This letter proposes a situation-adaptive detection algorithm for the improved efficiency of the detection performance and complexity in the MIMO-OFDM system. The proposed algorithm adaptively uses the QRD-M, DFE, and iterative detection scheme in according to the detection environment. Especially, the proposed algorithm effectively reduces the occurrence probability of error in the successive interference cancellation procedure by the unit of the spatial stream. The simulations demonstrate that the adaptive detection method using the proposed algorithm provides a better trade-off between detection performance and complexity in the MIMO-OFDM system.

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This paper addresses the designability of Boolean networks, i.e., the existence of a Boolean function satisfying an attractor condition under a given network structure. In particular, we present here a necessary and sufficient condition of the designability of Boolean networks with multiple attractors. The condition is characterized by the cyclicity of network structures, which allows us to easily determine the designability.

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Symmetric predicate encryption schemes support a rich class of predicates over keyword ciphertexts while preserving both keyword privacy and predicate privacy. Most of these schemes treat each keyword as the smallest unit to be processed in the generation of ciphertexts and predicate tokens. To extend the class of predicates, we treat each symbol of a keyword as the smallest unit to be processed. In this letter, we propose a novel encoding to construct a symmetric inner-product encryption scheme for position-aware symbol-based predicates. The resulting scheme can be applied to a number of secure filtering and online storage services.

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In this article, we investigate the depth distribution and the depth spectra of linear codes over the ring R=F₂+uF₂+u²F₂, where u³=1. By using homomorphism of abelian groups from R to F₂ and the generator matrices of linear codes over R, the depth spectra of linear codes of type 8^k₁4^k₂2^k₃ are obtained. We also give the depth distribution of a linear code C over R. Finally, some examples are presented to illustrate our obtained results.

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Recently, many neural networks have been proposed for radar sea clutter suppression. However, they have poor performance under the condition of low signal to interference plus noise ratio (SINR). In this letter, we put forward a novel method to detect a small target embedded in sea clutter based on an optimal filter. The proposed method keeps the energy in the frequency cell under test (FCUT) invariant, at the same time, it minimizes other frequency signals. Finally, detect target by judging the output SINR of every frequency cell. Compared with the neural networks, the algorithm proposed can detect under lower SINR. Using real-life radar data, we show that our method can detect the target effectively when the SINR is higher than -39dB which is 23dB lower than that needed by the neural networks.

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Current research trends in computer vision have tended towards achieving the goal of recognizing human action, due to the potential utility of such recognition in various applications. Among many potential approaches, an approach involving Gaussian Mixture Model (GMM) supervectors with a Support Vector Machine (SVM) and a nonlinear GMM KL kernel has been proven to yield improved performance for recognizing human activities. In this study, based on tensor analysis, we develop and exploit an extended class of action features that we refer to as gradient-flow tensor divergence. The proposed method has shown a best recognition rate of 96.3% for a KTH dataset, and reduced processing time.

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