Fourier Transforms

The 21st century ushered in a new era of technology that has been reshaping everyday life, simplifying outdated processes, and even giving rise to entirely new business sectors. Today, contemporary users of products and services expect more and more personalized products and services that can meet their unique needs. In that sense, it is necessary to further develop existing methods, adapt them to new applications, or even discover new methods. This book provides a thorough review of some methods that have an increasing impact on humanity today and that can solve different types of problems even in specific industries. Upgrading with Fourier Transformation gives a different meaning to these methods that support the development of new technologies and have a good projected acceleration in the future

Empire Rules: Cultures of U.S. Imperialism in Multi-Ethnic Literature of the U.S.

This dissertation concerns contemporary multi-ethnic literature of the U.S. (MELUS) and empire. Namely, contemporary MELUS invites a reckoning with U.S. Empire, an amalgamation of settler colonialism, imperialism, and capitalism, which works through ahistorical and transhistorical cultural narratives. In turn, contemporary multi-ethnic writers uncover our obscured colonial and imperial histories and legacies that racialize, criminalize, and otherize people of color in the U.S within our present moment. This dissertation, then, analyzes recent novels and poetry collections by African American, Native American, Latinx, and African diasporic writers to unmask the efforts of empire-building with the material effects on colonized, marginalized peoples. Reckoning with U.S. Empire within the literary space of contemporary MELUS, I argue, illustrates how the symbolic and material rules of U.S. Empire were established and how these rules concurrently erase their authors and beneficiaries, thereby naturalizing or normalizing our social, political, and economic hierarchies. Observing our contemporary moment portrayed in the fiction and poetry of MELUS has shown that nothing in our society—race, ethnicity, citizenship, gender—is an accident or created by chance. These are the rules of U.S. Empire employed since the constitutional foundation of the U.S., and now contemporary African American, Native, Latinx, and West African women writers are unveiling these rules, their construction, and at times their undoing

Adaptive dynamic programming with eligibility traces and complexity reduction of high-dimensional systems

This dissertation investigates the application of a variety of computational intelligence techniques, particularly clustering and adaptive dynamic programming (ADP) designs especially heuristic dynamic programming (HDP) and dual heuristic programming (DHP). Moreover, a one-step temporal-difference (TD(0)) and n-step TD (TD(λ)) with their gradients are utilized as learning algorithms to train and online-adapt the families of ADP. The dissertation is organized into seven papers. The first paper demonstrates the robustness of model order reduction (MOR) for simulating complex dynamical systems. Agglomerative hierarchical clustering based on performance evaluation is introduced for MOR. This method computes the reduced order denominator of the transfer function by clustering system poles in a hierarchical dendrogram. Several numerical examples of reducing techniques are taken from the literature to compare with our work. In the second paper, a HDP is combined with the Dyna algorithm for path planning. The third paper uses DHP with an eligibility trace parameter (λ) to track a reference trajectory under uncertainties for a nonholonomic mobile robot by using a first-order Sugeno fuzzy neural network structure for the critic and actor networks. In the fourth and fifth papers, a stability analysis for a model-free action-dependent HDP(λ) is demonstrated with batch- and online-implementation learning, respectively. The sixth work combines two different gradient prediction levels of critic networks. In this work, we provide a convergence proofs. The seventh paper develops a two-hybrid recurrent fuzzy neural network structures for both critic and actor networks. They use a novel n-step gradient temporal-difference (gradient of TD(λ)) of an advanced ADP algorithm called value-gradient learning (VGL(λ)), and convergence proofs are given. Furthermore, the seventh paper is the first to combine the single network adaptive critic with VGL(λ). --Abstract, page iv