Sijia Liu

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We consider the problem of finding optimal time-periodic sensor schedules for estimating the state of a large-scale dynamical system. We assume that a large number of sensors have been deployed and that the sensors are subject to resource constraints, which limits the number of times each can be activated over one period. We seek an algorithm that strikes a(More)
—In the context of distributed estimation, we consider the problem of sensor collaboration, which refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. While incorporating the cost of sensor collaboration, we aim to find optimal sparse collaboration schemes subject to a certain information or energy(More)
—In this paper, we consider the problem of estimating a spatially varying field in a wireless sensor network, where resource constraints limit the number of sensors selected in the network that provide their measurements for field estimation. Based on a one-to-one correspondence between the selected sensors and the nonzero elements of Kriging weights, we(More)
— In this paper, we present an optimal sensor staggering strategy to estimate a spatially and temporally varying field using quantized sensor data in wireless sensor networks. In order to predict the field intensity at a particular field point of interest, we first extend ordinary kriging to the case of quantized sensor data. Then, we derive the Average(More)
—In this letter, we consider the problem of detecting a high dimensional signal based on compressed measurements with physical layer secrecy guarantees. We assume that the network operates in the presence of an eavesdropper who intends to discover the state of the nature being monitored by the system. We design measurement matrices which maximize the(More)
In this working note, we introduce our participation at the ImageCLEF 2016 Handwritten Document Retrieval Task. We mainly focused on hyphenation detection using line images and information retrieval using n-best results. The hyphenation detection step utilizes extracted image features from beginning and end of a line and a binary classifier to determine if(More)