Interference Problem Research Articles

Post-Cancellation-Based LLR Refining for MIMO Multiple ARQ Systems

In multiple-input multiple-output (MIMO) multiple automatic repeat request (ARQ) systems, multiple streams with independent hybrid ARQ (HARQ) processes can be simultaneously sent. Thus, the interference from other streams can affect future retransmissions of a packet as well as the current transmission, and proper management of interference at the receiver is required. Therefore, in this paper, a post-cancellation-based log-likelihood ratio (LLR) refining scheme is proposed for MIMO multiple ARQ (MMARQ) systems. In the proposed scheme, after the end of the entire conventional reception procedure for packet decoding, LLR refining is performed for the non-terminated packets that will be sent during the next transmission time interval. For LLR refining, the packet cancellation is performed to cancel only the successfully decoded packets. Thus, the LLRs of the non-terminated packets are refined without any error propagation, including the inter-transmission error propagation. Consequently, the proposed scheme can compensate for the interference problem in MMARQ systems and improve system performance. In order to utilize the error detection results of decoded codewords, the proposed scheme should be performed after the end of the entire reception procedure for packet decoding. Therefore, as the post-processing scheme, the proposed scheme can be employed to any existing LLR-level combining-based MMARQ receiver without changing the original procedure. Simulation results verify that the proposed scheme can significantly improve the error performance and throughput of MMARQ systems, especially for hard-decision interference cancellation-based receivers and high-order modulation. In addition, compared with the conventional reception procedure, the proposed scheme requires a smaller computational complexity in most of the simulated SNR region. Therefore, the proposed LLR refining scheme can be considered as an effective and practical post-processing scheme for an MMARQ receiver.

Analysis of arbitrary tooth profiles of cylindrical gears using normal polar coordinates (Application to the generation of a gear tooth profile by a given tooth profile of rack cutter and its interference problem)

Analysis of arbitrary tooth profiles of cylindrical gears using normal polar coordinates (Application to the generation of a gear tooth profile by a given tooth profile of rack cutter and its interference problem)

On the problem of semantic interference of nomen in teaching RFL

On the problem of semantic interference of nomen in teaching RFL

Research on Pedestrian Re-identification Techniques in Dynamic Scenes Using Convolutional Neural Networks

Abstract Pedestrian re-recognition is the process of retrieving pedestrians with the same identity information as a given pedestrian from a cross-domain view candidate image dataset or a non-overlapping surveillance video sequence using computer vision techniques. The goal of this paper is to use convolutional neural network techniques to re-recognize pedestrians in dynamic scenes. Through the use of convolutional calculations, activation function selection, and other techniques, this paper provides basic technical support for the research of pedestrian re-recognition technology. A Siamese network is obtained by applying convolutional neural networks to pedestrian recognition as the main discriminative model for subsequent research. In order to effectively solve the problem of occlusion, illumination, and other possible interference with the recognition effect in dynamic scenes, this paper adopts the image enhancement method of random erasure and introduces the attention mechanism to improve the robustness of the model to the occlusion of pedestrian images. Through the examination of the model on the dataset of the average accuracy mean (MAP) and other indicators and the actual application in the construction site and other scenes, it is proved that the pedestrian re-recognition model used in this paper has a more significant recognition performance compared with other models, and can still maintain more than 80% of the accuracy rate under the application of dynamic and complex scenes.

Co-Channel Interference Mitigation in 5g Network using Particle Swarm Optimization

The co-channel interference problem in 5G wireless networks is the subject of this research project. Even with the improvements brought forth by 5G, co-channel interference still degrades network speed and signal quality. Particle Swarm Optimization (PSO) is the method used in this study to minimize this interference and improve network performance by fine-tuning resource allocation settings. PSO-based interference mitigation, network modeling, simulation, an overview of the literature, and comparative analysis are all included in the study. The study is valuable for communication engineers since it explores PSO as a means of improving network quality and expanding our knowledge of wireless technology, even though it is restricted to co-channel interference and PSO. In the sphere of 5G wireless communication, this discovery represents a substantial advancement. The application of particle swarm optimization has shown to be innovative in addressing the complex problem of co-channel interference. Insights into the possibilities of clever optimization strategies for interference reduction can be gained from the applied models and results. With 5G networks set to revolutionize the digital environment, these findings provide a strong basis for future research efforts focused at refining interference avoidance techniques and ultimately boosting the performance and dependability of these networks.

