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Electroencephalography (EEG) recordings during sleep are often contaminated by muscle and ocular artefacts, which can affect the results of spectral power analyses significantly. However, the extent to which these artefacts affect EEG spectral power across different sleep states has not been quantified explicitly. Consequently, the effectiveness of automated artefact-rejection algorithms in minimizing these effects has not been characterized fully. To address these issues, we analysed standard 10-channel EEG recordings from 20 subjects during one night of sleep. We compared their spectral power when the recordings were contaminated by artefacts and after we removed them by visual inspection or by using automated artefact-rejection algorithms. During both rapid eye movement (REM) and non-REM (NREM) sleep, muscle artefacts contaminated no more than 5% of the EEG data across all channels. However, they corrupted delta, beta and gamma power levels substantially by up to 126, 171 and 938%, respectively, relative to the power level computed from artefact-free data. Although ocular artefacts were infrequent during NREM sleep, they affected up to 16% of the frontal and temporal EEG channels during REM sleep, primarily corrupting delta power by up to 33%. For both REM and NREM sleep, the automated artefact-rejection algorithms matched power levels to within ~10% of the artefact-free power level for each EEG channel and frequency band. In summary, although muscle and ocular artefacts affect only a small fraction of EEG data, they affect EEG spectral power significantly. This suggests the importance of using artefact-rejection algorithms before analysing EEG data., (© 2017 European Sleep Research Society.)

Existing mathematical models for predicting neurobehavioural performance are not suited for mobile computing platforms because they cannot adapt model parameters automatically in real time to reflect individual differences in the effects of sleep loss. We used an extended Kalman filter to develop a computationally efficient algorithm that continually adapts the parameters of the recently developed Unified Model of Performance (UMP) to an individual. The algorithm accomplishes this in real time as new performance data for the individual become available. We assessed the algorithm's performance by simulating real-time model individualization for 18 subjects subjected to 64 h of total sleep deprivation (TSD) and 7 days of chronic sleep restriction (CSR) with 3 h of time in bed per night, using psychomotor vigilance task (PVT) data collected every 2 h during wakefulness. This UMP individualization process produced parameter estimates that progressively approached the solution produced by a post-hoc fitting of model parameters using all data. The minimum number of PVT measurements needed to individualize the model parameters depended upon the type of sleep-loss challenge, with ~30 required for TSD and ~70 for CSR. However, model individualization depended upon the overall duration of data collection, yielding increasingly accurate model parameters with greater number of days. Interestingly, reducing the PVT sampling frequency by a factor of two did not notably hamper model individualization. The proposed algorithm facilitates real-time learning of an individual's trait-like responses to sleep loss and enables the development of individualized performance prediction models for use in a mobile computing platform., (© 2017 European Sleep Research Society.)

Interior tomography is clinically desired to reduce the radiation dose rendered to patients. In this work, a new statistical interior tomography approach for computed tomography is proposed. The developed design focuses on taking into account the statistical nature of local projection data and recovering fine structures which are lost in the conventional total-variation (TV)-minimization reconstruction. The proposed method falls within the compressed sensing framework of TV minimization, which only assumes that the interior ROI is piecewise constant or polynomial and does not need any additional prior knowledge. To integrate the statistical distribution property of projection data, the objective function is built under the criteria of penalized weighed least-square (PWLS-TV). In the implementation of the proposed method, the interior projection extrapolation based FBP reconstruction is first used as the initial guess to mitigate truncation artifacts and also provide an extended field-of-view. Moreover, an interior feature refinement step, as an important processing operation is performed after each iteration of PWLS-TV to recover the desired structure information which is lost during the TV minimization. Here, a feature descriptor is specifically designed and employed to distinguish structure from noise and noise-like artifacts. A modified steepest descent algorithm is adopted to minimize the associated objective function. The proposed method is applied to both digital phantom and in vivo Micro-CT datasets, and compared to FBP, ART-TV and PWLS-TV. The reconstruction results demonstrate that the proposed method performs better than other conventional methods in suppressing noise, reducing truncated and streak artifacts, and preserving features. The proposed approach demonstrates its potential usefulness for feature preservation of interior tomography under truncated projection measurements.

Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate MR image reconstruction from undersampled K-space data. Integrating IFR with CSMRI which is equipped with fixed transforms, we develop an IFR-CS method to restore meaningful structures and details that are originally discarded without introducing too much additional complexity. Specifically, the proposed IFR-CS is realized with three iterative steps, namely sparsity-promoting denoising, feature refinement and Tikhonov regularization. Experimental results on both simulated and in vivo MR datasets have shown that the proposed module has a strong capability to capture image details, and that IFR-CS is comparable and even superior to other state-of-the-art reconstruction approaches.

Compressed sensing magnetic resonance imaging (CSMRI) employs image sparsity to reconstruct MR images from incoherently undersampled K-space data. Existing CSMRI approaches have exploited analysis transform, synthesis dictionary, and their variants to trigger image sparsity. Nevertheless, the accuracy, efficiency, or acceleration rate of existing CSMRI methods can still be improved due to either lack of adaptability, high complexity of the training, or insufficient sparsity promotion. To properly balance the three factors, this paper proposes a two-layer tight frame sparsifying (TRIMS) model for CSMRI by sparsifying the image with a product of a fixed tight frame and an adaptively learned tight frame. The two-layer sparsifying and adaptive learning nature of TRIMS has enabled accurate MR reconstruction from highly undersampled data with efficiency. To solve the reconstruction problem, a three-level Bregman numerical algorithm is developed. The proposed approach has been compared to three state-of-the-art methods over scanned physical phantom and in vivo MR datasets and encouraging performances have been achieved., Competing Interests: The authors declare that they have no competing interests.

