Facial expression recognition of intercepted video sequences based on feature point movement trend and feature block texture variation

Facial Expression Recognition (FER) is an important subject of human–computer interaction and has long been a research area of great interest. Accurate Facial Expression Sequence Interception (FESI) and discriminative expression feature extraction are two enormous challenges for the video-based FER. This paper proposes a framework of FER for the intercepted video sequences by using feature point movement trend and feature block texture variation. Firstly, the feature points are marked by Active Appearance Model (AAM) and the most representative 24 of them are selected. Secondly, facial expression sequence is intercepted from the face video by determining two key frames whose emotional intensities are minimum and maximum, respectively. Thirdly, the trend curve which represents the Euclidean distance variations between any two selected feature points is fitted, and the slopes of specific points on the trend curve are calculated. Finally, combining Slope Set which is composed by the calculated slopes with the proposed Feature Block Texture Difference (FBTD) which refers to the texture variation of facial patch, the final expressional feature are formed and inputted to One-dimensional Convolution Neural Network (1DCNN) for FER. Five experiments are conducted in this research, and three average FER rates 95.2%, 96.5%, and 97% for Beihang University (BHU) facial expression database, MMI facial expression database, and the combination of two databases, respectively, have shown the significant advantages of the proposed method over the existing ones.     

Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.     

A novel approach to adaptive relational association rule mining

The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.     

Low-cost and two-cycle hardware structures of PRINCE lightweight block cipher

In this paper, low-cost and two-cycle hardware structures of the PRINCE lightweight block cipher are presented. In the first structure, we proposed an area-constrained structure, and in the second structure, a high-speed implementation of the PRINCE cipher is presented. The substitution box (S-box) and the inverse of S-box (S-box⁻¹) blocks are the most complex blocks in the PRINCE cipher. These blocks are designed by an efficient structure with low critical path delay. In the low-cost structure, the S-boxes and S-boxes⁻¹ are shared between the round computations and the intermediate step of PRINCE cipher. Therefore, the proposed architecture is implemented based on the lowest number of computation resources. The two-cycle implementation of PRINCE cipher is designed by a processing element (PE), which is a general and reconfigurable element. This structure has a regular form with the minimum number of the control signal. Implementation results of the proposed structures in 180-nm CMOS technology and Virtex-4 and Virtex-6 FPGA families are achieved. The proposed structures, based on the results, have better critical path delay and throughput compared with other's related works.     

测井资料在优选压裂层位中的应用

压裂是低孔、低渗油层改造的主要增产措施。根据常规测井资料、岩石物理实验分析数据及试油成果。研究储层四性关系，确定区块的低孔、低渗储层评价标准。进一步对油层进行分类，有针对性地为油层压裂提供施工层位和准确的储层参数。通过商543区块实例，阐述了选择压裂层位的方法。     

泌阳凹陷复杂断块群油气分布控制因素

泌阳凹陷北部斜坡复杂断块群油田以断鼻、断块及断层+岩性油气藏为主,垂向含油层系多,平面上叠合连片,油气主要富集在鼻状构造主体部位的断鼻断块内。通过对研究区油气控制因素的分析,指出继承性的鼻状构造控制了油气聚集方向和富集程度,油气藏含油范围和油气藏高度则主要受断层封闭性和封闭程度的控制,油气分布规律受伸展构造所伴生的各种断块群构造型式的控制。     

浇筑式沥青混凝土心墙型式改进的建议

本文根据浇筑式沥青混凝土主、副心墙材料的性能，分折了沥青混凝土心墙施工或运行中破坏的原因，建议用编织一无纺复合布取代预制块，以避免主、副心墙材料变形性能相差悬残对心墙运行的不利影响，从而发挥浇筑式沥青混凝土心墙的优越性。     

MULTICARRIER DS—CDMA WITH ADAPTIVE MODULATION AND POWER ALLOCATION

Adaptive modulation and power allocation is introduced into the multicarrier DSCDMA system to improve the system performance and bandwidth efficiency.First,the system design appropriate for adaptive modulation and power allocation is given,then the algorithm of adaptive modulation and power allocation is applied.Simulation results demonstrate great performance improvement compared with the fixed modulated one.     

智能天线对移动通信网性能的影响

智能天线是目前移动通信中的热点话题之一，也是提升网络容量和改善传输质量的关键技术。本文简要介绍了智能天线基本概念，原理及其在无线网络中的重大作用。     

合成丁苯嵌段共聚物的影响因素

采用负离子溶液聚合方法制备了丁苯嵌段共聚物，研究了引发剂、活化剂、偶合剂、终止剂对聚合产物性能的影响。