In this letter, we examine the linear complexity of some 3-ary sequences, proposed by No, of period 3n-1(n=3ek, e, k integer) with the ideal autocorrelation property. The exact value of linear complexity k(6e)w is determined when the parameter r =. Furthermore, the upper bound of the linear complexity is given when the other forms of the value r is taken. Finally, a Maple program is designed to illustrate the validity of the results.

This letter presents the globally optimal data replication in the distributed networks. We propose a distributed approach based on the metropolis-hastings algorithm to achieve the globally optimal data replication without requiring any global information. Experimental results show that the proposed approach works well and the error can be held below 0.6% easily.

In this letter, we explore joint optimization of perceptual gain function and deep neural networks (DNNs) for a single-channel speech enhancement task. A DNN architecture is proposed which incorporates the masking properties of the human auditory system to make the residual noise inaudible. This new DNN architecture directly trains a perceptual gain function which is used to estimate the magnitude spectrum of clean speech from noisy speech features. Experimental results demonstrate that the proposed speech enhancement approach can achieve significant improvements over the baselines when tested with TIMIT sentences corrupted by various types of noise, no matter whether the noise conditions are included in the training set or not.

This paper focuses on the problem of performance degradation in mismatched speech recognition. The F-Ratio analysis method is utilized to analyze the significance of different frequency bands for speech unit classification, and we find that frequencies around 1 kHz and 3 kHz, which are the upper bounds of the first and the second formants for most of the vowels, should be emphasized in comparison to the Mel-frequency cepstral coefficients (MFCC). The analysis result is further observed to be stable in several typical mismatched situations. Similar to the Mel-Frequency scale, another frequency scale called the F-Ratio-scale is thus proposed to optimize the filter bank design for the MFCC features, and make each subband contains equal significance for speech unit classification. Under comparable conditions, with the modified features we get a relative 43.20% decrease compared with the MFCC in sentence error rate for the emotion affected speech recognition, 35.54%, 23.03% for the noisy speech recognition at 15 dB and 0 dB SNR (signal to noise ratio) respectively, and 64.50% for the three years' 863 test data. The application of the F-Ratio analysis on the clean training set of the Aurora2 database demonstrates its robustness over languages, texts and sampling rates.

Due to the reuse factor reduction, the attendant increase in co-channel interference (CCI) becomes the limiting factor in the performance of the orthogonal frequency division multiplexing (OFDM) based cellular systems. In the previous work, we proposed the least mean square-blind joint maximum likelihood sequence estimation (LMS-BJMLSE) algorithm, which is effective for CCI cancellation in OFDM systems with only one receive antenna. However, LMS-BJMLSE requires a long training sequence (TS) for channel estimation, which reduces the transmission efficiency. In this paper, we propose a subcarrier identification and interpolation algorithm, in which the subcarriers are divided into groups based on the coherence bandwidth, and the slowest converging subcarrier in each group is identified by exploiting the correlation between the mean-square error (MSE) produced by LMS and the mean-square deviation (MSD) of the desired channel estimate. The identified poor channel estimate is replaced by the interpolation result using the adjacent subcarriers' channel estimates. Simulation results demonstrate that the proposed algorithm can reduce the required training sequence dramatically for both the cases of single interference and dual interference. We also generalize LMS-BJMLSE from single antenna to receiver diversity, which is shown to provide a huge improvement.

This paper studies the impact of integrating pricing with connection admission control (CAC) on the congestion management practices in contention-based wireless random access networks. Notably, when the network is free of charge, each self-interested user tries to occupy the channel as much as possible, resulting in the inefficient utilization of network resources. Pricing is therefore adopted as incentive mechanism to encourage users to choose their access probabilities considering the real-time network congestion level. A Stackelberg leader-follower game is formulated to analyze the competitive interaction between the service provider and the users. In particular, each user chooses the access probability that optimizes its payoff, while the self-interested service provider decides whether to admit or to reject the user's connection request in order to optimize its revenue. The stability of the Stackelberg leader-follower game in terms of convergence to the Nash equilibrium is established. The proposed CAC scheme is completely distributed and can be implemented by individual access points using only local information. Compared to the existing schemes, the proposed scheme achieves higher revenue gain, higher user payoff, and higher QoS performance.

A novel routing protocol, named candidate-based routing, for mobile ad hoc networks is presented. Instead of flooding over the whole network, it improves and rebuilds routing paths among a limited number of candidate nodes, which are dynamically elected without incurring or exchanging any additional information. Experimental results show that the proposed protocol performs well in terms of overhead and improvement in route efficiency, especially in the high mobility speed environments.

