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To promote the deployment of quadrupedal robots, this study proposes a novel bio-inspired control scheme. Specifically, inspired by the differentiated modalities of the animal's proximal and distal joints, a multi-model fusion scheme is constructed. First, the hip movement in joint space is obtained by a central pattern generator(CPG), whereby motion gaits, including trotting and galloping, are generated by a coupling network. Then, to generate the knee motion, a CPG-driven finite state machine is first proposed to determine the gait state.

We developed a unique and valuable dataset specifically for advancing Brain-Computer Interface (BCI) systems by recording brain activity from a dedicated volunteer. The participant was asked to pronounce 100 carefully selected Malayalam words, along with their English translations, which were chosen for their relevance to astronauts during human space missions. The volunteer pronounced these words both vocally and subvocally, each word being repeated 50 times. Non-invasive Electroencephalography (EEG) sensors were employed to capture the brain activity associated with these tasks.

This dataset contains simulation values from thermo-mechanical finite element analysis simulations using ABAQUS.  Each simulation is one of 192 unique process parameter settings which includes varying laser power, scan speed, layer height and cooling assumptions. The geometry for each simulation is a hollow rectangluar box with rounded corners such that they form semi-circles. The wall thickness of each simulation is exactly the width of the focused laser.

The Deepfake face detection task involves a facial image of unknown authenticity for testing. While most deepfake detection methods take only the image as input, our literature demonstrates that conditioning the deepfake detector on identity—i.e., knowing whose deepfake face the picture might be—can enhance detection performance. Existing deepfake detection datasets, such as FaceForensics++ and DFDC, do not include identity information for authentic and deepfake faces.

This data set contains: 

- Training dataset: 271 CT-scans of inner ears used for optimization and training of the model. 

- Validation dataset: 70 CT-scans of inner ears used for external validation. 

- U-net architecture deep-learning model's weight after optimized training. 

- All manual segmentations performed for both datasets. 

- All post-processed automated segmentations performed by the model for bothd atasets. 

This data provides an overview of the simulation of Hybrid Electric Vehicles (HEVs) using MATLAB. HEVs have emerged as a promising solution for reducing emissions and improving fuel efficiency. MATLAB offers a flexible platform to accurately model and analyze the complex dynamics of hybrid powertrains, including various components and control strategies. The simulation capabilities enable the evaluation of HEV performance under different conditions, optimization of designs, and exploration of factors impacting energy consumption.

The following dataset is used to test lossless compression algorithm on already compressed data, i.e. reduce data size even further without compromising its integrity. This Dataset has 1.000 “.txt” text files extracted from other Datasets and compressed in “.zip” format with a maximum size of 1 kB (Kilobyte), that is, 1.024 bytes. The largest Dataset file has 1670 characters including “text_zip (970).zip” spaces, and the smallest repository file has 14 characters with “text_zip (1).zip” spaces.

Although surface coating is commonly utilized in many industries for improving the aesthetics and functionality of the end product, our tactile perception of coated surfaces has not been investigated in depth yet. In fact, there are only a few studies investigating the effect of coating material on our tactile perception of extremely smooth surfaces having roughness amplitudes in the order of a few nanometers.