A Light Weight Neural Network for Automatic Modulation Classification in OFDM Systems

Automatic Modulation Classification (AMC) is a vital component in the development of intelligent and adaptive transceivers for future wireless communication systems. Existing statistically-based blind modulation classification methods for Orthogonal Frequency Division Multiplexing (OFDM) often fail to achieve the required accuracy and performance. Consequently, the modulation classification research community has shifted its focus toward deep learning techniques, which demonstrate promising performance, but come with increased computational complexity. In this paper, we propose a lightweight subcarrier-based modulation classification method for OFDM systems. In the proposed approach, a selected set of subcarriers in an OFDM frame is classified first, followed by the prediction of the modulation types for the remaining subcarriers based on the initial results. A Lightweight Neural Network (LWNN) is employed to identify the initially selected set of subcarriers, and its output is fed into a Recurrent Neural Network (RNN) as an embedded vector to predict the modulation schemes of the remaining subcarriers in the OFDM frame.

Automatic Wire-Harness Color Sequence Detector

Wire harness inspection process remains a labor-intensive process prone to errors in the modern Electronics Manufacturing Services (EMS) industry. This paper introduces a semiautomated machine vision system capable of verifying correct wire positioning, correctness of the connector polarity and correctness of color sequences for both linear and circular wire harness configurations. Five industrial standard CMOS cameras are integrated into a modularized mechanical framework in the physical structure of the solution and a HSV and RGB color domain value comparison based color sequence classifier is used in the operation. For each harness batch, a user can train the system using at least five reference samples; the trained file is stored and reused for similar harness types. The Solution is deployed at GPV Lanka Pvt. Ltd. (Fig. 2) and the system achieved 100% detection accuracy and reduced inspection time by 44% compared to manual methods. Additional features include user management, adjustable lighting, session data storage, and secure login. Results of this product usage in the real world situation demonstrate that this approach delivers reliable and efficient inspection capabilities.