'Reconstruction filter' presentation slideshows

Sampling and Reconstruction

Sampling and Reconstruction. Reading. Required: Watt, Section 14.1 Recommended: Ron Bracewell, The Fourier Transform and Its Applications, McGraw-Hill.

486 views • 38 slides

CMSC 635

CMSC 635. Sampling and Antialiasing. Aliasing in images. Aliasing in animation. High frequencies alias as low frequencies. Aliasing. Abstract Vector Spaces. Addition C = A + B = B + A (A + B) + C = A + (B + C) given A, B, A + X = B for only one X Scalar multiply C = a A

295 views • 29 slides

CMSC 635

CMSC 635. Sampling and Antialiasing. Aliasing in images. Aliasing in animation. High frequencies alias as low frequencies. Aliasing. Abstract Vector Spaces. Addition C = A + B = B + A (A + B) + C = A + (B + C) given A, B, A + X = B for only one X Scalar multiply C = a A

294 views • 29 slides

Time and Frequency Characterization of Signals & Systems

Time and Frequency Characterization of Signals & Systems. Frequency Domain Characterization through multiplication of Fourier Transform of input signal and system frequency response. ( Transfer Function). Time Domain Characterization through convolution of input signal

642 views • 26 slides

Fundamentals of ...

Fundamentals of. Fundamentals of Digital Audio. Analog vs. Digital. Analog vs. Digital. Analog. AMPLIFIER. MICROPHONE. SOUND PRESSUTRE WAVE. SOUND PRESSUTRE WAVE. VOLTAGE IN WIRE. Digital. DIGTAL TO ANALOG CONVERTER. ANALOG TO DIGITAL CONVERTER. COMUTER MEMORY. AMPLIFIER.

325 views • 22 slides

Sampling

294 views • 13 slides

Review of Discrete-Time Signal Processing

Review of Discrete-Time Signal Processing. Sampling. CT. DT. Zero-Order Hold (ZOH). Sampling. Impulse train. Sampling. ? (t-nT). Spectrum of Sampled Signal ( ? s >2 ? N ). The spectrum of the sampled signal is periodic in ? s =2 ? /T. Spectrum of Sampled Signal ( ? s <2 ? N ).

242 views • 8 slides

Sampling and quantization

586 views • 6 slides

Data Tracking Quadrature Condition Using Joint Histogram Method

To use the characteristic points of the image is an excellent choice for accurate monitoring data. The main disadvantages are that the properties of the monitoring body easily affected by noise and the data must often large enough to facilitate the monitoring to be dimensioned. In contrast, the facial features Gabor jet features are better than other types of functions of mother wavelets. This paper shows a novel filter for Joint Histogram Method.

50 views • 4 slides