In this paper, the problem of lifetime extension of wireless sensor networks (WSNs) with redundant sensor nodes deployed in 3D vegetation-covered fields is modeled, which includes building communication models, network model and energy model. Generally, such a problem cannot be solved by a conventional method directly. Here we propose an Artificial Bee Colony (ABC) based optimal grouping algorithm (ABC-OG) to solve it. The main contribution of the algorithm is to find the optimal number of feasible subsets (FSs) of WSN and assign them to work in rotation. It is verified that reasonably grouping sensors into FSs can average the network energy consumption and prolong the lifetime of the network. In order to further verify the effectiveness of ABC-OG, two other algorithms are included for comparison. The experimental results show that the proposed ABC-OG algorithm provides better optimization performance.

This paper investigates the resource allocation problem for the downlink of non-orthogonal multiple access (NOMA) networks. A novel resource allocation method is proposed to deal with the problem of maximizing the system capacity while taking into account user fairness. Since the optimization problem is nonconvex and intractable, we adopt the idea of step-by-step optimization, decomposing it into user pairing, subchannel and power allocation subproblems. First, all users are paired according to their different channel gains. Then, the subchannel allocation is executed by the proposed subchannel selection algorithm (SSA) based on channel priority. Once the subchannel allocation is fixed, to further improve the system capacity, the subchannel power allocation is implemented by the successive convex approximation (SCA) approach where the nonconvex optimization problem is transformed into the approximated convex optimization problem in each iteration. To ensure user fairness, the upper and lower bounds of the power allocation coefficients are derived and combined by introducing the tuning coefficients. The power allocation coefficients are dynamically adjustable by adjusting the tuning coefficients, thus the diversified quality of service (QoS) requirements can be satisfied. Finally, simulation results demonstrate the superiority of the proposed method over the existing methods in terms of system performance, furthermore, a good tradeoff between the system capacity and user fairness can be achieved.

The throughput performance of a slotted, non-persistent Idle-Signal Casting Multiple Access (ICMA) protocol under the effects of various combinations of Rayleigh fading, lognormal shadowing, and spatial distribution of mobile users is studied. The opposing effects of propagation impairments on the performance of the protocol through simultaneously increasing the probability of receiver capture and attenuation of the received signal power level are demonstrated.

Nowadays, malware is a serious threat to the Internet. Traditional signature-based malware detection method can be easily evaded by code obfuscation. Therefore, many researchers use the high-level structure of malware like function call graph, which is impacted less from the obfuscation, to find the malware variants. However, existing graph match methods rely on approximate calculation, which are inefficient and the accuracy cannot be effectively guaranteed. Inspired by the successful application of graph convolutional network in node classification and graph classification, we propose a novel malware similarity metric method based on graph convolutional network. We use graph convolutional network to compute the graph embedding vectors, and then we calculate the similarity metric of two graph based on the distance between two graph embedding vectors. Experimental results on the Kaggle dataset show that our method can applied to the graph based malware similarity metric method, and the accuracy of clustering application with our method reaches to 97% with high time efficiency.

A novel compact 5-pole bandpass filter (BPF) using two different types of resonators, one is coaxial TEM-mode resonator and the other dielectric triple-mode resonator, is proposed in this paper. The coaxial resonator is a simple single-mode resonator, while the triple-mode dielectric resonator (DR) includes one TM01δ mode and two degenerate HE11 modes. An excellent spurious performance of the BPF is obtained due to the different resonant behaviors of these two types of resonators used in the BPF. The coupling scheme of the 5-pole BPF includes two cascade triplets (CTs) which produce two transmission zeros (TZs) and a sharp skirt of the passband. Behaviors of the resonances, the inter-resonance couplings, as well as their tuning methods are investigated in detail. A procedure of mapping the coupling matrix of the BPF to its physical dimensions is developed, and an optimization of these physical dimensions is implemented to achieve best performance of the filter. The designed BPF is operated at 1.84GHz with a bandwidth of 51MHz. The stopband rejection is better than 20dB up to 9.7GHz (about 5.39×f0) except 7.85GHz. Good agreement between the designed and theoretically synthesized responses of the BPF is reached, verifying well the proposed configuration of the BPF and its design method.

