Interference Problem Research Articles

Adaptive Ultra-Wideband/Pedestrian Dead Reckoning Localization Algorithm Based on Maximum Point-by-Point Distance

Positioning using ultra-wideband (UWB) signals can be used to achieve centimeter-level indoor positioning. UWB has been widely used in indoor localization, vehicle networking, industrial IoT, etc. However, due to non-line-of-sight (NLOS) and multipath interference problems, UWB cannot provide adequate position information, which affects the final positioning accuracy. This paper proposes an adaptive UWB/PDR localization algorithm based on the maximum point-by-point distance to solve the problems of poor UWB performance and the error accumulation of the pedestrian dead reckoning (PDR) algorithm in NLOS scenarios that is used to enhance the robustness and accuracy of indoor positioning. Specifically, firstly, the cumulative distribution function (CDF) map of localization under normal conditions is obtained through offline pretraining and then compared with the CDF obtained when pedestrians are moving on the line. Then, the maximum point-by-point distance algorithm is used to identify the abnormal base stations. Then, the standard base stations are filtered out for localization. To further improve the localization accuracy, this paper proposes a UWB/PDR algorithm based on an improved adaptive extended Kalman filtering (EKF), which dynamically adjusts the position information through the adaptive factor, eliminates the influence of significant errors on the current position information and realizes multi-sensor fusion positioning. The realization results show that the algorithm in this paper has a solid ability to identify abnormal base stations and that the adaptive extended Kalman filtering (AEKF) algorithm is improved by 81.27%, 58.50%, 29.76%, and 18.06% compared to the PDR, UWB, EKF, and AEKF algorithms, respectively.

Improved Electromagnetic Interference Shielding in Lightweight Polyvinylidene Fluoride‐Carbon Nanotube Composite Films

ABSTRACTAs electronic devices become smaller and more common, the problem of electromagnetic interference (EMI) becomes more serious, requiring better shielding solutions. In this study, lightweight polyvinylidene fluoride (PVDF) composite films mixed with multiwalled carbon nanotubes (MWCNT) were developed to improve EMI shielding. A solution casting method and a thin film applicator ensured uniform thickness and flexibility in PVDF‐MWCNT films with varying MWCNT concentrations (0, 0.1, 1, and 2 wt%). Structural and surface properties were analyzed using X‐ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and energy dispersive X‐ray spectroscopy (EDS). Dielectric response, AC conductivity, and EMI shielding effectiveness (EMI‐SE) in the Ku band (12–18 GHz) were measured. The PVDF film with 2 wt% MWCNT, at a thickness of 190 ± 10 μm, showed the highest complex permittivity and a record EMI‐SE value of 53.08 dB. This high EMI‐SE is attributed to increased multiple internal reflections within the thin film, enhancing attenuation and reducing reflection losses. These polymer nanocomposites offer a promising and lightweight option for advanced EMI shielding in the growing number of electronic devices.

COSMIC-2 RFI Prediction Model Based on CNN-BiLSTM-Attention for Interference Detection and Location.

As the application of the Global Navigation Satellite System (GNSS) continues to expand, its stability and safety issues are receiving more and more attention, especially the interference problem. Interference reduces the signal reception quality of ground terminals and may even lead to the paralysis of GNSS function in severe cases. In recent years, Low Earth Orbit (LEO) satellites have been highly emphasized for their unique advantages in GNSS interference detection, and related commercial and academic activities have increased rapidly. In this context, based on the signal-to-noise ratio (SNR) and radio-frequency interference (RFI) measurements data from COSMIC-2 satellites, this paper explores a method of predicting RFI measurements using SNR correlation variations in different GNSS signal channels for application to the detection and localization of civil terrestrial GNSS interference signals. Research shows that the SNR in different GNSS signal channels shows a correlated change under the influence of RFI. To this end, a CNN-BiLSTM-Attention model combining a convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM), and attention mechanism is proposed in this paper, and the model takes the multi-channel SNR time series of the GNSS as the input and outputs the maximum measured value of RFI in the multi-channels. The experimental results show that compared with the traditional band-pass filtering inter-correlation method and other deep learning models, the model in this paper has a root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R2) of 1.0185, 1.8567, and 0.9693, respectively, in RFI prediction, which demonstrates a higher RFI detection accuracy and a wide range of rough localization capabilities, showing significant competitiveness. Since the correlation changes in the SNR can be processed to decouple the signal strength, this model is also suitable for future GNSS-RO missions (such as COSMIC-1, CHAMP, GRACE, and Spire) for which no RFI measurements have yet been made.

