This is a dataset about minimizing maritime passenger transfer in ship routing. consists of data on the distance between ports, the number of passengers from the port of origin to the port of destination, ship speed, and the duration of berthing at ports.
The widespread adoption of smartphones has transformed how users engage with digital content, particularly for reading. Unlike desktop systems, which rely on peripherals like a mouse and keyboard, reading on smartphones involves direct interaction with the touchscreen. Actions such as pinch-to-zoom, tapping, scrolling, changing screen orientation, and taking screenshots are key components of smartphone reading behavior. While studies on desktop peripherals have provided insights into implicit feedback from user interactions, similar research for smartphones remains underexplored.
This dataset comprises audio recordings of ultra-high-frequency ambient noise stored in the lossless waveform format (WAW). The recordings were sampled at a frequency sample rate of 2.048 MHz and then provided at a downsampled audio rate of 48 kHz for compatibility and practical usage. The total length of the dataset is 01:30:29, consisting of approximately 260 million data points. (2024-03-30)
To access this dataset without purchasing an IEEE Dataport subscription, please visit: https://zenodo.org/doi/10.5281/zenodo.11711229
Please cite the following paper when using this dataset:
A craniometry study was undertaken to obtain anthropometric measurements of three hundred and five (305) medical staff within Trinidad & Tobago which is a twin island republic situated in the Caribbean. A non-contact measurement method was used involving 3D scanning equipment to record the geometry of each subject’s head as a digital file. The digital files were then processed using CAD software to obtain measurements for twenty-two (22) facial points of interest. In addition, the gender of each staff member was recorded.
The Partial Discharge - Localisation Dataset, abbreviated: PD-Loc Dataset is an extensive collection of acoustic data specifically curated for the advancement of Partial Discharge (PD) localisation techniques within electrical machinery. Developed using a precision-engineered 32-sensor acoustic array, this dataset encompasses a wide array of signals, including chirps, white Gaussian noise, and PD signals.
New capabilities involving sensors, data collection, and data analysis have enabled innovations in how engineered systems are monitored and maintained. Whereas each new evolution of maintenance philosophies has relied upon the current technological state, this research examines potential future capabilities in the field of prognostics and health management (PHM). PHM algorithms for predicting the estimated time to failure for a system are based on sensor data, physical models, or a combination of both.
These datasets are gathered from an array of four gas sensors to be used for the odor detection and recognition system. The smell inspector Kit IX-16 used to create the dataset. each of 4 sensor has 16 channels of readings. Odors of different 12 samples are taken from these six sensors
1- Natural Air
2- Fresh Onion
3- Fresh Garlic
4- Black Lemon
5- Tomato
6- Petrol
7- Gasoline
8- Coffee
9- Orange
This is a test dataset for comparison with the latest multi-objective evolutionary algorithms. We have split the experiment into two groups in high and low dimensions respectively, and the experimental results are outstanding. We used IGD as the performance metric, and the data in parentheses are the std of 20 independent repetitions of the experiment and were analyzed for significance.