Ad hoc networks are becoming an interesting research area, as they inherently support unique network applications for the wireless communications in a rugged environment, which requires rapid deployment and is difficult to be provided by an infrastructure network. Many issues need to be addressed for the ad hoc networks. In this paper, we propose an efficient distributed coordination function on the media access control protocol to enhance the power conservation of mobile hosts by using a power control algorithm and the network throughput of an ad hoc network by using an algorithm for simultaneous frame transmissions. Extensive simulation is studied to evaluate the improvement of the proposed method. The results of the simulation exhibit significant improvement to the standard access control protocol. With slight improvement of network throughput, up to 85% of the consumed energy was able to be saved in compared to the standard protocol and up to 7 times of the energy efficiency was enhanced with the proposed method.

Bag-of-Visual-Words representation has recently become popular for scene classification. However, learning the visual words in an unsupervised manner suffers from the problem when faced these patches with similar appearances corresponding to distinct semantic concepts. This paper proposes a novel supervised learning framework, which aims at taking full advantage of label information to address the problem. Specifically, the Gaussian Mixture Modeling (GMM) is firstly applied to obtain "semantic interpretation" of patches using scene labels. Each scene induces a probability density on the low-level visual features space, and patches are represented as vectors of posterior scene semantic concepts probabilities. And then the Information Bottleneck (IB) algorithm is introduce to cluster the patches into "visual words" via a supervised manner, from the perspective of semantic interpretations. Such operation can maximize the semantic information of the visual words. Once obtained the visual words, the appearing frequency of the corresponding visual words in a given image forms a histogram, which can be subsequently used in the scene categorization task via the Support Vector Machine (SVM) classifier. Experiments on a challenging dataset show that the proposed visual words better perform scene classification task than most existing methods.

This paper presents an inter-satellite link (ISL) reassignment method to optimize the snapshot routing performance for polar-orbit LEO satellite networks. When the snapshot routing tables are switching simultaneously in all satellites, we propose to reassign the inter-plane ISLs with regularity to improve the quality of the next snapshot, such as snapshot duration, on-board transceiver utilization ratio and end to end delay. Evaluations indicate that our method can attain equal-length snapshots regardless of the latitude of the polar area border, and so is superior to the natural partition method. Meanwhile, compared with the equal partition method which is used in the Iridium system, our method can prolong 82.87% snapshot duration, increase 8.68% on-board transceiver utilization ratio and reduce 5.30% average end to end delay of the whole network. Therefore, we believe that the ISL reassignment method can be efficiently applied in all practical polar-orbit LEO satellite networks.

Fused Blocked Pattern Matching is a kind of approximate matching based on Blocked Pattern Matching, and can be used in identification of fused peptides in tumor genomes. In this paper, we propose a new algorithm for fused blocked pattern matching. We give a comparison between Julio's solution and ours, which shows our algorithm is more efficient.

In this paper, we consider the problem of bearing estimation for spatially distributed sources in unknown spatially-correlated noise. Assumed that the noise covariance matrix is centro-Hermitian, a differential denoising scheme is developed. Combined it with the classic DSPE algorithm, a differential denoising estimator is formulated. Its modified version is also derived. Exactly, the differential processing is first imposed on the covariance matrix of array outputs. The resulting differential signal subspace (DSS) is then utilized to weight array outputs. The noise components orthogonal to DSS are eliminated. Based on eigenvalue decomposition of the covariance matrix of weighted array outputs, the DSPE null spectrum is constructed. The asymptotic performance of the proposed bearing estimator is evaluated in a closed form. Moreover, in order to improve the performance of bearing estimation in case of low signal-to-noise ratio, a modified differential denoising estimator is proposed. Simulation results show the effectiveness of the proposed estimators under the low SNR case. The impacts of angular spread and number of sensors are also investigated.

