"Optimizing sEMG Gesture Recognition with Stacked Autoencoder Neural Network for Bionic Hand".

This study presents a novel deep learning approach for surface electromyography (sEMG) gesture recognition using stacked autoencoder neural network (SAE)s. The method leverages hierarchical representation learning to extract meaningful features from raw sEMG signals, enhancing the precision and robustness of gesture classification.•Feature Extraction and Classification MODWT Decomposition: The sEMG signals were decomposed using the MODWT DECOMPOSITION(Maximal Overlap Discrete Wavelet Transform) to capture various frequency components.

Facilitating spice recognition and classification: An image dataset of Indian spices.

This data paper presents a comprehensive visual dataset of 19 distinct types of Indian spices, consisting of high-quality images meticulously curated to facilitate various research and educational applications. The dataset includes extensive imagery of the following spices: Asafoetida, Bay Leaf, Black Cardamom, Black Pepper, Caraway Seeds, Cinnamon Stick, Cloves, Coriander Seeds, Cubeb Pepper, Cumin Seeds, Dry Ginger, Dry Red Chilly, Fennel Seeds, Green Cardamom, Mace, Nutmeg, Poppy Seeds, Star Anise, and Stone Flowers. Each image in the dataset has been captured under controlled conditions to ensure consistency and clarity, making it an invaluable resource for studies in food science, agriculture, and culinary arts.

Exploring the natural pothole dataset generated by the abrasion and cavitation effects of river water on rocks.

The Natural Pothole Dataset within River Environments is an extensive collection of 3992 high-resolution images [1] documenting various natural potholes located in riverine settings. Each image has been rigorously annotated utilizing the YOLO (You Only Look Once) object detection framework, which ensures precise bounding box coordinates and accurate class labels for identified potholes. The annotations are provided in XML format, facilitating seamless integration with machine learning algorithms and computer vision applications.

Novel low-temperature synthesis of Y O :Eu nanophosphor using combustion with thioglycerol as fuel for near-UV light-emitting diodes.

Luminescent materials used in flat panel displays, compact fluorescent lamps, and light-emitting diodes require high purity, uniform particle size, clean surfaces, spherical shape, and dense morphology to ensure long-term stability. Y O :Eu is a widely studied red phosphor known for its characteristic photoluminescence (PL) emission at 613 nm with near-UV excitation at 392 nm. Many methods have been explored to synthesize Y O :Eu nanoparticles with exceptional purity, consistent phases, and uniform particle sizes.