Search Results (276)

Based on the generalized cross-correlation method (GCC), the period peaks in the cross-correlation function (CCF) of frequency shift keying (FSK) signals will degrade the estimation performance of time difference of arrival (TDOA), as well as the estimation performance of frequency difference of arrival (FDOA), through the cross ambiguity function (CAF), in the case of a low signal-to-noise ratio (SNR). An improved TDOA/FDOA estimation algorithm made using period peaks is proposed in this paper to better understand the performance of TDOA/FDOA estimation for FSK signals. First, the cross ambiguity function of FSK signals is computed, and the peak position of the CAF is found to obtain coarse TDOA/FDOA estimation results, as per the usual method. Next, the mainlobe and period sidelobes are found according to the peak position and period interval; then, the peaks of each sidelobe around the mainlobe are found, and the period TDOA estimations are obtained. Then, the improved TDOA estimation can be used to calculate the average value of period TDOA estimations. Last, the period sidelobes accumulate to the mainlobe, and the improved FDOA estimation result are obtained by finding the peak position of the accumulated mainlobe. Simulations are performed to demonstrate that the proposed algorithm provides a better performance. Full article

Semantic segmentation of three-dimensional (3D) plant point clouds at the stem-leaf level is foundational and indispensable for high-throughput tomato phenotyping systems. However, existing semantic segmentation methods often suffer from issues such as low precision and slow inference speed. To address these challenges, we propose an innovative encoding-decoding structure, incorporating voxel sparse convolution (SpConv) and attention-based feature fusion (VSCAFF) to enhance semantic segmentation of the point clouds of high-resolution tomato seedling images. Tomato seedling point clouds from the Pheno4D dataset labeled into semantic classes of ‘leaf’, ‘stem’, and ‘soil’ are applied for the semantic segmentation. In order to reduce the number of parameters so as to further improve the inference speed, the SpConv module is designed to function through the residual concatenation of the skeleton convolution kernel and the regular convolution kernel. The feature fusion module based on the attention mechanism is designed by giving the corresponding attention weights to the voxel diffusion features and the point features in order to avoid the ambiguity of points with different semantics having the same characteristics caused by the diffusion module, in addition to suppressing noise. Finally, to solve model training class bias caused by the uneven distribution of point cloud classes, the composite loss function of Lovász-Softmax and weighted cross-entropy is introduced to supervise the model training and improve its performance. The results show that mIoU of VSCAFF is 86.96%, which outperformed the performance of PointNet, PointNet++, and DGCNN, respectively. IoU of VSCAFF achieves 99.63% in the soil class, 64.47% in the stem class, and 96.72% in the leaf class. The time delay of 35ms in inference speed is better than PointNet++ and DGCNN. The results demonstrate that VSCAFF has high performance and inference speed for semantic segmentation of high-resolution tomato point clouds, and can provide technical support for the high-throughput automatic phenotypic analysis of tomato plants. Full article

Background/Objectives: Cystoscopy is necessary for diagnosing bladder cancer, but it has limitations in identifying ambiguous lesions, such as carcinoma in situ (CIS), which leads to a high recurrence rate of bladder cancer. With the significant advancements in deep learning in the medical field, several studies have explored its application in cystoscopy. This study aimed to utilize the VGG19 and Deeplab v3+ deep learning models to classify and segment cystoscope images, respectively. Methods: We classified cystoscope images obtained from 772 patients based on morphology (normal, papillary, flat, mixed) and biopsy results (normal, Ta, T1, T2, CIS, etc.). Experienced urologists annotated and labeled the lesion areas and image categories. The classification model for bladder cancer lesion, annotated with pathological results, was developed using VGG19 with an additional fully connected layer, utilizing sparse categorical cross-entropy as the loss function. The Deeplab v3+ model was used for segmenting each morphological type of bladder cancer in the cystoscope images, employing the dice coefficient loss function. The classification model was evaluated using validation accuracy and correlation with biopsy results, while the segmentation model was assessed using the Intersection over Union (IoU) combined with binary accuracy. Results: The dataset was split into training and validation sets with a 4:1 ratio. The VGG19 classification model achieved an accuracy score of 0.912. The Deeplab v3+ segmentation model achieved an IoU of 0.833 and a binary accuracy of 0.951. Visual analysis revealed a high similarity between the lesions identified by Deeplab v3+ and those labeled by experts. Conclusions: In this study, we applied two deep learning models using well-annotated datasets of cystoscopic images. Both VGG19 and Deeplab v3+ demonstrated high performance in classification and segmentation, respectively. These models can serve as valuable tools for bladder cancer research and may aid in the diagnosis of bladder cancer. Full article

