arXiv subjects

Tomoki Murakami

Publications and source records attributed to Tomoki Murakami.

7 recordsLinked to original sources

arXiv2026

The Silicon Tracking System of the E16 experiment at J-PARC: construction, installation and commissioning in beam test experiments

The J-PARC E16 experiment aims to search for signatures of chiral symmetry restoration. It studies in-medium modifications of vector mesons that decay via the dielectron channel. The measurements use a high-intensity 30 GeV proton beam with C and Cu targets at rates up to 10 MHz. To achieve this, the experiment upgrades its tracking, by introducing innermost detector modules constructed with the same technology and procedures as the modules of the Silicon Tracking System (STS) of the Compressed Baryonic Matter (CBM) experiment at Facility for Antiproton and Ion Research (FAIR). A total of 15 modules were assembled, tested, characterized and then installed in the E16 detector setup. The detector was commissioned in a beam test experiment at Tsukuba, where the detector modules could be exposed to a 3 GeV electron beam. In preparation for the beam test the modules were characterized and calibrated, and performance studies were accomplished to assess the quality of the setup. During beamtime, three modules were operated and illuminated in two planes by the electron beam. This paper presents the results of the construction, characterization, commissioning, and operation of the E16-STS modules in beam test experiments.

physics.ins-det↗

arXiv2026

First measurement of $\phi$ meson production in 30 GeV proton-nucleus reactions via di-electron decay at J-PARC

We present the first measurement of the production of the $\phi$ meson in 30 GeV proton-nucleus interactions on carbon and copper targets via the di-electron decay channel. The measurement was conducted at the high-momentum beamline of the J-PARC Hadron Experimental Facility, which was commissioned in 2020. The $e^+e^-$ pairs were detected using the E16 spectrometer, during a commissioning run of the J-PARC E16 experiment. The $\phi$ mesons are successfully reconstructed on all experimental targets. The obtained yields are converted to the total production cross section, assuming a kinematical distribution of the event generator JAM. The total cross sections derived are 2.0 $\pm$ 0.9 (stat.) $\pm$ 1.0 (syst.) mb on the carbon target and 10.3 $\pm$ 4.4 (stat.) $\pm$ 4.4 (syst.) mb on the copper target. The mass-number dependence of the cross section is discussed using the parameter $\alpha$, defined as $\sigma \propto A^\alpha$, resulting in $\alpha = $ 0.99 $\pm$ 0.38 (stat.) $\pm$ 0.34 (syst.). The extrapolation to $A=1$, which means that the cross section of proton-proton reactions, is in good agreement with the existing measurements at comparable energies.

nucl-ex↗

arXiv2025

Outdoor Crowd Flow Estimation Using RSRP from Commercial LTE Base Station: A Field Study

With the advent of the 6G era, Integrated Sensing and Communications (ISAC) has attracted increasing attention. One representative of use cases is crowd flow estimation on outdoor streets. However, most existing studies have focused on indoor environments or vehicles, and demonstrations of outdoor crowd flow estimation using commercial LTE base station remain limited. This study addresses this use case and proposes an analysis of a crowd flow estimation method using Reference Signal Received Power (RSRP) obtained from a commercial LTE base station. Specifically, pedestrian counts derived from a camera-based object recognition algorithm were associated with the variance of RSRP. The features obtained from the variance were quantitatively evaluated by combining a CatBoost regression model with SHapley Additive exPlanations (SHAP) analysis. Through this investigation, we clarified that an optimal variance window size for RSRP is 0.1 to 0.2 seconds and that enlarging the counting area increased the features obtained from the variance of RSRP, for machine learning. Consequently, this study is the first to quantitatively demonstrate the effectiveness of outdoor crowd flow estimation using commercial LTE, while also revealing the characteristic behavior of variance window size and counting area size in feature design.

eess.SP↗

arXiv2025

Unsupervised Learning based Element Resource Allocation for Reconfigurable Intelligent Surfaces in mmWave Network

