Online or In-Person Course? Meaningful Discussion of Learning Effectiveness and Satisfaction of Microwave Filter Design Course in Graduate Students

Contribution: A research on applying blended teaching in microwave filter design in graduate students. Background: The Covid-19 epidemic has caused many universities worldwide to switch to online courses. Taiwan did not have a large-scale local infection in 2020, so the school has implemented a blended teaching plan, combining online and in-person courses. Intended Outcomes: Discuss the effectiveness and satisfaction of the Microwave Filter Design Course in Graduate Students for two classes, Online or In-person course. Application Design: This study uses a quasi-experiment to teach microwave filter courses in the two classes. The teacher integrated into the Flipped Classroom and Interactive Response System (IRS). Students must use the APP to complete the preclass preview and prepare materials. Class A <inline-formula> <tex-math notation="LaTeX">$(N$</tex-math> </inline-formula> <inline-formula> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 14) uses in-person classrooms for the whole course;Class B uses blended teaching. The first eight weeks are synchronized online, then mid-term exams, and in-person courses are used for the next ten weeks. Students in two classes in the last week filled out the course satisfaction questionnaire. Findings: Class B achieved better results in the eighth midterm exam week, showing better learning results. Although students in both classes are highly satisfied with the course, Class A is more satisfied than Class B. For graduate students participating in the microwave filter design course, in-person classrooms and blended teaching can achieve good learning results and satisfaction. However, teachers must pay attention to students’reception and understanding of flipped classrooms when using online teaching. And timely and in-depth guidance on the accuracy of APP use. IEEE

Analyzing the Role of Emotional Intelligence on the Performance of Small and Medium Enterprises (SMEs) Using AI-Based Convolutional Neural Networks (CNNs)

Human emotion detection is necessary for social interaction and plays an important role in our daily lives. Artificial intelligence research is rising, focusing on automated emotion detection. The capability to identify the emotion, which is considered one of the traits of emotional intelligence, is a component of human intelligence. Although the study is limited dependent on facial expressions or voice is flourishing, it is identifying emotions via body movements, a less researched issue. To attain emotional intelligence, this study suggests a deep learning approach. Here initially the video can be converted into image frames after the converted image frames can be preprocessed using the Glitter bandpass butter worth filter and contrast stretch histogram equalization. Then from the enhanced image, the features can be clustered using the hybrid Gaussian BIRCH algorithm. Then the specialized features are retrieved from the body of human gestures using the AdaDelta bacteria foraging optimization algorithm, and the selected features are fed to a supervised Kernel Boosting LENET deep-learning algorithm. The experiment is conducted using Geneva multimodal emotion portrayals (GEMEPs) corpus data set. This data set includes, human body gestures portraying the archetypes of five emotions, such as anger, fear, joy, pride, and sad. In these emotion detection techniques, the suggested Kernel Boosting LENET classifier achieves 98.5% accuracy, 94% precision, 95% sensitivity, and F-Score 93% outperformed better than the other existing classifiers. As a result, emotional acknowledgment may help small and medium enterprises (SMEs) to improve their performance and entrepreneurial orientation. The correlation coefficient of 188 and the significance coefficient of 0.00 show that emotional intelligence and SMEs performance have a significant and positive association.

Attention Detection using Electro-oculography Signals in E-learning Environment

Recently, on account of the Covid-19 pandemic online learning has become a main strategy of learning in any educational environment. The main problem is the lack of focus or attention during a student watching an online lecture or video due to ways of distraction that can occur and that's what interrupts the students and make them lose their focus. In this research paper, we propose the attention detection system using an Electro-oculogram (EOG) signal generated by eye movement. The first goal of this study was detecting the un-focusing periods in the video. The proposed process of recognizing the un-focusing period depends on recognizing the eye movement direction. 6 classes of eye movements were classified in this study: left, right, up, down, blinking, and no movement. The process started by collecting our benchmark dataset from 50 subjects, which was considered the largest dataset at all for eye movements, using a special hardware device. The pre-processing step was down to filter the signals from any noise that may be caused by the surrounding environment or involuntary movements using the band-pass filter. Finally, the classification step was down. Three models of deep learning were examined for the best classifier: Convolution Neural Network (CNN), Inception Network, and VGG Network. Our experiments achieved the best results with the Inception model, reaching an average accuracy of 93.63%. The second goal of this study was to recognize the Attention-deficit hyperactivity disorder (ADHD) pattern in the student's recording. The model succeeded to recognize this disordered pattern. © 2021 IEEE.

Quad-port MIMO Filtenna with High Isolation Employing BPF with High out of Band Rejection

A quad-port multiple-input multiple-output (MIMO) filtenna with compact dimensions of 50 ×50 mm2 are configured, in which each element is placed orthogonally to its adjacent to enhance the isolation. The MIMO element is configured based on the novel COVID-19 virus shape with a co-planar waveguide feeding structure (CPW) and dimensions 17 ×22 mm2. The element bandwidth is ranging from 3.3 GHz to more than 60 GHz. Three frequency notches are designed at 3.5 GHz for WiMAX and 5.5 GHz for WLAN, and 8.5 GHz for X-band applications. A bandpass filter (BPF) with high out of band rejection is used as a decoupling structure (DS) to improve the isolation to more than 30 dB across most of the bandwidth. The equivalent circuit model is scrutinized to investigate the enactment of the decoupling structure. The proposed MIMO filtenna system provides an impedance bandwidth of 2.4–18 GHz, a peak gain of 13.2 dBi, and an envelope correlation coefficient (ECC) less than 0.00021. In turn, channel capacity loss does not exceed 0.2. The MIMO filtenna is fabricated and measured. Good agreement between the measured and simulation results is achieved. Author