Cathinone and its synthetic derivatives belong to organic compounds with narcotic properties. Their structural diversity and massive illegal use create the need to develop new analytical methods for their identification in different matrices. NMR spectroscopy is one of the most versatile methods for identifying the structure of organic substances. However, its use could sometimes be very difficult and time-consuming due to the complexity of NMR spectra, as well as the technical limitations of measurements. In such cases, molecular modeling serves as a good supporting technique for interpreting ambiguous spectral data. Theoretical prediction of NMR spectra includes calculation of nuclear magnetic shieldings and sometimes also indirect spin–spin coupling constants (SSCC). The quality of theoretical prediction is strongly dependent on the choice of the theory level. In the current study, cathinone and its 12 fluorinated derivatives were selected for gauge-including atomic orbital (GIAO) NMR calculations using Hartree–Fock (HF) and 28 density functionals combined with 6-311++G** basis set to find the optimal level of theory for ¹H, ¹³C, and ¹⁹F chemical shifts modeling. All calculations were performed in the gas phase, and solutions were modeled with a polarized-continuum model (PCM) and solvation model based on density (SMD). The results were critically compared with available experimental data. Full article

Deep-water reverberation caused by multiple reflections from the seafloor and sea surface can affect the performance of active sonars. To detect a moving target under reverberation conditions, a reverberation suppression method using multipath Doppler shift in deep water and wideband ambiguity function (WAF) is proposed. Firstly, the multipath Doppler factors in the deep-water direct zone are analyzed, and they are introduced into the target scattered sound field to obtain the echo of the moving target. The mesh method is used to simulate the deep-water reverberation waveform in time domain. Then, a simulation model for an active sonar based on the source and short vertical line array is established. Reverberation and target echo in the received signal can be separated in the Doppler shift domain of the WAF. The multipath Doppler shifts in the echo are used to estimate the multipath arrival angles, which can be used for target localization. The simulation model and the reverberation suppression detection method can provide theoretical support and a technical reference for the active detection of moving targets in deep water. Full article

Background: Recent studies on bronchiectasis have revealed significant structural abnormalities and pathophysiological changes. However, there is limited research focused on pulmonary venous variability and congenital variation. Through our surgical observations, we noted that coarctation of pulmonary veins and atrophied lung volume are relatively common in bronchiectasis patients. Therefore, we conducted a retrospective study to explore pulmonary venous variation and secondary manifestations in bronchiectasis cases, utilizing 3D reconstruction software (Mimics Innovation Suite 21.0, Materialise Dental, Leuven, Belgium) to draw conclusions supported by statistical evidence. Method: This retrospective study included patients with bronchiectasis and healthy individuals who underwent CT examinations at Beijing Chao-Yang Hospital between January 2017 and July 2023. Chest CT data were reconstructed using Materialise Mimics. Pulmonary veins and lung lobes were segmented from surrounding tissue based on an appropriate threshold determined by local grey values and image gradients. Subsequently, venous cross-sectional areas and lung volumes were measured for statistical analysis. Result: CT data from 174 inpatients with bronchiectasis and 75 cases from the health examination center were included. Three-dimensional reconstruction data revealed a significant reduction in cross-sectional areas of pulmonary veins in the left lower lobe (p < 0.001), the right lower lobe (p = 0.030), and the right middle lobe (p = 0.009) of bronchiectasis patients. Subgroup analyses indicated that approximately 73.5% of localized cases of the left lower lobe exhibited pulmonary vein stenosis, while in the diffuse group, this proportion was only 52.6%. Furthermore, the cross-sectional area of pulmonary veins had a gradually decreasing trend, based on a small sample. Lung function tests showed significant reductions in FEV1, FVC, and FEV1% in bronchiectasis patients, attributed to the loss of lung volume in the left lower lobe, which accounted for 60.9% of the included sample. Conclusions: Our recent findings suggest that pulmonary venous stenosis is a common variation in bronchiectasis and is often observed concurrently with reduced lung volume, particularly affecting the left lower lobe. Moreover, localized cases are more likely to suffer from pulmonary venous stenosis, with an ambiguous downtrend as the disease progresses. In conclusion, increased attention to pulmonary venous variation in bronchiectasis is warranted, and exploring new therapies to intervene in the early stages or alleviate obstruction may be beneficial. Full article

