Genre ja rekisteri populaarimusiikissa

Genre substanssina ; Genre relaationa ; Tekstikeskeisen genretutkimuksen dilemmat ; Genre kommunikatiivisena tasona ; Genre diskurssina ; Genre substantifikaationa.

Purchasing portfolio analysis and supplier classification

The purpose of the thesis is to classify suppliers and to enhance strategic purchasing in the case company. Supplier classification is conducted to fulfill the requirements of the company quality manual and international quality standards. To gain more benefit, a strategic purchasing tool, Kraljic’s purchasing portfolio and analytical hierarchy process are utilized for the base of supplier classification. Purchasing portfolio is used to give quick and easy visual insight on product group management form the viewpoint of purchasing. From the base on purchasing portfolio alternative purchasing and supplier strategies can be formed that enhance the strategic orientation of purchasing. Thus purchasing portfolio forces the company to orient on proactive and strategic purchasing. As a result a survey method for implementing purchasing portfolio in the company is developed that exploits analytical hierarchy process. Experts from the company appoint the categorization criteria and in addition, participate in the survey to categorize product groups on the portfolio. Alternative purchasing strategies are formed. Suppliers are classified depending on the importance and characteristics of the product groups supplied.

Support vector machines in spectral data classification

Työssä käydään läpi tukivektorikoneiden teoreettista pohjaa sekä tutkitaan eri parametrien vaikutusta spektridatan luokitteluun.

Video transcoding in a distributed cloud computing environment

Video transcoding refers to the process of converting a digital video from one format into another format. It is a compute-intensive operation. Therefore, transcoding of a large number of simultaneous video streams requires a large amount of computing resources. Moreover, to handle di erent load conditions in a cost-e cient manner, the video transcoding service should be dynamically scalable. Infrastructure as a Service Clouds currently offer computing resources, such as virtual machines, under the pay-per-use business model. Thus the IaaS Clouds can be leveraged to provide a coste cient, dynamically scalable video transcoding service. To use computing resources e ciently in a cloud computing environment, cost-e cient virtual machine provisioning is required to avoid overutilization and under-utilization of virtual machines. This thesis presents proactive virtual machine resource allocation and de-allocation algorithms for video transcoding in cloud computing. Since users' requests for videos may change at di erent times, a check is required to see if the current computing resources are adequate for the video requests. Therefore, the work on admission control is also provided. In addition to admission control, temporal resolution reduction is used to avoid jitters in a video. Furthermore, in a cloud computing environment such as Amazon EC2, the computing resources are more expensive as compared with the storage resources. Therefore, to avoid repetition of transcoding operations, a transcoded video needs to be stored for a certain time. To store all videos for the same amount of time is also not cost-e cient because popular transcoded videos have high access rate while unpopular transcoded videos are rarely accessed. This thesis provides a cost-e cient computation and storage trade-o strategy, which stores videos in the video repository as long as it is cost-e cient to store them. This thesis also proposes video segmentation strategies for bit rate reduction and spatial resolution reduction video transcoding. The evaluation of proposed strategies is performed using a message passing interface based video transcoder, which uses a coarse-grain parallel processing approach where video is segmented at group of pictures level.

Local Features in Image and Video Processing - Object Class Matching and Video Shot Detection

The usage of digital content, such as video clips and images, has increased dramatically during the last decade. Local image features have been applied increasingly in various image and video retrieval applications. This thesis evaluates local features and applies them to image and video processing tasks. The results of the study show that 1) the performance of different local feature detector and descriptor methods vary significantly in object class matching, 2) local features can be applied in image alignment with superior results against the state-of-the-art, 3) the local feature based shot boundary detection method produces promising results, and 4) the local feature based hierarchical video summarization method shows promising new new research direction. In conclusion, this thesis presents the local features as a powerful tool in many applications and the imminent future work should concentrate on improving the quality of the local features.

