The use of millimeter-wave radar (mmWave radar) for human motion detection is essential in the realm of smart home Internet-of-Things (IoT) scenarios due to its non-intrusive nature, privacy considerations, interactive capabilities, and cost-effectiveness. However, the existing methods fail to address the issue of low real-time detection rates. Moreover, the design of the action does not align with the requirements of smart home IoT systems, while the complexity of the algorithm leads to high computing hardware costs. To address these problems, we propose mmMotion, a novel mmWave radar-based human motion detection system capable of accurately estimating six well-designed human motions (sit down, stand up, get up, lie down, wave hands, and punch) commonly observed in smart home scenarios with cost-effectiveness and real-time processing speed. By incorporating the spatial feature of “Height”, we are the first to propose a novel feature extraction method using the “Doppler-Height-Time” (DHT) feature maps as system input for human motion detection. A combined lightweight Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture is developed to simultaneously capture spatial and temporal features, leveraging the strengths of both architectures. Furthermore, we propose a novel Real-time Classifier combined with the Static Clutter Removal (SCR) method and Duo Filter algorithm to enhance the real-time detection capability of mm-Motion. Experiment results demonstrate that mmMotion achieves an impressive offline training accuracy rate of 98.13% and a real-time motion recognition rate of 90.5% for six designated motions. Furthermore, it exhibits excellent generalization ability across different individuals and robustness to the interference of large and small actions during real-time performance testing.

We propose a novel mmWave radar-based system capable of accurately estimating six human motions (Sit down, stand up, snap, swing hands, get up, and lie down) commonly observed in smart home scenarios with cost-effectiveness and real-time processing speed. We are the first to propose the “Doppler-Height-SNR-Time” feature maps as system input for human motion estimation. We propose a novel approach that combines lightweight CNN and LSTM architectures. Our approach leverages the strengths of both models to enhance system accuracy by effectively capturing spatial and doppler velocity features using CNN while extracting temporal features related to motion continuity using LSTM. Our model achieves an impressive offline testing accuracy of 96.7% and demonstrates a 73.33% recognition rate for predefined actions in real-time scenarios, along with robustness against non-target motions in experiments.

English has become an utterly crucial device to take part in global verbal exchange and competition. It is essential to enhance English teaching's flexibility to meet the desires to improve the market economy. Therefore, powerful coaching strategies and language identification are considered challenging factors in existing methods. The proposed model includes hypothesized relationships among college students' conception of learning English, their perceptions of the study room environment, and their approaches to learning. They are examined using the Pre-trained Teacher–Student Fixed Interactive Model (PTSFIM). This model proposes a new way to develop the teaching process providing the baseline of record excellence towards a strategic performance control framework for an institute. The traditional strategies emphasize the benefits of the interactive approach and accentuate their effectiveness through Structural Multivariate Equation (SME) analysis in enhancing students' innovative thinking, research, and reasoning abilities. The reciprocal instructional analysis optimizes students' models to memorize for a longer duration. The evaluation of the study's outcomes suggests that interactive learning can assist college students that predict different results in participating inside the speech system and gain the best knowledge. The simulation analysis is performed based on accuracy, performance, and efficiency proves the reliability of the proposed framework.

The number of sentence pairs in the bilingual corpus is a key to translation accuracy in computational machine translations. However, if the amount goes beyond a certain degree, the increasing number of cases has less impact on the translation while the construction of translation systems requires a considerable amount of time and energy, thus preventing the development of a statistical translation by the computer. This article offers a number of classifications for measuring the amount of information for each pair of sentences, using the Heuristic Bilingual Graph Corpus Network (HBGCN) to form an improved method of corpus selection that takes the difference between the first amount of information between the pairs of sentences into account. Using a graphic-based selector method as a training set, they achieve a close translation result through our experiments with the whole body and achieve better results than basic results for the following based on the Document Inverse Frequency (DIF) ranking approach.