Image Noise Research Articles

Research on Radar Target Detection Based on the Electromagnetic Scattering Imaging Algorithm and the YOLO Network

In this paper, the radar imaging technology based on the time-domain (TD) electromagnetic scattering algorithm is used to generate image datasets quickly and apply them to target detection research. Considering that radar images are different from optical images, this paper proposes an improved strategy for the traditional You Only Look Once (YOLO)v3 network to improve target detection accuracy on radar images. The speckle noise in radar images can cover the real information of a target image and increase the difficulty of target detection. The attention mechanisms are added to the traditional YOLOv3 network to strengthen the weight of the target region. By comparing the target detection accuracy under different attention mechanisms, an attention module with higher detection accuracy is obtained. The validity of the proposed detection network is verified on a simulation dataset, a measured real dataset, and a mixed dataset. This paper is about an interdisciplinary study of computational electromagnetics, remote sensing, and artificial intelligence. Experiments verify that the proposed composite network has better detection performance.

Abdominal Applications of Photon-Counting CT.

Photon-counting computed tomography (PCCT) has shown promising advancements in abdominal imaging in clinical use. Though more peer-reviewed primary literature is needed, this commentary explores PCCT's potential applications, focusing on enhancing diagnostic accuracy, optimizing radiation dose management, and improving patient care. PCCT offers improved spatial and contrast resolution, lower image noise, and reduced radiation dose. Increased spatial resolution provides better detail in abdominal imaging, aiding in the detection of small lesions and subtle pathological changes. However, this generates more images per scan, raising concerns about "image overload" in PACS, potentially leading to longer reading times and increased stress for radiologists. PCCT's improved contrast resolution enhances tissue differentiation, reducing the need for intravenous contrast agents. The technology's advanced tissue characterization provides several advantages, such as non-invasive and opportunistic liver disease evaluation and improved differentiation of renal and adrenal masses. PCCT's optimized radiation dose management is crucial for patients requiring frequent scans. Enhanced diagnostic accuracy through spectral information aids in tissue differentiation, improving confidence in diagnoses. Streamlined workflows, particularly in emergency settings, and oncologic imaging, are potential benefits, reducing the need for additional imaging studies. Future integration of PCCT into clinical practice requires collaboration, education, and research to fully harness its potential, ensuring optimized abdominal imaging and improved patient care.

Computed tomography of the equine caudal spine and pelvis: Technique, image quality and anatomical variation in 56 clinical cases (2018-2023).

Cross-sectional imaging improves the diagnostic accuracy of complex anatomical regions. Computed tomography (CT) of the pelvis and caudal spine in a large group of live horses and ponies has not been previously reported. To describe the procedure for acquiring CT images of horses' caudal spine/pelvis under general anaesthesia (GA) and to detail the image quality, artefacts and anatomical variations in this region. Retrospective case series. Horses with CT of the caudal spine/pelvis were included. Horses under 6 months and CT examination performed post-mortem were excluded. Protocols, image quality, region of interest, anatomical features and morbidities were analysed. Fifty-six horses (8 months to 20 years, 85-680 kg) met the inclusion criteria. GA ranged from 10 to 60 min (mean: 30, median: 32). There were no adverse events recorded in any of the horses associated with the procedure. Images of all horses were considered of diagnostic quality. Anatomical variations were common and included the location of diverging (widest) interspinous space, the presence of spina bifida in the lumbar and sacral spine, the shape of the last lumbar vertebra and the location of intertransverse joints in terms of where they were present and the degree of fusion/modelling. Not all horses underwent CT examination of the same regions, the upper size limit of horses is unknown and will vary depending on bore size and table infrastructure. Image noise, particularly in large horses and beam hardening artefacts from hardware and pelvis degraded image quality. Images were of insufficient quality in large horses for soft tissue interpretation. CT of the caudal spine and pelvis in live horses with wide-bore CT machines and modified patient infrastructure was safe and produced diagnostic images.

