Automatic Search Dense Connection Module for Super-Resolution

The development of display technology has continuously increased the requirements for image resolution. However, the imaging systems of many cameras are limited by their physical conditions, and the image resolution is often restrictive. Recently, several models based on deep convolutional neural network (CNN) have gained significant performance for image super-resolution (SR), while extensive memory consumption and computation overhead hinder practical applications. For this purpose, we present a lightweight network that automatically searches dense connection (ASDCN) for image super-resolution (SR), which effectively reduces redundancy in dense connection and focuses on more valuable features. We employ neural architecture search (NAS) to model the searching of dense connections. Qualitative and quantitative experiments on five public datasets show that our derived model achieves superior performance over the state-of-the-art models.     

The AME 2020 atomic mass evaluation (I). Evaluation of input data,and adjustment procedures

This is the first of two articles(Part I and Part II)that presents the results of the new atomic mass evaluation,Ame2020.It includes complete information on the experimental input data that were used to derive the tables of recommended values which are given in Part II.This article describes the evaluation philosophy and procedures that were implemented in the selection of specific nuclear reaction,decay and mass-spectrometric data which were used in a least-squares fit adjustment in order to determine the recommended mass values and their uncertainties.All input data,including both the accepted and rejected ones,are tabulated and compared with the adjusted values obtained from the least-squares fit analysis.Differences with the previous Ame2016 evaluation are discussed and specific examples are presented for several nuclides that may be of interest to Ame users.     

Statistical and Visual Analysis of Audio,Text, and Image Features for Multi-Modal Music Genre Recognition

We present a multi-modal genre recognition framework that considers the modalities audio, text, and image by features extracted from audio signals, album cover images, and lyrics of music tracks. In contrast to pure learning of features by a neural network as done in the related work, handcrafted features designed for a respective modality are also integrated, allowing for higher interpretability of created models and further theoretical analysis of the impact of individual features on genre prediction. Genre recognition is performed by binary classification of a music track with respect to each genre based on combinations of elementary features. For feature combination a two-level technique is used, which combines aggregation into fixed-length feature vectors with confidence-based fusion of classification results. Extensive experiments have been conducted for three classifier models (Naïve Bayes, Support Vector Machine, and Random Forest) and numerous feature combinations. The results are presented visually, with data reduction for improved perceptibility achieved by multi-objective analysis and restriction to non-dominated data. Feature- and classifier-related hypotheses are formulated based on the data, and their statistical significance is formally analyzed. The statistical analysis shows that the combination of two modalities almost always leads to a significant increase of performance and the combination of three modalities in several cases.     

A modular attachment mechanism for software network evolution

A modular attachment mechanism of software network evolution is presented in this paper. Compared with the previous models, our treatment of object-oriented software system as a network of modularity is inherently more realistic. To acquire incoming and outgoing links in directed networks when new nodes attach to the existing network, a new definition of asymmetric probabilities is given. Based on this, modular attachment instead of single node attachment in the previous models is then adopted. The proposed mechanism is demonstrated to be able to generate networks with features of power-law, small-world, and modularity, which represents more realistic properties of actual software networks. This work therefore contributes to a more accurate understanding of the evolutionary mechanism of software systems. What is more, explorations of the effects of various software development principles on the structure of software systems have been carried out, which are expected to be beneficial to the software engineering practices.     

Combined effects of high pressure and sodium hydrogen carbonate treatment on pork ham: improvement of texture and palatability

In this study, the combined effects of high pressure and sodium hydrogen carbonate (NaHCO₃) treatment on the physical and chemical properties, and palatability of pork ham, a tough and under-utilized meat, were investigated. Assessment of meat properties with heat treatment, after exposure to NaHCO₃ and high pressure treatment, revealed an increase in water content, and decreased weight reduction and rupture stress. The free amino acid content of meat samples increased with NaHCO₃ and high pressure treatment. The effect of high pressure processing was especially notable at a pressure of 300 MPa. Sensory evaluation showed that meat subjected to high pressure processing after NaHCO₃ treatment was tender and juicy. In addition, the sample produced minimal residue in the mouth and was characterized by a good taste.     

电磁防护材料性能的评价方法

阐述了东南大学电磁兼容研究室近三十年来所接触的电子产品常用电磁防护材料电磁特性的评价方法,并针对这些方法的原理和适用领域,以及在材料电磁防护性能表述中出现的一些问题提出了看法。最后,介绍了目前防护材料电磁特性评价方法的研究动态。     

面向密集用户区域的无线网络重构体系架构

现有蜂窝移动通信系统支持的业务量相对稳定,不能很好地满足密集用户应急突发业务的需求,在资源综合优化利用方面还存在局限性。无线网络逻辑重构是一种在已有蜂窝系统物理架构上,通过调整或重新配置资源的组织运用方式,以最大限度地提升系统容量的方法。本文首先分析了无线网络重构对于提升蜂窝系统容量的重要性,接着设计了基于SAE的无线网络重构体系架构,在此基础上给出了网络重构的控制方式以及信令实现流程。     

一种提高语音干扰效果评估准确度的方法

语音信号的干扰效果是检验通信对抗装备的重要指标之一,基于客观的评估方法是当前研究的重点。客观评估方法的基础是对语音信号的预处理,介绍语音信号端点识别和语音信号分段处理方法的基础上,提出了一种基于时间统一设备的端点识别与分段方法,解决了加噪语音信号在仿真试验中的分段问题,该方法不受信噪比影响,具有较强的适应性。     

Adaptive Call Admission Control for QoS/Revenue Optimization in CDMA Cellular Networks

In this paper, we show how online management of both quality of service (QoS) and provider revenue can be performed in CDMA cellular networks by adaptive control of system parameters to changing traffic conditions. The key contribution is the introduction of a novel call admission control and bandwidth degradation scheme for real-time traffic as well as the development of a Markov model for the admission controller. This Markov model incorporates important features of 3G cellular networks, such as CDMA intra- and inter-cell interference, different call priorities and soft handover. From the results of the Markov model the threshold for maximal call degradation is periodically adjusted according to the currently measured traffic in the radio access network. As a consequence, QoS and revenue measures can be optimized with respect to a predefined goal. To illustrate the effectiveness of the proposed QoS/revenue management approach, we present quantitative results for the Markov model and a comprehensive simulation study considering a half-day window of a daily usage pattern.     

An Area Optimization Method for Digital Filter Design

In this paper, we propose an efficient design method for area optimization in a digital filter. The conventional methods to reduce the number of adders in a filter have the problem of a long critical path delay caused by the deep logic depth of the filter due to adder sharing. Furthermore, there is such a disadvantage that they use the transposed direct form (TDF) filter which needs more registers than those of the direct form (DF) filter. In this paper, we present a hybrid structure of a TDF and DF based on the flattened coefficients method so that it can reduce the number of flip‐flops and full‐adders without additional critical path delay. We also propose a resource sharing method and sharing‐pattern searching algorithm to reduce the number of adders without deepening the logic depth. Simulation results show that the proposed structure can save the number of adders and registers by 22 and 26%, respectively, compared to the best one used in the past.