IDMoB: IoT Data Marketplace on Blockchain

Today, Internet of Things (IoT) devices are the powerhouse of data generation with their ever-increasing numbers and widespread penetration. Similarly, artificial intelligence (AI) and machine learning (ML) solutions are getting integrated to all kinds of services, making products significantly more "smarter". The centerpiece of these technologies is "data". IoT device vendors should be able keep up with the increased throughput and come up with new business models. On the other hand, AI/ML solutions will produce better results if training data is diverse and plentiful. In this paper, we propose a blockchain-based, decentralized and trustless data marketplace where IoT device vendors and AI/ML solution providers may interact and collaborate. By facilitating a transparent data exchange platform, access to consented data will be democratized and the variety of services targeting end-users will increase. Proposed data marketplace is implemented as a smart contract on Ethereum blockchain and Swarm is used as the distributed storage platform.Comment: Presented at Crypto Valley Conference on Blockchain Technology (CVCBT 2018), 20-22 June 2018 - published version may diffe

Nanoparticle Classification in Wide-field Interferometric Microscopy by Supervised Learning from Model

Interference enhanced wide-field nanoparticle imaging is a highly sensitive technique that has found numerous applications in labeled and label-free sub-diffraction-limited pathogen detection. It also provides unique opportunities for nanoparticle classification upon detection. More specif- ically, the nanoparticle defocus images result in a particle-specific response that can be of great utility for nanoparticle classification, particularly based on type and size. In this work, we com- bine a model based supervised learning algorithm with a wide-field common-path interferometric microscopy method to achieve accurate nanoparticle classification. We verify our classification schemes experimentally by using gold and polystyrene nanospheres.Comment: 5 pages, 2 figure

Ölümünün 84 yıl sonra yine Tevfik Fikret

Taha Toros Arşivi, Dosya No: 98/A-Tevfik Fikretİstanbul Kalkınma Ajansı (TR10/14/YEN/0033) İstanbul Development Agency (TR10/14/YEN/0033

Proportioning for performance-based concrete pavement mixtures

The work presented in this dissertation involves an effort to develop a mix proportioning tool that can be used to determine the required type and amount of concrete components in a mixture based on the desired fresh and hardened concrete properties. Concrete performance is affected by the quantity and quality of the paste, and aggregate systems. Therefore, this study analyzed the effect of binder systems with different types and content; the paste quality; and size, shape, texture and gradation of different aggregate systems on various fresh and hardened concrete properties. In this experimental program, a total of 178 mixtures were prepared with 7 different gradation systems, 12 binder systems, 25 binder contents, 6 different water-to-binder ratio (w/b), and 3 different nominal air content. Fresh properties of slump, air content, air-void system, setting time, unit weight, and temperature were tested. Hardened properties of compressive strength, rapid chloride penetration, surface resistivity, air permeability, and shrinkage were tested at various ages. However, to develop such a tool, this study overall focused on the assessment of workability, compressive strength, and durability as these three properties are commonly used as indicators of concrete performance. Durability was assessed by testing the rapid chloride penetration, and surface resistivity at 28-days. As part of this study, an artificial neural network (ANN) approach has been used for concrete mix proportion design to analyze the complexity between concrete properties and concrete components. Results have shown that development of a performance-based mix proportioning tool is possible for mixtures when aggregate gradation is not varied. Development of a mix proportioning tool with addition of the various aggregates systems generally was not as successful due to the increased variability of the mix design parameters such as size, shape, texture, and gradation of aggregates. The proposed mix proportioning tool is promising and achievable in terms of predicting the values of the tested properties based on the mix design variables. Although the proposed mix proportioning tool is not completely ready for prime time, the findings of this study can be implemented in real time when this approach is used with a larger data set