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Articles 1  4 of 4
FullText Articles in Computational Engineering
The Impact Of Quantum Size Effects On Thermoelectric Performance In Semiconductor Nanostructures, Adithya Kommini
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 solidstate, energy conversion and refrigeration using semiconductorbased 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 wellknown 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 ...
Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan
Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan
Masters Theses
Recent advances in cloudbased bigdata 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 shortterm 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.
Stateoftheart nowcasting systems today are based on numerical models which describe the physical processes leading to ...
MagnetoElectric Approximate Computational Framework For Bayesian Inference, Sourabh Kulkarni
MagnetoElectric Approximate Computational Framework For Bayesian Inference, Sourabh Kulkarni
Masters Theses
Probabilistic graphical models like Bayesian Networks (BNs) are powerful artificialintelligence formalisms, with similarities to cognition and higher order reasoning in the human brain. These models have been, to great success, applied to several challenging realworld applications. Use of these formalisms to a greater set of applications is impeded by the limitations of the currently used softwarebased implementations. New emergingtechnology 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: MemristorBased 3d Ic For Artificial Neural Networks, Sachin Bhat
Skynet: MemristorBased 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 2terminal 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 neuronâ€™s functionality is spread across layers of CMOS and memristor crossbar and thus cannot support the required connectivity to implement largescale multilayered ANNs.
This work proposes a new finegrained 3D integrated ...