42 skills found · Page 1 of 2
qcadoo / Mesqcadoo MES - friendly web manufacturing software
Telecominfraproject / OpenCellularOpenCellular (OC) is open source access platform with main focus on rural connectivity. OC is an eco-system of open-source project(s) focusing on hardware, software, testing automation, manufacturing and building tools for ease of deployments and operations.
RespiraWorks / VentilatorFully-featured ICU ventilator design, optimized for manufacture using commonly available components and free to license. Repository tracks all mechanical, electrical and systems design, software, requirements and regulatory documentation.
MSLattice / MSLattice WindowsMSLattice is a software that can be used to generate the geometries of various types of lattices that are known as periodic cellular materials or architected materials. In particular, lattices based on triply periodic minimal surfaces. The user can generate the STL files needed for fabricating the lattices using additive manufacturing (commonly known as 3D printing) and can generate the files needed for conducting finite element simulations or computational fluid dynamics simulation in order to estimate the performance of lattices under different loading conditions.
computed-axial-lithography / CAL Software MatlabSoftware to support volumetric additive manufacturing by tomographic reconstruction (education, research and not-for-profit use)
Blb3D / Filaops3D Print Farm ERP - Production-grade manufacturing resource planning for additive manufacturing
adamantine-sim / AdamantineSoftware to simulate heat transfer for additive manufacturing
Abhishekkt15 / Pharmacy Management SystemThe main aim of the project is the management of the database of the pharmaceutical shop. This project is insight into the design and implementation of a Pharmacy Management System. This is done by creating a database on the available medicines in the shop. The primary aim of Pharmacy Management System is to improve accuracy and enhance safety and efficiency in the pharmaceutical store. The aim this project is to develop software for the effective management of a pharmaceutical store. We have developed this software for ensuring effective policing by providing statistics of the drugs in stock. Pharmacy Management system is useful to maintain correct database by providing an option to update the drugs in stock. It is used to manage most pharmacy related activities in the pharmacy. Pharmacy Management System is a management system that is designed to improve accuracy and to enhance safety and efficiency in the pharmaceutical store. This program can be used in any pharmaceutical shops having a database to maintain. The software used can generate reports, as per the user’s requirements. Using this pharmacy management system user is also able to generate report within a specified period of time. The system allows the user to enter a manufacturing and expiry date for a particular product or drug during opening stock and sales transaction. The software can print invoices, bills, receipts etc. It can also maintain the record of supplies sent in by the supplier. The system will also give report showing the list of products expiry after a specified date before the product eventually expires. The system services and goals are established by consultation with system user. It also involves manual entry upon arrival of new batches of drugs and upon drug movement out of the pharmacy for a certain period. Pharmacy management system is being build. Pharmacy management system is robust, integrated technology. every month, the pharmacist may want to generate report for the movement of drugs in and out of the pharmacy, getting information about the drugs e.g. expiry date, date purchased, number of drug type left, location of a drug in the pharmacy. Pharmacy management system deals with the maintenance of drugs and consumables in the pharmacy unit. This pharmacy management system is user friendly.
popupcad / Code PopupcadpopupCAD is a software package which facilitates the development of laminate devices, popup mechanisms, and flat foldable structures. The Software is designed to provide a foundation for common mathematical operations, with many higher-level plugins available for automating the design process, analyzing manufacturability, and generating the structures used to fabricate such devices.
ttlock / Android SDK DemoTTLock is a global provider of solutions for smart locks. We provide PCBA and softwares for lock manufactures and end users. We provide open API/SDK for developers. With these APIs/SDKs, you can develop your own applications to manage smart locks. The SDK here is for mobile app to communicate with locks via bluetooth.
ciyam / CiyamCIYAM Software Manufacturing
ttlock / IOS SDK DemoTTLock is a global provider of solutions for smart locks. We provide PCBA and softwares for lock manufactures and end users. We provide open API/SDK for developers. With these APIs/SDKs, you can develop your own applications to manage smart locks. The SDK here is for mobile app to communicate with locks via bluetooth.
