Critical path analysis and low-complexity implementation of the lms adaptive algorithm

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Engineering

Logic Mind Technologies
Vijayangar (Near Maruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Critical-Path Analysis and Low-Complexity
Implementation of the LMS Adaptive
Algorithm
Abstract—This paper presents a precise analysis of the critical path of the least-
mean-square (LMS) adaptive filter for deriving its architectures for high-speed and
low-complexity implementation. It is shown that the direct-form LMS adaptive
filter has nearly the same critical path as its transpose-form counterpart, but
provides much faster convergence and lower register complexity. From the critical-
path evaluation, it is further shown that no pipelining is required for implementing
a direct-form LMS adaptive filter for most practical cases, and can be realized with
a very small adaptation delay in cases where a very high sampling rate is required.
Based on these findings, this paper proposes three structures of the LMS adaptive
filter: (i) Design 1 having no adaptation delays, (ii) Design 2 with only one
adaptation delay, and (iii) Design 3 with two adaptation delays. Design 1 involves
the minimum area and the minimum energy per sample (EPS). The best of existing
direct- form structures requires 80.4% more area and 41.9% more EPS compared
to Design 1. Designs 2 and 3 involve slightly more EPS than the Design 1 but offer
nearly twice and thrice the MUF at a cost of 55.0% and 60.6% more area,
respectively.

SOFTWARE REQUIREMENT:
 ModelSim6.4c.
 Xilinx 9.1/13.2.
HARDWARE REQUIREMENT:
 FPGA Spartan 3.
PROJECT FLOW:
First Review:
Literature Survey
Paper Explanation
Design of Project
Project Enhancement explanation
Second Review:
Implementing 40% of Base Paper
Third Review
Implementing Remaining 60% of Base Paper with Future Enhancement (Modification)
For More Details please contact
Logic Mind Technologies
Vijayangar (NearMaruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Mail: logicmindtech@gmail.com

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About Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol. • Remote control: Parallel or serial interface. • Compatible with MAFI CCR system. • Compatible with IDM8000 CCR. • Compatible with Backplane mount serial communication. • Compatible with commercial and Defence aviation CCR system. • Remote control system for accessing CCR and allied system over serial or TCP. • Indigenized local Support/presence in India. • Easy in configuration using DIP switches. Technical Specifications Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol. Key Features Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol. • Remote control: Parallel or serial interface • Compatible with MAFI CCR system • Copatiable with IDM8000 CCR • Compatible with Backplane mount serial communication. • Compatible with commercial and Defence aviation CCR system. • Remote control system for accessing CCR and allied system over serial or TCP. • Indigenized local Support/presence in India. Application • Remote control: Parallel or serial interface. • Compatible with MAFI CCR system. • Compatible with IDM8000 CCR. • Compatible with Backplane mount serial communication. • Compatible with commercial and Defence aviation CCR system. • Remote control system for accessing CCR and allied system over serial or TCP. • Indigenized local Support/presence in India. • Easy in configuration using DIP switches.

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Standard Reomte Control Interface - Neometrix

Critical path analysis and low-complexity implementation of the lms adaptive algorithm

1. Logic Mind Technologies Vijayangar (Near Maruthi Medicals), Bangalore-40 Ph: 8123668124 // 8123668066 Critical-Path Analysis and Low-Complexity Implementation of the LMS Adaptive Algorithm Abstract—This paper presents a precise analysis of the critical path of the least- mean-square (LMS) adaptive filter for deriving its architectures for high-speed and low-complexity implementation. It is shown that the direct-form LMS adaptive filter has nearly the same critical path as its transpose-form counterpart, but provides much faster convergence and lower register complexity. From the critical- path evaluation, it is further shown that no pipelining is required for implementing a direct-form LMS adaptive filter for most practical cases, and can be realized with a very small adaptation delay in cases where a very high sampling rate is required. Based on these findings, this paper proposes three structures of the LMS adaptive filter: (i) Design 1 having no adaptation delays, (ii) Design 2 with only one adaptation delay, and (iii) Design 3 with two adaptation delays. Design 1 involves the minimum area and the minimum energy per sample (EPS). The best of existing direct- form structures requires 80.4% more area and 41.9% more EPS compared to Design 1. Designs 2 and 3 involve slightly more EPS than the Design 1 but offer nearly twice and thrice the MUF at a cost of 55.0% and 60.6% more area, respectively.

2. SOFTWARE REQUIREMENT:  ModelSim6.4c.  Xilinx 9.1/13.2. HARDWARE REQUIREMENT:  FPGA Spartan 3. PROJECT FLOW: First Review: Literature Survey Paper Explanation Design of Project Project Enhancement explanation Second Review: Implementing 40% of Base Paper Third Review Implementing Remaining 60% of Base Paper with Future Enhancement (Modification) For More Details please contact Logic Mind Technologies Vijayangar (NearMaruthi Medicals), Bangalore-40 Ph: 8123668124 // 8123668066 Mail: logicmindtech@gmail.com

Critical path analysis and low-complexity implementation of the lms adaptive algorithm

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