In the past few years, discriminant analysis and manifold learning have been widely used in feature extraction. Recently, the sparse representation technique has advanced the development of pattern recognition. In this paper, we combine both discriminant analysis and manifold learning with sparse representation technique and propose a novel feature extraction approach named sparsity preserving embedding with manifold learning and discriminant analysis. It seeks an embedded space, where not only the sparse reconstructive relations among original samples are preserved, but also the manifold and discriminant information of both original sample set and the corresponding reconstructed sample set is maintained. Experimental results on the public AR and FERET face databases show that our approach outperforms relevant methods in recognition performance.

Survivable virtual network embedding (SVNE) is one of major challenges of network virtualization. In order to improve the utilization rate of the substrate network (SN) resources with virtual network (VN) topology connectivity guarantee under link failure in SN, we first establishes an Integer Linear Programming (ILP) model for that under SN supports path splitting. Then we designs a novel survivable VN topology protection method based on particle swarm optimization (VNE-PSO), which redefines the parameters and related operations of particles with the embedding overhead as the fitness function. Simulation results show that the solution significantly improves the long-term average revenue of the SN, the acceptance rate of VN requests, and reduces the embedding time compared with the existing research results.

5G has achieved large-scale commercialization across the world and the global 6G research and development is accelerating. To support more new use cases, 6G mobile communication systems should satisfy extreme performance requirements far beyond 5G. The physical layer key technologies are the basis of the evolution of mobile communication systems of each generation, among which three key technologies, i.e., duplex, waveform and multiple access, are the iconic characteristics of mobile communication systems of each generation. In this paper, we systematically review the development history and trend of the three key technologies and define the Non-Orthogonal Physical Layer (NOPHY) concept for 6G, including Non-Orthogonal Duplex (NOD), Non-Orthogonal Multiple Access (NOMA) and Non-Orthogonal Waveform (NOW). Firstly, we analyze the necessity and feasibility of NOPHY from the perspective of capacity gain and implementation complexity. Then we discuss the recent progress of NOD, NOMA and NOW, and highlight several candidate technologies and their potential performance gain. Finally, combined with the new trend of 6G, we put forward a unified physical layer design based on NOPHY that well balances performance against flexibility, and point out the possible direction for the research and development of 6G physical layer key technologies.

In deep sub-micrometer CMOS process, owing to the thin gate oxide and small subthreshold voltage, the leakage current becomes more and more serious. The leakage current has made the impact on phase-locked loops (PLLs). In this paper, the compensation circuits are presented to reduce the leakage current on the charge pump circuit and the MOS capacitor as the loop filter. The proposed circuit has been fabricated in 0.13-µm CMOS process. The power consumption is 3 mW and the die area is 0.270.3 mm2.

In this paper, we propose a novel self-supervised learning of video representation which is capable to anticipate the video category by only reading its short clip. The key idea is that we employ the Siamese convolutional network to model the self-supervised feature learning as two different image matching problems. By using frame encoding, the proposed video representation could be extracted from different temporal scales. We refine the training process via a motion-based temporal segmentation strategy. The learned representations for videos can be not only applied to action anticipation, but also to action recognition. We verify the effectiveness of the proposed approach on both action anticipation and action recognition using two datasets namely UCF101 and HMDB51. The experiments show that we can achieve comparable results with the state-of-the-art self-supervised learning methods on both tasks.

In this paper, we present the channel estimation (CE) problem in the orthogonal frequency division multiplexing system with offset quadrature amplitude modulation (OFDM/OQAM). Most CE methods rely on the assumption of a low frequency selective channel to tackle the problem in a way similar to OFDM. However, these methods would result in a severe performance degradation of the channel estimation when the assumption is not quite inaccurate. Instead, we focus on estimating the channel impulse response (CIR) itself which makes no assumption on the degree of frequency selectivity of the channels. After describing the main idea of this technique, we present an iterative CE method that does not require zero-value guard symbols in the preamble and consequently improves the spectral efficiency. This is done by the iterative estimation of the unknown transmitted data adjacent to the preamble. Analysis and simulation results validate the efficacy of the proposed method in multipath fading channels.

