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The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as “canonical” frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the “Connectivity-based Meta-Bands” (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the “canonical” frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity. •Canonical frequency bands disregard relevant subject-specific neural idiosyncrasies.•A novel frequency band segmentation approach is proposed, the CMB algorithm.•The CMB algorithm is based on the frequency-dependent topology of the functional connectivity.•The CMB algorithm provides an automatic user-specific band segmentation.•The CMB algorithm opens the way for novel, personalized, data-driven connectivity analyses.

Both the pre-supplementary motor area (preSMA) and the right inferior frontal gyrus (rIFG) are important for stopping action outright. These regions are also engaged when preparing to stop. We aimed to elucidate the roles of these regions by harnessing the high spatio-temporal resolution of electrocorticography (ECoG), and by using a task that engages both preparing to stop and stopping outright. First, we validated the task using fMRI in 16 healthy control participants to confirm that both the preSMA and the rIFG were active. Next, we studied a rare patient with intracranial grid coverage of both these regions, using macrostimulation, diffusion tractography, cortico-cortical evoked potentials (CCEPs) and task-based ECoG. Macrostimulation of the preSMA induced behavioral motor arrest. Diffusion tractography revealed a structural connection between the preSMA and rIFG. CCEP analysis showed that stimulation of the preSMA evoked strong local field potentials within 30ms in rIFG. During the task, when preparing to stop, there was increased high gamma amplitude (~70–250Hz) in both regions, with preSMA preceding rIFG by ~750ms. For outright stopping there was also a high gamma amplitude increase in both regions, again with preSMA preceding rIFG. Further, at the time of stopping, there was an increase in beta band activity (~16Hz) in both regions, with significantly stronger inter-regional coherence for successful vs. unsuccessful stop trials. The results complement earlier reports of a structural/functional action control network between the preSMA and rIFG. They go further by revealing between-region timing differences in the high gamma band when preparing to stop and stopping outright. They also reveal strong between-region coherence in the beta band when stopping is successful. Implications for theories of action control are discussed. ► We used fMRI in healthy adults and multiple methods in a single epilepsy patient. ► We studied the presupplementary motor area and right inferior frontal gyrus. ► The preSMA and rIFG are structurally and functionally connected. ► Gamma activity in preSMA occurs before rIFG in anticipation of action control. ► Beta-band coherence between preSMA and rIFG occurs during action control.

Accurate characterization of spatial multipath channels at millimeter wave bands has gained significant interest both in industry and academia. A channel measurement was conducted at three different frequency bands, i.e., 2−4, 14−16, and 28−30 GHz in a line-of-sight (LOS) and an obstructed-LOS (O-LOS) scenarios in an empty room environment. A vector network analyzer connected to a virtual uniform circular array and to a rotational directional horn antenna was used in the measurements, respectively. Angle-of-arrivals and delay-of-arrivals of the multipath components were obtained from the measurements for the three frequency bands. Room electromagnetic properties for the three different frequencies at different propagation scenarios were investigated as well.

Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC) to select salient features from the marginal spectrum of vibration signals by Hilbert–Huang Transform (HHT). In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS) into window spectrums, following which Rand Index (RI) criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs). Next, a hybrid REBs fault diagnosis is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines). The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU). The said test results evidence three major advantages of the novel method. First, the fault classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, fault classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500–800 and a m range of 50–300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR) = 5. Experimental results indicate that the proposed WMSC method yields a high REBs fault classification accuracy and a good performance in Gauss white noise reduction.

The sensorimotor cortex is a frequently targeted brain area for the development of Brain-Computer Interfaces (BCIs) for communication in people with severe paralysis and communication problems (locked-in syndrome; LIS). It is widely acknowledged that this area displays an increase in high-frequency band (HFB) power and a decrease in the power of the low frequency band (LFB) during movement of, for example, the hand. Upon termination of hand movement, activity in the LFB band typically shows a short increase (rebound). The ability to modulate the neural signal in the sensorimotor cortex by imagining or attempting to move is crucial for the implementation of sensorimotor BCI in people who are unable to execute movements. This may not always be self-evident, since the most common causes of LIS, amyotrophic lateral sclerosis (ALS) and brain stem stroke, are associated with significant damage to the brain, potentially affecting the generation of baseline neural activity in the sensorimotor cortex and the modulation thereof by imagined or attempted hand movement. In the Utrecht NeuroProsthesis (UNP) study, a participant with LIS caused by ALS and a participant with LIS due to brain stem stroke were implanted with a fully implantable BCI, including subdural electrocorticography (ECoG) electrodes over the sensorimotor area, with the purpose of achieving ECoG-BCI-based communication. We noted differences between these participants in the spectral power changes generated by attempted movement of the hand. To better understand the nature and origin of these differences, we compared the baseline spectral features and task-induced modulation of the neural signal of the LIS participants, with those of a group of able-bodied people with epilepsy who received a subchronic implant with ECoG electrodes for diagnostic purposes. Our data show that baseline LFB oscillatory components and changes generated in the LFB power of the sensorimotor cortex by (attempted) hand movement differ between participants, despite consistent HFB responses in this area. We conclude that the etiology of LIS may have significant effects on the LFB spectral components in the sensorimotor cortex, which is relevant for the development of communication-BCIs for this population.

