arXiv subjects

Tomoya Tanaka

Publications and source records attributed to Tomoya Tanaka.

5 recordsLinked to original sources

arXiv2025

Comprehensive Deployment-Oriented Assessment for Cross-Environment Generalization in Deep Learning-Based mmWave Radar Sensing

This study presents the first comprehensive evaluation of spatial generalization techniques, which are essential for the practical deployment of deep learning-based radio-frequency (RF) sensing. Focusing on people counting in indoor environments using frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) radar, we systematically investigate a broad set of approaches, including amplitude-based statistical preprocessing (sigmoid weighting and threshold zeroing), frequency-domain filtering, autoencoder-based background suppression, data augmentation strategies, and transfer learning. Experimental results collected across two environments with different layouts demonstrate that sigmoid-based amplitude weighting consistently achieves superior cross-environment performance, yielding 50.1% and 55.2% reductions in root-mean-square error (RMSE) and mean absolute error (MAE), respectively, compared with baseline methods. Data augmentation provides additional though modest benefits, with improvements up to 8.8% in MAE. By contrast, transfer learning proves indispensable for large spatial shifts, achieving 82.1% and 91.3% reductions in RMSE and MAE, respectively, with 540 target-domain samples. Taken together, these findings establish a highly practical direction for developing radar sensing systems capable of maintaining robust accuracy under spatial variations by integrating deep learning models with amplitude-based preprocessing and efficient transfer learning.

cs.CV↗

arXiv2025

Comprehensive Evaluation of Rule-Based, Machine Learning, and Deep Learning in Human Estimation Using Radio Wave Sensing: Accuracy, Spatial Generalization, and Output Granularity Trade-offs

This study presents the first comprehensive comparison of rule-based methods, traditional machine learning models, and deep learning models in radio wave sensing with frequency modulated continuous wave multiple input multiple output radar. We systematically evaluated five approaches in two indoor environments with distinct layouts: a rule-based connected component method; three traditional machine learning models, namely k-nearest neighbors, random forest, and support vector machine; and a deep learning model combining a convolutional neural network and long short term memory. In the training environment, the convolutional neural network long short term memory model achieved the highest accuracy, while traditional machine learning models provided moderate performance. In a new layout, however, all learning based methods showed significant degradation, whereas the rule-based method remained stable. Notably, for binary detection of presence versus absence of people, all models consistently achieved high accuracy across layouts. These results demonstrate that high capacity models can produce fine grained outputs with high accuracy in the same environment, but they are vulnerable to domain shift. In contrast, rule-based methods cannot provide fine grained outputs but exhibit robustness against domain shift. Moreover, regardless of the model type, a clear trade off was revealed between spatial generalization performance and output granularity.

cs.CV↗

arXiv2025

X-ray free-electron laser observation of giant and anisotropic magnetostriction in $\beta$-O$_{2}$ at 110 Tesla

In strong magnetic fields beyond 100 T, the significant Zeeman energy competes with the lattice interactions, where a considerable magnetostriction is expected. However, the microscopic observation of the magnetostriction above 100 T has been hindered due to the short pulse duration of $\mu$-seconds and the coil's destruction. Here, we report the observation of the giant and anisotropic magnetostriction of $\sim 1$ % at 110 T in the spin-controlled crystal, $\beta$-O$_{2}$, by combining the single-shot diffraction of x-ray free-electron laser (XFEL) and the newly developed portable 100 T generator (PINK-02). The very soft and anisotropic response of $\beta$-O$_{2}$ should originate in the competing van der Waals force and exchange interaction, and also the frustration of spin and lattice on the triangular network. The XFEL experiment above 100 T using PINK-02 enables microscopic investigations on materials' properties at high magnetic fields, providing insights into how spins contribute to the stability of crystal structures.

cond-mat.str-el↗

arXiv2024

Validation of Practicality for CSI Sensing Utilizing Machine Learning

In this study, we leveraged Channel State Information (CSI), commonly utilized in WLAN communication, as training data to develop and evaluate five distinct machine learning models for recognizing human postures: standing, sitting, and lying down. The models we employed were: (i) Linear Discriminant Analysis, (ii) Naive Bayes-Support Vector Machine, (iii) Kernel-Support Vector Machine, (iv) Random Forest, and (v) Deep Learning. We systematically analyzed how the accuracy of these models varied with different amounts of training data. Additionally, to assess their spatial generalization capabilities, we evaluated the models' performance in a setting distinct from the one used for data collection. The experimental findings indicated that while two models -- (ii) Naive Bayes-Support Vector Machine and (v) Deep Learning -- achieved 85% or more accuracy in the original setting, their accuracy dropped to approximately 30% when applied in a different environment. These results underscore that although CSI-based machine learning models can attain high accuracy within a consistent spatial structure, their performance diminishes considerably with changes in spatial conditions, highlighting a significant challenge in their generalization capabilities.

eess.SP↗

arXiv2021

Topic Allocation Method on Edge Servers for Latency-sensitive Notification Service

The importance of real-time notification has been growing for social services and Intelligent Transporting System (ITS). As an advanced version of Pub/Sub systems, publish-process-subscribe systems, where published messages are spooled and processed on edge servers, have been proposed to achieve data-driven intelligent notifications. In this paper, we present a system that allows a topic to be managed on multiple edge servers so that messages are processed near the publishers, even when publishers are spread over a wide area. Duplicating messages on geographically distributed servers could enable immediate notification to neighboring subscribers. However, the duplicated message spool may cause exhaustion of resources. We prepare a formal model of our publish-process-subscribe system and formulate the topic allocation as an optimization problem under the resource constraints of edge servers. As the optimization problem is NP-hard, we propose heuristics leveraging the locality and the pub/sub relationships observed between clients to use the edge server resources efficiently. Our performance evaluation shows that our method reduces the delay to deliver notifications and the effectiveness of the strategy exploiting the relationships between clients.

cs.NI↗