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germal's Projects

spencer_people_tracking icon spencer_people_tracking

Multi-modal ROS-based people detection and tracking framework for mobile robots developed within the context of the EU FP7 project SPENCER.

story-generation icon story-generation

Code for Improving Neural Story Generation by Targeted Common Sense Grounding

swarm_evolve icon swarm_evolve

Model-Based Stochastic Search for Large Scale Optimization of Multi-Agent UAV Swarms

system-design-primer icon system-design-primer

Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

t265-genom3 icon t265-genom3

Genom3 component used to get pose and images asynchronously from Intel RealSense T265 sensor. Pose is published at 200Hz. Processing on images can be enabled to detect AprilTags and output the desired parameters (corners, center, pose, message,...)

t265_depth icon t265_depth

a ros node to publish depth from stereo block matching using the Realsense T265 Tracking Camera

t265_mavconn icon t265_mavconn

Send T265 VIO pose message to ArduPilot using libmavconn

t265controlsystem icon t265controlsystem

A control system that uses the RealSense T265 camera to report drone position

t265matlabanalysis icon t265matlabanalysis

This repository contains matlab scripts for analysis and graphing of data collected by the T265 camera and the motion capture system

targetpersontracker icon targetpersontracker

A tracker implementation which tracks target person using collected ORB features from the target.

te-orb_slam2 icon te-orb_slam2

This repository utilizes the high-dimensional info extracted from YOLO v3 and merges it in ORB-SLAM2.

telesculptor icon telesculptor

TeleSculptor: Aerial Photogrammetry Application powered by KWIVER

tf-pose-estimation icon tf-pose-estimation

Hey! This is a clone of the tf-pose-estimation by Ildoo Kim modified to work with Tensorflow 2.0+!

tfnet-for-environmental-sound-classification icon tfnet-for-environmental-sound-classification

Learning discriminative and robust time-frequency representations for environmental sound classification: Convolutional neural networks (CNN) are one of the best-performing neural network architectures for environmental sound classification (ESC). Recently, attention mechanisms have been used in CNN to capture the useful information from the audio signal for sound classification, especially for weakly labelled data where the timing information about the acoustic events is not available in the training data, apart from the availability of sound class labels. In these methods, however, the inherent time-frequency characteristics and variations are not explicitly exploited when obtaining the deep features. In this paper, we propose a new method, called time-frequency enhancement block (TFBlock), which temporal attention and frequency attention are employed to enhance the features from relevant frames and frequency bands. Compared with other attention mechanisms, in our method, parallel branches are constructed which allow the temporal and frequency features to be attended respectively in order to mitigate interference from the sections where no sound events happened in the acoustic environments. The experiments on three benchmark ESC datasets show that our method improves the classification performance and also exhibits robustness to noise.

topics_timer icon topics_timer

ROS package to measure elapsed time between each message of a "sensor_msgs::Image" topic and the corresponding message on the "sensor_msgs::CompressedImage" topic.

topological_tools icon topological_tools

A ROS package that provides functionalities to describe topological maps and abstract complex navigation maps into discrete labeled locations

trajectoryclustering icon trajectoryclustering

This application written in Python is capable of fully automatic clustering of 2D trajectory data.

transformer-xl-chinese icon transformer-xl-chinese

transformer xl在中文文本生成上的尝试(可写小说、古诗)(transformer xl for text generation of chinese)

transunet icon transunet

This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

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