Selected mapping (SLM) is a promising distortionless technique for controlling the high peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) systems. In SLM, phase sequence selection plays an important role for efficient PAPR reduction. Although some phase sequence sets have been proposed in past studies, we show that an optimal selection is still desired for the phase sequences from the same set. Therefore, this letter develops a measureable phase sequence selection criterion to optimally select the phase sequences from both the same and different sets, so as to achieve near optimum PAPR reduction performance in SLM-OFDM systems.

In this paper, the research advances in SIW-like (Substrate Integrated Waveguide-like) guided wave structures and their applications in the State Key Laboratory of Millimeter Waves of China is reviewed. Our work is concerned with the investigations on the propagation characteristics of SIW, half-mode SIW (HMSIW) and the folded HMSIW (FHMSIW) as well as their applications in microwave and millimeter wave filters, diplexers, directional couplers, power dividers, antennas, power combiners, phase shifters and mixers etc. Selected results are presented to show the interesting features and advantages of those new techniques.

This letter presents a novel pre-transformed interleaved frequency division multiple access (IFDMA) transmission structure that improves system performance without the desire of channel information at the transmitter. Simulation results show that the proposed structure can provide improved system performance while only moderately increasing the complexity, and keeping the advantage of a low peak-to-average power ratio (PAPR) of the transmitted signal for SISO and MISO channels.

Fast identification is an urgent demand for modern RFID systems. In this paper, we propose a novel algorithm, access probability adjustment based fine-grained Q-algorithm (APAFQ), to enhance the efficiency of RFID identification with low computation overhead. Specifically, instead of estimation accuracy, the target of most proposed anti-collision algorithms, the APAFQ scheme is driven by updating Q value with two different weights, slot by slot. To achieve higher identification efficiency, the reader adopts fine-grained access probability during the identification process. Moreover, based on the responses from tags, APAFQ adjusts the access probability adaptively. Simulations show the superiority of APAFQ over existing Aloha-based algorithms.

In our previous work, we proposed to combine ConceptNet and WordNet for Word Sense Disambiguation (WSD). The ConceptNet was automatically disambiguated through Normalized Google Distance (NGD) similarity. In this letter, we present several techniques to enhance the performance of the ConceptNet disambiguation and use this enriched semantic knowledge in WSD task. We propose to enrich both the WordNet semantic knowledge and NGD to disambiguate the concepts in ConceptNet. Furthermore, we apply the enriched semantic knowledge to improve the performance of WSD. From a number of experiments, the proposed method has been obtained enhanced results.

Consider an undirected graph G=(V,E) with n (=|V|) vertices and m (=|E|) edges. It is well-known that the problem of computing the sequence Nn-1,Nn,...,Nm is #P-complete (see e.g.,[3]), where Ni denotes the number of connected spanning subgraphs with i (n-1!im) edges in G. In this paper, by proving new inequalities on the sequence Nn-1,Nn,...,Nm, we show an interesting and stronger property that the sequence γn-1,γn,...,γm, where γi denotes the average number of spanning trees in the connected spanning subgraphs with i edges, is a convex sequence as well as a monotonically increasing sequence, although this property does not hold for the sequence Nn-1,Nn,...,Nm.

In this letter, we investigate the physical layer security in multi-user multi-relay networks, where each relay is not merely a traditional helper, but at the same time, can become a potential eavesdropper. We first propose an efficient low-complexity user and relay selection scheme to significantly reduce the amount of channel estimation as well as the amount of potential links for comparison. For the proposed scheme, we derive the closed-form expression for the lower bound of ergodic secrecy rate (ESR) to evaluate the system secrecy performance. Simulation results are provided to verify the validity of our expressions and demonstrate how the ESR scales with the number of users and relays.

In this study, an extraction method of failure sound signal which is strongly contaminated by noise is investigated by genetic algorithm and statistical tests of the frequency domain for the failure diagnosis of machinery. In order to check the extraction accuracy of the failure signal and obtain the optimum extraction of failure signal, the "existing probability Ps (t*k) of failure signal" and statistical information Iqp are defined as the standard indices for evaluation of the extraction results. It has been proven by practical field data and application of the inspection and diagnosis robot that the extraction method discussed in this paper is effective for detection of a failure and distinction of it's origin in the diagnosis of machinery.

