Real-time Processing Efficiency Research Articles

Improving Speech Recognition with Convolutional Neural Networks

This project explores advanced techniques in speech recognition, focusing on emotion identification using Convolutional Neural Networks for improved accuracy and real-time processing efficiency. Emotion recognition from speech signals plays a crucial role in various applications, including human-computer interaction, customer service, mental health monitoring, and entertainment. This project proposes an innovative approach to emotion recognition using Convolutional Neural Networks (CNNs) applied to speech data. By leveraging advanced deep learning techniques, the proposed system aims to accurately identify and classify emotions conveyed through vocal expressions. The project begins with a comprehensive review of existing literature on emotion recognition and speech processing, identifying key challenges and opportunities in the field. Building upon prior research, the project introduces a novel CNN architecture optimized for emotion recognition tasks. This architecture is designed to extract relevant features from speech signals and capture subtle nuances indicative of different emotional states. One of the distinguishing features of the proposed approach is its multi-modal integration, which combines information from both audio and visual modalities to enhance emotion recognition accuracy. In addition to analysing speech signals, the system incorporates visual cues such as facial expressions and gestures, providing a more comprehensive understanding of the speaker's emotional state. Real-time processing efficiency is prioritized in the design of the system, ensuring prompt and responsive emotion recognition in interactive applications. Optimization techniques such as model quantization and lightweight architecture design are employed to minimize computational overhead while maintaining high accuracy. To address the variability and subjectivity of emotional expression, the system incorporates user-specific adaptation mechanisms. Through continuous learning and feedback integration, the system dynamically adapts to individual speakers' speech patterns and emotional characteristics, enhancing its ability to accurately recognize emotions in diverse contexts. The project also explores ensemble learning strategies to improve robustness and generalization performance. By combining predictions from multiple CNN models trained on diverse datasets, the system achieves greater resilience to variations in emotional expression and environmental factors. Ethical considerations, including privacy protection and responsible data handling, are integral aspects of the project's design and implementation. Measures are implemented to ensure the ethical collection, storage, and usage of speech data, safeguarding user privacy and maintaining trust in the system. Overall, the proposed system represents a significant advancement in emotion recognition technology, offering a sophisticated and versatile solution for accurately identifying emotions from speech signals. By leveraging deep learning techniques, multi-modal integration, real-time processing optimization, user-specific adaptation, and ensemble learning, the system demonstrates promising potential for various practical applications requiring robust and context-aware emotion recognition capabilities. Keywords: Speech Recognition, Emotion Identification, Convolutional Neural Networks (CNNs), Real-time Processing, Multi-modal Integration, User-specific Adaptation, Ensemble Learning, Deep Learning, Emotional Expression, Ethical Data Handling

Design and Implementation of Port Video Terminals Security Access Authentication System Using Blockchain Technology

In the face of the increasing scale of port video surveillance systems, edge computing is gradually used to improve the real-time processing efficiency of surveillance systems and reduce the load on the cloud with the advantages of distributed computing. In order to improve the security factor of terminal access in the edge computing environment, this paper proposes a decentralized terminal security access authentication system based on blockchain, which realizes the identity registration, revocation and mutual authentication of two interfaces between terminal devices and edge computing through the smart contract technology of blockchain, and establishes a trusted channel for data transmission between terminals and edge nodes. Through the functional test of the system and the security analysis of the system on the Hyperledger Fabric platform, it shows that the system has more security attributes and higher security.

A massive images classification method based on MapReduce parallel fuzzy C-means clustering

Aiming at the low performance of classifying images under the computing model of single node. With GLCM (Gray Level Co-occurrence Matrix) which fuses gray level with texture of image, a parallel fuzzy C-means clustering method based on MapReduce is designed to classify massive images and improve the real-time performance of classification. The experimental results show that the speedup ratio of this method is more than 10% higher than that of the other two methods, moreover, the accuracy of image classification has not decreased. It shows that this method has high real-time processing efficiency in massive images classification.

사실적인 게임 배경 제작을 위한 나무 성장 모델 설계

본 연구에서는 게임의 실외 지형을 구성하는 많은 수의 다양한 나무를 효율적이고 자연스럽게 표현하기 위한 나무 성장 모델을 설계한다. 제안하는 나무 성장 모델은 (1) 다양한 종류의 나무를 보다 직관적이면서 자연스럽고 효율적으로 모델링하기 위한 성장 볼륨 및 약수 함수의 합성 곱 기반의 나무 모델링 방법, (2) 복잡한 구조의 다수 나무들의 실시간 처리를 위하여 인스턴싱 기반의 가지의 세분화 단계를 통한 렌더링 방법, 그리고 (3) 이를 조합하여 게임 배경을 효율적으로 구축하는 방법으로 구성된 나무 성장 모델이다. 제안한 나무 성장 모델을 통하여 자연스럽고 다양한 나무의 성장과 이를 통한 자연스러운 게임 배경의 구축 가능성 및 실시간 처리의 효율성을 실험을 통해 확인한다.