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Accurate reconstruction of hyperspectral image(HSI) from a few random sampled measurements is crucial for hyperspectal compressive sensing. The underlying sparsity of HSI is one crucial factor for HSI reconstruction. However, the s-parsity is unknown in reality and varied with different noise. To address this problem, a novel nonseparable sparsity based hyperspectral compressive sensing(NSHCS) method...
IoT (Internet of Things) bridges the physical world and information space. IoT services are environment sensitive and event-driven. The new IoT service architecture should adapt to these features. This paper analyses IoT sensing service characteristics and proposes the future services architecture. It is focused on the middleware architecture and the interface presentation technology. In the middleware...
In this paper, we propose a novel spectrum sensing method for Time Domain Synchronous-Orthogonal Frequency Division Multiplexing (TDS-OFDM) signals in Cognitive Radio (CR) Networks and the key idea is to employ the Pseudo-Noise (PN) sequences embedded in the frame headers to carry out the spectrum sensing. We employ the modulation method of the PN sequence rather than the cyclostationary of PN sequence...
To reduce huge consumption of processing hyperspectral images(HSI), a novel Bayesian unmixing compressive sensing framework is proposed to compress and reconstruct HSI effectively, called structured sparse Bayesian umixing compressive sensing(SSBUCS). SSBUCS unites compressive sensing and hyperspectral linear mixed model in Bayesian framework. An HSI is decomposed as a linear combination of endmembers...
In recent years, new business and research opportunities have been increasingly emerging in the field of large-scale context-aware pervasive systems (e.g. pervasive health-care, city traffic monitoring, environmental monitoring, smart grids). These large-scale pervasive systems are characterized by the need to employ large number of context sources, process massive amounts of real-time context data,...
Coverage holes often exist in wireless sensor networks deployed with static nodes. In order to patch the holes and recover the network performance, an algorithm based on mobile nodes is presented, which guides the mobile nodes to the position of holes, and then get the resilient coverage. The static sensors bound a coverage hole can compute the optimum position for the mobile sensors which, after...
Object tracking is one of the fundamental applications in Wireless Sensor Networks (WSNs). To detect and track the appearance and movement of malicious object(s), a number of sensors are usually deployed randomly in the area of interest specially for hostile application scenarios. Following random deployment strategy, the resulting WSNs conform to Poisson or Gaussian distribution, depending on specific...
This paper addresses the problem of estimating the odor path which is most likely taken by the odor patch detected by the concentration sensor on a mobile robot moving in an indoor dynamic airflow environment. The odor path estimation is useful for plume tracing and odor source declaration. A novel algorithm for odor path likelihood mapping in the dynamic airflow environment is proposed. The algorithm...
An improved single odor/gas source searching approach using a mobile robot by combining image recognition in complex environments is presented. First, color image segmentation of prospective visual candidates is achieved using support vector machines (SVM). Second, the features of those candidates, such as color, shape and orientation (the posture of the object) are extracted. Third, the robot finds...
A single-step, bottom-up technique has been used to fabricate sensors, based on conducting polymer nanofibers. A small amount of an aqueous solution containing aniline, a dopant, and an oxidant was placed on an interdigitated electrode array. Ultraviolet (UV)-irradiation of the solutions affected polymerization, yielding a highly porous film of polyaniline nanofibers with a mean diameter of around...
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