Robust Sound Event Classification using Deep Neural Networks

The automatic recognition of sound events by computers is an important aspect of emerging applications such as automated surveillance, machine hearing and auditory scene understanding. Recent advances in machine learning, as well as in computational models of the human auditory system, have contributed to advances in this increasingly popular research field. Robust sound event classification, the ability to recognise sounds under real-world noisy conditions, is an especially challenging task. Classification methods translated from the speech recognition domain, using features such as mel-frequency cepstral coefficients, have been shown to perform reasonably well for the sound event classification task, although spectrogram-based or auditory image analysis techniques reportedly achieve superior performance in noise. This paper outlines a sound event classification framework that compares auditory image front end features with spectrogram image-based front end features, using support vector machine and deep neural network classifiers. Performance is evaluated on a standard robust classification task in different levels of corrupting noise, and with several system enhancements, and shown to compare very well with current state-of-the-art classification techniques

Encoder-Decoder-Based Intra-Frame Block Partitioning Decision

The recursive intra-frame block partitioning decision process, a crucial component of the next-generation video coding standards, exerts significant influence over the encoding time. In this paper, we propose an encoder-decoder neural network (NN) to accelerate this process. Specifically, a CNN is utilized to compress the pixel data of the largest coding unit (LCU) into a fixed-length vector. Subsequently, a Transformer decoder is employed to transcribe the fixed-length vector into a variable-length vector, which represents the block partitioning outcomes of the encoding LCU. The vector transcription process adheres to the constraints imposed by the block partitioning algorithm. By fully parallelizing the NN prediction in the intra-mode decision, substantial time savings can be attained during the decision phase. The experimental results obtained from high-definition (HD) sequences coding demonstrate that this framework achieves a remarkable 87.84\% reduction in encoding time, with a relatively small loss (8.09\%) of coding performance compared to AVS3 HPM4.0

Experimental Research on Passive Control of Steel Frame Structure Using SMA Wires

Mechanical properties of shape memory alloy (SMA) wires were experimentally researched in this paper, and an energy dissipater made of SMA wire cable was designed and applied in a steel frame structure model by using superelasticity characteristics of SMAs to passively reduce dynamic responses of the steel frame structure under seismic load. For the characteristics of large relative displacements between the stories of the steel frame structure on both diagonal ends and the consideration of initial prestrain effects of the SMA cables, three kinds of the whole control, the part control, and no control of the shaking table tests and numerical simulations were carried, respectively. Through the results of the shaking table test and numerical simulation analysis, the dynamic responses such as the maximum displacement, velocity, and acceleration at the top layer of the steel frame structure applied with SMA cables are significantly decreased compared with the no control case. However, considering the premise of both effectiveness and efficiency, the part control effect is superior to the whole control. In many cases, it can meet the control requirement of reducing the maximum displacement and acceleration, while the superelasticity of SMAs can be sufficiently played, realizing the passive control purposes of the steel frame structure based on the energy dispassion through the application of the SMA cables. The proposed method has broad application prospects in the passive control field of building structures

Resource Allocation for Vertical Sectorization in LTE-Advanced Systems

Massive multiple input multiple output (MIMO) technology has been discussed widely in the past few years. Three-dimensional MIMO (3D MIMO) can be seen as a promising technique to realize massive MIMO to enhance the performance of LTE-Advanced systems. Vertical sectorization can be introduced by means of adjusting the downtilt of transmitting antennas. Thus, the radiowave from a base station (BS) to a group of user equipments (UE) can be divided into two beams which point at two different areas within a cell. Intrasector interference is inevitable since the resources are overlapped. In this paper, the influence of intrasector interference is analyzed and an enhanced resource allocation scheme for vertical sectorization is proposed as a method of interference cancellation. Compared with the conventional 2D MIMO scenarios, cell average throughput of the whole system can be improved by vertical sectorization. System level simulation is performed to evaluate the performance of the proposed scheme. In addition, the impacts of downtilt parameters and intersite distance (ISD) on spectral efficiency and cell coverage are presented