Car crashes are among the top ten leading causes of death; they could mainly be attributed to distracted drivers. An advanced driver-assistance technique (ADAT) is a procedure that can notify the ...driver about a dangerous scenario, reduce traffic crashes, and improve road safety. The main contribution of this work involved utilizing the driver's attention to build an efficient ADAT. To obtain this "attention value", the gaze tracking method is proposed. The gaze direction of the driver is critical toward understanding/discerning fatal distractions, pertaining to when it is obligatory to notify the driver about the risks on the road. A real-time gaze tracking system is proposed in this paper for the development of an ADAT that obtains and communicates the gaze information of the driver. The developed ADAT system detects various head poses of the driver and estimates eye gaze directions, which play important roles in assisting the driver and avoiding any unwanted circumstances. The first (and more significant) task in this research work involved the development of a benchmark image dataset consisting of head poses and horizontal and vertical direction gazes of the driver's eyes. To detect the driver's face accurately and efficiently, the You Only Look Once (YOLO-V4) face detector was used by modifying it with the Inception-v3 CNN model for robust feature learning and improved face detection. Finally, transfer learning in the InceptionResNet-v2 CNN model was performed, where the CNN was used as a classification model for head pose detection and eye gaze angle estimation; a regression layer to the InceptionResNet-v2 CNN was added instead of SoftMax and the classification output layer. The proposed model detects and estimates head pose directions and eye directions with higher accuracy. The average accuracy achieved by the head pose detection system was 91%; the model achieved a RMSE of 2.68 for vertical and 3.61 for horizontal eye gaze estimations.
To solve the problem of low accuracy of pavement crack detection caused by natural environment interference, this paper designed a lightweight detection framework named PCDETR (Pavement Crack ...DEtection TRansformer) network, based on the fusion of the convolution features with the sequence features and proposed an efficient pavement crack detection method. Firstly, the scalable Swin-Transformer network and the residual network are used as two parallel channels of the backbone network to extract the long-sequence global features and the underlying visual local features of the pavement cracks, respectively, which are concatenated and fused to enrich the extracted feature information. Then, the encoder and decoder of the transformer detection framework are optimized; the location and category information of the pavement cracks can be obtained directly using the set prediction, which provided a low-code method to reduce the implementation complexity. The research result shows that the highest AP (Average Precision) of this method reaches 45.8% on the COCO dataset, which is significantly higher than that of DETR and its variants model Conditional DETR where the AP values are 36.9% and 42.8%, respectively. On the self-collected pavement crack dataset, the AP of the proposed method reaches 45.6%, which is 3.8% higher than that of Mask R-CNN (Region-based Convolution Neural Network) and 8.8% higher than that of Faster R-CNN. Therefore, this method is an efficient pavement crack detection algorithm.
It is critical for intelligent vehicles to be capable of monitoring the health and well-being of the drivers they transport on a continuous basis. This is especially true in the case of autonomous ...vehicles. To address the issue, an automatic system is developed for driver’s real emotion recognizer (DRER) using deep learning. The emotional values of drivers in indoor vehicles are symmetrically mapped to image design in order to investigate the characteristics of abstract expressions, expression design principles, and an experimental evaluation is conducted based on existing research on the design of driver facial expressions for intelligent products. By substituting a custom-created CNN features learning block with the base 11 layers CNN model in this paper for the development of an improved faster R-CNN face detector that detects the driver’s face at a high frame per second (FPS). Transfer learning is performed in the NasNet large CNN model in order to recognize the driver’s various emotions. Additionally, a custom driver emotion recognition image dataset is being developed as part of this research task. The proposed model, which is a combination of an improved faster R-CNN and transfer learning in NasNet-Large CNN architecture for DER based on facial images, enables greater accuracy than previously possible for DER based on facial images. The proposed model outperforms some recently updated state-of-the-art techniques in terms of accuracy. The proposed model achieved the following accuracy on various benchmark datasets: JAFFE 98.48%, CK+ 99.73%, FER-2013 99.95%, AffectNet 95.28%, and 99.15% on a custom-developed dataset.
