Pengurangan Noise Pada RTL-SDR Menggunakan Least Mean Square Dan Recursive Least Square

Noise reduction is an important process in a communication system, one of which is radio communication. In the process of broadcasting radio Frequency Modulation (FM) often encountered noise so that listeners find it difficult to understand the information provided. In the past, noise reduction used traditional filters that were only able to filter certain frequencies. However, for future technologies an adaptive filter is needed that can dynamically reduce noise effectively. Register Level-Software Defined Radio (RTL-SDR) can capture signals with a very wide frequency range but has a less clear sound quality. So it needs to be done noise reduction. In this study, two methods are used, namely Least Mean Square (LMS) and Recursive Least Square (RLS). The data used five radio stations in Malang. The results showed that the LMS algorithm is stable but has a slow convergence speed, whereas the RLS algorithm has poor stability but has a high convergence speed. From the test, it can be concluded that the performance of RLS is better than LMS for noise reduction in RTL-SDR. The best performance is the reduction of White Noise using RLS on the Oryza radio station with an Normalized Weight Differences (NWD) value of -13.93 dB.Pengurangan noise merupakan proses penting dalam suatu sistem komunikasi, salah satunya pada komunikasi radio. Pada proses broadcasting radio Frequency Modulation (FM) sering dijumpai noise sehingga pendengar sulit memahami informasi yang diberikan. Dulu, pengurangan noise menggunakan filter tradisional yang hanya mampu memfilter frekuensi tertentu. Namun, untuk teknologi yang akan datang diperlukan penggunaan filter adaptif yang secara dinamis dapat efektif mengurangi noise. Register Transfer Level-Software Defined Radio (RTL-SDR) bisa menangkap sinyal dengan range frekuensi yang sangat luas namun mempunyai kualitas suara yang kurang jernih. Sehingga perlu dilakukan pengurangan  noise. Pada penelitian ini digunakan dua metode yaitu Least Mean Square (LMS) dan Recursive Least Square (RLS). Data yang digunakan dalam penelitian adalah lima stasiun radio yang ada di Malang. Hasil penelitian menunjukkan bahwa algoritma LMS ini stabil namun memiliki kecepatan konvergensi yang lambat, sedangkan pada algoritma RLS memiliki kestabilan yang kurang baik namun memiliki kecepatan konvergensi yang tinggi. Dari pengujian dapat disimpulkan bahwa kinerja RLS lebih baik daripada LMS untuk pengurangan noise pada RTL-SDR. Kinerja terbaik yaitu pengurangan White Noise menggunakan RLS pada stasiun radio Oryza dengan nilai Normalized Weight Differences (NWD) -13.93 dB

Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research

A common problem in neural recordings is the low signal-to-noise ratio (SNR), particularly when using non-invasive techniques like magneto- or electroencephalography (M/EEG). To address this problem, experimental designs often include repeated trials, which are then averaged to improve the SNR or to infer statistics that can be used in the design of a denoising spatial filter. However, collecting enough repeated trials is often impractical and even impossible in some paradigms, while analyses on existing data sets may be hampered when these do not contain such repeated trials. Therefore, we present a data-driven method that takes advantage of the knowledge of the presented stimulus, to achieve a joint noise reduction and dimensionality reduction without the need for repeated trials. The method first estimates the stimulus-driven neural response using the given stimulus, which is then used to find a set of spatial filters that maximize the SNR based on a generalized eigenvalue decomposition. As the method is fully data-driven, the dimensionality reduction enables researchers to perform their analyses without having to rely on their knowledge of brain regions of interest, which increases accuracy and reduces the human factor in the results. In the context of neural tracking of a speech stimulus using EEG, our method resulted in more accurate short-term temporal response function (TRF) estimates, higher correlations between predicted and actual neural responses, and higher attention decoding accuracies compared to existing TRF-based decoding methods. We also provide an extensive discussion on the central role played by the generalized eigenvalue decomposition in various denoising methods in the literature, and address the conceptual similarities and differences with our proposed method.status: publishe