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Computational Engineering Commons

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Full-Text Articles in Computational Engineering

Reward Allocation For Maximizing Energy Savings In A Transportation System, Adewale O. Oduwole Jan 2018

Reward Allocation For Maximizing Energy Savings In A Transportation System, Adewale O. Oduwole

Masters Theses

Transportation has a major impact on our society and environment, contributing 70% of U.S petroleum use, 28% of U.S. greenhouse gas (GHG) emissions, over 34,000 fatalities and 2.2 million injuries in 2013. Punitive approaches to used to tackle environmental issues in the transportation sector, such as congestion pricing have been well documented, although the use of incentives or rewards lags behind in comparison. In addition to the use of more fuel-efficient, alternate energy vehicles and various other energy reduction strategies; energy consumption can be lowered through incentivizing alternative modes of transportation. This paper focused on modifying ...


Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan Jan 2017

Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan

Masters Theses

Recent advances in cloud-based big-data technologies now makes data driven solutions feasible for increasing numbers of scientific computing applications. One such data driven solution approach is machine learning where patterns in large data sets are brought to the surface by finding complex mathematical relationships within the data. Nowcasting or short-term prediction of rainfall in a given region is an important problem in meteorology. In this thesis we explore the nowcasting problem through a data driven approach by formulating it as a machine learning problem.

State-of-the-art nowcasting systems today are based on numerical models which describe the physical processes leading to ...


Magneto-Electric Approximate Computational Framework For Bayesian Inference, Sourabh Kulkarni Jan 2017

Magneto-Electric Approximate Computational Framework For Bayesian Inference, Sourabh Kulkarni

Masters Theses

Probabilistic graphical models like Bayesian Networks (BNs) are powerful artificial-intelligence formalisms, with similarities to cognition and higher order reasoning in the human brain. These models have been, to great success, applied to several challenging real-world applications. Use of these formalisms to a greater set of applications is impeded by the limitations of the currently used software-based implementations. New emerging-technology based circuit paradigms which leverage physical equivalence, i.e., operating directly on probabilities vs. introducing layers of abstraction, promise orders of magnitude increase in performance and efficiency of BN implementations, enabling networks with millions of random variables. While majority of applications ...


Skynet: Memristor-Based 3d Ic For Artificial Neural Networks, Sachin Bhat Jan 2017

Skynet: Memristor-Based 3d Ic For Artificial Neural Networks, Sachin Bhat

Masters Theses

Hardware implementations of artificial neural networks (ANNs) have become feasible due to the advent of persistent 2-terminal devices such as memristor, phase change memory, MTJs, etc. Hybrid memristor crossbar/CMOS systems have been studied extensively and demonstrated experimentally. In these circuits, memristors located at each cross point in a crossbar are, however, stacked on top of CMOS circuits using back end of line processing (BOEL), limiting scaling. Each neuron’s functionality is spread across layers of CMOS and memristor crossbar and thus cannot support the required connectivity to implement large-scale multi-layered ANNs.

This work proposes a new fine-grained 3D integrated ...


The Impact Of Quantum Size Effects On Thermoelectric Performance In Semiconductor Nanostructures, Adithya Kommini Jan 2017

The Impact Of Quantum Size Effects On Thermoelectric Performance In Semiconductor Nanostructures, Adithya Kommini

Masters Theses

An increasing need for effective thermal sensors, together with dwindling energy resources, have created renewed interests in thermoelectric (TE), or solid-state, energy conversion and refrigeration using semiconductor-based nanostructures. Effective control of electron and phonon transport due to confinement, interface, and quantum effects has made nanostructures a good way to achieve more efficient thermoelectric energy conversion. This thesis studies the two well-known approaches: confinement and energy filtering, and implements improvements to achieve higher thermoelectric performance. The effect of confinement is evaluated using a 2D material with a gate and utilizing the features in the density of states. In addition to that ...