Multi-scale input layers and dense decoder aggregation network for COVID-19 lesion segmentation from CT scans

Accurate segmentation of COVID-19 lesions from medical images is essential for achieving precise diagnosis and developing effective treatment strategies. Unfortunately, this task presents significant challenges, owing to the complex and diverse characteristics of opaque areas, subtle differences between infected and healthy tissue, and the presence of noise in CT images. To address these difficulties, this paper designs a new deep-learning architecture (named MD-Net) based on multi-scale input layers and dense decoder aggregation network for COVID-19 lesion segmentation. In our framework, the U-shaped structure serves as the cornerstone to facilitate complex hierarchical representations essential for accurate segmentation. Then, by introducing the multi-scale input layers (MIL), the network can effectively analyze both fine-grained details and contextual information in the original image. Furthermore, we introduce an SE-Conv module in the encoder network, which can enhance the ability to identify relevant information while simultaneously suppressing the transmission of extraneous or non-lesion information. Additionally, we design a dense decoder aggregation (DDA) module to integrate feature distributions and important COVID-19 lesion information from adjacent encoder layers. Finally, we conducted a comprehensive quantitative analysis and comparison between two publicly available datasets, namely Vid-QU-EX and QaTa-COV19-v2, to assess the robustness and versatility of MD-Net in segmenting COVID-19 lesions. The experimental results show that the proposed MD-Net has superior performance compared to its competitors, and it exhibits higher scores on the Dice value, Matthews correlation coefficient (Mcc), and Jaccard index. In addition, we also conducted ablation studies on the Vid-QU-EX dataset to evaluate the contributions of each key component within the proposed architecture.

Assessment of Emphysema on X-ray Equivalent Dose Photon-Counting Detector CT

Objectives The aim of this study was to evaluate the feasibility and efficacy of visual scoring, low-attenuation volume (LAV), and deep learning methods for estimating emphysema extent in x-ray dose photon-counting detector computed tomography (PCD-CT), aiming to explore future dose reduction potentials. Methods One hundred one prospectively enrolled patients underwent noncontrast low- and chest x-ray dose CT scans in the same study using PCD-CT. Overall image quality, sharpness, and noise, as well as visual emphysema pattern (no, trace, mild, moderate, confluent, and advanced destructive emphysema; as defined by the Fleischner Society), were independently assessed by 2 experienced radiologists for low- and x-ray dose images, followed by an expert consensus read. In the second step, automated emphysema quantification was performed using an established LAV algorithm with a threshold of −950 HU and a commercially available deep learning model for automated emphysema quantification. Automated estimations of emphysema extent were converted and compared with visual scoring ratings. Results X-ray dose scans exhibited a significantly lower computed tomography dose index than low-dose scans (low-dose: 0.66 ± 0.16 mGy, x-ray dose: 0.11 ± 0.03 mGy, P < 0.001). Interreader agreement between low- and x-ray dose for visual emphysema scoring was excellent (κ = 0.83). Visual emphysema scoring consensus showed good agreement between low-dose and x-ray dose scans (κ = 0.70), with significant and strong correlation (Spearman ρ = 0.79). Although trace emphysema was underestimated in x-ray dose scans, there was no significant difference in the detection of higher-grade (mild to advanced destructive) emphysema (P = 0.125) between the 2 scan doses. Although predicted emphysema volumes on x-ray dose scans for the LAV method showed strong and the deep learning model excellent significant correlations with predictions on low-dose scans, both methods significantly overestimated emphysema volumes on lower quality scans (P < 0.001), with the deep learning model being more robust. Further, deep learning emphysema severity estimations showed higher agreement (κ = 0.65) and correlation (Spearman ρ = 0.64) with visual scoring for low-dose scans than LAV predictions (κ = 0.48, Spearman ρ = 0.45). Conclusions The severity of emphysema can be reliably estimated using visual scoring on CT scans performed with x-ray equivalent doses on a PCD-CT. A deep learning algorithm demonstrated good agreement and strong correlation with the visual scoring method on low-dose scans. However, both the deep learning and LAV algorithms overestimated emphysema extent on x-ray dose scans. Nonetheless, x-ray equivalent radiation dose scans may revolutionize the detection and monitoring of disease in chronic obstructive pulmonary disease patients.

