We propose an impact and high-pitch noise-suppression method based on spectral entropy. Spectral entropy takes a large value for flat spectral amplitude and a small value for spectra with several lines. We model the impact noise as a flat spectral signal and its damped oscillation as a high-pitch periodic signal consisting of spectra with several lines. We discriminate between the current noise situations by using spectral entropy and adaptively change the noise-suppression parameters used in a zero phase-based impact-noise-suppression method. Simulation results show that the proposed method can improve the perceptual evaluation of the speech quality and speech-recognition rate compared to conventional methods.

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High efficiency video coding (HEVC) is a video compression standard that outperforms the predecessor H.264/AVC by doubling the compression efficiency. To enhance the coding accuracy, HEVC adopts sample adaptive offset (SAO), which reduces the distortion of reconstructed pixels using classification based non-linear filtering. In the traditional coding tree unit (CTU) grain based VLSI encoder implementation, during the pixel classification stage, SAO cannot use the raw samples in the boundary of the current CTU because these pixels have not been processed by deblocking filter (DF). This paper proposes a hardware-oriented category determination algorithm based on estimating the deblocking strengths on CTU boundaries and selectively adopting the promising samples in these areas during SAO classification. Compared with HEVC test mode (HM11.0), experimental results indicate that the proposed method achieves an average 0.13%, 0.14%, and 0.12% BD-bitrate reduction (equivalent to 0.0055dB, 0.0058dB, and 0.0097dB increases in PSNR) in CTU sizes of 64 × 64, 32 × 32, and 16 × 16, respectively.

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In this paper, we report the design of an organic thin-film transistor (OTFT) driver circuit for the actuator of an organic fluid pump, which can be integrated in a portable-size fully-organic artificial lung. Compared to traditional pump designs, lightness, compactness and scalability are achieved by adopting a creative pumping mechanism with a completely organic-material-based system concept. The transportable fluid volume is verified to be flexibly adjustable, enabling on-demand controllability and scalability of the pump's fluid-flow rate. The simulations, based on an accurate surface-potential OTFT compact model, demonstrate that the necessary driving waveforms can be efficiently generated and adjusted to the actuator requirements. At the actuator-driving-circuit frequency of 0.98Hz, an all-organic fluid pump with 40cm length and 0.2cm height is able to achieve a flow rate of 0.847L/min, which satisfies the requirements for artificial-lung assist systems to a weakened normal lung.

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In this paper, we propose a true random number generator (TRNG) exploiting jitter and the chaotic behavior in cross ring oscillators (CROs). We make a further study of the feedback ring architecture and cross-connect the XOR gates and inverters to form an oscillator. The CRO utilizes totally digital logic circuits, and gains a high and robust entropy rate, as the jitter in the CRO can accumulate locally between adjacent stages. Two specific working modes of CRO in which the CRO can work in a consistent state and a free-running state respectively are introduced and analyzed both theoretically and experimentally. Finally, different stage lengths of cross ring true random number generators (CRTRNGs) are tested in different Field Programmable Gate Arrays (FPGAs) and test results are analyzed and compared. Especially, random data achieved from a design of 63-stage CRTRNG in Altera Cyclone IV passes both the NIST and Diehard test suites at a rate as high as 240Mbit/s.

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In this correspondence, a method of constructing optimal zero correlation zone (ZCZ) sequence sets over the 16-QAM+ constellation is presented. Based on 16-QAM orthogonal matrices and perfect ternary sequences, 16-QAM+ ZCZ sequence sets are obtained. The resulting ZCZ sequence sets are optimal with respect to the Tang-Fan-Matsufuji bound. Moreover, methods for transforming binary or quaternary orthogonal matrices into 16-QAM orthogonal matrices are proposed. The proposed 16-QAM+ ZCZ sequence sets can be potentially applied to communication systems using a 16-QAM constellation to remove the multiple access interference (MAI) and multi-path interference (MPI).

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Web Service Composition (WSC) has been well recognized as a convenient and flexible way of service sharing and integration in service-oriented application fields. WSC aims at selecting and composing a set of initial services with respect to the Quality of Service (QoS) values of their attributes (e.g., price), in order to complete a complex task and meet user requirements. A major research challenge of the QoS-aware WSC problem is to select a proper set of services to maximize the QoS of the composite service meeting several QoS constraints upon various attributes, e.g. total price or runtime. In this article, a fast algorithm based on QoS-aware sampling (FAQS) is proposed, which can efficiently find the near-optimal composition result from sampled services. FAQS consists of five steps as follows. 1) QoS normalization is performed to unify different metrics for QoS attributes. 2) The normalized services are sampled and categorized by guaranteeing similar number of services in each class. 3) The frequencies of the sampled services are calculated to guarantee the composed services are the most frequent ones. This process ensures that the sampled services cover as many as possible initial services. 4) The sampled services are composed by solving a linear programming problem. 5) The initial composition results are further optimized by solving a modified multi-choice multi-dimensional knapsack problem (MMKP). Experimental results indicate that FAQS is much faster than existing algorithms and could obtain stable near-optimal result.

