Through the Internet, learners can access available information on e-learning platforms to facilitate their studies or to acquire new skills. However, finding the right information for their specific needs among the numerous available choices is a tedious task due to information overload. Recommender systems are a good solution to personalize e-learning by proposing useful and relevant information adapted to each learner using a set of techniques and algorithms. Collaborative filtering (CF) is one of the techniques widely used in such systems. However, the high dimensions and sparsity of the data are major problems. Since the concept of deep learning has grown in popularity, various studies have emerged to improve this form of filtering. In this work, we used an autoencoder, which is a powerful model in data dimension reduction, feature extraction and data reconstruction, to learn and predict student preferences in an e-learning recommendation system based on collaborative filtering. Experimental results obtained using the database created by Kulkarni et al. show that this model is more accurate and outperforms models based on K-nearest neighbor (KNN), singular value decomposition (SVD), singular value decomposition plus plus (SVD++) and non-negative matrix factorization (NMF) in terms of the root-mean-square error (RMSE) and mean absolute error (MAE).

This novel research introduces a game-changing architecture design for Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) decoders in Fifth-Generation New-Radio (5G-NR) wireless communications, specifically designed to meet precise specifications and leveraging the layered Min-Sum (MS) algorithm. Our innovative approach presents a fully parallel architecture that is precisely engineered to cater to the demanding high-throughput requirements of enhanced Mobile Broadband (eMBB) applications. To ensure smooth computation in the MS algorithm, we use the Sub-Optimal Low-Latency (SOLL) technique to optimize the critical check node process. Thus, our design has the potential to greatly benefit certain Ultra-Reliable Low-Latency Communications (URLLC) scenarios. We conducted precise Bit Error Rate (BER) performance analysis on our LDPC decoder using a Hardware Description Language (HDL) Co-Simulation (MATLAB/Simulink/ModelSim) for two codeword rates (2/3 and 1/3), simulating the challenging Additive White Gaussian Noise (AWGN) channel environment.

The epitaxy of III-V semiconductors on silicon substrates remains challenging because of lattice parameter and material polarity differences. In this work, we report on the Metal Organic Chemical Vapor Deposition (MOCVD) and characterization of InAs/GaAs Quantum Dots (QDs) epitaxially grown on quasi-nominal 300 mm Ge/Si(001) and GaAs(001) substrates. QD properties were studied by Atomic Force Microscopy (AFM) and Photoluminescence (PL) spectroscopy. A wafer level µPL mapping of the entire 300 mm Ge/Si substrate shows the homogeneity of the three-stacked InAs QDs emitting at 1.30 ± 0.04 µm at room temperature. The correlation between PL spectroscopy and numerical modeling revealed, in accordance with transmission electron microscopy images, that buried QDs had a truncated pyramidal shape with base sides and heights around 29 and 4 nm, respectively. InAs QDs on Ge/Si substrate had the same shape as QDs on GaAs substrates, with a slightly increased size and reduced luminescence intensity. Our results suggest that 1.3 μm emitting InAs QDs quantum dots can be successfully grown on CMOS compatible Ge/Si substrates.

In this study, we propose a “New Reliability Ratio Weighted Bit Flipping” (NRRWBF) algorithm for Low‐Density Parity‐Check (LDPC) codes. This algorithm improves the “Reliability Ratio Weighted Bit Flipping” (RRWBF) algorithm by modifying the reliability ratio. It surpasses the RRWBF in performance, reaching a 0.6 dB coding gain at a Binary Error Rate (BER) of 10⁻⁴ over the Additive White Gaussian Noise (AWGN) channel, and presents a significant reduction in the decoding complexity. Furthermore, we improved NRRWBF using the sum of the syndromes as a criterion to avoid the infinite loop. This will enable the decoder to attain a more efficient and effective decoding performance.