Design of electric power emergency communication system based on multi-network integration

Natural disasters and emergencies generally pose serious threats to power facilities. Therefore, it is necessary to establish an efficient electric power emergency communication (EPEC) system. Existing systems based on a single network carrier (for example,4G, satellite, and Wi-Fi) may not meet the requirements of complex environments. We design a new EPEC system based on multi-network convergence technology by integrating the above three network carriers. It overcomes the limitations of single network carrier and improves the efficiency of power emergency communication. The test results show that the system can improve the transmission line bandwidth, automatically switch to the optimal signal network, and guarantee the security and stability of data transmission.

4.2 K sensitivity-tunable radio frequency reflectometry of a physically defined p-channel silicon quantum dot

We demonstrate the measurement of p-channel silicon-on-insulator quantum dots at liquid helium temperatures by using a radio frequency (rf) reflectometry circuit comprising of two independently tunable GaAs varactors. This arrangement allows observing Coulomb diamonds at 4.2 K under nearly best matching condition and optimal signal-to-noise ratio. We also discuss the rf leakage induced by the presence of the large top gate in MOS nanostructures and its consequence on the efficiency of rf-reflectometry. These results open the way to fast and sensitive readout in multi-gate architectures, including multi qubit platforms.

An Image-Based Workflow for Objective Vessel Wall Enhancement Quantification in Intracranial Aneurysms

Background: VWE in contrast-enhanced magnetic resonance imaging (MRI) is a potential biomarker for the evaluation of IA. The common practice to identify IAs with VWE is mainly based on a visual inspection of MR images, which is subject to errors and inconsistencies. Here, we develop and validate a tool for the visualization, quantification and objective identification of regions with VWE. Methods: N = 41 3D T1-MRI and 3D TOF-MRA IA images from 38 patients were obtained and co-registered. A contrast-enhanced MRI was normalized by the enhancement intensity of the pituitary stalk and signal intensities were mapped onto the surface of IA models generated from segmented MRA. N = 30 IAs were used to identify the optimal signal intensity value to distinguish the enhancing and non-enhancing regions (marked by an experienced neuroradiologist). The remaining IAs (n = 11) were used to validate the threshold. We tested if the enhancement area ratio (EAR—ratio of the enhancing area to the IA surface-area) could identify high risk aneurysms as identified by the ISUIA clinical score. Results: A normalized intensity of 0.276 was the optimal threshold to delineate enhancing regions, with a validation accuracy of 81.7%. In comparing the overlap between the identified enhancement regions against those marked by the neuroradiologist, our method had a dice coefficient of 71.1%. An EAR of 23% was able to discriminate high-risk cases with an AUC of 0.7. Conclusions: We developed and validated a pipeline for the visualization and objective identification of VWE regions that could potentially help evaluation of IAs become more reliable and consistent.

Geometric Approach to Analytic Marginalisation of the Likelihood Ratio for Continuous Gravitational Wave Searches

The likelihood ratio for a continuous gravitational wave signal is viewed geometrically as a function of the orientation of two vectors; one representing the optimal signal-to-noise ratio, and the other representing the maximised likelihood ratio or F-statistic. Analytic marginalisation over the angle between the vectors yields a marginalised likelihood ratio, which is a function of the F-statistic. Further analytic marginalisation over the optimal signal-to-noise ratio is explored using different choices of prior. Monte-Carlo simulations show that the marginalised likelihood ratios had identical detection power to the F-statistic. This approach demonstrates a route to viewing the F-statistic in a Bayesian context, while retaining the advantages of its efficient computation.

Untuned broadband spiral micro-coils achieve sensitive multi-nuclear NMR TX/RX from microfluidic samples

The low frequency plateau in the frequency response of an untuned micro-resonator permits broadband radio-frequency reception, albeit at the expense of optimal signal-to-noise ratio for a particular nucleus. In this contribution we determine useful figures of merit for broadband micro-coils, and thereby explore the parametric design space towards acceptable simultaneous excitation and reception of a microfluidic sample over a wide frequency band ranging from 13C to 1H, i.e., 125–500 MHz in an 11.74 T magnet. The detector achieves 37% of the performance of a comparably sized, tuned and matched resonator, and a linewidth of 17 ppb using standard magnet shims. The use of broadband detectors circumvents numerous difficulties introduced by multi-resonant RF detector circuits, including sample loading effects on matching, channel isolation, and field distortion.

Simulations with Australian dragon lizards suggest movement-based signal effectiveness is dependent on display structure and environmental conditions

Habitat-specific characteristics can affect signal transmission such that different habitats dictate the optimal signal. One way to examine how the environment influences signals is by comparing changes in signal effectiveness in different habitats. Examinations of signal effectiveness between different habitats has helped to explain signal divergence/convergence between populations and species using acoustic and colour signals. Although previous research has provided evidence for local adaptations and signal divergence in many species of lizards, comparative studies in movement-based signals are rare due to technical difficulties in quantifying movements in nature and ethical restrictions in translocating animals between habitats. We demonstrate herein that these issues can be addressed using 3D animations, and compared the relative performance of the displays of four Australian lizard species in the habitats of each species under varying environmental conditions. Our simulations show that habitats differentially affect signal performance, and an interaction between display and habitat structure. Interestingly, our results are consistent with the hypothesis that the signal adapted to the noisier environment does not show an advantage in signal effectiveness, but the noisy habitat was detrimental to the performance of all displays. Our study is one of the first studies for movement-based signals that directly compares signal performance in multiple habitats, and our approach has laid the foundation for future investigations in motion ecology that have been intractable to conventional research methods.

Fusing Nature with Computational Science for Optimal Signal Extraction

Fusing nature with computational science has been proved paramount importance and researchers have also shown growing enthusiasm on inventing and developing nature inspired algorithms for solving complex problems across subjects. Inevitably, these advancements have rapidly promoted the development of data science, where nature inspired algorithms are changing the traditional way of data processing. This paper proposes the hybrid approach, namely SSA-GA, which incorporates the optimization merits of genetic algorithm (GA) for the advancements of Singular Spectrum Analysis (SSA). This approach further boosts the performance of SSA forecasting via better and more efficient grouping. Given the performances of SSA-GA on 100 real time series data across various subjects, this newly proposed SSA-GA approach is proved to be computationally efficient and robust with improved forecasting performance.