Download Ambient diagnostics by Yang Cai PDF

By Yang Cai

ISBN-10: 1466510412

ISBN-13: 9781466510418

"In way of life, we regularly realize styles from ad-hoc sensors and make intuitive judgements. This approach is called ambient diagnostics. This textbook is written for rising classes in biomedical engineering, electric engineering, and machine technology that discover clever software program diagnostics from reasonable units, akin to sensors on cellphones, microphones, cameras, and gyroscopes. The text Read more...

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Chapters 11 and 12 explore some do-it-yourself (DIY) approaches for building personalized microscopes and spectrometers for a smartphone. 8 BASIC ALGORITHMS To implement ambient diagnostic systems, it is inevitable to use algorithms to ­filter, visualize, cluster, and classify data and recognize patterns. The algorithms in this book cover a broad spectrum of multimedia analysis (Chapters 4–14), physical ­modeling (Chapters 9–14), and rudimentary data mining (Chapters 2–3). As we discussed in the section on the science of simplicity, rudimentary algorithms are essential to many applications, and they are often more robust than complicated ones.

For example, the intensity of pixels in an image is a feature, and the speed of walking gestures is a feature. A raw dataset can be represented in many forms of features based on the need of an analysis. Here, we elaborate the feature representations in a variety of forms, including time series, multichannel time series, spatiotemporal, shape, image, frequency domain, and multiresolution. Attributes are measurements of a feature. Attributes allow us to specify ­different variants of a feature. For example, if we have a feature vector to describe a face, then the attributes would be the parameters of the biometric measurements in the ­vector, such as the distance between eyes, nose length, lip width, and so on.

In engineering, it is common to use pseudocolor maps that intuitively map the data values to temperatures, such as minimum values to blue (cold) and maximum to white (hot). 8 FREQUENCY DOMAIN FEATURES Any spatial, time domain, or sequential data can be written as a sum of periodic functions, called Fourier functions. For example, when you talk on your cell phone, the processor inside converts the time domain sound waveforms to features in a frequency domain and sends those compressed features through a microwave.

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Ambient diagnostics by Yang Cai

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