Machine Learning

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.

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To test the feasibility of the idea: Using the processed data of sentinel-2 and GlobeLand30 as the input image and ground truth of subspace clustering for land cover classification, a dataset named 'MSI_Gwadar' is created.

'MSI_Gwadar' is a multi-spectral remote sensing image of Gwadar (town and seaport, southwestern Pakistan) and its four regions of interest, which includes MATLAB data files and ground truth files of the study area and its four regions of interest.

 

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Problems of neurodegenerative disorder patients can be solved by developing Brain-Computer Interface (BCI) based solutions. This requires datasets relevant to the languages spoken by patients. For example, Marathi, a prominent language spoken by over 83 million people in India, lacks BCI datasets based on the language for research purposes. To tackle this gap, we have created a dataset comprising Electroencephalograph (EEG) signal samples of selected common Marathi words.

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With the goal of improving machine learning approaches in inverse scattering, we provide an experimental data set collected with a 2D near-field microwave imaging system. Machine learning approaches often train solely on synthetic data, and one of the reasons for this is that no experimentally-derived public data set exists. The imaging system consists of 24 antennas surrounding the imaging region, connected via a switch to a vector network analyzer. The data set contains over 1000 full Scattering parameter scans of five targets at numerous positions from 3-5 GHz.

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The results are based on the measurements conducted on small drones and a bionic bird using a 60 GHz millimeter wave radar, analyzing their micro-Doppler characteristics in both time and frequency domain. The results are presented in .pkl format. The more detailed description of the data and how the authors processed it will be updated soon.

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This dataset presents real-world IoT device traffic captured under a scenario termed "Active," reflecting typical usage patterns encountered by everyday users. Our methodology emphasizes the collection of authentic data, employing rigorous testing and system evaluations to ensure fidelity to real-world conditions while minimizing noise and irrelevant capture.

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Weconsiderfivebenchmarkdatasets-Pokec-z,NBA,

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Standard dataset of the Tennessee–Eastman (TE) process.

The overall process consists of five operating units: reactor, condenser, vapor-liquid separator, recycle compressor and product stripper.

It has standard training and test data sets for soft sensor, fault detection and diagnosis, fault  classification, etc. Each data set is under different operating conditions.

 

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This dataset contains the online appendix of the paper titled "The effectiveness of hidden dependence metrics in bug prediction"

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The rapid evolution of wireless technology has led to the proliferation of small, low-power IoT devices, often constrained by traditional battery limitations, resulting in size, weight, and maintenance challenges. In response, ambient radio frequency (RF) energy harvesting has emerged as a promising solution to power IoT devices using RF energy from the environment. However, optimizing the placement of energy harvesters is crucial for maximizing energy reception. This paper employs machine learning (ML) techniques to predict areas with high power intensity for RF energy harvesting.

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