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In this paper, we present new feedforward FFT hardware architectures based on rotator allocation. The rotator allocation approach consists in distributing the rotations of the FFT in such a way that the number of edges in the FFT that need rotators and the complexity of the rotators are reduced. Radix-2 and radix-2k feedforward architectures based on rotator allocation are presented in this paper. Experimental results show that the proposed architectures reduce the hardware cost significantly with respect to previous FFT architectures. [ABSTRACT FROM PUBLISHER]

This paper is concerned with a general class of distributed constrained optimization problems over a multiagent network, where the global objective function is represented by the sum of all local objective functions. Each agent in the network only knows its own local objective function, and is restricted to a global nonempty closed convex set. We discuss the scenario where the communication of the whole multiagent network is expressed as a sequence of time-varying general unbalanced directed graphs. The directed graphs are required to be uniformly jointly strongly connected and the weight matrices are only row-stochastic. To collaboratively deal with the optimization problems, existing distributed methods mostly require the communication graph to be fixed or balanced, which is impractical and hardly inevitable. In contrast, we propose a new distributed projection subgradient algorithm which is applicable to the time-varying general unbalanced directed graphs and does not need each agent to know its in-neighbors’ out-degree. When the objective functions are convex and Lipschitz continuous, it is proved that the proposed algorithm exactly converges to the optimal solution. Simulation results on a numerical experiment are shown to substantiate feasibility of the proposed algorithm and correctness of the theoretical findings. [ABSTRACT FROM AUTHOR]

With an increase in the capabilities of sensors, wireless visual sensor networks (WVSNs) are being researched to perform more complicated tasks as each visual sensor performs video capture, processing and sharing. Because each visual sensor operates by using its own resources including computation and communication under a limited power supply, it is necessary to develop an energy management scheme suitable for WVSNs. In particular, when multiple camera modules of visual sensors are aimed at some regions with different fields of views (FOVs), undesirable power consumption for encoding may occur among distributed visual sensors. Because some overlapped FOVs among captured images give rise to encoding redundancy, this leads to an increase in data quantity and power consumption for the encoding and communication. To resolve this problem, in this paper, we present a novel strategy for lifetime maximization of the WVSN. In order to estimate overlapped FOVs without using complicated procedures such as pattern or object recognition, we propose a geographical model to estimate overlapped FOVs based on location and visual direction. Based on this model, the power-rate-distortion (P-R-D) is determined and used to construct an optimization problem for minimizing the power consumption of each node. Through the proposed scheme, including distributed power allocation and node scheduling under simple information sharing, the network lifetime is maximized. Numerical results demonstrate the validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]