Perfect Blind-Channel Shortening for Multicarrier Systems

In multicarrier systems, when the order of a channel impulse response is larger than the length of the cyclic prefix (CP), there is a significant performance degradation due to interblock interference (IBI). This paper proposes a blind-channel shortening method in which the equalizer parameter vector is formed by the noise subspace of the received signal correlation matrix so that the output power is maximized. The proposed method can not only shorten the effective channel impulse response to within the CP length but also maximize the output signal-to-interference-and-noise ratio while eliminating the IBI. We point out that the performance depends on the choice of a decision delay and propose a simple method for determining the appropriate delay. We propose both a batch algorithm and an adaptive algorithm and show by simulation that they are superior to the conventional algorithms.     

汽车无线电DSP推动汽车音响发展

随着MP3音频压缩和CD技术的普及，消费者开始希望这些功能和便利性也能在其他常用设备包括汽车中实现。因此，制造商开始在汽车内引入数字媒体处理技术，使司机和乘客能数字化地获得娱乐和信息。     

Application of an improved control-variate scheme to local neoclassical transport simulations

A new radially local neoclassical transport code is developed based on the radially global two-weight δf$\delta f$ Monte Carlo code, FORTEC-3D (Satake et al., 2008). In the collisional two-weight δf$\delta f$ method, the variance of weight increases in time due to the so-called weight spreading, which leads to an increasing numerical noise in long time simulations. A new improved control-variate scheme was proposed by Kleiber et al. (2011) to reduce the variance. We investigate the effectiveness and validity of the new control-variate scheme for a practical collisional transport problem in a plasma described by the drift kinetic equation. It is demonstrated that the new scheme reduces the variance of weight by approximately 75% in an axisymmetric magnetic field configuration. The burst-like behavior of a transport observable caused by a large numerical noise can be avoided. As a result, the time evolution of a transport observable can be successfully smoothed.     

Reinforcement of polypropylene by cellulose microfibers modified with polydopamine and octadecylamine

Cellulose microfibers (CMFs) having surfaces modified with polydopamine (PDPA) and octadecylamine (ODA) were prepared, and their reinforcing abilities for polypropylene (PP) were investigated. The PDPA coating was made via self-polymerization of dopamine (P-CMF), and subsequent alkylation was conducted by the reaction with ODA (OP-CMF). The modified CMFs exhibited improved dispersibility in the PP matrix due to the reduced hydrophilicity. The OP-CMF/PP composite prepared by batch mixing had a higher tensile modulus compared to that for the pure PP and composites with unmodified CMFs. However, excess alkylation lowered the tensile modulus, and the presence of an optimal degree of alkylation was demonstrated. The CMF/PP-IM composites fabricated by injection molding exhibited improved tensile properties compared to those prepared by batch mixing. Both the tensile modulus and yield stress were increased by increasing the CMF content and improved by the surface modification of the CMFs.     

Spike train probability models for stimulus-driven leaky integrate-and-fire neurons

Mathematical models of neurons are widely used to improve understanding of neuronal spiking behavior. These models can produce artificial spike trains that resemble actual spike train data in important ways, but they are not very easy to apply to the analysis of spike train data. Instead, statistical methods based on point process models of spike trains provide a wide range of data-analytical techniques. Two simplified point process models have been introduced in the literature: the time-rescaled renewal process (TRRP) and the multiplicative inhomogeneous Markov interval (m-IMI) model. In this letter we investigate the extent to which the TRRP and m-IMI models are able to fit spike trains produced by stimulus-driven leaky integrate-and-fire (LIF) neurons. With a constant stimulus, the LIF spike train is a renewal process, and the m-IMI and TRRP models will describe accurately the LIF spike train variability. With a time-varying stimulus, the probability of spiking under all three of these models depends on both the experimental clock time relative to the stimulus and the time since the previous spike, but it does so differently for the LIF, m-IMI, and TRRP models. We assessed the distance between the LIF model and each of the two empirical models in the presence of a time-varying stimulus. We found that while lack of fit of a Poisson model to LIF spike train data can be evident even in small samples, the m-IMI and TRRP models tend to fit well, and much larger samples are required before there is statistical evidence of lack of fit of the m-IMI or TRRP models. We also found that when the mean of the stimulus varies across time, the m-IMI model provides a better fit to the LIF data than the TRRP, and when the variance of the stimulus varies across time, the TRRP provides the better fit.     

Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder

Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data.

     

CuO/SnO2-In2O3 sensor for monitoring CO concentration in a reducing atmosphere

The CO sensing property of CuO-loaded SnO₂-In₂O₃ sensor was investigated in a reducing atmosphere. The sensor response to CO for CuO/SnO₂-In₂O₃ (8/2) was much higher than that for CuO/SnO₂ in the range of 200-1000 ppm of CO concentration. Such a high sensor response of CuO/SnO₂-In₂O₃ may originate from the high dispersion of CuO playing a role as sensing site.     

Intracellular recovery of gold by microbial reduction of AuCl4 ions using the anaerobic bacterium Shewanella algae

Microbial reduction and intracellular precipitation of gold was achieved at 25 °C and pH 7 by using the mesophilic anaerobic bacterium Shewanella algae with H₂ as the electron donor. The reductive precipitation of gold by S. algae was a fast process: 0.1–1 mol/m³ AuCl₄⁻ ions were completely reduced to insoluble gold within 30 min. The biogenic precipitates were crystalline gold nanoparticles of 10–20 nm present in the periplasmic space. The reducing power of S. algae at 3.2 × 10¹⁵ cells/m³ and 25 °C was comparable to that of aqueous citric acid solution (chemical reductant) at 20 mol/m³ and 50 °C. The intracellular recovery of gold is potentially attractive as an environmentally friendly alternative to conventional methods.