Biophysical and biochemical characterization of yeast tRNA nucleotidyltransferase variants

The enzyme ATP(CTP):tRNA-specific tRNA nucleotidyltransferase adds cytidine-cytidine-adenosine (CCA) to the 3’ end of eukaryotic tRNAs during their maturation. This CCA sequence plays a vital role in aminoacylation and hence in protein synthesis. In yeast, this enzyme is defined as a Class II tRNA nucleotidyltransferase due to the presence of five conserved N-terminal motifs (A to E). Based on the available crystal structures of related tRNA nucleotidyltransferases, specific functions have been assigned to each of these motifs. We previously have shown that mutations in motif C that reduce enzyme activity can be overcome by a mutation in motif A that restores this activity. Here we explore the roles of two acidic residues (glutamate 189 and aspartate 190) found within motif C and one residue (arginine 64) found in motif A to understand better the role of motif C and the potential interactions between motifs A and C. Site-directed mutagenesis was used to change arginine 64 (to tryptophan), or glutamate 189 (to glutamine, lysine, alanine or phenylalanine) or aspartate 190 (to alanine or phenylalanine) alone, or in combination with the arginine 64 tryptophan substitution and the effects of these amino acid alterations on enzyme structure and function were studied. Biophysical analyses (circular dichroism and fluorescence spectroscopy and thermal denaturation experiments) suggest no major changes in structure in almost all of the variants tested. Kinetic analysis revealed no alterations in substrate binding (Km), but a large drop in turnover number (kcat) for the 189 and 190 variants (but not the arginine 64 variant). The reduced activity in the 189 and 190 variants is alleviated when accompanied by the change of arginine 64 to tryptophan, which also suppresses the temperature-sensitive phenotype. Taken together these data suggest that arginine 64 is not required for enzyme activity unlike glutamate 189 and aspartate 190. Moreover, they suggest an interaction between motifs A and C, and that motif C plays a role in accommodating and orienting the substrates to promote catalysis involving motif A

Multiple drone classification using millimeter-wave CW radar micro-Doppler data

Funding: Army Research Laboratory under Cooperative Agreement Number: W911NF-19-2-0075.This paper investigates the prospect of classifying different types of rotary wing drones using radar. The proposed method is based on the hypothesis that the rotor blades of different sizes and shapes will exhibit distinct Doppler features. When sampled unambiguously, these features can be properly extracted and then can be used for classification. We investigate various continuous wave (CW) spectrogram features of different drones obtained with a low phase noise, coherent radar operating at 94 GHz. Two quadcopters of different sizes (DJI Phantom Standard 3 and Joyance JT5L-404) and a hexacopter (DJI S900) have been used during the experimental trial for data collection. For classification training, we first show the limitation of the feature extraction based method. We then propose a convolutional neural network (CNN) based approach in which the classification training is done by using micro-Doppler spectrogram images. We have created an extensive dataset of spectrogram images for classification training, which have been fed to the existing GoogLeNet model. The trained model then has been tested with unseen and unlabelled data for performance verification. Validation accuracy of above 99% is achieved along with very accurate testing results, demonstrating the potential of using neural networks for multiple drone classification.Postprin

Key Agreement

WPA and WPA2 (Wi-Fi Protected Access) is a certification program developed by the Wi-Fi Alliance to indicate compliance with the security protocol created by the WiFi alliance to secure wireless networks. The alliance defined the protocol in response to several weaknesses researchers had found in the previous Wired Equivalent Privacy (WEP) system. Many sophisticated authentication and encryption techniques have been embedded into WPA but it is still facing a lot of challenging situations. In this paper we discuss the vulnerabilit

Classification of drones and birds using convolutional neural networks applied to radar micro-Doppler spectrogram images

Funding: UK Science and Technology Facilities Council ST/N006569/1 (DR).This study presents a convolutional neural network (CNN) based drone classification method. The primary criterion for a high-fidelity neural network based classification is a real dataset of large size and diversity for training. The first goal of the study was to create a large database of micro-Doppler spectrogram images of in-flight drones and birds. Two separate datasets with the same images have been created, one with RGB images and other with grayscale images. The RGB dataset was used for GoogLeNet architecture-based training. The grayscale dataset was used for training with a series architecture developed during this study. Each dataset was further divided into two categories, one with four classes (drone, bird, clutter and noise) and the other with two classes (drone and non-drone). During training, 20% of the dataset has been used as a validation set. After the completion of training, the models were tested with previously unseen and unlabelled sets of data. The validation and testing accuracy for the developed series network have been found to be 99.6% and 94.4% respectively for four classes and 99.3% and 98.3% respectively for two classes. The GoogLenet based model showed both validation and testing accuracies to be around 99% for all the cases.PostprintPeer reviewe

A new simulation methodology for generating accurate drone micro-Doppler with experimental validation

Authors acknowledge the financial support of the Engineering and Physical Sciences Council and QinetiQ who are funding MM's PhD.Unmanned Aerial Vehicles, or drones, pose a significant threat to privacy and security. To understand and assess this threat, classification between different drone models and types is required. One way in which this has been demonstrated experimentally is through this use of micro-Doppler information from radars. Classifiers capable of exploiting differences in micro-Doppler spectra will require large amounts of data but obtaining such data experimentally is expensive and time consuming. The authors present the methodology and results of a drone micro-Doppler simulation framework which uses accurate 3D models of drone components to yield detailed and realistic synthetic micro-Doppler signatures. This is followed by the description of a purpose-built validation radar that has been developed specifically to gather high-fidelity experimental drone micro-Doppler data with which is used to validate the simulation. Detailed comparisons between the experimental and simulated micro-Doppler spectra from three models of drones with differently shaped propellers are given, showing very good agreement. The aim is to introduce the simulation methodology. Validation using single propeller micro-Doppler is provided, although the simulation can be extended to multiple propellers. The simulation framework offers the potential to generate large quantities of realistic drone micro-Doppler signatures for training classification algorithms.Publisher PDFPeer reviewe

