Optical flow approximation based motion object extraction for MPEG-2 video stream

This paper presents a compressed-domain motion object extraction algorithm based on optical flow approximation for MPEG-2 video stream. The discrete cosine transform (DCT) coefficients of P and B frames are estimated to reconstruct DC + 2AC image using their motion vectors and the DCT coefficients in I frames, which can be directly extracted from MPEG-2 compressed domain. Initial optical flow is estimated with Black’s optical flow estimation framework, in which DC image is substituted by DC + 2AC image to provide more intensity information. A high confidence measure is exploited to generate dense and accurate motion vector field by removing noisy and false motion vectors. Global motion estimation and iterative rejection are further utilized to separate foreground and background motion vectors. Region growing with automatic seed selection is performed to extract accurate object boundary by motion consistency model. The object boundary is further refined by partially decoding the boundary blocks to improve the accuracy. Experimental results on several test sequences demonstrate that the proposed approach can achieve compressed-domain video object extraction for MPEG-2 video stream in CIF format with real-time performance.     

基于J2ME和J2EE的城市公交查询系统的设计与实现

J2ME和J2EE技术是目前Java两大主要技术,并在应用中取得了成功。本文基于J2ME和J2EE技术实现了一个移动手机版的城市公交查询系统。其中,J2ME技术拥有移动手机客户端的开发,而J2EE技术则用于后台服务器和手机客户端的交互,以及后台服务器信息的发布。本系统实现了三种查询功能:路线查询、站点查询和站站查询。通过本系统将极大地方便手机用户对公交信息的查询。     

“AI+智能”赋能高校智慧教学生态体系构建研究

随着人工智能技术的快速发展,深化“AI+智能”应用以推进高校教学生态体系的优化与升级,已成为教育领域发展的一种重要趋势。为顺应这一趋势,加速构建智慧教学生态体系,更好地利用“AI+智能”技术推动教学质效升级,成为当前高校教学创新发展的重要举措。基于“AI+智能”的高校智慧教学生态体系主要通过综合应用多种智能技术,实现教学内容的多元化,为学生提供个性化的学习体验。文中从智慧教学生态体系的基础理论入手,在精准把握“AI+智能”技术在高等教育中的应用优势的基础上,进一步提出了“AI+智能”赋能高校智慧教学生态体系构建的实践策略,以期为推动高校实现智慧化教学提供有益的路径参考。     

Balancing MXene Surface Termination and Interlayer Spacing Enables Superior Microwave Absorption

Surface chemistry and interlayer engineering determines the electrical properties of 2D MXene. However, it remains challenging to regulate the surface and interfacial chemistry of MXene simultaneously. Herein, simultaneous modulation of Ti₃C₂T_x MXene surface termination and layer spacing by alkali treatment are achieved. The electrical and electromagnetic properties of Ti₃C₂T_x are investigated in detail with respect to KOH and ammonia concentration dependence. A high concentration of KOH caused the Ti₃C₂T_x layer spacing to expand to 13.7 Å and the surface O/F ratio to increase to 33.84. Because of its weaker ionization effect, ammonia provides finer tuning compared to the drastic intercalation of KOH with a thorough sweeping of the F-containing groups. Ti₃C₂T_x is enriched with conductive -OH termination after ammonia treatment, which achieves an effective balance with the increased interlayer resistance. Therefore, NH₃H₂O-Ti₃C₂T_x achieves broad-band impedance matching and exhibits an efficient microwave loss of −49.1 dB at a low thickness of 1.7 mm, with an effective frequency bandwidth of 3.9 GHz. The results herein optimize the electrical properties of Ti₃C₂T_x using surface and interfacial chemistry to achieve broad microwave absorption, providing a framework for enhancing the electromagnetic wave loss of intrinsic MXene.     

Doped/Undoped A1-A2 Typed Copolymers as ETLs for Highly Efficient Organic Solar Cells

The electron transport layer (ETL) is a critical component in achieving high device performance and stability in organic solar cells. Conjugated polyelectrolytes (CPEs) have become an attractive alternative due to film-forming properties and ease of preparation. However, p-type CPEs generally exhibit poor charge mobility and conductivity, incorporation of electron-withdrawing units forming alternated D-A conjugated backbone can make up for these deficiencies. Herein, the ratio of electron withdrawing moieties are further increased and two poly(A1-alt-A2) typed PIIDNDI-Br and PDPPNDI-Br based on the combination of naphthalene diimide (NDI) with isoindigo (IID) or diketopyrrolopyrrole (DPP) via direct arylation polycondensation are synthesized. These CPEs possess excellent alcohol solubility, a suitable lowest unocuppied molecular orbital energy level, and work function tunability. Surprisingly, the incorporation of IID and DPP units generate distinct self-doping behaviors, which are confirmed by UV–vis absorption and ESR spectra. However, no matter doped or undoped, both CPEs present better charge-transporting properties and conductivity when utilized as ETLs. The PIIDNDI-Br and PDPPNDI-Br display good universal compatibility with the blend of PM6:Y6 and PM6:L8-BO, and PCEs of 18.32% and 18.36% are obtained, respectively, which also present excellent storage stability. In short, the combination of two different acceptors demonstrates an efficient strategy to design highly efficient ETLs for high performance photovoltaic devices.     

