Cross-modal perception, the integration of information from multiple senses, plays a critical role in shaping emotional experiences. This study examines the interactions between visual and olfactory stimuli and their effects on emotional responses, a topic rarely addressed in prior research. Experiments employed five distinct visual stimulation methods that were combined with olfactory stimuli. Participants' emotional responses were assessed via surveys and electroencephalography (EEG) signal analysis. The study varied the color and movement direction of augmented particles to investigate their impact on EEG signals and emotional states. The findings demonstrated significant differences in emotional state classification under the influence of visual-olfactory interactions. Specifically, with backward-moving particles with matching colors (M4), classification accuracy was comparable to that of unimodal olfactory conditions (M1). Other visual stimuli generally caused confusion in classifying emotional responses. The increased valence ratings for pleasant aromas across all visual conditions did not consistently align with EEG-based classification results, suggesting that visual stimuli may introduce complexities into neural signals. These results highlight the intricate dynamics of multisensory interactions, emphasizing the role of visual stimuli in modulating emotional responses. The findings also suggest the potential of visual-olfactory interactions in developing augmented reality (AR) systems. By aligning visual and olfactory cues, AR environments can enhance the user experience and create immersive emotional landscapes, leading to applications for mood modulation and stress relief. This study underscores the relevance of multisensory integration in advancing emotion analysis and affective computing.

Eye-writing, the act of drawing letters using eye movements, offers a promising avenue for human-computer interaction, especially when coupled with electrooculogram (EOG) recognition techniques. However, achieving high accuracy in eye-writing recognition using deep learning requires large datasets, which is challenging due to the time-consuming nature of data collection and privacy concerns. In this paper, we introduce a diffusion model capable of producing synthetic EOG signals, demonstrating the feasibility of generating high-quality bio-signal data. This approach not only mitigates the challenges of data collection but also facilitates the improvement of pattern recognition accuracies in small bio-signal datasets.

This This study aims to improve significant wave height estimation using X-band radar images from Sokcho Beach, South Korea, collected during the summer rainy season. Traditional methods, such as those based on Signal to Noise Ratio (SNR), face limitations due to linear assumptions. To address this, a 3D Convolutional Neural Network (CNN) was implemented, with data preprocessing to manage class imbalance. The proposed model achieved a correlation coefficient of 0.9607 and an RMSE of 0.3449 m, showing significant improvement over existing methods. While the model demonstrates enhanced accuracy, the study is limited by its focus on a single marine area during a specific season. Future research should extend validation across different weather conditions and locations to further refine wave height estimation.

The monitoring and prediction of joint load based on physical activity is important but challenging in the medical field due to the limited neural perception of cartilage and technical obstacles in real-time monitoring of dynamic daily activities. This study explores a novel, non-invasive method to measure load-generated potentials in knee cartilage using surface electrodes. Twenty subjects performed both active and passive knee extensions while potentials were recorded from seven electrodes around the knee. The signals were analyzed across five phases: initial flat, activity, post-activity, return, and post-return. Results showed significantly higher mean amplitudes in active extensions compared to passive ones, particularly in the post-activity and post-return phases. This study identifies specific bioelectric signals correlating with knee kinematics, offering potential for improved assessment of joint health and rehabilitation strategies.

Recent advancements in deep neural networks (DNN) and augmented reality (AR) have improved the efficiency and automation of agriculture. This study proposes an AR grape thinning support system to assist in grape thinning operations. The proposed system uses DNN to predict grape berries that need to be thinned and uses the optical see-through Head-Mounted Display (HMD) HoloLens 2 to superimpose contour information over the real target berry, making it easy for users to identify the berry to be thinned. Additionally, the hand-tracking function of HoloLens 2 is utilized to monitor the thinning operation in real-time and provide voice instructions to improve work efficiency and user experience. The evaluation experiment compared three interfaces: “image only”, “image with contour overlay”, and “image with contour overlay and voice instructions”, evaluating using the metric of time taken to thin one grape cluster and the usability and user experience. The results showed that the image with contour overlay and voice instructions could significantly improve usability.

This study investigates the impact of visual augmentation on the olfactory system by analyzing users’ emotional responses. Augmented particles were presented using HoloLens through five methods, involving adjustment in color and movement, alongside six odors. Through the experiments with 30 participants, we discovered that augmented particles could intensify or reduce emotional reactions based on their colors and movement directions.

A lot of image data has been digitized and preserved in computers. In order to search for an image, an efficient and accurate retrieval method is needed. This paper is concerned with shape retrieval which is one of the searching methods for finding similar images in a database. Shape, the outer form of a picture, is considered the most promising feature to identify entities in an image. The problem of shape retrieval takes a lot of time because an exhaustive search is mainly used in literatures. This paper suggests the use of a clustering method known as dendrogram for shape retrieval. In addition, we proposed the automatic decision of a threshold to determine a number of clusters in dendrogram. Through the experimental result, the proposed method proved fast retrieval preserving almost the same level accuracy.

This paper addresses the problem of dynamic time warping (DTW) causing unintended matching correspondences when it is employed for online two-dimensional (2D) handwriting signals, and proposes the concept of dynamic positional warping (DPW) in conjunction with DTW for online handwriting matching problems. The proposed DPW allows subsignal translations without any additional costs when their starting points are matched to other points through the matching process. Because the movement of subsignals is cost-free, except for the distance between the two starting points, an adequate movement — finding and matching similar subsignals — could significantly reduce the matching cost. This feature causes a tendency to match with the least amount of subsignal movement in the result. In order to evaluate the proposed method of solving this problem, two experiments were conducted: an accuracy test for matching similar handwriting and a utility test for signature verification. For the former, we proposed the new concept of ideal matching between two handwriting signals using the affine transformation; the results of this test verified the superiority of the proposed DPW over conventional methods in reducing matching errors. Further, DPW significantly outperformed the conventional methods in the latter test.