Due to the demand for real-time processing in real-time automatic target recognition (RTATR) systems, fast connected components analysis (CCA) is significant to RTATR performance improvement. Conventional single-pass CCA algorithms need horizontal blanking periods to resolve the equivalence, which are difficult to be applied when the streamed data is transmitted without horizontal blanking periods. In this paper, a real-time single-pass CCA algorithm is proposed. Unlike the conventional ones, we adopt the pixel as a scan unit while the line as a labeling unit and manage the correspondence of labels between adjacent rows by designing a multi-layer-index structure. Equivalence is resolved when the image is scanning, without extra processing time. The proposed algorithm is suitable for hardware acceleration, and the streamed image data can be processed during image transmission without horizontal blanking periods. Experimental results indicate that the hardware acceleration of algorithm achieves real-time CCA in RTATR system.
Music style is tightly connected with listeners’ emotional processes and neural activities. However, it remains unclear how the brain works when different music styles are processed emotionally. The current study analyzed the neural activation associated with five music styles during emotion-evoking. Twenty non-musicians participated in the functional magnetic resonance imaging (fMRI) scanning and the emotional ratings of pleasure and arousal evoked by pop, rock, jazz, folk, and classical music. Results showed that classical music was associated with the highest pleasure rating and deactivation of the corpus callosum. Rock music was associated with the highest arousal rating and deactivation of the cingulate gyrus. Pop music activated the bilateral supplementary motor areas (SMA) and the superior temporal gyrus (STG) with moderate pleasure and arousal. As the first fMRI experiment investigating the relationship between the music style and emotion, it provides neural correlates of different music styles during emotion-evoking.
The impulse of love at first sight (ILFS) is a well known but rarely studied phenomenon. Despite the privacy of these emotions, knowing how attractive one finds a partner may be beneficial for building a future relationship in an open society, where partners are accepting each other. Therefore, this study adopted the electrocardiograph (ECG) signal collection method, which has been widely used in wearable devices, to collect signals and conduct corresponding recognition analysis. First, we used photos to induce ILFS and obtained ECG signals from 46 healthy students (24 women and 22 men) in a laboratory. Second, we extracted the time- and frequency-domain features of the ECG signals and performed a nonlinear analysis. We subsequently used a feature selection algorithm and a set of classifiers to classify the features. Combined with the sequence floating forward selection and random forest algorithms, the identification accuracy of the ILFS was 69.07%. The sensitivity, specificity, F1, and area under the curve of the other parameters were all greater than 0.6. The classification of ECG signals according to their characteristics demonstrated that the signals could be recognized. Through the information provided by the ECG signals, it can be determined whether the participant possesses the desire to fall in love, helping to determine the right partner in the fastest time; this is conducive to establishing a romantic relationship.
Love at first sight is a well-known and interesting phenomenon, and denotes the strong attraction to a person of the opposite sex when first meeting. As far as we know, there are no studies on the changes in physiological signals between the opposite sexes when this phenomenon occurs. Although privacy is involved, knowing how attractive a partner is may be beneficial to building a future relationship in an open society where both men and women accept each other. Therefore, this study adopts the photoplethysmography (PPG) signal acquisition method (already applied in wearable devices) to collect signals that are beneficial for utilizing the results of the analysis. In particular, this study proposes a love pulse signal recognition algorithm based on a PPG signal. First, given the high correlation between the impulse signals of love at first sight and those for physical attractiveness, photos of people with different levels of attractiveness are used to induce real emotions. Then, the PPG signal is analyzed in the time, frequency, and nonlinear domains, respectively, in order to extract its physiological characteristics. Finally, we propose the use of a variety of machine learning techniques (support vector machine (SVM), random forest (RF), linear discriminant analysis (LDA), and extreme gradient enhancement (XGBoost)) for identifying the impulsive states of love, with or without feature selection. The results show that the XGBoost classifier has the highest classification accuracy (71.09%) when using the feature selection.
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