This letter investigates the space-time block codes from quasi-orthogonal design as a tradeoff between high transmission rate and low decoding complexity. By studying the role orthogonality plays in space-time block codes, upper bound of transmission rate and lower bound of decoding complexity for quasi-orthogonal design are claimed. From this point of view, novel algorithms are developed to construct specific quasi-orthogonal designs achieving these bounds.

We previously proposed an unsupervised model using the inclusion-exclusion principle to compute sentence information content. Though it can achieve desirable experimental results in sentence semantic similarity, the computational complexity is more than O(2n). In this paper, we propose an efficient method to calculate sentence information content, which employs the thinking of the difference set in hierarchical network. Impressively, experimental results show that the computational complexity decreases to O(n). We prove the algorithm in the form of theorems. Performance analysis and experiments are also provided.

In this paper, a practical adaptive TuCM scheme is proposed, and its adaptive method is described. With some hardware considerations, a suboptimal optimization algorithm which shows that the number of fading regions is variable is put forward. The proposed adaptive TuCM comes within 3 dB of fading channel capacity, exhibits about 3 dB power gain over conventional adaptive TCM, and is easy to realize by hardware. Considering delay and channel estimation error, the BER performance of adaptive TuCM is analyzed and simulated. In the performance analysis, the method of data fitting is applied to obtain the BER expression for TuCM, and a fitting mathematical model is proposed. Results show that adaptive TuCM is very sensitive to delay and channel estimation error. To alleviate these problems, we proposed an improved power adaptation that can make adaptive TuCM practical.

The energy efficiency of Internet of Things (IoT) could be improved by RF energy transfer technologies.Aiming at IoT applications with a mobility-constrained mobile sink, a double-sourced energy transfer (D-ET) scheme is proposed. Based on the hierarchical routing information of network nodes, the Simultaneous Wireless Information and Power Transfer (SWIPT) method helps to improve the global data gathering performance. A genetic algorithm and graph theory are combined to analyze the node energy consumption distribution. Then dedicated charger nodes are deployed on the basis of the genetic algorithm's output. Experiments are conducted using Network Simulator-3 (NS-3) to evaluate the performance of the D-ET scheme. The simulation results show D-ET outperforms other schemes in terms of network lifetime and data gathering performance.

Word sense disambiguation (WSD) is to identify the right sense of ambiguous words via mining their context information. Previous studies show that classifier combination is an effective approach to enhance the performance of WSD. In this paper, we systematically review state-of-the-art methods for classifier combination based WSD, including probability-based and voting-based approaches. Furthermore, a new classifier combination based WSD, namely the probability weighted voting method with dynamic self-adaptation, is proposed in this paper. Compared with existing approaches, the new method can take into consideration both the differences of classifiers and ambiguous instances. Exhaustive experiments are performed on a real-world dataset, the results show the superiority of our method over state-of-the-art methods.

Exploring the structural information as prior to facial images is a key issue of face super-resolution (SR). Although deep convolutional neural networks (CNNs) own powerful representation ability, how to accurately use facial structural information remains challenges. In this paper, we proposed a new residual fusion network to utilize the multi-scale structural information for face SR. Different from the existing methods of increasing network depth, the bottleneck attention module is introduced to extract fine facial structural features by exploring correlation from feature maps. Finally, hierarchical scales of structural information is fused for generating a high-resolution (HR) facial image. Experimental results show the proposed network outperforms some existing state-of-the-art CNNs based face SR algorithms.

Sentence similarity computation is an increasingly important task in applications of natural language processing such as information retrieval, machine translation, text summarization and so on. From the viewpoint of information theory, the essential attribute of natural language is that the carrier of information and the capacity of information can be measured by information content which is already successfully used for word similarity computation in simple ways. Existing sentence similarity methods don't emphasize the information contained by the sentence, and the complicated models they employ often need using empirical parameters or training parameters. This paper presents a fully unsupervised computational model of sentence semantic similarity. It is also a simply and straightforward model that neither needs any empirical parameter nor rely on other NLP tools. The method can obtain state-of-the-art experimental results which show that sentence similarity evaluated by the model is closer to human judgment than multiple competing baselines. The paper also tests the proposed model on the influence of external corpus, the performance of various sizes of the semantic net, and the relationship between efficiency and accuracy.

Linear codes have wide applications in many fields such as data storage, communication, cryptography, combinatorics. As a subclass of linear codes, minimal linear codes can be used to construct secret sharing schemes with good access structures. In this paper, we first construct some new classes of linear codes by selecting definition set properly. Then, the lengths, dimensions and the weight distribution of the codes are determined by investigating whether the intersections of the supports of vectors and the definition sets are empty. Results show that both wide and narrow minimal linear codes are contained in the new codes. Finally, we extend some existing results to general cases.

Unlike Radio Frequency Identification (RFID), emerging Computational RFID (CRFID) integrates the RF front-end and MCU with multiple sensors. CRFIDs need to transmit data within the interrogator range, so when the tags moved rapidly or the contact duration with interrogator is limited, the sensor data collected by CRFID must be transferred to interrogator quickly. In this paper, we focus on throughput optimization for backscatter link, take physical and medium access control (MAC) layers both into consideration, put forward our scheme called ORRIS. On physical layer, we propose Cluster Gather Degree (CGD) indicator, which is the clustering degree of signal in IQ domain. Then CGD is regarded as the criterion to adaptively adjust the rate encoding mode and link frequency, accordingly achieve adaptive rate transmission. On MAC layer, based on the idea of asynchronous transfer, we utilize the the number of clusters in IQ domain to select the optimal Q value as much as possible. So that achieve burst transmission or bulk data transmission. Experiments and analyses on the static and mobile scenarios show that our proposal has significantly better mean throughput than BLINK or CARA, which demonstrate the effectiveness of our scheme.