jiankangren

-REAL-TIME-VEHICLE-CLASSIFICATION-AND-LOCALIZATION-USING-EDGE-COMPUTING-

The project aims to develop a traffic monitoring system using convolution neural network. We had modified existing tiny YOLO model for Indian vehicle such as auto rickshaw, bicycle, motorbikes etc. To do this, first we developed a data set of these vehicles then we retrained the existing tiny YOLO model. Moreover, with raspberry Pi we have developed a prototype for edge device which can count incoming and outgoing traffic from a particular point. There are various applications of such devices for example, it can be used as traffic monitoring system, surveillance, traffic load prediction. This is a fully functional independent device. All the decision has been done locally, this make this device highly useful for IoTs.

2018-DAC-System-Design-Contest

4ceedframework

Real-time Acquisition and Analysis Framework for Materials-related Cyber-Physical Environments

Abnormal-Behavior-Detection-Based-On-Optical-Flow-Features

CSC2515-University of Toronto. This project applied computer vision and mechine learning methods aimed to detect abnormal behaved object in crowd, by Hanwen Liang and Haohan Li.

abstract-value-iteration

Code for experiments in the paper "Abstract Value Iteration for Hierarchical Reinforcement Learning" (https://arxiv.org/abs/2010.15638)

CL-Embedding-CNN

Character-level Embedding with Convolutional Neural Network for Malicious Web Request Detection

Hierarchical-Fuzzy-Tree

Software: The Hierarchical Fuzzy Inference Tree Software Toolbox is a function approximation and feature selection tool that uses genetic programming for constructing tree like structure to construct an adaptive multi-layer perceptron. This standalone software toolbox solves prediction problems. The developed algorithm performs multiobjective in which it adaptively creates a simple models with high generalization ability.

online-mec

Validation for an online resource allocation algorithm for mobile edge computing

Quantum_Computation

量子计算相关学习

Simulation_MEC

Implementing and applying offloading algorithms on simulation-based MEC.

jiankangren/benchmarks_rtss16

Benchmarks for IEEE Real-Time Systems Symposium (RTSS), 2016 paper.

jiankangren/CAV-Sim

jiankangren/cleverhans

An adversarial example library for constructing attacks, building defenses, and benchmarking both

jiankangren/contiki

The official git repository for Contiki, the open source OS for the Internet of Things

jiankangren/COWOMO_2018_demo

jiankangren/Crowd_Behavior_Analysis

Crowd behavior analysis is an important field of research in modern world. It has wide applications in surveillance and public safety which are one of the prime social concerns. One way to analyze crowd behavior is obtain crowd movement data and then find out outliers in the individual trajectories to infer any abnormal behavior in the crowd.

jiankangren/DAC2020-Evaluations

jiankangren/dagsched

A deterministic scheduling algorithm for parallel DAG programs.

jiankangren/DeepSniffer

jiankangren/distiller

Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://nervanasystems.github.io/distiller

jiankangren/GEKKO

GEKKO Python for Machine Learning and Dynamic Optimization

jiankangren/gfpdag

Experiments for analysis of Global Fixed Priority scheduling for parallel tasks.

jiankangren/googletest

Googletest - Google Testing and Mocking Framework

jiankangren/lingua-franca

A coordination language for defining and composing reactors. See the Wiki for documentation.

jiankangren/llvm-project

The LLVM Project is a collection of modular and reusable compiler and toolchain technologies. Note: the repository does not accept github pull requests at this moment. Please submit your patches at http://reviews.llvm.org.

jiankangren/Minimizing-Resource-Consumption-Cost-of-DAG-Applications-with-Reliability-Requirement

Support code for paper "Minimizing Resource Consumption Cost of DAG Applications with Reliability Requirement on Heterogeneous Processor Systems"

jiankangren/Neural-Networks-on-Silicon

This is a collection of works on neural networks and neural accelerators.

jiankangren/ONNF

Open Neural Network Frontend

jiankangren/racecar_simulator

A lightweight simulator of the MIT Racecar.

jiankangren/Real-Time-Multicore-Scheduling

An implementation of the models of the paper cited

jiankangren/Reinforcement-Learning-for-Real-time-Pricing-and-Scheduling-Control-in-EV-Charging-Stations

Reinforcement Learning for Real time Pricing and Scheduling Control in EV Charging Stations

jiankangren/rnd-dag-gen

Randomized Direct Acyclic Graph (DAG) generator for scheduling and allocation.

jiankangren/ros-lite

jiankangren/rtrl

jiankangren/Scheduling-Real-Time-Parallel-Applications-in-Cloud-to-Minimize-Energy-Consumption

Support code for the paper "Scheduling Real-Time Parallel Applications in Cloud to Minimize Energy Consumption"

jiankangren/Simple-Neural-Network-Accelerator

Project where we conceptualized and designed a simple neural network accelerator, loosely based on the Eyeriss architecture, to accelerate the inference on a database of 10000 images. The trained model was made using KANN C libraries and the accelerator was synthesized as part of an SoC on an Altera FPGA.

jiankangren/spinlocks_clustering

Experiments for IEEE Transactions on Parallel and Distributed Systems (TPDS), 2018 paper.

jiankangren/UAV-NDFT

[ICCV 2019] "Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach"

jiankangren/Underwater-Dataset

Dataset of Underwater images

jiankangren/UWCNN

Code and Datasets for "Underwater Scene Prior Inspired Deep Underwater Image and Video Enhancement", Pattern Recognition, 2019