementary phases to superpose and generate a variable interference standing wave. The interferometric standing wave and weak FP interferences are used to form a spatial-hole burning to promote the excitation of multi-format and multi-wavelength lasers. The proposed system enables dual-wavelength spacing ranging from 0.3 nm to 3.35 nm, with a switchable wavelength position at approximately 1527 nm to 1535 nm, providing flexible tunability.

Range-Doppler image classification for interference detection in over-the-horizon radar

Sky-wave over-the-horizon radar (OTHR), as a useful tool for beyond-line-of-sight target detection, is inevitably affected by radio frequency interference (RFI) and transient interference (TSI). Conventional interference detection is usually based on radar signal processing. This paper proposes to detect the interference by classifying the range-Doppler (RD) image into three categories, i.e. RFI, TSI, and NoI (No Interference), according to the texture differences. Unlike the conventional way, RD image classification has direct reflection of the interference's influence on target detection. To design the RD image classifier, we develop signal models to construct the simulated database, use real data to construct the real database, and employ various classification theories, e.g. deep learning, traditional machine learning, feature fusion, and ensemble learning. Experimental results show that the RD image classifier is an effective and promising tool for interference detection. Strong interference can be accurately detected, while weak interference needs more study in the future.

The radial-tangential anisotropy of numerosity perception.

Humans can estimate the number of visually presented items without counting. In most studies on numerosity perception, items are uniformly distributed across displays, with identical distributions in central and eccentric parts. However, the neural and perceptual representation of the human visual field differs between the fovea and the periphery. For example, in peripheral vision, there are strong asymmetries with regard to perceptual interferences between visual items. In particular, items arranged radially usually interfere more strongly with each other than items arranged tangentially (the radial-tangential anisotropy). This has been shown for crowding (the deleterious effect of clutter on target identification) and redundancy masking (the reduction of the number of perceived items in repeating patterns). In the present study, we tested how the radial-tangential anisotropy of peripheral vision impacts numerosity perception. In four experiments, we presented displays with varying numbers of discs that were predominantly arranged radially or tangentially, forming strong and weak interference conditions, respectively. Participants were asked to report the number of discs. We found that radial displays were reported as less numerous than tangential displays for all radial and tangential manipulations: weak (Experiment 1), strong (Experiment 2), and when using displays with mixed contrast polarity discs (Experiments 3 and 4). We propose that numerosity perception exhibits a significant radial-tangential anisotropy, resulting from local spatial interactions between items.

Fluorescence Lifetime Measurement For Studying Evaporation Of Bi-Component Droplet

Sprays are essential in engineering applications like evaporators and combustion chambers. Predicting the evaporation of multi-component liquid droplets is complex due to changing composition and properties. Measuring droplet composition is challenging, with limited non-intrusive methods available. Raman spectroscopy faces hurdles like weak signals and interference. Intensity based Laser-Induced Fluorescence (LIF) requires optical models to interpret data due to varying droplet size and position. Fluorescent dyes in liquid mixtures are sensitive to temperature, complicating measurements. Fluorescence lifetime measurements offer a promising alternative, being an absolute quantity less affected by reabsorption. This study develops a novel method using fluorescence lifetime to analyze droplet composition, leveraging Time-Correlated Single Photon Counting (TCSPC) for precise measurements. Factors such as dye concentration, solvent polarity, and temperature sensitivity are investigated. Calibration curves for ethanol-water mixtures correlate fluorescence lifetime with volume fraction. Experimental setups use TCSPC and femtosecond lasers for dye excitation. Results show that fluorescence lifetime decreases linearly with solvent polarity and is influenced by temperature and dye concentration. High dye concentrations cause self-quenching, altering temperature sensitivity. The method was applied to study the evaporation of ethanol-water droplets under acoustic levitation. Findings indicate that evaporation rates slow as the water fraction increases, with initial measurements showing higher water content due to partial evaporation. This research introduces a novel technique for droplet compositional analysis using fluorescence lifetime, providing insights into optimizing measurement accuracy and advancing understanding in engineering and related fields.

Synergistic enhancement of NC-GCN/Ag2O photocatalytic reduction of uranium by D-A and p-n junction structures

Recently, how to improve the photocatalytic uranium removal performance of Graphite-phase carbon nitride (GCN) has always been a hot research topic. In this paper, a new strategy based on p-n junction and D-A structure is proposed to improve the photocatalytic performance. And as a result, a novel cyano modified photocatalytic material NC-GCN/Ag2O was designed and prepared for the reduction of U(VI). Interestingly, the material shows better adsorption performance for U(VI) in the dark treatment stage, and its removal of U(VI) is close to 70 % under the initial U(VI) concentration of 20 ppm and the solid–liquid ratio of 0.2 g/L. And the adsorption is in accordance with the Langmuir model and Pseudo second order model. After 30 min of illumination, the removal rate of U(VI) can reach 93.2 %, significantly higher than that of GCN (13.6 %) and NC-GCN10(37.7 %). It’s worth mentioning that photocatalytic reduction dominated the adsorption-reduction process for U(VI) removal after light exposure. In addition, after four cycles, the removal of U(VI) from the composite material decreased by only 5 % with no change in the crystal structure, i.e., it has good reusability and stability. Meanwhile, there was no significant interference with other ions except for Cu2+ and V5+, which had a weak interference. Finally, a new mechanism for U(Ⅵ) reduction was proposed.

