6,144 research outputs found

    Microwave Characteristics of an Independently Biased 3-stack InGaP/GaAs HBT Configuration

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    This paper investigates various important microwave characteristics of an independently biased 3-stack InGaP/GaAs heterojunction bipolar transistor (HBT) monolithic microwave integrated circuit (MMIC) chip at both small-signal and large-signal operation. By taking the advantage of the independently biased functionality, bias condition for individual transistor can be adjusted flexibly, resulting in the ability of independent control for both small-signal and large-signal performances. It was found that at small-signal operation stability and isolation characteristics of the proposed configuration can be significantly improved by controlling bias condition of the second-stage and the third-stage transistors while at large-signal operation its linearity and power gain can be improved through controlling the bias condition of the first-stage and the third-stage transistors. To demonstrate the benefits of using such an independently biased configuration, a measured optimum large-signal performance at an operation frequency of 1.6 GHz under an optimum bias condition for the high gain, low distortion were obtained as: PAE = 23.5 %, Pout = 12 dBm; Gain = 32.6 dB at IMD3 = -35 dBc. Moreover, to demonstrate the superior advantage of the proposed configuration, its small-signal and large-signal performance were also compared to that of a single stage common-emitter, a conventional 2-stack, an independently biased 2-stack and a conventional 3-stack configuration. The compared results showed that the independently biased 3-stack is the best candidate among the configurations for various wireless communications applications

    Acoustic echo and noise canceller for personal hands-free video IP phone

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    This paper presents implementation and evaluation of a proposed acoustic echo and noise canceller (AENC) for videotelephony-enabled personal hands-free Internet protocol (IP) phones. This canceller has the following features: noise-robust performance, low processing delay, and low computational complexity. The AENC employs an adaptive digital filter (ADF) and noise reduction (NR) methods that can effectively eliminate undesired acoustic echo and background noise included in a microphone signal even in a noisy environment. The ADF method uses the step-size control approach according to the level of disturbance such as background noise; it can minimize the effect of disturbance in a noisy environment. The NR method estimates the noise level under an assumption that the noise amplitude spectrum is constant in a short period, which cannot be applied to the amplitude spectrum of speech. In addition, this paper presents the method for decreasing the computational complexity of the ADF process without increasing the processing delay to make the processing suitable for real-time implementation. The experimental results demonstrate that the proposed AENC suppresses echo and noise sufficiently in a noisy environment; thus, resulting in natural-sounding speech

    DNN-Based Source Enhancement to Increase Objective Sound Quality Assessment Score

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    We propose a training method for deep neural network (DNN)-based source enhancement to increase objective sound quality assessment (OSQA) scores such as the perceptual evaluation of speech quality (PESQ). In many conventional studies, DNNs have been used as a mapping function to estimate time-frequency masks and trained to minimize an analytically tractable objective function such as the mean squared error (MSE). Since OSQA scores have been used widely for soundquality evaluation, constructing DNNs to increase OSQA scores would be better than using the minimum-MSE to create highquality output signals. However, since most OSQA scores are not analytically tractable, i.e., they are black boxes, the gradient of the objective function cannot be calculated by simply applying back-propagation. To calculate the gradient of the OSQA-based objective function, we formulated a DNN optimization scheme on the basis of black-box optimization, which is used for training a computer that plays a game. For a black-box-optimization scheme, we adopt the policy gradient method for calculating the gradient on the basis of a sampling algorithm. To simulate output signals using the sampling algorithm, DNNs are used to estimate the probability-density function of the output signals that maximize OSQA scores. The OSQA scores are calculated from the simulated output signals, and the DNNs are trained to increase the probability of generating the simulated output signals that achieve high OSQA scores. Through several experiments, we found that OSQA scores significantly increased by applying the proposed method, even though the MSE was not minimized

    Finite-Block-Length Analysis in Classical and Quantum Information Theory

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    Coding technology is used in several information processing tasks. In particular, when noise during transmission disturbs communications, coding technology is employed to protect the information. However, there are two types of coding technology: coding in classical information theory and coding in quantum information theory. Although the physical media used to transmit information ultimately obey quantum mechanics, we need to choose the type of coding depending on the kind of information device, classical or quantum, that is being used. In both branches of information theory, there are many elegant theoretical results under the ideal assumption that an infinitely large system is available. In a realistic situation, we need to account for finite size effects. The present paper reviews finite size effects in classical and quantum information theory with respect to various topics, including applied aspects

    Selective Sampling for Example-based Word Sense Disambiguation

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    This paper proposes an efficient example sampling method for example-based word sense disambiguation systems. To construct a database of practical size, a considerable overhead for manual sense disambiguation (overhead for supervision) is required. In addition, the time complexity of searching a large-sized database poses a considerable problem (overhead for search). To counter these problems, our method selectively samples a smaller-sized effective subset from a given example set for use in word sense disambiguation. Our method is characterized by the reliance on the notion of training utility: the degree to which each example is informative for future example sampling when used for the training of the system. The system progressively collects examples by selecting those with greatest utility. The paper reports the effectiveness of our method through experiments on about one thousand sentences. Compared to experiments with other example sampling methods, our method reduced both the overhead for supervision and the overhead for search, without the degeneration of the performance of the system.Comment: 25 pages, 14 Postscript figure
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