Fundamental Limits on Detection of UAVs by Existing Terrestrial RF Networks

Detection of drones carries critical importance for safely and effectively managing unmanned aerial system traffic in the future. Given the ubiquitous presence of the drones across all kinds of environments in the near future, wide area drone detection and surveillance capability are highly desirable, which require careful planning and design of drone sensing networks. In this paper, we seek to meet this need by using the existing terrestrial radio frequency (RF) networks for passive sensing of drones. To this end we develop an analytical framework that provides the fundamental limits on the network-wide drone detection probability. In particular, we characterize the joint impact of the salient features of the terrestrial RF networks, such as the spatial randomness of the node locations, the directional 3D antenna patterns, and the mixed line of sight/non line of sight (LoS/NLoS) propagation characteristics of the air-to-ground (A2G) channels. Since the strength of the drone signal and the aggregate interference in a sensing network are fundamentally limited by the 3D network geometry and the inherent spatial randomness, we use tools from stochastic geometry to derive the closed-form expressions for the probabilities of detection, false alarm and coverage. This, in turn, demonstrates the impact of the sensor density, beam tilt angle, half power beam width (HPBW) and different degrees of LoS dominance, on the projected detec?tion performance. Our analysis reveals optimal beam tilt angles, and sensor density that maximize the network-wide detection of the drones.

Fundamental Limits on Detection of UAVs by Existing Terrestrial RF Networks

Detection of drones carries critical importance for safely and effectively managing unmanned aerial system traffic in the future. Given the ubiquitous presence of the drones across all kinds of environments in the near future, wide area drone detection and surveillance capability are highly desirable, which require careful planning and design of drone sensing networks. In this paper, we seek to meet this need by using the existing terrestrial radio frequency (RF) networks for passive sensing of drones. To this end we develop an analytical framework that provides the fundamental limits on the network-wide drone detection probability. In particular, we characterize the joint impact of the salient features of the terrestrial RF networks, such as the spatial randomness of the node locations, the directional 3D antenna patterns, and the mixed line of sight/non line of sight (LoS/NLoS) propagation characteristics of the air-to-ground (A2G) channels. Since the strength of the drone signal and the aggregate interference in a sensing network are fundamentally limited by the 3D network geometry and the inherent spatial randomness, we use tools from stochastic geometry to derive the closed-form expressions for the probabilities of detection, false alarm and coverage. This, in turn, demonstrates the impact of the sensor density, beam tilt angle, half power beam width (HPBW) and different degrees of LoS dominance, on the projected detec?tion performance. Our analysis reveals optimal beam tilt angles, and sensor density that maximize the network-wide detection of the drones.

Scaling complexity comparison of an ACO-based routing algorithm used as an IoT network core

This paper proposes a routing method that is based on an Ant Colony Algorithm (ACO) for minimizing energy consumption in Wireless Sensor Networks (WSNs). The routing method is used as the backbone of the Internet of Things (IoT) platform. It also considers the critical design issues of a WSN, such as the energy constraint of sensor nodes, network load balancing, and sensor density in the field. Special attention is paid to the impact of network scaling on the performance of the ACO-based routing algorithm.

A Comparative and Review Study on Shape and Stress Sensing of Flat/Curved Shell Geometries Using C0-Continuous Family of iFEM Elements

In this study, we methodologically compare and review the accuracy and performance of C0-continuous flat and curved inverse-shell elements (i.e., iMIN3, iQS4, and iCS8) for inverse finite element method (iFEM) in terms of shape, strain, and stress monitoring, and damage detection on various plane and curved geometries subjected to different loading and constraint conditions. For this purpose, four different benchmark problems are proposed, namely, a tapered plate, a quarter of a cylindrical shell, a stiffened curved plate, and a curved plate with a degraded material region in stiffness, representing a damage. The complexity of these test cases is increased systematically to reveal the advantages and shortcomings of the elements under different sensor density deployments. The reference displacement solutions and strain-sensor data used in the benchmark problems are established numerically, utilizing direct finite element analysis. After performing shape-, strain-, and stress-sensing analyses, the reference solutions are compared to the reconstructed solutions of iMIN3, iQS4, and iCS8 models. For plane geometries with sparse sensor configurations, these three elements provide rather close reconstructed-displacement fields with slightly more accurate stress sensing using iCS8 than when using iMIN3/iQS4. It is demonstrated on the curved geometry that the cross-diagonal meshing of a quadrilateral element pattern (e.g., leading to four iMIN3 elements) improves the accuracy of the displacement reconstruction as compared to a single-diagonal meshing strategy (e.g., two iMIN3 elements in a quad-shape element) utilizing iMIN3 element. Nevertheless, regardless of any geometry, sensor density, and meshing strategy, iQS4 has better shape and stress-sensing than iMIN3. As the complexity of the problem is elevated, the predictive capabilities of iCS8 element become obviously superior to that of flat inverse-shell elements (e.g., iMIN3 and iQS4) in terms of both shape sensing and damage detection. Comprehensively speaking, we envisage that the set of scrupulously selected test cases proposed herein can be reliable benchmarks for testing/validating/comparing for the features of newly developed inverse elements.

