application of the Digital Signal Processing. Spectral analysis is used in many applications such as to get the target location and its velocity information in the radar applications [9]. In general many practical applications such as Ocean noise‚ Wind speed give a time series data [10]. This data can be analyzed using spectral analysis. Spectrum estimation is a problem that involves estimating the power spectrum of the signal from a finite number of noisy measurements of the signal. The techniques adopted
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Hyderabad. DIGITAL SIGNAL PROCESSING LAB MANUAL III YEAR II SEMESTER (ECE) Prepared by: K. Ashok Kumar Reddy Department of Electronics & Communications Engineering‚ Visvesvaraya College of Engineering & Technology‚ Ibrahimpatnam. Digital Signal Processing Lab Manual Page 1 www.jntuworld.com www.jntuworld.com Department of Electronics and Communication VCET‚Hyderabad. JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD III Year B.Tech. ECE - II Sem L T/P/D C 0 -/3/2 DIGITAL SIGNAL PROCESSING LAB
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Master Thesis ICT Time to Digital Converter used in ALL digital PLL Master of Science Thesis In System-on-Chip Design By Chen Yao Stockholm‚ 08‚ 2011 Supervisor: Dr. Fredrik Jonsson and Dr. Jian Chen Examiner: Prof. Li-Rong Zheng Master Thesis TRITA-ICT-EX-2011:212 1 ACKNOWLEDGEMENTS I would like to thank: Professor Li-Rong Zheng for giving me the opportunity to do my master thesis project in IPACK group at KTH. Dr. Fredrik Jonsson for providing me with the interesting topic and
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Transform (FFT). Time Issues Due to the rate-change operators in the filter bank‚ the discrete WT is not time-invariant but actually very sensitive to the alignment of the signal in time. To address the time-varying problem of wavelet transforms‚ Mallat and Zhong proposed a new algorithm for wavelet representation of a signal‚ which is invariant to time shifts.[3] According to this algorithm‚ which is called a TI-DWT‚ only the scale parameter is sampled along the dyadic sequence 2^j (j∈Z) and
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2. WINS SYSTEM ARCHITECTURE 3. WINS NODE ARCHITECTURE 4. WINS MICRO SENSOR 5. WINS MICROSENSOR INTERFACE CIRCUITS 6. ROUTING BETWEEN NODES 7. SHORTEST DISTANCE ALGORITHM 8. WINS DIGITAL SIGNAL PROCESSING 9. PSD COMPARSON 10. WINS MICROPOWER EMBEDDED RADIO 11. HISTORY 12. APPLICATION 13. PROS AND CONS 14. CONCLUSION REFERENCES LIST OF FIGURES
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techniques and the paper also presents the real-time image processing technique used to measure traffic queue parameters. TABLE OF CONTENTS (1) Introduction……………………………………………………… 3 (2) Signal & Image Processing applied to traffic 2.1 Digital Signal 2.2 Need for processing……………………………………… 4 2.3 Parameters measurable…………………………………... 4 2.4 Image analysis system structure…………………………. 4 (3) Stages of image analysis 0 Pipeline preprocessing…………………………………………
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wavefront-array architecture is proposed for the real-time implementation of second-order spatially-bandpass (SBP) 2-D infinite impulse response (IIR) beam filters having potential applications in broadband beamforming of temporally down-converted RF signals. The higher speed of operation and potentially reduced power consumption of the asynchronous architecture of wavefront-array processors (WAPs) in comparison to the conventional synchronous hardware has emerging applications in radio-astronomy‚ radar
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the system. This is don e by designing the input signal‚ which is applied to the system [3]‚ whereas output is taken as impulse response of system. Input signal is used for excitation. In this paper identification has been achieved by basic approaches as variable frequency signa l (chirp)‚ coherence function and pseudo inverse. II. SYSTEM IDENTIFICATION A suitable system is considered for the application at hand. Then a special input signal is designed such that the system captures the behavior
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traditional multi-bit ADC with a sine-wave input signal. This input is sampled at a frequency Fs. According to Nyquist theory‚ Fs must be at least twice the bandwidth of the input signal. When observing the result of an FFT analysis on the digital output‚ we see a single tone and lots of random noise extending from DC to Fs/2 (Figure 1). Known as quantization noise‚ this effect results from the following consideration: the ADC input is a continuous signal with an infinite number of possible states‚ but
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pulse-width modulation (PWM) techniques with a field-programmable gate array (FPGA). Basic PWM and audio playing methods are explained in detail. A sigma-delta modulation module‚ specifically‚ was programmed into a Basys FPGA in Verilog to produce the audio signal. In addition‚ several other Verilog modules were needed to complete the player: a 25 MHz to 44.1 kHz clock converter‚ address counter‚ and byte readers. Documented code for all the modules is provided. The techniques explored here are practical‚ efficient
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