A study whether animations can help algorithms students understand computational complexity

Undergraduate thesis submitted to the Department of Computer Science, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2019This paper seeks to discover if using animations to explain computational complexity to Algorithms students is better than using only handouts. As researchers in the field have shown, theoretical topics such as computational complexity are often difficult for students to understand especially because these students find the math and reductions too abstract to understand. In this paper, the author developed a visualisation system with key animations to improve students understanding. Students taking an Algorithms course were the participants of the study. They were equally divided into a control group and experimental group. The study took place in this order: all students took a class on computational complexity, then a pre-test, the control group used handouts while the experimental group used the animation system to learn computational complexity, finally everyone took a post-test. After running the Mann Whitney test, the results showed that there was no significant difference between the scores of the control group and experimental group. Hence, both the handouts and animation provide a similar level of understanding.Ashesi Universit

Key Elements for Motion Planning Algorithms

Non

Organizational Intelligence in Digital Innovation: Evidence from Georgia State University

The fourth industrial revolution challenges organizations to cope with dynamic business landscapes as they seek to improve their competitive position through rapid and pervasive digitalization of products, services, processes, and business models. As organizations sense and respond to new opportunities and threats, digital innovations are not only meeting new requirements, unarticulated needs, and market demands, they also lead to disruptive transformation of sociotechnical structures. Despite the practical relevance and theoretical significance of digital innovations, we still have limited knowledge on how digital innovation initiatives are rationalized, realized, and managed to improve organizational performance. Drawing on a longitudinal study of digital innovations to improve student success at Georgia State University, we develop a theory of organizational intelligence to help understand how organizations’ digital innovation initiatives are organized and managed to improve their performance over time in the broader context of organizational transformation. We posit that organizational intelligence enables an organization to gather, process, and manipulate information and to communicate, share, and make sense of the knowledge it creates, so it can increase its adaptive potential in the dynamic environment in which it operates. Moreover, we elaborate how organizational intelligence is constituted as human and material agency come together in analytical and relational intelligence to help organizations effectively manage digital innovations, and how organizational intelligence both shapes and is shaped by an organization’s digital innovation initiatives. Hence, while current research on organizational intelligence predominantly emphasizes analytic capabilities, this research puts equal emphasis on relational capabilities. Similarly, while current research on organizational intelligence focuses only on human agency, this research focuses equally on material agency. Our proposed theory of organizational intelligence responds to recent calls to position IS theories along the sociotechnical axis of cohesion and has pronounced implications for both theory and practice

Metaheuristics and combinatorial optimization problems

This thesis will use the traveling salesman problem (TSP) as a tool to help present and investigate several new techniques that improve the overall performance of genetic algorithms (GA). Improvements include a new parent selection algorithm, harem select, that outperforms all other parent selection algorithms tested, some by up to 600%. Other techniques investigated include population seeding, random restart, heuristic crossovers, and hybrid genetic algorithms, all of which posted improvements in the range of 1% up to 1100%. Also studied will be a new algorithm, GRASP, that is just starting to enjoy a lot of interest in the research community and will also been applied to the traveling salesman problem (TSP). Given very little time to run, relative to other popular metaheuristic algorithms, GRASP was able to come within 5% of optimal on several of the TSPLIB maps used for testing. Both the GA and the GRASP algorithms will be compared with commonly used metaheuristic algorithms such as simulated annealing (SA) and reactive tabu search (RTS) as well as a simple neighborhood search - greedy search

Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

Short-term generation scheduling in a hydrothermal power system.

SIGLEAvailable from British Library Document Supply Centre- DSC:D173872 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Massively Parallel Approach to Modeling 3D Objects in Machine Vision

Electrical Engineerin

Multi-image classification and compression using vector quantization

Vector Quantization (VQ) is an image processing technique based on statistical clustering, and designed originally for image compression. In this dissertation, several methods for multi-image classification and compression based on a VQ design are presented. It is demonstrated that VQ can perform joint multi-image classification and compression by associating a class identifier with each multi-spectral signature codevector. We extend the Weighted Bayes Risk VQ (WBRVQ) method, previously used for single-component images, that explicitly incorporates a Bayes risk component into the distortion measure used in the VQ quantizer design and thereby permits a flexible trade-off between classification and compression priorities. In the specific case of multi-spectral images, we investigate the application of the Multi-scale Retinex algorithm as a preprocessing stage, before classification and compression, that performs dynamic range compression, reduces the dependence on lighting conditions, and generally enhances apparent spatial resolution. The goals of this research are four-fold: (1) to study the interrelationship between statistical clustering, classification and compression in a multi-image VQ context; (2) to study mixed-pixel classification and combined classification and compression for simulated and actual, multispectral and hyperspectral multi-images; (3) to study the effects of multi-image enhancement on class spectral signatures; and (4) to study the preservation of scientific data integrity as a function of compression. In this research, a key issue is not just the subjective quality of the resulting images after classification and compression but also the effect of multi-image dimensionality on the complexity of the optimal coder design