Author details

Sector Industry Standard Scale 1 Scale 2 Scale 3 Scale 4 Scale 5 Scale 6 Business Equipment Chips �3.8316 �3.6320 �3.7992 �4.6833 �4.4293 �4.9713 �3.3804 Business Equipment Hardw �4.8485 �4.7393 �5.0597 �5.2962 �6.1684 �6.4471 �5.7814 Business Equipment Softw �4.9513 �4.2904 �4.7301 �5.5315 �6.4523 �6.0185 �8.9519 Chemicals Chems 1.5807 1.0305 1.5022 2.0940 2.7400 2.9636 4.0330 Consumer Durables Hshld �2.3809 �2.0684 �2.3437 �2.4775 �3.7354 �5.6384 �4.9896 Consumer Non-Durables Agric 0.0740 �0.1157 0.0024 0.4892 0.3488 �0.5291 �0.0022 Consumer Non-Durables Beer �1.5311 �1.3189 �1.5000 �1.8565 �2.1101 �2.3938 �1.5355 Consumer Non-Durables Books 0.9144 0.9247 0.9670 1.1610 0.5773 1.1321 2.4769 Consumer Non-Durables Clths 0.9467 0.9735 0.9728 0.9999 1.2157 0.3739 0.5315 Consumer Non-Durables Food �0.4112 0.0323 �0.3587 �0.8661 �1.6998 �0.7677 �0.8502 Consumer Non-Durables Smoke �0.9420 �1.0478 �0.9154 �0.9083 �0.7504 �0.5228 �0.6924 Consumer Non-Durables Soda �0.8866 �0.7657 �0.7021 �1.2258 �1.5206 �1.9345 �2.9555 Consumer Non-Durables Toys �0.6113 �0.3523 �0.7208 �1.2232 �0.2576 �0.4538 �1.7159 Consumer Non-Durables Txtls 2.3341 2.0792 2.3535 2.7470 1.8222 2.0718 4.8854 Energy Coal 1.4393 0.9462 1.5578 1.7379 2.4123 2.6359 3.6478 Energy Mines 2.0426 1.5405 2.2404 2.5477 3.3198 2.5847 3.8822 Energy Oil 2.6581 2.3133 2.7992 3.2025 3.9142 4.3390 4.3149 Health Drugs �5.0907 �4.3717 �4.8950 �5.6002 �6.1844 �6.6855 �8.9212 Health Hlth �0.6160 �0.1308 �0.6923 �0.9001 �1.3981 �0.7229 �2.5513 Health MedEq �2.7898 �1.9996 �2.4451 �3.7228 �4.1971 �5.7059 �5.6987 Manufacturing Aero 0.9648 0.6773 1.0958 1.5436 1.9928 0.6222 1.5235 Manufacturing Autos 3.2835 2.9080 3.1879 3.9504 4.1288 4.5340 5.8731 Manufacturing Boxes 0.6010 0.7789 0.5898 0.4788 0.7280 0.3423 �2.5781 Manufacturing ElcEq �0.3872 �0.3341 �0.4123 0.0537 �1.0260 �2.1100 �1.3577 Manufacturing FabPr 1.4724 1.6384 1.1517 1.2185 1.6245 1.3601 1.6359 Manufacturing Guns 0.6623 0.7106 0.3766 0.9148 0.6122 0.6942 1.9906 Manufacturing LabEq �2.2409 �1.9613 �1.9003 �2.3894 �3.1570 �4.9967 �6.9546 Manufacturing Mach 1.4261 1.1052 1.5513 1.7953 1.6190 1.2927 2.5047 Manufacturing Paper 1.8917 1.6192 1.9279 2.3544 2.4226 1.2835 4.0063 Manufacturing Rubbr 1.5629 1.6763 1.5835 1.5672 1.4813 0.5814 1.0477 Manufacturing Ships 1.1188 1.0080 1.1144 1.4306 1.5571 1.2472 0.2335 Manufacturing Steel 4.4322 3.7856 4.3600 5.3271 6.1578 5.2726 6.4820 Money Banks 6.5329 6.1530 6.1435 6.6598 6.7497 7.9405 10.1990 Money Fin 4.4794 3.9067 4.2282 4.8100 5.5739 5.1944 6.3825 Money Insur 3.8657 3.7464 3.8372 3.8679 4.0888 4.6434 5.1487 Money RlEst 2.2449 2.0399 2.1946 1.8982 2.0844 2.7702 5.0913

