基于SpringBoot的后台管理系统，实现了系统权限、动态菜单，用户权限，数据字典等基础功能。基于uniapp的商城移动端，实现了商品分类，用户注册和登录，下单和购物车等操作，由于近期比较忙，后期更新优惠券，拼团、抢购的功能，另外数据库sql里面已经有相关的表设计可自行二开。
基于springboot cloud构建的一个商城项目，包括前端，后端和h5应用，小程序，作为zscat应用实践的模板项目。基于SpringBoot2.x、SpringCloud和SpringCloudAlibaba并采用前后端分离的企业级微服务敏捷开发系统架构。并引入组件化的思想实现高内聚低耦合，[ 微信 + 支付宝 + 百度 + 头条 ] 小程序 + APP + 公众号 + PC + H5 项目代码简洁注释丰富上手容易，适合学习和企业中使用。真正实现了基于RBAC、jwt和oauth2的无状态统一权限认证的解决方案，面向互联网设计同时适合B端和C端用户，支持CI/CD多环境部署，积分商城，分销商城并提供应用管理方便第三方系统接入；同时还集合各种微服务治理功能和监控功能。模块包括:企业级的认证系统、开发平台、应用监控、慢sql监控、统一日志、单点登录、Redis分布式高速缓存、配置中心、分布式任务调度、接口文档、代码生成等等
Tumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.
Tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and hence they have different treatments. Detection of tumor in the earlier stages makes the treatment possible. Here we review different segmentation methods associated with feature extraction from Magnetic Resonance Imaging (MRI) of brain. We also discuss different machine learning and classification algorithms that use to classify normal and cancerous tissues. Finally, we propose an automatic tumor detection system
Linux 0.11 内核实验室 —— 基于 Docker/Qemu/Bochs 的极速 Linux 0.11 内核学习和开发环境。
