感測器解析 Part 1
透過資料採礦洞見分析目標及獲得重要知識
(Gaining Insights into Activities through Data Mining)

有愈來愈多的應用需要在時間序列及感測器資料上,同時進行訊號處理及機器學習的處理技巧。MATLAB可以在單一的環境下提供一系列完整的建模及設計能力,幫助加快資料的解析以及感測器處理系統的開發。

本演講我們會介紹MATLAB常見的訊號處理方法(包含數位濾波器及頻域分析),有助於從原始波形萃取出可描述的特徵,我們也將為您展示如何利用平行運算來加速大量資料集的處理。接下來,我們會討論如何利用程式及互動的方式來探索、測試不同類別的演算法(例如決策樹、支持向量機、或類神經網路)。

我們也將演示如何利用MATLAB轉檔佈署的工具,來建構一個簡潔流暢的嵌入式感測器分析的分類演算法。

An increasing number of applications require the joint use of signal processing and machine learning techniques on time series and sensor data. MATLAB can accelerate the development of data analytics and sensor processing systems by providing a full range of modelling and design capabilities within a single environment.

In this session, we introduce common signal processing methods in MATLAB (including digital filtering and frequency-domain analysis) that help extract descripting features from raw waveforms, and we show how parallel computing can accelerate the processing of large datasets. We then discuss how to explore and test different classification algorithms (such as decision trees, support vector machines, or neural networks) both programmatically and interactively.

We also demonstrate the use of MATLAB deployment tools to architect a streaming classification algorithm for embedded sensor analytics.