Denoising astronomical images using an enhanced Pix2Pix model

Astronomical images are frequently affected by sensor noise, which can negatively impact the accuracy of subsequent data analysis. To address this issue, this study proposes an enhanced Pix2Pix generative adversarial network model that incorporates Residual Blocks and Self-Attention mechanisms to improve denoising performance. The effectiveness of the proposed model is evaluated by comparing it with traditional denoising methods, standard Pix2Pix, Pix2Pix with Residual Blocks, and Pix2Pix with Self-Attention using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. The findings demonstrate that the Pix2Pix model, when combined with both Residual Blocks and Self-Attention, significantly outperforms other models in noise reduction and detail preservation. This improved approach offers a robust solution for high-quality processing of astronomical images, providing clearer and more reliable data for scientific analysis. The results highlight the potential of advanced deep learning techniques in overcoming the challenges posed by sensor noise in astronomical imaging.

Performance of Lung-Nodule Computer-Aided Detection Systems on Standard-Dose and Low-Dose Pediatric CT Scans: An Intraindividual Comparison.

Background: When applying lung-nodule computer-aided detection (CAD) systems for pediatric CT, performance may be degraded on low-dose scans due to increased image noise. Objective: To conduct an intraindividual comparison of the performance for lung nodule detection of two CAD systems trained using adult data between low-dose and standard-dose pediatric chest CT scans. Methods: This retrospective study included 73 patients (32 female, 41 male; mean age, 14.7 years; age range, 4-20 years) who underwent both clinical standard-dose and investigational low-dose chest CT examinations within the same encounter from November 30, 2018 to August 31, 2020 as part of an earlier prospective study. Fellowship-trained pediatric radiologists annotated lung nodules to serve as the reference standard. Both CT scans were processed using two publicly available lung-nodule CAD systems previously trained using adult data: FlyerScan and Medical Open Network for Artificial Intelligence (MONAI). The systems' sensitivities for nodules measuring 3-30 mm (n=247) were calculated when operating at a fixed frequency of two false-positives per scan. Results: FlyerScan exhibited detection sensitivities of 76.9% (190/247; 95% CI: 73.3-80.8%) on standard-dose scans and 66.8% (165/247; 95% CI: 62.6-71.5) on low-dose scans. MONAI exhibited detection sensitivities of 67.6% (167/247, 95% CI: 61.5-72.1) on standard-dose scans and 62.3% (154/247, 95% CI: 56.1-66.5%) on low-dose scans. The number of detected nodules for standard-dose versus low-dose scans for 3-mm nodules was 33 versus 24 (FlyerScan) and 16 versus 13 (MONAI), 4-mm nodules was 46 versus 42 (FlyerScan) and 39 versus 30 (MONAI), 5-mm nodules was 38 versus 33 (FlyerScan) and 32 versus 31 (MONAI), and 6-mm nodules was 27 versus 20 (FlyerScan) and 24 versus 24 (MONAI). For nodules measuring ≥7 mm, detection did not show a consistent pattern between standard-dose and low-dose scans for either system. Conclusions: Two lung-nodule CAD systems demonstrated decreased sensitivity on low-dose versus standard-dose pediatric CT scans performed in the same patients. The reduced detection at low dose was overall more pronounced for nodules measuring less than 5 mm. Clinical Impact: Caution is needed when using low-dose CT protocols in combination with CAD systems to help detect small lung nodules in pediatric patients.

New tunneling source follower with low 1/f noise and high voltage gain

We have designed a tunneling source follower (TSF) for image sensors that achieves high voltage gain (Av) and low 1/f noise simultaneously. The TSF is composed of N-P-N-N, especially the p-doped grounded area, which serves to amplify the insufficient tunneling current, allowing the source follower (SF) to utilize band-to-band tunneling (BTBT). Compared to thermionic emission, tunneling based structures contribute to increasing Av through lower channel length modulation and lower body effect. As a result, TSF achieves a higher Av (~ 1.0 V/V) than conventional SF (~ 0.9 V/V). Moreover, the n-doped channel makes the buried conductive channel farther from the interface, lowering the noise. The input-referred voltage noise spectral density (SV) is approximately 10 times lower in TSF than that of in conventional SF. Therefore, our TSF can be a possible candidate for High Av and low 1/f noise image sensors.

Image reconstruction parameters and the standardized uptake value ratios in brain amyloid PET.

The present study investigated various image reconstruction protocols for amyloid PET using phantom test criteria published by the Japanese Society of Nuclear Medicine (JSNM) and compared them with the composite standardized uptake value ratio (cSUVR) in clinical imaging. Hoffman 3D phantoms and cylindrical phantoms were collected for 30 min according to the JSNM guidelines. Images were created under various reconstruction protocols by three physical evaluation items in the guidelines and were assessed: gray matter/white matter contrast (%contrast), uniformity (SDuROImean), and image noise [coefficient of variation (CV)]. We compared the cSUVR of images reconstructed under 15 protocols using 18F-flutemetamol and 18F-florbetapir in 15 cases each and the guidelines for physical evaluation of reconstruction parameters. No significant differences were observed in cSUVR between reconstruction protocols that satisfied the guidelines' criteria for %contrast and CV and those that did not; however, the visual impression of images differed. SDuROImean, which evaluated uniformity, met the criteria in all data. Reconstruction protocols should be selected appropriately using guidelines and other information, as cSUVR remains largely the same even if the visual impression of the images differs between different reconstruction protocols. When the relationship between %contrast and CV is expressed in terms of several reconstruction protocols, the graph shows a curved shape, and the optimal protocols for both %contrast and CV are near its center. Since cSUVR is similar to optimal parameters, even under parameters outside this range, multiple parameters need to be considered when selecting image reconstruction protocols for amyloid PET.

Modeling and Calculation of Limit Magnitude Detection of Orbital Optoelectric Tracking System

In order to evaluate the tracking capability of optoelectric tracking for an orbital target, the limit magnitude detection performance calculation model and its calculation method are studied. Combining the optical signal characteristics of the tracked orbital target, the background, and the CCD noise, the framework of the limit magnitude calculation model of the system for dynamic target detection is constructed. The relationships between the limit magnitude and the signal-to-noise ratio threshold of the optical signal characteristics, the exposure time of the CCD camera, and the dark current of the CCD imaging are studied and analyzed while considering the sunlight illumination condition, so that the calculation function and its change curve are given. The limit magnitude detection capability of the system is verified by the simulated experiment and the synchronized tracking test, and the detection distance maximum error of the model calculation is 3.6 m. The results show that under certain illumination conditions, when the exposure time of the CCD camera is longer and the SNR threshold is lower, the limit magnitude detection performance of the system is better, and the tracking performance of the system is more stable.

Improved Discriminability of Severe Lung Injury and Atelectasis in Thoracic Trauma at Low keV Virtual Monoenergetic Images from Photon-Counting Detector CT

Objectives: To evaluate the value of virtual monoenergetic images (VMI) from photon-counting detector CT (PCD-CT) for discriminability of severe lung injury and atelectasis in polytraumatized patients. Materials & Methods: Contrast-enhanced PCD-CT examinations of 20 polytraumatized patients with severe thoracic trauma were included in this retrospective study. Spectral PCD-CT data were reconstructed using a noise-optimized virtual monoenergetic imaging (VMI) algorithm with calculated VMIs ranging from 40 to 120 keV at 10 keV increments. Injury-to-atelectasis contrast-to-noise ratio (CNR) was calculated and compared at each energy level based on CT number measurements in severely injured as well as atelectatic lung areas. Three radiologists assessed subjective discriminability, noise perception, and overall image quality. Results: CT values for atelectasis decreased as photon energy increased from 40 keV to 120 keV (mean Hounsfield units (HU): 69 at 40 keV; 342 at 120 keV), whereas CT values for severe lung injury remained near-constant from 40 keV to 120 keV (mean HU: 42 at 40 keV; 44 at 120 keV) with significant differences at each keV level (p < 0.001). The optimal injury-to-atelectasis CNR was observed at 40 keV in comparison with the remaining energy levels (p < 0.001) except for 50 keV (p > 0.05). In line with this, VMIs at 40 keV were rated best regarding subjective discriminability. VMIs at 60–70 keV, however, provided the highest subjective observer parameters regarding subjective image noise as well as image quality. Conclusions: Discriminability between severely injured and atelectatic lung areas after thoracic trauma can be substantially improved by virtual monoenergetic imaging from PCD-CT with superior contrast and visual discriminability at 40–50 keV.

