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Previous research has shown that meaning in life (MIL) includes feelings of coherence, purpose, and external value (i.e., significance or mattering). Our studies aim to contribute to this framework by testing whether internal value should also be considered a basic component of MIL. To meet this aim, we developed a quadripartite existential meaning scale (QEMS) that incorporates items assessing one’s perceived internal value of life, and examined its relationship with other relevant measures. Results from three samples of undergraduates showed that QEMS had sound psychometric properties (e.g., good factor structure and reliability) and could effectively differentiate four sub-constructs of MIL. Regression and relative importance analysis demonstrated that each QEMS subscale carried unique predictive utility for unidimensional meaning measures, relevant well-being variables, and exhibited differential associations with other theoretically related variables (e.g., self-reflection, commitment making, self-esteem, and self or other interest orientation). Finally, exploratory structural equation modelling analysis showed that the four MIL sub-constructs formed an association pattern where comprehension fosters purpose, and purpose, in turn, contributes to both external and internal value. Implications for considering internal value as a core component of meaning in life are discussed.

In this work, a quaternion-valued model is proposed in lieu of the Clarke’s α, β transformation to convert three-phase quantities to a hypercomplex single-phase signal. The concatenated signal can be used for harmonic distortion detection in three-phase power systems. In particular, the proposed model maps all the harmonic frequencies into frequencies in the quaternion domain, while the Clarke’s transformation-based methods will fail to detect the zero sequence voltages. Based on the quaternion-valued model, the Fourier transform, the minimum variance distortionless response (MVDR) algorithm and the multiple signal classification (MUSIC) algorithm are presented as examples to detect harmonic distortion. Simulations are provided to demonstrate the potentials of this new modeling method.

A new model for three-dimensional processes based on the trinion algebra is introduced for the first time. Compared to the pure quaternion model, the trinion model is more compact and computationally more efficient, while having similar or comparable performance in terms of adaptive linear filtering. Moreover, the trinion model can effectively represent the general relationship of state evolution in Kalman filtering, where the pure quaternion model fails. Simulations on real-world wind recordings and synthetic data sets are provided to demonstrate the potential of this new modeling method.

The diagonal loading (DL) technique is the most widely used method to improve the robustness of the Capon beamformer in the presence of imprecise knowledge of the covariance matrix and the desired signal's steering vector. The selection of the DL level is challenging in practice and might depend on some user-defined parameters which are possibly hard to be determined. A fully automatic and training-free method for the DL level selection is herein presented to extract the desired signal with constant modulus, which is a common feature for communication signals. Simulated results of the beamforming performance have demonstrated the efficacy of the proposed method.

Directional modulation (DM) can be achieved based on uniform linear arrays where the maximum spacing between adjacent antennas is half-wavelength of the frequency of interest in order to avoid spatial aliasing. To exploit the additional degrees of freedom provided in the spatial domain, sparse antenna arrays can be employed for more effective DM. In this study, the spare array design problem in the context of DM is formulated from the viewpoint of compressive sensing (CS), so that it can be solved using standard convex optimisation toolboxes in the CS area. In detail, a common set of active antennas needs to be found for all modulation symbols generating a response close to the desired one. The key to the solution is to realise that group sparsity has to be employed, as a common antenna set cannot be guaranteed if the antenna locations are optimised for each modulation symbol individually. Moreover, two practical scenarios are considered for the proposed design: robust design with model errors and design with practical non-zero-sized antennas, and corresponding solutions are found by modifying the proposed standard solution.

An augmented charrelation matrix (ACM), which can utilize both the conventional and the conjugate statistical information in the complex domain, is proposed. The ACM additionally makes use of the conjugate Hessian matrix (namely conjugate charrelation matrix) of the observations of noncircular sources. A blind separation scheme built on the approximate joint diagonalization (AJD) principle is introduced, which precedes some numerical examples to demonstrate the improved performance of the ACM-AJD approach compared with some algorithms in the literature.

