Adaptive self-management of teams of autonomous vehicles

Unmanned Autonomous Vehicles (UAVs) are increasingly deployed for missions that are deemed dangerous or impractical to perform by humans in many military and disaster scenarios. Collaborating UAVs in a team form a Self- Managed Cell (SMC) with at least one commander. UAVs in an SMC may need to operate independently or in sub- groups, out of contact with the commander and the rest of the team in order to perform specific tasks, but must still be able to eventually synchronise state information. The SMC must also cope with intermittent and permanent communication failures as well permanent UAV failures. This paper describes a failure management scheme that copes with both communication link and UAV failures, which may result in temporary disjoint sub-networks within the SMC. A communication management protocol is proposed to control UAVs performing disconnected individual operations, while maintaining the SMCs structure by trying to ensure that all members of the mission regardless of destination or task, can communicate by moving UAVs to act as relays or by allowing the UAVs to rendezvous at intermittent intervals. Copyright 2008 ACM.Accepted versio

On The Negative Pell Equation

The negative pell equation represented by the binary quadratic equationnbspnbsp is analyzed for its non-zero distinct integer solutions. A few interesting relations among the solutions are presented. Employing the solutions of the equation under consideration, the integer solutions for a few choices of hyperbola and parabola are obtained

Integral solutions of the heptic equation with five unknowns

The non-homogeneous Diophantine equation of degree seven with five variables represented by is analyzed for its non-zero distinct integer solutions. A few interesting relation between the solutions and special numbers namely Polygonal numbers, Pyramidal numbers, centered Polygonal numbers are exhibited

Earthquake-triggered landslides and Environmental Seismic Intensity: insights from the 2018 Papua New Guinea earthquake (Mw 7.5)

On the 25 February 2018, an earthquake of magnitude M(w)7.5 struck the region of Porgera in Papua New Guinea (PNG), triggering numerous landslides. Planetscope images are used to derive a partial inventory of 2941 landslides in a cloud-free area of 2686 km(2). The average area of landslides in the study area is 18,500 m(2). We use the Environmental Seismic Intensity (ESI) scale to assess the damage due to the triggered landslides. Local intensity values are assigned to individual landslides by calculating their volume using various area-volume relations. We observe that different empirical relations yield similar volume values for individual landslides (local ESI intensity & GE; X). The spatial variation of landslide density and areal coverage within the study area in cells of 1 km(2) is investigated and compared to the probability predicted by the USGS model. We observe that high probability corresponds to a significant number of landslides. An ESI epicentral intensity of XI is estimated based on primary and secondary effects. This study represents the first application of the ESI scale to an earthquake in PNG. The Porgera earthquake fits well with past case studies worldwide in terms of ESI scale epicentral intensity and triggered landslide number as a function of earthquake magnitude

Remote Sensing Data Visualization, Fusion and Analysis via Giovanni

We describe Giovanni, the NASA Goddard developed online visualization and analysis tool that allows users explore various phenomena without learning remote sensing data formats and downloading voluminous data. Using MODIS aerosol data as an example, we formulate an approach to the data fusion for Giovanni to further enrich online multi-sensor remote sensing data comparison and analysis

Latent Space Model for Multi-Modal Social Data

With the emergence of social networking services, researchers enjoy the increasing availability of large-scale heterogenous datasets capturing online user interactions and behaviors. Traditional analysis of techno-social systems data has focused mainly on describing either the dynamics of social interactions, or the attributes and behaviors of the users. However, overwhelming empirical evidence suggests that the two dimensions affect one another, and therefore they should be jointly modeled and analyzed in a multi-modal framework. The benefits of such an approach include the ability to build better predictive models, leveraging social network information as well as user behavioral signals. To this purpose, here we propose the Constrained Latent Space Model (CLSM), a generalized framework that combines Mixed Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA) incorporating a constraint that forces the latent space to concurrently describe the multiple data modalities. We derive an efficient inference algorithm based on Variational Expectation Maximization that has a computational cost linear in the size of the network, thus making it feasible to analyze massive social datasets. We validate the proposed framework on two problems: prediction of social interactions from user attributes and behaviors, and behavior prediction exploiting network information. We perform experiments with a variety of multi-modal social systems, spanning location-based social networks (Gowalla), social media services (Instagram, Orkut), e-commerce and review sites (Amazon, Ciao), and finally citation networks (Cora). The results indicate significant improvement in prediction accuracy over state of the art methods, and demonstrate the flexibility of the proposed approach for addressing a variety of different learning problems commonly occurring with multi-modal social data.Comment: 12 pages, 7 figures, 2 table

Smartphone Based E-Learning

Children often attend schools intermittently in rural areas in Africa and India due to socio-economic conditions which make pupils augment their family income by working. An e-Learning solution could aid in raising the level of education by making it easier for children to fit schoolwork into the day, acting as a complement to when they are able to attend school. Traditional distance learning solutions based on computers are not suitable due to lack of infrastructure support. In this paper, we evaluate both text and voice based smartphone prototype environments which could provide the tools and services for pupils to download educational content, interact with teachers as well as other pupils to discuss topics. These have been implemented as a proof-of- concept and the initial evaluation feedback, although not from target users, was very promising. We intend to re-implement the prototype and do a proper evaluation with rural-area school children.Accepted versio

A Mission Management Framework for Unmanned Autonomous Vehicles

Accepted versio