Interference Problem Research Articles

Analysis of Deep-Water Low-Frequency Sound Multipath Arrivals and Interference Striation Patterns

The deep-water sound multipath arrivals and interference striation patterns not only vary with range and depth but also with frequency. The first two cases can be solved using a ray-mode method in the high-frequency limit. However, this method may cause unsatisfactory results in treating low-frequency sounds (as low as tens of Hertz) because of the Wentzel-Kramers-Brillouin (WKB) high-frequency approximation assumption. To overcome this problem, the authors exploited phase information to modify the ray parameters, such as the propagation range, the time delay, and the group velocity. Following that, a waveguide invariant (WI) expression was derived to address low-frequency sound interference problems. Numerical investigations demonstrated that the physical mechanism of the frequency dependence of the multipath arrivals and interference striation patterns could be better understood using this modified ray-mode method. In addition, the influence of bottom sound speed on multipath interference structures is also considered.

Retrieval of Total Suspended Matter Concentration Based on the Iterative Analysis of Multiple Equations: A Case Study of a Lake Taihu Image from the First Sustainable Development Goals Science Satellite’s Multispectral Imager for Inshore

Inland waters consist of multiple concentrations of constituents, and solving the interference problem of chlorophyll-a and colored dissolved organic matter (CDOM) can help to accurately invert total suspended matter concentration (Ctsm). In this study, according to the characteristics of the Multispectral Imager for Inshore (MII) equipped with the first Sustainable Development Goals Science Satellite (SDGSAT-1), an iterative inversion model was established based on the iterative analysis of multiple linear regression to estimate Ctsm. The Hydrolight radiative transfer model was used to simulate the radiative transfer process of Lake Taihu, and it analyzed the effect of three component concentrations on remote sensing reflectance. The characteristic band combinations B6/3 and B6/5 for multiple linear regression were determined using the correlation of the three component concentrations with different bands and band combinations. By combining the two multiple linear regression models, a complete closed iterative inversion model for solving Ctsm was formed, which was successfully verified by using the modeling data (R2 = 0.97, RMSE = 4.89 g/m3, MAPE = 11.48%) and the SDGSAT-1 MII image verification data (R2 = 0.87, RMSE = 3.92 g/m3, MAPE = 8.13%). And it was compared with iterative inversion models constructed based on other combinations of feature bands and other published models. Remote sensing monitoring Ctsm was carried out using SDGSAT-1 MII images of Lake Taihu in 2022–2023. This study can serve as a technical reference for the SDGSAT-1 satellite in terms of remote sensing monitoring of Ctsm, as well as monitoring and improving the water environment.

5G‐based passive radar on a moving platform—Detection and imaging

AbstractIn recent times, the development of radar systems for automotive applications has gathered significant interest due to the increasing necessity of vehicle situational awareness for new active protection systems and the intensive development of higher autonomous level driving solutions. Therefore, the number of active radars in automotive applications is growing, causing spectrum sharing and interference between radars operating in the same band. Alternative solutions without dedicated electromagnetic transmissions, such as joint communication and radar systems, or even no transmission at all, such as passive radars, have emerged in recent years and are promising solutions to help mitigate the interference problem. A passive automotive radar based on 5G communication signals is proposed as an alternative to active radars to provide situational awareness. The data downlink transmissions provided by an operative 5G base station were combined with a dual‐channel passive radar (PR) system deployed on a moving platform to provide moving target detection and radar imaging of the vehicle's surroundings. The outcomes show a possibility of commensal utilisation of the new telecommunication standard for automotive radar applications. The idea of mounting the PR receiver on a moving platform was tested using simulated and real‐life data, which shows great potential for joining the new radio (NR) with sensing capabilities using PR. The theory, numerical experiments, and measurement results are dealt with a cooperative 5G base station and PR demonstrator.