Avalon Media System project update: a Hydra solution for digital audio and video access

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Audio and Video Repositories at Scale: Indiana University’s Media Digitization and Preservation Initiative

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Service offering classification for logistics services

This thesis studies the development of service offering model that creates added-value for customers in the field of logistics services. The study focusses on offering classification and structures of model. The purpose of model is to provide value-added solutions for customers and enable superior service experience. The aim of thesis is to define what customers expect from logistics solution provider and what value customers appreciate so greatly that they could invest in value-added services. Value propositions, costs structures of offerings and appropriate pricing methods are studied. First, literature review of creating solution business model and customer value is conducted. Customer value is found out with customer interviews and qualitative empiric data is used. To exploit expertise knowledge of logistics, innovation workshop tool is utilized. Customers and experts are involved in the design process of model. As a result of thesis, three-level value-added service offering model is created based on empiric and theoretical data. Offerings with value propositions are proposed and the level of model reflects the deepness of customer-provider relationship and the amount of added value. Performance efficiency improvements and cost savings create the most added value for customers. Value-based pricing methods, such as performance-based models are suggested to apply. Results indicate the interest of benefitting networks and partnership in field of logistics services. Networks development is proposed to be investigated further.

The Role of Video Based Content Marketing as a Part of Modern Marketing Communications

The thesis studies the role of video based content marketing as a part of modern marketing communications.

Differential evolution based classification with pool of distances and aggregation operators

The objective of this thesis is to develop and generalize further the differential evolution based data classification method. For many years, evolutionary algorithms have been successfully applied to many classification tasks. Evolution algorithms are population based, stochastic search algorithms that mimic natural selection and genetics. Differential evolution is an evolutionary algorithm that has gained popularity because of its simplicity and good observed performance. In this thesis a differential evolution classifier with pool of distances is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, to determine the optimal values for all free parameters of the classifier model during the training phase of the classifier. The differential evolution classifier applies the individually optimized distance measure for each new data set to be classified is generalized to cover a pool of distances. Instead of optimizing a single distance measure for the given data set, the selection of the optimal distance measure from a predefined pool of alternative measures is attempted systematically and automatically. Furthermore, instead of only selecting the optimal distance measure from a set of alternatives, an attempt is made to optimize the values of the possible control parameters related with the selected distance measure. Specifically, a pool of alternative distance measures is first created and then the differential evolution algorithm is applied to select the optimal distance measure that yields the highest classification accuracy with the current data. After determining the optimal distance measures for the given data set together with their optimal parameters, all determined distance measures are aggregated to form a single total distance measure. The total distance measure is applied to the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; a sample belongs to the class represented by the nearest prototype vector when measured with the optimized total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method confirm that the choice of distance measure is one of the most crucial factors for obtaining higher classification accuracy. The results also demonstrate that it is possible to build a classifier that is able to select the optimal distance measure for the given data set automatically and systematically. After finding optimal distance measures together with optimal parameters from the particular distance measure results are then aggregated to form a total distance, which will be used to form the deviation between the class vectors and samples and thus classify the samples. This thesis also discusses two types of aggregation operators, namely, ordered weighted averaging (OWA) based multi-distances and generalized ordered weighted averaging (GOWA). These aggregation operators were applied in this work to the aggregation of the normalized distance values. The results demonstrate that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy. The main outcomes of the work are the six new generalized versions of previous method called differential evolution classifier. All these DE classifier demonstrated good results in the classification tasks.

