In this paper, Mind Wave technology based brain-controlled robotic car is implemented. The objective is to create an innovative human machine interface, enabling users to control the cars movements seamlessly through ...
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ISBN:
(数字)9798350375466
ISBN:
(纸本)9798350375473
In this paper, Mind Wave technology based brain-controlled robotic car is implemented. The objective is to create an innovative human machine interface, enabling users to control the cars movements seamlessly through their brain signals and facial movement. The Mind Wave headset, equipped with EEG sensors, captures and interprets the users brainwave pattern, translating them into specific commands for the robotic car. The system integrates real time signalprocessingalgorithms to extract relevant features from the EEG data, mapping them to predefined control commands for the robotic car. A comprehension control architecture is established to facilitate smooth navigation, including forward, backward, left, and right movements Additionally, advanced features such as speed modulation and obstacle avoidance are implemented to enhance the user experience and ensure safety. Furthermore, the work explores the challenges and limitations of the proposed brain-controlled robotic car system, addressing issues related to signal noise,user training,and the adaptability of the *** directions for improvement and expansion of the system are discussed, aiming to refine the interface, increase responsiveness, and broken the range of supported commands.
Using a cutting-edge neural network framework, “PulseSync BP” estimates blood pressure without contact. Using photoplethysmogram (PPG) signals and other physiological data, this novel model uses advancedsignal proc...
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ISBN:
(数字)9798350364828
ISBN:
(纸本)9798350364835
Using a cutting-edge neural network framework, “PulseSync BP” estimates blood pressure without contact. Using photoplethysmogram (PPG) signals and other physiological data, this novel model uses advancedsignalprocessing. The suggested framework changes blood pressure monitoring by eliminating cuff-based measures. PulseSync BP's RNN architecture is carefully designed for optimal prediction accuracy. The algorithm effectively estimates blood pressure through extensive experimentation, demonstrating its potential for continuous healthcare applications. This framework's capacity to use various physiological cues shows its adaptability and versatility. PulseSync BP allows real-time, non-intrusive blood pressure monitoring, advancing individualized health tracking solutions. Cuffless blood pressure estimate is advanced here, demonstrating how artificial intelligence could improve healthcare accessibility and patient-centric monitoring. PulseSync BP and other revolutionary technologies can transform blood pressure monitoring in healthcare.
This research paper introduces a novel approach for car parking slot detection using YOLOv8, an advanced object detection algorithm renowned for its state-of-the-art performance. The objective of this study is to addr...
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ISBN:
(数字)9798350364590
ISBN:
(纸本)9798350375381
This research paper introduces a novel approach for car parking slot detection using YOLOv8, an advanced object detection algorithm renowned for its state-of-the-art performance. The objective of this study is to address the increasing demand for efficient parking management in urban areas, where optimizing parking space utilization is essential to alleviate traffic congestion. The proposed model efficiently uses the power of YOLOv8's single-shot detection. It uses anchor boxes to automatically identify and classify vacant and occupied parking slots in real time. By utilizing YOLOv8's deep learning capabilities, the proposed method achieved impressive accuracy and efficiency in car parking slot detection. The simulation results showcase the system's effectiveness, providing valuable insights for urban mobility planning and management.
Recognizing handwritten characters is a complex task in pattern recognition and computer vision, demanding advanced techniques to enable computers to discern and transform handwritten or printed characters into a digi...
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ISBN:
(数字)9798331540685
ISBN:
(纸本)9798331540692
Recognizing handwritten characters is a complex task in pattern recognition and computer vision, demanding advanced techniques to enable computers to discern and transform handwritten or printed characters into a digital format. The integration of the GRU algorithm within the RNN-CNN hybrid methodology marks a significant advancement in handwritten character recognition. By transitioning from a character-by-character to a line-by-line recognition approach, the system aims to improve the conversion of handwritten text into digital formats. GRU enhances context awareness by predicting subsequent letters in incomplete words, thereby facilitating more accurate recognition. Leveraging the IAM dataset not only provides ample training data but also aids in error detection and correction, enhancing the overall accuracy and reliability of the system. This innovative approach underscores a commitment to advancing OCR capabilities beyond isolated characters and words, toward more comprehensive and context-aware text recognition, thereby paving the way for more effective digitization of handwritten documents.
To address the issue of unsatisfactory burst signal detection and insufficient time-of-arrival estimation accuracy in low SNR environments in modern digital signal receivers, this paper investigates a two-step high pr...
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ISBN:
(数字)9798350316537
ISBN:
(纸本)9798350316544
To address the issue of unsatisfactory burst signal detection and insufficient time-of-arrival estimation accuracy in low SNR environments in modern digital signal receivers, this paper investigates a two-step high precision real-time signal detection method. The first step involves using the Short-Time Fourier Transform and Constant False Alarm Rate detection in the frequency domain to improve the detection of weak signals, and obtain the existence and rough position of signals. By using time-domain autocorrelation detection based on the detection results, the TOA of the signal is accurately estimated. The method is then implemented on the FPGA platform and verified in an actual environment, demonstrating practical value for engineering.
Recurrent Neural Network (RNN) algorithm, is renowned for its effectiveness in object localization and classification tasks within the realm of computer vision. This study combines RNN capabilities with the analysis o...
