In many wireless sensor applications, skyline monitoring queries that continuously retrieve the skyline objects as well as the complete set of nodes that reported them play an important role. This paper presents SKYMON, a novel energy-efficient monitoring approach. The basic idea is to prune nodes that cannot yield a skyline result at the sink, as indicated by their (error bounded) prediction values, to suppress unnecessary sensor updates. Every node is associated with a prediction model, which is maintained at both the node and the sink. Sensors check sensed data against model-predicted values and transmit prediction errors to the sink. A data representation scheme is then developed to calculate an approximate view of each node's reading based on prediction errors and prediction values, which facilitates safe node pruning at the sink. We also develop a piecewise linear prediction model to maximize the benefit of making the predictions. Our proposed approach returns the exact results, while deceasing the number of queried nodes and transferred data. Extensive simulation results show that SKYMON substantially outperforms the existing TAG-based approach and MINMAX approach in terms of energy consumption.

According to the match-degree between lightpaths, an HC-sharing approach is proposed to assign wavelength for an arriving transmission request for dynamic traffic in LOBS-based datacenter networks. The simulation results demonstrate that the proposed approach can provide lower block probability than other approaches for both unicast and multicast transmissions.

In this letter, we present a radix-R regular interconnection pattern family of factorizations for the WHT-FFT with identical stage-to-stage interconnection pattern in a unified form, where R is any power of 2. This family of algorithms has identical sparse matrix factorization in each stage and can be implemented in a merged butterfly structure, which conduce to regular and efficient memory managing scalable to high radices. And in each stage, the butterflies with same twiddle factor set are aggregated together, which can reduce the twiddle factor evaluations or accesses to the lookup table. The kinds of factorization can also be extended to FFT, WHT and SCHT with identical stage-to-stage interconnection pattern.

In this paper, a miniaturized absorptive/transmissive radome with switchable passband and wide absorbing band is designed. Pin diodes are loaded on the radome in order to obtain switchable passband and miniaturized unit cells, while the resistor loaded double square loops are used to absorb the incident wave. The total thickness of the radome is only 4.5mm. Its transmission and absorbing properties are verified by both synthetic experiments and measurements in the anechoic chamber. Furthermore, the switchable passband of the radome is also evaluated using a waveguide simulator.

Transfer boosting, a branch of instance-based transfer learning, is a commonly adopted transfer learning method. However, currently popular transfer boosting methods focus on binary classification problems even though there are many multi-classification tasks in practice. In this paper, we developed a new algorithm called MultiTransferBoost on the basis of TransferBoost for multi-classification. MultiTransferBoost firstly separated the multi-classification problem into several orthogonal binary classification problems. During each iteration, MultiTransferBoost boosted weighted instances from different source domains while each instance's weight was assigned and updated by evaluating the difficulty of the instance being correctly classified and the “transferability” of the instance's corresponding source domain to the target. The updating process repeated until it reached the predefined training error or iteration number. The weight update factors, which were analyzed and adjusted to minimize the Hamming loss of the output coding, strengthened the connections among the sub binary problems during each iteration. Experimental results demonstrated that MultiTransferBoost had better classification performance and less computational burden than existing instance-based algorithms using the One-Against-One (OAO) strategy.

We succeeded in detoxification of hexachloro-benzene adsorbed on artificially produced fly ash in air by irradiating microwave (2.45 GHz) in the presence of activated carbon powder. Hexachlorobenzene was decomposed by 50-90% at 200-300 by MW irradiation of 1-1.5 min when the ash contained activated carbon by 12 wt% and water by 10 wt%. Chlorinated benzene derivatives are dechlorinated through substitution of chloride anion with hydroxylation produced by basic CaO in the co-presence of activated carbon effectively heated by MW. This method using microwave irradiation enables us to treat the contaminated fly ash in a shorter time and decompose hexachlorobenzene more efficiently than the conventional heating.

Distributed channel selection schemes are proposed in this paper to mitigate the flow-in-the-middle (FIM) starvation in dense wireless local area networks (WLANs). The FIM starvation occurs when the middle transmitter is within the carrier sense range of two exterior transmitters, while the two exterior transmitters are not within the carrier sense range of each other. Since an exterior transmitter sends a frame regardless of the other, the middle transmitter has a high probability of detecting the channel being occupied. Under heavy traffic conditions, the middle transmitter suffers from extremely low transmission opportunities, i.e., throughput starvation. The basic idea of the proposed schemes is to let each access point (AP) select the channel which has less three-node-chain topologies within its two-hop neighborhood. The proposed schemes are formulated in strategic form games. Payoff functions are designed so that they are proved to be potential games. Therefore, the convergence is guaranteed when the proposed schemes are conducted in a distributed manner by using unilateral improvement dynamics. Moreover, we conduct evaluations through graph-based simulations and the ns-3 simulator. Simulations confirm that the FIM starvation has been mitigated since the number of three-node-chain topologies has been significantly reduced. The 5th percentile throughput has been improved.

The rapid development of information techniques has lead to more and more high-dimensional datasets, making classification more difficult. However, not all of the features are useful for classification, and some of these features may even cause low classification accuracy. Feature selection is a useful technique, which aims to reduce the dimensionality of datasets, for solving classification problems. In this paper, we propose a modified bat algorithm (BA) for feature selection, called MBAFS, using a SVM. Some mechanisms are designed for avoiding the premature convergence. On the one hand, in order to maintain the diversity of bats, they are guided by the combination of a random bat and the global best bat. On the other hand, to enhance the ability of escaping from local optimization, MBAFS employs one mutation mechanism while the algorithm trapped into local optima. Furthermore, the performance of MBAFS was tested on twelve benchmark datasets, and was compared with other BA based algorithms and some well-known BPSO based algorithms. Experimental results indicated that the proposed algorithm outperforms than other methods. Also, the comparison details showed that MBAFS is competitive in terms of computational time.

Spectrum sensing is the first task performed by cognitive radio (CR) networks. In this paper we propose a spectrum sensing algorithm for orthogonal frequency division multiplex (OFDM) signal based on deep learning and covariance matrix graph. The advantage of deep learning in image processing is applied to the spectrum sensing of OFDM signals. We start by building the spectrum sensing model of OFDM signal, and then analyze structural characteristics of covariance matrix (CM). Once CM has been normalized and transformed into a gray level representation, the gray scale map of covariance matrix (GSM-CM) is established. Then, the convolutional neural network (CNN) is designed based on the LeNet-5 network, which is used to learn the training data to obtain more abstract features hierarchically. Finally, the test data is input into the trained spectrum sensing network model, based on which spectrum sensing of OFDM signals is completed. Simulation results show that this method can complete the spectrum sensing task by taking advantage of the GSM-CM model, which has better spectrum sensing performance for OFDM signals under low SNR than existing methods.