106 Wavelet Theory and Its Applications

Bruce D. McNevin<sup>1</sup> and Joan Nix2 \*


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108 Wavelet Theory and Its Applications

abstract 891567

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**Chapter 6**

Provisional chapter

**A Comparative Performance of Discrete Wavelet**

DOI: 10.5772/intechopen.76522

A Comparative Performance of Discrete Wavelet

**Transform Implementations Using Multiplierless**

Using discrete wavelet transform (DWT) in high-speed signal-processing applications imposes a high degree of care to hardware resource availability, latency, and power consumption. In this chapter, the design aspects and performance of multiplierless DWT is analyzed. We presented the two key multiplierless approaches, namely the distributed arithmetic algorithm (DAA) and the residue number system (RNS). We aim to estimate the performance requirements and hardware resources for each approach, allowing for the selection of proper algorithm and implementation of multi-level DAA- and RNS-based DWT. The design has been implemented and synthesized in Xilinx Virtex 6 ML605, taking

Keywords: discrete wavelet transform (DWT), distributed arithmetic algorithm (DAA), field programmable gate array (FPGA), residue number system (RNS), multiplierless

The architecture of the embedded platform plays a significant role in ensuring that real-time systems meet the performance requirements. Moreover, software development suffers from increased implementation complexity and a lack of standard methodology for partitioning the implementation of signal-processing functionalities to heterogeneous hardware platforms. For instance, digital signal processor (DSP) is cheaper, consumes less power, and is easy to develop software applications, but it has a considerable latency and less throughput compared with field programmable gate arrays (FPGAs) [1]. For high-speed signal-processing (HSP) communication systems, such as cognitive radio (CR) [2, 3] and software-defined radio (SDR) [4], DSP may fail to capture and process the received data due to data loss. In addition, implementing

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

Transform Implementations Using Multiplierless

Husam Alzaq and Burak Berk Üstündağ

Husam Alzaq and Burak Berk Üstündağ

http://dx.doi.org/10.5772/intechopen.76522

Abstract

implementation

1. Introduction

Additional information is available at the end of the chapter

advantage of Virtex 6's embedded block RAMs (BRAMs).

Additional information is available at the end of the chapter

#### **A Comparative Performance of Discrete Wavelet Transform Implementations Using Multiplierless** A Comparative Performance of Discrete Wavelet Transform Implementations Using Multiplierless

DOI: 10.5772/intechopen.76522

Husam Alzaq and Burak Berk Üstündağ Husam Alzaq and Burak Berk Üstündağ

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.76522

#### Abstract

Using discrete wavelet transform (DWT) in high-speed signal-processing applications imposes a high degree of care to hardware resource availability, latency, and power consumption. In this chapter, the design aspects and performance of multiplierless DWT is analyzed. We presented the two key multiplierless approaches, namely the distributed arithmetic algorithm (DAA) and the residue number system (RNS). We aim to estimate the performance requirements and hardware resources for each approach, allowing for the selection of proper algorithm and implementation of multi-level DAA- and RNS-based DWT. The design has been implemented and synthesized in Xilinx Virtex 6 ML605, taking advantage of Virtex 6's embedded block RAMs (BRAMs).

Keywords: discrete wavelet transform (DWT), distributed arithmetic algorithm (DAA), field programmable gate array (FPGA), residue number system (RNS), multiplierless implementation