In this paper, we present a hybrid speech emotion recognition system exploiting both spectral and prosodic features in speech. For capturing the emotional information in the spectral domain, we propose a new spectral feature extraction method by applying a novel non-uniform subband processing, instead of the mel-frequency subbands used in Mel-Frequency Cepstral Coefficients (MFCC). For prosodic features, a set of features that are closely correlated with speech emotional states are selected. In the proposed hybrid emotion recognition system, due to the inherently different characteristics of these two kinds of features (e.g., data size), the newly extracted spectral features are modeled by Gaussian Mixture Model (GMM) and the selected prosodic features are modeled by Support Vector Machine (SVM). The final result of the proposed emotion recognition system is obtained by combining the results from these two subsystems. Experimental results show that (1) the proposed non-uniform spectral features are more effective than the traditional MFCC features for emotion recognition; (2) the proposed hybrid emotion recognition system using both spectral and prosodic features yields the relative recognition error reduction rate of 17.0% over the traditional recognition systems using only the spectral features, and 62.3% over those using only the prosodic features.

Primitive linear recurring sequences over rings are important in modern communication technology, and character sums of such sequences are used to analyze their statistical properties. We obtain a new upper bound for the character sum of primitive sequences of order n over the residue ring modulo a square-free odd integer m, and thereby improve previously known bound mn/2.

Due to the reuse factor reduction, the same frequencies are reused in adjacent neighboring cells, which causes an attendant increase in co-channel interference (CCI). CCI has already become the limiting factor in the performance of orthogonal frequency division multiplexing (OFDM) based cellular systems. Joint maximum likelihood sequence estimation (JMLSE) based interference cancellation algorithms have been under intense research. However, despite the fact that the error probability of JMLSE is critical for analyzing the performance, to the best of our knowledge, the mathematical expression has not been derived for MQAM-OFDM yet. Direct computation of the error probability involves integrating a multi-dimensional Gaussian distribution that has no closed-form solution. Therefore, an alternative way is to upper and lower bound the error probability with computable quantities. In this paper, firstly, both the upper and the conventional lower error probability bounds of JMLSE are derived for MQAM-OFDM systems based on a genie-aided receiver. Secondly, in order to reduce the gap between the conventional lower bound and the simulation results, a tighter lower bound is derived by replacing the genie with a less generous one. Thirdly, those derived error probability bounds are generalized to the receiver diversity scheme. These error probability bounds are important new analytical results that can be used to provide rapid and accurate estimation of the BER performance over any MQAM scheme and an arbitrary number of interferers and receive antennas.

In a 1-out-of-n oblivious signature scheme, a user provides a set of messages to a signer for signatures but he/she can only obtain a valid signature for a specific message chosen from the message set. There are two security requirements for 1-out-of-n oblivious signature. The first is ambiguity, which requires that the signer is not aware which message among the set is signed. The other one is unforgeability which requires that the user is not able to derive any other valid signature for any messages beyond the one that he/she has chosen. In this paper, we provide a generic construction of 1-out-of-n oblivious signature. Our generic construction consists of two building blocks, a commitment scheme and a standard signature scheme. Our construction is round efficient since it only asks one interaction (i.e., two rounds) between the user and signer. Meanwhile, in our construction, the ambiguity of the 1-out-of-n oblivious signature scheme is based on the hiding property of the underlying commitment, while the unforgeability is based on the binding property of the underlying commitment scheme and the unforgeability of the underlying signature scheme. Moreover, our construction can also enjoy strong unforgeability as long as the underlying building blocks have strong binding property and strong unforgeability respectively. Given the fact that commitment and digital signature are well-studied topics in cryptography and numerous concrete schemes have been proposed in the standard model, our generic construction immediately yields a bunch of instantiations in the standard model based on well-known assumptions, including not only traditional assumptions like Decision Diffie-Hellman (DDH), Decision Composite Residue (DCR), etc., but also some post-quantum assumption like Learning with Errors (LWE). As far as we know, our construction admits the first 1-out-of-n oblivious signature schemes based on the standard model.

Image retouching is fundamental in photography, which is widely used to improve the perceptual quality of a low-quality image. Traditional image quality metrics are designed for degraded images, so they are limited in evaluating the quality of retouched images. This letter presents a RETouched Image QUality Evaluation (RETIQUE) algorithm by measuring structure and color changes between the original and retouched images. Structure changes are measured by gradient similarity. Color colorfulness and saturation are utilized to measure color changes. The overall quality score of a retouched image is computed as the linear combination of gradient similarity and color similarity. The performance of RETIQUE is evaluated on a public Digitally Retouched Image Quality (DRIQ) database. Experimental results demonstrate that the proposed metric outperforms the state-of-the-arts.

MICKEY-family ciphers are lightweight cryptographic primitives and include a register R determined by two related maximal-period linear transformations. Provided that primitivity is efficiently decided in finite fields, it is shown by quantitative analysis that potential parameters for R can be found in probabilistic polynomial time.

