Genetic Programming for Computationally Efficient Land Use Allocation Optimization

Land use allocation optimization is essential to identify ideal landscape compositions for the future. However, due to the solution encoding, standard land use allocation algorithms cannot cope with large land use allocation problems. Solutions are encoded as sequences of elements, in which each element represents a land unit or a group of land units. As a consequence, computation times increase with every additional land unit. We present an alternative solution encoding: functions describing a variable in space. Function encoding yields the potential to evolve solutions detached from individual land units and evolve fields representing the landscape as a single object. In this study, we use a genetic programming algorithm to evolve functions representing continuous fields, which we then map to nominal land use maps. We compare the scalability of the new approach with the scalability of two state-of-the-art algorithms with standard encoding. We perform the benchmark on one raster and one vector land use allocation problem with multiple objectives and constraints, with ten problem sizes each. The results prove that the run times increase exponentially with the problem size for standard encoding schemes, while the increase is linear with genetic programming. Genetic programming was up to 722 times faster than the benchmark algorithm. The improvement in computation time does not reduce the algorithm performance in finding optimal solutions; often, it even increases. We conclude that evolving functions enables more efficient land use allocation planning and yields much potential for other spatial optimization applications

Genetic Programming for Computationally Efficient Land Use Allocation Optimization

Land use allocation optimization is essential to identify ideal landscape compositions for the future. However, due to the solution encoding, standard land use allocation algorithms cannot cope with large land use allocation problems. Solutions are encoded as sequences of elements, in which each element represents a land unit or a group of land units. As a consequence, computation times increase with every additional land unit. We present an alternative solution encoding: functions describing a variable in space. Function encoding yields the potential to evolve solutions detached from individual land units and evolve fields representing the landscape as a single object. In this study, we use a genetic programming algorithm to evolve functions representing continuous fields, which we then map to nominal land use maps. We compare the scalability of the new approach with the scalability of two state-of-the-art algorithms with standard encoding. We perform the benchmark on one raster and one vector land use allocation problem with multiple objectives and constraints, with ten problem sizes each. The results prove that the run times increase exponentially with the problem size for standard encoding schemes, while the increase is linear with genetic programming. Genetic programming was up to 722 times faster than the benchmark algorithm. The improvement in computation time does not reduce the algorithm performance in finding optimal solutions; often, it even increases. We conclude that evolving functions enables more efficient land use allocation planning and yields much potential for other spatial optimization applications

An Artificial Intelligence Approach to Concatenative Sound Synthesis

Sound examples are included with this thesisTechnological advancement such as the increase in processing power, hard disk capacity and network bandwidth has opened up many exciting new techniques to synthesise sounds, one of which is Concatenative Sound Synthesis (CSS). CSS uses data-driven method to synthesise new sounds from a large corpus of small sound snippets. This technique closely resembles the art of mosaicing, where small tiles are arranged together to create a larger image. A ‘target’ sound is often specified by users so that segments in the database that match those of the target sound can be identified and then concatenated together to generate the output sound. Whilst the practicality of CSS in synthesising sounds currently looks promising, there are still areas to be explored and improved, in particular the algorithm that is used to find the matching segments in the database. One of the main issues in CSS is the basis of similarity, as there are many perceptual attributes which sound similarity can be based on, for example it can be based on timbre, loudness, rhythm, and tempo and so on. An ideal CSS system needs to be able to decipher which of these perceptual attributes are anticipated by the users and then accommodate them by synthesising sounds that are similar with respect to the particular attribute. Failure to communicate the basis of sound similarity between the user and the CSS system generally results in output that mismatches the sound which has been envisioned by the user. In order to understand how humans perceive sound similarity, several elements that affected sound similarity judgment were first investigated. Of the four elements tested (timbre, melody, loudness, tempo), it was found that the basis of similarity is dependent on humans’ musical training where musicians based similarity on the timbral information, whilst non-musicians rely on melodic information. Thus, for the rest of the study, only features that represent the timbral information were included, as musicians are the target user for the findings of this study. Another issue with the current state of CSS systems is the user control flexibility, in particular during segment matching, where features can be assigned with different weights depending on their importance to the search. Typically, the weights (in some existing CSS systems that support the weight assigning mechanism) can only be assigned manually, resulting in a process that is both labour intensive and time consuming. Additionally, another problem was identified in this study, which is the lack of mechanism to handle homosonic and equidistant segments. These conditions arise when too few features are compared causing otherwise aurally different sounds to be represented by the same sonic values, or can also be a result of rounding off the values of the features extracted. This study addresses both of these problems through an extended use of Artificial Intelligence (AI). The Analysis Hierarchy Process (AHP) is employed to enable order dependent features selection, allowing weights to be assigned for each audio feature according to their relative importance. Concatenation distance is used to overcome the issues with homosonic and equidistant sound segments. The inclusion of AI results in a more intelligent system that can better handle tedious tasks and minimize human error, allowing users (composers) to worry less of the mundane tasks, and focusing more on the creative aspects of music making. In addition to the above, this study also aims to enhance user control flexibility in a CSS system and improve similarity result. The key factors that affect the synthesis results of CSS were first identified and then included as parametric options which users can control in order to communicate their intended creations to the system to synthesise. Comprehensive evaluations were carried out to validate the feasibility and effectiveness of the proposed solutions (timbral-based features set, AHP, and concatenation distance). The final part of the study investigates the relationship between perceived sound similarity and perceived sound interestingness. A new framework that integrates all these solutions, the query-based CSS framework, was then proposed. The proof-of-concept of this study, ConQuer, was developed based on this framework. This study has critically analysed the problems in existing CSS systems. Novel solutions have been proposed to overcome them and their effectiveness has been tested and discussed, and these are also the main contributions of this study.Malaysian Minsitry of Higher Education, Universiti Putra Malaysi