EI-ISOA-VMD: Adaptive denoising and detrending method for nuclear circulating water pump impeller

As a key component of the nuclear circulating water pump, it is difficult to extract effective information from low-quality impeller signal because of the interference noise and non-stationary trend, such as water flow impact, vibrations from unrelated components, and sensor noise. Traditional signal denoising methods, particularly those utilizing variational mode decomposition, often require manual parameter tuning, resulting in unsatisfactory outcome. To address these issues, this paper proposes a signal denoising and detrending method based on the evaluation index driven the improved seagull algorithm optimized for variational mode decomposition (EI-ISOA-VMD). During the optimization process, sinusoidal chaotic mapping is utilized for initializing the seagull population, while a tanh function is employed to design nonlinearly decreasing inertia weights. Furthermore, the Levy flight mechanism enhances the randomness of position updates and optimizes seagull individuals beyond the boundary range in order to form the improved seagull optimization algorithm. The denoising, detrending process, and evaluation index proposed in this paper are utilized as the fitness function for the improved seagull optimization algorithm to optimize key parameters of variational mode decomposition. During the denoising and detrending process, the trend component is initially selected using the mean ratio method. The useful, low noise and high noise intrinsic mode functions are screened by correlation coefficient and weighted permutation entropy. Subsequently, the low noise components are denoised by wavelet thresholding method, and the high noise components are discarded to finally reconstruct the signal. The experimental results demonstrate that the proposed method effectively eliminates noise and trend components in low-quality signal while preserving more valuable information.

Noise sensors - Part 1: Usage for the redesign of neighbourhoods

An interdisciplinary research project at the Stuttgart University of Applied Sciences with participation of the departments of urban planning, surveying and geoinformatics, business psychology and acoustics, present alternative urban scenarios. Different aspects for the redesign of the district, especially measures to reduce the individual motorised transport, were investigated. Virtual reality and augmented reality experiments are used in public spaces, to show redesign possibilities. Surveys of people, who had the experience of the future scenarios via the AR application and VR application, were carried out. Additionally, acoustical surveys and measurements in this research project were performed using so called noise sensors. These sensors based on MEMS microphones were developed for a simple and cost-effective measurement of external noise in a citizen sciences project. The measurement results of the noise sensors are saved online and can be accessed on the homepage "Sensor.Community". One of the noise sensors was used in the project area over a longer period. Measurement values were generated in third octave bands at intervals of 5 s and are available for further evaluations, e.g. for the calculation of indoor noise levels. The paper presents the research project and reports about the application of the noise sensor.

Relation between measured noise levels and traffic flow

Road-traffic flow parameters are a critical input to urban noise pollution evaluation frameworks such as CNOSSOS-EU. The accuracy and resolution of the traffic flow improves the quality of the noise exposure assessment. Highly resolved traffic flow within an urban network is difficult to reliably measure or predict. The quality and availability of such traffic information may be improved using data from low-cost noise sensors that are mounted beside representative segments of the road network. A model is presented, which relates the noise levels measured at a single noise sensor with the local traffic flow conditions. The noise data is obtained from a noise sensor mounted alongside a road, which is part of a test-bed in Stockholm, Sweden. The local traffic flow is obtained from commercial vehicle-counting sensors mounted alongside the noise sensor. Effect of seasonality is observed when comparing models trained on different subsets of the training data. The effect of seasonality on the noise levels is investigated.