A salivary urea sensor based on a microsieve disposable gate AlGaN/GaN high electron mobility transistor.

The abundant bio-markers in saliva provide a new option for non-invasive testing. However, due to the presence of impurities in the saliva background, most of the existing saliva testing methods rely on pre-processing, which limits the application of saliva testing as a convenient means of testing in daily life. Herein, a disposable-gate AlGaN/GaN high electron mobility transistor (HEMT) biosensor integrated with a micro-sieve was introduced to solve the problem of signal interference caused by charged impurities in saliva for HEMT based biosensors, where the micro-sieve was utilized as a pre-treatment unit to remove large particles of impurities from saliva through the size effect and thus greatly improving the accuracy of detection. The experimental results showed that the HEMT based biosensor has excellent linearity (R2 = 0.9977) and a high sensitivity of 6.552 μA dec-1 for urea sensing from 1 fM to 100 mM in 0.1× PBS solution. When it comes to artificial saliva detection, compared to the HEMT sensor without the micro-sieve (sensitivity = 3.07432 μA dec-1), the sensitivity of the HEMT sensor integrated with the micro-sieve showed almost no change. Moreover, to verify that urea can be detected in actual saliva, urea is sensed directly in human saliva. The addition of the microsieve module provides a new way for biosensors to detect specific markers in saliva in real time, and the designed HEMT biosensor with the microsieve function has a wide range of application potential in rapid saliva detection.

Modifikasi Protokol Routing DSDV Menggunakan Algoritma Dynamic-power transmission untuk Mengurangi Interferensi Sinyal dalam Pengiriman Data Berdasarkan Tingkat Kepadatan Node di Jaringan MANET

Mobile Ad-Hoc Network (MANET) is a decentralized collection of nodes that exchange information temporarily via wireless transmission. When nodes communicate, all nodes in the communication range will be affected by signal interference from other nodes. The problem that arises due to this signal interference is that it will affect or interfere with the signal transmitted to the receiver, resulting in a decrease in network performance which will impact the quality of service (QoS). To overcome the problem of signal interference, use interference management. This study applies the Dynamic-Power Transmission (DPT) algorithm to the Destination Sequence Distance Vector (DSDV) routing protocol framework. DPT-DSDV will adjust the communication range automatically based on the node density level. When a node sends data packets to neighbouring nodes, the communication range will be adjusted automatically based on the selected level. The performance of DPT-DSDV will be compared with standard DSDV and standard AODV by looking at performance based on three parameters, namely throughput, packet delivery ratio (PDR), and end-to-end delay. Based on the results of research trials, applying the dynamic-power transmission algorithm can improve the performance of the standard DSDV routing protocol. Throughput results on DPT-DSDV increased by 0.90%. The packet delivery ratio (PDR) increased by 0.50%, and end-to-end delay decreased by 2.80%.
 Key words: MANET, Interference, Quality of Service, Routing, DSDV, Dynamic-Power Transmission.

An Efficient Single Image De-raining Model with Decoupled Deep Networks.

