This paper presents the first comprehensive review on vigilance enhancement using both conventional and unconventional means, and further discusses the resulting contradictory findings. It highlights the key differences observed between the research findings and argues that variations of the experimental protocol could be a significant contributing factor towards such contradictory results. Furthermore, the paper reveals the effectiveness of unconventional means of enhancement in significant reduction of vigilance decrement compared to conventional means. Meanwhile, a discussion on the challenges of enhancement techniques is presented, with several suggested recommendations and alternative strategies to maintain an adequate level of vigilance for the task at hand. Additionally, this review provides evidence in support of the use of unconventional means of enhancement on vigilance studies, regardless of their practical challenges.

The use of radar technology in the field of human activity recognition (HAR) has garnered considerable interest due to its notable benefits in terms of accuracy, resilience, and safeguarding of privacy. As the back-scattered radar returns are composed of multiple components & non-stationary in behavior, the time-frequency analysis like Wigner-Ville distribution (WVD) can play a significant role, but the presence of cross-terms limits the suitability of WVD. This study introduces a time segmentation and frequency windowing-based novel strategy for cross-terms free WVD of multi-component non-stationary signals. The effectiveness of the proposed method is validated on synthetic signals which outperforms the existing related methods for different performance measures. The proposed TFR is also applied for HAR from the received signals of frequency-modulated continuous-wave (FMCW) radar. A novel analysis of extracting slow-time signatures from the received signal captured by FMCW radar is developed and the proposed technique is utilized to generate cross-term free TFRs from these signatures for each activity. Threshold-based time windowing followed by 2-D segmented features extraction is performed on the TFRs for activity recognition system development. The optimization of ensemble classifier yields an impressive classification accuracy rate of 99.51%, surpassing the performances achieved by previous methodologies on the same dataset.

In this paper, we present a method to quantify the coupling between brain regions under vigilance and enhanced mental states by utilizing partial directed coherence (PDC) and graph theory analysis (GTA). The vigilance state is induced using a modified version of stroop color-word task (SCWT) while the enhancement state is based on audio stimulation with a pure tone of 250 Hz. The audio stimulation was presented to the right and left ears simultaneously for one-hour while participants perform the SCWT. The quantification of mental states was performed by means of statistical analysis of indexes based on GTA, behavioral responses of time-on-task (TOT), and Brunel Mood Scale (BRMUS). The results show that PDC is very sensitive to vigilance decrement and shows that the brain connectivity network is significantly reduced with increasing TOT, p < 0.05. Meanwhile, during the enhanced state, the connectivity network maintains high connectivity as time passes and shows significant improvements compared to vigilance state. The audio stimulation enhances the connectivity network over the frontal and parietal regions and the right hemisphere. The increase in the connectivity network correlates with individual differences in the magnitude of the vigilance enhancement assessed by response time to stimuli. Our results provide evidence for enhancement of cognitive processing efficiency with audio stimulation. The BRMUS was used to evaluate the emotional states of vigilance task before and after using the audio stimulation. BRMUS factors, such as fatigue, depression, and anger, significantly decrease in the enhancement group compared to vigilance group. On the other hand, happy and calmness factors increased with audio stimulation, p < 0.05.

Unlike a conventional phased array that offers directivity only in the angle domain, a frequency diverse array (FDA) is capable of producing a beampattern that is dependent on both angle and range. In this letter, a transmit beamforming method for an FDA is proposed that uses logarithmic frequency increments that have a nonuniform spacing. Compared to existing methods, the proposed architecture offers an improved range-angle localization with reduced sidelobe values. Simulations results for single and multiple target beampatterns are shown in order to assess the efficacy of the proposed architecture.

Our experiments explored the effect of visual stimuli degradation on cognitive workload. We investigated the subjective assessment, event-related potentials (ERPs) as well as electroencephalogram (EEG) as measures of cognitive workload. These experiments confirm that degradation of visual stimuli increases cognitive workload as assessed by subjective NASA task load index and confirmed by the observed P300 amplitude attenuation. Furthermore, the single-trial multi-level classification using features extracted from ERPs and EEG is found to be promising. Specifically, the adopted single-trial oscillatory EEG/ERP detection method achieved an average accuracy of 85% for discriminating 4 workload levels. Additionally, we found from the spatial patterns obtained from EEG signals that the frontal parts carry information that can be used for differentiating workload levels. Our results show that visual stimuli can modulate cognitive workload, and the modulation can be measured by the single trial EEG/ERP detection method.

This paper studies localizing sources of electromagnetic energy using a passive sensor array whose manifold is only nominally known. The problem of source localization is studied in the context of an airborne array that is able to observe a ground-based source from multiple angles. External and self-calibration algorithms are developed as a means to obtain accurate source localization estimates when the sensor manifold is perturbed. External calibration establishes the expected difference between the actual and modeled array manifold using signals at known locations. Self-calibration assumes that this expected difference is known only approximately and relies on signals of opportunity in the environment to provide updates. Several novel calibration algorithms are proposed, and their performance is tested on experimental data. The results indicate that significant performance gains are achieved with the use of the proposed calibration algorithms