A biquaternion-based direction-finding algorithm for noncircular sources is presented. The covariance and conjugate covariance matrices of the array output are utilized symmetrically within a frame of biquaternions. The direction-of-arrivals are found where the biquaternion steering vectors are orthogonal to the noise subspace in the biquaternion domain. Simulations show the improved performance of the proposed method compared to its complex counterparts.

Direction-of-arrival (DOA) estimation for noncircular sources is addressed within the hypercomplex framework utilizing fourth-order (FO) cumulants and a MUSIC-like estimator is proposed. Simulation results show the better performance of the proposed method compared to its complex counterpart in terms of both accuracy and robustness to model errors due to the stronger orthogonality in the biquaternion domain.

The major flaw of the conventional diagonal loading (DL) method is that it is unclear to choose appropriate DL levels or user-parameters (UPs), though several remarkable contributions have been made to regularize model errors without UPs. An UP-free algorithm for two-component vector-sensor arrays, which is robust to steering vector errors, is considered. The algorithm is within the hypercomplex framework using quaternions, and the optimal solution is found at the maximal correlation between the quaternionic and complex outputs. The performance of the proposed beamformer is illustrated via numerical simulations and is compared with several other UP-free adaptive beamformers. K e y w o r d s: array signal processing, robust adaptive beamforming, vector-sensor, user-parameter-free, quaternion

Directional modulation (DM) can be achieved based on uniform linear arrays (ULAs), where the maximum half wavelength spacing is needed to avoid spatial aliasing. To exploit the degrees of freedom (DOFs) in the spatial domain, sparse arrays can be employed for more effective DM design. In this paper, the problem of antenna location optimisation for sparse arrays in the context of DM is addressed for the first time, where compressive sensing based formulations are proposed employing the group sparsity concept. Design examples are provided to verify the effectiveness of the proposed designs.

The problem of three-dimensional (3-D) wind profile prediction is addressed based a trinion wind model, which inherently reckons the coupling of the three perpendicular components of a wind field. The augmented trinion statistics are developed and employed to enhance the prediction performance due to its full exploitation of the second-order statistics. The proposed trinion domain processing can be regarded as a more compact version of the existing quaternion-valued approach, with a lower computational complexity. Simulations based on recorded wind data are provided to demonstrate the effectiveness of the proposed methods., Comment: 4 pages, 4 figures, to be published in Proc. of the IEEE International Conference on Digital Signal Processing, Singapore, July 2015

Robust adaptive beamforming algorithms with fully automatic diagonal level computation are desired but scarce in the literature. We note that the an imaginary-valued diagonal loading level well approximates but differs from its real-valued counterpart in terms of output signal-to-interference-plus-noise ratio (SINR), and an estimate of the optimal diagonal loading level can be found by the highest double-sided peak of the cross-correlation-coefficient curve of the two outputs using realand imaginary-valued diagonal loading levels with the same absolute values. The performance of the resulting beamformer is illustrated via numerical examples, compared with several other hyperparameter-free algorithms.

Background The calculation of arterial oxygen saturation (SpO2) relies heavily on the amplitude information of the high-quality photoplethysmographic (PPG) signals, which could be contaminated by motion artifacts (MA) during monitoring. Methods A new method combining temporally constrained independent component analysis (cICA) and adaptive filters is presented here to extract the clean PPG signals from the MA corrupted PPG signals with the amplitude information reserved. The underlying PPG signal could be extracted from the MA contaminated PPG signals automatically by using cICA algorithm. Then the amplitude information of the PPG signals could be recovered by using adaptive filters. Results Compared with conventional ICA algorithms, the proposed approach is permutation and scale ambiguity-free. Numerical examples with both synthetic datasets and real-world MA corrupted PPG signals demonstrate that the proposed method could remove the MA from MA contaminated PPG signals more effectively than the two existing FFT-LMS and moving average filter (MAF) methods. Conclusions This paper presents a new method which combines the cICA algorithm and adaptive filter to extract the underlying PPG signals from the MA contaminated PPG signals with the amplitude information reserved. The new method could be used in the situations where one wants to extract the interested source automatically from the mixed observed signals with the amplitude information reserved. The results of study demonstrated the efficacy of this proposed method.

