Research and Implementation of High-Speed Data Streams Symbol Synchronization Algorithm Using Training Sequence in IMDD-OOFDM System

We propose a symbol synchronization algorithm for high-speed data streams in IMDD-OOFDM system using a training sequence. Sampling point phase offset approximately sustains within ±π/32 and symbol synchronization deviation stabilizes within ±0.5 sampling point in a real-time system of 1.5Gsa/s.

Blind Adaptive Equalization for Aerospace Communication Channels

In some communication systems, it is desirable for the receiver to synchronize to the received signal and to adjust the equalizer without having knowledge of a training sequence. Blind equalization uses the initial adjustment of the coefficients without making use of a training sequence. Different adaptive blind equalization algorithms have been developed over the past four decades. In this paper, we investigate the effect of blind equalization on space communication channels. The space channel under investigation is considered to be a multipath frequency selective channel having four paths. The channel is subjected to the phenomenon of InterSymbol Interference (ISI) which severely degrades the performance of the space communication system. Two blind algorithms are used in equalizer adjustment. The impulse responses of the space channel, the blind equalizer and the combination of channel and equalizer for QPSK and 16-QAM transmission are shown. The scatter diagrams for the transmitted sequence, received sequence, and the output of the equalizer using two of the blind algorithms are shown.

Low-Complexity Robust Adaptive Beamforming Based on INCM Reconstruction via Subspace Projection

Adaptive beamforming is sensitive to steering vector (SV) and covariance matrix mismatches, especially when the signal of interest (SOI) component exists in the training sequence. In this paper, we present a low-complexity robust adaptive beamforming (RAB) method based on an interference–noise covariance matrix (INCM) reconstruction and SOI SV estimation. First, the proposed method employs the minimum mean square error criterion to construct the blocking matrix. Then, the projection matrix is obtained by projecting the blocking matrix onto the signal subspace of the sample covariance matrix (SCM). The INCM is reconstructed by replacing part of the eigenvector columns of the SCM with the corresponding eigenvectors of the projection matrix. On the other hand, the SOI SV is estimated via the iterative mismatch approximation method. The proposed method only needs to know the priori-knowledge of the array geometry and angular region where the SOI is located. The simulation results showed that the proposed method can deal with multiple types of mismatches, while taking into account both low complexity and high robustness.

Constructing benchmark test sets for biological sequence analysis using independent set algorithms

Statistical inference and machine learning methods are benchmarked on test data independent of the data used to train the method. Biological sequence families are highly non-independent because they are related by evolution, so the strategy for splitting data into separate training and test sets is a nontrivial choice in bench marking sequence analysis methods. A random split is insufficient because it will yield test sequences that are closely related or even identical to training sequences. Adapting ideas from independent set graph algorithms, we describe two new meth- ods for splitting sequence data into dissimilar training and test sets. These algo rithms input a sequence family and produce a split in which each test sequence is less than p % identical to any individual training sequence. These algorithms successfully split more families than a previous approach, enabling construction of more diverse benchmark datasets.

A Simple Approach to Relating the Optimal Learning and the Meaningful Learning Experience in Students Age 14–16

Using a questionnaire applied in real time to students in stages 14–16 during a distance class, the authors appraise whether they experience feelings that lead to a central experience of flow, according to the flow theory of positive psychology. Students are exposed to a planned session that considers the moments of the training sequence and consciously integrates technological tools to support learning. A formal evaluation system, which includes formative and summative evaluations, determines if students build meaningful learning. This research contributes to understanding that an optimal learning experience characterized by the pedagogical principles of curiosity, concentration, challenge, and enjoyment, favor the construction of meaningful learning. Furthermore, the simplicity of the proposed experimental design suggests a direct way to replicate the study in later learning stages and assess the efficiency of new technology-based pedagogies within the distance education paradigm imposed by the 2020 pandemic crisis.

Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen

Species harbor extensive structural variation underpinning recent adaptive evolution. However, the causality between genomic features and the induction of new rearrangements is poorly established. Here, we analyze a global set of telomere-to-telomere genome assemblies of a fungal pathogen of wheat to establish a nucleotide-level map of structural variation. We show that the recent emergence of pesticide resistance has been disproportionally driven by rearrangements. We use machine learning to train a model on structural variation events based on 30 chromosomal sequence features. We show that base composition and gene density are the major determinants of structural variation. Retrotransposons explain most inversion, indel and duplication events. We apply our model to Arabidopsis thaliana and show that our approach extends to more complex genomes. Finally, we analyze complete genomes of haploid offspring in a four-generation pedigree. Meiotic crossover locations are enriched for new rearrangements consistent with crossovers being mutational hotspots. The model trained on species-wide structural variation accurately predicts the position of >74% of newly generated variants along the pedigree. The predictive power highlights causality between specific sequence features and the induction of chromosomal rearrangements. Our work demonstrates that training sequence-derived models can accurately identify regions of intrinsic DNA instability in eukaryotic genomes.