Novel intravascular tantalum oxide-based contrast agent achieves improved vascular contrast enhancement and conspicuity compared to Iopamidol in an animal multiphase CT protocol

BackgroundTo assess thoracic vascular computed tomography (CT) contrast enhancement of a novel intravenous tantalum oxide nanoparticle contrast agent (carboxybetaine zwitterionic tantalum oxide, TaCZ) compared to a conventional iodinated contrast agent (Iopamidol) in a rabbit multiphase protocol.MethodsFive rabbits were scanned inside a human-torso-sized encasement on a clinical CT system at various scan delays after intravenous injection of 540 mg element (Ta or I) per kg of bodyweight of TaCZ or Iopamidol. Net contrast enhancement of various arteries and veins, as well as image noise, were measured. Randomized scan series were reviewed by three independent readers on a clinical workstation and assessed for vascular conspicuity and image artifacts on 5-point Likert scales.ResultsOverall, net vascular contrast enhancement achieved with TaCZ was superior to Iopamidol (p ≤ 0.036 with the exception of the inferior vena cava at 6 s (p = 0.131). Vascular contrast enhancement achieved with TaCZ at delays of 6 s, 40 s, and 75 s was superior to optimum achieved Iopamidol contrast enhancement at 6 s (p ≤ 0.036. Vascular conspicuity was higher for TaCZ in 269 of 300 (89.7%) arterial and 269 of 300 (89.7%) venous vessel assessments, respectively (p ≤ 0.005), with substantial inter-reader reliability (κ = 0.61; p < 0.001) and strong positive monotonic correlation between conspicuity scores and contrast enhancement measurements (ρ = 0.828; p < 0.001).ConclusionTaCZ provides absolute and relative contrast advantages compared to Iopamidol for improved visualization of thoracic arteries and veins in a multiphase CT protocol.Relevance statementThe tantalum-oxide nanoparticle is an experimental intravenous CT contrast agent with superior cardiovascular and venous contrast capacity per injected elemental mass in an animal model, providing improved maximum contrast enhancement and prolonged contrast conspicuity. Further translational research on promising high-Z and nanoparticle contrast agents is warranted.Key PointsThere have been no major advancements in intravenous CT contrast agents over decades.Iodinated CT contrast agents require optimal timing for angiography and phlebography.Tantalum-oxide demonstrated increased CT attenuation per elemental mass compared to Iopamidol.Nanoparticle contrast agent design facilitates prolonged vascular conspicuity.Graphical

Noise and inflow velocity measurements of fixed pitch rotors in edgewise flight

A common design of many of the proposed generation of electric Vertical Takeoff and Landing (eVTOL) vehicles involves fixed pitch rotors in edgewise flight. These applications typically have significantly lower tip Mach numbers than conventional rotor-craft and involve distributed smaller rotors making them operate at lower Reynolds numbers and hence likely having a greater sensitivity to gusts and environmental turbulence. This work will present results of studies with fixed pitch rotors conducted in the UF Anechoic Wind Tunnel Facility. The studies involve radiated noise, thrust and stereoscopic Particle Image Velocimetry to measure the inflow velocity to the rotor plane. The measurements were taken for un-altered free stream conditions as well as ones subjected to three different turbulence generation grids. The results provided show that there can be effects at moderate levels of free stream to turbulence to the rotor efficiency and radiated noise.

One-third octave spectrum calculation to meet the attenuation limits of a Class 1 analyzer (IEC 61260-1:2014) for low-power noise sensors

Thanks to the advances in technology and the cost reductions of electronic components, nowadays it is possible to produce low-cost and low-power acoustic sensors with extended capabilities such as the one-third octave band spectrum calculation. However, there are some doubts about the accuracy of these devices. Thus, to meet the attenuation limits of a Class 1 analyzer for low frequencies as stated in IEC 61260-1:2014 normative, this research proposes two algorithms to calculate the one-third octave band spectrum, both algorithms are based on the Power Spectral Density (PSD). The first one consists of a subdivision of frequency bins, where a PSD bin is divided into smaller parts, gain adjusted, and assigned to its corresponding one third-octave band. The other one is based on signal decimation-accumulation, where samples, that are processed every 125 ms due to the sensor design, are first decimated and then accumulated to increase the frequency bin resolution. Both algorithms are programmed into the sensor and the power consumption and acoustical performance are evaluated and compared to a typical multi-rate filter bank. It is observed that both proposed algorithms improve the performance of the low-cost sensor in terms of energy consumption and accuracy.