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ISBN:
(数字)9798350354218
ISBN:
(纸本)9798350354225
Recurrent Neural Network (RNN) algorithm, is renowned for its effectiveness in object localization and classification tasks within the realm of computer vision. This study combines RNN capabilities with the analysis of Mel-Frequency Cepstral Coefficients(MFCC) features, which have demonstrated effectiveness in capturing unique audio signal characteristics. Mel-Frequency Cepstral Coefficients (MFCCs) serve as a compact representation of the spectral content of audio signals, enabling the identification of distinctive features crucial for detecting anomalies introduced during the generation of deepfake audio. The Recurrent Neural Network (RNN) algorithm is applied to these MFCC features. RNN excels in tasks such as image recognition, object detection, and semantic segmentation. Previous research in the field of audio forensics has explored various methodologies for detecting manipulated audio content. While some approaches focus on signalprocessing techniques, others employ machine learning algorithms. MFCCs have consistently demonstrated their efficacy in capturing audio features relevant to authenticity. The proposed approach enhances the detection of fake audio, a critical challenge in the era of advanced digital media manipulation. By combining the strengths of MFCC features and the RNN algorithm, the model aims to learn and identify intricate patterns associated with genuine audio, enabling accurate differentiation between authentic and manipulated audio sources. By adapting RNN to analyze MFCC features, the model gains the capability to discern intricate patterns associated with genuine audio, thereby distinguishing between authentic and manipulated audio sources.
In recent years, brain-computer interfaces (BCIs) have advanced significantly through the integration of electroencephalogram (EEG) data, though the complexity of EEG signals remains challenging for traditional classi...
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ISBN:
(数字)9798350374957
ISBN:
(纸本)9798350374964
In recent years, brain-computer interfaces (BCIs) have advanced significantly through the integration of electroencephalogram (EEG) data, though the complexity of EEG signals remains challenging for traditional classification methods. This paper introduces a novel machine learning framework to enhance classification efficiency in EEG-based BCIs. We employ advancedalgorithms, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to capture temporal and spatial dependencies in EEG data. Our approach leverages a combination of feature extraction techniques and dimensionality reduction to optimize classification, improving accuracy and speed. We performed a comparative analysis using a standardized EEG dataset, revealing our model’s significant improvement in classification accuracy and computational efficiency over traditional classifiers. These results highlight our method’s potential in realtime BCI applications. This study contributes to the theoretical understanding of EEG signalprocessing and paves the way for more responsive and user-friendly BCI systems.
In this work, wearable electroencephalogram (EEG) technology is investigated as a cutting-edge stress assessment and treatment method. An increasing number of people require simple, practical methods for monitoring an...
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ISBN:
(数字)9798331539948
ISBN:
(纸本)9798331539955
In this work, wearable electroencephalogram (EEG) technology is investigated as a cutting-edge stress assessment and treatment method. An increasing number of people require simple, practical methods for monitoring and managing their stress levels because of the increasing prevalence of illnesses associated with stress. Wearable electroencephalography (EEG) devices offer a feasible path for the high temporal resolution monitoring of stress circumstances by permitting real-time, noninvasive brain activity measurements. This abstract examines the current status of wearable EEG technology, emphasizing how advancements in sensor technology, hardware downsizing, and signalprocessing techniques have made it simpler to integrate EEG monitoring into everyday life. The project also investigates the utility of EEG-based Brain-Computer Interfaces (BCIs) for stress detection and control, as well as the potential of EEG signals as biomarkers for stress-related cognitive processes. The paper also covers the methodological aspects of implementing EEG-based stress assessment protocols, including feature extraction, signalprocessingalgorithms, and machine learning techniques for stress prediction. The end of the abstract discusses the therapeutic applications of wearable EEG devices in stress management therapy and points to potential directions for future study in this rapidly emerging field. Because wearable electroencephalography (EEG) technology offers new insights into the neurophysiological foundation of stress and makes individualized therapies based on individual stress profiles possible, it has the potential to completely transform paradigms for stress measurement and regulation.
Wind power generation has grown in importance as renewable energy technology has advanced; nonetheless, wind speed forecasting remains a concern. Traditional approaches are inadequate in dealing with the nonlinearity ...
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ISBN:
(数字)9798331541460
ISBN:
(纸本)9798331541477
Wind power generation has grown in importance as renewable energy technology has advanced; nonetheless, wind speed forecasting remains a concern. Traditional approaches are inadequate in dealing with the nonlinearity and long-term dependency of wind speed data. The suggested method aims to present a deep learning architecture that incorporates several meteorological elements, with a hybrid loss employed to increase the wind speed prediction effect. The experiment used meteorological data from the Hangzhou Asian Games. Comparative results show that our suggested strategy outperformed ARIMA and Random Forest models in measures by up to 10.86%. Ablation studies demonstrated that including wind speed along with other meteorological data can improve performance, highlighting the importance of wind speed in predicting.
Facial recognition technology plays a crucial role in various applications, from enhancing security at banks and organizations to streamlining attendance tracking in public gatherings and educational institutions. Tra...
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ISBN:
(数字)9798350350654
ISBN:
(纸本)9798350350661
Facial recognition technology plays a crucial role in various applications, from enhancing security at banks and organizations to streamlining attendance tracking in public gatherings and educational institutions. Traditional methods of attendance marking, such as signatures, names, and biometrics, can be time-consuming and error-prone. To address these challenges, a smart attendance system is proposed, leveraging Deep Learning, Convolutional Neural Networks (CNN), and the OpenCV library in Python for efficient face detection and recognition. The system utilizes advancedalgorithms, including Eigen faces and fisher faces, to recognize faces accurately. While deep learning models excel with large datasets, they may not perform optimally with few samples. By comparing input faces with images in the dataset, the system automatically updates recognized names and timestamps into a CSV file, which is then sent to the respective organization's head. Additionally, the system allows users to upload a single photo or a group photo, and it returns matched photos as output using a CNN. This feature enhances the system's flexibility and usability, providing users with a convenient way.
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