Mother Tongue Interference and Its Effects: A Case Study of a Selected Secondary School in Matabeleland South in Zimbabwe

The study was conducted in a selected secondary school in Matabeleland South Province of Zimbabwe where the problem of mother tongue interference on the teaching and learning involving African indigenous languages was detected. The inquiry adopted a qualitative paradigm while interviews and observations were data gathering instruments. The population comprised the Head of Department (HOD), IsiNdebele teachers and learners. The sample consisted of an HOD, two IsiNdebele teachers and forty form one and form two learners altogether. Collected data were thematically presented, discussed and analysed. Revealed in the study was that mother tongue interference is inevitable and has negative and positive effects. Although mother tongue interference is perceived negatively, its importance in education is that it provides the basis for acquiring of the target language. Hence, this spontaneous featuring should not be perceived as interference. Instead, it should be used to consolidate the acquisition of concepts in the target language.

Hierarchical configuration design of PAN/Gr@MWCNTs composite film with superior electromagnetic shielding and excellent Joule heating via in-situ sedimentation strategy

With the rapid development of 5G communication technology, the surge in the number of antenna modules for intelligent terminal equipment has led to the intensification of electromagnetic radiation and interference problems, posing a major threat to electronic equipment safety, information systems stability, and human health. Traditional electromagnetic shielding materials have made it difficult to meet the demand for intelligent, integrated electronics. In this study, we proposed an in-situ sedimentation strategy to fabricate flexible polyacrylonitrile/graphene@multi-walled carbon nanotubes (PAN/Gr@MWCNTs) composite film with hierarchical double-layered structures, thus realizing ultra-thin, highly conductive, and excellent electromagnetic shielding and Joule heating properties. Particularly, the as-formed hierarchical film possessed an ultra-thin thickness of 0.158 mm and a high conductivity of 1660.26 S/m, simultaneously accompanied by an electromagnetic interference shielding efficiency (EMI SE) of 42.82 dB and a specific shielding efficiency (SSE) of 271.01 dB/mm. In addition, the film exhibited excellent Joule heating capability in the voltage range of 1.50-2.25 V. This study provides innovative ideas for developing flexible electromagnetic shielding films, which are of great scientific significance and potential application in electromagnetic radiation protection.

Influence of Modal Decomposition Algorithms on Nonlinear Time Series Machine Learning Prediction Models in Engineering: A Case Study of Subway Tunnel Settlement

The settlement values of subway tunnels during the construction period exhibit significant nonlinear and spatial‒temporal variation characteristics. To overcome the problems of historical data interference and spatiotemporal characteristics in tunnel settlement prediction models, this paper proposes a tunnel settlement prediction method based on data decomposition, reconstruction, and optimization. First, the original data are optimized via the SSA, which has global optimization capability, high noise immunity, and high adaptivity. The original signal is subsequently decomposed into multiple subsignal sequences via a variational modal decomposition (VMD) algorithm combined with a rolling decomposition technique. Finally, the decomposed signals are fed into the machine learning model to construct a high-precision settlement prediction model based on rolling decomposition. The prediction accuracy of different models was analyzed via the measured settlement data during the construction period of the Beijing subway as an example. The results show that the prediction model with the integrated decomposition algorithm reduces the RMSE and MAE by 33% and 37%, respectively, which significantly improves the prediction accuracy and generalization ability of the neural network to meet the demand of practical engineering prediction and simultaneously enhances the risk warning ability of the model.