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In this letter, a novel perceptually motivated single channel speech enhancement approach based on Deep Neural Network (DNN) is presented. Taking into account the good masking properties of the human auditory system, a new DNN architecture is proposed to reduce the perceptual effect of the residual noise. This new DNN architecture is directly trained to learn a gain function which is used to estimate the power spectrum of clean speech and shape the spectrum of the residual noise at the same time. Experimental results demonstrate that the proposed perceptually motivated speech enhancement approach could achieve better objective speech quality 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.

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In this letter, a new scheme for multirate coprime sampling and reconstructing of sparse multiband signals with very high carrier frequencies is proposed, where the locations of the signal bands are not known a priori. Simulation results show that the new scheme can simultaneously reduce both the number of sampling channels and the sampling rate for perfect reconstruction, compared to the existing schemes requiring high number of sampling channels or high sampling rate.

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A partitioning parallelization of the multi-objective evolutionary algorithm based on decomposition, pMOEA/D, is proposed in this letter to achieve significant time reductions for expensive bi-objective optimization problems (BOPs) on message-passing clusters. Each sub-population of pMOEA/D resides on a separate processor in a cluster and consists of a non-overlapping partition and some extra overlapping individuals for updating neighbors. Additionally, sub-populations cooperate across separate processors by the hybrid migration of elitist individuals and utopian points. Experimental results on two benchmark BOPs and the wireless sensor network layout problem indicate that pMOEA/D achieves satisfactory performance in terms of speedup and quality of solutions on message-passing clusters.

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Safety is the foremost requirement of avionics systems on aircraft. So far, avionics systems have evolved into an integrated system, i.e., integrated avionics system, and the derivative functions occur when the avionics systems are upgraded. However, the traditional safety analysis method is insufficient to be utilized in upgraded avionics systems due to these derivative functions. In this letter, a safety evaluation scheme is proposed to quantitatively evaluate the safety of the upgraded avionics systems. All the functions including the derivative functions can be traced and covered. Meanwhile, a set of safety issues based on different views is established to evaluate the safety capability from three layers, i.e., the mission layer, function layer and resource layer. The proposed scheme can be considered as an efficient scheme in the safety validation and verification in the upgraded avionics systems.

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Rotation symmetric Boolean functions (RSBFs) that are invariant under circular translation of indices have been used as components of different cryptosystems. In this paper, odd-variable balanced RSBFs with maximum algebraic immunity (AI) are investigated. We provide a construction of n-variable (n=2k+1 odd and n ≥ 13) RSBFs with maximum AI and nonlinearity ≥ 2^n-1-¥binom{n-1}{k}+2^k+2^k-2-k, which have nonlinearities significantly higher than the previous nonlinearity of RSBFs with maximum AI.

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In this paper, we construct ideal and probabilistic secret sharing schemes for some multipartite access structures, including the General Hierarchical Access Structure and Compartmented Access Structures. We devise an ideal scheme which implements the general hierarchical access structure. For the compartmented access structures, we consider three special access structures. We propose ideal and probabilistic schemes for these three compartmented access structures by bivariate interpolation.

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The orthogonal array is an important object in combinatorial design theory, and it is applied to many fields, such as computer science, coding theory and cryptography etc. This paper mainly studies the existence of the mixed orthogonal arrays of strength two with seven factors and presents some new constructions. Consequently, a few new mixed orthogonal arrays are obtained.

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In this letter, we propose a lattice reduction (LR) aided joint precoding design for MIMO-relay broadcast communication with the average bit error rate (BER) criterion. We jointly design the signal process flow at both the base station (BS), and the relay station (RS), using the reduced basis of two-stage channel matrices. We further modify the basic precoding design with a novel shift method and a modulo method to improve the power efficiency at the BS and the RS respectively. In addition, the MMSE-SIC algorithm is employed to improve the performance of precoding. Simulations show that, the proposed schemes achieve higher diversity order than the traditional precoding without LR, and the modified schemes significantly outperform the basic design, proving the effectiveness of the proposed methods.

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In this letter, several new families of binary sequence pairs with period N=np, where p is a prime and gcd(n,p)=1, and optimal correlation values 1 and -3 are constructed. These classes of binary sequence pairs are based on Chinese remainder theorem. The constructed sequence pairs have optimum balance among 0's and 1's.

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Electroencephalography (EEG) and magnetoencephalography (MEG) measure the brain signal from spatially-distributed electrodes. In order to detect event-related synchronization and desynchronization (ERS/ERD), which are utilized for brain-computer/machine interfaces (BCI/BMI), spatial filtering techniques are often used. Common spatial potential (CSP) filtering and its extensions which are the spatial filtering methods have been widely used for BCIs. CSP transforms brain signals that have a spatial and temporal index into vectors via a covariance representation. However, the variance-covariance structure is essentially different from the vector space, and not all the information can be transformed into an element of the vector structure. Grassmannian embedding methods, therefore, have been proposed to utilize the variance-covariance structure of variational patterns. In this paper, we propose a metric learning method to classify the brain signal utilizing the covariance structure. We embed the brain signal in the extended Grassmann manifold, and classify it on the manifold using the proposed metric. Due to this embedding, the pattern structure is fully utilized for the classification. We conducted an experiment using an open benchmark dataset and found that the proposed method exhibited a better performance than CSP and its extensions.

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