Sharing data might bring the risk of disclosing the sensitive knowledge in it. Usually, the data owner may choose to sanitize data by modifying some items in it to hide sensitive knowledge prior to sharing. This paper focuses on protecting sensitive knowledge in the form of frequent itemsets by data sanitization. The sanitization process may result in side effects, i.e., the data distortion and the damage to the non-sensitive frequent itemsets. How to minimize these side effects is a challenging problem faced by the research community. Actually, there is a trade-off when trying to minimize both side effects simultaneously. In view of this, we propose a data sanitization method based on evolutionary multi-objective optimization (EMO). This method can hide specified sensitive itemsets completely while minimizing the accompanying side effects. Experiments on real datasets show that the proposed approach is very effective in performing the hiding task with fewer damage to the original data and non-sensitive knowledge.

One major challenge to implement orthogonal frequency division multiplexing (OFDM) systems over doubly selective channels is the non-negligible intercarrier interference (ICI), which significantly degrades the system performance. Existing solutions to cope with ICI include zero-forcing (ZF), minimum mean square error (MMSE) and other linear or nonlinear equalization methods. However, these schemes fail to achieve a satisfactory tradeoff between performance and computational complexity. To address this problem, in this paper we propose two novel nonlinear ICI cancellation techniques, which are referred to as parallel interference cancelation (PIC) and hybrid interference cancelation (HIC). Taking advantage of the special structure of basis expansion model (BEM) based channel matrices, our proposed schemes enjoy low computational complexity and are capable of cancelling ICI effectively. Moreover, since the proposed schemes can flexibly select different basis functions and be independent of the channel statistics, they are applicable to practical OFDM based systems such as DVB-T2 over doubly selective channels. Theoretical analysis and simulation results both confirm their performance-complexity advantages in comparison with some existing methods.

We model a road network as a directed graph G(V,E) with a source s and a sink t, where each edge e has a positive length l(e) and each vertex v has a distribution function αv with respect to the traffic entering and leaving v. This paper proposes a polynomial time algorithm for evaluating the importance of each edge e E whicn is defined to be the traffic f(e) passing through e in order to assign the required traffic Fst(0) from s to t along only shortest s-t paths in accordance with the distribution function αv at each vertex v.

A low-power low-noise intermediate-frequency (IF) circuit is proposed for Gaussian frequency shift keying (GFSK) low-IF receivers. The proposed IF circuit is realized by an all-analog architecture composed of a couple of limiting amplifiers (LAs) and received signal strength indicators (RSSIs), a couple of band-pass filters (BPFs), a frequency detector (FD), a low-pass filter (LPF) and a slicer. The LA and RSSI are realized by an optimized combination of folded amplifiers and current subtractor based rectifiers to avoid the process induced depressing on accuracy. In addition, taking into account the nonlinearity and static current of rectifiers, we propose an analytical model as an accurate approximation of RSSIs' transfer character. An active-RC based GFSK demodulation scheme is proposed, and then both low power consumption and a large dynamic range are obtained. The chip is implemented with HJTC 0.18 µm CMOS technology and measured under an intermediate frequency of 200 kHz, a data rate of 100 kb/s and a modulation index of 1. The RSSI has a dynamic range of 51 dB with a logarithmic linearity error of less than 1 dB, and the slope is 23.9 mV/dB. For 0.1% bit error ratio (BER), the proposed IF circuit has the minimum input signal-to-noise ratio (SNR) of 5 dB and an input dynamic range of 55.4 dB, whereas it can tolerate a frequency offset of -3%+9.5% at 6 dB input SNR. The total power consumption is 5.655.89 mW.

Tag collision has a negative impact on the performance of RFID systems. In this letter, we propose an algorithm termed anti-collision protocol based on improved collision detection (ACP-ICD). In this protocol, dual prefixes matching and collision bit detection technique are employed to reduce the number of queries and promptly identify tags. According to the dual prefixes matching method and collision bit detection in the process of collision arbitration, idle slots are eliminated. Moreover, the reader makes full use of collision to improve identification efficiency. Both analytical and simulation results are presented to show that the performance of ACP-ICD outperforms existing anti-collision algorithms.

This paper discusses how to compute the parametric sensitivity function in continuous-time Markov chains (CTMC). The sensitivity function is the first derivative of the steady-state probability vector regarding a CTMC parameter. Since the sensitivity function is given as a solution of linear equations with a sparse matrix, several linear equation solvers are available to obtain it. In this paper, we consider Jacobi and successive-over relaxation as variants of the Gauss-Seidel algorithm. In addition, we develop an algorithm based on the Takahashi method for the sensitivity function. In numerical experiments, we comprehensively evaluate the performance of these algorithms from the viewpoint of computation time and accuracy.

