Res-ECA-UNet++: an automatic segmentation model for ovarian tumor ultrasound images based on residual networks and channel attention mechanism

ObjectiveUltrasound imaging has emerged as the preferred imaging modality for ovarian tumor screening due to its non-invasive nature and real-time dynamic imaging capabilities. However, in many developing countries, ultrasound diagnosis remains dependent on specialist physicians, where the shortage of skilled professionals and the relatively low accuracy of manual diagnoses significantly constrain screening efficiency. Although deep learning has achieved remarkable progress in medical image segmentation in recent years, existing methods still face challenges in ovarian tumor ultrasound segmentation, including insufficient robustness, imprecise boundary delineation, and dependence on high-performance hardware facilities. This study proposes a deep learning-based automatic segmentation model, Res-ECA-UNet++, designed to enhance segmentation accuracy while alleviating the strain on limited healthcare resources.MethodsThe Res-ECA-UNet++ model employs UNet++ as its fundamental architecture with ResNet34 serving as the backbone network. To effectively address the vanishing gradient problem in deep networks, residual modules are incorporated into the skip connections between the encoding and decoding processes. This integration enhances feature extraction efficiency while improving model stability and generalization capabilities. Furthermore, the ECA-Net channel attention mechanism is introduced during the downsampling phase. This mechanism adaptively emphasizes tumor region-related channel information through global feature recalibration, thereby improving recognition accuracy and localization precision for tumor areas.ResultsBased on clinical ultrasound datasets of ovarian tumors, experimental results demonstrate that Res-ECA-UNet++ achieves outstanding performance in clinical validation, with a Dice coefficient of 95.63%, mean Intersection over Union (mIoU) of 91.84%, and accuracy of 99.75%. Compared to the baseline UNet, Res-ECA-UNet++ improves these three metrics by 0.45, 4.42, and 1.57%, respectively. Comparative analyses of ROC curves and AUC values further indicate that Res-ECA-UNet++ exhibits superior segmentation accuracy and enhanced generalization capabilities on the test set. In terms of computational efficiency, the inference time of Res-ECA-UNet++ meets clinical real-time requirements on both high-end and low-end hardware, demonstrating its suitability for deployment on resource-constrained devices. Additionally, comparative experiments on the public OTU2D dataset validate the model’s superior segmentation performance, highlighting its strong potential for practical applications.ConclusionThe proposed Res-ECA-UNet++ model demonstrates exceptional accuracy and robustness in the segmentation of ovarian tumor ultrasound images, highlighting its potential for clinical application. Its ability to enhance segmentation precision and aid clinicians in diagnosis underscores broad prospects for practical implementation. Future research will focus on optimizing the model architecture to further improve its adaptability to diverse pathological types and imaging characteristics, thereby expanding its clinical diagnostic utility

Building Occupancy Detection and Localisation using CCTV Camera and Deep Learning

Occupancy information plays a key role in analysing and improving building energy performance. The advances of Internet of Things (IoT) technologies have engendered a shift in measuring building occupancy with IoT sensors, in which cameras in Closed-Circuit Television (CCTV) systems can provide richer measurements. However, existing camera-based occupancy detection approaches cannot function well when scanning videos with a number of occupants and determining occupants’ locations. This paper aims to develop a novel deep learning based approach for better building occupancy detection based on CCTV cameras. To doing so, this research proposes a deep learning model to detect the number of occupants and determine their locations in videos. This model consists of two main modules namely feature extraction and three-stage occupancy detection. The first module presents a deep convolutional neural network to perform residual and multi-branch convolutional calculation to extract shallow and semantic features, and constructs feature pyramids through a bi-directional feature network. The second module performs a three-stage detection procedure with three sequential and homogeneous detectors which have increasing Intersection over Union (IoU) thresholds. Empirical experiments evaluate the detection performance of the approach with CCTV videos from a university building. Experimental results show that the approach achieves superior detection performance when compared with baseline models.Natural Science Foundation of Chin

A Contextual Ontology for Distributed Urban Data Management

The evolution of information and communication technology in the construction domain has yielded a variety of heterogeneous data sources. While bespoke approaches have been developed to explore data merging for a variety of purposes, few have explored how to develop a multipurpose information organisation that can be reconfigured on a per-project basis. This paper describes an approach that, using a lightweight central server, is used to investigate the effectiveness of loose federations of information sources that together serve the information needs of a project. The central server provides both a common context through which relationships between the information sources can be expressed and a data register to enable information discovery. The paper describes the creation of an ontology to capture this context and a software architecture to support its use. The efficacy of the approach is illustrated through describing the use of the server for marshalling data used in a renovation project.Science Foundation IrelandUniversity College Dubli

