University of Electro-Communications

Distinguishing photon arrival time and position is crucial for advancing quantum technology. However, capturing spatial and temporal information efficiently remains challenging. Here, we present a novel photon detection technique to achieve a significantly more efficient measurement of frequency-entangled biphoton than conventional photon detectors. We utilize a delay-line-anode single-photon detector (DLD), which consists of a position-sensitive delay-line anode sensor behind a microchannel plate. Biphotons are obtained from the decay of biexcitons in the copper chloride semiconductor crystal. Two DLDs are coupled with a grating spectrometer exit to measure the joint spectral distributions of the biphoton. The resulting non-scanning process requires only a few minutes to obtain a temporally and spectrally resolved image, much quicker than the scanning biphoton frequency measurements such as monochromator or Fourier spectroscopy. Our technique paves the way for all experiments in multi-mode quantum science requiring coincidence measurement.

Persistent organic room‐temperature phosphorescence (RTP) enables high‐resolution afterglow bioimaging, independent of autofluorescence. However, the yield of organic RTP in the long‐wavelength region is generally low, which limits the high‐resolution information that can be obtained from the long‐wavelength region. Moreover, this makes it impossible to obtain multicolor and high‐resolution afterglow images. This report describes a molecule containing no atoms from the fourth or higher period that exhibits efficient red RTP in high yield. A molecule with red phosphorescent chromophores substituted with multiple phenylthio groups reached an RTP yield of 46.3% and an RTP lifetime of 0.43 s in an appropriate crystalline host medium. The selective lower‐occupied through‐bond or through‐space interactions among molecules significantly enhance the phosphorescence in the long‐wavelength region. The highly efficient and bright red persistent RTP induces a red afterglow from individual nanoparticles. Tuning the selective lower‐occupied through‐bond or through‐space interactions allows for the design of high‐performance RTP dyes and offers a novel approach to explore high‐resolution full‐color afterglow imaging.

One‐ and two‐electron‐oxidized species of two π‐conjugated systems that adopt close π‐stacking structures are referred to as π‐mers and π‐dimers, respectively. These cationic species exhibit intriguing physicochemical properties that are markedly distinct from those of their parent monomeric radical cations. In this study, we have achieved the chemical oxidation of a newly designed tethered dimer comprising two planar sulfur‐ and carbon‐bridged triphenylamine (SCBT) units linked by a 1,8‐naphthalene moiety to obtain the corresponding π‐mer and π‐dimer as crystalline materials. Single‐crystal x‐ray diffraction analyses revealed that these cationic species adopt almost perfectly overlapping face‐to‐face close π‐stacking structures. As the degree of oxidation increases from the neutral form to the monocation and the dication, the interatomic S–‐S distance decreases drastically from 5.35 to 3.03 Å. The injection of electron spins into the neutral species effectively induces covalent‐like bonding interactions between the sulfur atoms and changes the degree of structural freedom of the molecule. The salt of the π‐mer exhibited an electrical conductivity of 1.9 × 10⁻⁶ S cm⁻¹ at room temperature. The injection of spins by chemical oxidation also dramatically changed the dielectric response by generating dipole moments that affect the symmetry of the molecule.

Although lead sulfide (PbS) colloidal quantum dot (CQD) solar cells demonstrate excellent storage stability under ambient conditions, the operational stability is still rather poor for devices based on both organic or inorganic hole transport layer (HTL), seriously limiting their practical applications. In this work, it is find that both the CQD/polymer HTL bottom interface and the polymer HTL/electrode top interface are critical factors limiting device performance and operational stability. By proposing a double‐side interfacial engineering strategy to achieve surface energy matching and energy level grading, a high efficiency of 14.28% is realized using the classic P3HT HTL material, which is the highest reported efficiency for PbS CQD solar cells with organic HTLs. More importantly, the unencapsulated device can maintain 90% of its initial power (T90) after ≈520 hours at the maximum power point (MPP) in ambient air, far exceeding the highest value previously reported in the literature (260 hours). This work provides new insights into the development of stable CQD‐based optoelectronic devices.

To introduce the concept of the “constraint tolerance” (i.e., a feasibility of solutions) in the flight scheduling problem, this paper proposes the optimization method that can find the feasible flight schedules by optimizing the original objective function while maximizing the constraint tolerance as much as possible. The proposed method further is improved by integrating it with the local search and archive mechanisms to obtain a wide range of Pareto-optimal solutions with a high constraint tolerance. A comparison between the proposed method and the conventional methods with or without adding a new objective function to maximize the constraint tolerance shows the statistical superiority of the proposed method.

Understanding the structures of the unresolved transition array (UTA) observed in extreme ultraviolet (EUV) spectra from many-electron atoms is crucial for various applications, including fusion science and nanolithography. To measure the fine structure of the UTA from tungsten and tin at around 5 and 13.5 nm at the Tokyo electron beam ion trap, we developed a high-resolution EUV spectrometer. The designed spectrometer achieves a resolving power of λ/dλ > 5000 at 5 and 13.5 nm. The fabricated large-area grating was experimentally examined at beamline BL5B of the UVSOR synchrotron facility to evaluate the diffraction efficiencies and their variation across the ruled area. The measured diffraction efficiencies are 0.65% ± 0.07% at 5 nm (second order) and 7.9% ± 0.2% at 13.5 nm (first order). The variation in the diffraction efficiency across the ruled area is 2.2%, 13.6%, and 10.0% in zeroth, first, and second order diffractions, respectively. The discrepancies in diffraction efficiencies between the experiments and the calculations were 5.2%, 29%, and 35% for the zeroth, first, and second diffraction orders, respectively.

