Rapid developments in digital technology have expedited the dissemination of information on social media platforms like as Twitter, Facebook, and Weibo. Unverified information can create protests and mislead the publi...
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This research study proposes the development of comprehensive vital sign surveillance designed for real-time patient assessment. The system integrates multiple sensors, including the LM35 temperature sensor, Heartbeat...
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ISBN:
(纸本)9798350359299
This research study proposes the development of comprehensive vital sign surveillance designed for real-time patient assessment. The system integrates multiple sensors, including the LM35 temperature sensor, Heartbeat Rate (HBR) and Blood Oxygen (SpO2) Sensor, DHT11 humidity and temperature sensor, OLED display, and gyroscope sensor, to provide a holistic approach to vital signs. The LM35 sensor is employed for accurate temperature measurement, providing crucial data for monitoring the patient's thermal status. This information is essential for detecting fever or abnormal temperature variations, aiding in the early identification of potential health issues. The HBR and SpO2 sensor plays a vital role in cardiovascular vital signs. The system can assess the patient's cardiac and respiratory well-being by measuring the heart rate and blood oxygen levels. Abnormalities in these parameters can trigger timely alerts, enabling swift medical intervention. The DHT11 sensor enhances the system's capabilities by monitoring ambient temperature and humidity. This additional environmental data contributes to a more comprehensive understanding of the patient's surroundings, ensuring that external factors are considered in the health assessment process. Integrating an OLED display serves as an intuitive user interface, providing real-time feedback on the measured parameters. This ensures that patients and healthcare providers can easily interpret and respond to the health data presented by the system. The display can showcase temperature, humidity, heart rate, blood oxygen levels, and other relevant information in a user-friendly format. Furthermore, the gyroscope sensor adds a layer of sophistication to vital sign surveillance by enabling the assessment of body movement and posture. This feature is particularly beneficial for patients with mobility issues or those prone to falls. The gyroscope data can be analyzed to detect sudden movements or abnormal postures, triggering alerts
One of the most challenging issues in computer imaging is the automated segmentation of brain tumors using Magnetic Resonance Images (MRI). Several approaches are explored using Deep Neural Networks in image segmentat...
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Customer churn prediction is an important task in customer relationship management because it helps businesses know who is at risk of leaving and retain such at-risk *** and time-efficient churn prediction is essentia...
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This paper presents an integrated AI-based pipeline for law enforcement applications, specifically targeting the creation of first information report (FIR) in the Kannada language. The pipeline comprises a speech proc...
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Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly ***,detecting sarcasm in various forms of communication can be difficult due to conflicting *** t...
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Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly ***,detecting sarcasm in various forms of communication can be difficult due to conflicting *** this paper,we introduce a contrasting sentiment-based model for multimodal sarcasm detection(CS4MSD),which identifies inconsistent emotions by leveraging the CLIP knowledge module to produce sentiment features in both text and ***,five external sentiments are introduced to prompt the model learning sentimental preferences among ***,we highlight the importance of verbal descriptions embedded in illustrations and incorporate additional knowledge-sharing modules to fuse such imagelike *** results demonstrate that our model achieves state-of-the-art performance on the public multimodal sarcasm dataset.
With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. ...
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With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.
Accurate skin tumor identification and categorization are crucial for quick diagnosis and treatment in dermatological care. In our research, we developed a reliable model that successfully categorizes a wide range of ...
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The rapid evolution of smartphone technology and the diverse range of available models have made selecting a cost-effective mobile phone a complex decision for consumers. Although brand, internal memory, camera qualit...
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This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden *** MVS under consideration consists of a leader vehicle with a...
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This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden *** MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles,connected via a directed interaction topology,subject to simultaneous unknown heterogeneous nonlinearities and external *** central aim is to achieve effective and collisionfree formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering,while not demanding global information of the interaction *** this goal,a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle ***,a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is *** is proved that,with the proposed protocol,the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is *** simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.
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