Single image de-raining is an emerging paradigm for many outdoor computer vision applications since rain streaks can significantly degrade the visibility and render the function compromised. The introduction of deep learning (DL) has brought about substantial advancement on de-raining methods. However, most existing DL-based methods use single homogeneous network architecture to generate de-rained images in a general image restoration manner, ignoring the discrepancy between rain location detection and rain intensity estimation. We find that this discrepancy would cause feature interference and representation ability degradation problems which significantly affect de-raining performance. In this paper, we propose a novel heterogeneous de-raining architecture aiming to decouple rain location detection and rain intensity estimation (DLINet). For these two subtasks, we provide dedicated network structures according to their differential properties to meet their respective performance requirements. To coordinate the decoupled subnetworks, we develop a high-order collaborative network learning the dynamic inter-layer interactions between rain location and intensity. To effectively supervise the decoupled subnetworks during training, we propose a novel training strategy that imposes task-oriented supervision using the label learned via joint training. Extensive experiments on synthetic datasets and real-world rainy scenes demonstrate that the proposed method has great advantages over existing state-of-the-art methods.

Temperature control system of hot and cold alternating treatment system based on Kalman filter combined with fuzzy logic

Abstract Aiming at the lag, overshoot, easy interference, and other problems of the temperature control of the heat exchanger in the current alternating hot and cold treatment of deep vein thrombosis equipment, a fuzzy temperature control method based on Kalman filtering is proposed to complete the filtering for the measurement noise through Kalman filtering. Finally, based on the fuzzy strategy to achieve fast response and overshoot control of temperature control, we analyze its fuzzy rules and reasoning through the study of fuzzy PID control. By using MATLAB software for simulation and comparison, it is proved that the accuracy of the designed heat exchanger temperature control system is greatly improved, the robustness of the system is better, the response speed is obviously improved, the amount of overshooting is reduced, the regulation time becomes shorter, and there is also a strong inhibition effect on the interference.

UWB ANTENNA WITH TRIPLE BAND NOTCH CHARACTERISTICS

This study presents a compact ultra-wideband (UWB) patch antenna with three band notches for use in the industrial, scientific, and medical (ISM) bands. On a FR4 substrate with dimensions of 15 mm, 20 mm, and 1.4 mm, a modified circular patch with microstrip fed by a coplanar waveguide (CPW) is created with a fractional bandwidth of 133%. The small antenna operates at 2.45 GHz, which is frequently necessary for the effective operation of ISM usage. The bandwidth used by UWB systems and the current bands is the same. The suggested antenna is ISM band-compatible (1.5-3.29 GHz). Triple notches are used in this design to reject interference from the downlink X- and C-bands. The results from simulations and measurements show good agreement. In this article, a resolution to the problems of interference and miniaturization is proposed.

Investigation of Travel Interference Problems in Taxi-Type AGV Transport Systems and Its Estimation under SLAM Operations

Automated Guided Vehicles (AGVs) are becoming increasingly important in flexible manufacturing systems because they can adapt to changes in facilities and factory layouts. However, AGV transport systems suffer from system shutdowns when the AGVs collide. In this study, our objective was to investigate how the interference time of taxitype AGV transport systems is altered when a passing place is installed at the pick-up and drop-off points (P/Ds) to reduce the interference between AGVs. Consequently, it was inferred that the main cause of interference during taxi-type autonomous driving was when the AGV, a type of autonomous mobile robot, waited at the P/D, and installing the passing place was effective. In recent years, there has also been a growing trend toward autonomous AGVs that use simultaneous localization and mapping for self-position estimation and environmental mapping. In autonomous driving, a high level of self-position estimation is important because it is related to interference between AGVs. However, the accuracy of AGV self-position estimation depends on the environment in which the AGV is used and its traveling speed. Therefore, our objective was to evaluate the accuracy of selfposition estimation when the environment was used and the AGV speed was varied. The results demonstrated that the speed and standard deviation were positively correlated. The standard deviation was smaller for the rectangle (b) than the wall (a) on both sides. Therefore, the possibility of interference between AGVs increases when the speed is high or when there are few features, such as walls, because the accuracy of self-position estimation decreases.