Heart rate variability, with modern technology detects the differences between each electro cardiac signal of the heartbeat, and is being utilized across a broad number of fields in aviation and space medicine, such as simulation training, assessment of performance and improvement in safety and wellbeing and has potential in the prediction of incapacitation in Civil Aviation Medicine, from both physical and mental health problems, and monitoring of astronauts in space flight. HRV has seen increasing utility across aviation and aerospace medicine but also has potential as a marker of -chronological aging, stress, cognitive load, recovery and optimal performance. Data needs to be gathered on the population being studied at baseline, and under physiological stress and recovery in the long term and potentially in the occupational role. Aviation Medical examiners need to be skilled at critical analysis of HRV data in predicting incapacity particularly in the aging pilot where there may be many cofounders. HRV biofeedback may enhance aviation medical wellbeing interventions. Aviation Medical Examiners would benefit from understanding the current use and potential of Heart rate variability data.

An innovative antenna radiation method for exciting a dipole-type radiation mode in the ground plane of mobile devices is proposed. This method is based on a capacitively loaded slot placed at the centre of the ground plane. The ground plane is 50 × 15 mm in length and width, respectively, which is the size typically used in USB dongles and headsets. The −10 dB impedance bandwidth is 12.9% at 2.4 GHz, fully covering the WiMax, Wi-Fi and Bluetooth frequency bands.

Multi-modal medical image fusion has emerged as a famous and efficient tool in medical applications. The major goal of this fusion is to fuse diverse multi-modal medical images attained from varied imaging modalities into a single fused image, which is broadly utilized by surgeons for the precise diagnosis and treatment of diseases. Multi-modal medical images generally consist of captured images with the specific organ of a patient. These images will indicate a modality, which will provide the observed organ in a different way that leads to dissimilar examinations of a specific incident like stroke. The detection with more suitable clinical decisions is taken by the accurate analysis of each modality. Multi-modal medical imaging is an efficient research area that incorporates the improvement of robust techniques, which can facilitate the information of image fusion attained with diverse sets of modalities. The major goal of this paper is to develop the multi-modal medical image fusion model using the new hybrid meta-heuristic approach. At first, the high-frequency sub-bands and low frequency sub-bands of the images that to be fused split by the weighted fast discrete curvelet transform (W-FDCuT). Once the sub-bands are split, the high-frequency sub-bands of the two images are fused by the optimized Type-2 fuzzy entropy. On the other hand, Averaging approach is used for performing the fusion of low frequency sub-bands. Finally, the inverse W-FDCuT is done for generating the final fused image. To improvise the performance of W-FDCuT and Type-2 fuzzy entropy, the hybrid meta-heuristic algorithm named hybrid jaya with sun flower optimization (HJ-SFO) is adopted, thus enhances the significant parameters by maximizing the structural similarity index measure (SSIM). The comparison over the conventional image fusion models proves the efficiency of the proposed model in terms of diverse analysis.

It is known that the Aeronautical Radio Navigation Systems sharing the Global Navigation Satellite Systems (GNSS) frequency band represent a threat to the satellite-based navigation services. Distance Measuring Equipment (DME) and Tactical Air Navigation (TACAN) systems broadcast strong pulsed ranging signals within the Global Positioning System L5 and Galileo E5a frequency bands where the aviation positioning aids services are allocated. This study provides an experimental assessment of the DME/TACAN interference effect on the GNSS receivers performance in scenarios where the presence of several transmitters in view generates radio-frequency interference hard to mitigate by means of the classical solutions. In detail, analysis in terms of the receiver performance will be presented by showing the effect of the non-ideal pulse blanking on the GNSS signal quality. The optimal set-up of the mitigation process, investigated by means of a software simulation, is provided.

In the past decade, high spatial resolution Synthetic Aperture Radar (SAR) sensors have provided information that contributed significantly to cropland monitoring. However, the specific configurations of SAR sensors (e.g., band frequency, polarization mode) used to identify land-use types remains underexplored. This study investigates the contribution of C/L-Band frequency, dual/quad polarization and the density of image time-series to winter land-use identification in an agricultural area of approximately 130 km² located in northwestern France. First, SAR parameters were derived from RADARSAT-2, Sentinel-1 and Advanced Land Observing Satellite 2 (ALOS-2) time-series, and one quad-pol and six dual-pol datasets with different spatial resolutions and densities were calculated. Then, land use was classified using the Random Forest algorithm with each of these seven SAR datasets to determine the most suitable SAR configuration for identifying winter land-use. Results highlighted that (i) the C-Band (F1-score 0.70) outperformed the L-Band (F1-score 0.57), (ii) quad polarization (F1-score 0.69) outperformed dual polarization (F1-score 0.59) and (iii) a dense Sentinel-1 time-series (F1-score 0.70) outperformed RADARSAT-2 and ALOS-2 time-series (F1-score 0.69 and 0.29, respectively). In addition, Shannon Entropy and SPAN were the SAR parameters most important for discriminating winter land-use. Thus, the results of this study emphasize the interest of using Sentinel-1 time-series data for identifying winter land-use.