图像处理代表工程和计算机科学的专业化内的骨干研究领域。这是今天迅速成长的技术，并创立于生物医学领域特别是癌症疾病的各个方面及其应用。乳腺癌被认为是根据最近的统计在全世界所有的癌症类型的致命之一。这是最常见的女性癌症和女性癌症死亡的第二大原因。关于在发展中国家和发达国家的总癌症病例的23％。在这项工作中，使用内插处理对乳腺癌分类为主要类型，良性的和恶性的。该方案依赖于乳腺肿块的形态谱。恶性肿瘤有较良性肿瘤不规则形状百分点。通过这种方式，肿瘤的边界将被追加像素进行内插以使边界平滑越好，这些所需的像素是与所述肿瘤的不规则形状成比例的，在插值像素意味着肿瘤，使得增加朝向恶性情况下前进。所提出的系统是用MATLAB编程实现，并在从乳房X线图像分析协会（MIAS）图像数据库采取了一些图片进行测试。 MIA中提供了乳腺研究常规分类。该系统的工作速度更快，使任何放射科医生可拍摄约钙化的目测外观一个明确的决定。

Automated Driving Systems (ADS) have rapidly evolved in recent years and their architecture becomes sophisticated. Ensuring robustness, reliability and safety of performance is particularly important. The main challenge in building an ADS is the ability to meet certain stringent performance requirements in terms of both making safe operational decisions and finishing processing in real-time. Middlewares play a crucial role to handle these requirements in ADS. The way middlewares share data between the different system components has a direct impact on the overall performance, particularly the latency overhead. To this end, this paper presents FastCycle as a lightweight multi-threaded zero-copy messaging broker to meet the requirements of a high fidelity ADS in terms of modularity, real-time performance and security. We discuss the architecture and the main features of the proposed framework. Evaluation of the proposed framework based on standard metrics in comparison with popular middlewares used in robotics and automated driving shows the improved performance of our framework. The implementation of FastCycle and the associated comparisons with other frameworks are open sourced.