基于广义回归神经网络的情感语音韵律特征预测

韵律特征是语音信号中情感信息的主要表征之一。为了更好地进行情感语音合成的研究,本文通过提取普通话情感语音的韵律特征进行分析,采用广义回归神经网络构建了一个情感语音韵律特征预测模型,并根据所提取的测试集数据文本语境信息进行韵律特征预测,实验获得了相应的结果。实验结果表明,情感语音韵律特征预测效果较好。     

《遥感技术与应用》30年论文发表趋势

自《遥感技术与应用》1986年创刊以来,走过了30年的历程,现已成为国内遥感研究成果的主要载体之一。利用现有的CSCD和CNKI数据库,从文献计量角度分析《遥感技术与应用》30年的发文趋势。结果表明:30a来,《遥感技术与应用》的论文发表数量总体呈快速增长趋势;相关研究机构主要集中在西北和北京两地;Landsat和MODIS是研究最多的两种遥感数据;土地利用是关注最多的遥感应用研究。引文分析的结果表明《遥感技术与应用》刊载的论文被CSCD和SCI引用频次和期刊影响因子不断提高,进一步说明了学界对遥感技术相关问题的关注程度不断加强。此外,计算机技术、数据科学、大数据等技术的发展促进了遥感科学的发展;另一方面,自然灾害、大气环境问题、粮食安全和可持续发展问题以及"Future Earth"的战略部署,都需要依托遥感技术获取更为可靠的数据源、构建更加系统的数据平台及面向更加智能的决策平台。     

复杂环境下基于角点回归的全卷积神经网络的车牌定位

车牌定位是车牌识别系统中核心部分,具有较高的研究和应用价值。尽管近些年来该研究取得了很大的进展,但仍无法很好地解决低亮度、低分辨率和车辆倾斜等环境下的定位问题。本文提出了一种新的全卷积神经网络,通过回归车牌角点的方式准确地进行车牌定位。为了保证训练的有效性,对45 000幅含有车牌的图像进行人工标注。同时,对标注的图像随机进行平移、缩放、旋转和加噪,提高训练样本的数量和多样性。在本文构建的卡口图像数据集和复杂环境数据集上与两种方法进行了比较,验证了本文方法的有效性。     

城市交叉路口背景提取与车道标定算法

目的 为解决车辆对车道标记的遮挡问题,提出一种新的背景提取算法,同时基于透视变换实现了城市交叉路口的多车道标定。方法 首先,通过均值与帧间差分方法的融合,进行城市交叉路口的背景稳定与更新;然后,利用Canny算子及Hough直线检测得到各类直线;其次,基于透视变换、聚类分析和先验知识建立了车道线的筛选数学模型,实现了车道线标定;最后,通过实验对算法进行了验证。结果 采用10min长度、分辨率为2592×2048像素的某城市交叉路口实际监控视频进行交叉路口背景提取。本文算法的背景提取准确率比均值法和传统高斯混合模型法分别提升20%和30%左右,车道线标定也优于其他类似方法。结论 算法具有收敛速度快、准确率较高、稳定性较好等特点,在车流量大时可快速更新并消除车辆虚影,适用于光照条件正常的城市交叉种口的车道线标定。     

Letter: Solution‐processed flexible zinc‐tin oxide thin‐film transistors on ultra‐thin polyimide substrates

In this letter, solution‐processed flexible zinc‐tin oxide (Z_0.35T_0.65O_1.7) thin‐film transistors with electrochemically oxidized gate insulators (AlOx:Nd) fabricated on ultra‐thin (30 µm) polyimide substrates are presented. The AlOx:Nd insulators exhibited wonderful stability under bending and excellent insulating properties with low leakage current, high dielectric constant, and high breakdown field. The device exhibited a mobility of 3.9 cm²/V · s after annealing at 300 °C. In addition, the flexible device was able to maintain the electricity performance under various degrees of bending, which was attributed to the ultra‐thin polyimide substrate.     

Efficient near-duplicate image detection with a local-based binary representation

Efficient near-duplicate image detection is important for several applications that feature extraction and matching need to be taken online. Most image representations targeting at conventional image retrieval problems are either computationally expensive to extract and match, or limited in robustness. Aiming at this problem, in this paper, we propose an effective and efficient local-based representation method to encode an image as a binary vector, which is called Local-based Binary Representation (LBR). Local regions are extracted densely from the image, and each region is converted to a simple and effective feature describing its texture. A statistical histogram can be calculated over all the local features, and then it is encoded to a binary vector as the holistic image representation. The proposed binary representation jointly utilizes the local region texture and global visual distribution of the image, based on which a similarity measure can be applied to detect near-duplicate image effectively. The binary encoding scheme can not only greatly speed up the online computation, but also reduce memory cost in real applications. In experiments the precision and recall, as well as computational time of the proposed method are compared with other state-of-the-art image representations and LBR shows clear advantages on online near-duplicate image detection and video keyframe detection tasks.     

Arithmetic coding using hierarchical dependency context model for H.264/AVC video coding

In this paper, a hierarchical dependency context model (HDCM) is firstly proposed to exploit the statistical correlations of DCT (Discrete Cosine Transform) coefficients in H.264/AVC video coding standard, in which the number of non-zero coefficients in a DCT block and the scanned position are used to capture the magnitude varying tendency of DCT coefficients. Then a new binary arithmetic coding using hierarchical dependency context model (HDCMBAC) is proposed. HDCMBAC associates HDCM with binary arithmetic coding to code the syntax elements for a DCT block, which consist of the number of non-zero coefficients, significant flag and level information. Experimental results demonstrate that HDCMBAC can achieve similar coding performance as CABAC at low and high QPs (quantization parameter). Meanwhile the context modeling and the arithmetic decoding in HDCMBAC can be carried out in parallel, since the context dependency only exists among different parts of basic syntax elements in HDCM.