Number Recognition

Number Recognition

From an image predict the number from it

Artificial Intelligence

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Description

Number prediction

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Victor M. created project Smart E-commerce Atacadista

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Smart E-commerce Atacadista

O presente projeto de pesquisa propõe a construção de um sistema WEB comercial. Trata-se de um e-commerce para o seguimento business-to-business (B2B) em que empresas distribuidoras de mercadorias façam transações comerciais virtualmente com seus clientes varejistas. A proposta é informatizar os processos comerciais realizados presencialmente por um vendedor em um sistema que dê autonomia ao varejista para realizar suas compras periódicas. Essa virtualização do processo tem seu diferencial prático, tornando largamente vantajosa sua utilização, em comparação ao modo convencional. Esta plataforma requer o uso de tecnologias de desenvolvimento apropriadas para sua construção. Por tratar-se de um sistema online, exige-se que sejam elencadas as tecnologias ideais para o seu desenvolvimento, visando a evolução do sistema enquanto produto de software. Para isto, é necessário um planejamento refinado e o uso do mais adequado Processo de Desenvolvimento de Software, o qual seguirá os mais atuais conceitos de Engenharia de Software.

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Rose D. created project Home Fingerprint

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Home Fingerprint

Home automation is stepping into the realm of data science and machine learning, but by doing so homes need to become smarter, utilizing sensors to track and analyze data. A crucial part in doing so is developing a home identity, or fingerprint through sensors that maps when a user is home and the environment they live in verses when a user is not home and the environment. This analysis will show use a fingerprint of attributes that help define the pattern of the home. Through this, it can be determined the typical temperature range in the home with a high and low value, average humidity, noise, how often the user is home, sleep patterns and much more. This is a concept that can be integrated into many technologies or home ecosystems.  

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Carlos P. created project Solar Forecasting in Hawaii

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Solar Forecasting in Hawaii

Energy companies in Hawaii have started to use solar energy harvested in power plants as a more cost-effective alternative to provide energy to consumers. A problem with this renewable alternative is the dependence on weather, and consequent unpredictability of the next day solar irradiation.

In the University of Hawaii at Manoa ICS department together with Hawaiian Electric (HECO), I've investigated how to identify such patterns using probabilistic models and information theory.

More details of this work can be found on the International Conference on Machine Learning and Applications (ICMLA 2015).

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Carlos P. created project Social and Code Networks Motifs

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Social and Code Networks Motifs

Our lab at the University of Hawaii at Manoa, Siemens Germany, and Universitat Passau is studying how the alignment of social and code networks in open source software development, also known as Conway's Law (https://en.wikipedia.org/wiki/Conway%27s_law) affects various project outcomes. Different from previous research, our work extends the analysis through granularity (functions, files, modules), time windows (weeks, months, years) and different communication channels (mailing lists, issue trackers), to obtain the full picture.

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Carlos P. created project Net-Zero Energy Building and Thermal Comfort Modeling

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Net-Zero Energy Building and Thermal Comfort Modeling

Amid growing concerns about rising energy prices, energy independence, and the impact of climate change, statistics show buildings to be the primary energy consumer in the U.S. This fact underscores the importance of targeting building energy use as a key to decreasing the nation’s energy consumption.

One current solution to minimize building energy consumption is net-zero buildings. In the context of this project, a net-zero building is thought of as being capable of generating energy (from solar irradiance) and consuming energy through its various energy devices. The goal of every year is minimally to be Net-Zero, as Net-Negative will incur costs to purchase energy. The energy management, however, is not fully automated by the building: It is the responsability of the building’s users to reach the Net-Zero goal every year.

Understanding key patterns in several types of devices through time and user behavior to identify actionable insight that minimizes energy consumption is the vision of this project.

In the ERDL Lab, at the University of Hawaii at Manoa, my tasks vary from managing data acquisition sensors, data transformation, and thermal comfort modeling to identify opportunities for a more responsible way towards energy consumption, without compromising comfort. More recently, I have also started to perform analytics to understand wind patterns through renewable energy generation by wind turbines.

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Paul L. created project IoT Analytics Gateway

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IoT Analytics Gateway

Creation of a scaleable and efficient Analytics Gateway to collect anonymous usage data was achieved leveraging a "Serverless" Architecture. The "Serverless" Framework allows the construction auto-scaling, event driven components deployable to many cloud platforms such as Amazon Web Services, Azure and Google Cloud Platform or as the business case requires, deploying as easily to your own private cloud (Intel Private Cloud). Currently servicing sensor and usage data to over 20,000 live connected IoT devices deployed across the U.S., we are able to collect real-time, anonymous statistics for only pennies per device over the service life of the device.

Extremely Low latency (<100ms) and minimal downtime from connectivity disruption is achieved from this service model.

Exploration of usage of Analytics data with leading Machine Learning platforms: Caffe2 (on AWS), Apache Spark BigDL, Cloud AI (Google) & Watson (IBM).

Topics explored: * Serverless - https://serverless.com/ * Caffe2 - https://caffe2.ai/ * Google Cloud AI - https://cloud.google.com/products/machine-learning/ * Apache Spark BigDL - https://software.intel.com/en-us/articles/bigdl-distributed-deep-learning-on-apache-spark * IBM Watson Machine Learning - https://datascience.ibm.com/features#machinelearning * Intel Private Cloud - https://www.intel.com/content/www/us/en/cloud-computing/private-cloud-solutions.html

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Edwin M. created project Farm Assist

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Farm Assist

With everything going digital, there’s need for a digital platform that allows farmers to automate their farm activities such that they have visibility of the farm progress anytime anywhere on single click. Coming up with a digital online platform, Farm Assist (FA), an internet application software, will help farmers to solve these problems in a more efficient and less costly way leading to increase in production.

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