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Electroencephalography (EEG) is a physiological signal directly generated by the central nervous system. Brain rhythm is closely related to a person's emotional state and is widely used for EEG emotion recognition. In previous studies, the rhythm specificity between different brain channels was seldom explored. In this paper, the rhythm specificity of brain channels is studied to improve the accuracy of EEG emotion recognition. Variational mode decomposition is used to decompose rhythm signals and enhance features, and two kinds of information entropy, i.e., differential entropy (DE) and dispersion entropy (DispEn) are extracted. The rhythm being used to get the best result of single channel emotion recognition is selected as the representative rhythm, and the remove one method is employed to obtain rhythm information entropy feature. In the experiment, the DEAP database was used for EEG emotion recognition in valence-arousal space. The results showed that the best result of rhythm DE feature classification in the valence dimension is 77.04%, and the best result of rhythm DispEn feature classification in the arousal dimension is 79.25%. [ABSTRACT FROM AUTHOR]

The synthesis of high-performance rare-earth borides (e.g., RB2, RB4, R2B5, RB6, RB12, and RB66) typically necessitates sintering at high temperatures and high pressures for prolonged durations. Traditional methods, such as hot-pressing sintering and spark plasma sintering, are marked by high production costs and low efficiency. Here, using EuB6 as an example, we demonstrate the successful synthesis of rare-earth boride ceramics utilizing the one-step ultrafast joule heating technology. Using this method, we optimized the sintering temperature and duration and synthesized EuB6 ceramics with superior electronic and magnetic properties at 1600 °C in just 5 min. The EuB6 ceramics prepared under optimized conditions show distinct paramagnetic to ferromagnetic phase transition, significant negative magnetoresistance, and enhanced magnetization. The combination of the one-step ultrafast Joule heating technology and boron thermal reduction method offers a simple and fast synthesis approach for fabricating polycrystalline EuB6 and may be extended to prepare other rare-earth and transition-metal borides. [ABSTRACT FROM AUTHOR]

Initiative service is a key research direction for the new generation of service robots. It is important to automatically track humans for initiative service in human-robot interaction. To solve the problems of low precision and poor anti-interference capability of only using single-modal (audio or visual) information, a speaker positioning and tracking method based on an audio-visual bimodal combination is proposed. First, the azimuth of the speaker is obtained based on the time difference of arrival using a microphone array, and face detection based on AdaBoost is carried out using the camera. A distance and azimuth calculation model is established to obtain the position of the speaker. Second, a speaker positioning strategy based on an audio-visual bimodal combination is designed to handle different situations. Third, the path is planned by which the azimuth and distance between the robot and the speaker are maintained in a limited range. Different azimuths and distances for speaker tracking are set to perform various simulations. Finally, the mobile robot is driven to follow the path using the STM32 real-time control system. Information from the microphone array and the camera is collected and processed by Raspberry Pi. The tracking accuracy was tested under a single-face situation by setting 20 different target points, and 10 tests were carried out under each point. Under multi-face situations, the audio-visual bimodal information is combined to identify the speaker, and then the Kalman filter is used in face tracking. The experimental results demonstrate that the running trajectory of the mobile robot is close to the ideal trajectory, which ensures effective speaker tracking. [ABSTRACT FROM AUTHOR]