Characterization of unstructured video

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 1999.Includes bibliographical references (p. 135-139).In this work, we examine video retrieval from a synthesis perspective in co-operation with the more common analysis perspective. Specifically, we target our algorithms for one particular domain- unstructured video material. The goal is to make this unstructured video available for manipulation in interesting ways. I.e, take video that may have been shot with no specific intent and use it in different settings. For example, we build a set of interfaces that will enable taking a collection of home videos and making Christmas cards, Refrigerator magnets, family dramas etc out of them. The work is divided into three parts. First, we study features and models for characterization of video. Examples are VideoBook with its extensions and Hidden Markov Models for video analysis. Secondly, we examine clustering as an approach for characterization of unstructured video. Clustering alleviates some of the common problems with "query-by- example" and presents groupings that rely on the user's abilities to make relevant connections. The clustering techniques we employ operate in the probability density space. One of our goals is to employ these techniques with sophisticated models such as Bayesian Networks and HMMs, which give similar descriptions. The clustering techniques we employ are shown to be optimal in an information theoretic and Gibbs Free Energy sense. Finally, we present a set of interfaces that use these features and groupings to enable browsing and editing of unstructured video content.by Giridharan Ranganathan Iyengar.Ph.D

Utilizing Science and Technology to Enhance a Future Planetary Mission: Applications to Europa

abstract: A thorough understanding of Europa's geology through the synergy of science and technology, by combining geologic mapping with autonomous onboard processing methods, enhances the science potential of future outer solar system missions. Mapping outlines the current state of knowledge of Europa's surface and near sub-surface, indicates the prevalence of distinctive geologic features, and enables a uniform perspective of formation mechanisms responsible for generating those features. I have produced a global geologic map of Europa at 1:15 million scale and appraised formation scenarios with respect to conditions necessary to produce observed morphologies and variability of those conditions over Europa's visible geologic history. Mapping identifies areas of interest relevant for autonomous study; it serves as an index for change detection and classification and aids pre-encounter targeting. Therefore, determining the detectability of geophysical activity is essential. Activity is evident by the presence of volcanic plumes or outgassing, disrupted surface morphologies, or changes in morphology, color, temperature, or composition; these characteristics reflect important constraints on the interior dynamics and evolutions of planetary bodies. By adapting machine learning and data mining techniques to signatures of plumes, morphology, and spectra, I have successfully demonstrated autonomous rule-based response and detection, identification, and classification of known events and features on outer planetary bodies using the following methods: 1. Edge-detection, which identifies the planetary horizon and highlights features extending beyond the limb; 2. Spectral matching using a superpixel endmember detection algorithm that identifies mean spectral signatures; and 3. Scale invariant feature transforms combined with supervised classification, which examines brightness gradients throughout an image, highlights extreme gradient regions, and classifies those regions based on a manually selected library of features. I have demonstrated autonomous: detection of volcanic plumes or jets at Io, Enceladus, and several comets, correlation between spectral signatures and morphological appearances of Europa's individual tectonic features, detection of ≤94% of known transient events on multiple planetary bodies, and classification of similar geologic features. Applying these results to conditions expected for Europa enables a prediction of the potential for detection and recommendations for mission concepts to increase the science return and efficiency of future missions to observe Europa.Dissertation/ThesisPh.D. Geological Sciences 201

Proceedings of the 3rd Open Source Geospatial Research & Education Symposium OGRS 2014

The third Open Source Geospatial Research & Education Symposium (OGRS) was held in Helsinki, Finland, on 10 to 13 June 2014. The symposium was hosted and organized by the Department of Civil and Environmental Engineering, Aalto University School of Engineering, in partnership with the OGRS Community, on the Espoo campus of Aalto University. These proceedings contain the 20 papers presented at the symposium. OGRS is a meeting dedicated to exchanging ideas in and results from the development and use of open source geospatial software in both research and education.  The symposium offers several opportunities for discussing, learning, and presenting results, principles, methods and practices while supporting a primary theme: how to carry out research and educate academic students using, contributing to, and launching open source geospatial initiatives. Participating in open source initiatives can potentially boost innovation as a value creating process requiring joint collaborations between academia, foundations, associations, developer communities and industry. Additionally, open source software can improve the efficiency and impact of university education by introducing open and freely usable tools and research results to students, and encouraging them to get involved in projects. This may eventually lead to new community projects and businesses. The symposium contributes to the validation of the open source model in research and education in geoinformatics

Creating music by listening

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 127-139).Machines have the power and potential to make expressive music on their own. This thesis aims to computationally model the process of creating music using experience from listening to examples. Our unbiased signal-based solution models the life cycle of listening, composing, and performing, turning the machine into an active musician, instead of simply an instrument. We accomplish this through an analysis-synthesis technique by combined perceptual and structural modeling of the musical surface, which leads to a minimal data representation. We introduce a music cognition framework that results from the interaction of psychoacoustically grounded causal listening, a time-lag embedded feature representation, and perceptual similarity clustering. Our bottom-up analysis intends to be generic and uniform by recursively revealing metrical hierarchies and structures of pitch, rhythm, and timbre. Training is suggested for top-down un-biased supervision, and is demonstrated with the prediction of downbeat. This musical intelligence enables a range of original manipulations including song alignment, music restoration, cross-synthesis or song morphing, and ultimately the synthesis of original pieces.by Tristan Jehan.Ph.D

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum