An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.     

Ku频段小型化低噪声放大器的设计

设计制作了一款基于微组装工艺的小型化低噪声放大器（LNA）。该器件广泛选用裸管芯、芯片电容等微型器件,采用两级放大电路结构,使用AWR与HFSS电磁仿真软件进行设计、优化和仿真,运用键合金丝微波特性进行噪声系数调试,实现较好的低噪声微波特性。最终实现了在12.25GHz-12.75GHz 工作频段,增益大于20dB,噪声系数小于1.2dB的低噪声放大器,整体电路尺寸仅为12mm×10mm×7mm。     

基于GIS的土地利用数据库缩编

为了满足县级制图以及数据快速查询显示的要求,需要制作1∶50 000土地利用数据库,主要介绍了基于GIS技术,由1∶10 000缩编到1∶50 000的土地利用数据库缩编方法,包括小图斑综合处理、线状地物处理、图形概况、拓扑关系检查等内容.合理地选取地类图斑,科学地概括图斑形状,使得缩编前后的地类图斑比例保持基本一致,轮廓表达相似,充分反映各地类的分布规律是缩编的关键.数据库采用的为MDB格式,缩编软件为ARCGIS 9.2.