Lasing from a single quantum wire

A laser with an active volume consisting of only a single quantum wire in the 1-dimensional (1-D) ground state is demonstrated. The single wire is formed quantum-mechanically at the T-intersection of a 14 nm Al_{0.07}Ga_{0.93}As quantum well and a 6 nm GaAs quantum well, and is embedded in a 1-D single-mode optical waveguide. We observe single-mode lasing from the quantum wire ground state by optical pumping. The laser operates from 5 to 60 K, and has a low threshold pumping power of 5 mW at 5 K.Comment: 4 pages including 4 figure

Imaging of emission patterns in a T-shaped quantum wire laser

Spatially and spectrally resolved microscopic images of spontaneous and stimulated emissions are imaged at the mirror facets of a GaAs T-shaped quantum wire laser with high uniformity. Laser emission from the one-dimensional ground state reveals a circular image located at the core of a T-shaped optical waveguide but significantly smaller in area than the low power spontaneous emission from the same waveguide. These images unambiguously allow assignment of all spontaneous and laser emissions to the wire ground state and respective intersecting wells in the structure.Comment: 4 pages, 3 figure

A Study on the Applicability of the Lesamnta-LW Lightweight Hash Function to TPMS

The Tire Pressure Monitoring System (TPMS) is used to monitor the pressure of the tires and to inform the driver of it. This equipment is mandatory for vehicles in US and EU. To ensure the security of TPMS, it is important to reduce the cost of the cryptographic mechanisms implemented in resourced-constrained devices. To address this problem, previous work has proposed countermeasures employing lightweight block ciphers such as PRESENT, SPECK, or KATAN. However, it is not clear to us that any of these works have addressed the issues of software optimization that considers TPMS-packet protection as well as session key updates for architectures consisting of the vehicle TPMS ECU and four low-cost TPM sensors equipped with the tires. In this paper, we propose to application of the ISO/IEC 29192-5 lightweight hash function Lesamnta-LW to address this issue. Our approach is to apply the known method of converting Lesamnta-LW to multiple independent pseudo-random functions (PRFs) in TPMS. In our case, we generate five PRFs this way and then use one PRF for MAC-generation and four for key derivation. Although we follow the NIST SP 800-108 framework of converting PRFs to key derivation functions, we confirm the significant advantage of Lesamnta-LW-based PRFs over HMAC-SHA-256 by evaluating the performance on AVR 8-bit micro-controllers, on which we consider simulating TPMS sensors. We expect that our method to achieve multiple-purposes with a single cryptographic primitive will help to reduce the total implementation cost required for TPMS security

Performance Evaluation of NIST LWC Finalists on AVR ATmega and ARM Cortex-M3 Microcontrollers

This paper presents results of performance evaluation of NIST Lightweight Cryptography standardization finalists which are implemented by us. Our implementation method puts on the target to reduce RAM consumption on embedded devices. Our target microcontrollers are AVR ATmega 128 and ARM Cortex-M3. We apply our implementation method to five AEAD schemes which include four finalists of the NIST lightweight cryptography standardization and demonstrate the performance evaluation on target microcontrollers. From our performance evaluation of the RAM size, we have achieved 117-byte TinyJAMBU-128 on ATmega 128 and 140-byte TinyJAMBU-128 on ARM Cortex-M3. Our implementation of TinyJAMBU-128 has the smallest RAM of all the target AEAD schemes

Development of Attitude Sensor using Deep Learning

A new method for attitude determination utilizing color earth images taken with COTS visible light camera is presented. The traditional earth camera has been used for coarse attitude determination by detecting the edge of the earth, and therefore it can only provide coarse and 2-axis information. In contrast, our method recognizes the ground pattern with an accuracy of sub-degrees and can provide 3-axis attitude information by comparing the detected ground pattern and the global map. Moreover, this method has advantages in the size, mass and cost of the detector system which consists of a cheap optical color camera and a single board computer. To demonstrate the method in space, we have developed a sensor system named “Deep Learning Attitude Sensor (DLAS)”. DLAS uses COTS camera modules and single board computers to reduce the cost. The obtained images are promptly analyzed with a newly developed real-time image recognition algorithms

Factors Associated with Improvement in Activities of Daily Living during Hospitalization: A Retrospective Study of Older Patients with Hip Fractures

Background In this study, we aimed to examine the changes in delirium during hospitalization of patients and its association with behavioral and psychological symptoms of dementia (BPSD), as well as improvements in activities of daily living (ADL). Methods A longitudinal, retrospective cohort study was conducted involving 83 older adults (≥65 years) with hip fractures. We collected Mini-Mental State Examination (MMSE) and Functional Independence Measure-motor domain (m-FIM) assessment results from the medical charts at two time points: baseline (first week of hospitalization) and pre-discharge (final week before discharge). Additionally, we collected data on delirium and BPSD at three points: baseline, week 2 post-admission, and pre-discharge. We performed univariate logistic regression analysis using changes in m-FIM scores as the dependent variable and MMSE and m-FIM scores at baseline and pre-discharge, along with delirium and BPSD subtypes at baseline, week 2 post-admission, and pre-discharge, as the explanatory variables. Finally, we performed a multivariate logistic regression analysis incorporating the significant variables from the univariate analysis to identify factors associated with ADL improvement during hospitalization. Results We observed significant correlations between ADL improvement during hospitalization and baseline m-FIM and MMSE scores, hypoactive delirium state, and BPSD subtype pre-discharge. Notably, all participants with hypoactive symptoms before discharge exhibited some subtype of delirium and BPSD at baseline. Conclusion Besides ADL ability and cognitive function at admission, the presence of hypoactive delirium and BPSD subtype before discharge may hinder ADL improvement during hospitalization

Observation of biexcitonic emission at extremely low power density in tungsten disulfide atomic layers grown on hexagonal boron nitride

Monolayer transition metal dichalcogenides (TMDCs) including WS2, MoS2, WSe2 and WS2, are two-dimensional semiconductors with direct bandgap, providing an excellent field for exploration of many-body effects in 2-dimensions (2D) through optical measurements. To fully explore the physics of TMDCs, the prerequisite is preparation of high-quality samples to observe their intrinsic properties. For this purpose, we have focused on high-quality samples, WS2 grown by chemical vapor deposition method with hexagonal boron nitride as substrates. We observed sharp exciton emissions, whose linewidth is typically 22~23 meV, in photoluminescence spectra at room temperature, which result clearly demonstrates the high-quality of the current samples. We found that biexcitons formed with extremely low-excitation power (240 W/cm^2) at 80 K, and this should originate from the minimal amount of localization centers in the present high-quality samples. The results clearly demonstrate that the present samples can provide an excellent field, where one can observe various excitonic states, offering possibility of exploring optical physics in 2D and finding new condensates

Development and Initial On-orbit Performance of Multi-Functional Attitude Sensor using Image Recognition

This paper describes a multi-functional attitude sensor mounted on the “Innovative Satellite 1st” led by Japan Aerospace Exploration Agency which was launched in January 2019. In order to achieve the high accuracy determination in low cost, we developed a novel attitude sensor utilizing real-time image recognition technology, named “Deep Learning Attitude Sensor (DLAS)”. DLAS has two type of attitude sensors: Star Tracker(STT) and Earth Camera (ECAM). For the low-cost development, we adopted commercial off-the-shelf cameras. DLAS uses real-time image recognition technology and a new attitude determination algorithm. In this paper, we present the missions, methods and system configuration of DLAS and initial results of on-orbit experiment that was conducted after the middle of February 2019, and it is confirmed that attitude determinations using ECAM and STT are performed correctly