Improving cache-enabled D2D communications using actor–critic networks over licensed and unlicensed spectrum

Cache-enabled Device-to-Device (D2D) communications is an effective way to improve data sharing. User Equipment (UE)-level caching holds the potential to reduce the data traffic burden on the core network. Licensed spectrum is utilized for D2D communications, but due to spectrum scarcity, exploring unlicensed spectrum is essential to enhance network capacity. In this paper, we propose caching at the UE-level and exploit both licensed and unlicensed spectrum for optimizing throughput. First, we propose a reinforcement learning-based data caching scheme leveraging an actor–critic network to improve cache-enabled D2D communications. Besides, licensed and unlicensed spectrum are devised for D2D communications considering interference from existing cellular and Wi-Fi users. A duty cycle-based unlicensed spectrum access algorithm is employed, guaranteeing the Signal-to-Interference and Noise Ratio (SINR) required by the users. The unlicensed spectrum is prone to data packets collisions. Therefore, Request-to-Send/Clear-to-Send (RTS/CTS) mechanism is utilized in conjunction with Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) to alleviate both the interference and packets collision problems of the unlicensed spectrum. Extensive simulations are performed to analyze the performance gain of our proposed scheme compared to the benchmarks under different network scenarios. The obtained results demonstrate that our proposed scheme possesses the potential to optimize network performance.

A General Strategy to Rapid and Sensitive Determination of Five High Boiling Point Solvents in Pharmaceuticals by RP-HPLC Combined with Precipitation Method.

The difficulty on determination of high boiling point solvents, always exist due to their low vapor pressure in headspace (HS) vial. What's worse, the commonly used GC diluents, such as N, N-dimethylacetamide, 1,3-dimethyl-2-imidazolidinone, dimethyl sulfoxide, N, N-dimethylformamide and N-methylpyrrolidone are often remained in HS instrument after frequent injection into HS, thus would result in terrible positive interference problem. In this work, a fast and sensitive HPLC method in combination with a sample precipitation method, was proposed for the determination of above five high boiling point solvents in drug substances within 14.5min. The final method was validated with regard to limit of detection and limit of quantitation, precision, linearity and accuracy in accordance with ICH Q2 (R1). Finally, the proposed strategy was successfully applied for the analysis of high boiling point solvents used in three active pharmaceutical ingredients. The established generic strategy would provide another choice for the determination of high boiling point solvents in pharmaceuticals.

Research on pulse laser detection anti-interference method in smoke environment

To address the problem of smoke medium interference on pulse laser detection signals, this study employs Monte Carlo method to precisely establish the backscattered signals model of pulsed lasers in smoke conditions, and proposes an anti-interference strategy combining particle swarm optimization (PSO) and convolutional neural network (CNN) by echo signals collected experimentally. Specifically, this study uses the Monte Carlo method and scattering phase function to simulate particle collisions and constructs a sampling model of reflected photons based on the target surface’s reflective properties. Semi-analytical reception technology is used to extract the received backscattered photon signals. Finally, a CNN is built taking all the backscattered data points as the input. The parameter of the network is optimized using PSO algorithm. To verify the accuracy of the simulated backscattered echo signals and the effectiveness of the anti-interference algorithm, a laser detection experiment in a smoke environment was conducted. The experimental results show that the simulated detection backscattered signals and the experimental backscattered signals have a high degree of consistency, with a maximum root mean square error of only 0.043. The recognition accuracy of the CNN optimized by the PSO algorithm is 96.154 %, with a computational parameter size of 193.008 KB. This study provides solid theoretical support and technical assurance for the pulse laser detection technology in the anti-interference field.

