University of Electro-Communications

About: University of Electro-Communications is a education organization based out in Tokyo, Japan. It is known for research contribution in the topics: Laser & Robot. The organization has 8041 authors who have published 16950 publications receiving 235832 citations. The organization is also known as: UEC & Denki-Tsūshin Daigaku.

Noise Thresholds for Discrete LDPC Decoding Mappings

Abstract: For decoding low-density parity-check (LDPC) codes on discrete memoryless channels, a method to quantize messages and to find message-passing decoding functions for the variable and check nodes is developed. These are used to obtain noise thresholds by density evolution. The message-passing decoding alphabet is restricted to be discrete with a fixed maximum alphabet size. Discrete quantization is required to obtain this fixed alphabet size; a greedy algorithm which uses the mutual information between the code bit and message is presented. It is argued that using this message-passing decoding framework is more efficient for approaching channel capacity than simply quantizing the belief-propagation algorithm. This method is evaluated using regular LDPC codes on the binary symmetric channel. Using a maximum alphabet size of 16 (4 bits), noise thresholds close to those of belief propagation are obtained.

Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study

Abstract: A comprehensive screening method using machine learning and many factors (biological characteristics, Helicobacter pylori infection status, endoscopic findings and blood test results), accumulated daily as data in hospitals, could improve the accuracy of screening to classify patients at high or low risk of developing gastric cancer. We used XGBoost, a classification method known for achieving numerous winning solutions in data analysis competitions, to capture nonlinear relations among many input variables and outcomes using the boosting approach to machine learning. Longitudinal and comprehensive medical check-up data were collected from 25,942 participants who underwent multiple endoscopies from 2006 to 2017 at a single facility in Japan. The participants were classified into a case group (y = 1) or a control group (y = 0) if gastric cancer was or was not detected, respectively, during a 122-month period. Among 1,431 total participants (89 cases and 1,342 controls), 1,144 (80%) were randomly selected for use in training 10 classification models; the remaining 287 (20%) were used to evaluate the models. The results showed that XGBoost outperformed logistic regression and showed the highest area under the curve value (0.899). Accumulating more data in the facility and performing further analyses including other input variables may help expand the clinical utility.

The influence of location of electrode on muscle fiber conduction velocity and EMG power spectrum during voluntary isometric contraction measured with surface array electrodes.

Abstract: A surface array electrode was used to investigate muscle fiber conduction velocity (MFCV) and EMG power spectrum during voluntary isometric contraction of m. biceps brachii. The mean power frequency (MPF) and the total power (TP) at various locations of the muscle were obtained from the power spectrum. MFCVs at various locations of the muscle were measured directly using the averaging method. The values of MPF, TP and MFCV were identified with respect to the electrode locations on the m. biceps brachii. MPF was shown as high near the end-plate and low near the tendon of the muscle during contraction of 40% of isometric maximum voluntary contraction (MVC). TP showed low value near the end-plate and the tendon of the muscle fiber and different values at different location setting electrode during voluntary isometric contraction. TP at each location on the muscle surface increased when the contraction levels increased in the contractions range of 20 to 60% MVC. MFCVs showed a high value near the end-plate and the tendon of the muscle during the contraction of 40% MVC. These results indicated that MPF, TP and MFCV at different locations on the muscle were different along the length of the muscle fiber.

Electronic structures of B 2p and C 2p levels in boron-doped diamond films studied using soft x-ray absorption and emission spectroscopy

Abstract: X-ray absorption (XAS) and emission (XES) spectroscopy near B K and C K edges have been performed on metallic ($\ensuremath{\sim}01\phantom{\rule{03em}{0ex}}\mathrm{at}\phantom{\rule{02em}{0ex}}%$ B, B-diamond) and semiconducting ($\ensuremath{\sim}003\phantom{\rule{03em}{0ex}}\mathrm{at}\phantom{\rule{02em}{0ex}}%$ B and N, BN-diamond) doped diamond films Both B K XAS and XES spectra show a metallic partial density of states (PDOS) with the Fermi energy of $1853\phantom{\rule{03em}{0ex}}\mathrm{eV}$, and there is no apparent boron-concentration dependence in contrast to the different electric property In C K XAS spectrum of B-diamond, the impurity state ascribed to boron is clearly observed near the Fermi level The Fermi energy is found to be almost same with the top of the valence band of nondoped diamond: ${E}_{V}=2839\phantom{\rule{03em}{0ex}}\mathrm{eV}$ C K XAS of BN-diamond shows both the B-induced shallow level and N-induced deep and broad levels as the in-gap states, in which the shallow level is in good agreement with the activation energy $({E}_{a}=037\phantom{\rule{03em}{0ex}}\mathrm{eV})$ estimated from the temperature dependence of the conductivity; namely, the change in $\mathrm{C}\phantom{\rule{02em}{0ex}}2p$ PDOS of impurity-induced metallization is directly observed The electric property of this diamond is ascribed mainly to the electronic structure of $\mathrm{C}\phantom{\rule{02em}{0ex}}2p$ near the Fermi level The observed XES spectra are compared with the discrete variational $\mathrm{X}\ensuremath{\alpha}$ ($\mathrm{DVX}\ensuremath{\alpha}$) cluster calculation The $\mathrm{DVX}\ensuremath{\alpha}$ result supports the strong hybridization between $\mathrm{B}\phantom{\rule{02em}{0ex}}2p$ and $\mathrm{C}\phantom{\rule{02em}{0ex}}2p$ observed in XAS and XES spectra, and suggests that the small amount of boron $(\ensuremath{\leqslant}01\phantom{\rule{03em}{0ex}}\mathrm{at}\phantom{\rule{02em}{0ex}}%)$ in diamond occupies the substitutional site rather than interstitial site

Prediction models discriminating between nonlocomotive and locomotive activities in children using a triaxial accelerometer with a gravity-removal physical activity classification algorithm.

Abstract: The aims of our study were to examine whether a gravity-removal physical activity classification algorithm (GRPACA) is applicable for discrimination between nonlocomotive and locomotive activities for various physical activities (PAs) of children and to prove that this approach improves the estimation accuracy of a prediction model for children using an accelerometer. Japanese children (42 boys and 26 girls) attending primary school were invited to participate in this study. We used a triaxial accelerometer with a sampling interval of 32 Hz and within a measurement range of ±6 G. Participants were asked to perform 6 nonlocomotive and 5 locomotive activities. We measured raw synthetic acceleration with the triaxial accelerometer and monitored oxygen consumption and carbon dioxide production during each activity with the Douglas bag method. In addition, the resting metabolic rate (RMR) was measured with the subject sitting on a chair to calculate metabolic equivalents (METs). When the ratio of unfiltered synthetic acceleration (USA) and filtered synthetic acceleration (FSA) was 1.12, the rate of correct discrimination between nonlocomotive and locomotive activities was excellent, at 99.1% on average. As a result, a strong linear relationship was found for both nonlocomotive (METs = 0.013×synthetic acceleration +1.220, R2 = 0.772) and locomotive (METs = 0.005×synthetic acceleration +0.944, R2 = 0.880) activities, except for climbing down and up. The mean differences between the values predicted by our model and measured METs were −0.50 to 0.23 for moderate to vigorous intensity (>3.5 METs) PAs like running, ball throwing and washing the floor, which were regarded as unpredictable PAs. In addition, the difference was within 0.25 METs for sedentary to mild moderate PAs (<3.5 METs). Our specific calibration model that discriminates between nonlocomotive and locomotive activities for children can be useful to evaluate the sedentary to vigorous PAs intensity of both nonlocomotive and locomotive activities.