We classify nef vector bundles on a smooth quadric surface with first Chern class (2, 2) over an algebraically closed field of characteristic zero.

With the rapid development of artificial intelligence and the Internet of Things, along with the growing demand for privacy-preserving transmission, the need for efficient and secure communication systems has become increasingly urgent. Traditional communication methods transmit data at the bit level without considering its semantic significance, leading to redundant transmission overhead and reduced efficiency. Semantic communication addresses this issue by extracting and transmitting only the most meaningful semantic information, thereby improving bandwidth efficiency. However, despite reducing the volume of data, it remains vulnerable to privacy risks, as semantic features may still expose sensitive information. To address this, we propose an entropy-bottleneck-based privacy protection mechanism for semantic communication. Our approach uses semantic segmentation to partition images into regions of interest (ROI) and regions of non-interest (RONI) based on the receiver’s needs, enabling differentiated semantic transmission. By focusing transmission on ROIs, bandwidth usage is optimized, and non-essential data is minimized. The entropy bottleneck model probabilistically encodes the semantic information into a compact bit stream, reducing correlation between the transmitted content and the original data, thus enhancing privacy protection. The proposed framework is systematically evaluated in terms of compression efficiency, semantic fidelity, and privacy preservation. Through comparative experiments with traditional and state-of-the-art methods, we demonstrate that the approach significantly reduces data transmission, maintains the quality of semantically important regions, and ensures robust privacy protection.

With the continuous advancement of computer vision, image processing technologies, volumetric video, represented by point cloud videos, holds the potential for extensive applications in areas such as Virtual Reality (VR) and Augmented Reality (AR). Viewport prediction, also referred to as Field of View (FoV) prediction, is a crucial component in emerging VR and AR applications, playing a vital role in the transmission of point cloud videos. Currently, models for viewpoint prediction that integrate feature extraction and FoV information heavily rely on the spatial-temporal features extracted by convolutional neural networks. However, the drawback of 3D convolution lies in its inability to effectively capture long-term spatial-temporal dependencies within videos. Moreover, the temporal contrast layer used for time feature extraction only compares features within each block, leading to matching errors and inaccurate temporal feature extraction, consequently diminishing predictive performance. To address these limitations, we propose a Transformer-based Volumetric Point Cloud Video Viewport Prediction Network (VPFormer) that can efficiently extract spatial-temporal features from point cloud videos. VPFormer constitutes a viewport prediction framework that combines the spatial-temporal features of point cloud videos with user trajectory information. Specifically, we introduce a novel sampling method that effectively preserves spatial-temporal information while reducing computational complexity. Additionally, we incorporate context-aware dynamic positional encoding to capture inter-frame spatial-temporal context information. Subsequently, we introduce a voxel-based temporal contrast layer and partition the point cloud into smaller voxel blocks during feature matching, significantly reducing matching errors and enhancing the analysis and extraction of temporal features. Finally, by combining the spatial-temporal features of point cloud videos with user head trajectory information, we successfully predict future user viewpoints. Experimental results demonstrate that this approach outperforms other solutions in terms of performance.

This article proposes a low cost of USD 72.5 BoM with a small size of water level monitoring IoT sensor without Ultra-Sonic level sensors. The power consumption for sensing and RF transmission is minimized to less than 100 μW in the manner of the Beat Sensor IoT system so that power is supplied by energy harvesting power supply using a card-size solar cell. Communication range can be reached 2 km using LoRa WAN and Quasi-isotropic Ω -Antenna. The system continuously measures the water level at a pond for 21 days without a battery, and the RMSE of the water level measurement reaches 2.5 mm. The practical evaluations show that this proposal can be applicable to several IoT systems for monitoring water levels.

This paper proposes an accurate Doppler velocity–associated radar imaging method for millimeter wave short-range radar. Doppler velocity–based data decomposition is promising in terms of enhancing the spatial resolution of radar images, especially for multiple objects with different motion velocities, e.g ., human walking models. However, the traditional short-time Fourier transform (STFT) or other time–frequency analysis methods inherently suffer from inaccuracy due to a limited unambiguous velocity range or the trade-off between temporal and frequency resolutions. To address this problem, the proposed method incorporates time-of-flight (TOF) based Doppler velocity estimation, i.e ., the weighted kernel density (WKD) method, which overcomes the limitations of unambiguous velocity range and the trade-off between temporal and frequency resolutions in the Doppler velocity decomposed and associated radar imaging. Experimental results obtained using 79-GHz bandMMWradar equipment demonstrate the effectiveness of the proposed method for two use cases, i.e ., multiple rotating spheres and a human walking model.

The gliding motility of bacteria is not linear but somehow exhibits a curved trajectory. This general observation is explained by the helical structure of protein tracks (Nakane et al., 2013) or the asymmetric array of gliding machineries (Morio et al., 2016), but these interpretations have not been directly examined. Here, we introduced a simple assumption: the gliding trajectory of M. mobile is guided by the cell shape. To test this idea, the intensity profile of a bacterium, Mycoplasma mobile, was analyzed and reconstructed at the single-cell level from images captured under a highly stable dark-field microscope, which minimized the mechanical drift and noise during sequential image recording. The raw image with the size of ~1 μm, which is about four times larger than the diffraction limit of visible light, was successfully fitted by double Gaussians to quantitatively determine the curved configuration of its shape. By comparing the shape and curvature of a gliding motility, we found that the protruded portion of M. mobile correlated with, or possibly guided, its gliding direction. Considering the balance between decomposed gliding force and torque as a drag, a simple and general model that explains the curved trajectory of biomolecules under a low Reynolds number is proposed. Fullsize Image