Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems

The emerging concept of drone swarms creates new opportunities with major societal implications. However, future drone swarm applications and services pose new communications and sensing challenges, particularly for collaborative tasks. To address these challenges, in this paper, we integrate sensor arrays and communication to propose a mathematical model to route a collection of autonomous unmanned aerial systems (AUAS), a so-called drone swarm or AUAS swarm, without having a base station of communication but communicating with each other using multiple spatio-temporal data. The theories of structured matrices, concepts in multi-beam beamforming, and sensor arrays are utilized to propose a swarm routing algorithm. We address the routing algorithm’s computational and arithmetic complexities, precision, and reliability. We measure bit-error-rate (BER) based on the number of elements in sensor arrays and beamformed output of the members of the swarm to authenticate and secure the routing for the decentralized AUAS networking. The proposed model has the potential to enable future drone swarm applications and services. Finally, we discuss future work on obtaining a machine-learning-based low-cost drone swarm routing algorithm

Specifying geospatial data product characteristics for forest and fuel management applications

One of the greatest challenges for land managers is to maintain a multitude of ecosystem services while reducing hazards posed by wildfires, insect outbreaks, and other disturbances accelerating due to climate change. In response to limited available resources and improved technical abilities, natural resource managers are increasingly using geospatial data to plan and evaluate their management actions. Large amounts of public resources are invested in research and development to improve geospatial datasets, yet there is limited knowledge about the specific data types and data characteristics that clients (e.g. land managers) prefer. Our overall objective was to investigate what geospatial data characteristics are preferred by natural resource professionals to monitor and manage forests and fuels across large landscapes. We performed an online survey and collected supplemental data at a subsequent workshop during the 2020 Operational Lidar Inventory meeting to investigate preferred data use and data characteristics of data users of the Pacific Northwest. Our online survey was completed by 69 respondents represented by managers and natural resource professionals from tribal/state, federal, academic, and industry/consulting entities. We found that metrics related to species composition, total biomass/timber volume, and vegetation height were the most preferred attributes, yet preference differed slightly by employment type. From the workshop we found that metric preferences depend upon which management priorities are central to the management application. There was preference for data with Landsat pixel-level (30 m) spatial resolution, annual temporal resolution, and at regional spatial extents. To maintain viable ecosystem services in the long term, it is important to understand the metrics and their data characteristics that are most useful. We conclude that our study is a useful way to understand (a) how to improve the data utility for the users (clients) and (b) the development and investment needs for the data developers and funders

Detection of inflammation in vivo by surface-enhanced Raman scattering provides higher sensitivity than conventional fluorescence imaging

The detection of inflammatory changes is a key aim for the early diagnosis and treatment of several autoimmune, infectious, and metastatic diseases. While surface-enhanced Raman scattering (SERS) has the capability to provide noninvasive, in vivo imaging at sufficient depth to achieve this goal, this approach has not been exploited in the study of inflammation. SERS-active nanoparticles were coded with a unique Raman signal that was protected under a wide range of conditions and stimuli. To detect early-stage inflammation, gold nanoparticle clusters containing Raman-active molecules were conjugated to intercellular adhesion molecule 1- (ICAM-1-) specific monoclonal antibodies. SERS allowed noninvasive measurement of ICAM-1 expression in vivo with twice the sensitivity of two-photon fluorescence. This is the first time SERS has been used for in vivo detection of inflammation and is a major advance in the ever-growing toolkit of approaches for use in noninvasive, next-generation in vivo imaging