A Class of Second Order Difference Approximation for Solving Space Fractional Diffusion Equations

A class of second order approximations, called the weighted and shifted Gr\"{u}nwald difference operators, are proposed for Riemann-Liouville fractional derivatives, with their effective applications to numerically solving space fractional diffusion equations in one and two dimensions. The stability and convergence of our difference schemes for space fractional diffusion equations with constant coefficients in one and two dimensions are theoretically established. Several numerical examples are implemented to testify the efficiency of the numerical schemes and confirm the convergence order, and the numerical results for variable coefficients problem are also presented.Comment: 24 Page

A Compact and Discriminative Feature Based on Auditory Summary Statistics for Acoustic Scene Classification

One of the biggest challenges of acoustic scene classification (ASC) is to find proper features to better represent and characterize environmental sounds. Environmental sounds generally involve more sound sources while exhibiting less structure in temporal spectral representations. However, the background of an acoustic scene exhibits temporal homogeneity in acoustic properties, suggesting it could be characterized by distribution statistics rather than temporal details. In this work, we investigated using auditory summary statistics as the feature for ASC tasks. The inspiration comes from a recent neuroscience study, which shows the human auditory system tends to perceive sound textures through time-averaged statistics. Based on these statistics, we further proposed to use linear discriminant analysis to eliminate redundancies among these statistics while keeping the discriminative information, providing an extreme com-pact representation for acoustic scenes. Experimental results show the outstanding performance of the proposed feature over the conventional handcrafted features.Comment: Accepted as a conference paper of Interspeech 201

Critical behavior of a stochastic anisotropic Bak-Sneppen model

In this paper we present our study on the critical behavior of a stochastic anisotropic Bak-Sneppen (saBS) model, in which a parameter $\alpha$ is introduced to describe the interaction strength among nearest species. We estimate the threshold fitness $f_c$ and the critical exponent $\tau_r$ by numerically integrating a master equation for the distribution of avalanche spatial sizes. Other critical exponents are then evaluated from previously known scaling relations. The numerical results are in good agreement with the counterparts yielded by the Monte Carlo simulations. Our results indicate that all saBS models with nonzero interaction strength exhibit self-organized criticality, and fall into the same universality class, by sharing the universal critical exponents.Comment: 9 pages, 7 figures. arXiv admin note: text overlap with arXiv:cond-mat/9803068 by other author

Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events

In this paper, we propose a new strategy for acoustic scene classification (ASC) , namely recognizing acoustic scenes through identifying distinct sound events. This differs from existing strategies, which focus on characterizing global acoustical distributions of audio or the temporal evolution of short-term audio features, without analysis down to the level of sound events. To identify distinct sound events for each scene, we formulate ASC in a multi-instance learning (MIL) framework, where each audio recording is mapped into a bag-of-instances representation. Here, instances can be seen as high-level representations for sound events inside a scene. We also propose a MIL neural networks model, which implicitly identifies distinct instances (i.e., sound events). Furthermore, we propose two specially designed modules that model the multi-temporal scale and multi-modal natures of the sound events respectively. The experiments were conducted on the official development set of the DCASE2018 Task1 Subtask B, and our best-performing model improves over the official baseline by 9.4% (68.3% vs 58.9%) in terms of classification accuracy. This study indicates that recognizing acoustic scenes by identifying distinct sound events is effective and paves the way for future studies that combine this strategy with previous ones.Comment: code URL typo, code is available at https://github.com/hackerekcah/distinct-events-asc.gi

Unified probe launch pattern design and methodology of differential probe characterization

Differential probe is wildly used in the signal integrity area to do the accuracy signal measurement in frequency domain or time domain. Comparing with traditional SMA connector measurement, the probe measurement has several advantages such as the high flexibility and measurement efficiency. Nevertheless, the probe has some disadvantages such as multiple design patterns and the difficulty of fast landing. In this thesis, a unified probe landing pattern is provided to solve the con of probes and a probe testing fixture is designed for characterize probe and extract the probe model. In the first portion, a unified differential probe launching pattern is proposed for universal usage of different types of differential probes. Full wave-modeling of the transition with the unified probe launching pattern is developed for optimization of dimensions. For the unified probe launching pattern evaluation, 16-layer test vehicles were designed with engineered transitions for performance up to 40 GHz. Four-port measurement results of different differential pairs from the test vehicle are used as the 2x thru reference and DUT for de-embedding. By using GSSG probe, accurate DK and DF along with frequency can be extracted. In the second portion, a probe testing fixture is designed based on the unified probe launch pattern design to characterize the performance of probe based on the smart fixture de-embedding method. The full wave model is extracted from the fixture design to server future probing measurement design and the circuit model is extracted to study the effectiveness from the specific portion behavior --Abstract, page iii