首页|Study Data from Oral Roberts University Provide New Insights into Machine Learni ng (Comparison of Denoising Methods in Improving V2V/V2X Communication)
Study Data from Oral Roberts University Provide New Insights into Machine Learni ng (Comparison of Denoising Methods in Improving V2V/V2X Communication)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news originating from Oral Roberts Univers ity by NewsRx editors, the research stated, “Vehicle-to-vehicle (V2V/V2X) commun ication is essential to our current transportation systems; it enables vehicles to exchange crucial data for better efficiency and safety.” The news reporters obtained a quote from the research from Oral Roberts Universi ty: “However, communication channels in these networks are susceptible to differ ent forms of interference and noise, which causes a deterioration in signal qual ity and communication reliability. This paper compares different signal denoisin g techniques for V2V communication channels, focusing on four prominent methods: Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT), machine learnin g, and deep residual networks. We evaluate the denoising performance of each met hod using simulated signals corrupted by different noises and interference. Our experimental results demonstrate the effectiveness of each approach in mitigatin g noise and possibly improving communication reliability. Specifically, we obser ve that FFT and DWT offer efficient frequency and time-frequency domain represen tations for denoising signals. Traditional machine learning methods and residual networks (ResNets) demonstrate superior denoising performance.”
Oral Roberts UniversityCyborgsEmergi ng TechnologiesMachine Learning
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