Hydrogeological Investigation of a Goaf and Subsidence Area Based on a Ground-to-Air Transient Electromagnetic Sounding Method

Water hazards in coal mines have always been the main geological hazard that restricts the safety of coal mine production. The traditional electromagnetic method is difficult to accurately detect the hydrogeological conditions of underground goafs due to accuracy and interference problems. The ground-to-air transient electromagnetic method is an electromagnetic detection technology with strong adaptability to various terrains, a large detection depth, a wide coverage, a high resolution, and fast speed. This paper mainly applies the ground-to-air transient electromagnetic method to conduct a hydrogeological engineering geological survey in a mine subsidence area. By using this method effectively, changes in hydrogeological and engineering geological conditions in the mine subsidence area are identified and water filling conditions below section 310 and section 250 of the roadway are determined. This study provides accurate and reliable basic data support for rescue operations and post-disaster reconstruction efforts, while also offering valuable insights for similar exploration projects.

Local maximum synchrosqueezing reassigning chirplet transform and its application to gearbox fault diagnosis

Abstract Aiming at the problems of poor time-frequency aggregation and severe noise interference when traditional time-frequency analysis methods deal with complex multi-component signals, this paper proposes a new time-frequency analysis method – local maximum synchrosqueezing reassigning chirplet transform (LMSRCT). The core idea of the method is to introduce the principle of general linear chirplet transform (GLCT) into synchro-reassigning transform (SRT), followed by reassigning the results of local maximum synchrosqueezing chirplet transform (LMSSCT), and then introducing the innovative synchro-reassigning operator (SRO). This results to a novel three-step method for extracting instantaneous frequency, which ultimately yields the final time-frequency representation. This method ensures the integrity of each instantaneous frequency, solves the problem of energy divergence problems, and improves aggregation. Simulation and experimental results show that the LMSRCT can provide better characterization results compared to other methods and can effectively solve the different situations that occur between the instantaneous frequencies of complex signals. The proposed method can effectively estimate transmission speed, which achieves fault diagnosis under tacholess conditions, and the order analysis results are more accurate and reliable.

FPGA-based interference elimination for health monitoring in dynamic environments

Since flexible wearable sensing devices are mostly used to acquire the human health information by fitting, the vibration generated by the wearer’s movement, physiological state, environment, and other dynamic factors will interfere the accuracy of collected signals. In order to solve the signal interference problem in dynamic environments, a system is needed to eliminate the strong interfering signals in disturbing scenarios in order to acquire the target signal. In this paper, a FPGA-based system is designed to eliminate the signal interference, where a PFGA chipset is served as the core hardware part, an adaptive notch filter is used to process the sensing signals and the FPGA and the host computer are interacted in real time, thus various vibration noises suffered by the flexible sensing device could be eliminated when it is applied to health monitoring in dynamic environments.

Advanced NOMA-aided indoor optical wireless communications using improved quantum optimized resource

At present, Non-Orthogonal Multiple Access (NOMA) has become the most efficient technique to solve Data Rate (DR) requirements in Visible Light Communication (VLC) systems. However, present NOMA systems show high interference and increase the Peak-to-Average Power Ratio (PAPR), especially in wider applications. To overcome this issue, several techniques have been undertaken in the past and proven to better communication performance. However, the existing studies fail to provide a better Quality of Services (QoS) for the recent multi-carrier Optical Communication System (OCS). Hence, this study put forth a novel Generalized Frequency Division Multiplexing (GFDM) scheme to minimize the PAPR in an indoor-based NOMA-VLC system. To enhance the performance of the GFDM system, a novel Offset-based Quadrature Amplitude Modulation (OQAM) technique is introduced that enhances the signal quality and prevents the Co-Channel Interference (CCI) problems effectively. Moreover, the proposed study introduces a novel Quantum-enabled Rabbit Optimization (QRO) technique for solving Resource Allocation (RA) problems in the NOMA-VLC system. The proposed method is processed via the MATLAB platform and various performance measures like Sum Rate (SR), Signal-to-Interference Noise Ratio (SINR), and Symbol Error Rate (SER) are analyzed and distinguished with various existing studies. In the simulation scenario, the proposed method achieves the SR of 178Mbps, SINR of 16 dB, and SER of compared to conventional techniques.

