Human-object interaction (HOI) detection for capturing relationships between humans and objects is an important task in the semantic understanding of images. When processing human and object keypoints extracted from an image using a graph convolutional network (GCN) to detect HOI, it is crucial to extract appropriate object keypoints regardless of the object type and to design a GCN that accurately captures the spatial relationships between keypoints. This paper presents the human and object keypoint-based extension module (HOKEM) as an easy-to-use extension module to improve the accuracy of the conventional detection models. The proposed object keypoint extraction method is simple yet accurately represents the shapes of various objects. Moreover, the proposed human-object adaptive GCN (HO-AGCN), which introduces adaptive graph optimization and attention mechanism, accurately captures the spatial relationships between keypoints. Experiments using the HOI dataset, V-COCO, showed that HOKEM boosted the accuracy of an appearance-based model by a large margin.

Skeleton-based two-person interaction recognition has been gaining increasing attention as advancements are made in pose estimation and graph convolutional networks. Although the accuracy has been gradually improving, the increasing computational complexity makes it more impractical for a real-world environment. There is still room for accuracy improvement as the conventional methods do not fully represent the relationship between inter-body joints. In this paper, we propose a lightweight model for accurately recognizing two-person interactions. In addition to the architecture, which incorporates middle fusion, we introduce a factorized convolution technique to reduce the weight parameters of the model. We also introduce a network stream that accounts for relative distance changes between inter-body joints to improve accuracy. Experiments using two large-scale datasets, NTU RGB+D 60 and 120, show that our method simultaneously achieved the highest accuracy and relatively low computational complexity compared with the conventional methods.

Traveling waves are commonly observed across the brain. While previous studies have suggested the role of traveling waves in learning, the mechanism remains unclear. We adopted a computational approach to investigate the effect of traveling waves on synaptic plasticity. Our results indicate that traveling waves facilitate the learning of poly-synaptic network paths when combined with a reward-dependent local synaptic plasticity rule. We also demonstrate that traveling waves expedite finding the shortest paths and learning nonlinear input/output mapping, such as exclusive or (XOR) function.

In this paper, we propose a hardware development of a high accuracy Precision Time Protocol (PTP) on Software Defined Radio (SDR) for Industrial WLAN systems. To increase the throughput of the transmission system, we propose a low overhead 3-frames exchange PTP instead of 4-frames transmission as IEEE 1588 PTP protocol. Besides, our hardware-software co-design of the full SoC MAC-PHY can select the PTP timestamp point to remove the random transmission jitters caused by higher layers and the imbalanced timing made by the rugged hardware designs between master and slave. In previous work, the PTP synchronization has been evaluated on the M820-ZYNQ platform with Dummy PHY. In this paper, the proposed PTP system is developed on the SDR platform with real PHY. The SDR platform can reduce the processing delay between software and hardware compared to the previous platform. The result of our demonstration experiments shows that an oscilloscope captures the time synchronization of the proposed system in high accuracy with the nano-second level, which is less than 200 ns.

In this paper, we propose a design of asynchronous communication for an industrial wireless LAN system. In our design, MAC is synchronous multi-user round-robin transmission protocol. Low overhead Packet Division Multiple Access scheme is utilized for Multi-user Downlink transmission up to 80 MHz, while Frequency Division Multiple Access is employed for the uplink transmission. The designed of PHY system is developed based on a fast and deterministic communication featuring the synchronous multi-user system. In this paper, the industrial robots are not only controlled by synchronous communication but also by asynchronous communication via our industrial WLAN (iWLAN) system. This paper presents the hardware and software cooperative design. In the verification, up to 10 motors can be controlled by our iWLAN system by asynchronous communication, and the delay of each robot measured is 188 μs on average. Besides, the time synchronization captured by oscilloscope is from 40 to 100 ns as in our target.

In this paper, we present supervised fractional-order embedding multiview canonical correlation analysis (SFEMCCA). SFEMCCA is a CCA method realizing the following three points: (1) learning noisy data with small number of samples and large number of dimensions, (2) multiview learning that can integrate three or more kinds of features, and (3) supervised learning using labels corresponding to the samples. In real data, it is necessary to deal with high dimensional noisy data with limited number of samples, and there are many cases where three or more kinds of multimodal and supervised data are treated in order to calculate more accurate projections. Therefore, SFEMCCA, which takes the above advantages (1)-(3) into account, is effective for data obtained from real environments. From experimental results, it was confirmed that accuracy improvements using SFEMCCA were statistically significant compared to the several conventional methods of supervised multiview CCA.

This paper presents a novel method for personalized video preference estimation based on early fusion using multiple users' viewing behavior. The proposed method adopts supervised Multi-View Canonical Correlation Analysis (sMVCCA) to estimate correlation between different types of features. Specifically, we estimate optimal projections maximizing the correlation between three features of video, target user's viewing behavior and evaluation scores for video. Then novel video features (canonical video features), which reflect the target user's individual preference, are obtained by the estimated projections. Furthermore, our method computes sMVCCA-based canonical video features by using multiple users' viewing behavior and a target user's evaluation scores. This non-conventional approach using the multiple users' viewing behavior for the preference estimation of the target user is the biggest contribution of our method, and it enables early fusion of the canonical video features. Consequently, successful video recommendation that reflects the users' individual preference can be expected via the evaluation score prediction from the integrated canonical video features. Experimental results show the effectiveness of our method.