Traffic regulations provide a solid foundation for the safety of all road users; however, the ambiguous provisions and unclear safety thresholds within these regulations pose significant challenges to compliance, particularly concerning the safe operation of autonomous vehicles. To address this issue, this paper conducts an in-depth analysis of vehicle emergency braking behavior based on the Aerial Dataset for China Congested Highway and Expressway (AD4CHE). The extraction method for the emergency braking risk scenario of natural driving data is proposed, and the correlation between safe distance, safe speed, and driving safety under the scenario of a slightly congested expressway is elaborated in detail. The safety threshold of ambiguous traffic rules obtained can be used for the digitalization of traffic rules that can support the functional development and traffic safety testing of automated driving systems. Full article

Depth estimation plays a pivotal role in advancing human–robot interactions, especially in indoor environments where accurate 3D scene reconstruction is essential for tasks like navigation and object handling. Monocular depth estimation, which relies on a single RGB camera, offers a more affordable solution compared to traditional methods that use stereo cameras or LiDAR. However, despite recent progress, many monocular approaches struggle with accurately defining depth boundaries, leading to less precise reconstructions. In response to these challenges, this study introduces a novel depth estimation framework that leverages latent space features within a deep convolutional neural network to enhance the precision of monocular depth maps. The proposed model features dual encoder–decoder architecture, enabling both color-to-depth and depth-to-depth transformations. This structure allows for refined depth estimation through latent space encoding. To further improve the accuracy of depth boundaries and local features, a new loss function is introduced. This function combines latent loss with gradient loss, helping the model maintain the integrity of depth boundaries. The framework is thoroughly tested using the NYU Depth V2 dataset, where it sets a new benchmark, particularly excelling in complex indoor scenarios. The results clearly show that this approach effectively reduces depth ambiguities and blurring, making it a promising solution for applications in human–robot interaction and 3D scene reconstruction. Full article

With rapid urbanization, maintaining a high quality of life (QoL) for city residents has become a critical challenge for policy-makers and urban planners. Smart cities, leveraging advanced technologies and data analytics, present a promising pathway to enhance urban services and promote sustainability. This paper introduces an innovative adaptation of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, integrating a Belief Structure (BTOPSIS) to improve the evaluation and interpretation of survey data. Our approach effectively addresses the distribution of responses across categories and the uncertainty often present in such data, including missing or ambiguous answers. Additionally, we perform a sensitivity analysis to assess the stability of the BTOPSIS rankings under varying utility function parameters, further validating the robustness of our method. We apply this framework to the 2023 ‘Quality of Life in European Cities’ survey, analyzing diverse urban factors such as public transport, healthcare, cultural facilities, green spaces, education, air quality, noise levels, and cleanliness. Additionally, our study offers a comparative analysis of BTOPSIS against other multi-criteria methods used for evaluation data from this report, showcasing its strengths and limitations in addressing the dataset’s complexity. Our findings reveal significant variations in residents’ perceived QoL across European cities, both between cities and within countries. Zurich and Groningen rank highest in satisfaction, while Tirana, Skopje, and Palermo are ranked lowest. Notably, residents of cities with populations under 500,000 report higher satisfaction levels than those in larger cities, and satisfaction levels are generally higher in EU and EFTA cities compared to those in the Western Balkans, with the highest satisfaction observed in northern and western Member States. To aid urban planners and policy-makers, we propose a ranking tool using the BTOPSIS method, capturing nuanced resident perceptions of living conditions across cities. These insights provide valuable guidance for strategic urban development and advancing the smart city agenda across Europe. Full article

