The Internet of Things and edge computing are fostering a future of ecosystems

hosting complex decentralized computations, deeply integrated with our very dynamic

environments. Digitalized buildings, communities of people, and cities will be the

next-generation “hardware and platform”, counting myriads of interconnected devices, on top of

which intrinsically-distributed computational processes will run and self-organize. They will

spontaneously spawn, diffuse to pertinent logical/physical regions, cooperate and compete,


Clamp-on ultrasonic transit time difference flow meters provide opportunities for metering where it is impractical or undesirable to cut into an existing pipeline to install an alternative flow meter. Up until now, it has been difficult to perform this type of measurement on thin-walled metal pipes, due to the difficulty of interpreting the guided wave modes in the combined pipe wall and internal fluid system, but a new method has been reported recently that utilises these guided wave modes for flow measurement.


This dataset provides a dataset of high resolution image-grade LiDAR SLAM in .bag format.


For the interaction of humans with machines but also for the interaction of machines with the environment, e.g. in robotic manipulation tasks, large area sensors like sensor skins are of high interest. Capacitive sensor have become widely used for touch sensor and proximity sensors and are well suited for such large area sensing.


In the present work, Prosopis Juliflora, an invasive alien plant species, is processed and converted into a useful biomass carbon material. For gas sensing application, SnO2 nanoparticles are anchored on the Prosopis Juliflora biomass carbon leaves (BML) using a sol-gel process. The BML/SnO2 nanocomposite is fabricated and tested for hazardous NO2 gas sensing applications. The biomass carbon support and uniform distribution of SnO2 nanoparticles enabled the adsorption/desorption of target gas on the surface of the nanocomposite.


The number of private vehicles is still increasing from year to year. In order to limit environmental damage, a proper way of dealing with this trend is the introduction of intelligent automotive infrastructure. Besides traffic management solutions, smart parking guidance systems are important for reducing unnecessary traffic. For this, a key prerequisite are sensor networks that provide information about the occupancy state of every single parking spot in the parking infrastructure of high traffic targets e.g. nearby an airport or shopping mall.


This data set was generated and used in determining the workability of a homemade Intelligent IoT Weather Station Using an Embedded System.


ReSysTDepth collects trajectories of people in indoor spaces. Each trajectory is composed of a sequence of three-dimensional coordinates that capture the center of mass of people moving in the corridor of a building. It is composed of two datasets:

  • Real Dataset - real data captured by a depth camera (Microsoft Kinect v2).
  • Synthetic dataset - synthetic data generated artificially using splines.



A synthetic dataset designed to evaluate transfer learning performance for RF domain adaptation in the publication Assessing the Value of Transfer Learning Metrics for RF Domain Adaptation. The dataset contains a total of 13.8 million examples, with 600k examples each of 22 modulation schemes (given below) and AWGN noise (200k each for training, validation, and testing); 512 raw IQ samples per example.


This dataset was generated using Python wrappers around liquid-dsp (, and is saved in SigMF format such that each example is saved in an individual ‘.sigmf-data’ file with an associated ‘.sigmf-meta’ file  of the same name. The ‘.sigmf-data’ file contains the interleaved raw IQ samples in binary format and can be read using the numpy.load() function. The ‘.sigmf-meta’ file contains all metadata parameters used to generate the example including the number of samples, modulation type, signal-to-noise ratio, frequency offset, and filtering parameters, is in json format ,and can be read using json.load(). Further details and code examples for loading the dataset can be found at


Minimally-Invasive Surgeries can benefit from having miniaturized sensors on surgical graspers to provide additional information to the surgeons. One such potential sensor is an ultrasound transducer. At long travel distances, the ultrasound transducer can accurately measure its ultrasound wave's time of flight, and from it, classify the grasped tissue. However, the ultrasound transducer has a ringing artifact arising from the decaying oscillation of its piezo element, and at short travel distances, the artifact blends with the acoustic echo.