Signal Processing

The accuracy and reliability of an Ultra- WideBand (UWB) Indoor Positioning System (IPS) are compromised owing to the positioning error caused by the Non-Line-of-Sight (NLoS) signals. To address this, Machine Learning (ML) has been employed to classify Line-of-Sight (LoS) and NLoS components. However, the performance of ML algorithms degrades due to the disproportion of the number of LoS and NLoS signal components. A Weighted Naive Bayes (WNB) algorithm is proposed in this paper to mitigate this issue.


This dataset contains the raw data to figure 6 of the paper "Modeling and evaluation of a rate-based transcutaneous blood gas monitor" that has been submitted to IEEE Transactions on Biomedical Engineering


This dataset contains the measurements used in the validation example described in the manuscript "Fourier-based microwave imaging with antenna pattern compensation". These measurements were collected using the facility described in [1].


The human gait is unique and so is the impact of a walking human on the propagation of wireless signals within a wireless network. Using appropriate pattern recognition techniques, a person can thus be identified just from a time series of Received Signal Strength (RSS) measurements. This dataset holds bidirectional RSS measurements recorded within a mesh network of four Bluetooth sensor devices. During the measurements, a total of 14 subjects walked individually through the setup. A total of more than 10,000 recordings are provided.


The Bluetooth 5.1 Core Specification brought Angle of Arrival (AoA) based Indoor Localization to the Bluetooth Standard. This feature is usually referred to as Bluetooth Direction Finding. Besides localization, this new technology can be used to implement a radar system just using commodity Bluetooth chipsets. This dataset holds measurements recorded using a demonstration setup for such kind of radar system. Along with this dataset, the experimental setup, the used sensor devices, and the structure of the provided data files are described in detail.


 The drawback of inter-subcarrier interference in OFDM systems makes the channel estimation and signal detection performance of OFDM systems with few pilots and short cyclic prefixes (CP) poor. Thus, we use deep learning to assist OFDM in recovering nonlinearly distorted transmission data. Specifically, we use a self-normalizing network (SNN) for channel estimation, combined with a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) for signal detection, thus proposing a novel SNN-CNN-BiGRU network structure (SCBiGNet). 


The data collection questionnaire consisted of two sections. One section involved the collection of data via Google Forms questionnaires, and the other involved the collection of WhatsApp voice samples. There were three subsections in the questionnaire section. The first consisted of the individual's basic information, such as email address, name, and identification number. The second was the personal health questionnaire depression scale (PHQ8), which included 8 groups of statements, and the third was the Beck Depression Inventory-II, which contained 21 groups of statements.


<p>Two-electrode straight open magnetic source (TSOMS) has been widely used on the magnetic minesweeper and magnetic decoy. Fast-forward modeling and obtaining the high-precision magnetic field in the air are the prerequisites for real-time inversion and positioning of the TSOMS. In this paper, we propose an algorithm to calculate the magnetic field of the two-electrode straight open magnetic source (MFTSOMS) by previous theoretical research. And a classical algorithm of electric dipole magnetic field is used to verify the correctness of our algorithm.


We provided a synthetic dataset for the RIS-aided mmWave channel estimation


These data is state estimation accuracy of the proposed algorithm When the equivalent measurement loss probability is 0.1


These data includes the position estimation accuracy and velocity estimation accuracy of the algorithm.

The data are explained below:

save_bikf_pos_p1, save_kf_pos_p1, save_okf_pos_p1, save_bakf_pos_p1, save_vakf_pos_p1 are the position accuracy of the BKF, KF, OKF, the proposed BAKF-GIWM and the VAKF-GIWM, respectively.