Street tree segmentation method combining image semantic segmentation and point cloud clustering

Abstract As a common object in the park scene, the 3D information of street trees is crucial for digital cities, and extracting individual trees from point cloud data accurately and efficiently is a hotspot and a difficult point-in-point cloud processing. Aiming at the interference problem of complex non-tree objects and dynamic objects in the park environment, a tree point cloud segmentation method combining a deep learning network framework with a point cloud clustering algorithm is proposed in this paper. First, this paper applies the YOLOv8 to the semantic segmentation of camera images, and fuses image semantics with point cloud data, to build the semantic point cloud model. Then, preliminary extraction for street trees is performed using the semantic information of this point cloud model, and radius filtering is used to reduce the outliers. Finally, the individual tree segmentation is realized using an enhanced multi-level Euclidean clustering algorithm based on KD-Tree. Experimental results indicate that the proposed method performs well in extracting and segmenting the roadway trees, with a recall of 94.5%, a precision of 80.7%, and an F1-score of 87.0%.

Phase coded waveforms for integrated sensing and communication systems

AbstractNowadays, especially with the developments in the automotive industry, it has become a highly popular topic for radar and communication systems to operate on the same platform and perform joint tasks to increase situational awareness. Sharing the hardware of the radar and communication systems and using a joint waveform for both functions eliminate interference problems between the systems, increasing the effectiveness of the joint task. In studies where radar signals are utilised for communication functions, continuous wave frequency modulation or intra‐pulse frequency modulation is generally employed to transmit communication signals. There are a few studies in the literature on joint radar and communication waveforms using phase modulation. In these studies, phase modulation is not employed directly for both functions. As a contribution to the literature, this study evaluates the usage of joint radar and communications waveforms, such as Barker sequences and Zadoff–Chu sequences as opposed to linear frequency modulation. We compare these waveforms terms of range‐speed detection and symbol error rate.

Research on registration method for enface image using multi-feature fusion

Objective.The purpose of this work is to accurately and quickly register the Optical coherence tomography (OCT) projection (enface) images at adjacent time points, and to solve the problem of interference caused by CNV lesions on the registration features.Approach.In this work, a multi-feature registration strategy was proposed, in which a combined feature (com-feature) containing 3D information, intersection information and SURF feature was designed. Firstly, the coordinates of all feature points were extracted as combined features, and then these feature coordinates were added to the initial vascular coordinate set simplified by the Douglas-Peucker algorithm as the point set for registration. Finally, the coherent point drift registration algorithm was used to register the enface coordinate point sets of adjacent time series.Main results.The newly designed features significantly improve the success rate of global registration of vascular networks in enface images, while the simplification step greatly improves the registration speed on the basis of preserving vascular features. The MSE, DSC and time complexity of the proposed method are 0.07993, 0.9693 and 42.7016 s, respectively.Significance.CNV is a serious retinal disease in ophthalmology. The registration of OCT enface images at adjacent time points can timely monitor the progress of the disease and assist doctors in making diagnoses. The proposed method not only improves the accuracy of OCT enface image registration, but also significantly reduces the time complexity. It has good registration results in clinical routine and provides a more efficient method for clinical diagnosis and treatment.

Deep Learning Models For Symbol Detection in UFMC Systems

The limited availability of the frequency band in wireless communication systems is one of the major obstacles to achieving high-speed data transmission. To overcome this obstacle, multicarrier systems, which utilize the available frequency bandwidth most efficiently to ensure spectral efficiency and consequently high data rate transmission, are used. In the Universal Filtered Multi-Carrier (UFMC) technique, which is one of the multi-carrier systems, in addition to high-speed data transmission, the bandwidth is divided into many sub-bands and only the lower sidebands are filtered, and as a result, the inter-channel interference problem is minimized. However, in UFMC systems, the error-free reception of symbols at the receiver is directly dependent on the performance of the symbol detection algorithm. In this study, symbol detection was performed in UFMC systems by taking advantage of the learning ability of deep learning methods, providing flexible solutions in solving nonlinear problems, reducing the hardware load by using fewer parameters and the ability to perform parallel processing, and thus the symbol detection performance of the system under bad channel conditions was increased.

