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This paper addresses the problem of target detection in dynamic environments in a semi-supervised data-driven setting with low-cost passive sensors. A key challenge here is to simultaneously achieve high probabilities of correct detection with low probabilities of false alarm under the constraints of limited computation and communication resources. In general, the changes in a dynamic environment...
This paper addresses the sensor selection problem in a passive sensor network under dynamically varying environment. It is assumed that sensors in the same local area share similar dynamic environmental characteristics but only a few may have events within their respective sensing radii. The main challenge is to select sensors for separation in two categories: (i) those sensors whose outputs contain...
This paper presents a symbolic dynamic method for real-time estimation of battery state-of-charge (SOC). In the proposed method, symbol strings are generated by partitioning (finite-length) time windows of synchronized input-output (e.g., current-voltage) pairs in the respective two-dimensional space. Then, a special class of probabilistic finite state automata (PFSA), called D-Markov machine, is...
This paper addresses the problem of target detection in dynamic environments. A key challenge here is to simultaneously achieve high probabilities of correct detection with low false alarm rates under limited computation and communication resources. To this end, a procedure of binary hypothesis testing is proposed based on agglomerative hierarchical feature clustering. The proposed procedure has been...
This paper presents real-time parameter identification in battery systems as a paradigm of dynamic data-driven application systems (DDDAS). In the proposed method, symbol sequences are generated by partitioning (finite-length) time series data of synchronized input-output (i.e., current-voltage) pairs in the respective two-dimensional space. Then, a special class of probabilistic finite state automata...
This paper addresses sensor network-based surveillance of target detection and target type & motion classification. The performance of target detection and classification could be compromised (e.g., due to high rates of false alarm and misclassification), because of inadequacies of feature extraction from (possibly noisy) sensor data and subsequent pattern classification over the network. A feature...
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