Identification of rice disease under complex background based on PSOC-DRCNet

Rice is a crucial agricultural crop, yet it frequently suffers from various diseases, leading to decreased yields and, in severe cases, crop failure. Diseases significantly affect rice growth and yield, resulting in economic losses and food security challenges. The role of image recognition in identifying rice diseases is critical in agricultural production. It enables automated and efficient detection of rice diseases, which is essential for effective management, ensuring food security and sustainable agriculture. To address issues like background noise and edge blurring in rice disease image capture, as well as challenges in determining the optimal learning rate during the training of traditional rice disease recognition networks, a novel method based on PSOC-DRCNet is proposed for rice disease recognition.. First, tto solve the problem of background interference, Dual Mode Attention (DMA) is proposed to adaptively capture meaningful regions in rice disease images. Second, the Residual Adaptive Block(RAB) is proposed, which utilizes dimensional changes and channel attention to solve edge blur problems. Then, a Cross entropy and regularized mixed Loss function (CerLoss), is proposed to optimize the learning strategy of the model in the process of processing datasets and enhance the performance and generalization ability of the model to avoid overfitting problems. Ultimately, In response to the cumbersome problem of finding the optimal learning rate, we propose using Particle Swarm Optimization Chameleon (PSOC) to find the optimal learning rate and train the PSOC-DRCNet model on our custom dataset and compare it with other existing methods and the final average classification accuracy of PSOC-DRCNet is 93.88% with an F1 score of 0.940. We compare it with other existing methods. It is proved that the average classification accuracy of our model under hyper-parameter unification is 92.65% F1 score is 0.928. We validated the PSOC-DRCNet by conducting comparative analyses with other models and through generalization experiments and module effectiveness tests. Additionally, the practicality of PSOC-DRCNet was confirmed through its application in real-world scenarios. The methods proposed in this paper successfully enable the identification of various diseases in rice leaves, offering a practical solution for incorporating deep learning into the agricultural production process. Furthermore, these findings serve as a valuable reference for disease identification in other crops.

Color-polarization synergistic target detection method considering shadow interference

Aiming at the problem of shadow interference in UAV's ground reconnaissance, a color and polarization synergistic target detection method is proposed for anti-shadow interference, based on the influence of two physical characteristics (wavelength and polarization) under different illuminations. A valid fusion strategy is proposed via integrating two separate detection results on color and polarization images. Moreover, a local enhancement and recognition module (LER) is introduced to boost the performance. Based on our built dataset, experimental results show that our method achieves mAP of 87.76%, and 12.37% higher than that of color image and 14.68% higher than that of polarization image.

Planar wideband balanced-to-balanced microstrip diplexer with reflectionless characteristics

In this paper, a planar wideband balanced-to-balanced microstrip diplexer with excellent reflectionless characteristics is presented. In order to solve the problem of interference caused by reflected energy in wireless communication systems for conventional balanced diplexers, a novel design concept of a balanced absorptive diplexer with reflectionless characteristics is realized by loading reflectionless stubs with different operating frequencies at differential transmission ports and differential reception ports. To confirm the validation of modeling design, a microstrip prototype of a balanced absorptive diplexer is manufactured and measured with lower and upper channels centered at 1.2 and 1.8 GHz, with a 3-dB bandwidth of 36.7 % and 33.3 %, respectively.

