43 skills found · Page 1 of 2
Jashcraf / PokePoke (pronounced poh-keh) is a Polarization Ray Tracing and Gaussian Beamlet module for Python
DavidT3 / XGAX-ray: Generate and Analyse is a module designed to make the analysis of XMM observations simple and efficient. It provides an interface with SAS for the creation of XMM data products, as well as a way to easily perform fits (scalable for multiple observations) and retrieve information about an object, all within a Python package.
pierangeloc / Ray Tracer ZioA ray tracer to learn ZIO modules
youngjun-ko / Ct Mar AttentionCodes and data for paper "Rigid and Non-rigid Motion Artifact Reduction in X-ray CT using Attention Module"
tkn-tub / Ns3sionnans3sionna is a software module that brings realistic channel simulation using ray tracing from Sionna to the ns-3 network simulator
DavidT3 / DAXADemocratising Archival X-ray Astronomy (DAXA) is an easy-to-use Python module for downloading multi-mission X-ray telescope data and processing it into usable archives. Users can acquire entire archives, or filter observations based on ID/positions/time. Supports XMM/eROSITA/Chandra; partial support NuSTAR/Swift/Suzaku/ASCA/ROSAT/INTEGRAL/XRISM
Auditory-Biophysics-Lab / Slicer BoneThicknessMapping3D Slicer module that creates a gradient map representing the bone thickness of a volume using VTK ray-casting.
IS2AI / Chest X Ray ModuleLeveraging the recent advances in machine learning and availability of public medical imaging datasets, we created a Free Online X-Ray Diagnostic Tool using deep learning that can determine the X-ray type and visualize the pathology.
hashlookup / A Ray Grassa-ray-grass is a yara module that provides support for DCSO-format bloom filters in yara. In the context of hashlookup, it allows quickly discard known files "pour séparer le grain de l'ivraie"
priyamittal15 / Implementation Of Different Deep Learning Algorithms For Fracture Detection Image ClassificationUsing-Deep-Learning-Techniques-perform-Fracture-Detection-Image-Processing Using Different Image Processing techniques Implementing Fracture Detection on X rays Images on 8000 + images of dataset Description About Project: Bones are the stiff organs that protect vital organs such as the brain, heart, lungs, and other internal organs in the human body. There are 206 bones in the human body, all of which has different shapes, sizes, and structures. The femur bones are the largest, and the auditory ossicles are the smallest. Humans suffer from bone fractures on a regular basis. Bone fractures can happen as a result of an accident or any other situation in which the bones are put under a lot of pressure. Oblique, complex, comminute, spiral, greenstick, and transverse bone fractures are among the many forms that can occur. X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and other types of medical imaging techniques are available to detect various types of disorders. So we design the architecture of it using Neural Networks different models, compare the accuracy, and get a result of which model works better for our dataset and which model delivers correct results on a specific related dataset with 10 classes. Basically our main motive is to check that which model works better on our dataset so in future reference we all get an idea that which model gives better type of accuracy for a respective dataset . Proposed Method for Project: we decided to make this project because we have seen a lot of times that report that are generated by computer produce error sometimes so we wanted to find out which model gives good accuracy and produce less error so we start to research over image processing nd those libraries which are used in image processing like Keras , Matplot lib , Image Generator , tensor flow and other libraries and used some of them and implement it on different image processing algorithm like as CNN , VGG-16 Model ,ResNet50 Model , InceptionV3 Model . and then find the best model which gives best accuracy for that we generate classification report using predefined libraries in python such as precision , recall ,r2score , mean square error etc by importing Sklearn. Methodology of Project: Phase 1: Requirement analysis: • Study concepts of Basic Python programming. • Study of Tensor flow, keras and Python API interface . • Study of basic algorithms of Image Processing and neural network And deep learning concepts. • Collect the dataset from different resources and describe it into Different classes(5 Fractured + 5 non fractured). Phase 2: Designing and development: The stages of design and development are further segmented. This step starts with data from the Requirement and Analysis phase, which will lead to the model construction phase, where a model will be created and an algorithm will be devised. After the algorithm design phase is completed, the focus will shift to algorithm analysis and implementation in this project. Phase 3: Coding Phase: Before real coding begins, the task is divided into modules/units and assigned to team members once the system design papers are received. Because code is developed during this phase, it is the developers' primary emphasis. The most time-consuming aspect of the project will be this. This project's implementation begins with the development of a program in the relevant programming language and the production of an error-free executable program. Phase 4: Testing Phase: When it comes to the testing phase, we may test our model based on the classification report it generates, which contains a variety of factors such as accuracy, f1score, precision, and recall, and we can also test our model based on its training and testing accuracy. Phase 5: Deployment Phase: One of our goals is to bring all of the previous steps together and put them into practice. Another goal is to deploy our model into a python-based interface application after comparing the classification reports and determining which model is best for our dataset.
Micosilent / MFOSmodA transcription to KiCAD of Ray Wilson's synthesizer modules
purdue-aalp / RayflexChisel RTL module of a unified ray tracer datapath pipeline. Supports ray-box intersection, ray-triangle intersection, acceleration for Euclidean distance and cosine similarity calculation.
SaiHarish-7 / Fire DetectionFire Alarm Systems are very common in commercial building and factories, these devices usual contain a cluster of sensors that constantly monitors for any flame, gas or fire in the building and triggers an alarm if it detects any of these. One of the simplest way to detect fire is by using anIR Flame sensor, these sensors have an IR photodiode which is sensitive to IR light. Now, in the event of a fire, the fire will not only produce heat but will also emit IR rays, yes every burning flame will emit some level of IR light, this light is not visible to human eyes but our flame sensor can detect it and alert a microcontroller like Arduino that a fire has been detected. In this Project we interface Flame Sensor with Arduinoand learn all the steps to build Fire Alarm System by using Arduino and flame sensor. Flame sensor module has a photodiode to detect the light and an op-amp to control the sensitivity. It is used to detect fire and provide a HIGH signal upon the detection. Arduino reads the signal and provides alert by turning on the buzzer and LED. The flame sensor used here is an IR based flame sensor.
havardlovas / Gref4hsiThis is a repository for georeferencing of pushbroom hyperspectral imagery and includes ray-intersection, orthorectification and a coregistration module (at dev stage).
pierreamir123 / VTK Pyqt 3D Visualisation DICOMpyqt5 GUI application use VTK tools to visualize DICOM files in 3D modules in two modes Surface Rending and Ray Casting
JaXRTS / JaxrtsPython module for modelling X-ray Thomson scattering spectra
Nanunanuk / SMARTIRay tracing tool for solar cell and module optics
scottprahl / PygrinA python module to ray trace through gradient index (GRIN) lenses.
idaholab / MacawMaCaw is a MOOSE-based application that enables domain-decomposed neutral particle transport calculations in MOOSE. It leverages the ray tracing MOOSE module for unstructured mesh tracking and OpenMC for collision physics, handling material definitions, and tallying quantities.
ray-di / Ray.AuraSqlModuleAura.Sql module for Ray.Di
