Hands-free communications between cellular phones must be robust enough to withstand echo-path variation, and highly nonlinear echoes must be suppressed at low cost, when acoustic echo cancellation or suppression is applied to them. This paper proposes a spectrum-selective nonlinear echo suppression (SS-ES) approach as a solution to these issues. SS-ES is characterized by the selection of either a spectrum of the residual signal from an adaptive filter or a spectrum of the sending input signal depending on the amount of linear echo cancellation in an adaptive filter. Compared to conventional methods, the objective evaluation results of the SS-ES approach show an improvement of approximately 0.8-2.2dB, 0.23-2.39dB, and 0.26-0.50 in average echo return loss enhancement (ERLE), average root-mean-square log-spectral distortion (RMS-LSD), and the perceptual evaluation of speech quality (PESQ) value, respectively, under echo-path variation and double-talk conditions.

This paper presents a spatial fading emulator for evaluating handset MIMO antennas in a cluster environment. The proposed emulator is based on Clarke's model and has the ability to control RF signals directly in spatial domain to generate an accurate radio propagation channel model, which includes both uniform and non-uniform angular power spectra (APS) in the horizontal plane. Characteristics of a propagation channel such as fading correlations, eigenvalues and MIMO channel capacities of handset antennas located in the vicinity of the emulator's ring can be evaluated. The measured results show that the fading emulator with 31 antenna probes is sufficient to evaluate fading correlation and MIMO channel capacity of handset antenna in the case of a narrow APS with the standard deviation of more than 20 degrees.

Visual quality evaluation is crucially important for various video and image processing systems. Traditionally, subjective image quality assessment (IQA) given by the judgments of people can be perfectly consistent with human visual system (HVS). However, subjective IQA metrics are cumbersome and easily affected by experimental environment. These problems further limits its applications of evaluating massive pictures. Therefore, objective IQA metrics are desired which can be incorporated into machines and automatically evaluate image quality. Effective objective IQA methods should predict accurate quality in accord with the subjective evaluation. Motivated by observations that HVS is highly adapted to extract irregularity information of textures in a scene, we introduce multifractal formalism into an image quality assessment scheme in this paper. Based on multifractal analysis, statistical complexity features of nature images are extracted robustly. Then a novel framework for image quality assessment is further proposed by quantifying the discrepancies between multifractal spectrums of images. A total of 982 images are used to validate the proposed algorithm, including five type of distortions: JPEG2000 compression, JPEG compression, white noise, Gaussian blur, and Fast Fading. Experimental results demonstrate that the proposed metric is highly effective for evaluating perceived image quality and it outperforms many state-of-the-art methods.

This paper proposes a new spectrum sharing scheme which uses one-sided collaboration. In the proposed system, the transmitter of the secondary system relays the primary signal and overlays its own data on the retransmitted primary signal. The results of the theoretical analysis show that the proposed scheme with regenerative relay allows the secondary system to communicate at the same speed as the primary system that disregards the presence of the secondary system.

Many detailed studies ranging from networking to hardware as well as standardization activities over the last few years have advanced the performance of the elastic optical network. Thanks to these intensive works, the elastic optical network has been becoming feasible. This paper reviews the recent advances in the elastic optical network from the aspects of networking technology and hardware design. For the former, we focus on the efficient elastic network design technology related to routing and spectrum assignment (RSA) of elastic optical paths including network optimization or standardization activities, and for the latter, two key enabling technologies are discussed: elastic transponders/regenerators and gridless optical switches. Making closely-dependent networking and hardware technologies work synergistically is the key factor in implementing truly effective elastic optical networks.

A maximum-likelihood code acquisition scheme is investigated for frequency-selective fading channels with an emphasis on the decision strategies. Using the maximum-likelihood estimation technique, we first derive an optimal decision rule, which is optimal in the viewpoint of probability of detection. Based on the derived optimal decision rule, a practical and simple decision rule is also developed, and its performance is assessed for both single dwell and double dwell acquisition systems. Simulation results demonstrate that the proposed acquisition scheme significantly outperforms the previously proposed schemes in frequency-selective fading channels.