Underwater wireless sensor networks (UWSNs) have gained prominence in wireless sensor technology, featuring resource-limited sensor nodes deployed in challenging underwater environments. To address ...challenges like power consumption, network lifetime, node deployment, topology, and propagation delays, cooperative transmission protocols like co-operative (Co-UWSN) and co-operative energy-efficient routing (CEER) have been proposed. These protocols utilize broadcast capabilities and neighbor head node (NHN) selection for cooperative routing. This research introduces NBEER, a novel neighbor-based energy-efficient routing protocol tailored for UWSNs. NBEER aims to surpass the limitations of Co-UWSN and CEER by optimizing NHNS and cooperative mechanisms to achieve load balancing and enhance network performance. Through comprehensive MATLAB simulations, we evaluated NBEER against Co-UWSN and CEER, demonstrating its superior performance across various metrics. NBEER significantly maximizes end-to-end delay, reduces energy consumption, improves packet delivery ratio, extends network lifetime, and enhances total received packets analysis compared to the existing protocols.
The lack of skid resistance performance is a crucial factor leading to road traffic accidents. The pavement surface friction is an essential indicator for measuring the skid resistance. The surface ...texture structure significantly affects the friction between the tire and the pavement, determining the pavement skid resistance. To deeply study the relationship between surface texture structure and pavement skid resistance performance, two types of asphalt mixture specimens, asphalt concrete (AC) and open-graded friction course (OGFC), are prepared for the skid resistance performance test. Firstly, a high-precision 3D smart sensor Gocator 3110 is used to collect the 3D point cloud data of the asphalt mixture surface texture. The British pendulum tester is used to measure the friction. Secondly, ten feature parameters are extracted to describe the 3D macrotexture characteristics. A data set containing 10 features and 200 groups of texture and friction data was also constructed. Meanwhile, the influence of macro-texture features on the skid resistance performance is discussed. Finally, an optimized Bayesian-LightGBM model is trained based on the constructed dataset. Compared with LightGBM, XGBoost, RF, and SVR algorithms, the Bayesian-LightGBM model can evaluate skid resistance more accurately. The R2 value of the proposed model is 92.83%. The research results prove that ten macrotexture features contribute to the evaluation of skid resistance to varying degrees. Furthermore, compared with AC mixture specimen, the texture surface of OGFC mixture specimen has more obvious height characteristics and higher roughness. The skid resistance of OGFC mixture specimens is better than that of AC.
Sorting gangue from raw coal is an essential concern in coal mining engineering. Prior to separation, the location and shape of the gangue should be extracted from the raw coal image. Several ...approaches regarding automatic detection of gangue have been proposed to date; however, none of them is satisfying. Therefore, this paper aims to conduct gangue segmentation using a U-shape fully convolutional neural network (U-Net). The proposed network is trained to segment gangue from raw coal images collected under complex environmental conditions. The probability map outputted by the network was used to obtain the location and shape information of gangue. The proposed solution was trained on a dataset consisting of 54 shortwave infrared (SWIR) raw coal images collected from Datong Coalfield. The performance of the network was tested with six never seen images, achieving an average area under the receiver operating characteristics (AUROC) value of 0.96. The resulting intersection over union (IoU) was on average equal to 0.86. The results show the potential of using deep learning methods to perform gangue segmentation under various conditions.
Objective
Embryonal tumors with multilayered rosettes (ETMRs) are a histologically heterogeneous entity and gather embryonal tumors with abundant neuropil and true rosettes (ETANTRs), ...ependymoblastoma, and medulloepithelioma. ETMRs are highly aggressive and associated with poorer clinical courses. However, cases of this entity are rare, and advances in molecular genetics and therapy are minor. The purpose of our study was to retrospectively analyze the clinical, pathological features, and prognostic factors of ETMRs.
Methods
Our cohort consisted of 17 patients diagnosed with ETMRs in our hospital from 2018 to 2022, and two of them were lost to follow-up. Clinical data were retrieved, and immunohistochemistry and genetic analyses were performed.
Results
Among 17 cases, 16 were ETANTRs, and one was medulloepithelioma. Morphologically, tumor cells of ETANTRs could transform into anaplasia and lose the biphasic architecture during tumor progression. Immunohistochemistry of LIN28A revealed positive expression in 17 cases, and the expression of LIN28A was more intense and diffuse in the recurrent lesions than in primaries. The increased
N-MYC
copy numbers were detected in the primary tumor and recurrence of patient 8. Moreover, the incidence of metastatic disease was 100% in patients aged > 4 years and 18% in the younger group. For patients receiving chemotherapy, the median overall survival time was 7.4 months, while that of those who didn’t receive it was 1.2 months. Nevertheless, surgical approaches, radiotherapy, age at presentation, gender, tumor location, and metastatic status were not associated with independent prognosis.
