Abstract :

Modern Signal Processing Systems Demand Highly Efficient Forward Wavelet Packet Transform (FWPT) Architectures To Support Real-time Multimedia, Biomedical, Wireless Communication, And Embedded IoT Applications. Existing FWPT Architectures Utilize Dedicated Processing Elements (PEs) And Fixed Scheduling Mechanisms To Perform Recursive Packet Decomposition. Although These Architectures Provide Parallel Processing Capabilities, They Suffer From Excessive Hardware Resource Utilization, Increased Interconnection Complexity, Higher Power Consumption, Limited Scalability, And Inefficient Utilization Of Processing Elements. Furthermore, Fixed Lifting Parameters Restrict Adaptability To Varying Signal Characteristics, Thereby Affecting Transform Accuracy And Reconstruction Quality. To Overcome These Limitations, This Paper Proposes An Adaptive Time-Multiplexed Systolic Wavelet Packet Transform (ATMS-WPT) Architecture That Integrates A Proposed Splitting Unit, TimeMultiplexed Processing Unit, And Proposed Lifting Unit Within A Parameterized Systolic Framework. The Proposed Splitting Unit Efficiently Separates Incoming Signals Into Low-pass And High-pass Components Through Adaptive Alignment Mechanisms, While The Timemultiplexed Processing Unit Dynamically Reuses Computational Resources Across Multiple Packet Nodes Using Adaptive Scheduling. Additionally, The Proposed Lifting Unit Incorporates Coefficient Tuning And Feedback-based Coefficient Update Mechanisms To Optimize Transform Parameters According To Signal Conditions While Preserving Perfect Reconstruction. The Regular Systolic Dataflow Ensures Deterministic Latency, Reduced Hardware Complexity, Lower Power Consumption, Improved Scalability, And Enhanced Transform Quality, Making The Proposed ATMS-WPT Architecture A Highly Efficient Solution For Next-generation VLSI Signal Processing Systems.

Published:

01-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1-19

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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