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本文主要包括：6种粒子群算法的程序设计的一般结构。 介绍在Matlab编程中一些需要注意的细节。在实际编程实现中，结合Matlab 语言的特色，可以将程序效率发挥到极致。

本文主要包含以下内容： 遗传算法和粒子群算法的程序设计的一般结构。主要介绍两类算法的程序设计中的主要思想。 介绍在Matlab编程中一些需要注意的细节。在实际编程实现中，结合Matlab 语言的特色，可以将程序效率发挥到极致。 一个遗传算法实例和一个粒子群算法实例。

PSO比较有潜力的应用包括系统设计、多目标优化、分类、模式识别、调度、信号处理、决策、机器人应用等。其中具体应用实例有：模糊控制器设计、车间作业调度、机器人实时路径规划、自动目标检测、时频分析等。

粒子祥优化是一种新兴的老于体智话的后发式全局拟索法，子祥优化算法远过粒子间的充争和协作以实在复杂拟东空间中号找全局爆优点。它是有易犯解、易实呢、全局拟东能力奶等特点，天科学与工程领质的广泛关注，己经成为发展快的智能优化算法之一。论文介绍了粒子祥优化算法的老本原理，分析了其特点。论文中四楼子优化算法的原理、特点、参数设置与应用等方进行全画述，点利用单国子方差分析方法，分析了粒粹优化算法中的性权慎，加速国子的设置对算法老构性能的影响，给出算法中的经这参数设量。最后对其未来的研是出了一些议及研究方向的展。

现代高级成像技术显著改变了缺血性脑卒中的诊疗。通过神经影像技术，如多模态成像技术，可以快速获得患者的组织变异、血管状态和血流灌注情况，基于这些信息实现对缺血性脑卒中成因快速诊断，选择时间窗内外适合进行溶栓治疗的缺血性脑卒中患者，进一步辅助缺血性脑卒中的治疗决策，提高溶栓获益[LA12，Lie09]。对脑缺血患者的个性化诊疗是当今临床上的迫切需求，而神经影像的最重要的作用之一就是选取急性期脑卒中适合溶栓治疗的患者[DD02]。

The increased availability of labeled X-ray image archives(e.g. ChestX-ray14 dataset) hastriggereda growing interest in deep learning techniques. To provide better insight into the different approaches, and their applicationsto chest X-ray classification, we investigate a powerful network architecturein detail: the ResNet-50. Building on prior workin this domain, we consider transfer learning with and without fine-tuning as well as the training of a dedicated X-ray network from scratch. To leveragethe high spatial resolution of X-ray data, we also include an extended ResNet-50 architecture, andanetwork integrating non-image data(patient age, gender and acquisition type) in the classification process. In a concluding experiment, we also investigate multiple ResNet depths(i.e. ResNet-38and ResNet-101). In a systematic evaluation, using 5-fold re-sampling and a multi-label loss function, we comparethe performance of the different approachesfor pathology classification by ROC statistics andanalyze differences between the classifiers using rank correlation. Overall, we observe a considerable spread in the achieved performance and conclude thatthe X-ray-specific ResNet-38, integrating non-image data yields the best overall results. Furthermore, class activation maps are used to understand the classification process, and a detailed analysis of the impact of non-image featuresisprovided.

CT相对于其他医学影像技术而言具有超高的时空分辨率的优点，可以将病人体内微小组织结构及其变化呈现给医生。鉴于CT优异的结构显示能力，目前CT已经被应用与临床常规疾病的诊断中如中枢神经系统疾病、头颈部疾病、胸部疾病、心脏疾病和腹部疾病等。尤其是针对肺部肿瘤筛查，CT系统已经成为了最为常用的医学影像设备。

目前最新的计算断层成像（CT）、核磁共振成像（MRI）、超声影像（US）等多模态医学成像技术不仅可以实现对肿瘤病变高分辨率的成像，同时还能够结构及功能多模态成像，从而实现对肿瘤病变无创检测。还作为最常用的治疗规划及监测治疗反应和提供肿瘤关键信息的方法，已经成为临床上对癌症诊疗的主要技术手段[2]，有效克服了常规临床病理取样和分析的难题。但是目前临床基于影像的肿瘤诊断和治疗都依赖于医生的经验主观评判和操作，然而目前成像模态多样化，再加上多维空间数据获取，数据量和信息量也越来越多，如何通过计算机信息技术尤其是人工智能技术对肿瘤影像数据进行全自动、精准的诊断和治疗规划一直是研究热点和难点[3]。

Non-intrusive inspection systerms based on X-ray radiography techriques are rou tinely used at transport hubs to ensure the conforrmity of catgo content with the supplied shipping manifest. As trade volurmes increase and regulatiors become more stringent, manual inspection by trairned operatos is less and less viable dus to low throusghput. Macline vision techniques can assist operators in their task by autormating parts of the inspection worlflow. Since cats are toutinely involvedin trafficking, export fraul, and tax erasion schermes, they represent an attractive target for autormated detection and flagging for subsequent irspection by operators. In this contribution, we deecribe a rmethod for the detection of cars in X-ray caep images based on trained-from-scratch Contolutional Neural Networlks. By introducing an oversarmpling scherme that suitably addresses the low nurmber of oar images available for training we achieved 100% car irmage classification rate for a false positive rate of 1-in -454. Cars that were partially or completely obscured by other oods,a mocdus operandi frequently adopted by ctirminals, were cotrectly detected.We believe that this level of performance sugeests that the method is suitable fordeployrment in the field. It is expected that the eneric object detection worlkflow described can be extended to other object classes gjven the avail ability of suitable trainingdata.

有限视角光声断层成像的迭代重建算法研究_刘学彦，有限视角光声断层成像的迭代重建算法研究_刘学彦