DataSet used in learning process of the traditional technique's operation, considering different devices and scenarios, the proposed approach can adapt its response to the device in use, identifying the MAC layer protocol, perform the commutation through the protocol in use, and make the device to operate with the best possible configuration.
Vehicular networks have various characteristics that can be helpful in their inter-relations identifications. Considering that two vehicles are moving at a certain speed and distance, it is important to know about their communication capability. The vehicles can communicate within their communication range. However, given previous data of a road segment, our dataset can identify the compatibility time between two selected vehicles. The compatibility time is defined as the time two vehicles will be within the communication range of each other.
Recognition and classification of currency is one of the important task. It is a very crucial task for visually impaired people. It helps them while doing day to day financial transactions with shopkeepers while traveling, exchanging money at banks, hospitals, etc. The main objectives to create this dataset were:
1) Create a dataset of old and new Indian currency.
2) Create a dataset of Thai Currency.
Message Queuing Telemetry Transport (MQTT) protocol is one of the most used standards used in Internet of Things (IoT) machine to machine communication. The increase in the number of available IoT devices and used protocols reinforce the need for new and robust Intrusion Detection Systems (IDS). However, building IoT IDS requires the availability of datasets to process, train and evaluate these models. The dataset presented in this paper is the first to simulate an MQTT-based network. The dataset is generated using a simulated MQTT network architecture.
Dataset used for "A Machine Learning Approach for Wi-Fi RTT Ranging" paper (ION ITM 2019). The dataset includes almost 30,000 Wi-Fi RTT (FTM) raw channel measurements from real-life client and access points, from an office environment. This data can be used for Time of Arrival (ToA), ranging, positioning, navigation and other types of research in Wi-Fi indoor location. The zip file includes a README file, a CSV file with the dataset and several Matlab functions to help the user plot the data and demonstrate how to estimate the range.
Imagine you just moved to your brand-new home and hired your energy provider. They tell you that based on the provided information they will set up a direct debit of €50/month. However, at the end of the year, that prediction was not quite accurate, and you end up paying a settlement amount of €300, or if you are lucky, they give you back some money. Either way, you will probably be disappointed with your energy provider and might consider moving on to another one. Predicting energy consumption is currently a key challenge for the energy industry as a whole.
The aircraft fuel distribution system has two primary functions: storing fuel and distributing fuel to the engines. These functions are provided in refuelling and consumption phases, respectively. During refuelling, the fuel is first loaded in the Central Reservation Tank and then distributed to the Front and Rear Tanks. In the consumption phase, the two engines receive an adequate level of fuel from the appropriate tanks. For instance, the Port Engine (PE) will receive fuel from Front Tank and the Starboard Engine (SE) will receive fuel from Rear Tank.
The database contains the raw range-azimuth measurements obtained from mmWave MIMO radars (IWR1843BOOST http://www.ti.com/tool/IWR1843BOOST) deployed in different positions around a robotic manipulator. Data has been collected inside a workspace environment. The data is used to train a machine learning model for the detection of a human operator placed in different positions (see the image). The Python code examples (on github link) use the data for federated learning.
We collected experimental field data with a prototype open-ended waveguide sensor (WR975) operating between 600 MHz - 1300 MHz. With our prototype sensor we collected reflection coefficient measurements at a total of 50 unique 1-ft^2 sites across two separate established cranberry beds in central Wisconsin. The sensor was placed directly on top of cranberry-crop bed canopies, and we obtained 12 independent reflection coefficient measurements (each defined as one S11 sweep across frequency) at each 1-ft^2 site by randomly rotating and/or tran
