Communication at mmWave bands carries critical importance for 5G wireless networks. In this paper, we study the characterization of mmWave air-to-ground (AG) channels for unmanned aerial vehicle (UAV) communications. In particular, we use ray tracing simulations using Remcom Wireless InSite software to study the behavior of AG mmWave bands at two different frequencies: 28 GHz and 60 GHz. Received signal strength (RSS) and root mean square delay spread (RMS-DS) of multipath components (MPCs) are analyzed for different UAV heights considering four different environments: urban, suburban, rural, and over sea. It is observed that the RSS mostly follows the two ray propagation model along the UAV flight path for higher altitudes. This two ray propagation model is affected by the presence of high rise scatterers in urban scenario. Moreover, we present details of a universal serial radio peripheral (USRP) based channel sounder that can be used for AG channel measurements for mmWave (60 GHz) UAV communications.

✓ The authors describe two rare occurrences of radiographically, surgically, and pathologically confirmed spinal intradural arachnoid cysts (not associated with additional pathological entities) that were located anterior to the cervical spinal cord. These lesions have been reported previously in only eight patients. The patients described in this report were young adults who presented with progressive spastic tetraparesis shortly after sustaining mild cervical trauma and in whom no neurological deficit or bone fracture was demonstrated. The presence of an intradural arachnoid cyst was detected on postcontrast computerized tomography (CT) myelography and on magnetic resonance imaging; both diagnostic tools correctly characterized the cystic nature of the lesion. Plain radiography, plain tomography, and contrast-enhanced CT scans were not diagnostic. In both cases a laminectomy was performed, and the wall of the cyst was excised and fenestrated with subarachnoid space. Postoperatively, the patients made complete neurological recoveries. Based on a review of the literature, arachnoid cysts of the spinal canal may be classified as either extra- or intradural. Intradural arachnoid cysts usually arise posterior to the spinal cord in the thoracic spine region; however, these cysts very rarely develop in the cervical region. The pathogenesis of arachnoid cysts is unclear, although congenital, traumatic and inflammatory causes have been postulated. The authors believe that the formation of an arachnoid cyst cannot be explained by simply one mechanism because, in some reported cases, there has been accidental or iatrogenic trauma in association with congenital lesions. They also note that an intradural arachnoid cyst located anterior to the cervical spinal cord is an extremely rare disorder that may cause progressive myelopathy; however, the postoperative prognosis is good.

The future 5G networks are expected to use millimeter wave (mmWave) frequency bands to take advantage of large unused spectrum. However, due to the high path loss at mmWave frequencies, coverage of mmWave signals can get severely reduced, especially for non-line-of-sight (NLOS) scenarios as mmWave signals are severely attenuated when going through obstructions. In this work, we study the use of passive metallic reflectors of different shapes/sizes to improve 28 GHz mmWave signal coverage for both indoor and outdoor NLOS scenarios. We quantify the gains that can be achieved in the link quality with metallic reflectors using measurements, analytical expressions, and ray tracing simulations. In particular, we provide an analytical model for the end-to-end received power in an NLOS scenario using reflectors of different shapes and sizes. For a given size of the flat metallic sheet reflector approaching to the size of incident plane waves, we show that the reflected received power for the NLOS link is same as line-of-sight (LOS) free space received power of the same link distance. Extensive results are provided to study impact of environmental features and reflector characteristics on NLOS link quality.

This paper investigates the problem of detection and classification of unmanned aerial vehicles (UAVs) in the presence of wireless interference signals using a passive radio frequency (RF) surveillance system. The system uses a multistage detector to distinguish signals transmitted by a UAV controller from the background noise and interference signals. First, RF signals from any source are detected using a Markov models-based naïve Bayes decision mechanism. When the receiver operates at a signal-to-noise ratio (SNR) of 10 dB, and the threshold, which defines the states of the models, is set at a level 3.5 times the standard deviation of the preprocessed noise data, a detection accuracy of 99.8% with a false alarm rate of 2.8% is achieved. Second, signals from Wi-Fi and Bluetooth emitters, if present, are detected based on the bandwidth and modulation features of the detected RF signal. Once the input signal is identified as a UAV controller signal, it is classified using machine learning (ML) techniques. Fifteen statistical features extracted from the energy transients of the UAV controller signals are fed to neighborhood component analysis (NCA), and the three most significant features are selected. The performance of the NCA and five different ML classifiers are studied for 15 different types of UAV controllers. A classification accuracy of 98.13% is achieved by k-nearest neighbor classifier at 25 dB SNR. Classification performance is also investigated at different SNR levels and for a set of 17 UAV controllers which includes two pairs from the same UAV controller models.

This paper focuses on the detection and classification of micro-unmanned aerial vehicles (UAVs) using radio frequency (RF) fingerprints of the signals transmitted from the controller to the micro-UAV. In the detection phase, raw signals are split into frames and transformed into the wavelet domain to remove the bias in the signals and reduce the size of data to be processed. A naive Bayes approach, which is based on Markov models generated separately for UAV and non-UAV classes, is used to check for the presence of a UAV in each frame. In the classification phase, unlike the traditional approaches that rely solely on time-domain signals and corresponding features, the proposed technique uses the energy transient signal. This approach is more robust to noise and can cope with different modulation techniques. First, the normalized energy trajectory is generated from the energy-time-frequency distribution of the raw control signal. Next, the start and end points of the energy transient are detected by searching for the most abrupt changes in the mean of the energy trajectory. Then, a set of statistical features is extracted from the energy transient. Significant features are selected by performing neighborhood component analysis (NCA) to keep the computational cost of the algorithm low. Finally, selected features are fed to several machine learning algorithms for classification. The algorithms are evaluated experimentally using a database containing 100 RF signals from each of 14 different UAV controllers. The signals are recorded wirelessly using a high-frequency oscilloscope. The data set is randomly partitioned into training and test sets for validation with the ratio 4:1. Ten Monte Carlo simulations are run and results are averaged to assess the performance of the methods. All the micro-UAVs are detected correctly and an average accuracy of 96.3% is achieved using the k-nearest neighbor (kNN) classification. Proposed methods are also tested for different signal-to-noise ratio (SNR) levels and results are reported.

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