ContinuousMultimodal Global Optimization with Differential Evolution-Based Methods

Metaheuristic methods have become increasingly popular approaches in solving global optimization problems. From a practical viewpoint, it is often desirable to perform multimodal optimization which, enables the search of more than one optimal solution to the task at hand. Population-based metaheuristic methods offer a natural basis for multimodal optimization. The topic has received increasing interest especially in the evolutionary computation community. Several niching approaches have been suggested to allow multimodal optimization using evolutionary algorithms. Most global optimization approaches, including metaheuristics, contain global and local search phases. The requirement to locate several optima sets additional requirements for the design of algorithms to be effective in both respects in the context of multimodal optimization. In this thesis, several different multimodal optimization algorithms are studied in regard to how their implementation in the global and local search phases affect their performance in different problems. The study concentrates especially on variations of the Differential Evolution algorithm and their capabilities in multimodal optimization. To separate the global and local search search phases, three multimodal optimization algorithms are proposed, two of which hybridize the Differential Evolution with a local search method. As the theoretical background behind the operation of metaheuristics is not generally thoroughly understood, the research relies heavily on experimental studies in finding out the properties of different approaches. To achieve reliable experimental information, the experimental environment must be carefully chosen to contain appropriate and adequately varying problems. The available selection of multimodal test problems is, however, rather limited, and no general framework exists. As a part of this thesis, such a framework for generating tunable test functions for evaluating different methods of multimodal optimization experimentally is provided and used for testing the algorithms. The results demonstrate that an efficient local phase is essential for creating efficient multimodal optimization algorithms. Adding a suitable global phase has the potential to boost the performance significantly, but the weak local phase may invalidate the advantages gained from the global phase.

Moment Operators of Observables in Quantum Mechanics, with Applications to Quantization and Homodyne Detection

The questions studied in this thesis are centered around the moment operators of a quantum observable, the latter being represented by a normalized positive operator measure. The moment operators of an observable are physically relevant, in the sense that these operators give, as averages, the moments of the outcome statistics for the measurement of the observable. The main questions under consideration in this work arise from the fact that, unlike a projection valued observable of the von Neumann formulation, a general positive operator measure cannot be characterized by its first moment operator. The possibility of characterizing certain observables by also involving higher moment operators is investigated and utilized in three different cases: a characterization of projection valued measures among all the observables is given, a quantization scheme for unbounded classical variables using translation covariant phase space operator measures is presented, and, finally, a mathematically rigorous description is obtained for the measurements of rotated quadratures and phase space observables via the high amplitude limit in the balanced homodyne and eight-port homodyne detectors, respectively. In addition, the structure of the covariant phase space operator measures, which is essential for the above quantization, is analyzed in detail in the context of a (not necessarily unimodular) locally compact group as the phase space.

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presented

The best practices in the formation phase of Sino-Finnish joint ventures in China: For Finnish software companies

This study discusses the formation phase of Chinese-Finnish joint ventures in China. The purpose of this thesis is to create best practices for Finnish software companies in forming a joint venture with a local Chinese company in China. Therefore, the main research question, in what are the best practices for forming Sino-Finnish joint ventures in China for Finnish software firms, is examined through four different themes within the joint venture formation phase; the motives, the partner se- lection, the choice of a joint venture type and joint venture negotiations. The theoretical background of the study consists of literature relating to the establishment process of Sino-Western joint ventures in China. The empirical research conducted for this study is based on the expert interviews. The empirical data was gathered via nine semi-structured interviews with both Chinese and Finnish experts in software and technology industry, who have experience or knowledge in establishing Sino-Finnish joint ventures in China. Thematic analysis was used to cat- egorize and interpret the interview data. In addition, a thematic network was built to act as a basis of the analysis. According to the main findings, the main motives for Finnish software companies to establish a joint venture in China are lack of skills or experience, little resources to enter on their own, and China’s large market. The main motives for Chinese companies are to gain new technology or man- agerial skills, and expand internationally. The intellectual property rights (IPR) have recently im- proved a lot in China, but the Finnish companies’ knowledge on IPR is inadequate. The Finnish software companies should conduct a market and industry research in order to understand their po- sition in the market and to find a suitable location and potential joint venture partners. It is essential to define partner selection criteria and partner attributes. In addition, it is important to build the joint venture around complementary motives and a win-win situation between the joint venture partners. The Finnish companies should be prepared that the joint venture negotiations will be challenging and they will take a long time. The challenges can be overcome by gaining understanding about the Chinese culture and business environment. The findings of this study enhance understanding of the joint venture formation phase in China. This study provides guidelines for Finnish software companies to establish a joint venture in China. In addition, this study brings new insights to the Sino-Western joint venture literature with its soft- ware industry context. Future research is, however, necessary in order to gain an understanding of the advantages and disadvantages of a joint venture as an entry mode into China for Finnish soft- ware companies

The best practices in the formation phase of Sino-Finnish joint ventures in China: For Finnish software companies

This study discusses the formation phase of Chinese-Finnish joint ventures in China. The purpose of this thesis is to create best practices for Finnish software companies in forming a joint venture with a local Chinese company in China. Therefore, the main research question, in what are the best practices for forming Sino-Finnish joint ventures in China for Finnish software firms, is examined through four different themes within the joint venture formation phase; the motives, the partner se-lection, the choice of a joint venture type and joint venture negotiations. The theoretical background of the study consists of literature relating to the establishment process of Sino-Western joint ventures in China. The empirical research conducted for this study is based on the expert interviews. The empirical data was gathered via nine semi-structured interviews with both Chinese and Finnish experts in software and technology industry, who have experience or knowledge in establishing Sino-Finnish joint ventures in China. Thematic analysis was used to cat-egorize and interpret the interview data. In addition, a thematic network was built to act as a basis of the analysis. According to the main findings, the main motives for Finnish software companies to establish a joint venture in China are lack of skills or experience, little resources to enter on their own, and China’s large market. The main motives for Chinese companies are to gain new technology or man-agerial skills, and expand internationally. The intellectual property rights (IPR) have recently im-proved a lot in China, but the Finnish companies’ knowledge on IPR is inadequate. The Finnish software companies should conduct a market and industry research in order to understand their po-sition in the market and to find a suitable location and potential joint venture partners. It is essential to define partner selection criteria and partner attributes. In addition, it is important to build the joint venture around complementary motives and a win-win situation between the joint venture partners. The Finnish companies should be prepared that the joint venture negotiations will be challenging and they will take a long time. The challenges can be overcome by gaining understanding about the Chinese culture and business environment. The findings of this study enhance understanding of the joint venture formation phase in China. This study provides guidelines for Finnish software companies to establish a joint venture in China. In addition, this study brings new insights to the Sino-Western joint venture literature with its soft-ware industry context. Future research is, however, necessary in order to gain an understanding of the advantages and disadvantages of a joint venture as an entry mode into China for Finnish soft-ware companies.