The increasing demand for high data rates and seamless connectivity in wireless systems has sparked significant interest in reconfigurable intelligent surfaces (RIS) and artificial intelligence-based wireless applications. RIS typically comprises passive reflective antenna elements that control the wireless propagation environment by adequately tuning the phase of the reflective elements. The allocation of RIS elements to multipleuser equipment (UEs) is crucial for efficiently utilizing RIS. In this work, we formulate a joint optimization problem that optimizes the RIS phase configuration and resource allocation under an $\alpha$-fair scheduling framework and propose an efficient way of allocating RIS elements. Conventional iterative optimization methods, however, suffer from exponentially increasing computational complexity as the number of RIS elements increases and also complicate the generation of training labels for supervised learning. To overcome these challenges, we propose a five-layer fully connected neural network (FNN) combined with a preprocessing technique to significantly reduce input dimensionality, lower computational complexity, and enhance scalability. The simulation results show that our proposed NN-based solution reduces computational overhead while significantly improving system throughput by 6.8% compared to existing RIS element allocation schemes. Furthermore, the proposed system achieves better performance while reducing computational complexity, making it significantly more scalable than the iterative optimization algorithms.

cs.LG↗

arXiv2024

Device-free Indoor WLAN Localization with Distributed Antenna Placement Optimization and Spatially Localized Regression

Wireless sensing is a promising technology for future wireless communication networks to realize various application services. Wireless local area network (WLAN)-based localization approaches using channel state information (CSI) have been investigated intensively. Further improvements in detection performance will depend on selecting appropriate feature information and determining the placements of distributed antenna elements. This paper presents a proposal of an enhanced device-free WLAN-based localization scheme with beam-tracing based antenna placement optimization and spatially localized regression, where beam-forming weights (BFWs) are used as feature information for training machine-learning (ML)-based models localized to partitioned areas. By this scheme, the antenna placement at the access point (AP) is determined by solving a combinational optimization problem with beam-tracing between AP and station (STA) without knowing the CSI. Additionally, we propose the use of localized regression to improve localization accuracy with low complexity, where classification and regression based ML models are used for coarse and precise estimations of the target position. We evaluate the proposed scheme effects on localization performance in an indoor environment. Experiment results demonstrate that the proposed antenna placement and localized regression scheme improve the localization accuracy while reducing the required complexity for both off-line training and on-line localization relative to other reference schemes.

eess.SP↗

arXiv2020

Data acquisition system in Run-0a for the J-PARC E16 experiment

J-PARC E16 is an experiment to examine the origin of hadron mass through a systematic measurement of spectral changes of vector mesons in nuclei. The measurement of $e^{+}e^{-}$ pairs from the decay of vector mesons will provide the information of the partial restoration of the chiral symmetry in a normal nuclear density. To resolve a pulse pile-up and achieve good discrimination of $e^{\pm}$ from the background of a reaction rate of an order of 10 MHz, the data acquisition (DAQ) system uses waveform sampling chips of APV25 and DRS4. The trigger rate and data rate are expected to be 1 kHz and 130--330 MiB/s, respectively. The DAQ system for readout of APV25 and DRS4 were developed, where events were synchronized by common trigger and tag data. The first commissioning in beam, called Run-0a, was performed in June 2020 with about 1/4 of the designed setup. The DAQ worked with a trigger rate of 300 Hz in the Run-0a and the main bottleneck was a large data size of APV25. Further optimization of the DAQ system will improve the performance.

physics.ins-det↗

arXiv2020

CSI2Image: Image Reconstruction from Channel State Information Using Generative Adversarial Networks

This study aims to find the upper limit of the wireless sensing capability of acquiring physical space information. This is a challenging objective, because at present, wireless sensing studies continue to succeed in acquiring novel phenomena. Thus, although a complete answer cannot be obtained yet, a step is taken towards it here. To achieve this, CSI2Image, a novel channel-state-information (CSI)-to-image conversion method based on generative adversarial networks (GANs), is proposed. The type of physical information acquired using wireless sensing can be estimated by checking wheth\-er the reconstructed image captures the desired physical space information. Three types of learning methods are demonstrated: gen\-er\-a\-tor-only learning, GAN-only learning, and hybrid learning. Evaluating the performance of CSI2Image is difficult, because both the clarity of the image and the presence of the desired physical space information must be evaluated. To solve this problem, a quantitative evaluation methodology using an object detection library is also proposed. CSI2Image was implemented using IEEE 802.11ac compressed CSI, and the evaluation results show that the image was successfully reconstructed. The results demonstrate that gen\-er\-a\-tor-only learning is sufficient for simple wireless sensing problems, but in complex wireless sensing problems, GANs are important for reconstructing generalized images with more accurate physical space information.

cs.CV↗