On Superposition Lattice Codes for the K-User Gaussian Interference Channel.

In this study, we work with lattice Gaussian coding for a K-user Gaussian interference channel. Following the procedure of Etkin et al., in which the capacity is found to be within 1 bit/s/Hz of the capacity of a two-user Gaussian interference channel for each type of interference using random codes, we work with lattices to take advantage of their structure and potential for interference alignment. We mimic random codes using a Gaussian distribution over the lattice. Imposing constraints on the flatness factor of the lattices, the common and private message powers, and the channel coefficients, we find the conditions to obtain the same constant gap to the optimal rate for the two-user weak Gaussian interference channel and the generalized degrees of freedom as those obtained with random codes, as found by Etkin et al. Finally, we show how it is possible to extend these results to a K-user weak Gaussian interference channel using lattice alignment.

A Spatial Small Target Detection Method Based on a Multi-Scale Feature Fusion Pyramid Spatial Small Target Detection Method Based on Multi-scale Feature Fusion Pyramid

Small target detection has become an important part of space exploration missions. The existence of weak illumination and interference from the background of star charts in deep and distant space has brought great challenges to space target detection. In addition, the distance of space targets is usually far, so most of them are small targets in the image, and the detection of small targets is also very difficult. To solve the above problems, we propose a multi-scale feature fusion pyramid network. First, we propose the CST module of a CNN fused with Swin Transformer as the feature extraction module of the feature pyramid network to enhance the extraction of target features. Then, we improve the SE attention mechanism and construct the CSE module to find the attention region in the dense star map background. Finally, we introduce improved spatial pyramid pooling to fuse more features to increase the sensory field to obtain multi-scale object information and improve detection performance for small targets. We provide two versions and conducted a detailed ablation study to empirically validate the effectiveness and efficiency of the design of each component in our network architecture. The experimental results show that our network improved in performance compared to the existing feature pyramid.

Research on the processing and interpretation methods of distributed fiber optic vibration signal logging injection profiles

The oil and gas industry currently faces the dual challenges of improving extraction efficiency and reducing environmental impacts. Traditional well monitoring technologies are increasingly unable to meet the demands of modern oil and gas extraction due to technological limitations and environmental concerns. Distributed Acoustic Sensing (DAS) technology, which utilizes optical fibers as sensing elements, enables real-time and accurate monitoring of the CO2 injection process in wells. However, DAS technology encounters issues such as weak amplitude signals and noise interference in practical applications. In response, a comprehensive method for processing and interpreting DAS logging data for CO2 injection wells has been proposed. This method involves restoring amplitude signals through wavefront energy compensation and applying Gaussian filtering and wavelet threshold denoising algorithms for noise reduction. The processed results are classified using the K-Medoids clustering algorithm and the CO2 absorption volume in the injection layer is calculated using the integral area method. The accuracy and practicality of this method have been validated through comparative analysis with pulse neutron oxygen activation logging results. This approach aims to address the challenges faced by DAS technology in the oil and gas industry, thereby providing technical support for the efficient, environmentally friendly, and intelligent management of oil and gas fields.

G-formula for observational studies under stratified interference, with application to bed net use on malaria.

Assessing population-level effects of vaccines and other infectious disease prevention measures is important to the field of public health. In infectious disease studies, one person's treatment may affect another individual's outcome, that is, there may be interference between units. For example, the use of bed nets to prevent malaria by one individual may have an indirect effect on other individuals living in close proximity. In some settings, individuals may form groups or clusters where interference only occurs within groups, that is, there is partial interference. Inverse probability weighted estimators have previously been developed for observational studies with partial interference. Unfortunately, these estimators are not well suited for studies with large clusters. Therefore, in this paper, the parametric g-formula is extended to allow for partial interference. G-formula estimators are proposed for overall effects, effects when treated, and effects when untreated. The proposed estimators can accommodate large clusters and do not suffer from the g-null paradox that may occur in the absence of interference. The large sample properties of the proposed estimators are derived assuming no unmeasured confounders and that the partial interference takes a particular form (referred to as 'weak stratified interference'). Simulation studies are presented demonstrating the finite-sample performance of the proposed estimators. The Demographic and Health Survey from the Democratic Republic of the Congo is then analyzed using the proposed g-formula estimators to assess the effects of bed net use on malaria.