A Novel Approach for Mixed Manual/Connected Automated Freeway Traffic Management

Freeway traffic management and control often rely on input from fixed-point sensors. A sufficiently high sensor density is required to ensure data reliability and accuracy, which results in high installation and maintenance costs. Moreover, fixed-point sensors encounter difficulties to provide spatiotemporally and wide-ranging information due to the limited observable area. This research exploits the utilization of connected automated vehicles (CAVs) as an alternative data source for freeway traffic management. To handle inherent uncertainty associated with CAV data, we develop an interval type 2 fuzzy logic-based variable speed limit (VSL) system for mixed traffic. The simulation results demonstrate that when more 10% CAVs are deployed, the performance of the proposed CAV-based system can approach that of the detector-based system. It is demonstrated in addition that the introduction of CAVs may make VSL obsolete at very high CAV-equipment rates.

Observations on the Ultrastructure of Antennal Sensilla of Adult Glenea cantor (Cerambycidae: Lamiinae)

The external morphology and distribution of antennal sensilla of Glenea cantor Fabricius were studied with scanning electron microscopy. The antennae of G. cantor were observed to be filiform, consisting of scape, pedicel, and flagellum (nine flagellomeres). Four distinct types of sensory receptors were observed, including sensilla chaetica, sensilla trichodea, sensilla basiconica, and Böhm bristles. Three morphological subtypes of sensilla chaetica were found on the antennae, and sensilla trichodea were also categorized into three morphological subtypes. Sensilla basiconica was grouped into two morphological subtypes that were found on subsegments F2-F9 of the flagellum, and Böhm bristles were only found at the intersegmental joints between the scape and the head and between the scape and the pedicel. The antennae of male and female adults were similar in shape, length, and diameter. However, the length, diameter, distribution, and number of each of the four distinct types of sensilla on the males were significantly different from those on females. The types, lengths, diameters, numbers, and distributions of these sensilla were described, and their possible functions were also discussed. The results indicated that the base and end of an antennal segment have a similar sensillum density, but the middle section sensor density is significantly greater, especially for olfactory and gustatory sensilla, possibly because the joints are more involved in mechanical sensing. The density of sensors is closely related to its sensing function; so, future studies on the biology of olfaction and sexual communication in G. cantor will be facilitated by these observations.

On-scalp MEG SQUIDs are sensitive to early somatosensory activity unseen by conventional MEG

Magnetoencephalography (MEG) has a unique capacity to resolve the spatio-temporal development of brain activity from non-invasive measurements. Conventional MEG, however, relies on sensors that sample from a distance (20-40 mm) to the head due to thermal insulation requirements (the MEG sensors function at 4 K in a helmet). A gain in signal strength and spatial resolution may be achieved if sensors are moved closer to the head. Here, we report a study comparing measurements from a seven-channel on-scalp SQUID MEG system to those from a conventional (in-helmet) SQUID MEG system.We compared spatio-temporal resolution between on-scalp and conventional MEG by comparing the discrimination accuracy for neural activity patterns resulting from stimulating five different phalanges of the right hand. Because of proximity and sensor density differences between on-scalp and conventional MEG, we hypothesized that on-scalp MEG would allow for a more high-resolved assessment of these activity patterns, and therefore also a better classification performance in discriminating between neural activations from the different phalanges.We observed that on-scalp MEG provided better classification performance during an early post-stimulus period (15-30 ms). This corresponded to electroencephalographic (EEG) response components N16 and P23, and was an unexpected observation as these components are usually not observed in conventional MEG. They indicate that on-scalp MEG opens up for a richer registration of the cortical signal, allowing for sensitivity to what are potentially sources in the thalamo-cortical radiation and to quasi-radial sources.We had originally expected that on-scalp MEG would provide better classification accuracy based on activity in proximity to the P60m component compared to conventional MEG. This component indeed allowed for the best classification performance for both MEG systems (60-75%, chance 50%). However, we did not find that on-scalp MEG allowed for better classification than conventional MEG at this latency. We believe this may be due to the limited sensor coverage in the recording, in combination with our strategy for positioning the on-scalp MEG sensors. We discuss how sensor density and coverage as well as between-phalange source field dissimilarities may influence our hypothesis testing, which we believe to be useful for future benchmarking measurements.