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Number 1: Spectral analysis and modulation
- Part 1 explains the Discrete Fourier Transform (DFT) and its Fast Fourier Transform (FFT) implementation.
- Part 2 discusses estimation of power spectrum or spectral density using windowed FFTs and auto-correlation. It illustrates periodogram averaging with the Welsh and Bartlet periodograms.
- Part 3 explores model-based, i.e., parametric spectral estimation, covering topics including auto-regressive moving average (ARMA) models and the Yule-Walker method. It looks at wavelet analysis and the Haar and Morlet wavelets.
- Part 4 examines amplitude and frequency modulation schemes including amplitude shift keying, frequency shift keying, binary frequency shift keying, minimum shift keying, and pulse amplitude modulation.
- Part 5 introduces Phase Shift Keying (PSK) and its variants BPSK, QPSK, QAM, and GMSK. We look at complex modulation, a method encompassing all PSK methods, and the Hilbert Transformer, a filter model for sideband (SSB) signals.
Number 2: Fixed-Point DSP and Algorithm Implementation provides a primer on the use of fixed-point arithmetic in DSP algorithms. It covers concepts such as two's complement representation, dynamic range, overflow, truncation, and saturation. The article also introduces key filtering concepts
Number 3: Multirate DSP
- Part 1 explains how to upsample and downsample by an integer factor.
- Part 2 shows how to change the sampling rate by a non-integer factor. It also looks at multistage decimation and polyphase filters.
- Part 3 looks at oversampling in analog-to-digital converters. It applies these principles to a sigma-delta ADC and revisits the CD player case study.
- Part 4 shows how to undersample bandpass signals.
Number 4: Tutorial: The H.264 Scalable Video Codec (SVC) explains how H.264 SVC reduces network bandwidth, eliminates transcoding, and simplifies storage management.
Number 5: The math of DSP
- Part 1 introduces the basic math needed for DSP. Topics covered include polynomials, transcendentals, series, limits, integration, polar notation, and frequency.
- Part 2 explains complex numbers. Topics covered include real and imaginary numbers, periodic signals, digital frequencies, and discrete arithmetic.
- Part 3 explains the basics of low-pass and high-pass filters. It also explains the concept of causality.
- Part 4 looks at convolution, the Fourier series, and the Nyquist sampling theorem.
- Part 5 explains the concept of orthogonality and introduces quadrature signals.
Number 6: Frequency domain tutorial
- Part 1 explains the mathematics and notation of FFTs and the discrete frequency domain. It starts by discussing the ambiguities of discrete signals.
- Part 2 introduces quadrature (complex) signals, and explains the nature, and notation, of the spectral diagrams used in DSP.
Number 7: Basics of ADCs and DACs
- Part 1 introduces the concept of sampling and explains Nyquist's sampling criteria. It also shows how to use undersampling and antialiasing filters.
- Part 2 explains how ADCs and DACs introduce noise through quantization errors, offset errors, and other "DC" errors. It also explains the characteristics of an ideal ADC.
- Part 3 examines distortion and noise in practical ADCs.
- Part 4 examines jitter, delay, and other errors in ADCs.
- Part 5 examines DAC performance, including glitches and rolloff.
Number 8: Background subtraction tutorial
- Part 1 implements three background subtraction algorithms in MATLAB: frame difference, approximate median, and mixture of Gaussians. The article provides m-code and test videos showing how each works.
- Part 2 shows how to convert the background subtraction models from MATLAB to C using Agility's MCS tool.
- Part 3 highlights advanced topics in MATLAB to C conversion using the mixture-of-Gaussians background subtraction method.
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