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The authors have developed a semitransparent, multilayered cathode of indium tin oxide (ITO)/Ag/tungsten oxide (WO3) for transparent organic light-emitting diodes. The device showed a weak negative differential resistance (NDR), until the operating voltage of 8V was reached. NDR was due to the resonant tunneling by both the quantum barrier and quantum well. The silver oxide (Ag2O) on the Ag metal was confirmed by x-ray photoelectron spectroscopy, and the energy levels of Ag2O were quantized due to the quantum size effect and this produced the resonant tunneling channels. The device using ITO∕Ag∕WO3 with a LiF∕Al bilayer was superior to those devices which only used ITO or WO3, mainly because the out coupling was enhanced by employing a WO3 material, which is much more transparent than ITO.

In patients with SLE, (1)H MR spectroscopic findings seem to reflect the cerebral metabolic disturbance related to the severity of the neuropsychiatric symptoms and are not related to the presence of abnormal MR imaging findings.

Abstract-In most wireless communications research, the channel models considered experience less severe fading than the classic Rayleigh fading case. In this work, however, we investigate MIMO channels where the fading is more severe. In these environments, we show that the coefficient of variation of the channel amplitudes is a good predictor of the link mutual information, for a variety of models. We propose a novel channel model for severely fading channels based on the complex multivariate t distribution. For this model, we are able to compute exact results for the ergodic mutual information and approximations to the outage probabilities for the mutual information. Applications of this work include wireless sensors, RF tagging, land-mobile, indoor-mobile, ground-penetrating radar, and ionospheric radio links. Finally, we point out that the methodology can also be extended to evaluate the mutual information of a cellular MIMO link and the performance of various MIMO receivers in a cellular scenario. In these cellular applications, the channel itself is not severely fading but the multivariate t distribution can be applied to model the effects of inter-cell interference.

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This paper proposes a deep learning-based non-intrusive objective speech intelligibility estimation method based on recurrent neural network (RNN) with long short-term memory (LSTM) structure. Conventional non-intrusive estimation methods such as standard P.563 have poor estimation performance and lack of consistency, especially, in various noise and reverberation environments. The proposed method trains the LSTM RNN model parameters by utilizing the STOI that is the standard intrusive intelligibility estimation method with reference speech signal. The input and output of the LSTM RNN are the MFCC vector and the frame-wise STOI value, respectively. Experimental results show that the proposed objective intelligibility estimation method outperforms the conventional standard P.563 in various noisy and reverberant environments.