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Ptychography is an imaging technique that uses the redundancy of information generated by the overlapping of adjacent light regions to calculate the relative phase of adjacent regions and reconstruct the image. In the terahertz domain, in order to make the ptychography technology better serve engineering applications, we propose a set of deep learning terahertz ptychography system that is easier to realize in engineering and plays an outstanding role. To address this issue, we propose to use a powerful deep blind degradation model which uses isotropic and anisotropic Gaussian kernels for random blurring, chooses the downsampling modes from nearest interpolation, bilinear interpolation, bicubic interpolation and down-up-sampling method, and introduces Gaussian noise, JPEG compression noise, and processed detector noise. Additionally, a random shuffle strategy is used to further expand the degradation space of the image. Using paired low/high resolution images generated by the deep blind degradation model, we trained a multi-layer residual network with residual scaling parameters and dense connection structure to achieve the neural network super-resolution of terahertz ptychography for the first time. We use two representative neural networks, SwinIR and RealESRGAN, to compare with our model. Experimental result shows that the proposed method achieved better accuracy and visual improvement than other terahertz ptychographic image super-resolution algorithms. Further quantitative calculation proved that our method has significant advantages in terahertz ptychographic image super-resolution, achieving a resolution of 33.09 dB on the peak signal-to-noise ratio (PSNR) index and 3.05 on the naturalness image quality estimator (NIQE) index. This efficient and engineered approach fills the gap in the improvement of terahertz ptychography by using neural networks.

Transformer-based light field (LF) super-resolution (SR) methods have recently achieved significant performance improvements due to global feature modeling by self-attention mechanisms. However, as a method designed for natural language processing, 4D LFs are reshaped into 1D sequences with an immense set of tokens, which results in a quadratic computational complexity cost. In this paper, a spatial–angular–epipolar swin transformer (SAEST) is proposed for spatial and angular SR (SASR), which sufficiently extracts SR information in the spatial, angular, and epipolar domains using local self-attention with shifted windows. Specifically, in SAEST, a spatial swin transformer and an angular standard transformer are firstly cascaded to extract spatial and angular SR features, separately. Then, the extracted SR feature is reshaped into the epipolar plane image pattern and fed into an epipolar swin transformer to extract the spatial–angular correlation information. Finally, several SAEST blocks are cascaded in a Unet framework to extract multi-scale SR features for SASR. Experiment results indicate that SAEST is a fast transformer-based SASR method with less running time and GPU consumption and has outstanding performance on simulated and real-world public datasets.

In order to accurately identify occluding targets and infer the motion state of objects, we propose a Bird’s-Eye View Object Detection Network based on Temporal-Spatial feature fusion (TS-BEV), which replaces the previous multi-frame sampling method by using the cyclic propagation mode of historical frame instance information. We design a new Temporal-Spatial feature fusion attention module, which fully integrates temporal information and spatial features, and improves the inference and training speed. In response to realize multi-frame feature fusion across multiple scales and views, we propose an efficient Temporal-Spatial deformable aggregation module, which performs feature sampling and weighted summation from multiple feature maps of historical frames and current frames, and makes full use of the parallel computing capabilities of GPUs and AI chips to further improve efficiency. Furthermore, in order to solve the lack of global inference in the context of temporal-spatial fusion BEV features and the inability of instance features distributed in different locations to fully interact, we further design the BEV self-attention mechanism module to perform global operation of features, enhance global inference ability and fully interact with instance features. We have carried out extensive experimental experiments on the challenging BEV object detection nuScenes dataset, quantitative results show that our method achieves excellent performance of 61.5% mAP and 68.5% NDS in camera-only 3D object detection tasks, and qualitative results show that TS-BEV can effectively solve the problem of 3D object detection in complex traffic background with lack of light at night, with good robustness and scalability.