5G new radio multiuser precoding in geostationary satellite networks

SummaryThe multiuser precoding procedures standardized by the 3rd Generation Partnership Project (3GPP) for new radio (NR) represent a new opportunity to realize satellite precoding for capacity improvement, given the integration of non‐terrestrial networks in the 3GPP framework. This work analyses and numerically evaluates them for the geostationary satellite scenarios consolidated by the 3GPP. It shows that the preferred precoding matrix indicated by NR codebooks is not able to handle properly the inter‐beam interference and should only be used to estimate the propagation channel. To this aim, we propose the use of modified type II port selection codebooks reporting on the strongest beams, which correspond to the higher interferences. Since the obtention of high precoding gains requires the operation in rather high signal‐to‐noise regimes and the proper estimation of the interfering contributions from many beams, we propose to enlarge the maximum number of beams reported by each precoding matrix indicator (PMI) report, currently limited to four. As alternative, we also evaluate a sequential multi‐report framework considering the effects of channel‐state information aging and the user equipment (UE) processing times. It preserves the current limits on the number of reported beams but increases the system overhead signalling. The quantization of estimated coefficients is also analysed, not only from the overhead point of view but also to properly report weak interferers corresponding to distant beams, due to the increase in the number of reported beams. Moreover, we identify the minimum interference power a UE should be able to detect to achieve the theoretical large precoding gains.

Phonon Blockade in a Hybrid Quadratically Coupled Optomechanical System

AbstractPhonon statistics in a hybrid optomechanical system, where a mechanical resonator is quadratically coupled to a cavity and couples with another resonator via Coulomb interaction is studied. It is found that the conventional and unconventional phonon blockade can be both achieved at the same parameter regime, which is confirmed by analytical and numerical results. By analyzing the energy level structure, it is found that the anharmonicity of the system is the joint action of both the Coulomb interaction and the optomechanical coupling, where the nonlinearity introduced by the latter has a more significant effect on one branch of the conventional phonon blockade. In addition, the phonon blockade can be enhanced by introducing the mechanical parametric amplifier with optimal parameters, where the physical mechanism underlying comes from the multi‐pathway destructive interference. It is found that the phonon blockade is fragile toward thermal noise due to the weak quantum interference, which can be overcome by increasing the driving strength and applying the mechanical parametric amplifier with suitable parameters. This work may provide a way to manipulate the phonon blockade and can be helpful to the application of quantum information processing.

Identification of Interwell Interference Based on Hydrogeochemical Characteristics of Produced Water from Coalbed Methane Wells: A Case Study in the Southern Qinshui Basin, China

Summary Interwell interference is the superposition effect of coalbed methane (CBM) reservoir pressure. This study aims to provide a new direction for the quantitative analysis of interwell interference from the hydrogeochemical characteristics of produced water from CBM wells. A total of 24 produced water samples collected from the Panhe (PH) group, Shizhuangnan (SZN)-1 group, and SZN-2 group in Qinshui Basin were selected for the comparative analysis. The water type of all water samples is characterized by Na-HCO3, with Na+ being the main total dissolved solids (TDS) provider. The self-similar major ionic characteristics of the PH and SZN-2 groups are prone to the occurrence of interwell interference. The δD and δ18O show that the main source of produced water is atmospheric circulating water. The similar isotope characteristics of produced water in the PH and SZN-2 groups represent that there is remarkable interwell interference. Sr, As, Cu, Ga, Li, Rb, Sn, Mo, and V are selected as indicator elements. In the cluster analysis, all CBM wells form a single cluster in the PH and SZN-2 groups in the first three iterations, indicating interwell interference. According to the established fuzzy discriminative model, interwell interference is divided into two types—strong interwell interference and weak interwell interference. Most CBM wells in the PH and SZN-2 groups show strong interwell interference. This study can provide theoretical foundations for the dynamic pressure regulation and well pattern infilling of CBM wells.

“Zero” Intrinsic Fluorescence Sensing‐Platforms Enable Ultrasensitive Whole Blood Diagnosis and In Vivo Imaging

AbstractAbnormal physiological processes and diseases can lead to content or activity fluctuations of biocomponents in organelles and whole blood. However, precise monitoring of these abnormalities remains extremely challenging due to the insufficient sensitivity and accuracy of available fluorescence probes, which can be attributed to the background fluorescence arising from two sources, 1) biocomponent autofluorescence (BCAF) and 2) probe intrinsic fluorescence (PIF). To overcome these obstacles, we have re‐engineered far‐red to NIR II rhodol derivatives that possess weak BCAF interference. And a series of “zero” PIF sensing‐platforms were created by systematically regulating the open‐loop/spirocyclic forms. Leveraging these advancements, we devised various ultra‐sensitive NIR indicators, achieving substantial fluorescence boosts (190 to 1300‐fold). Among these indicators, 8‐LAP demonstrated accurate tracking and quantifying of leucine aminopeptidase (LAP) in whole blood at various stages of tumor metastasis. Furthermore, coupling 8‐LAP with an endoplasmic reticulum‐targeting element enabled the detection of ERAP1 activity in HCT116 cells with p53 abnormalities. This delicate design of eliminating PIF provides insights into enhancing the sensitivity and accuracy of existing fluorescence probes toward the detection and imaging of biocomponents in abnormal physiological processes and diseases.