In cognitive radar systems (CRSs), target scattering coefficients (TSC) can be utilized to improve the performance of target identification and classification. This work considers the problem of TSC estimation for temporally correlated target. Multiple receive antennas are adopted to receive the echo waveforms, which are interfered by the signal-dependent clutter. Unlike existing estimation methods in time domain, a novel estimation method based on Kalman filtering (KF) is proposed in frequency domain to exploit the temporal TSC correlation, and reduce the complexity of subsequent waveform optimization. Additionally, to minimize the mean square error of estimated TSC at each KF iteration, in contrary to existing works, we directly model the design process as an optimization problem, which is non-convex and cannot be solved efficiently. Therefore, we propose a novel method, similar in some way to semi-definite programming (SDP), to convert the non-convex problem into a convex one. Simulation results demonstrate that the estimation performance can be significantly improved by the KF estimation with optimized waveform.

With the development of Internet technology, the demand for signing electronic contracts has been greatly increased. The electronic contract generated by the participants in an online way enjoys the same legal effect as paper contract. The fairness is the key issue in jointly signing electronic contracts by the involved participants, so that all participants can either get the same copy of the contract or nothing. Most existing solutions only focus on the fairness of electronic contract generation between two participants, where the digital signature can effectively guarantee the fairness of the exchange of electronic contracts and becomes the conventional technology in designing the contract signing protocol. In this paper, an efficient blockchain-based multi-party electronic contract signing (MECS) protocol is presented, which not only offers the fairness of electronic contract generation for multiple participants, but also allows each participant to aggregate validate the signed copy of others. Security analysis shows that the proposed MECS protocol enjoys unforgeability, non-repudiation and fairness of electronic contracts, and performance analysis demonstrates the high efficiency of our construction.

In this paper, an automatic retransmission request (ARQ) scheme for IEEE 802.11ac is presented, which can solve the problem of severe packet loss and greatly improve the performance in error-prone environments. The proposed solution only requires to be deployed on the sender and is compatible with the 802.11 protocol. The algorithm utilizes the basic strategy of sliding retransmission and then adds the method of copying frames. The media access control (MAC) protocol data unit (MPDU) lost in the transmission and the newly added data frame brought by the sliding window change are replicated. The scheme retransmits the duplicated aggregated packet and further improves the throughput by increasing the probability of successful transmission of sub-frames. Besides, we also establish a mathematical model to analyze the performance of the proposed scheme. We introduce the concept of average aggregated sub-frames and express the sliding retransmission strategy as the aggregated transmission of average aggregated sub-frames, thereby simplifying the model and effectively analyzing the theoretical throughput of the proposed algorithm. The simulation results of Network simulator 3 (NS-3) simulation results demonstrate that the performance of the proposed algorithm is better than the traditional sliding retransmission ARQ algorithm in error-prone channels with a higher physical layer rate.

In this paper, we propose a polynomial time algorithm for computing the expected maximum number of vertex-disjoint s-t paths in a probabilistic basically series-parallel directed graph and a probabilistic series-parallel undirected graph with distinguished source s and sink t(st), where each edge has a mutually independent failure probability and each vertex is assumed to be failure-free.

Let Ni be the number of connected spanning subgraphs with i(n-1 i m) edges in an n-vertex m-edge undirected graph G=(V,E). Although Nn-1 is computed in polynomial time by the Matrix-tree theorem, whether Nn is efficiently computed for a graph G is an open problem (see e.g., [2]). On the other hand, whether Nn2≥ Nn-1Nn+1 for a graph G is also open as a part of log concave conjecture (see e.g., [6],[12]). In this paper, for a complete graph Kn, we give the formulas for Nn, Nn+1, by which Nn, Nn+1 are respectively computed in polynomial time on n, and, in particular, prove Nn2> Nn-1Nn+1 as well.

Human gender recognition in the wild is a challenging task due to complex face variations, such as poses, lighting, occlusions, etc. In this letter, learnable Gabor convolutional network (LGCN), a new neural network computing framework for gender recognition was proposed. In LGCN, a learnable Gabor filter (LGF) is introduced and combined with the convolutional neural network (CNN). Specifically, the proposed framework is constructed by replacing some first layer convolutional kernels of a standard CNN with LGFs. Here, LGFs learn intrinsic parameters by using standard back propagation method, so that the values of those parameters are no longer fixed by experience as traditional methods, but can be modified by self-learning automatically. In addition, the performance of LGCN in gender recognition is further improved by applying a proposed feature combination strategy. The experimental results demonstrate that, compared to the standard CNNs with identical network architecture, our approach achieves better performance on three challenging public datasets without introducing any sacrifice in parameter size.