Scraper Point Attitude Optimization in the Scraping Process of Aviation Composite Curved Surface

Aiming at the interference problem between the tool and the surface during the contact between the blade position and the composite material during the off-line programming scraping process of aviation composite parts, the contact effect of the blade during the scraping process of the composite surface is discussed, and a rotation projection attitude optimization strategy is proposed. Firstly, by constructing the mathematical model of the contact between the scraper and the surface, the geometric properties of the scraper and its contact on the surface are analyzed. Then, the non-uniform rational B-spline (NURBS) surface is used to reconstruct the surface, and the position information of the local area of the contact point is obtained to obtain the normal direction and curvature. In order to evaluate the contact effect of the scraper, an objective function is defined to minimize the contact error between the scraper linear trajectory and its projection on the surface, thereby quantifying the contact accuracy. By comparing the position and attitude of the scraper before and after optimization, the effectiveness of the proposed optimization method is verified. The optimized scraper posture significantly improves the contact accuracy with the composite surface, better adapts to the geometric characteristics of the complex surface, and ensures the efficiency and consistency of the scraping process. The research results provide a new perspective and method for the future development of automatic scraping process, and also provide an effective solution and technical means for other industries in the application scenario of curved scraping.

Development of the headspace gas chromatography-tandem mass spectrometry method for ethylene oxide and ethylene chlorohydrin residue in medical devices.

Ethylene oxide (EO) sterilization is commonly employed for the sterilization of medical devices and has a very high market share. However, EO and its metabolite ethylene chlorohydrin (ECH) are toxic to humans. In compliance with the classification and residue limits of medical devices defined by ISO 10993-7, our study established two extraction methods for the testing of EO and ECH. The first method involves simulated-use extraction using water as the extraction solvent. While the second, exhaustive extraction, directly extracts sample through headspace sampling analysis. Gas chromatography-tandem mass spectrometry in multiple reaction monitoring mode was utilized, requiring only 16 min. Then, the developed method was applied to assess 10 commercially available medical devices sterilized by EO. In simulated-use extraction, calibration curves were evaluated in the range of 1-100 and 5-500 μg for EO and ECH, respectively (r > 0.999). Inter-day recoveries ranged from 85.0% to 95.2% and from 94.8% to 102.4%. In exhaustive extraction, calibration curves spanned 0.5-50 and 2-200 μg for EO and ECH, respectively (r > 0.999). Inter-day recoveries ranged from 101.6% to 102.1% for EO and from 98.1% to 102.2% for ECH. After analysis of the 10 commercially available medical devices, two cotton swabs were found to have ECH of 35.1 and 28.4μg per device, and four medical devices were found to have EO with concentration below the limit of quantification. Meanwhile, we found that the EO internal standard (propylene oxide) recommended by ISO 10993-7 had interference problems with other similar substances and was not suitable as an internal standard for EO. This study offers a sensitive and straightforward analytical approach to EO and ECH residues in a variety of medical devices. In addition, the results show that the EO or ECH content of these types of medical devices in our study falls below the regulatory limits, therefore instilling confidence among consumers regarding their safe use.

Recent Patents on Piano Tuning System

Piano tuning plays a very important role in piano playing and music education. Regular tuning can prolong the service life of the piano, improve the sound saturation, and make the sound more concentrated and full. Automated mechanical equipment, controllers, sensors, and other equipment are used to automatically tune pianos to solve the problems of human error, overtone resonance interference, string breakage, etc., occurring in manual tuning. To solve the above problems, based on the synthesis of related patents and research results, the patents on automatic tuning system and methods for modulating piano are introduced in detail. An automatic tuning system can replace manual tuning, simplify the complex process of tuning, and consider the problem of overtone interference caused by the vibration of the strings to improve the accuracy of tuning; it can, at the same time, perform real-time monitoring of the string tension, protecting the strings from breaking due to tuning. The automatic tuning system has a simple structure and a high degree of automation; its method is highly accurate and it can replace professional tuning technicians, being highly innovative and widely practical.