Conclusion
ETANTR might not present as the typical morphologies during tumor progression, so analyses of
C19MC
amplification and Lin28A antibody are indispensable for diagnosing ETMRs accurately. Children aged > 4 years tend to have a higher rate of metastasis in ETMRs. Chemotherapy is the only prognostic factor for ETMRs patients with a favorable prognosis. The biological nature and clinical patterns for recurrent diseases need to be further demonstrated to predict prognosis and guide treatment.
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•A superhydrophobic blood-repellent tube was developed, which efficiently delays blood coagulation time in comparison with a clinical Bioline heparin-coated tube.•This ...superhydrophobic blood-repellent tube has good blood-bouncing properties and biocompatibility.•The physiological mechanism underlying the ability of superhydrophobic coatings to prolong clotting time was first interpreted from the perspective of inhibiting thromboxane-A2 secretion during blood coagulation.
Superhydrophobic coatings are gaining importance not only in military technology, industrial engineering, power systems and transportation, but also in food science and biomedicine, where they have enormous potential for real-time applications. Many research institutes have recently been working on the development of superhydrophobic blood-repellent tubes to replace clinical heparin-coated tubes during ECMO therapy, but biological toxicity and high blood adhesion have significantly hampered their practical applications. In this study, we created a cardiopulmonary bypass tube with good superhydrophobicity and blood repellency. In terms of biotoxicity, coagulation time, protein and platelet adsorption, the results show that the superhydrophobic-treated tube clotted in 36 min, compared to 21 min for a clinical Bioline heparin-coated tube and 14 min for bare PVC tube. When compared to the bare PVC tube, the clotting time of the superhydrophobic-treated tube was increased by 157% due to the Cassie-Baxter wetting state of the resulting superhydrophobic blood-repellent coating. Meanwhile, the protein and platelet adsorption on the superhydrophobic-treated tube were decreased by 32% and 74%, respectively. It should be noted that we were the first to explain the superhydrophobic anti-platelet adsorption mechanism from the perspective of inhibiting thromboxane-A2 secretion during blood coagulation. Clarification of platelet activation process and activating substances is vital for understanding the anticoagulant properties of superhydrophobic coatings. On the other hand, non-inflammatory response of the organism was confirmed in the vivo experiments and the cell viability after a 48-hour cytotoxicity assay was about 101%, indicating that the superhydrophobic-treated tubes are non-toxic.
A super-resolution reconstruction approach based on an improved generative adversarial network is presented to overcome the huge disparities in image quality due to variable equipment and ...illumination conditions in the image-collecting stage of intelligent pavement detection. The nonlinear network of the generator is first improved, and the Residual Dense Block (RDB) is created to serve as Batch Normalization (BN). The Attention Module is then formed by combining the RDB, Gated Recurrent Unit (GRU), and Conv Layer. Finally, a loss function based on the L1 norm is utilized to replace the original loss function. The experimental findings demonstrate that the self-built pavement crack dataset's Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) of the reconstructed images reach 29.21 dB and 0.854, respectively. The results improved compared to the Set5, Set14, and BSD100 datasets. Additionally, by employing Faster-RCNN and a Fully Convolutional Network (FCN), the effects of image reconstruction on detection and segmentation are confirmed. The findings indicate that the segmentation results' F1 is enhanced by 0.012 to 0.737 and the detection results' confidence is increased by 0.031 to 0.9102 when compared to state-of-the-art methods. It has a significant engineering application value and can successfully increase pavement crack-detecting accuracy.
The existing pavement crack detection algorithms are all dealing with detection and segmentation separately, ignoring the feature correlation between the bounding box coordinates and mask ...information, and there are still problems such as low crack detection accuracy, incomplete detection, and segmentation fracture in practical application. In view of the above situation, this paper took the detection bounding box coordinates and mask information as multimodal features of the same pavement crack area, and proposed a One-Stage MFFNet (Multimodal Feature Fusion Network), which improved the pavement crack detection accuracy and segmentation integrity obviously. And experimental results of different models were compared on self-collected datasets and two public datasets (CFD and CRACK500) respectively. Compared with Mask R-CNN, the average detection accuracy and average segmentation accuracy were improved by 2.6% and 4.7%, respectively. Compared with the optimised model of RDSNet, the detection accuracy and processing speed were improved by 1.8% and 2FPS, respectively. In addition, MFFNet had significantly improved the integrity of pavement crack segmentation results. The results showed that the proposed MFFNet model achieved the best detection and segmentation accuracy, and was an effective and high-precision pavement crack detection model.
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