The dataset consists all the Telugu characters that contains Vowels, Consonants and combine characters such as Othulu (Consonant-Consonant) and Guninthamulu (Consonant-Volwels). The main objective of this dataset to recognize handwritten Telugu characters, from that convert handwritten document into editable electronic copy.

Instructions: 

All the images are in the same size and all images are scanned by scanner and segmented manually and all images are jpeg images.

 Acknowledgement:

 The work is carried out under Collaborative Research Project Sponsored by JNTU Hyderabad, India. The project file no. JNTUH/TEQIP-III/CRS/2019/CSE/12 and Titled as "Deep Learning Aided-OCR for Handwritten Telugu Character".

 

 

Categories:
476 Views

Training, Test, and Validation data pertaining to the real-time packet data captured in Sonic Firewall is attached herewith.

Categories:
61 Views

The select_mmsi.xls file is the MMIS number were selected. The information include the gear type of each vessel, the startdate and enddate of random selected 5000 position points. The txt files are the raw information of each position points for all vessel. 

Instructions: 

The select_mmsi.xls file is the MMIS number were selected. The information include the gear type of each vessel, the startdate and enddate of random selected 5000 position points.

The txt files are the raw information of each position points for all vessel. For each vessel, 5000 position points were selected.

Categories:
21 Views

The data collection was carried out over several months and across several cities including but not limited to Quetta, Islamabad and Karachi, Pakistan. Ultimately, the number of images collected as part of the Pakistani dataset were, albeit in a very small quantity. The images taken were also distributed across the classes unevenly, just like the German dataset. All the 359 images were then manually cropped to filter out the unwanted image background data. All the images were sorted into folders with names corresponding to the label of the images.

Instructions: 

Dataset is divided by classes and the images inside the folder are named randomly and contain no useful labels in their names.

Categories:
257 Views

This is a supplementary data file, providing the data used to evaluate the performance of our 3D fully convolutional neural network. This network removes reverberation noise from ultrasound channel data. This dataset is simulated ultrasound channel data, simulated in Field II Pro, and has artificial reverberation and thermal noise added. This dataset will be linked to our publication, once it is accepted. 

Categories:
57 Views

Holoscopic micro-gesture recognition (HoMG) database was recorded using a holoscopic 3D camera, which have 3 conventional gestures from 40 participants under different settings and conditions. The principle of holoscopic 3D (H3D) imaging mimics fly’s eye technique that captures a true 3D optical model of the scene using a microlens array. For the purpose of H3D micro-gesture recognition. HoMG database has two subsets. The video subset has 960 videos and the image subset has 30635 images, while both have three type of microgestures (classes).

Instructions: 

Holoscopic micro-gesture recognition (HoMG) database consists of 3 hand gestures: Button, Dial and Slider from 40 subjects with various ages and settings, which includes the right and left hand, two of record distance.

For video subset: There are 40 subjects, and each subject has 24 videos due to the different setting and three gestures. For each video, the frame rate is 25 frames per second and length of videos are from few seconds to 20 seconds and not equally. The whole dataset was divided into 3 parts. 20 subjects for the training set, 10 subjects for development set and another 10 subjects for testing set.

For image subset: Video can capture the motion information of the micro-gesture and it is a good way for micro-gesture recognition. From each video recording, the different number of frames were selected as the still micro-gesture images. The image resolution 1920 by 1080. In total, there are 30635 images selected. The whole dataset was split into three partitions: A Training, Development, and Testing partition. There are 15237 images in the training subsets of 20 participants with 8364 in close distance and 6853 in the far distance. There are 6956 images in the development subsets of 10 participants with 3077 in close distance and 3879 in far distance. There are 8442 images in the testing subsets of 10 participants with 3930 in close distance and 4512 in far distance.

Categories:
137 Views

 food recognition  

 

Instructions: 

The data consists of 222430 training and 55096 testing images belonging to 2 classes. For the preparation of this dataset, we used images from the existing image datasets of UECFOOD256, Caltech 256, Instagram Images, Flickr Image Dataset, Food101, Malaysian Food Dataset(gathered and crawled by us), Indoor Scene recognition Dataset, 15 scene dataset.

Please only cite our work, for Food/Non-Food detection, in case of classification problems on the individual datasets, please cite and use them.

Categories:
152 Views

We study the ability of neural networks to steer or control trajectories of dynamical systems on graphs, which we represent with neural ordinary differential equations (neural ODEs). To do so, we introduce a neural-ODE control (NODEC) framework and find that it can learn control signals that drive graph dynamical systems into desired target states. While we use loss functions that do not constrain the control energy, our results show that NODEC produces control signals that are highly correlated with optimal (or minimum energy) control signals.

Categories:
75 Views

In this work, physical parameter‐based modeling of small signal parameters for a metal‐semiconductor field‐effect transistor (MESFET) has been carried out as continuous functions of drain voltage, gate voltage, frequency, and gate width. For this purpose, a device simulator has been used to generate a big dataset of which the physical device parameters included material type, doping concentration and profile, contact type, gate length, gate width, and work function.

Categories:
55 Views

IEEE Access "A Process-aware memory compact-device model using long-short term memory"

Categories:
29 Views

Pages