Fenomenologinen tutkimus video-EEG potilaiden kohtauskokemuksista

Tässä fenomenologisessa tutkimuksessa kuvaillaan Video-EEG –tutkimukseen (VEEG) tulevien potilaiden kokemuksia kohtauksistaan. Tutkimusasetelmana on käytetty fenomenologiseen psykologiaan kuuluvaa Giorgin menetelmää soveltaen sitä hoitotieteen tutkimukseen. Tutkimuksen tarkoituksena oli kuvailla neurologisten kohtausoireiden vuoksi VEEG-tutkimukseen tulleiden potilaiden kokemuksia kohtauksistaan ja tunnistaa sekä kuvailla kokemukseen liittyviä tekijöitä. Tutkimuksen tavoitteena oli lisäta terveydenhoitohenkilökunnan ymmärrystä neurologisia kohtausoireita saavien ihmisten ohjaustarpeista. Materiaali kerättiin kahdeksalta potilaalta avoimilla haastatteluilla ja analysoitiin Giorgin analyysimenetelmällä. Aineistoon yhdistettiin kliinisen neurofysiologin lausunto ja muodostettiin kokemuskertomukset. Aineistosta tunnistettiin fenomenologista reduktiota käyttäen keskeiset kohtauksiin ja sairauteen liittyvät kokemukset. Käsitteiden suhdetta toisiinsa ja merkitystä sopeutumiselle analysoitiin käyttäen apuna Uncertainty in illness -mallia. Keskeisten kokemusten pohjalta toteutettiin kirjallisuushaku, jonka tuloksia reflektoitiin tämän tutkimuksen tuloksiin. Aineistosta muodostui kolme erillistä kokemuskertomusta: kertomus konkreettisista tapahtumista, kokemus hallinnan menettämisestä ja kokemus sairauden kanssa elämisesta. Keskeisiksi kokemussisällöiksi tunnistettiin kokemus terveysongelman hallinnasta, kokemus hallinnan menettämisestä, kokemus ympäristön negatiivisesta suhtautumisesta ja huoli läheisistä. Aikaisempaa tutkimusta löytyi kokemuksista terveysongelman hallinnasta ja hallinnan menetyksestä sekä ympäristön suhtautumisesta.

Fenomenologinen tutkimus video-EEG potilaiden kohtauskokemuksista

Tässä fenomenologisessa tutkimuksessa kuvaillaan Video-EEG –tutkimukseen (VEEG) tulevien potilaiden kokemuksia kohtauksistaan. Tutkimusasetelmana on käytetty fenomenologiseen psykologiaan kuuluvaa Giorgin menetelmää soveltaen sitä hoitotieteen tutkimukseen. Tutkimuksen tarkoituksena oli kuvailla neurologisten kohtausoireiden vuoksi VEEGtutkimukseen tulleiden potilaiden kokemuksia kohtauksistaan ja tunnistaa sekä kuvailla kokemukseen liittyviä tekijöitä. Tutkimuksen tavoitteena oli lisätä terveydenhoitohenkilökunnan ymmärrystä neurologisia kohtausoireita saavien ihmisten ohjaustarpeista. Materiaali kerättiin kahdeksalta potilaalta avoimilla haastatteluilla ja analysoitiin Giorgin analyysimenetelmällä. Aineistoon yhdistettiin kliinisen neurofysiologin lausunto ja muodostettiin kokemuskertomukset. Aineistosta tunnistettiin fenomenologista reduktiota käyttäen keskeiset kohtauksiin ja sairauteen liittyvät kokemukset. Käsitteiden suhdetta toisiinsa ja merkitystä sopeutumiselle analysoitiin käyttäen apuna Uncertainty in illness -mallia. Keskeisten kokemusten pohjalta toteutettiin kirjallisuushaku, jonka tuloksia reflektoitiin tämän tutkimuksen tuloksiin. Aineistosta muodostui kolme erillistä kokemuskertomusta: kertomus konkreettisista tapahtumista, kokemus hallinnan menettämisestä ja kokemus sairauden kanssa elämisestä. Keskeisiksi kokemussisällöiksi tunnistettiin kokemus terveysongelman hallinnasta, kokemus hallinnan menettämisestä, kokemus ympäristön negatiivisesta suhtautumisesta ja huoli läheisistä. Aikaisempaa tutkimusta löytyi kokemuksista terveysongelman hallinnasta ja hallinnan menetyksestä sekä ympäristön suhtautumisesta.