Designing of a nanoscale Quantum Well (QW) heterostructure with a well thickness of ∼60 Å is critical for many applications and remains a challenge. This paper has a detailed study directed towards designing of In0.29Ga0.71As0.99N0.01/GaAs straddled nanoscale-heterostructure having a single QW of thickness ∼60 Å and optimization of optical and lasing characteristics such as optical and mode gain, differential gain, gain compression, anti-guiding factor, transparency wavelength, relaxation oscillation frequency (ROF), optical power and their mutual variation behavior. The outcomes of the simulation study imply that for the carrier concentration of ∼2 × 1018cm−3 the optical gain of the nano-heterostructure is of 2100 cm−1 at the wavelength is of 1.30 μm. Though the obtained gain is almost half of the gain of InGaAlAs/InP heterostructure, but from the wavelength point of view the InGaAsN/GaAs nano-heterostructure is also more desirable because the 1.30 μm wavelength is attractive due to negligible dispersion in the silica based optical fiber. Hence, the InGaAsN/GaAs nano-heterostructure can be very valuable in optical fiber based communication systems.
The paper stresses the issue of strong temperature influence on the gain of a Silicon Photomultiplier (SiPM). High sensitivity of the detector to light (single photons) requires stable parameters during measurement, including gain. The paper presents a method of compensating the change of gain caused by temperature variations, by adjusting a suitable voltage bias provided by a precise power module. The methodology of the research takes in account applications with a large number of SiPMs (20 thousand), explains the challenges and presents the results of the gain stabilization algorithm.
A new approach to passive electromagnetic modelling of coupled–cavity quantum cascade lasers is presented in this paper. One of challenges in the rigorous analysis of such eigenvalue problem is its large size as compared to wavelength and a high quality factor, which prompts for substantial computational efforts. For those reasons, it is proposed in this paper to consider such a coupled-cavity Fabry-Perot resonant structure with partially transparent mirrors as a two-port network, which can be considered as a deterministic problem. Thanks to such a novel approach, passive analysis of an electrically long laser can be split into a cascade of relatively short sections having low quality factor, thus, substantially speeding up rigorous electromagnetic analysis of the whole quantum cascade laser. The proposed method allows to determine unequivocally resonant frequencies of the structure and the corresponding spectrum of a threshold gain. Eventually, the proposed method is used to elaborate basic synthesis rules of coupled–cavity quantum cascade lasers.
Vibrating plates have been recently used for a number of active noise control applications. They are resistant to difficult environmental conditions including dust, humidity, and even precipitation. However, their properties significantly depend on temperature. The plate temperature changes, caused by ambient temperature changes or plate heating due to internal friction, result in varying response of the plate, and may make it significantly different than response of a fixed model. Such mismatch may deteriorate performance of an active noise control system or even lead to divergence of a model-based adaptation algorithm.
In this paper effects of vibrating plate temperature variation on a feedforward adaptive active noise reduction system with the multichannel Filtered-reference LMS algorithm are examined. For that purpose, a thin aluminum plate is excited with multiple Macro-Fiber Composite actuators. The plate temperature is forced by a set of Peltier cells, what allows for both cooling and heating the plate. The noise is generated at one side of the plate, and a major part of it is transmitted through the plate. The goal of the control system is to reduce sound pressure level at a specified area on the other side of the plate.
To guarantee successful operation of the control system in face of plate temperature variation, a gain-scheduling scheme is proposed to support the Filtered-reference LMS algorithm.
The Histogram Test method is a popular technique in analog-to-digital converter (ADC) testing. The presence of additive noise in the test setup or in the ADC itself can potentially affect the accuracy of the test results. In this study, we demonstrate that additive noise causes a bias in the terminal based estimation of the gain but not in the estimation of the offset. The estimation error is determined analytically as a function of the sinusoidal stimulus signal amplitude and the noise standard deviation. We derive an exact but computationally difficult expression as well as a simpler closed form approximation that provides an upper bound of the bias of the terminal based gain. The estimators are validated numerically using a Monte Carlo procedure with simulated and experimental data.
Time-interleaved analog-to-digital converter (ADC) architecture is crucial to increase the maximum sample rate. However, offset mismatch, gain mismatch, and timing error between time-interleaved channels degrade the performance of time-interleaved ADCs. This paper focuses on the gain mismatch and timing error. Techniques based on Discrete Fourier Transform (DFT) for estimating and correcting gain mismatch and timing error in an M-channel ADC are depicted. Numerical simulations are used to verify the proposed estimation and correction algorithm.
The influence of bandwidth of OPA on frequency characteristics was investigated in this paper. The analysis of frequency properties was carried out for two exemplary structures. For operational amplifier it was assumed a typical frequency macromodel with 1-pole characteristic. Deformation of the frequency characteristic and the structure bandwidth in dependence on amplifiers bandwidth were analyzed. It was proved that shape of the characteristic to some degree depends on some elements values. The procedure was proposed for optimal choice of the values of (RC) elements, that ensures the characteristic is most approached to ideal one. Optimal values of these (RC) elements ensure that the characteristic of structures do not have any distortion in all frequencies, and these structures can be used in high frequency applications.
An automatic analysis of product reviews requires deep understanding of the natural language text by machine. The limitation of bag-of-words (BoW) model is that a large amount of word relation information from the original sentence is lost and the word order is ignored. Higher-order-N-grams also fail to capture the long-range dependency relations and word order information. To address these issues, syntactic features extracted from the dependency relations can be used for machine learning based document-level sentiment classification. Generalization of syntactic dependency features and negation handling is used to achieve more accurate classification. Further to reduce the huge dimensionality of the feature space, feature selection methods based on information gain (IG) and weighted frequency and odds (WFO) are used. A supervised feature weighting scheme called delta term frequency-inverse document frequency (TF-IDF) is also employed to boost the importance of discriminative features using the observed uneven distribution of features between the two classes. Experimental results show the effectiveness of generalized syntactic dependency features over standard features for sentiment classification using Boolean multinomial naive Bayes (BMNB) classifier.
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