Privacy-preserving biometric verification with outsourced correlation filter computation

Multimedia Tools and Applications - In traditional biometric verification systems, personal computer stores biometric database and performs verification process. Because of limited storage,...     

Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.     

Enhancing level set brain tumor segmentation using fuzzy shape prior information and deep learning

Magnetic resonance imaging (MRI) brain tumor segmentation is a crucial task for clinical treatment. However, it is challenging owing to variations in type, size, and location of tumors. In addition, anatomical variation in individuals, intensity non-uniformity, and noises adversely affect brain tumor segmentation. To address these challenges, an automatic region-based brain tumor segmentation approach is presented in this paper which combines fuzzy shape prior term and deep learning. We define a new energy function in which an Adaptively Regularized Kernel-Based Fuzzy C-Means (ARKFCM) Clustering algorithm is utilized for inferring the shape of the tumor to be embedded into the level set method. In this way, some shortcomings of traditional level set methods such as contour leakage and shrinkage have been eliminated. Moreover, a fully automated method is achieved by using U-Net to obtain the initial contour, reducing sensitivity to initial contour selection. The proposed method is validated on the BraTS 2017 benchmark dataset for brain tumor segmentation. Average values of Dice, Jaccard, Sensitivity and specificity are 0.93 ± 0.03, 0.86 ± 0.06, 0.95 ± 0.04, and 0.99 ± 0.003, respectively. Experimental results indicate that the proposed method outperforms the other state-of-the-art methods in brain tumor segmentation.     

A novel method for voice conversion based on non-parallel corpus

This article puts forward a new algorithm for voice conversion which not only removes the necessity of parallel corpus in the training phase but also resolves the issue of insufficiency of the target speaker’s corpus. The proposed approach is based on one of the new voice conversion models utilizing classical LPC analysis-synthesis model combined with GMM. Through this algorithm, the conversion functions among vowels and demi-syllables are derived. We assumed that these functions are rather the same for different speakers if their genders, accents, and languages are alike. Therefore, we will be able to produce the demi-syllables with just having access to few sentences from the target speaker and forming the GMM for one of his/her vowels. The results from the appraisal of the proposed method for voice conversion clarifies that this method has the ability to efficiently realize the speech features of the target speaker. It can also provide results comparable to the ones obtained through the parallel-corpus-based approaches.     

Integrated Fuzzy DEA-ANFIS to Measure the Success Effect of Human Resource Spirituality

Workplace spirituality has gained attention as it is proven to be a contributor to organizational performance improvement. This paper aims to assess the impact of human resource spirituality on the success of organizational strategic change projects. The success of the projects is measured by the well-known criterion of deviation from the planned budget cost. Data collection is based on a questionnaire survey of 252 personnel in 36 large and medium-scale organizational change projects in power industry. The paper proposes an integrated algorithm of fuzzy data envelopment analysis (FDEA) and adaptive network-based fuzzy inference system (ANFIS) for measuring the pure effect of human resource spirituality on the success of organizational change projects in the power industry. It also achieves a verified tool capable of addressing complexity, nonlinearity, ambiguity, and fuzziness for measuring spirituality of human resources in the projects. Results show that spirituality of the project team has a significant effect on project success.     

Improvement of time alignment of the speech signals to be used in voice conversion

One of the main applications of time alignment is parallel corpus based voice conversion. In the literature, various methods such as dynamic time warping (DTW) and hidden Markov model have been suggested for time alignment of two speech signals. In this paper, we introduce some modifications to DTW in order to decrease the time alignment error. These modifications are refinement, which is done by exerting a threshold, normalization, and comparisons between the preceding and the following frames to make sound correspondence between two different parallel corpus-based speakers’ speeches. Evaluation of this approach which has been done on some corpus sentences indicates a significant improvement of time alignment. At least about 4% and in some cases 15% decrease of error in comparison with DTW has been achieved.     

A Modified Method Based on the Discrete Sliding Norm Transform to Reduce the PAPR in OFDM Systems

Orthogonal frequency division multiplexing (OFDM) is a modulation technique that allows the transmission of high data rates over wideband radio channels subject to frequency selective fading by dividing the data into several narrowband and flat fading channels. OFDM has high spectral efficiency and channel robustness. However, a major drawback of OFDM is that the peak‐to‐average power ratio (PAPR) of the transmitted signals is high, which causes nonlinear distortion in the received data and reduces the efficiency of the high power amplifier in the transmitter. The most straightforward method to solve this problem is to use a nonlinear mapping algorithm to transform the signal into a new signal that has a smaller PAPR. One of the latest nonlinear methods proposed to reduce the PAPR is the L₂‐by‐3 algorithm, which is based on the discrete sliding norm transform. In this paper, a new algorithm based on the L₂‐by‐3 method is proposed. The proposed method is very simple and has a low complexity analysis. Simulation results show that the proposed method performs better, has better power spectral density, and is less sensitive to the modulation type and number of subcarriers than L₂‐by‐3.     

An investigation into the strength development of Wastepaper Sludge Ash blended with Ground Granulated Blastfurnace Slag

Wastepaper Sludge Ash (WSA) contains reactive components such as free lime (CaO), and less reactive and inert mineral species. When water is added to WSA some constituents hydrate faster than others, lime being the fastest giving high alkalinity to the solution. If the WSA is combined with Ground Granulated Blastfurnace Slag (GGBS) then the latter, activated by the raised pH, produces new minerals which will contribute further to the strength development of the paste. The kinetics of the hydration reactions and the possible ways of making lime a more effective contributor in activating other phases in the WSA–GGBS system are discussed.