Studying the morphology and interior movement of lymphocytes in intravital microscopic images is essential to understanding and treating various biological processes and pathological situations. A method combing features of shape, deformation, and intracellular motion for quantitatively characterizing the dynamic behavior of a single lymphocyte is proposed in this paper. The method is tested on a set of image sequences of lymphocytes obtained from the peripheral blood of mice undergoing skin transplantation using a phase contrast microscope. Experimental results coincide with the clinical observation and pathological analysis, demonstrating that the extracted cell morphology and motion features can provide new insights into the relationship between the dynamic behavior of lymphocytes and the occurrence of graft rejection.

This paper proposes an extension of the NOncircularity Rate Maximization (NORM) beamformer that takes into account noncircular interferences. The main idea of the new approach is to utilize the spatial diversity and noncircularity dissimilarity between the desired signal and interferences plus noise simultaneously. For this purpose, the weight vector is designed by maximizing the non-circularity rate of desired signal with imposing derivative constraints on the interference directions. The proposed derivative constrained NORM algorithm could broaden the width of pattern null, therefore it provides robustness in the presence of noncircular interferences. Besides that, the new method could also be adjusted to apply in rapidly moving jammer environments, without the requirement of desired signal-free sample data. Numerical examples are included to illustrate the performance of the proposed beamformer.

Adaptive beamforming using a crossed-dipole array is addressed in the hypercomplex framework. A new variant of Capon beamformer, called Quaternion-Capon (Q-Capon) beamformer, is herein proposed to fulfill the linearly constrained minimization operation in the quaternion domain. Simulation results show that the Q-Capon beamformer has a better convergence performance than the standard "long-vector" Capon beamformer, and is more robust than the latter to the uncertainty in signal's steering vector especially under the scenario of high signal to noise ratio values.

Automatic beamforming for noncircular signals with distinct noncircularity-rates can be achieved via Takagi factorization based on the second-order statistics. However, the requirement on noncircularity-rate diversity can not be satisfied by rectilinear signals (e.g., AM and BPSK signals), which are used in many practical communications systems. A mixed-order Takagi factorization scheme is herein proposed to handle coexisted rectilinear signals, wherein the fourth-order statistics is used for adjusting virtually the noncircularity-rates of signals, while the second-order statistics is utilized for blind identification. Numerical examples are also included to illustrate the performance of the proposed beamformer.

Using an acoustic vector sensor array, adaptive beamforming is addressed based on hypercomplex algebra. To utilize the minimum-variance distortionless response beamforming in the biquaternion domain, a variant of Capon beamformer named Biquaternion Capon (BQ-Capon) is proposed. Simulations show that the BQ-Capon beamformer outperforms the conventional long vector Capon beamformer with the convergence rate, and is less sensitive to the inaccuracy of the steering vector of the signal-of-interest (SOI) than the latter.

Many researchers have studied the various affective variables which influence the use of the target language. Willingness to communicate is often regarded to be main causes of the frequency of L2 use. At first, the concept and importance of willingness to communicate is illustrated. Next, the essay argues about the personality-based variables underlying willingness. Finally, the correlation of the personality-based variables will be illustrated. The purpose of this essay is to make a literature of willingness to communicate and provide a theoretical support on the further research on willingness to communicate.

A hypercomplex variant of Capon beamformer, called biquaternion Capon BQ-Capon, is herein proposed for signal strengthening with four-component vector-sensor arrays. This BQ-Capon beamformer extracts the signal-of-interest SOI while rejecting interferences plus noise in a multiple-input-multiple-output MIMO fashion. Simulation results show that BQ-Capon beamformer outperforms the standard long-vector counterpart in convergence and sensitivity to mismatch in SOI steering vector.