Therefore, our aim is to find a reliable and efficient emotion recognition scheme, which can be used for non-behavior-based emotion recognition in real-time.
This paper examines the energy efficiency optimization problem of intelligent reflective surface (IRS)-assisted multi-user rate division multiple access (RSMA) downlink systems under terahertz propagation.
To reconstruct the spatial distribution of micro vibrations, this paper proposes a new method based on a coincidence imaging system.
We use Velostat as a force sensor resistance (FSR) to construct a sensor matrix over the mat to receive the pressure distribution of the patient's body, and then upload the processed distribution information to the PC for data visualization through Arduino.
In this study, we propose a dynamic RIS subarray structure to improve the performance of a THz MIMO communication system.
The ability to visually verify some element of a remotely controlled agricultural automation system through a photograph is valuable in many cases, not only in the operational phase of the system, but especially in the design and implementation phases.
Second, we investigate some deep learning models based on CNN (ResNet34, hierarchical structure) and other deep learning models (LSTM, CLDNN).
With the fast advancement of smart devices and Internet of Things (IoT) technologies, certain established situations are opening up new avenues of exploration.
Various emotions can produce variations in electrocardiograph (ECG) signals, distinct emotions can be distinguished by different changes in ECG signals.
This system overall accuracy for the heart and respiration rate estimation can reach 99. 109% and 98. 581%, respectively.
Seizure onset detection in electroencephalography (EEG) signals is a challenging task due to the non-stereotyped seizure activities as well as their stochastic and non-stationary characteristics in nature.
Moreover, the conditions of maintaining the orthogonality of the RIS-aided THz channel are derived in support of spatial multiplexing.
Cardiovascular disease has become one of the most significant threats endangering human life and health.
With recently successful applications of deep learning in computer vision and general signal processing, deep learning has shown many unique advantages in medical signal processing.
Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI).
The newly emerged machine learning (e. g. deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems.
By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use $k$-means algorithm for this purpose.
In this paper, we incorporate a new type of side information based on consecutive location changes of users and propose a new metric called transition-entropy to investigate the location privacy preservation, followed by two algorithms to improve the transition-entropy for a given dummy generation algorithm.