Dynamic Positioning Control of Large Ships in Rough Sea Based on an Improved Closed-Loop Gain Shaping Algorithm

In order to solve the problem of the dynamic positioning control of large ships in rough sea and to meet the need for fixed-point operations, this paper proposes a dynamic positioning controller that can effectively achieve large ships’ fixed-point control during Level 9 sea states (wind force Beaufort No. 10). To achieve a better control effect, a large ship’s forward motion is decoupled to establish a mathematical model of the headwind stationary state. Meanwhile, the closed-loop gain shaping algorithm is combined with the exact feedback linearization algorithm to design the speed controller and the course-keeping controller. This effectively solves the problem of strong external interferences impacting the control system in rough seas and guarantees the comprehensive index of robustness performance. In this paper, three large ships—the “Mariner”, “Taian kou”, and “Galaxy”—are selected as the research objects for simulation research and the final fixing error is less than 10 m. It is proven that the method is safe, feasible, practical, and effective, and provides technical support for the design and development of intelligent marine equipment for use in rough seas.

Implementation and analysis of a parallel kalman filter algorithm for lidar localization based on CUDA technology.

Introduction: Navigation satellite systems can fail to work or work incorrectly in a number of conditions: signal shadowing, electromagnetic interference, atmospheric conditions, and technical problems. All of these factors can significantly affect the localization accuracy of autonomous driving systems. This emphasizes the need for other localization technologies, such as Lidar. Methods: The use of the Kalman filter in combination with Lidar can be very effective in various applications due to the synergy of their capabilities. The Kalman filter can improve the accuracy of lidar measurements by taking into account the noise and inaccuracies present in the measurements. Results: In this paper, we propose a parallel Kalman algorithm in three-dimensional space to speed up the computational speed of Lidar localization. At the same time, the initial localization accuracy of the latter is preserved. A distinctive feature of the proposed approach is that the Kalman localization algorithm itself is parallelized, rather than the process of building a map for navigation. The proposed algorithm allows us to obtain the result 3.8 times faster without compromising the localization accuracy, which was 3% for both cases, making it effective for real-time decision-making. Discussion: The reliability of this result is confirmed by a preliminary theoretical estimate of the acceleration rate based on Ambdahl's law. Accelerating the Kalman filter with CUDA for Lidar localization can be of significant practical value, especially in real-time and in conditions where large amounts of data from Lidar sensors need to be processed.

A Practical Solution to Reduce Interference from Led Lights

Abstract The study explores the detection of a harmful, wide-band interference signal, whose origin is a traditional desk LED (light-emitting diode) lamp. The interference signal was noticed on the sensitive solar spectrometer observing system, which was operating at a frequency range between 100 and 300 MHz. The interference signal was so strong and wide-band that it destroyed totally the solar observations. The study introduces two practical EMI (Electromagnetic Interference) reducing methods: ferrite cores and shielding (shielding effectiveness, S.E.). Their theoretical background is presented, and, in addition, these methods are tested in practice. The measurements and tests showed that even simple ferrite core was a very effective method to reduce interference effects. It is important that a suitable setup will be found: a single ferrite core cannot solve a whole problem. This interference problem is very demonstrative and it can be used in educational purposes at an undergraduate level at Metropolia University of Applied Sciences, Finland. This study reveals the fact that standard lamps, which are on the market, do not necessarily follow good EMC (Electromagnetic Compatibility) practices.

Unmanned aerial vehicle fault diagnosis based on ensemble deep learning model

To address the problems of external interference during unmanned aerial vehicle (UAV) flight and the low accuracy and weak generalization ability of the current single fault diagnosis model, this work proposes a weighted ensemble deep learning UAV fault diagnosis method. First, considering the differences in training methods and fault feature recognition principles of deep networks with different structures, three hybrid fault diagnosis models are constructed. These models are constructed by combining convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM) and bidirectional gate recurrent unit (BiGRU). CNN is used to extract the features of the UAV flight data and the obtained feature information is fed into BiLSTM and BiGRU to explore the fault information inherent in the time series data. Then, the three hybrid fault diagnosis models are used as the individual model of the ensemble learning algorithm, and the weights of the three individual models are optimized using a random grid search algorithm to construct a UAV fault diagnosis model based on hybrid deep learning weighted ensemble, which further improves the fault diagnosis performance. Finally, it is demonstrated experimentally that the proposed hybrid deep learning weighted ensemble fault diagnosis model can effectively identify the fault of UAV with an accuracy of 99.22% and 99.62% on binary and multivariate classification, respectively, and reflects better generalization performance in the metrics of precision, recall, and F1 score.

