Signal Processing

The dataset is intended to cover core issues pertaining to the area of a traffic optimization via RET motors inside the antenna on the mobile base station system (BSS). The principle of RET operation was already known to scientists; however, the use of a machine learning and big data provides the possibility of creation an autonomous system, which control RET system.

Last Updated On: 
Sun, 10/23/2022 - 08:50

Parkinson’s Disease (PD) is the second most common neurodegenerative disorder with resting tremor (RT) being it's most common motor symptom. This study aimed to determine the features of wrist velocity and acceleration that can be used as objective, reliable, and sensitive detectors of RT. Forty-five healthy young adults imitated RT in both hands after observing a video of RT in a person with PD. Inertial measurement units placed on both wrists recorded the linear acceleration and angular velocity, which were used to calculate linear velocity and angular acceleration.


The dataset includes processed sequences of optical time domain reflectometry (OTDR) traces incorporating different types of fiber faults namely fiber cut, fiber eavesdropping (fiber tapping), dirty connector and bad splice. The dataset can be used for developping ML-based approaches for optical fiber fault detection, localization, idenification, and characterization. 


Oral health problems are closely associated with the analysis of dental tissue changes and the stomatologic treatment that follows. The associated paper explores the use of diffuse reflectance spectroscopy in the detection of dental tissue disorders. The data set includes 78 out of 343 measurements of teeth spectra in the wavelength range from 400 to 1700 nm. The proposed methodology focuses on computational and statistical methods and the use of these methods for the classification of dental tissue into two classes (healthy and unhealthy) by estimating the probability of class membership.


This dataset contains pathloss and ToA radio maps generated by the ray-tracing software WinProp from Altair. The dataset allows to develop and test the accuracies of pathloss radio map estimation methods and localization algorithms based on RSS or ToA in realistic urban scenarios.


More than 85% of traffic utilization via mobile phones are consumed in the urban area, and most of the traffic is used for downloading. Improving the throughput in LTE for 1 user equipment (UE) in cities is an urgent problem. The collected data is intended to study a dependence of the KPI mobile base station and neighboring from installation extra technology. This study will support the development of methods for comparing traffic utilization of urban area and carry out recommendations for the Channel Quality Indicator (CQI) increases.


This dataset is about UAV signals.

Both indoor and outdoor experiments are conducted.


Microwave Filter Design Kit, this CAD tool is used to design and analyse filters.


Human activity data based on wearable sensors, such as the Inertial Measurement Unit (IMU), have been widely used in human activity recognition. However, most publicly available datasets only collected data from few body parts and the type of data collected is relatively homogeneous. Activity data from local body parts is challenging for recognizing specific activities or complex activities. Hence, we create a new  HAR dataset which is colledted from the project named MPJA HAD: A Multi-Position Joint Angles Dataset for Human Activity Recognition Using Wearable Sensors.


Due to the smaller size, low cost, and easy operational features, small unmanned aerial vehicles (SUAVs) have become more popular for various defense as well as civil applications. They can also give threat to national security if intentionally operated by any hostile actor(s). Since all the SUAV targets have a high degree of resemblances in their micro-Doppler (m-D) space, their accurate detection/classification can be highly guaranteed by the appropriate deep convolutional neural network (DCNN) architecture.