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This paper presents an approach to exploit the superimposed training (ST)-based primary users’ (PUs) transmissions in the context of spectrum sensing for cognitive radio. In the low signal-to-noise ratio (SNR), the proposed scheme splits the spectrum sensing phase into two sample processing periods, allowing a secondary user (SU) to carry out a training sequence synchronization (with a small probability of error) before the implementation of a robust spectrum sensing algorithm that enhances the detection, based on the deterministic signal components embedded in the ST PU’s signals along with the unknown data signal. The overall sensing performance is improved using a reasonable number of samples to achieve a high probability of detection, resulting in a reduced spectrum sensing duration. Furthermore, a low computational complexity version of the proposed ST combined approach for a reduced phase (SCAR-Phase) of spectrum sensing is presented, which attains the same detection performance with a smaller number of real operations in the low SNR. In the practical consideration of imperfect training sequence synchronizations, the results show the advantages of exploiting the ST sequence to perform spectrum sensing, thus quantifying the significant improvement in detection performance and the maximum SU’s achievable throughput.

Over the past decade, noninvasive brain-computer interfaces (BCIs) leveraging electroencephalography(EEG) to decode gait intention have matured from proof-of-concept studies to nearclinicalimplementations. We systematically reviewed 55 studies (January 2015-April 2024) usingPRISMA guidelines, focusing on neurophysiological markers (MRCPs, ERD/ERS, high-γ), signalprocessing pipelines (artifact suppression, time-frequency transforms), machine-learning classifiers(CSP-SVM, ERD SVM), and deep-learning frameworks (spatio-spectral CNNs, LSTM RNNs). Acrossstudies, median classification accuracy rose from 75% (2015-2018) to 87% (2021-2024), while detectionlatency fell below 200ms. Innovations include enhancing intention detection with emotionevokingmusic stimuli (up to early ERD and improved accuracy), decoding pediatric gait kinematicswith state-space models (r = 0.71 hip, 0.59 knee), and session-to-session transfer learning withoutrecalibration (≤4% performance drop). Challenges remain in artifact mitigation, small sample sizes,and limited multi-centre trials. We propose open, standardized datasets, transfer-learning pipelines,and larger clinical validations to accelerate translation.

The spectrum sensing function allows a cognitive radio to determine the absence/presence of primary users' (PUs) signals in a frequency band of interest. These signals might exhibit very low-power at cognitive (or secondary) users' receivers. Thus requiring detection algorithms that work well in the very low signal-to-noise ratio (SNR) region. It is known that secondary users (SUs) can improve its detection performance if some known information about PUs' signals is available. In this regard, some PUs can use superimposed training (ST) technique for channel estimation and synchronization purposes in their own networks. However, to the authors knowledge, the exploitation of this ST information by SUs in the context of cognitive radio has not been studied yet. In this paper, a new spectrum sensing algorithm for superimposed trained PUs' signals is designed based on the Neyman-Pearson criterion. The proposed algorithm takes advantage of the ST sequence to improve the detection performance of SUs. Results show that, even with a small training-to-information power ratio, the superimposed training-based detector (STD) significantly outperforms the energy detector; specifically in the very low SNR, which is of interest in cognitive radio. Moreover, if ST is not used, the proposed STD reduces to the energy detector.

Superimposed training (ST) technique can be used at primary users' transmitters to improve parameter estimation tasks (e.g. channel estimation) at primary users' receivers. Since ST adds the training sequence to the data sequence the total available bandwidth is used for data transmission. The exploitation of the ST sequence in the context of cognitive radio networks leads to a significant increase in the detection performance of secondary users operating in the very low signal-to-noise ratio region. Hence, a considerably smaller number of samples are required for sensing. In this paper, the performance of ST-based spectrum sensing in a cooperative centralized cognitive radio network with soft-decision fusion is studied. Furthermore, a throughput analysis is carried out to quantify the benefits of using ST in the context of cognitive radio for both primary and secondary users.

This paper presents an automated system for monitoring production lines, managing key indicators like production rate, rejection rate, and downtime. Integrated with a Manufacturing Execution System (MES), it collects essential data such as part numbers and production figures, enabling periodic queries and minute-by-minute chart updates. At the end of each shift, it generates automatic PDF reports. Built using a distributed database architecture with MS SQL Server, the system integrates production data, downtime, and rejection rates. Users manage settings and access through ASP.NET MVC, with ASP.NET Identity for authentication and authorization. Scalable and efficient, it supports adding new production lines without performance issues and has been implemented across multiple plants. Statistical tests revealed significant improvements in production (-1923.80), rejection (-210.87), and downtime (-2.87), with p-values < 0.001. Future enhancements include early detection rules for process failures, enabling preventive alerts to further improve quality and efficiency.