Research on numerical prediction of hydrodynamic interference under coupling of pod propeller and hull

Abstract Taking the cable-laying ship and the pod propeller as the research objects, based on the Reynolds time-averaged RANS equation and sliding moving grid technology, the self-propulsion numerical calculation of the coupling of the pod propeller and the hull was completed. By comparing the resistance of the bare hull and the thrust of the pod propeller, the hydrodynamic interference under the coupling state of the pod propeller and the hull is analyzed. The results show that when Fr=0.295, the total resistance of the ship increases by 21.6%, the pressure difference resistance increases by 30.5%, the wake fraction is 0.011, and the thrust derating is 0.178. The research results provide theoretical and technical guidance on the interference problem between the hull and the pod propeller.

Design of an Integrated System for Spaceborne SAR Imaging and Data Transmission.

In response to the conflicting demands between real-time satellite communication and high-resolution synthetic aperture radar (SAR) imaging, we propose a method that aligns the data transmission rate with the imaging data volume. This approach balances SAR performance with the requirements for real-time data transmission. To meet the need for mobile user terminals to access real-time SAR imagery data of their surroundings without depending on large traditional ground data transmission stations, we developed an application system based on filter bank multicarrier offset quadrature amplitude modulation (FBMC-OQAM). To address the interference problem with SAR signals' transmission and reception, we developed a signal sequence based on spaceborne SAR echo and data transmission and reception. This system enables SAR and data transmission signals to share the same frequency band, radio frequency transmission system, and antenna, creating an integrated sensing and communication system. Simulation experiments showed that, compared to the equal power allocation scheme for subcarriers, the echo image signal-to-noise ratio (SNR) improved by 2.79 dB and the data transmission rate increased by 24.075 Mbps.

تحليل وتصميم شبكة محلية لاسلكية آمنة للحرم الجامعي في الجامعات الليبية

This research presents the wireless university campus network and discusses about the strengths and weaknesses of Design a secure wireless campus networks for ABC University in Libya, and about the costs and advantages of different vendor solutions. Wireless offers mobility and is available in locations where a wired outlet doesn't exist. The disadvantages of wireless include some minor interference problems and a quickly evolving and competitive group of standards. The purpose of the research is designing and implementing a secure campus network for Libyan university based on Cisco equipments. Moreover, the research can provide practical experience in network design, configuration, and security for helping organizations and institutions protect their network resources and data. Two end products or deliverables will result from the research: the first is a new architectural design for an efficient and advanced WLAN-friendly network to ensure the high performance of the data transaction process and ensure high availability of data transmissions. The second product is a report for network administrators or network architecture to guide users towards the achievement of WLAN-friendly by utilising advanced networking tools and technologies. Keywords: - Campus network; Wireless Mesh Network; wireless network; Encryption; Cisco equipment; security.

Stochastic Evolutionary Analysis of an Aerial Attack–Defense Game in Uncertain Environments

Aiming at the problem of random environment interference in the process of strategy interaction and the behavioral evolution of an aerial attack–defense game, this paper considers the influence of the difference in the performance and value between both game players in terms of strategy evolution; explores the randomness of the complex battlefield environment, the uncertainty of the behavioral state of game players, and the limitations of the emergent situation; constructs a mathematical model of the stochastic evolution of an aerial-coordinated attack–defense game in uncertain environments; and studies the stability of the strategy interaction and behavioral decision-making process of both players of the aerial attack–defense game. Simulation results show that many factors of the performance and value between both game players have a greater impact on the strategy evolution trend in both game players, which not only causes changes in the results of the strategy selection but also affects the rate of strategy evolution for the game players. In addition, random environmental factors cause a certain degree of interference to the strategy evolution process of the game players, which usually accelerates the game players’ strategy evolution rate and greatly affects the evolution process of the game players’ strategy. This study can provide a theoretical basis and feasible reference for improving mission decision-making, response mechanisms, and system modeling of an aerial attack–defense game, which has important theoretical value and practical significance.