Jun Li

Across domains such as visual processing, computer graphics, neuroscience, and biological sciences, geons are recognized as fundamental components of complex shapes. Their theoretical significance has been extensively acknowledged in scientific research. However, accurately identifying and extracting these structural components remains a persistent challenge. This study integrates theoretical foundations from signal processing, computer graphics, neuroscience, and biological sciences. We employ specific object-of-interest imaging and neural networks to mathematically operationalize visual geon characterization, thereby elucidating their intrinsic properties. Experiments validate the core hypothesis of geon theory, namely that geons are foundational components for the visual system to recognize complex objects. Through training, neural networks are capable of identifying distinct basic geons and, on this basis, performing target recognition in more complex scenarios. These findings provide empirical confirmation of geons’ existence and their critical role in visual recognition, establishing novel computational paradigms and theoretical foundations for interdisciplinary research.

In various fields including visual processing, computer graphics, neuroscience, and biological sciences, geons are widely recognized as fundamental units of complex shapes. Their importance has been broadly acknowledged. However, accurately identifying and extracting these geons remains a challenge.This study integrates theories from signal processing, computer graphics, neuroscience, and biological sciences, utilizing "object imaging" and neural networks to describe mathematical operators, in order to reveal the essence of visual geons. Experiments validate the core hypothesis of geon theory, namely that geons are foundational components for the visual system to recognize complex objects. Through training, neural networks are capable of identifying distinct basic geons and, based on this foundation, performing target recognition in more complex scenarios. This effectively confirms the existence of geons and their critical role in visual recognition, providing new tools and theoretical foundations for related research fields.

In recent years, the application of functional near‐infrared spectroscopy (fNIRS) and deep learning techniques has emerged as a promising method for personal identification. In this study, we innovatively utilized a deep learning framework and fNIRS data for personal identification. The framework is a one‐dimensional convolutional neural network (Conv1D) trained on resting‐state fNIRS signals collected from the frontal cortex of adults. In data preprocessing, we employed a sliding window‐based data augmentation technique and high‐pass filter, which could result in the highest identification accuracy through multiple experiments. Based on a data set consisting of 56 adult participants, the identification accuracy of 90.36% is achieved for training data with a window size of approximately 4.62 s; with the increase in training data window size, the identification accuracy can reach (97.65 ± 2.35)%. Our results suggest that deep learning is valuable for fNIRS‐based personal identification, with potential applications in security, biometrics, and healthcare.

RGBD salient object detection, based on the convolutional neural network, has achieved rapid development in recent years. However, existing models often focus on detecting salient object edges, instead of objects. Importantly, detecting objects can more intuitively display the complete information of the detection target. To take care of this issue, we propose a RGBD salient object detection method, based on specific object imaging, which can quickly capture and process important information on object features, and effectively screen out the salient objects in the scene. The screened target objects include not only the edge of the object, but also the complete feature information of the object, which realizes the detection and imaging of the salient objects. We conduct experiments on benchmark datasets and validate with two common metrics, and the results show that our method reduces the error by 0.003 and 0.201 (MAE) on D3Net and JLDCF, respectively. In addition, our method can still achieve a very good detection and imaging performance in the case of the greatly reduced training data.

To distinguish between children with autism spectrum disorder (ASD) and typically developing (TD) children, we have uncovered a new discriminative feature, hemoglobin coupling. Functional near‐infrared spectroscopy (fNIRS) was used to record resting‐state hemodynamic fluctuations in the bilateral temporal lobes in 25 children with ASD and 22 TD children, in which the coupling between low frequency oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (Hb) fluctuations was evaluated by Pearson correlation coefficient. The results showed significantly weak coupling in children with ASD in both the left and right, and throughout the whole temporal cortex. To explain this observation, a simulation study was performed using a balloon model, in which we found four related parameters could impact the coupling. This study suggested that hemoglobin coupling might be applied as a new cerebral hemodynamic characteristic for ASD screening or diagnostics.

Integrated modulators are essential components for optical sensing and communication applications. However, most efforts have been focused on infrared telecommunications wavelengths, with very little consideration for visible light applications. Here, we design an electro-optic (EO) modulator at 532 nm with the waveguides formed by SU8 polymer structures on the thin-film lithium niobate (LN). The simulation results show a low loss, high modulation efficiency, and large bandwidth simultaneously for the modulator. The predicted low loss is attributed to the LN etchless waveguide and high coupling efficiency, i.e., 93%, with the single-mode fiber theoretically using a SU8 edge coupler. The simulated low voltage-length product of 1.15 V · cm and a 3-dB-bandwidth of >120 GHz at a length of 5 mm are superior to any other modulator in the optical communication band. The modeling of the modulator proposed here shows great potential for visible light communications applications such as energy-efficient and large-capacity underwater wireless optical interconnects.