Wavelet transform is an important signal analysis tool. However, it is not well-supported like the Fourier transform in embedded systems domain. implementations of the wavelet transform in the embedded systems domain ...
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ISBN:
(纸本)9781424408399
Wavelet transform is an important signal analysis tool. However, it is not well-supported like the Fourier transform in embedded systems domain. implementations of the wavelet transform in the embedded systems domain are generally concentrated on specific applications like image/video processing, etc. In this paper, we present two fixed-point implementation frameworks for the discrete wavelet transform for real-time applications of one-dimensional embedded signal processing. These are based on the convolution and the lifting scheme. The implementation frameworks are realized and tested using 16-bit fixed-point Texas Instruments TMS320VC5510 DSP. Application examples, performance statistics and future scopes are presented in the scope of this paper.
Design techniques for high-performance, fixed-point, multiplierless filter banks are presented. Image compression using the biorthogonal 9/7 discrete wavelet transform provides a motivating example. Image compression ...
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Design techniques for high-performance, fixed-point, multiplierless filter banks are presented. Image compression using the biorthogonal 9/7 discrete wavelet transform provides a motivating example. Image compression and hardware performance of two commonly used filter structures, direct and cascade, and two known filter bank structures, nonpolyphase and polyphase, are compared. A technique is shown for designing a fixed-point polyphase filter structure, which is highly efficient from a hardware standpoint, such that image-compression quality is not significantly deteriorated by the use of fixed-point mathematics. The result is a polyphase structure with about twice the throughput rate of nonpolyphase structures, and peak signal-to-noise ratio values for lossy compression within 0.2 decibels of those achieved using floating-point filters.
The influence of fixed-point implementation in the unknown signal detection using a total-power radiometer is studied The detection is done using a general-purpose receiver structure. The system uses uniform analog-to...
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ISBN:
(纸本)9780780393592
The influence of fixed-point implementation in the unknown signal detection using a total-power radiometer is studied The detection is done using a general-purpose receiver structure. The system uses uniform analog-to-digital conversion (ADC) and internal calculations are performed using fixed-point. ADC and the finite internal precision cause the decision variable to be discrete. This leads to difficulties to maintain the constant false alarm rate required by the Neyman-Pearson criterion. Two internal calculation modes are compared. One removes the most significant bits and the other the least significant bits. From. a detection point of view the preservation of the least significant bits outperforms the preservation of the most significant bits.
Digital Signal Processing (DSP) chips are getting faster, with higher available performance and lower power consumption. Simultaneously, the complexity of algorithms that are being implemented on these devices is incr...
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ISBN:
(纸本)0780374029
Digital Signal Processing (DSP) chips are getting faster, with higher available performance and lower power consumption. Simultaneously, the complexity of algorithms that are being implemented on these devices is increasing rapidly. Currently, Texas Instruments (TI) has targeted the TMS320C5000, fixedpoint (16 bits), family of DSPs to be the most power efficient DSP engine. Implementing complex algorithms on a fixed-point platform is not a trivial task. fixed-point programs require variables to have a. quantization format, and prevent (minimize) overflow and underflow quantization errors The process of converting a successful floating-pointimplementation of an algorithm to fixed-point can be a long and tedious process. This paper presents software tool (Fast Quantization Tool (FaQT)), which simplifies the process of converting an algorithm from floating-point to fixed-point. The paper also presents the application of FaQT to a Noise Reduction (NR) algorithm used in speech processing for cell-phone and other audio applications.
The splitted generalized LeRoux-Gueguen algorithm performs a least-squares estimate of autoregressive parameters. Due to its lattice structure and finite memory length it seems to be well suited to fixed-point arithme...
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The splitted generalized LeRoux-Gueguen algorithm performs a least-squares estimate of autoregressive parameters. Due to its lattice structure and finite memory length it seems to be well suited to fixed-point arithmetic implementation. In the present paper the effect of round-off errors caused by such implementation is studied. This is done by computer simulation of the fixed-point implementation with varying wordlengths for characteristic quantities and comparison with a floating-pointimplementation as a reference. Input signals are determined by pole trajectories of the transfer functions of their autoregressive model filters. Comparison between the two implementations is carries out by a likelihood ratio. The simulation results lead to empirical guidelines for the choice of wordlengths.
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