Although there have been many previous studies on icon visual search and recognition performance, only a few have considered the effects of both the internal and external characteristics of icons. In this behavioral study, we employed a visual search task and a semantic recognition task to explore the effects of icon style, semantic distance (SD), and task difficulty on users’ performance in perceiving and identifying icons. First, we created and filtered 64 new icons, which were divided into four different groups (flat design & close SD, flat design & far SD, skeuomorphic design & close SD, skeuomorphic design & far SD) through expert evaluation. A total of 40 participants (13 men and 27 women, ages ranging from 19 to 25 years, mean age = 21.9 years, SD=1.93) were asked to perform an icon visual search task and an icon recognition task after a round of learning. Participants’ accuracy and response time were measured as a function of the following independent variables: two icon styles (flat or skeuomorphic style), two levels of SD (close or far), and two levels of task difficulty (easy or difficult). The results showed that flat icons had better visual search performance than skeuomorphic icons; this beneficial effect increased as the task difficulty increased. However, in the icon recognition task, participants’ performance in recalling skeuomorphic icons was significantly better than that in recalling flat icons. Furthermore, a strong interaction effect between icon style and task difficulty was observed for response time. As the task difficulty decreased, the difference in recognition performance between these two different icon styles increased significantly. These findings provide valuable guidance for the design of icons in human–computer interaction interfaces.

Neural Radiance Fields (NeRF) have recently emerged as a promising approach for synthesizing highly realistic images from 3D scenes. This technology has shown impressive results in capturing intricate details and producing photorealistic renderings. However, one of the limitations of traditional NeRF approaches is the difficulty in editing and manipulating the geometry of the scene once it has been captured. This restriction hinders creative freedom and practical applicability.

In this paper, we propose a method that enables interactive geometry editing for neural radiance fields manipulation. We use two proxy cages (inner cage and outer cage) to edit a scene. The inner cage defines the operation target, and the outer cage defines the adjustment space. Various operations apply to the two cages. After cage selection, operations on the inner cage lead to the desired transformation of the inner cage and adjustment of the outer cage. Users can edit the scene with translation, rotation, scaling, or combinations. The operations on the corners and edges of the cage are also supported. Our method does not need any explicit 3D geometry representations. The interactive geometry editing applies directly to the implicit neural radiance fields. Extensive experimental results demonstrate the effectiveness of our approach.

Virtual Reality (VR) sickness remains a significant challenge in the widespread adoption of VR technologies. The absence of a standardized benchmark system hinders progress in understanding and effectively countering VR sickness. This paper proposes an initial step towards a benchmark system, utilizing a novel methodological framework to serve as a common platform for evaluating contributing VR sickness factors and mitigation strategies. Our benchmark, grounded in established theories and leveraging existing research, features both small and large environments. In two research studies, we validated our system by demonstrating its capability to (1) quickly, reliably, and controllably induce VR sickness in both environments, followed by a rapid decline post-stimulus, facilitating cost and time-effective within-subject studies and increased statistical power, (2) integrate and evaluate established VR sickness mitigation methods — static and dynamic field of view reduction, blur, and virtual nose — demonstrating their effectiveness in reducing symptoms in the benchmark and their direct comparison within a standardized setting. Our proposed benchmark also enables broader, more comparative research into different technical, setup, and participant variables influencing VR sickness and overall user experience, ultimately paving the way for building a comprehensive database to identify the most effective strategies for specific VR applications.

Automated segmentation algorithms are a crucial component of medical image analysis, playing an essential role in assisting professionals to achieve accurate diagnoses. Traditional convolutional neural networks (CNNs) face challenges when dealing with complex and variable lesions: limited by the receptive field of convolutional operators, CNNs often struggle to capture long-range dependencies of complex lesions. The transformer’s outstanding ability to capture long-range dependencies offers a new perspective on addressing these challenges. Inspired by this, our research aims to combine the precise spatial detail extraction capabilities of CNNs with the global semantic understanding abilities of transformers. Unlike traditional fusion methods, we propose a fine-grained feature fusion strategy based on complementary attention, deeply exploring and complementarily fusing the feature representations of the encoder. Moreover, considering that merely relying on feature fusion might overlook critical texture details and key edge features in the segmentation task, we designed a feature enhancement module based on information entropy. This module emphasizes shallow texture features and edge information, enabling the model to more accurately capture and enhance multi-level details of the image, further optimizing segmentation results. Our method was tested on multiple public segmentation datasets of polyps and skin lesions,and performed better than state-of-the-art methods. Extensive qualitative experimental results indicate that our method maintains robust performance even when faced with challenging cases of narrow or blurry-boundary lesions.