Simulation and experimental validation of the mechanisms underlying the reduction and enhancement effects of Magnetic-Vibration composite treatment

Employing the COMSOL finite element software, this research has devised an intricate multi-physics field coupling simulation model focused on scrutinizing the macro-scale magnetic-vibration treatment process. Its aim is to offer a quantitative delineation of the mechanisms underpinning the enhancement effect associated with magnetic-vibration treatment. Through the application of modeling and simulation techniques, this inquiry adeptly replicates the synergistic reduction effect enhancement induced by magnetic-vibration treatment. The simulation findings provide quantitative substantiation of magnetic-vibration treatment's amplified reduction effect, approximating a factor of “1 + 1≈2.78″ when compared with individual treatment methods. Additionally, this investigation delves deeply into the implications of magnetic frequency and vibration frequency, shedding light on the mechanisms driving the augmentation of the residual stress reduction achieved through magnetic-vibration treatment. Notably, when the magnetic frequency aligns with the vibration frequency or becomes an integer multiple thereof, a pronounced enhancement effect is observed. Importantly, the study underscores the association between the efficacy of the magnetic-vibration treatment method and its enhancement effect with the thickness of the material component

Investigation of the influence of fatigue alternating loading on residual stress in materials

This study investigates the effect of pulsating cyclic fatigue loading on the internal stress of silicon steel material. A comparative analysis is conducted between the loaded material and the original material before treatment. The findings indicate that pulsating cyclic fatigue loading leads to a decrease in the residual stress of the material and an increase in its elongation, albeit with a reduction in its strength. Additionally, fatigue alternating loading reduces the roughness of the material and enhances its grain size, resulting in narrower grain boundaries. Transmission electron microscope (TEM) analysis indicates that finite fatigue loading causes an increase in the number of dislocations in the material, leading to local plastic deformation and contributing to the reduction of residual stress in the material. Notably, the mechanism underlying the reduction of residual stress resulting from pulsating cyclic fatigue loading is akin to that of non-temperature-affected residual stress reduction treatment, suggesting a possible similarity in their fundamental nature

An Ultra-Low-Power Reconfigurable Power-On Reset for Multi-Supply Voltages Applications

In this letter, an ultra-low-power power-on-reset circuit with reconfigurable trip-voltages is proposed. In order to reduce area and power consumption overhead, an all-MOS sub-threshold architecture based on threshold difference voltage references and current comparator is designed. By configuring the reference current and the different threshold transistors, the different trig voltages are generated to detect multi-supply voltages. Simulation results based on 55 nm CMOS process show that the proposed power-on-reset circuit generates trip voltages of 385.5 mV and 775.4 mV, consuming only 8.5 nA and 92.6 nA at the supply voltage of 0.5 V and 1 V, respectively. And the area of the proposed power-on-reset circuit is as low as 240 μm.</jats:p

A contextual ontology for distributed urban data management

The evolution of information and communication technology in the construction domain has yielded a variety of heterogeneous data sources. While bespoke approaches have been developed to explore data merging for a variety of purposes, few have explored how to develop a multipurpose information organisation that can be reconfigured on a per-project basis. This paper describes an approach that, using a lightweight central server, is used to investigate the effectiveness of loose federations of information sources that together serve the information needs of a project. The central server provides both a common context through which relationships between the information sources can be expressed and a data register to enable information discovery. The paper describes the creation of an ontology to capture this context and a software architecture to support its use. The efficacy of the approach is illustrated through describing the use of the server for marshalling data used in a renovation project. </jats:p

An investigation into the reduction mechanism of temperature-magnetic stress relief based on DO3 crystal

This study assesses a novel method proposed by the research group for reducing residual stress through temperature-magnetic stress relief (TMSR). Experimental findings indicate that this approach yields significant reductions in residual stress. Microscopic analysis reveals that following TMSR treatment, the labyrinthine domains within the material diminish while the parallel domains increase, prompting atomic migration and dislocation in regions of elevated stress. Moreover, utilizing density functional theory, this paper investigates the intrinsic strengthening mechanism of TMSR. The findings indicate that the atomic magnetic moment of the Fe3C (DO3) crystal exhibits an approximately increasing trend within a specific temperature range. This observation underscores the thermo-magnetic coupling strengthening effect inherent in the DO3 ferromagnetic crystal, which constitutes a pivotal element in the enhancement effect of TMSR. Furthermore, an examination of changes in the energy band distribution and electronic density of states at varying temperatures sheds light on the underlying cause of fluctuations in the atomic magnetic moment. The research outcomes indicate that upon surpassing a certain temperature threshold, the crystal phase undergoes changes, leading to fluctuations in the atomic magnetic moment. This study contributes novel insights aimed at enhancing comprehension of the TMSR method