This paper presents an electrolaryngeal (EL) speech enhancement method capable of significantly improving naturalness of EL speech while causing no degradation in its intelligibility. An electrolarynx is an external device that artificially generates excitation sounds to enable laryngectomees to produce EL speech. Although proficient laryngectomees can produce quite intelligible EL speech, it sounds very unnatural due to the mechanical excitation produced by the device. Moreover, the excitation sounds produced by the device often leak outside, adding to EL speech as noise. To address these issues, there are mainly two conventional approached to EL speech enhancement through either noise reduction or statistical voice conversion (VC). The former approach usually causes no degradation in intelligibility but yields only small improvements in naturalness as the mechanical excitation sounds remain essentially unchanged. On the other hand, the latter approach significantly improves naturalness of EL speech using spectral and excitation parameters of natural voices converted from acoustic parameters of EL speech, but it usually causes degradation in intelligibility owing to errors in conversion. We propose a hybrid approach using a noise reduction method for enhancing spectral parameters and statistical voice conversion method for predicting excitation parameters. Moreover, we further modify the prediction process of the excitation parameters to improve its prediction accuracy and reduce adverse effects caused by unvoiced/voiced prediction errors. The experimental results demonstrate the proposed method yields significant improvements in naturalness compared with EL speech while keeping intelligibility high enough.

We propose a cooperative compressed spectrum sensing scheme for correlated signals in wideband cognitive radio networks. In order to design a reconstruction algorithm which accurately recover the wideband signals from the compressed samples in low SNR (Signal-to-Noise Ratio) environments, we consider the multiple measurement vector model exploiting a sequence of input signals and propose a cooperative sparse Bayesian learning algorithm which models the temporal correlation of the input signals. Simulation results show that the proposed scheme outperforms existing compressed sensing algorithms for low SNRs.

Existing fault localization approaches usually do not provide a context for developers to understand the problem. Thus, this paper proposes a novel approach using the dynamic backward slicing technique to enrich contexts for existing approaches. Our empirical results show that our approach significantly outperforms five state-of-the-art fault localization techniques.

The perceived age of a singing voice is the age of the singer as perceived by the listener, and is one of the notable characteristics that determines perceptions of a song. In this paper, we describe an investigation of acoustic features that have an effect on the perceived age, and a novel voice timbre control technique based on the perceived age for singing voice conversion (SVC). Singers can sing expressively by controlling prosody and voice timbre, but the varieties of voices that singers can produce are limited by physical constraints. Previous work has attempted to overcome this limitation through the use of statistical voice conversion. This technique makes it possible to convert singing voice timbre of an arbitrary source singer into those of an arbitrary target singer. However, it is still difficult to intuitively control singing voice characteristics by manipulating parameters corresponding to specific physical traits, such as gender and age. In this paper, we first perform an investigation of the factors that play a part in the listener's perception of the singer's age at first. Then, we applied a multiple-regression Gaussian mixture models (MR-GMM) to SVC for the purpose of controlling voice timbre based on the perceived age and we propose SVC based on the modified MR-GMM for manipulating the perceived age while maintaining singer's individuality. The experimental results show that 1) the perceived age of singing voices corresponds relatively well to the actual age of the singer, 2) prosodic features have a larger effect on the perceived age than spectral features, 3) the individuality of a singer is influenced more heavily by segmental features than prosodic features 4) the proposed voice timbre control method makes it possible to change the singer's perceived age while not having an adverse effect on the perceived individuality.

