An Improved Sparse Distributed Memory for Anomaly Detection Applications

简而言之，我们的工业合作伙伴拥有经过验证的基于稀疏分布式内存（SDM）的技术， 流数据的异常检测，但它需要大量的内存，运行缓慢， 在设置时手动优化。我们的建议是设计、实施和测试可行的解决方案， 将缓解这些问题，从而扩大其技术的现实世界适用性。这些 增强功能将提高MindMick的商业价值，因为它将允许它将异常检测应用于GPS 用于安全和安保应用的跟踪数据（例如，实时监测阿尔茨海默病患者的行为）。它 还允许MindMick设想嵌入式软件设备，用于工业应用（例如， 关键机器部件的抢先维护警报）。如果没有 计算加速和动态内存分配的增强，我们计划。

In brief, our industrial partner has a proven Sparse Distributed Memory (or SDM) based technology capable of Anomaly Detection of streaming data, but it requires great amounts of memory, operates slowly and requires manual optimization at setup. What we propose is to design, implement and test feasible solutions, which would alleviate these problems and hence, widen the real-world applicability of their technology.These enhancements will boost MindMick's business value, as it will allow it to apply anomaly detection to GPS tracking data for security & safety applications (e.g., real-time monitoring of Alzheimer patient behaviour). It also allows MindMick to envision embedded software devices, to be used in industrial applications (e.g., pre-emptive maintenance alerts for critical machine parts). Neither of these directions are possible without the computational speed-up and dynamic memory allocation enhancements that we plan.