Self-learning enables students to learn at their own pace. The process itself has changed significantly from reading textbooks to viewing ebooks on electronic devices, asking questions on online discussion boards or watching video clips from other parts of the world. The change has come with a massive increase in the amount of information available. It is time-consuming for a student to obtain the exact item of class material wanted. We developed a search system that can store relevant class materials in its own database and give suggestions when a student needs them. The system is built as a chatbot in the LINE application, using the NLP algorithm, to interact between users and the system via Artificial Intelligence technology. The system was developed for two subjects in Electrical Engineering: Signals and Systems and Control Systems. It was able to correctly interpret the users’ requests with up to 80% accuracy. It can give suggestions of class materials based on rankings given by users’ scores. This search system makes self-learning less time consuming and may provide more encouragement to students.
The output from a digital Micro-Electrical- Mechanical System (MEMS) microphone in the form of PDM often needs to be converted to PCM before further processing, as PCM signals are easier to analyze. The current hardware-based PDM-to-PCM converters uses cascaded integrator-comb (CIC) based and finite impulse response (FIR) filters, which result in high hardware utilization to achieve a high signal-to-noise ratio (SNR). To strike a balance between performance and power consumption, a one-dimensional convolutional neural network (1D-CNN) has been applied in a PDM-to-PCM converter. Although this method resolves the aforementioned issues, an improvement to the system latency and throughput is possible. This paper proposes a fast method for a hardware-based PDM-to-PCM converter by cascading a digital low-pass filter and an existing ID-CNN-based low-pass filter. The approximation results show that the output PCM signal has the mean absolute error (MAE) of only 0.0026 compared to the original PCM signal. The proposed method has been implemented on the Xilinx PYNQ-ZI field programmable gate array (FPGA). While there is a slight increase in hardware utilization due to an additional required hardware, the latency has improved by 61% compared to the existing ID-CNN-based PDM-to-PCM converter. This research reduces the time taken to process each PCM data from PDM in a hardware-based system.
This research examines the quantum Hall effect of a GaAs/AlGaAs heterostructure sample at the temperature of 4.2 K and measures the longitudinal R xx and Hall resistance R H at $I = 10 \mu \mathrm{A}$. The corresponding Hall plateaus are represented by the depicted longitudinal R xx and Hall resistance R H curves, however, the Hall resistance R H ’s characteristics are not complete. The odd number’s plateau index i was not observed. At $i = 2$ and $i = 4$, the longitudinal resistance characteristic R xx approaches zero. Comparing the measurement results at 1.5 K (theoretical) and 4.2 K, it was discovered that there were deviations of $1.5 \mathrm{m}\Omega / \Omega$ ($i = 2$) and $5 \mathrm{m}\Omega / \Omega (i =4)$, respectively. The effect of rising temperature is a factor contributing to the quantum Hall effect’s incompleteness. We can identify abnormalities while measuring or determining the time period of operation from the characteristics of the measured results. Moreover, the external magnetic field is measured around the Quantum Hall Resistance (QHR) standard system. It will be able to securely evaluate the effects of magnetic field generation produced by the superconducting magnet in the cryostat system. From the results, there is a small external magnetic field around the QHR system.
Gamification has been widely used in recent years to enhance the learning experience of various subjects, including programming. However, there is a lack of clear and structured approach to implement gamification in programming education. This aim of this research is to develope a set of steps for efficiently learning programming through a game. The proposed steps cover the essential aspects of learning programming such as problem-solving, debugging, and understanding of programming concepts. The study also found that the proposed steps are flexible and also be applied to different programming languages and level of learners. The proposed steps also can be used as a guide for educators and trainers to implement the proposed step in programming education to make the learning process more engaging, interactive and fun. The study concludes the proposed step can be a valuable resources for educators and trainers to use gamification in education.
This paper proposes a performance analysis of using a static volt-amp-reactive (VAR) compensator for a three-phase self-excited induction generator (SEIG) operating as a single-phase induction generator. The study proposes appropriate capacitor for building up voltage supplying power to load with static VAR for regulating terminal voltage. The simulation model is analyzed by using MATLAB/Simulink under various conditions such as reactive power supplied, dynamic response, steady state, building up voltage for startup, linear load, non-linear load and harmonics in system. From the study (based on the study, the findings revealed it is found) that using the static VAR compensator connected in parallel with loads for supplying reactive power to the load systems is needed for maintaining terminal voltage.