Dynamic spectrum access is the key approach in cognitive wireless regional area networks, and it is adopted by secondary users to access the licensed radio spectrum opportunistically. In order to realize real-time secondary spectrum usage, a dynamic spectrum access model based on stochastic differential games is proposed to realize dynamic spectrum allocation; a Nash equilibrium solution to the model is given and analyzed in this paper. From an overall perspective, the relationships between available spectrum percentage and the spectrum access rate are studied. Changes in the available spectrum percentage of the cognitive wireless regional area networks involve a deterministic component and a stochastic component which depends upon an r-dimensional Wiener process. The Wiener process represents an accumulation of random influences over the interval, and it reflects stochastic and time-varying properties of the available spectrum percentage. Simulation results show that the dynamic spectrum access model is efficient, and it reflects the time-varying radio frequency environment. Differential games are useful tools for the spectrum access and management in the time-varying radio environment.

Pseudo-random sequences with high linear complexity play important roles in many domains. We give linear complexity of generalized cyclotomic quaternary sequences with period pq over Z4 via the weights of its Fourier spectral sequence. The results show that such sequences have high linear complexity.

Aspect-oriented software development (AOSD) helps to solve the problem of low scalability and high maintenance costs of legacy systems caused by code scattering and tangling by extracting cross-cutting concerns and inserting them into aspects. Identifying the cross-cutting concerns of legacy systems is the key to reconstructing such systems using the approach of AOSD. However, current dynamic approaches to the identification of cross-cutting concerns simply check the methods' execution sequence, but do not consider their calling context, which may cause low precision. In this paper, we propose an improved comprehensive approach to the identification of candidate cross-cutting concerns of legacy systems based on the combination of the analysis of recurring execution relations and fan-ins. We first analyse the execution trace with a given test case and identify four types of execution relations for neighbouring methods: exit-entry, entry-exit, entry-entry and exit-exit. Afterwards, we measure the methods' left cross-cutting degrees and right cross-cutting degrees. The former ensures that the candidate recurs in a similar running context, whereas the latter indicates how many times the candidate cross-cuts different methods. The final candidates are then obtained from those high fan-in methods, which not only cross-cut others more times than a predefined threshold, but are always entered or left under the same running context. The experiment conducted on three open source systems shows that our approach improves the precision of identifying cross-cutting concerns compared with tradition ones.

This paper proposes out-of-band emission reduction schemes for IEEE 802.11af based Wireless Local Area Network (WLAN) systems operating in TV White Spaces (TVWS). IEEE 802.11af adopts Orthogonal Frequency Division Multiplexing (OFDM) to exploit the TVWS spectrum effectively. The combination of the OFDM and TVWS may be able to solve the problem of frequency depletion. However the TVWS transmitter must satisfy a strict transmission spectrum mask and reduce out-of-band emission to protect the primary users. The digital convolution filter is one way of reducing the out-of-band emission. Unfortunately, implementing a strict mask needs a large number of filter taps, which causes high implementation complexity. Time-domain windowing is another effective approach. This scheme reduces out-of-band emission with low complexity but at the price of shortening the effective guard interval. This paper proposes a mechanism that jointly uses these two schemes for out-of-band emission reduction. Moreover, the appropriate windowing duration design is proposed in terms of both the out-of-band emission suppression and throughput performance for all mandatory mode of IEEE 802.11af system. The proposed time-domain windowing design reduces the number of multiplier by 96.5%.

Cognitive radio (CR) systems aim for more efficient spectrum utilization by having so called secondary users (SUs) transmit on a frequency band reserved for licensed primary users (PUs). The secondary transmissions are allowed provided that no harmful interference will be caused to the PUs. SU terminals with multiple antennas can employ transmit power control with transmit precoding in order to control the interference levels. In most of the existing works, perfect channel state information (CSI) is assumed to be available for the SUs. However, in practical systems where perfect CSI is not available, the SUs are not able to guarantee that the interference constraints are sufficiently satisfied. In this paper, we investigate the problem of spectrum sharing for multiantenna CR systems using estimated CSI. Due to the random nature of the estimation error, we set a probabilistic interference constraint and, in order to satisfy it, provide a density function for the interference power. In addition, we present a power control framework for the SU to meet the probabilistic interference constraint.