Segmentation of Seabed Sediment Images Based on Convolutional Neural Network

The analysis of particle sizes in seabed sediments plays an important role in various fields. However, acquiring digital sediment images has been challenging due to the characteristics of sediment particles and the complexity of the seabed exploration environment. The goal of this study is to address the issue of particle segmentation in sediment images and propose an innovative network called SAMU-Net. Innovations are as follows. Firstly, SAMU-Net combines spatial attention modules with the traditional U-Net model, which allows for contextual information extraction and adaptive perception of feature variations. Secondly, the model adopts the idea of multi-scale fusion, enabling networks to better understand the structural and semantic information of the images by fusing feature maps at different scales. In addition, using the drop blocks and normalization, we speed up the training convergence of the model and avoid the overfitting phenomenon. The results show that the segmentation results predicted by the SAMU-Net network are highly similar to the ground truth and have a better segmentation performance compared to the existing algorithms. At the same time, it provides a new solution idea to uneven illumination and other noise interference problems in the digital imaging information of sediments.

Radiofrequency interference in magnetic resonance imaging: Identification and rectification.

Radiofrequency (RF) interference artifact is a common type of magnetic resonance imaging (MRI) artifacts caused by the presence of unwanted RF field inside the scanner room. The artifact has the appearance of parallel bright lines or bands that resemble zippers, which can mimic pathology, obstruct the viewing of underlining tissues, and lower image signal-to-noise ratio, affecting the diagnostic evaluation of the image and sometimes even rendering it non-diagnostic. Due to the presence of multiple possible sources of RF interference in MRI and potential nonrelated MRI artifacts that resemble RF interference artifact, it may be difficult to effectively and timely resolve the artifact problem. The objective of this paper is to provide a review of RF interference in MRI and to offer guidance in the prompt and correct identification of the associated image artifacts as well as efficient approaches to resolve and prevent RF interference problems. This article should serve as a useful educational reference to magnetic resonance (MR) technologists and radiologists in dealing with MR image artifacts that may be caused by RF interference.

Interference Management Using Distance-based Clustering Method for D2D Communication Underlaying Multicell Cellular Network

Device-to-Device (D2D) communication is a technology candidate to support the next generation of cellular communication networks. D2D has the potential to boost the efficiency of frequency resources and system capacity. Generally, D2D performs in-band underlaying or shares frequency channels with traditional cellular users, which can cause co-channel interference problems between these two types of users. This paper offers a solution through a clustering technique for D2D users (DUEs) to reduce interference among DUEs. Clustering technique is performed on DUEs by allocating different frequency channels in a group, in order to reduce the interference effects experienced. Thus, it is expected that through this proposed method, both D2D and cellular users can experience better signal quality with minimal interference effects. Two systems have been considered i.e., the conventional/baseline system and the system with the proposed clustering method. The simulation results show that the SINR (Signal to Interference plus Noise Ratio) values and throughput for the proposed system compared to the baseline system has increased. The SINR result obtained is 16.8 dB for the baseline system and 17.68 dB for the proposed system, and resulting an improvement of 5.4%. Therefore, applying the proposed clustering method is able to increase acceptability of the desired signals for the observed DUEs. Then, the throughput value also increases by 5%, i.e., from 56.17 to 59 Mbps, which the system with the proposed clustering method provides a better increase in data transmission speed compared to the baseline system.

PA-Net: A phase attention network fusing venous and arterial phase features of CT images for liver tumor segmentation