Burn scar extraction using remote sensing data is an efficient way to precisely evaluate burn area and measure vegetation recovery. Traditional burn scar extraction methodologies have no well effect on burn scar image with blurred and irregular edges. To address these issues, this paper proposes an automatic method to extract burn scar based on Level Set Method (LSM). This method utilizes the advantages of the different features in remote sensing images, as well as considers the practical needs of extracting the burn scar rapidly and automatically. This approach integrates Change Vector Analysis (CVA), Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR) to obtain difference image and modifies conventional Level Set Method Chan-Vese (C-V) model with a new initial curve which results from a binary image applying K-means method on fitting errors of two near-infrared band images. Landsat 5 TM and Landsat 8 OLI data sets are used to validate the proposed method. Comparison with conventional C-V model, OSTU algorithm, Fuzzy C-mean (FCM) algorithm are made to show that the proposed approach can extract the outline curve of fire burn scar effectively and exactly. The method has higher extraction accuracy and less algorithm complexity than that of the conventional C-V model.

The self-organizing network (SON)-based multiple model system is a recently proposed method for identifying the dynamics of a general nonlinear system. It has been observed by researchers that cooperative learning among neighboring regions is sometimes important for the success of identification of a nonlinear system under the multiple model system framework. In this paper, we intend to formally evaluate the effects of cooperative learning and topology preservation in identification of multiple model system based on SON. The results of the mathematical analysis supports the heuristic that a good learning strategy for identifying the local model parameters of a SON-based multiple model system is to choose a neighborhood function whose effective region is initially wider and is reduced gradually during learning. An example of nonlinear function approximation is also provided at the end of this paper to demonstrate the results of the mathematical analysis.

Sentiment lexicon is used to judge the sentiments of words and plays a significant role in sentiment analysis. Existing sentiment lexicons ignore the sentimental ambiguity of words in different contexts and only assign sentiment positive or negative polarity for words. In this paper, we propose an automatic method for the construction of the domain-specific sentiment lexicon (SDS-lex) to avoid sentimental ambiguity, which incorporates the sentiment information not only from the existing lexicons but also from the corpus by using our improved TF-IDF algorithm (ITF-IDF). The ITF-IDF algorithm calculates the sentiment of words by considering both the importance of words and the distribution of different part-of-speech (POS) in a corpus labeled with different sentiment tendencies. Experiments on real-world datasets show that our constructed lexicon improves the sentimental ambiguity and outperforms many existing lexicons in terms of the coverage and the accuracy when performing text sentiment classification tasks. [ABSTRACT FROM AUTHOR]

With the development of cloud computing, more and more video services are moving to the cloud. How to realize fine-grained access control of those data on cloud becomes an urgent problem. Attribute-based encryption provides a solution. However, heavy computation is still a bottleneck restricting the wider application of attribute-based encryption in cloud computing. In addition, we find that expression of the access control structure on media cloud can be further improved. To solve these problems, we propose an enhanced media ciphertext-policy attribute-based encryption algorithm and introduce its two key components, the multiple access tree and the key chain. To increase the scalability of the proposed algorithm, we discussed the issues of multi-authorization, user revocation, and 1 − n multiple access tree. Security analysis shows that enhanced media ciphertext-policy attribute-based encryption can successfully resist chosen-plaintext attacks under the decisional bilinear Diffie–Hellman assumption. Performance analysis proves both theoretically and practically that the proposed algorithm incurs less computational cost than the traditional ciphertext-policy attribute-based encryption, multi-message ciphertext-policy attribute-based encryption, and scalable ciphertext-policy attribute-based encryption by optimizing the access control structure. Our proposed algorithm has strong practical significance in media cloud. [ABSTRACT FROM AUTHOR]

Smart Grids (SGs) expedite secure, large-scale and efficient two-way communication between the power supply and management, but under a sophisticated 5G communication infrastructure, the multi carrier system is the principle system. The high peak-to-average power ratio (PAPR) is one of the significant limitations of the 5G multi carrier (MC) system, as it impedes the efficient design of the 5G analogue front end. Tone reservation (TR) is a highly efficient scheme without signal distortion, which is designed by increasing the freedom in the frequency domain for PAPR reduction. In this paper, a particle swarm optimization (PSO) based TR (PSO-TR) scheme proceeding with an optimal input power back off-modulation error ratio (IBO-MER) convergence criterion is proposed to improve high power amplifier (HPA) efficiency for OFDM systems. A probabilistic analysis of TR predistribution and freedom in the frequency domain, in relationship with the amplitude of its constituent samples, is carried out. This yields the theoretical framework employed in design of the proposed high computing-enhanced solutions. The proposed PSO-TR essentially make the frequency domain distribution and operation itself adaptive, that is, it adjusts to comply with the changing HPA efficiency and redundant cost during application runtime. [ABSTRACT FROM AUTHOR]