Development of position tracking of BLDC motor using adaptive fuzzy logic controller

The brushless DC (BLDC) motor has many advantages including simple to construct, high torque capability, small inertia, low noise and long life operation. Unfortunately, it is a non-linear system whose internal parameter values will change slightly with different input commands and environments. In this proposed controller, Takagi-Sugeno-Kang method is developed. In this project, a FLC for position tracking and BLDC motor are modeled and simulated in MATLAB/SIMULINK. In order to verify the performance of the proposed controller, various position tracking reference are tested. The simulation results show that the proposed FLC has better performance compare the conventional PID controller

A Dual-Modality Emotion Recognition System of EEG and Facial Images and its Application in Educational Scene

With the development of computer science, people's interactions with computers or through computers have become more frequent. Some human-computer interactions or human-to-human interactions that are often seen in daily life: online chat, online banking services, facial recognition functions, etc. Only through text messaging, however, can the effect of information transfer be reduced to around 30% of the original. Communication becomes truly efficient when we can see one other's reactions and feel each other's emotions. This issue is especially noticeable in the educational field. Offline teaching is a classic teaching style in which teachers may determine a student's present emotional state based on their expressions and alter teaching methods accordingly. With the advancement of computers and the impact of Covid-19, an increasing number of schools and educational institutions are exploring employing online or video-based instruction. In such circumstances, it is difficult for teachers to get feedback from students. Therefore, an emotion recognition method is proposed in this thesis that can be used for educational scenarios, which can help teachers quantify the emotional state of students in class and be used to guide teachers in exploring or adjusting teaching methods. Text, physiological signals, gestures, facial photographs, and other data types are commonly used for emotion recognition. Data collection for facial images emotion recognition is particularly convenient and fast among them, although there is a problem that people may subjectively conceal true emotions, resulting in inaccurate recognition results. Emotion recognition based on EEG waves can compensate for this drawback. Taking into account the aforementioned issues, this thesis first employs the SVM-PCA to classify emotions in EEG data, then employs the deep-CNN to classify the emotions of the subject's facial images. Finally, the D-S evidence theory is used for fusing and analyzing the two classification results and obtains the final emotion recognition accuracy of 92%. The specific research content of this thesis is as follows: 1) The background of emotion recognition systems used in teaching scenarios is discussed, as well as the use of various single modality systems for emotion recognition. 2) Detailed analysis of EEG emotion recognition based on SVM. The theory of EEG signal generation, frequency band characteristics, and emotional dimensions is introduced. The EEG signal is first filtered and processed with artifact removal. The processed EEG signal is then used for feature extraction using wavelet transforms. It is finally fed into the proposed SVM-PCA for emotion recognition and the accuracy is 64%. 3) Using the proposed deep-CNN to recognize emotions in facial images. Firstly, the Adaboost algorithm is used to detect and intercept the face area in the image, and the gray level balance is performed on the captured image. Then the preprocessed images are trained and tested using the deep-CNN, and the average accuracy is 88%. 4) Fusion method based on decision-making layer. The data fusion at the decision level is carried out with the results of EEG emotion recognition and facial expression emotion recognition. The final dual-modality emotion recognition results and system accuracy of 92% are obtained using D-S evidence theory. 5) The dual-modality emotion recognition system's data collection approach is designed. Based on the process, the actual data in the educational scene is collected and analyzed. The final accuracy of the dual-modality system is 82%. Teachers can use the emotion recognition results as a guide and reference to improve their teaching efficacy

A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)

Implementation strategies for hyperspectral unmixing using Bayesian source separation

Bayesian Positive Source Separation (BPSS) is a useful unsupervised approach for hyperspectral data unmixing, where numerical non-negativity of spectra and abundances has to be ensured, such in remote sensing. Moreover, it is sensible to impose a sum-to-one (full additivity) constraint to the estimated source abundances in each pixel. Even though non-negativity and full additivity are two necessary properties to get physically interpretable results, the use of BPSS algorithms has been so far limited by high computation time and large memory requirements due to the Markov chain Monte Carlo calculations. An implementation strategy which allows one to apply these algorithms on a full hyperspectral image, as typical in Earth and Planetary Science, is introduced. Effects of pixel selection, the impact of such sampling on the relevance of the estimated component spectra and abundance maps, as well as on the computation times, are discussed. For that purpose, two different dataset have been used: a synthetic one and a real hyperspectral image from Mars.Comment: 10 pages, 6 figures, submitted to IEEE Transactions on Geoscience and Remote Sensing in the special issue on Hyperspectral Image and Signal Processing (WHISPERS

Deep Learning Based Real Time Devanagari Character Recognition

The revolutionization of the technology behind optical character recognition (OCR) has helped it to become one of those technologies that have found plenty of uses in the entire industrial space. Today, the OCR is available for several languages and have the capability to recognize the characters in real time, but there are some languages for which this technology has not developed much. All these advancements have been possible because of the introduction of concepts like artificial intelligence and deep learning. Deep Neural Networks have proven to be the best choice when it comes to a task involving recognition. There are many algorithms and models that can be used for this purpose. This project tries to implement and optimize a deep learning-based model which will be able to recognize Devanagari script’s characters in real time by analyzing the hand movements