Background and ObjectiveLiver cancer seriously threatens human health. In clinical diagnosis, contrast-enhanced computed tomography (CECT) images provide important supplementary information for accurate liver tumor segmentation. However, most of the existing methods of liver tumor automatic segmentation focus only on single-phase image features. And the existing multi-modal methods have limited segmentation effect due to the redundancy of fusion features. In addition, the spatial misalignment of multi-phase images causes feature interference. MethodsIn this paper, we propose a phase attention network (PA-Net) to adequately aggregate multi-phase information of CT images and improve segmentation performance for liver tumors. Specifically, we design a PA module to generate attention weight maps voxel by voxel to efficiently fuse multi-phase CT images features to avoid feature redundancy. In order to solve the problem of feature interference in the multi-phase image segmentation task, we design a new learning strategy and prove its effectiveness experimentally. ResultsWe conduct comparative experiments on the in-house clinical dataset and achieve the SOTA segmentation performance on multi-phase methods. In addition, our method has improved the mean dice score by 3.3% compared with the single-phase method based on nnUNet, and our learning strategy has improved the mean dice score by 1.51% compared with the ML strategy. ConclusionThe experimental results show that our method is superior to the existing multi-phase liver tumor segmentation method, and provides a scheme for dealing with missing modalities in multi-modal tasks. In addition, our proposed learning strategy makes more effective use of arterial phase image information and is proven to be the most effective in liver tumor segmentation tasks using thick-layer CT images. The source code is released on (https://github.com/Houjunfeng203934/PA-Net).

Synthesis of Cobalt Particles and Investigation of Their Electromagnetic Wave Absorption Characteristics.

As the integration technology for integrated circuit (IC) packaging continues to advance, the issue of electromagnetic interference in IC packaging becomes increasingly prominent. Magnetic materials, acknowledged for their superior electromagnetic absorption capabilities, play a pivotal role in mitigating electromagnetic interference problems. In this study, we employed a liquid-phase reduction method. We prepared three types of cobalt (Co) particles with distinct morphologies. Through variations in the synthesis process conditions, we were able to control the aspect ratio of protrusions on the surface of the Co particles. It was found that the sword-like Co particles exhibit superior electromagnetic wave absorption capabilities, showing a reflection loss value of up to -50.96 dB. Notably, when the coating thickness is only 1.6 mm, the effective absorption bandwidth is extended up to 7.6 GHz. The spatially expansive sword-like Co particles, with their unique structure featuring dipole polarization and interfacial polarization, demonstrated enhanced dielectric and magnetic loss capabilities, concurrently showcasing superior impedance-matching performance.

Covalent Coupling Assisted Hydrophilic Perovskite Spheres for Ratiometric Fluorescent Visual Multichannel Immunoassay.

Quantitative fluorescence immunoassay is essential for the construction of biosensing mechanisms and the quantification of trace markers. But the interference problems caused by low fluorescence efficiency and broad fluorescence spectrum of fluorescent probes have hindered the continued development of ratiometric fluorescence sensing in biosensing. Perovskite materials, with ultra-high color purity (FWHM < 30nm) and photoluminescence quantum yield (PLQY) (close to 100%), are expected to be next-generation fluorescent probes. However, poor water stability and biocompatibility are still non-negligible in biosensor applications. In this work, hyperstatic perovskite fluorescent microspheres prepared by swelling-shrinking method can be used as ratiometric fluorescence signals and biological immunoassay platforms. Meanwhile, inspired by p-aminophenol (AP) controlled synthesis and the catalytic reaction of 4-aminophenol phosphate (APP) triggered by alkaline phosphatase (ALP), a strategy to prepare fluorescent nanoparticles as fluorescence signals for ALP detection is proposed. Most importantly, it is proposed for the first time to combine this enzymatic fluorescence with perovskite materials using covalent linkage to create a novel cascade immunoassay and use it for quantitative and visualization determination of hepatitis B surface antigen (HBsAg)for application verification. These results indicate the biosensing potential of perovskite materials and provide a pathway for high sensitivity enzyme detection and enzyme triggered immune detection.