WPA 2 (Wi-Fi Protected Access 2) Security Enhancement: Analysis

WPA and WPA2 (Wi-Fi Protected Access) is a certification program developed by the Wi-Fi Alliance to indicate compliance with the security protocol created by the WiFi Alliance to secure wireless networks. The Alliance defined the protocol in response to several weaknesses researchers had found in the previous system: Wired Equivalent Privacy (WEP). Many sophisticated authentication and encryption techniques have been embedded into WPA2 but it still facing a lot of challenging situations. In this paper we discuss the benefit of WPA2, its vulnerabilit

G-band FMCW Doppler radar for sea clutter and target characterisation

Funding: UK Engineering and Physical Sciences Research Council under grant EP/S032851/1.Marine autonomy is a field receiving a high degree of interest for its many potential applications in terms of commerce, crew safety, and the military. A successful autonomous vessel depends on a sophisticated degree of situational awareness facilitated by sensors. We are investigating sub-THz radar sensors for this purpose, with the primary goal being the characterization of sea clutter and targets in terms of both amplitude and Doppler statistics at frequencies spanning 24 to 350 GHz, where presently there is a lack of data. Sub-THz frequencies are of particular interest due to improved range and Doppler resolutions, and reduced sensor size, factors expected to be critical in enabling anomaly detection in the dynamic marine environment. As part of this work, a new 207 GHz frequency modulated continuous wave (FMCW) radar is being developed for the collection of clutter and target phenomenology data. The architecture uses a direct digital synthesis (DDS) generated chirp which is upconverted onto a low phase noise microwave LO then frequency multiplied by 24 to the carrier frequency. Twin Gaussian optics lens antennas (GOLAs) are used for transmit and receive with beamwidths of 2° , with adjustable linear polarization. The radar head is gimbal mounted for raster scanning RCS maps or for use in staring mode Doppler measurements. A chirp bandwidth of 4 GHz enables range bins of a few centimeters and high speed chirps enable a maximum unambiguous velocity of ±5 m/s.Publisher PD

Amplitude characteristics of littoral sea clutter data at K-band and W-band

Funding: UK Engineering and Physical Sciences Research Council under grant EP/S032851/1.Sea clutter data at millimeter wave frequencies are quite limited in the literature. Recent advancements in millimeter wave radar technology have created a potential for its use in maritime surveillance and autonomy. Hence, collecting data at this frequency range is of great interest to both academia and industry. This study reports on a field trial conducted at St Andrews in winter 2020 to collect littoral sea clutter data using K-band (24 GHz) and W-band (94 GHz) radar systems. Extensive data collection was done during the trial, where this work specifically concentrates on analysis of the amplitude characteristics of the sea clutter returns. Analysis of the dataset shows that the radar backscatter was heavily dominated by sea-spikes. The modal normalized radar cross section (NRCS) values for Bragg, burst and whitecap scattering are measured to be -47, -30 and -17 dB respectively at 24 GHz in horizontal polarization and -48, -26 and -12 dB respectively at 94 GHz in circular polarization, measured at grazing angles of 1-3°. The backscatter from the smooth surface is found to be below the noise floor equivalent NRCS (-65 dB). Also, the power spectrum analysis of range-time intensity plots is discussed, revealing information on the sea surface dynamics.Postprin

Multiple drone type classification using machine learning techniques based on FMCW radar micro-Doppler data

Systems designed to detect the threat posed by drones should be able to both locate a drone and ideally determine its type in order to better estimate the level of threat. Previously, drone types have been discriminated using millimeter-wave Continuous Wave (CW) radar, which produces high quality micro-Doppler signatures of the drone propeller blades with fully sampled Doppler spectra. However, this method is unable to locate the target as it cannot measure range. By contrast, Frequency Modulated Continuous Wave (FMCW) data typically undersamples the micro-Doppler signatures of the blades but can be used to locate the target. In this paper we investigate FMCW features of four drones and if they can be used to discriminate the models using machine learning techniques, enabling both the location and classification of the drone. Millimeter-wave radar data are used for better Doppler sensitivity and shorter integration time. Experimentally collected data from Ttree quadcopters (DJI Phantom Standard 3, DJI Inspire 1, and Joyance JT5L-404) and a hexacopter (DJI S900) have been. For classification, feature extraction based machine learning was used. Several algorithms were developed for automated extraction of micro-Doppler strength, bulk Doppler to micro-Doppler ratio, and HERM line spacing from spectrograms. These feature values were fed to classifiers for training. The four models were classified with 85.1% accuracy. Higher accuracies greater than 95% were achieved for training using fewer drone models. The results are promising, establishing the potential for using FMCW radar to discriminate drone types.Publisher PD

Secure Authentication

Many complicated authentication and encryption techniques have been embedded into WiMAX but it still facing a lot of challenging situations. This paper shows that, GTEK Hash chain algorithm for Multi and Broadcast service of IEEE 802.16e facing a reduced forward secrecy problem. These vulnerabilities are the possibilities to forge key messages in Multiand Broadcast operation, which are susceptible to forgery and reveals important management information. In this paper, we also propose three UAKE protocols with PFS (Perfect Forward Secrecy) that are efficient and practical for mobile devices