Aichi Institute of Technology

ABSTRACT Yb-based intermediate-valence quasicrystals (IV-QCs) are known to exhibit robust quantum criticality against both applied physical and chemical pressure. In this study, we investigated the pressure dependance of the Yb mean valence (ν) in the quantum-critical Au–Al–Yb QC. We newly identified a critical pressure of 5.8 GPa at which a valence anomaly emerges. Furthermore, high pressure XRD measurements were performed to compare the effects of physical and chemical pressure, revealing that both lead to similar ν- a 6 D curves with a nearly identical critical Yb mean valence, although the anomaly is much sharper and the critical lattice parameter differs in the case of chemical pressure. These new findings provide valuable clues toward elucidating the origin of quantum criticality in IV-QCs.

Juggling is a complex motor skill that requires multiple sub-skills and cannot be mastered without extensive practice. Although prior studies have quantified performance differences between novice and expert jugglers, none have attempted to quantitatively decompose these components or model their contribution to juggling performance. This longitudinal study presents a multimodal evaluation system that integrates computer vision, motion capture, and biosensing to quantify three key elements of juggling ability: Sequencing, Prediction, and Accuracy. Twenty beginners completed a 10-day, three-ball juggling experiment combining visuo-haptic virtual reality (VR) and real-world practice, with half training in reduced gravity, previously shown to enhance early-stage motor learning. The fitted Gamma-Log generalized linear model (GLM) indicated that Sequencing is the dominant factor of early skill acquisition, followed by Prediction and Accuracy. This study provides the first computational decomposition of juggling, demonstrates how multiple elements jointly contribute to performance, and results in a principled approach to characterizing motor learning in complex real-world tasks.

Ambient backscatter communication has emerged as a promising paradigm for ultra-low-power wireless systems. To support diverse IoT applications, backscatter communication should support high uplink throughput, maintain ultra-low power consumption, and reuse existing Wi-Fi signals without special infrastructure. Achieving high-throughput backscatter over existing Wi-Fi signals, however, remains challenging because rapid fluctuation of ambient signals distort the backscattered signal, severely degrading decoding reliability and limiting throughput. Existing approaches, such as codeword translation and per-sample encoding, fail to meet all these requirements, particularly due to their limited throughput caused by single-carrier modulation and the need for additional hardware, such as dual receivers or full-duplex receivers. To address this issue, in this paper, we propose MCScatter, whereby an ultra-low-power device (ULPD) modulates its data using multi-carrier modulation and transmits data by reflecting ambient Wi-Fi signals. To mitigate distortion from ambient signal fluctuations, MCScatter estimates the effective channel, capturing both wireless propagation effect and fluctuations, and equalizes the received signal accordingly. To boost throughput, MCScatter incorporates two mechanisms: Frequency-aware Adaptive Modulation (FAM), which chooses different modulation schemes across subcarriers, and Maximum Likelihood Estimation (MLE), which enables reliable decoding of ULPD data embedded on the cyclic prefix (CP) of a Wi-Fi signal. To the best of our knowledge, MCScatter is the first design to jointly realize OFDM-based ULPD modulation with practical reuse of ambient Wi-Fi signals. To analytically characterize system behavior, we develop an analytical bit error rate (BER) model for MCScatter. Evaluation results demonstrate that MCScatter significantly outperforms benchmarks in both BER and throughput.

This study presents an automatic method for rally scene detection and score estimation in a broadcast table tennis video. Pre-match analysis typically requires extensive manual recording of player actions, shot trajectories, and scores from videos. This time-intensive process impacts preparation efficiency. The proposed methodology integrates YOLOv8 for player and table detection with Segment Anything Model (SAM) for scoreboard area segmentation. Rally scenes are identified through pixel change rate analysis combined with Mean Square Error calculations for frame similarity assessment. The research introduces a score recognition technique functioning independently of scoreboard design variations. Experimental evaluation demonstrates a 9 percentage point increase in precision compared to existing methods, with reduced false positives and missed detections. By incorporating score change timing data, the system overcomes challenges presented by variable camera angles and scene transitions. This work contributes to automated sports video analysis by demonstrating the feasibility of detection systems that enhance pre-match preparation while reducing manual analytical workload in professional table tennis contexts.

This paper describes the power conversion technology using special winding type transformers that the authors have developed. The authors have expanded the special winding transformer they have developed to date to 24 phases and discuss its effectiveness. In particular, when a power source is connected to the secondary side, power can be supplied from the secondary side to the primary side, as well as to DC and AC loads connected to the secondary side. The effectiveness of the proposed system has been verified through simulation and experimentation.

To connect DC power distribution systems to the power grid, AC/DC converters are essential. In this study, a multi-phase transformer with a specialized winding structure was used to investigate the effect of the number of transformer phases on DC power quality. Experiments were conducted with various phase configurations to examine the relationship between the number of phases and DC power quality.

This paper proposes a lightweight packet authentication method to enhance the security and robustness of the CYPHONIC Cloud, the core infrastructure of the CYber PHysical Overlay Network over Internet Communication (CYPHONIC). CYPHONIC enables secure, end-to-end communication between distributed devices by decoupling logical communication from physical network topology. This design ensures mobility, transparency, and interconnectivity across heterogeneous networks. The CYPHONIC Cloud manages authentication, session control, and routing. However, its central role exposes it to replay and denial-of-service (DoS) attacks using forged or replayed packets. To defend against these threats efficiently, the Cloud must verify packets with minimal delay and overhead. Traditional database-driven methods become bottlenecks under high traffic. To address this, the proposed method embeds a time-based Token in each packet. The Cloud verifies packets using hash functions and a synchronized time window, eliminating the need for database access or decryption. This primary screening enables fast, stateless authentication. Experiments in both wired and wireless network situations confirm that the method effectively filters replayed or delayed packets with low computational cost. This paper demonstrates that the proposed approach supports scalable, efficient security for decentralized overlay networks.