To decrease memory access of the decoder for double binary convolutional turbo code (DB CTC), an iterative decoding scheme is proposed. Instead of accessing all of the backward state metrics from the state metric cache (SMC), a part of them is computed by the recalculation unit (RU) in the forward direction. By analysis and simulations, both the amount of memory access and the size of SMC are reduced by about 45%. Moreover, combined with the scaling technique, the proposed scheme gets decoding performance near to that of the well-known Log-MAP algorithm.

The global avalanche characteristics measure the overall avalanche properties of Boolean functions, an n-variable balanced Boolean function of the sum-of-square indicator reaching σƒ=22n+2n+3 is an open problem. In this paper, we prove that there does not exist a balanced Boolean function with σƒ=22n+2n+3 for n≥4, if the hamming weight of one decomposition function belongs to the interval Q*. Some upper bounds on the order of propagation criterion of balanced Boolean functions with n (3≤n≤100) variables are given, if the number of vectors of propagation criterion is equal and less than 7·2n-3-1. Two lower bounds on the sum-of-square indicator for balanced Boolean functions with optimal autocorrelation distribution are obtained. Furthermore, the relationship between the sum-of-squares indicator and nonlinearity of balanced Boolean functions is deduced, the new nonlinearity improves the previously known nonlinearity.

Community detection is an important task in the social network analysis field. Many detection methods have been developed; however, they provide little semantic interpretation for the discovered communities. We develop a framework based on joint matrix factorization to integrate network topology and node content information, such that the communities and their semantic labels are derived simultaneously. Moreover, to improve the detection accuracy, we attempt to make the community relationships derived from two types of information consistent. Experimental results on real-world networks show the superior performance of the proposed method and demonstrate its ability to semantically annotate communities.

The involvement of IEEE 802.15.4 Wireless Sensor Networks (WSNs) in diverse applications has made the realistic analysis of sensor power dissipation in distributed network environments an essential research issue. In this paper, we propose and thoroughly analyze a power dissipation model for Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) based IEEE 802.15.4 distributed multi-hop WSNs. Our model takes the loss rate of frames, neighbor sensors density in communication range of a sensor, number of hops, distance of source to the sink, and density of the network into account. We evaluate the impact of these factors on overall power dissipation. We also perform comprehensive analysis of overheads caused by message routing through multi-hop distributed networks. We validate our proposed model through Monte Carlo simulations. Results show that our power dissipation model is more realistic compared to other proposed models in terms of accuracy and multiplicity of the environments.

The notion of the signal-to-noise ratio (SNR), proposed by Guilley, et al. in 2004, is a property that attempts to characterize the resilience of (n, m)-functions F=(f1,...,fm) (cryptographic S-boxes) against differential power analysis. But how to study the signal-to-noise ratio for a Boolean function still appears to be an important direction. In this paper, we give a tight upper and tight lower bounds on SNR for any (balanced) Boolean function. We also deduce some tight upper bounds on SNR for balanced Boolean function satisfying propagation criterion. Moreover, we obtain a SNR relationship between an n-variable Boolean function and two (n-1)-variable decomposition functions. Meanwhile, we give SNR(f⊞g) and SNR(f⊡g) for any balanced Boolean functions f, g. Finally, we give a lower bound on SNR(F), which determined by SNR(fi) (1≤i≤m), for (n, m)-function F=(f1,f2,…,fm).

In this paper, the robustness of the posterior-based confidence measures is improved by utilizing entropy information, which is calculated for speech-unit-level posteriors using only the best recognition result, without requiring a larger computational load than conventional methods. Using different normalization methods, two posterior-based entropy confidence measures are proposed. Practical details are discussed for two typical levels of hidden Markov model (HMM)-based posterior confidence measures, and both levels are compared in terms of their performances. Experiments show that the entropy information results in significant improvements in the posterior-based confidence measures. The absolute improvements of the out-of-vocabulary (OOV) rejection rate are more than 20% for both the phoneme-level confidence measures and the state-level confidence measures for our embedded test sets, without a significant decline of the in-vocabulary accuracy.

This paper provides a new method to implement substrate integrated defected ground structure (SIDGS)-based bandpass filter (BPF) with adjustable frequency and controllable bandwidth. Compared with previous literature, this method implements a new SIDGS-like resonator capable of tunable frequency in the same plane as the slotted line using a varactor diode, increasing the design flexibility. In addition, the method solves the problem that the tunable BPF constituted by the SIDGS resonator cannot control the bandwidth by introducing a T-shaped non-resonant unit. The theoretical design method and the structural design are shown. Moreover, the configured structure is fabricated and measured to show the validity of the design method in this paper.