Search Results (5)

The paper proposes a method for identifying a person based on EEG parameters recorded during the process of entering user password phrases on the keyboard. The method is presented in two versions: for a two-channel EEG (frontal leads only) and a six-channel EEG. A database of EEGs of 95 subjects was formed, who entered a password phrase on the keyboard, including states in an altered psychophysiological state (sleepy and tired). During the experiment, the subjects’ EEG data were recorded. The experiment on collecting data in each state was conducted on different days. The signals were segmented in such a way that the time of entering the password phrase corresponded to the time used during the EEG to identify the subject. The EEG signals are processed using two autoencoders trained on EEG data (on spectrograms of the original signals and their autocorrelation functions). The encoder is used to extract signal features. After identifying the features, identification is performed using the Bayesian classifier. The achieved error level was 0.8% for six-channel EEGs and 1.3% for two-channel EEGs. The advantages of the proposed identification method are that the subject does not need to be put into a state of rest, and no additional stimulation is required. Full article

Social robots are being conceived with different characteristics and being used in different applications. The growth of social robotics benefits from advances in fabrication, sensing, and actuation technologies, as well as signal processing and artificial intelligence. This paper presents a design and implementation of the humanoid robotic platform Adam, consisting of a motorized human-like head with precise movements of the eyes, jaw, and neck, together with capabilities of face tracking and vocal conversation using ChatGPT. Adam relies on 3D-printed parts together with a microphone, a camera, and proper servomotors, and it has high structural integrity and flexibility. Adam’s control framework consists of an adequate signal exploitation and motor command strategy that allows efficient social interactions. Adam is an innovative platform that combines manufacturability, user-friendliness, low costs, acceptability, and sustainability, offering advantages compared with other platforms. Indeed, the platform’s hardware and software components are adjustable and allow it to increase its abilities and adapt them to different applications in a variety of roles. Future work will entail the development of a body for Adam and the addition of skin-like materials to enhance its human-like appearance. Full article

Although commercial smart speakers are becoming increasingly popular, there is still much potential for investigation into their usability. In this study, we analyzed the usability of commercial smart speakers by focusing on the learnability of young users who are not yet familiar with voice user interface (VUI) operation. In the experiment, we conducted a task in which users repeatedly operated a smart speaker 10 times under four conditions, combining two experimental factors: the presence or absence of a screen on the smart speaker and the operation method (voice control only or in conjunction with remote-control operation). The usability of the smart speaker was analyzed in terms of task-completion time, task-completion rate, number of errors, subjective evaluation, and retrospective protocol analysis. In particular, we confirmed and compared the learning curves for each condition in terms of the performance metrics. The experimental results showed that there were no substantial differences in the learning curves between the presence and absence of a screen. In addition, the “lack of feedback” and “system response error” were identified as usability problems, and it was suggested that these problems led to “distrust of the system”. Full article

In computer vision and image processing, the shift from traditional cameras to emerging sensing tools, such as gesture recognition and object detection, addresses privacy concerns. This study navigates the Integrated Sensing and Communication (ISAC) era, using millimeter-wave signals as radar via a Convolutional Neural Network (CNN) model for event sensing. Our focus is on leveraging deep learning to detect security-critical gestures, converting millimeter-wave parameters into point cloud images, and enhancing recognition accuracy. CNNs present complexity challenges in deep learning. To address this, we developed flexible quantization methods, simplifying You Only Look Once (YOLO)-v4 operations with an 8-bit fixed-point number representation. Cross-simulation validation showed that CPU-based quantization improves speed by 300% with minimal accuracy loss, even doubling the YOLO-tiny model’s speed in a GPU environment. We established a Raspberry Pi 4-based system, combining simplified deep learning with Message Queuing Telemetry Transport (MQTT) Internet of Things (IoT) technology for nursing care. Our quantification method significantly boosted identification speed by nearly 2.9 times, enabling millimeter-wave sensing in embedded systems. Additionally, we implemented hardware-based quantization, directly quantifying data from images or weight files, leading to circuit synthesis and chip design. This work integrates AI with mmWave sensors in the domain of nursing security and hardware implementation to enhance recognition accuracy and computational efficiency. Employing millimeter-wave radar in medical institutions or homes offers a strong solution to privacy concerns compared to conventional cameras that capture and analyze the appearance of patients or residents. Full article

The modern tourist industry is characterized by an abundance of applied multicriteria decision-making tasks. Several researchers have demonstrated that such tasks can be effectively resolved using aggregation operators based on fuzzy integrals and fuzzy measures. At the same time, the implementation of this mathematical tool is limited by weak intuitive understanding by the practicing specialists of the aggregation process as well as fuzzy measures in general. Some researchers have proposed different aggregation visualization methods, but these methods have several properties that block their wide implementation in decision-making practice. The purpose of this study is to develop a decision-making approach that will allow practitioners to have a clear intuitive vision of the aggregation process and fuzzy measures. This article proposes an approach to decision making in the tourist industry based on the synthesis of the aggregation operator that includes 3D visualization graphics in virtual reality. Firstly, some research devoted to decision-making methods in tourism was assessed along with “smart” tourism, aggregation operators and their visualization. Secondly, a 3D visualization in the form of a balance model was introduced. Thirdly, the method of aggregation-operator synthesis based on the 3D balance model and the 2-order Choquet integral was developed. Finally, an illustrational example of implementing such an approach for resolving the task of assessing and choosing a hotel was described. Full article