Signal Processing
The dataset consists of acoustic signals acquired from the surface of the knee of 14 subjects. The description of study group and methodology of the experiment can be found in the publication: https://doi.org/10.3390/s21196495.
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IREYE4TASK is a dataset for wearable eye landmark detection and mental state analysis. Sensing the mental state induced by different task contexts, where cognition is a focus, is as important as sensing the affective state where emotion is induced in the foreground of consciousness, because completing tasks is part of every waking moment of life. However, few datasets are publicly available to advance mental state analysis, especially those using the eye as the sensing modality with detailed ground truth for eye behaviors.
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The dataset includes channel frequency response (CFR) data collected through an IEEE 802.11ax device for human activity recognition. This is the first dataset for Wi-Fi sensing with the IEEE 802.11ax standard which is the most updated Wi-Fi version available in commercial devices. The dataset has been collected within a single environment considering a single person as the purpose of the study was to evaluate the impact of communication parameters on the performance of sensing algorithms.
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Wearable long-term monitoring applications are becoming more and more popular in both the consumer and the medical market. In wearable ECG monitoring, the data quality depends on the properties of the electrodes and on how they contact the skin. Dry electrodes do not require any action from the user. They usually do not irritate the skin, and they provide sufficiently high-quality data for ECG monitoring purposes during low-intensity user activity. We investigated prospective motion artifact–resistant dry electrode materials for wearable ECG monitoring.
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Multidimensional geometric shaping has been shown to outperform uniform quadrature amplitude modulation (QAM) in optical communication systems but the complexity of symbol decision and bit mapping can often be significant as dimensionality increases. In this paper, a low-complexity geometric shaping method based on multidimensional lattices is investigated both in experiments and simulations. The modulation formats designed based on this method are called Voronoi constellations (VCs) and we study them in 8, 16, and 32 dimensions.
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To gather the dataset, we asked two participants to perform six basic knife activities. The layout of the system experiment is provided in Fig. 4. As it illustrates, we put the receiver on the right side and the ESP32 transceiver on the left side of the performing area. The performing area is a cutting board (30 x 46 cm) in this experiment. Each participant performs each activity five times in the performing area.
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The main objective of this project is to design and develop a collaborative framework which facilitates real-time tracking of a target person even when GPS signal is not available, while collecting motion data to infer his or her lifestyle and health status. The framework orchestrates a wide range of technologies such as localization technologies, machine learning and AI, sensor data analytics and cloud computing. The overall framework design also takes into consideration the culture, lifestyles, behaviours and infrastructures of ASEAN countries.
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In this paper, we study the advantages of improper Gaussian signaling (IGS) with the existence of hardware impairments (HWI) and imperfect successive interference cancellation (SIC) in the downlink two-user power-domain non-orthogonal multiple access (PD-NOMA) systems. We first provide the input-output relationships for the MIMO system with HWIs and then derive the rate expressions for each user. The improper signals are assumed to be generated by widely linear precoding (WLP), transmitted under the PD-NOMA scheme, and in the context of imperfect SIC.
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The formation and changes of the pulse wave areaffected by the functional status of the heart, blood, and arterial vessels. The pulse signal covers pathological information in the cardiovascular system, and the doctor of traditional Chinese medicine (TCM) can diagnose diseases by feeling the pulse. The diagnosis of diseases through feeling the pulse of TCM primarily relies on the doctor’s feelings and subjective experience, and it lacks objectified data.
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