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Abstract Sequence classification facilitates a fundamental understanding of the structure of microbial communities. Binary metagenomic sequence classifiers are insufficient because environmental metagenomes are typically derived from multiple sequence sources. Here we introduce a deep-learning based sequence classifier, DeepMicroClass, that classifies metagenomic contigs into five sequence classes, i.e. viruses infecting prokaryotic or eukaryotic hosts, eukaryotic or prokaryotic chromosomes, and prokaryotic plasmids. DeepMicroClass achieved high performance for all sequence classes at various tested sequence lengths ranging from 500 bp to 100 kbps. By benchmarking on a synthetic dataset with variable sequence class composition, we showed that DeepMicroClass obtained better performance for eukaryotic, plasmid and viral contig classification than other state-of-the-art predictors. DeepMicroClass achieved comparable performance on viral sequence classification with geNomad and VirSorter2 when benchmarked on the CAMI II marine dataset. Using a coastal daily time-series metagenomic dataset as a case study, we showed that microbial eukaryotes and prokaryotic viruses are integral to microbial communities. By analyzing monthly metagenomes collected at HOT and BATS, we found relatively higher viral read proportions in the subsurface layer in late summer, consistent with the seasonal viral infection patterns prevalent in these areas. We expect DeepMicroClass will promote metagenomic studies of under-appreciated sequence types. 

Mobile robots can access regions and collect data in structural locations not easily reached by humans. This includes confined spaces, such as inside walls, and underground pipes; and remote spaces, such as the underside of bridge decks. Robot access provides the opportunity to sense in these difficult to access spaces with robot mounted sensors, i.e. cameras and lidars, and with the robot placing and servicing standalone sensors. Teams of robots, sensors and AR-equipped humans have the potential to provide rapid and more comprehensive structural assessments. This paper presents results of studies using small robots to explore and collect structural condition data from remote and confined spaces including in walls, culverts, and bridge deck undersides. The presentation also covers system and network architecture, methods for automating data processing with localized and edge-based processors, the use of augmented reality (AR) human interfaces and discusses key technical challenges and possible solutions. 

Recent advances in precision manufacturing technology and a thorough understanding of the properties of piezoelectric materials have made it possible for researchers to develop innovative microrobotic systems, which draw more attention to the challenges of utilizing microrobots in areas that are inaccessible to ordinary robots. This review paper provides an overview of the recent advances in the application of piezoelectric materials in microrobots. The challenges of microrobots in the direction of autonomy are categorized into four sections: mechanisms, power, sensing, and control. In each section, innovative research ideas are presented to inspire researchers in their prospective microrobot designs according to specific applications. Novel mechanisms for the mobility of piezoelectric microrobots are reviewed and described. Additionally, as the piezoelectric micro-actuators require high-voltage electronics and onboard power supplies, we review ways of energy harvesting technology and lightweight micro-sensing mechanisms that contain piezoelectric devices to provide feedback, facilitating the use of control strategies to achieve the autonomous untethered movement of microrobots. 

This paper presents research concerning the use of visual-inertial Simultaneous Localization And Mapping (SLAM) algorithms to aid in Continuous Wave (CW) radar target mapping. SLAM is an established field in which radar has been used to internally contribute to the localization algorithms. Instead, the application in this case is to use SLAM outputs to localize radar data and construct three-dimensional target maps which can be viewed live in augmented reality. These methods are transferable to other types of radar units and sensors, but this paper presents the research showing how the methods can be applied to calculate depth efficiently with CW radar through triangulation using a Boolean intersection algorithm. Localization of the radar target is achieved through quaternion algebra. Due to the compact nature of the SLAM and CW devices, the radar unit can be operated entirely handheld. Targets are scanned in a free-form manner where there is no need to have a gridded scanning layout. The main advantage to this method is eliminating many hours of usage training and expertise, thereby eliminating ambiguity in the location, size and depth of buried or hidden targets. Additionally, this method grants the user the additional power, penetration and sensitivity of CW radar without the lack of range finding. Applications include pipe and buried structure location, avalanche rescue, structural health monitoring and historical site research. 

Frequency modulated continuous wave (FMCW) radar allows for a wide range of research applications. One primary use of this technology which is explored in this paper is the ground penetrating radar. To achieve high sensing performance, wide-band spectral reconstruction and sophisticated image reconstruction algorithm have been developed to overcome hardware limitations. Applications and future work include Synthetic Aperture Radar (SAR) imaging, innovative GPR, and unmanned aerial vehicle (UAV) radar systems. 

The conventional ground penetrating radar (GPR) data analysis methods, which use piecemeal approaches in processing the GPR data formulated in variant formats such as A-Scan, B-Scan, and C-Scan, fail to provide a global view of underground objects on the fly to adapt the operations of GPR systems in the field. To bridge the gap, in this paper, we propose a novel GPR data analysis approach termed “ScanCloud” which is focused on the whole in situ GPR dataset rather than on individual A-Scans, B-Scans or C-Scans. We also study the integration of ScanCloud and a deep reinforcement learning method called deep deterministic policy gradient (DDPG) to adapt the operation of GPR system. The proposed method is evaluated using GPR modeling software called GprMax. Simulation results show the efficacy of ScanCloud and the adaptive GPR system enabled by the integration of ScanCluod and DDPG. 

Radio-frequency sensing and communication systems which use a waveform for more than one function offer the promise of improved spectral efficiency and streamlined hardware requirements. Control of orbital angular momentum (OAM) may be used to increase data-rates and improve radar sensitivity to certain chiral targets. This paper presents finite-difference time-domain simulations which model a gigahertz-frequency OAM radar capable of transmitting information via OAM-mode modulation. The unique chirality-detection capability of OAM radar is demonstrated, as well as simple information transmission. Simulation scope and radar specifications are designed with an eye toward developing a dual function ground penetrating radar (GPR) with OAM mode control.