Modern field programmable gate arrays (FPGAs) with heterogeneous resources are partially reconfigurable. Existing methods of reconfiguration-aware floorplanning have limitations with regard to homogeneous resources; they solve only a part of the reconfigurable problem. In this paper, first, a precise model for partially reconfigurable FPGAs is formulated, and then, a two-phase floorplanning approach is presented. In the proposed approach, resource distribution is taken into consideration at all times. In the first step, a resource-aware insertion-after-remove perturbation is devised on the basis of the multi-layer sequence pair constraint graphs, and resource-aware slack-based moves (RASBM) are made to satisfy resource requirements. In the second step, a resource-aware fixed-outline floorplanner is used, and RASBM are applied to pack the reconfigurable regions on the FPGAs. Experimental results show that the proposed approach is resource- and reconfiguration-aware, and facilitates stable floorplanning. In addition, it reduces the wire-length by 4–28% in the first step, and by 12% on average in the second step compared to the wire-length in previous approaches.

The capacity of optical transport networks has been increasing steadily and the networks are becoming more dynamic, complex, and transparent. Though it is common to use worst case assumptions for estimating the quality of transmission (QoT) in the physical layer, over provisioning results in high margin requirements. Accurate estimation on the QoT for to-be-established lightpaths is crucial for reducing provisioning margins. Machine learning (ML) is regarded as one of the most powerful methodological approaches to perform network data analysis and enable automated network self-configuration. In this paper, an artificial neural network (ANN) framework, a branch of ML, to estimate the optical signal-to-noise ratio (OSNR) of to-be-established lightpaths is proposed. It takes account of both nonlinear interference between spectrum neighboring channels and optical monitoring uncertainties. The link information vector of the lightpath is used as input and the OSNR of the lightpath is the target for output of the ANN. The nonlinear interference impact of the number of neighboring channels on the estimation accuracy is considered. Extensive simulation results show that the proposed OSNR estimation scheme can work with any RWA algorithm. High estimation accuracy of over 98% with estimation errors of less than 0.5dB can be achieved given enough training data. ANN model with R=4 neighboring channels should be used to achieve more accurate OSNR estimates. Based on the results, it is expected that the proposed ANN-based OSNR estimation for new lightpath provisioning can be a promising tool for margin reduction and low-cost operation of future optical transport networks.

Audio applications for mobile phone and portable devices are increasingly popular. To attract consumer interest, a multi-standard design on a single device is the trend of current audio decoder development. This paper presents a configurable common filterbank processor (CCFP) for AC-3, MP3 and AAC audio decoder. It is used as an accelerator for general purpose processors to improve performance. All the filterbank transforms are derived to even- or odd-point IFFT flows. In the architecture, a fully pipelined approach is developed which can be configured for different operation modes. This design is synthesized using UMC 0.18 µm library and takes about 26.7 K gates. By the fast algorithm and fully pipelined architecture, the operation cycles are greatly reduced. Therefore, it can be executed at a very low operation frequency with the range of 1.3 to 3.6 MHz. Besides, the power consumption is only 0.9 mW, 3.2 mW and 1 mW for AC-3, MP3 and AAC respectively. We further port our design on an ARM Integrator platform to make a real play system. On average, over 50% ARM performance loading can be saved and used for handling other applications.

A cumulant-based lattice algorithm for multichannel adaptive filtering is proposed in this paper. Proposed algorithm takes into account the advantages of higer-order statistics, that is, improvement of estimation accuracy, blindness to colored Gaussian noise and the possibility to estimate the nonminimum-phase system etc. Without invoking the Instrumental Variable () method as used in other papers [1], [2], the algorithm is derived directly from the recursive pseudo-inverse matrix. The behavior of the algorithm is illustrated by numerical examples.