With the ever-growing prevalence of web 2.0, users can access information and resources easily and ubiquitously. It becomes increasingly important to understand the characteristics of user's complex behavior for efficient network management and security monitoring. In this paper, we develop a novel method to visualize and measure user's web-communication-behavior character in large-scale networks. First, we employ the active and passive monitoring methods to collect more than 20,000 IP addresses providing web services, which are divided into 12 types according to the content they provide, e.g. News, music, movie and etc, and then the IP address library is established with elements as (servicetype, IPaddress). User's behaviors are complex as they stay in multiple service types during any specific time period, we propose the behavior spectrum to model this kind of behavior characteristics in an easily understandable way. Secondly, two kinds of user's behavior characters are analyzed: the character at particular time instants and the dynamic changing characters among continuous time points. We then employ Renyi cross entropy to classify the users into different groups with the expectation that users in the same groups have similar behavior profiles. Finally, we demonstrated the application of behavior spectrum in profiling network traffic patterns and finding illegal users. The efficiency and correctness of the proposed methods are verified by the experimental results using the actual traffic traces collected from the Northwest Regional Center of China Education and Research Network (CERNET).

Manifold learning based image clustering models are usually employed at local level to deal with images sampled from nonlinear manifold. Multimode patterns in image data matrices can vary from nominal to significant due to images with different expressions, pose, illumination, or occlusion variations. We show that manifold learning based image clustering models are unable to achieve well separated images at local level for image datasets with significant multimode data patterns. Because gray level image features used in these clustering models are not able to capture the local neighborhood structure effectively for multimode image datasets. In this study, we use nearest neighborhood quality (NNQ) measure based criterion to improve local neighborhood structure in terms of correct nearest neighbors of images locally. We found Gist as the optimal image descriptor among HOG, Gist, SUN, SURF, and TED image descriptors based on an overall maximum NNQ measure on 10 benchmark image datasets. We observed significant performance improvement for recently reported clustering models such as Spectral Embedded Clustering (SEC) and Nonnegative Spectral Clustering with Discriminative Regularization (NSDR) using proposed approach. Experimentally, significant overall performance improvement of 10.5% (clustering accuracy) and 9.2% (normalized mutual information) on 13 benchmark image datasets is observed for SEC and NSDR clustering models. Further, overall computational cost of SEC model is reduced to 19% and clustering performance for challenging outdoor natural image databases is significantly improved by using proposed NNQ measure based optimal image representations.

The problem of resource allocation to minimize the outage probability for the secondary user (SU) groups in a cognitive radio (CR) multicast network is investigated. We propose a joint power and rate allocation scheme that provides significant improvement over the conventional scheme in terms of outage probability.

In this paper, we study an objective quality measure that approximates the subjective mean opinion score (MOS) for bandwidth-extended wideband speech with respect to narrowband speech. Bandwidth-extended speech should be widely evaluated by a subjective quality assessment such as MOS. However, such subjective quality assessments are expensive and time-consuming. This paper proposes a new objective quality measure that combines the perceptual evaluation of speech quality (PESQ) and spectral-distortion. We evaluated the correlation between our proposed scheme and MOS using AMR and AMR-WB speech codecs. The coefficient of correlation between the proposed scheme and the MOS value was found to be 0.973. We concluded that the proposed scheme is a valid and effective objective quality measure.

Cognitive Radio (CR) is aimed at increasing the efficiency of spectrum utilization by allowing unlicensed users to access, in an opportunistic and non-interfering manner, some licensed bands temporarily and/or spatially unoccupied by the licensed users. The analysis of CR systems usually requires the spectral activity of the licensed system to be represented and characterized in a simple and tractable, yet accurate manner, which is accomplished by means of spectrum models. In order to guarantee the realism and accuracy of such models, the use of empirical spectrum occupancy data is essential. In this context, this paper explains the complete process of spectrum modeling, from the realization of field measurements to the obtainment of the final validated model, and highlights the main relevant aspects to be taken into account when developing spectrum usage models for their application in the context of the CR technology.