Background and Objectives: Cognitive impairment is defined as a reduced ability to perform one or more cognitive functions, potentially leading to dementia if the condition worsens. With dementia being a rapidly growing public health issue affecting approximately 50 million people worldwide, understanding modifiable risk factors such as thyroid disease is crucial for prevention and early diagnosis. Thyroid hormones play a vital role in brain development and functioning, impacting processes such as neuron growth, myelination, and neurotransmitter synthesis. Recent decades have seen thyroid disorders emerging as potential independent risk factors for reversible cognitive impairment. Materials and Methods: The review adheres to PRISMA guidelines, utilizing a structured PICO question to explore whether individuals with thyroid diseases have a higher risk of developing dementia and cognitive impairments compared to those without. The literature search was conducted in PubMed, Cochrane, and ScienceDirect databases, including studies published from 1 January 2019 to 31 December 2023. The literature review discusses nine selected articles. Results: The findings highlight a complex association between thyroid dysfunction and cognitive decline, with some studies indicating significant links, particularly with hypothyroidism, and others suggesting the relationship may depend on the specific type of thyroid dysfunction or cognitive domain affected. Six out of nine articles found a link between thyroid disease and cognitive impairment, while three articles refuted this link. Conclusions: The review reveals a complex and ambiguous relationship between thyroid dysfunction and cognitive impairment. Further research is needed to elucidate the mechanisms underlying these associations and to determine whether thyroid dysfunction may be a modifiable risk factor for dementia. Full article

BCA methodically assesses the state of a building’s deterioration to support Maintenance, Safety, Function, and Compliance purposes. Originally used to assist in identifying urgent repair requirements, it has evolved and become one of the most used tools for assessing a building’s outstanding maintenance liability when a building is transacted or acquired. Nevertheless, current practices involve several conflicts; for example, high costs are associated with inspections, inconsistent building component registers, and ambiguity and consistency regarding reporting parameters, all of which lead to compounding errors that reduce reliability. To address these gaps, the current research, involving one hundred and eighteen (118) active facilities managers and asset inspectors, suggests the development of an extension of the deterioration scale (0–7) and methodologies to reduce errors and ambiguity. Furthermore, it suggests using weighted indices to focus on crucial building components, thus improving condition assessment. As was found, these tools improve the accuracy of BCA, facilitate better management of the asset’s life cycle, and provide support in decision-making. This study adds consistency, limits subjectivity, and provides a framework applicable to different building types, assisting future management for sustainability. It, therefore, stands to serve the field by providing detailed and concise best practices for conducting condition audits on built assets. Full article

Speckle noise is a granular interference that degrades image quality in coherent imaging systems, including underwater sonar, Synthetic Aperture Radar (SAR), and medical ultrasound. This study aims to enhance speckle noise reduction through advanced deep learning techniques. We introduce the Deep Gradient-Guidance Network (DGGNet), which features an architecture comprising one encoder and two decoders—one dedicated to image recovery and the other to gradient preservation. Our approach integrates a gradient map and fractional-order total variation into the loss function to guide training. The gradient map provides structural guidance for edge preservation and directs the denoising branch to focus on sharp regions, thereby preventing over-smoothing. The fractional-order total variation mitigates detail ambiguity and excessive smoothing, ensuring rich textures and detailed information are retained. Extensive experiments yield an average Peak Signal-to-Noise Ratio (PSNR) of 31.52 dB and a Structural Similarity Index (SSIM) of 0.863 across various benchmark datasets, including McMaster, Kodak24, BSD68, Set12, and Urban100. DGGNet outperforms existing methods, such as RIDNet, which achieved a PSNR of 31.42 dB and an SSIM of 0.853, thereby establishing new benchmarks in speckle noise reduction. Full article