Enhancing trajectory tracking accuracy in three-wheeled mobile robots using backstepping fuzzy sliding mode control

The rise in robotics technology has increased interest in ThreeWheeled Mobile Robots (TWMRs) due to their agility and adaptability across various applications. However, effectively controlling TWMRs presents a significant challenge owing to their inherent nonholonomic constraints, which restrict independent movement in all directions. Factors like sensor noise, nonlinear system dynamics, and uncertain system parameters also add to the complexity of controlling TWMRs. This research endeavors to enhance the precision of trajectory tracking in TWMRs. Specifically, it employs Backstepping Fuzzy Sliding Mode Control (BFSMC) with parameters optimized through Particle Swarm Optimization (PSO), coupled with the Extended Kalman Filter (EKF) for state estimation. The study conducts a comprehensive performance comparison between Backstepping Sliding Mode Control (BSMC) and Backstepping Fuzzy Sliding Mode Control(BFSMC) across various trajectory patterns, revealing substantial improvements in trajectory tracking accuracy with BFSMC. BFSMC demonstrates improvements in performance across various trajectory types when considering the integral time absolute error (IAE). Specifically, it achieves a 51.97% improvement for circular trajectories, an 82.09% improvement for infinity trajectories, and an 84.073% improvement for spiral trajectories. Moreover, BFSMC demonstrates superior robustness in the presence of disturbances, noise, parameter variations, and unmodeled dynamics compared to BSMC. Integrating the Extended Kalman Filter further improves accuracy, particularly in noisy conditions.

CRISP: A cross-modal integration framework based on the surprisingly popular algorithm for multimodal named entity recognition

The multimodal named entity recognition task on social media involves recognizing named entities with textual and visual information, which is of great significance for information processing. Nevertheless, many existing models still face the following challenges. First, in the process of cross-modal interaction, the attention mechanism sometimes focuses on trivial parts in the images that are not relevant to entities, which not only neglects valuable information but also inevitably introduces visual noise. Second, the gate mechanism is widely used for filtering out visual information to reduce the influence of noise on text understanding. However, the gate mechanism neglects capturing fine-grained semantic relevance between modalities, which easily affects the filtration process. To address these issues, we propose a cross-modal integration framework based on the surprisingly popular algorithm, aiming at enhancing the integration of effective visual guidance and reducing the interference of irrelevant visual noise. Specifically, we design a dual-branch interaction module that includes the attention mechanism and the surprisingly popular algorithm, allowing the model to focus on valuable but overlooked parts in the images. Furthermore, we compute the matching degree between modalities at the multi-granularity level, using the Choquet integral to establish a more reasonable basis for filtering out visual noise. We have conducted extensive experiments on public datasets, and the experimental results demonstrate the advantages of our model.

PE Gas Pipeline Defect Detection Algorithm based on Improved YOLO v5

In order to improve defects detection efficiency in polyethylene (PE) gas pipelines and decrease leakage or other pipelines abnormalities in operation, this research proposed an improved YOLO(You Only Look Once) v5 detection model. First, the collected pipeline defect images were processed in grey scale, which improved the computational efficiency of the computer; then, Gamma transform and double filtering algorithms were applied respectively for image enhancement and noise reduction filtering of defects, which enhanced image quality and reduced image noise. Finally, the improved Sobel algorithm was applied to detect defective image edges and the defects in the image were segmented by adaptive threshold segmentation method to obtain binary images. The obtained binary images were employed to train the improved YOLO v5 detection model. The obtained experimental results showed that, compared with the original algorithm, the improved detection algorithm had better detection efficiency and higher robustness as well as higher recognition for common defects,improved YOLOv5 mAP and recall were 97.18% and 98.03%, respectively, the mAP has increased by 1.33% and the recall has increased by 3.83%,which can achieve the detection and identification of defect types of effects in PE gas pipes.