SiamFCA: A new fish single object tracking method based on siamese network with coordinate attention in aquaculture

In aquaculture, tracking fish helps to obtain information about their growth, behavior, location and health, etc. However, there are some problems in the process of fish tracking. Firstly, to solve the noise problem of underwater images, we use the contrast limited adaptive histogram equalization (CLAHE) to carry out local histogram equalization in each small block, which increases the image contrast while retaining more details. This operation enhances the contrast of the image while suppressing noise to solve the problem of noisy images. Then, to solve the problems of similarity interference, occlusion and scale variation among fish, we propose a new method for fish single object tracking in aquaculture environments, called SiamFCA, by combining the coordinated attention mechanism (CA). The method is based on the siamese network framework and uses a modified AlexNet to extract the depth features of image pairs. And through the coordinate attention mechanism, the model can learn the relationship between different objects more accurately, which can improve the accuracy and robustness of the model when dealing with complex image scenes. Finally, the correlation operation between classification and regression is performed based on the region proposal network (RPN). By classifying, region suggestions can be labeled as positive or negative samples, and regression can calibrate the position and size of the region proposal to match targets more accurately. The experimental results show that the precision and success rate of the algorithm are 84.2 % and 62.4 %, respectively, which are better than the tracking methods such as SiamRPN and DaSiamRPN, etc. The SiamFCA runs at 83 fps, which meets the demand of real-time fish tracking in aquaculture.

The eco-ethical contribution of Menico Torchio – a forgotten pioneer of European Bioethics

BackgroundIn 1926, Fritz Jahr described bio-ethics (German: bio-ethik) as “the assumption of moral obligations not only towards humans, but towards all forms of life.” Jahr summarized his philosophy by declaring, “Respect every living being on principle as an end in itself and treat it, if possible, as such!.” Bioethics was thus originally an ethical system concerned with the “problems of interference with other living beings… and generally everything related to the balance of the ecosystem” according to the 1978 Encyclopedia of Bioethics. This definition was predicated on the work of Fritz Jahr, Menico Torchio, and Van Rensselaer Potter.MethodsIn order to proceed with depthful analysis of the origin and major bioethical flare up, we will use critical analysis of existing literature, followed by a study trip to relevant bioethical localities (collecting photo and other documentations regarding Menico Torchio).ResultsWhile Jahr and Potter are typically given intellectual credit for developing the field of bioethics, the eco-ethical contributions of Menico Torchio have been forgotten.This article will first trace the origins of “bioethics” – now commonly bifurcated into “biomedical ethics” and “environmental bioethics.” The former was developed by Tom Beauchamp from the Philosophy Department and James Childress of the Religious Studies department at Georgetown University and is based on principlism, with a narrow focus on medical settings. The latter addresses the environmental impact of the medical industry and climate change health hazards. Second, we will present a panorama of Torchio’s significant intellectual contribution to bioethics. Menico Torchio’s concept of bioethics synthesized work of both Jahr and Potter, advocating “the need to expand our ethical obligations and embrace the most developed groups of animals, not only physically but also psychologically.” Third, we will reflect on the lasting legacy of “bioethics” on biomedical and environmental bioethics today. Thematic elements such as interconnectedness of planetary health and human health, dedication to living in harmony with nature, and emphasis on systems and symbiosis remain unchanged from the legacy of Tochio onward.ConclusionOur conclusion will underscore the necessity of understanding the connections between planetary, environmental, and human health.

A Smart Amorphous Wire Composite with Tunable Electromagnetic Shielding

Nowadays, in view of the increasingly serious problems of electromagnetic interference (EMI) and radiation pollution, smart EMI shielding materials with tunable shielding effectiveness (SE), especially “On/Off” switchable capability, are highly desirable. Herein, Co‐based amorphous wires (AWs) with excellent soft magnetic and mechanical properties are regularly arranged on a 3D‐printed mold as tunable EMI shielding composites. The EMI SE can be easily tuned in the range of ≈0.6–24.7 dB by way of rotation, which has been confirmed to be an effective way to achieve free tuning of EMI SE by simulation. In addition, by compositing AWs with reduced graphene oxide and constructing a double‐layer structure with a gap of 1/4 wavelength in the X‐band, a much wider SE tuning range of ≈1.0–57.7 dB is further realized. The representative “On/Off” switchable shielding capability that can switch between EM transmission and effective shielding, together with the wide SE tuning range larger than 50 dB, shows the great potential of Co‐based AW composites in smart EMI shielding applications.