Mattress-Based Non-Influencing Sleep Apnea Monitoring System.

A mattress-type non-influencing sleep apnea monitoring system was designed to detect sleep apnea-hypopnea syndrome (SAHS). The pressure signals generated during sleep on the mattress were collected, and ballistocardiogram (BCG) and respiratory signals were extracted from the original signals. In the experiment, wavelet transform (WT) was used to reduce noise and decompose and reconstruct the signal to eliminate the influence of interference noise, which can directly and accurately separate the BCG signal and respiratory signal. In feature extraction, based on the five features commonly used in SAHS, an innovative respiratory waveform similarity feature was proposed in this work for the first time. In the SAHS detection, the binomial logistic regression was used to determine the sleep apnea symptoms in the signal segment. Simulation and experimental results showed that the device, algorithm, and system designed in this work were effective methods to detect, diagnose, and assist the diagnosis of SAHS.

Vibrational courtship disruption of Nilaparvata lugens using artificial disruptive signals.

The brown planthopper (BPH), Nilaparvata lugens (Stål; Hemiptera: Delphacidae) is a piercing-sucking pest that causes serious damage to rice plants by sucking the phloem sap from the plants and transmitting viruses. During courtship, the BPH vibrates its abdomen to produce signals that are transmitted to rice plants through its legs. Male BPHs search, locate, and mate with female BPHs after they exchange courtship signals with each other. Currently, spraying chemical pesticides is still the primary method for controlling BPH populations in paddy fields, although this approach has led to severe environmental pollution. A physical control method based on BPH courtship disruption to reduce the mating rate is a promising strategy for cutting environmental pollution. To acquire effective courtship disruptive signals, we developed a vibration signal recording, monitoring, and playback system for BPHs. Using this system, BPH courtship signals and male competition signals were collected and analyzed to obtain their frequency spectra. Results show that the mean main vibration frequency of female courtship signals is 234 Hz and the mean pulse rate is 23 Hz. The mean main vibration and pulse frequencies of the male courtship signals are 255 Hz and 82 Hz, respectively. Besides, the mean main vibration frequency of the male competition signal is 281 Hz. Seven different forms and frequencies of artificial signals were played back to male BPHs, then the courtship and behavioral responses of male BPHs were analyzed. Results indicate that a pure tone of 225 Hz prevents the males from recognizing female courtship signals. The male reply rate fell from 95.6 to 33.3% and the mean reply delay time increased from 5.3 s to 9.1 s. The reply rates of the other six artificial signals ranged from 42.9 to 83.7%, and the mean reply delays were between 5.0 s and 9.3 s. Therefore, the courtship behavior of BPHs can be disrupted by using specific artificial disruptive signals.

Foreign Language Teachers' Emotion Recognition in College Oral English Classroom Teaching.

One of the significant courses in Chinese universities is English. This course is usually taught by a foreign language instructor. There will, however, necessarily be some communication hurdles between "foreign language teachers" and "native students." This research presents an emotion recognition method for foreign language teachers in order to eliminate communication barriers between teachers and students and improve student learning efficiency. We discovered four factors of emotion recognition through literature analysis: smile, eye contact, gesture, and tone. We believe that differences in foreign language teachers' performance in these four areas will have an impact on students' emotion recognition and, as a result, on their learning efficiency. The influence of the foreign language teacher's eye contact and gestures is larger (the weight of a single variable accounted for 30% or more) in the decision whether can improve the students' classroom learning efficiency, according to 43 of the questionnaire data analysis. The second is the tone and smile (the weight of a single variable accounted for between 10 and 20%). Our research contributes to the body of knowledge on emotion recognition in university foreign language teachers by presenting a practical method for recognizing emotion in foreign language teachers. We recommend that college foreign language teachers pay attention to eye and gesture communication with students in English classrooms based on the findings. By enriching the style of emotional expression in class, college language teachers, particularly foreign language teachers, can improve communication, and connection with students.