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Construction workers’ behaviors directly affects labor productivity and their own safety, thereby influencing project quality. Recognizing and monitoring the construction-related behaviors is therefore crucial for high-quality management and orderly construction site operation. Recent strides in computer vision technology suggest its potential to replace traditional manual supervision approaches. This paper explores research on monitoring construction workers’ behaviors using computer vision. Through bibliometrics and content-based analysis, the authors present the latest research in this area from three perspectives: “Detection, Localization, and Tracking for Construction Workers,” “Recognition of Workers’ Construction Activities,” and “Occupational Health and Safety Behavior Monitoring.” In terms of the literature’s volume, there has been a notable increase in this field. Notably, the focus on safety-related literature is predominant, underscoring the concern for occupational health. Vision algorithms have witnessed an increase in the utilization of object detection. The ongoing and future research trajectory is anticipated to involve multi-algorithm integration and an emphasis on enhancing robustness. Then the authors summarize the review from engineering impact and technical suitability, and analyze the limitations of current research from the perspectives of technical approaches and application scenarios. Finally, it discusses future research directions in this field together with generative AI models. Furthermore, the authors hope this paper can serves as a valuable reference for both scholars and engineers.

High-resolution magnetic resonance imaging (MRI) sequences, such as 3D turbo or fast spin-echo (TSE/FSE) imaging, are clinically desirable but suffer from long scanning time-related blurring when reformatted into preferred orientations. Instead, multi-slice two-dimensional (2D) TSE imaging is commonly used because of its high in-plane resolution but is limited clinically by poor through-plane resolution due to elongated voxels and the inability to generate multi-planar reformations due to staircase artifacts. Therefore, multiple 2D TSE scans are acquired in various orthogonal imaging planes, increasing the overall MRI scan time. In this study, we propose a novel slice-profile transformation super-resolution (SPTSR) framework with deep generative learning for through-plane super-resolution (SR) of multi-slice 2D TSE imaging. The deep generative networks were trained by synthesized low-resolution training input via slice-profile downsampling (SP-DS), and the trained networks inferred on the slice profile convolved (SP-conv) testing input for 5.5x through-plane SR. The network output was further slice-profile deconvolved (SP-deconv) to achieve an isotropic super-resolution. Compared to SMORE SR method and the networks trained by conventional downsampling, our SPTSR framework demonstrated the best overall image quality from 50 testing cases, evaluated by two abdominal radiologists. The quantitative analysis cross-validated the expert reader study results. 3D simulation experiments confirmed the quantitative improvement of the proposed SPTSR and the effectiveness of the SP-deconv step, compared to 3D ground-truths. Ablation studies were conducted on the individual contributions of SP-DS and SP-conv, networks structure, training dataset size, and different slice profiles.

An efficient perturbation projection vector (PPV) modeling for a memristor-based oscillator is proposed and then applied to the simulation of the oscillatory neural network (ONN) for pattern recognition. Compared with the existing model, which is very time consuming, this novel modeling method features a fast and accurate simulation of a large-scale network with many oscillators. The proposed modeling is based on a highly efficient abstraction of the phase sensitivity characteristic of the memristor-based oscillator, i.e., its PPV, which enables reducing the complexity of the ONN description, speeding up the simulation considerably while retaining comparable precision. [ABSTRACT FROM AUTHOR]

Automatic segmentation of prostatic zones on multi-parametric MRI (mpMRI) can improve the diagnostic workflow of prostate cancer. We designed a spatial attentive Bayesian deep learning network for the automatic segmentation of the peripheral zone (PZ) and transition zone (TZ) of the prostate with uncertainty estimation. The proposed method was evaluated by using internal and external independent testing datasets, and overall uncertainties of the proposed model were calculated at different prostate locations (apex, middle, and base). The study cohort included 351 MRI scans, of which 304 scans were retrieved from a de-identified publicly available datasets (PROSTATEX) and 47 scans were extracted from a large U.S. tertiary referral center (external testing dataset; ETD)). All the PZ and TZ contours were drawn by research fellows under the supervision of expert genitourinary radiologists. Within the PROSTATEX dataset, 259 and 45 patients (internal testing dataset; ITD) were used to develop and validate the model. Then, the model was tested independently using the ETD only. The segmentation performance was evaluated using the Dice Similarity Coefficient (DSC). For PZ and TZ segmentation, the proposed method achieved mean DSCs of 0.80±0.05 and 0.89± 0.04 on ITD, as well as 0.79±0.06 and 0.87±0.07 on ETD. For both PZ and TZ, there was no significant difference between ITD and ETD for the proposed method. This DL-based method enabled the accuracy of the PZ and TZ segmentation, which outperformed the state-of-art methods (DeeplabV3+, Attention U-Net, R2U-Net, USE-Net and U-Net). We observed that segmentation uncertainty peaked at the junction between PZ, TZ and AFS. Also, the overall uncertainties were highly consistent with the actual model performance between PZ and TZ at three clinically relevant locations of the prostate.