Long-term use of desktop displays may increase the burden of the visual system and users can use low blue light mode for eye protection in terms of circadian effect. In this work, we investigated its influence from two aspects of brightness-visual effect, namely efficacy and circadian effect, and color quality, namely color difference Δu’v’ (chromaticity coordinate offset of two colors), and D_uv (deviation from blackbody locus). The decrease of brightness is accompanied by the increase of efficacy while diminishing circadian effect. The blue, cyan, and magenta have the largest Δu’v’, and the lower the saturation, the greater the Δu’v’. The lower the correlated color temperature (CCT), the greater the D_uv and the farther it deviates from the Planckian locus. We summarize three low blue light mode adjustment strategies based on red, green, and blue three-channel ratio of spectrum, and propose an optimized mode using genetic algorithm, which has two optional CCT ranges of 3500–5000 K and 2700–3000 K. Furthermore, we establish the relationship between brightness and gamut coverage to refine the screen brightness range for low blue light mode. This research provides valuable insights into low blue light mode application and their implications for human-centric healthy displays.

Human posture estimation is still a hot research topic. Previous algorithms based on traditional machine learning have difficulties in feature extraction and low fusion efficiency. To address these problems, we proposed a Transformer-based method. We combined three techniques, namely the Transformer-based feature extraction module, the multi-scale feature fusion module, and the occlusion processing mechanism, to capture the human pose. The Transformer-based feature extraction module uses the self-attention mechanism to extract key features from the input sequence, the multi-scale feature fusion module fuses feature information of different scales to enhance the perception ability of the model, and the occlusion processing mechanism can effectively handle occlusion in the data and effectively remove background interference. Our method has shown excellent performance through verification on the standard dataset Human3.6M and the wild video dataset, achieving accurate pose prediction in both complex actions and challenging samples.

In this work, we systematically investigate low-temperature polycrystalline silicon (LTPS)-based driving circuits of electronic paper for the aim of adopting small width/length ratio (W/L) of LTPS-based thin film transistors (TFTs) to reduce switch error and thus improve image sticking. Firstly, LTPS-TFTs with extremely low off-state leakage current (I_OFF) even at a large source-drain voltage (V_DS) of 30 V were obtained through detailed explorations of LTPS process technology. Meanwhile, the high on-state current (I_ON) of LTPS-TFTs also meet the requirements of fast signal writing to the storage capacitor due to their extremely high field-effect mobility (approximately 100 cm²/V⋅s), making it possible to fabricate TFTs with relatively small W/L, thereby minimizing switch error. The I_D-V_D test results reveal that the produced LTPS-TFTs can effectively withstand the maximum voltage difference of 30 V during product operation. Subsequently, the optimal W/L of the LTPS-TFT was determined through experimental results. Then, reliability test was conducted on the obtained LTPS-TFTs, revealing that the threshold voltage (V_TH) of the LTPS-TFTs shifted by 0.08 V after 7200 s under negative bias temperature stress (NBTS), and only by 0.19 V under positive bias temperature stress (PBTS). The aging test results of the aforementioned LTPS-TFTs exhibits a new physical phenomenon, that is, the I_OFF of the LTPS-TFTs has a strict matching characteristic with the aging direction. Next, we proposed a novel 2T2C driving circuit for the e-paper, which can effectively avoid the adverse effects of I_OFF on the frame holding period, and plotted it into an array layout. Finally, we combined the optimal fabricating process of the LTPS-TFTs with the 2T2C driving circuit design scheme to produce an e-paper with outstanding image sticking performance.