Tailoring of Photoluminescence Properties in All-Vacuum Deposited Perovskite via Ruddlesden–Popper Faults

Ruddlesden–Popper (RP) faults are well known in oxide perovskites, and are also observed in promising metal halide perovskites. However, the effect of RP faults on optical properties of perovskite has not been systematically investigated. In this study, it is found that RP faults are common planar faults in all-vacuum deposited CsPbBr₃-based perovskite polycrystal thin films, and the density of RP planar faults can be greatly increased by non-stoichiometric composition (Cs-rich) as well as reduced dimensionality (quasi-2D) strategies. The photoluminescence (PL) measurement reveals monotonically increasing peak intensities with higher densities of RP planar faults from Cs-rich, quasi-2D to Cs-rich & quasi-2D samples. The corresponding atomic-scale differential phase contrast maps indicate strongly confined charges within the RP planar fault network, which explains well the relationship between PL enhancement and the density of RP planar faults, and offers an alternative pathway for tailoring the optoelectronic properties of perovskite.     

基于深度神经网络的多指灵巧手抓取手势优化

基于深度神经网络模型,提出了一种适用于多指灵巧手的抓取手势优化方法。首先,在仿真环境下构建了一个抓取数据集,并在此基础上训练了一个卷积神经网络,依据目标物体单目视觉信息和多指灵巧手抓取位形来预测抓取质量函数,由此可以将多指灵巧手的抓取规划问题转化为使抓取质量最大化的优化问题,进一步,基于深度神经网络中的反向传播和梯度上升算法实现多指灵巧手抓取手势的迭代与优化。在仿真环境中,比较该网络和仿真平台对同一抓取位形的抓取质量评估结果,再利用所提出的优化方法对随机搜索到的初始手势进行优化,比较优化前后手势的力封闭指标。最后,在实际机器人平台上验证本文方法的优化效果,结果表明,本文方法对未知物体的抓取成功率在80%以上,对于失败的抓取,优化后成功的比例达到90%。     

深度学习屈光检测方法研究

屈光不正是一种非常常见且对视功能发育有严重危害的眼科问题。准确与方便的屈光检测技术,对于及时发现屈光不正问题以及采取相应措施进行干预具有非常重要的意义。目前的屈光筛查设备虽然能较快进行屈光检测,但主要存在两个问题：检测准确度较低,对被测者配合度要求较高。因此,提出一种新的屈光检测方法,此方法使用基于偏心摄影验光原理的光学系统获取人脸面部近红外图像,使用图像处理技术对面部近红外图像进行处理,得到左右瞳孔图像和瞳孔位置信息,使用提出的结合了深度可分离卷积和SE模块的混合数据多输入神经网络模型进行训练与屈光度的计算。与传统偏心摄影验光原理的屈光检测方法相比,此方法有望随着数据集的扩增而达到更高的准确度,并且此方法将瞳孔位置信息作为模型的输入,可以解决传统算法对被测者配合度要求较高的问题。该研究是对屈光检测新方法的一种有益探索,使用此方法有利于屈光筛查更便利地进行,为实现非接触自助式的屈光筛查提供基础。     

低温环境下FBG温度传感特性研究

在低温环境中,光纤光栅(Fiber Bragg Grating,FBG)材料的热膨胀系数和热光系数会发生改变,从而影响其温度传感特性。文章通过实验研究了裸光纤光栅传感器和黄铜管封装的光纤光栅传感器在低温下的温度传感特性。结果表明,在80~300 K温度范围,裸FBG温度传感器的灵敏度为6.43 pm/K,线性度为0.974,在80~230 K温度范围,温度与光纤光栅的中心波长呈现非线性关系;黄铜管封装的FBG温度传感器,在整个温度范围内灵敏度可达26 pm/K,线性度为0.996,较裸FBG温度传感器均有较大提升。对比实验表明,对光纤光栅进行封装,可以提高其温度灵敏度和线性度,改善温度传感特性。     

计及需求响应及抽水蓄能的含风电系统鲁棒机组组合

针对大规模风电接入电力系统带来的消纳问题,提出一种考虑需求响应及抽水蓄能的鲁棒机组组合优化方法。计及需求响应、抽水蓄能机组的运行约束及鲁棒可行性约束,采用考虑不确定预算的风电功率不确定集合,基于仿射补偿策略,构建鲁棒机组组合模型。同时根据对偶原理将鲁棒优化模型转化为确定性的数学规划问题,并在改进的IEEE39节点系统进行算例仿真,验证了所提方法的正确性和有效性。