Discover @chaets Open Source projects

Chetan Gupta (@chaets)

chaets

Stars
19
Global Rank 658,561 (Top 23 %)
Followers 6
Following 1
Registered almost 7 years ago
Most used languages

Java
30.8 %

C
23.1 %

Jupyter Notebook
23.1 %

Python
15.4 %

Assembly
7.7 %
Location 🇺🇸 United States
Country Total Rank 115,657
Country Ranking

Assembly
2,291

Cluster-Benchmarking

Cluster Benchmarking of CPU, RAM, I/O and network using C

Automatic-smear-detection-on-the-camera-lens-from-given-street-view-images

detect smear present on the camera lens through various image processing algorithms

Sort-using-Spark-and-Hadoop

This project covers sort through Hadoop and Spark on multiple nodes. We have used Linux system to implement your application and used the Proton Cluster accessible at 216.47.142.37; each Hadoop group (cluster) has 4 nodes, each node having 4-cores, 8GB of memory, and 80GB of SSD storage.

Understanding-the-cost-of-Computing-in-the-cloud

translates-the-virtual-address-into-Physical-address

uv2p() – uv2p system call translates the virtual address into Physical address.

Computer_Vision_projects

All the CV codes

Jupyter Notebook

Convolution-network-for-Bush-and-William-Data-Set

Convolution network for Bush and William Data Set to identify if they are in the pic or not

Jupyter Notebook

Heartbeat_data_analysis

Heartbeat_data_analysis after reading ECG data

Jupyter Notebook

Deep-neural-network-with-limiter-precision-on-GPUs-using-python

Deep neural network with limited precision on GPUs using python

Coupon-Inventory-System

Coupon Inventory System using JAVA

Sort-on-Single-shared-memory

sorting on external and multi-threaded programming

implement-semi-colon-and-and-operator-in-XV6

implement semi-colon and and operator in XV6

Deep-learning-with-limited-precision-using-GENANN

Deep learning with limited precision using GENANN in C. precisions like Int 8, int 16, Float 32 and Float 16

Generl-Sorting-for-Teragigabit-of-Data

External data sort and multithreaded programming is performed on single node shared memory for two given datasets of 2 GB and 20 GB each. The purpose of the project is to sort datasets larger than the size of the memory using External Sorting Technique. The results are evaluated and then comparison is carried out with the implementation of the results of Linux sorting. This document illustrates graphically the strategy for implementing the sorting followed by merging. The document also puts forward tabular performance evaluation for the same.