Abstract-We examine the problem of utilizing an autonomous underwater vehicle (AUV) to collect data from an underwater sensor network. The sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication. The AUV must plan a path that maximizes the information collected while minimizing travel time or fuel expenditure. We propose AUV path planning methods that extend algorithms for variants of the Traveling Salesperson Problem (TSP). While executing a path, the AUV can improve performance by communicating with multiple nodes in the network at once. Such multinode communication requires a scheduling protocol that is robust to channel variations and interference. To this end, we examine two multiple access protocols for the underwater data collection scenario, one based on deterministic access and another based on random access. We compare the proposed algorithms to baseline strategies through simulated experiments that utilize models derived from experimental test data. Our results demonstrate that properly designed communication models and scheduling protocols are essential for choosing the appropriate path planning algorithms for data collection.

The paper focuses on sensitivity-based identifiability analysis of parameters for mathematical models described by systems of nonlinear ordinary differential equations. This analysis is carried out using eigenvalue method and orthogonal method. Both methods allow one to globally evaluate and compare the influence of parameter values on measurement data. The sensitivity analysis and numerical experiments for mathematical model of the spread of TB and HIV co-infection are demonstrated. The numerical results show that 4 parameters (from 15 available) are identifiable uniquely by the given data only about 3 measured time-point functions during 5 years.

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Human activity prediction is an interesting problem with a wide variety of applications like intelligent virtual assistants, contextual marketing, etc. One formulation of this problem is jointly predicting human activities (viz. eating, commuting, etc.) with associated durations. Herein a deep learning system is proposed for this problem. Given a sequence of past activities and durations, the system estimates the probabilities for future activities and their durations. Two distinct Long-Short Term Memory (LSTM) networks are developed that cater to different assumptions about the data and achieve different modeling complexities and prediction accuracies. The networks are trained and tested with two real-world datasets, one being publicly available while the other collected from a field experiment. Modeling on the segment level public dataset mitigates the cold-start problem. Experiments indicate that compared to traditional approaches based on sequence mining or hidden Markov modeling, LSTM networks perform significantly better. The ability of LSTM networks to detect long term correlations in activity data is also demonstrated. The trained models are each less than 500KB in size and can be deployed to run in real-time on a mobile device without any dependencies on the cloud. This can help applications like mobile personal assistants by providing predictive context.