Smooth Signal Activity Detection in White Gaussian Noise: Application to P300 Detection

<div>Abstract—In this research, we have proposed a new scheme to detect and extract the activity of an unknown smooth template in presence of white Gaussian noise with unknown variance. In this regard, the problem is considered a binary hypothesis test, and it is solved employing the generalized likelihood ratio (GLR) method. GLR test uses the maximum likelihood (ML) estimation of unknown parameters under each hypothesis. The ML estimation of the desired signal yields an optimization problem with smoothness constraint which is in the form of a conventional least square error estimation problem and can be solved optimally. The proposed detection scheme is studied for P300 elicitation from the background electroencephalography signal. In addition, to assume the P300 smoothness, two prior knowledge are considered in terms of positivity and approximate occurrence time of P300. The performance of the method is assessed on both real and synthetic datasets in different noise levels and compared to a conventional signal detection scheme without considering smoothness priors, as well as state-of-theart linear and quadratic discriminant analysis. The results are illustrated in terms of detection probability, false alarm rate, and accuracy. The proposed method outperforms the counterparts in low signal-to-noise ratio situations.</div>

Smooth Signal Activity Detection in White Gaussian Noise: Application to P300 Detection

<div>Abstract—In this research, we have proposed a new scheme to detect and extract the activity of an unknown smooth template in presence of white Gaussian noise with unknown variance. In this regard, the problem is considered a binary hypothesis test, and it is solved employing the generalized likelihood ratio (GLR) method. GLR test uses the maximum likelihood (ML) estimation of unknown parameters under each hypothesis. The ML estimation of the desired signal yields an optimization problem with smoothness constraint which is in the form of a conventional least square error estimation problem and can be solved optimally. The proposed detection scheme is studied for P300 elicitation from the background electroencephalography signal. In addition, to assume the P300 smoothness, two prior knowledge are considered in terms of positivity and approximate occurrence time of P300. The performance of the method is assessed on both real and synthetic datasets in different noise levels and compared to a conventional signal detection scheme without considering smoothness priors, as well as state-of-theart linear and quadratic discriminant analysis. The results are illustrated in terms of detection probability, false alarm rate, and accuracy. The proposed method outperforms the counterparts in low signal-to-noise ratio situations.</div>

Perancangan Model Simulator dan Performansi Kesalahan Bit untuk Variasi Tipe Modulasi Digital Dalam Lingkungan Kanal Additive White Gaussian Noise (AWGN)

Penelitian ini dibuat berdasarkan permasalahan pada penelitian sebelumnya, dimana belum menampilkan gelombang sinyal sesudah melewati kanal Additive White Gaussian Noise (AWGN), serta menampilkan hasil simulasi perbandingan kesalahan bit yang ditampilkan dalam bentuk grafik kurva untuk berbagai tipe modulasi. Selain itu gelombang sinyal probabilitas kesalahan bit masih ditampilkan terpisah serta belum melakukan analisa antara perhitungan dengan hasil simulasi. Maka pada penelitian ini akan dilakukan pengembangan pada source code dan juga dibuatnya graphical user interface (GUI) Matlab beserta dengan perhitungan teoritisnya. Pada hasil simulasi jumlah bit minimal sebesar 100 bit didapatkan untuk modulasi amplitude Shift Keying (ASK), untuk modulasi frekuency shift keying (FSK) dan untuk binary phase shift keying (BPSK). Sedangkan pada jumlah bit maksimum bit sebesar 500 bit didapatkan untuk modulasi ASK, untuk modulasi FSK, dan untuk modulasi BPSK. Dari dua data tersebut menunjukan bahwa data simulasi sudah sesuai dengan teori yang ada, dimana semakin besar energi bit yang digunakan maka bit error rate (BER) yang dihasilkan akan semakin kecil. Kesesuaian simulasi dengan teori didukung juga oleh hasil perhitungan probabilitas kesalahan bit. Hasil simulasi dan teoritis probabilitas kesalahan bit memiliki karakteristik yang sama, dimana modulasi BPSK memiliki penurunan probabilias kesalahan bit yang lebih kecil dibandingkan modulasi ASK dan FSK. Selain itu didapatkan bahwa hasil perhitungan mengalami penurunan kesalahan bit yang lebih kecil serta ideal pada Eb/N0 yang sama untuk setiap percobaannya.

Nghiên cứu kỹ thuật điều chế sóng mang nghịch lưu một pha cascaded 5 bậc

Bài báo này nghiên cứu hiệu năng của kỹ thuật đa truy cập không trực giao (Non-Orthogonal Multiple Access – NOMA) trong thông tin vô tuyến. Đây là kỹ thuật được ứng dụng hiệu quả trong mạng 5G và hứa hẹn sẽ là ứng viên tiềm năng được sử dụng trong mạng 6G. Trong bài báo này, hiệu năng của NOMA được đánh giá qua giá trị tỉ lệ lỗi bit (Bit Error Rate – BER), tốc độ dữ liệu và xác suất dừng (Outage Probability – OP). Các giá trị này thu được thông qua mô phỏng hệ thống NOMA qua kênh truyền Rayleigh có chịu ảnh hưởng của nhiễu AWGN (Additive white Gaussian noise) ở cả hai trường hợp SIC (Successive Interference Cancellation) hoàn hảo và SIC không hoàn hảo. Nhìn chung, công suất phát càng tăng thì giá trị tốc độ dữ liệu sẽ càng cao đồng thời giá trị BER và OP sẽ được cải thiện đáng kể. Hơn nữa, kết quả phân tích cho thấy mô hình hệ thống NOMA cung cấp một nền tảng tốt phục vụ việc phát triển các kỹ thuật góp phần cải thiện chất lượng dịch vụ cho các hệ thống truyền thông dựa trên NOMA trong tương lai.

Medical Image Denoising Algorithm Based on Sparse Nonlocal Regularized Weighted Coding and Low Rank Constraint

Medical image information may be polluted by noise in the process of generation and transmission, which will seriously hinder the follow-up image processing and medical diagnosis. In medical images, there is a typical mixed noise composed of additive white Gaussian noise (AWGN) and impulse noise. In the conventional denoising methods, impulse noise is first removed, followed by the elimination of white Gaussian noise (WGN). However, it is difficult to separate the two kinds of noises completely in practical application. The existing denoising algorithm of weight coding based on sparse nonlocal regularization, which can simultaneously remove AWGN and impulse noise, is plagued by the problems of incomplete noise removal and serious loss of details. The denoising algorithm based on sparse representation and low rank constraint can preserve image details better. Thus, a medical image denoising algorithm based on sparse nonlocal regularization weighted coding and low rank constraint is proposed. The denoising effect of the proposed method and the original algorithm on computed tomography (CT) image and magnetic resonance (MR) image are compared. It is revealed that, under different σ and ρ values, the PSNR and FSIM values of CT and MRI images are evidently superior to those of traditional algorithms, suggesting that the algorithm proposed in this work has better denoising effects on medical images than traditional denoising algorithms.