Code-XYZxyz / Real Time Interferometric Measurement Control For Photopolymer Additive ManufacturingThis is a comprehensive MATLAB-based software platform developed for real-time measurement and feedback control of a custom mask-projection photopolymerization based additive manufacturing system (referred as "ECPL", i.e., Exposure Controlled Projection Lithography) using a lab-built interferometry (referred as "ICM&M", i.e., Interferometric Curing Monitoring and Measurement). A graphical user interface using the graphical user interface development environment (GUIDE) of MATLAB was created to implement the ICM&M method for the ECPL process. The software interfaces with the hardware of the ECPL system’s ultraviolet lamp and DMD, and the ICM&M system’s camera. It was designed to streamline the operation of the ECPL process with the aid of parallel computing that implements online both the ICM&M acquisition and measurement analysis as well as the feedback control method. The application logs the acquired interferogram video data, performs numerical computations for the ICM&M measurement algorithms and control law, saves the real-time data and measurement results for all voxels in the region of interest. Meanwhile, it displays interferogram frames and visualize the photocuring process without a substantial sacrifice in temporal performance of other key functions such as data acquisition and measurement & control analysis. The software could be extended to real-time process measurement and control for other additive manufacturing systems, for example, metal based additive manufacturing aided by in-situ thermal images analysis.
dia2018 / What Is The Difference Between AI And Machine LearningArtificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in. In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data. In this blog, we would dissect each term and understand how Artificial Intelligence and Machine Learning are related to each other. What is Artificial Intelligence? The term Artificial Intelligence was recognized first in the year 1956 by John Mccarthy in an AI conference. In layman terms, Artificial Intelligence is about creating intelligent machines which could perform human-like actions. AI is not a modern-day phenomenon. In fact, it has been around since the advent of computers. The only thing that has changed is how we perceive AI and define its applications in the present world. The exponential growth of AI in the last decade or so has affected every sphere of our lives. Starting from a simple google search which gives the best results of a query to the creation of Siri or Alexa, one of the significant breakthroughs of the 21st century is Artificial Intelligence. The Four types of Artificial Intelligence are:- Reactive AI – This type of AI lacks historical data to perform actions, and completely reacts to a certain action taken at the moment. It works on the principle of Deep Reinforcement learning where a prize is awarded for any successful action and penalized vice versa. Google’s AlphaGo defeated experts in Go using this approach. Limited Memory – In the case of the limited memory, the past data is kept on adding to the memory. For example, in the case of selecting the best restaurant, the past locations would be taken into account and would be suggested accordingly. Theory of Mind – Such type of AI is yet to be built as it involves dealing with human emotions, and psychology. Face and gesture detection comes close but nothing advanced enough to understand human emotions. Self-Aware – This is the future advancement of AI which could configure self-representations. The machines could be conscious, and super-intelligent. Two of the most common usage of AI is in the field of Computer Vision, and Natural Language Processing. Computer Vision is the study of identifying objects such as Face Recognition, Real-time object detection, and so on. Detection of such movements could go a long way in analyzing the sentiments conveyed by a human being. Natural Language Processing, on the other hand, deals with textual data to extract insights or sentiments from it. From ChatBot Development to Speech Recognition like Amazon’s Alexa or Apple’s Siri all uses Natural Language to extract relevant meaning from the data. It is one of the widely popular fields of AI which has found its usefulness in every organization. One other application of AI which has gained popularity in recent times is the self-driving cars. It uses reinforcement learning technique to learn its best moves and identify the restrictions or blockage in front of the road. Many automobile companies are gradually adopting the concept of self-driving cars. What is Machine Learning? Machine Learning is a state-of-the-art subset of Artificial Intelligence which let machines learn from past data, and make accurate predictions. Machine Learning has been around for decades, and the first ML application that got popular was the Email Spam Filter Classification. The system is trained with a set of emails labeled as ‘spam’ and ‘not spam’ known as the training instance. Then a new set of unknown emails is fed to the trained system which then categorizes it as ‘spam’ or ‘not spam.’ All these predictions are made by a certain group of Regression, and Classification algorithms like – Linear Regression, Logistic Regression, Decision Tree, Random Forest, XGBoost, and so on. The usability of these algorithms varies based on the problem statement and the data set in operation. Along with these basic algorithms, a sub-field of Machine Learning which has gained immense popularity in recent times is Deep Learning. However, Deep Learning requires enormous computational power and works best with a massive amount of data. It uses neural networks whose architecture is similar to the human brain. Machine Learning could be subdivided into three categories – Supervised Learning – In supervised learning problems, both the input feature and the corresponding target variable is present in the dataset. Unsupervised Learning – The dataset is not labeled in an unsupervised learning problem i.e., only the input features are present, but not the target variable. The algorithms need to find out the separate clusters in the dataset based on certain patterns. Reinforcement Learning – In this type of problems, the learner is rewarded with a prize for every correct move, and penalized for every incorrect move. The application of Machine Learning is diversified in various domains like Banking, Healthcare, Retail, etc. One of the use cases in the banking industry is predicting the probability of credit loan default by a borrower given its past transactions, credit history, debt ratio, annual income, and so on. In Healthcare, Machine Learning is often been used to predict patient’s stay in the hospital, the likelihood of occurrence of a disease, identifying abnormal patterns in the cell, etc. Many software companies have incorporated Machine Learning in their workflow to steadfast the process of testing. Various manual, repetitive tasks are being replaced by machine learning models. Comparison Between AI and Machine Learning Machine Learning is the subset of Artificial Intelligence which has taken the advancement in AI to a whole new level. The thought behind letting the computer learn from themselves and voluminous data that are getting generated from various sources in the present world has led to the emergence of Machine Learning. In Machine Learning, the concept of neural networks plays a significant role in allowing the system to learn from themselves as well as maintaining its speed, and accuracy. The group of neural nets lets a model rectifying its prior decision and make a more accurate prediction next time. Artificial Intelligence is about acquiring knowledge and applying them to ensure success instead of accuracy. It makes the computer intelligent to make smart decisions on its own akin to the decisions made by a human being. The more complex the problem is, the better it is for AI to solve the complexity. On the other hand, Machine Learning is mostly about acquiring knowledge and maintaining better accuracy instead of success. The primary aim is to learn from the data to automate specific tasks. The possibilities around Machine Learning and Neural Networks are endless. A set of sentiments could be understood from raw text. A machine learning application could also listen to music, and even play a piece of appropriate music based on a person’s mood. NLP, a field of AI which has made some ground-breaking innovations in recent years uses Machine Learning to understand the nuances in natural language and learn to respond accordingly. Different sectors like banking, healthcare, manufacturing, etc., are reaping the benefits of Artificial Intelligence, particularly Machine Learning. Several tedious tasks are getting automated through ML which saves both time and money. Machine Learning has been sold these days consistently by marketers even before it has reached its full potential. AI could be seen as something of the old by the marketers who believe Machine Learning is the Holy Grail in the field of analytics. The future is not far when we would see human-like AI. The rapid advancement in technology has taken us closer than ever before to inevitability. The recent progress in the working AI is much down to how Machine Learning operates. Both Artificial Intelligence and Machine Learning has its own business applications and its usage is completely dependent on the requirements of an organization. AI is an age-old concept with Machine Learning picking up the pace in recent times. Companies like TCS, Infosys are yet to unleash the full potential of Machine Learning and trying to incorporate ML in their applications to keep pace with the rapidly growing Analytics space. Conclusion The hype around Artificial Intelligence and Machine Learning are such that various companies and even individuals want to master the skills without even knowing the difference between the two. Often both the terms are misused in the same context. To master Machine Learning, one needs to have a natural intuition about the data, ask the right questions, and find out the correct algorithms to use to build a model. It often doesn’t requiem how computational capacity. On the other hand, AI is about building intelligent systems which require advanced tools and techniques and often used in big companies like Google, Facebook, etc. There is a whole host of resources to master Machine Learning and AI. The Data Science blogs of Dimensionless is a good place to start with. Also, There are Online Data Science Courses which cover the various nitty gritty of Machine Learning.
huzcodes / CraftIQ.InventoryCraftIQ.Inventory is a microservice dedicated to handling inventory management within the CraftIQ manufacturing software system.
ExascaleAM / AdditiveFOAMMirror for AdditiveFOAM https://github.com/ORNL/AdditiveFOAM: heat and mass transfer software for simulation of additive manufacturing, based on OpenFOAM
DESIGN4ADDITIVE / GPUCADforAMDesign Software for Additive Manufacturing
Jacob-Barhak / BIMThe BIM software registers point clouds to CAD models. It includes automatic registration algorithms. It is useful in cases where complex shapes are inspected by laser scanners. Sample cloud points and models are provided with the software. The BIM software was developed at the NSF Engineering Research Center for Reconfigurable Manufacturing Systems (ERC/RMS) at the University of Michigan. This is the version from 20 November 2005. The development team at the time was: Xinju Li, Jacob Barhak, Igor Guskov, Geoffrey W. Blake.
harry-io / Apple.comApple Inc (Apple) designs, manufactures, and markets smartphones, tablets, personal computers (PCs), portable and wearable devices. The company also offers software related services, accessories, and third-party digital content and applications.
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