• Image Formation & DisplayImage Formation & Display

    Images are a description of how a parameter varies over a surface. For example, standard visual images result from light intensity variations across a two-dimensional plane. However, light is not the only parameter used in scientific imaging. For example, an image can be formed of the temperature of an integrated circuit, blood velocity in a patien...

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  • Linear Image ProcessingLinear Image Processing

    Linear image processing is based on the same two techniques as conventional DSP: convolution and Fourier analysis. Convolution is the more important of these two, since images have their information encoded in the spatial domain rather than the frequency domain. Linear filtering can improve images in many ways: sharpening the edges of objects, redu...

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  • Special Imaging TechniquesSpecial Imaging Techniques

    This chapter presents four specific aspects of image processing. First, ways to characterize the spatial resolution are discussed. This describes the minimum size an object must be to be seen in an image. Second, the signal-to-noise ratio is examined, explaining how faint an object can be and still be detected. Third, morphological techniques are i...

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  • Data CompressionData Compression

    Data transmission and storage cost money. The more information being dealt with, the more it costs. In spite of this, most digital data are not stored in the most compact form. Rather, they are stored in whatever way makes them easiest to use, such as: ASCII text from word processors, binary code that can be executed on a computer, individual sampl...

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  • Digital Signal ProcessorsDigital Signal Processors

    Digital Signal Processing is carried out by mathematical operations. In comparison, word processing and similar programs merely rearrange stored data. This means that computers designed for business and other general applications are not optimized for algorithms such as digital filtering and Fourier analysis. Digital Signal Processors are microproc...

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  • Getting Started with DSPsGetting Started with DSPs

    Once you decide that a Digital Signal Processor is right for your application, you need a way to get started. Many manufacturers will sell you a low cost evaluation kit, allowing you to experience their products first-hand. These are a great educational tool; it doesn't matter if you are a novice or a pro, they are the best way to become familiar w...

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  • Complex NumbersComplex Numbers

    Complex numbers are an extension of the ordinary numbers used in everyday math. They have the unique property of representing and manipulating two variables as a single quantity. This fits very naturally with Fourier analysis, where the frequency domain is composed of two signals, the real and the imaginary parts. Complex numbers shorten the equati...

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  • The Complex Fourier TransformThe Complex Fourier Transform

    Although complex numbers are fundamentally disconnected from our reality, they can be used to solve science and engineering problems in two ways. First, the parameters from a real world problem can be substituted into a complex form, as presented in the last chapter. The second method is much more elegant and powerful, a way of making the complex n...

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  • The Laplace TransformThe Laplace Transform

    The two main techniques in signal processing, convolution and Fourier analysis, teach that a linear system can be completely understood from its impulse or frequency response. This is a very generalized approach, since the impulse and frequency responses can be of nearly any shape or form. In fact, it is too general for many applications in science...

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  • The z - TransformThe z - Transform

    Just as analog filters are designed using the Laplace transform, recursive digital filters are developed with a parallel technique called the z-transform. The overall strategy of these two transforms is the same: probe the impulse response with sinusoids and exponentials to find the system's poles and zeros. The Laplace transform deals with differe...

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