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Abstract-In this work we address the problem of estimating parameters of diffusion phenomena via autonomous wireless sensor networks. Diffusion phenomena, such as the propagation of a gas in the air or of a chemical agent in the water, can be modeled by means of partial differential equations (PDE's). In several scenarios, the parameters characterizing such models, i.e. the coefficients of the PDE's, are not known a-priori and need to be estimated. We develop an adaptive approach for the distributed identification of the parameters of diffusion models for both the cases of known and unknown boundary conditions (BC's). The technique also applies to the case of spatially varying parameters. We present simulation results to show the performance and the various trade-offs of the method.

Network communication and micro-electro-mechanical embedded technologies have attracted much attention in recent years. Through these technologies, the capabilities of sensing, identification, and communication can be embedded in various smart devices. These smart devices can automatically connect to the Internet and form an intelligent network called Internet of Things (IoT). However, these devices are embedded with different wireless communication interfaces such as Wi-Fi and ZigBee. This paper presents the design and implementation of an IoT access point that supports functionalities of coordination of various wireless transmission protocols. Based on the existing Wi-Fi access point, we have embedded a ZigBee module and implemented ZigBee and UPnP protocols into the designed IoT access point, which supports ZigBee communication capabilities over the Internet.

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Being motivated by the multilayer RECOS (REctified-COrrelations on a Sphere) transform, we develop a data-driven Saak (Subspace approximation with augmented kernels) transform in this work. The Saak transform consists of three steps: 1) building the optimal linear subspace approximation with orthonormal bases using the second-order statistics of input vectors, 2) augmenting each transform kernel with its negative, 3) applying the rectified linear unit (ReLU) to the transform output. The Karhunen-Loéve transform (KLT) is used in the first step. The integration of Steps 2 and 3 is powerful since they resolve the sign confusion problem, remove the rectification loss and allow a straightforward implementation of the inverse Saak transform at the same time. Multiple Saak transforms are cascaded to transform images of a larger size. All Saak transform kernels are derived from the second-order statistics of input random vectors in a one-pass feedforward manner. Neither data labels nor backpropagation is used in kernel determination. Multi-stage Saak transforms offer a family of joint spatial-spectral representations between two extremes; namely, the full spatial-domain representation and the full spectral-domain representation. We select Saak coefficients of higher discriminant power to form a feature vector for pattern recognition, and use the MNIST dataset classification problem as an illustrative example.