Research on ecosystem detection based on wireless communication network

Ecosystem monitoring is of great significance in environmental protection and sustainable development. Wireless communication networks provide a broad application prospect for ecosystem monitoring, but there is a lack of further research on the strengths and weaknesses of the existing research results and specific application scenarios, as well as on the reliability of data processing. This paper aims to explore the theoretical basis of the reliability of wireless communication networks in ecosystem monitoring. Firstly, the current status, characteristics and challenges of wireless communication networks in ecosystem monitoring are reviewed, the key elements in ecosystem monitoring and the requirements for data integrity and accuracy are analyzed in detail, and the reliability problems of signal interference, node failure and data loss in data transmission are discussed. Secondly, methods and strategies to improve the reliability of ecosystem monitoring based on wireless communication networks are explored. By reviewing and analyzing the existing research results, the advantages and disadvantages of different methods as well as the applicable scenarios are elaborated. Finally, the paper proposes directions and challenges for future research. The exploration of emerging technologies is emphasized. At the same time, issues that need to be addressed, network security, energy efficiency and cost feasibility are pointed out. The significance of this paper in academic and industry practice can further deepen the exploration of the theoretical basis of reliability and combine it with practical application scenarios for verification and optimization.

Modified Single–Working Portal Technique Using Percutaneous Spinal Needle Suture Passing in Arthroscopic Subscapularis Repair

The main method for arthroscopic repair of the subscapularis is repair with suture anchors. The surgeon generally establishes the anterior and anterolateral operation portals to complete anchor implantation and suture passing, respectively. The single–operation portal technique has been developed recently. However, in the traditional single–operation portal technique, the suture device and grasper are difficult to operate simultaneously. In addition, with the traditional rotator cuff suture device, it is easy to cause further iatrogenic injury to the rotator cuff because of its larger diameter. Therefore, we describe a modified single–operation portal technique for suture passing percutaneously with a spinal needle taking into account the shortcomings of existing techniques. Our modified technique avoids the use of traditional suturing devices and effectively avoids further damage to the rotator cuff. The use of a single operation portal makes the operation more minimally invasive and simple and effectively avoids the problem of interference between the suture device and grasper in the same portal. The entire operational process does not require the use of costly consumables, resulting in increased cost-effectiveness and a significantly reduced operating time. In conclusion, our modified technique achieves the use of a single operation portal to suture the subscapularis through spinal needle suture passing, which has good clinical value.

Dual quaternion hand-eye calibration algorithm for hunter-prey optimization based on twice opposition-learning and random differential variation

In order to solve the problem of noise interference in camera calibration and robot forward kinematics solution, this study proposes a dual quaternion hand-eye calibration algorithm based on twice opposition-learning and random differential variation (ODHPO). The hand-eye calibration equation was innovatively rewritten in the form of dual quaternion, and the F-norm minimization model of the rotation and translation error function was constructed for optimization. At the same time, the penalty function method is introduced to effectively transform the constrained optimization problem in hand-eye calibration into an unconstrained problem, which is further solved by the ODHPO algorithm for global optimization. Compared with the singular value decomposition algorithm based on the traditional dual quaternion method, the ODHPO algorithm performs better in global optimization ability and convergence stability. Through numerical simulation and real robot hand-eye calibration experiments, it is proved that the proposed algorithm is superior to the traditional dual quaternion (CDQ) algorithm, the classical algorithms Tsai method and Navy method in terms of solution accuracy, sensitivity to the number of pose transformations and stability, demonstrating its potential for application and practical significance in robot vision system.

