Convolutional neural network (CNN)

This dataset comprises a structured collection of control flow representations derived from microcontroller program execution traces, visualized as space-filling curves. The dataset is organized into eight folders, each containing 1,000 NumPy arrays representing individual image samples. These samples are grouped into four logical categories, each corresponding to a different abstraction level of program trace data: (1) complete execution traces, (2) function-call-only traces, (3) conditional-statement-only traces, and (4) scaled and truncated function-call traces.

The dataset trained the CNN for Zen-Ship, an automated audio censoring application. The dataset contains spectral data for either explicit or non-explicit words.

Event-based vision revolutionizes traditional image sensing by capturing asynchronous intensity variations rather than static frames, enabling ultrafast temporal resolution, sparse data encoding, and enhanced motion perception. While this paradigm offers significant advantages, conventional event-based datasets impose a fixed thresholding constraint to determine pixel activations, severely limiting adaptability to real-world environmental fluctuations.

4800 + 2400 chipless RFID measurements of a population of 16 tags. Magnitude and phase are phase to allow DSP in the time domain. The measurement are made in a monostatic configuration with linearly-polarized antennas. The population of tags include circular ring resonators and square ring resonators. The dataset is used to trained convolutional neural networks. The inputs of the CNN is the continuous wavelet transform of the signals. The CWT is get from a previously selected portion of th signal in time domain.

The 'AirScript' dataset consists of surface electromyography (sEMG) signals obtained while writing the uppercase English alphabets (A–Z) in free space. The Delsys Trigno device was used to record forearm muscle activity from 16 subjects. Every subject performs two trials for each letter, thus resulting in 52 samples per subject. sEMG signals obtained from all subjects were stored at a 2000 Hz sampling rate for high temporal resolution. The dataset consists of raw sEMG signals that are stored in subject-specific folders and saved as `.npy` files.

Smart focal-plane and in-chip image processing has emerged as a crucial technology for vision-enabled embedded systems with energy efficiency and privacy. However, the lack of special datasets providing examples of the data that these neuromorphic sensors compute to convey visual information has hindered the adoption of these promising technologies.

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

Internet-of-Things (IoT) technology such as Surveillance cameras are becoming a widespread feature of citizens' life. At the same time, the fear of crime in public spaces (e.g., terrorism) is ever-present and increasing but currently only a small number of studies researched automatic recognition of criminal incidents featuring artificial intelligence (AI), e.g., based on deep learning and computer vision. This is due to the fact that little to none real data is available due to legal and privacy regulations. Consequently, it is not possible to train and test deep learning models.

This dataset contains electrical measurements collected in the context of the paper "An Embedded Deep Learning NILM System: A Year-long Field Study in Real Houses".

Insecurity is a problem that affects all cities around the world to a greater or lesser extent, and some of them make use of video surveillance to combat it, setting up monitoring centres with hundreds of cameras. For the most part, these centres are staffed by personnel responsible for observation and incident response. The advancement of technology in the market offers the possibility to optimise and add value to these processes.