It is challenging to apply the receiver function method to teleseisms recorded by ocean-bottom seismographs (OBSs) due to a specific working environment that differs from land stations. Teleseismic incident waveforms reaching the area beneath stations are affected by multiple reflections generated by seawater and sediments and noise resulting from currents. Furthermore, inadequate coupling between OBSs and the seabed basement and the poor fidelity of OBSs reduce the signal-to-noise ratio (SNR) of seismograms, leading to the poor quality of extracted receiver functions or even the wrong deconvolution results. For instance, the poor results cause strong ambiguities regarding the Moho depth. This study uses numerical modeling to analyze the influences of multiple reflections generated by seawater and sediments on H-kappa stacking and the neighborhood algorithm. Numerical modeling shows that seawater multiple reflections are mixed with the coda waves of the direct P-wave and slightly impact the extracted receiver functions and can thus be ignored in subsequent inversion processing. However, synthetic seismograms have strong responses to the sediments. Compared to the waveforms of horizontal and vertical components, the sedimentary responses are too strong to identify the converted waves clearly. The extracted receiver functions correspond to the above influences, resulting in divergent results of H-kappa stacking (i.e., the Moho depth and crustal average V_P/V_S ratio are unstable and have great uncertainties). Fortunately, waveform inversion approaches (e.g., the neighborhood algorithm) are available and valid for obtaining the S-wave velocity structure of the crust–upper mantle beneath the station, with sediments varying in thickness and velocity. Full article

Background: Intracranial AVMs are a highly heterogeneous group of lesions that, while not very common, can pose significant risks. The therapeutic management of AVMs is complicated by ambiguous guidelines, particularly regarding which Spetzler–Martin grades should dictate specific treatment options. This study analyzed the clinical presentations and treatment approaches of 128 brain AVM cases managed between 2014 and 2022 at the National Institute of Neurology and Neurovascular Diseases in Bucharest, Romania. Methods: A retrospective analysis was conducted on patient demographics, clinical symptoms, Spetzler–Martin categorization, nidus localization, therapeutic management, and outcomes. Statistical analysis was performed using Python 3.10. Results: In our cohort of patients, the median age was 45 years, with a slight male predominance (67 males, 61 females). At admission, 51.5% presented with elevated blood pressure. The majority of patients had a Spetzler–Martin score of 2 (37.5%), followed by scores of 3 (31.3%) and 1 (20.3%). Treatment strategies included microsurgical resection in 32% of cases, conservative management in 31.2%, Gamma Knife radiosurgery in 22.6%, and endovascular embolization in 13.3%. Notably, open surgery was predominantly chosen for Grade II AVMs. The functional outcomes were favorable, with 69.5% achieving a good recovery score on the Glasgow Outcome Scale. Only four in-hospital deaths occurred, all in patients who underwent open surgery, and no deaths were recorded during the two-year follow-up. Conclusions: AVMs within the same Spetzler–Martin grade display considerable complexity, necessitating personalized treatment strategies. Our findings highlight the limitations of open surgery for Grade I cases but affirm its effectiveness for Grade II AVMs. Full article

When there are time division multiple access (TDMA) signals with large bandwidth, waveform aliasing, and fast frequency-hopping in space, current methods have difficulty achieving the accurate localization of radiation sources and signal-sorting from multiple network stations. To solve the above problems, a distributed passive positioning and network stations sorting method for broadband frequency-hopping signals based on two-level parameter estimation and joint clustering is proposed in this paper. Firstly, a two-stage filtering structure is designed to achieve control filtering for each frequency point. After narrowing down the parameter estimation range through adaptive threshold detection, the time difference of arrival (TDOA) and the velocity difference of arrival (VDOA) can be obtained via coherent accumulating based on the cross ambiguity function (CAF). Then, a multi-station positioning method based on the TDOA/VDOA is used to estimate the position of the target. Finally, the distributed joint eigenvectors of the multi-stations are constructed, and the signals belonging to different network stations are effectively classified using the improved K-means method. Numerical simulations indicate that the proposed method has a better positioning and sorting effect in low signal-to-noise (SNR) and low snapshot conditions compared with current methods. Full article