Optical fiber gas sensor with multi-parameter sensing and environmental anti-interference performance

Formic acid finds widespread applications in numerous fields, including the petrochemicals production, rubber industry, and leather tanning process, in which the operational environments are complex and diverse. The corresponding industrial systems are often intricate and highly integrated. Indeed, formic acid gas is insidious and poses a hazard to both operating personnel and industrial equipment. The existing detection technologies for formic acid gas suffer from inherent technical limitations, making it challenging to effectively shield against industrial interference in complex environments while accurately conveying information. Furthermore, the simultaneous transmission of essential environmental data alongside formic acid gas concentration remains a formidable challenge, which is crucial for facilitating the integration and simplification of industrial system. In response to the aforementioned challenges, this research firstly employs an optical fiber structure (SNS: single mode fiber-no-core fiber-single mode fiber) with natural resistance to electromagnetic interference as the substrate. Subsequently, considering the characteristics of spectral variations and the response mechanism of formic acid gas, and addressing the issue of humidity interference of the current-stage sensors based on sensitive material, the chitosan and chitosan@PDMS are chosen as sensitive membranes. Finally, a correction formula was introduced to address the temperature and humidity coupling issue of the sensor, resulting in the successful development of two formic acid gas sensors. The chitosan-based sensor has a detection sensitivity of up to 7.7 pm/ppm, whose dual parameter detection performance for temperature and formic acid gas has been experimentally demonstrated. It can also correct the humidity-induced detection error in real-time by the correction formula. The chitosan@PDMS-based sensor was fabricated by plating a hydrophobic and breathable PDMS membrane on the surface of the chitosan-based sensor. In addition to maintaining the performance compared to the original sensor (response and recovery time was increased), the latter sensor can effectively resist the humidity interference from the environment. Furthermore, the new capability has been complemented for the simultaneous detection of temperature and humidity. That is, it realizes the dual parametric detection for formic acid gas and humidity, meanwhile providing the compensation ability for temperature change. The proposed sensors have the outstanding sensitivity, selectivity, immunity, and safety. This research not only effectively resolves the interference problem in formic acid gas detection, but also provides a new idea for equipment simplification and information integration in complex industrial systems.

Printed graphene and its composite with copper for electromagnetic interference shielding applications.

Advances in mobile electronics and telecommunication systems along with 5G technologies have been escalating the electromagnetic interference (EMI) problem in recent years. Graphene-based material systems such as pristine graphene, graphene-polymer composites and other graphene-containing candidates have been shown to provide adequate EMI shielding performance. Besides achieving the needed shielding effectiveness (SE), the method of applying the candidate shielding material onto the object in need of protection is of enormous importance due to considerations of ease of application, reduced logistics and infrastructure, rapid prototyping and throughput, versatility to handle both rigid and flexible substrates and cost. Printing readily meets all these criteria and here we demonstrate plasma jet printing of thin films of graphene and its composite with copper to meet the EMI shielding needs. SE over 30 dB is achieved, which represents blocking over 99.9% of the incoming radiation. Graphene and its composite with copper yield higher green index compared to pure copper shields, implying reduced reflection of incoming electromagnetic waves to help reduce secondary pollution.

Optimization Techniques in Cooperative and Distributed MAC Protocols

The tremendous increase in wireless network application finds distributed allocation of resources allocation very useful in the network. Packet delivery ratio and delay can be improved by concentrating on payload size, mobility, and density of nodes in the network. In this article, a survey is carried out on different cooperative and distributed MAC protocols for communication and optimization algorithms for various applications and the mathematical issues related to game theory optimizations in MAC protocol. Spatial reuse of channel improved by (3-29) % and multi-channel improves throughput by 8% using distributed MAC protocol. The energy utility of individual players can be focused to get better network performance with NASH equilibrium. Fuzzy logic improves channel selection by 17% and secondary users' involvement by 8%. Jamming, interference problems can be addressed using cross layer approach in the MAC and simultaneous data, voice transmissions in IoT; WSN applications can be attained using hybrid distributed MAC protocol.