We contribute to approximate algorithms for the quadratic assignment problem also known as graph matching. Inspired by the success of the fusion moves technique developed for multilabel discrete Markov random fields, we investigate its applicability to graph matching. In particular, we show how fusion moves can be efficiently combined with the dedicated state-of-the-art dual methods that have recently shown superior results in computer vision and bioimaging applications. As our empirical evaluation on a wide variety of graph matching datasets suggests, fusion moves significantly improve performance of these methods in terms of speed and quality of the obtained solutions. Our method sets a new state-of-the-art with a notable margin with respect to its competitors.

We propose a fast approximate solver for the combinatorial problem known as tracking-by-assignment, which we apply to cell tracking. The latter plays a key role in discovery in many life sciences, especially in cell and developmental biology. So far, in the most general setting this problem was addressed by off-the-shelf solvers like Gurobi, whose run time and memory requirements rapidly grow with the size of the input. In contrast, for our method this growth is nearly linear. Our contribution consists of a new (1) decomposable compact representation of the problem; (2) dual block-coordinate ascent method for optimizing the decomposition-based dual; and (3) primal heuristics that reconstructs a feasible integer solution based on the dual information. Compared to solving the problem with Gurobi, we observe an up to 60 times speed-up, while reducing the memory footprint significantly. We demonstrate the efficacy of our method on real-world tracking problems.

We consider the MAP-inference problem for graphical models, which is a valued constraint satisfaction problem defined on real numbers with a natural summation operation. We propose a family of relaxations (different from the famous Sherali-Adams hierarchy), which naturally define lower bounds for its optimum. This family always contains a tight relaxation and we give an algorithm able to find it and therefore, solve the initial non-relaxed NP-hard problem. The relaxations we consider decompose the original problem into two non-overlapping parts: an easy LP-tight part and a difficult one. For the latter part a combinatorial solver must be used. As we show in our experiments, in a number of applications the second, difficult part constitutes only a small fraction of the whole problem. This property allows to significantly reduce the computational time of the combinatorial solver and therefore solve problems which were out of reach before.

Rapid development of extremely-low voltage high current MOSFETs allows reversing common design principles to explore new applications, such as battery powered traction drives. This enables the usage of multi-phase single-turn stator windings which can achieve a copper fill factor close to one. This paper briefly describes the proposed 2V, 1.1kW MOSFET commutated 13-phase permanent magnet DC motor and presents the efficiency and resistive loss measurements of the first prototype. The motor was successfully run with drive currents up to 520 A. Most of the obtained losses were resistive contact losses due to the flexible winding connections, less than 6% are dedicated to the MOSFETs. The results show that such a high current drive system is feasible and has great potential for further improvements, which is supported by the rapid development of extremely-low voltage high current semiconductors.

To verify the FEM simulation of the air-gap flux density, it is necessary to measure the flux density distribution as function of the angular machine position. This paper presents a scalable 3-D direct air-gap flux density magnitude measurement system for rotating electrical machines. A combination of eight linear Hall effect flux sensors and a rotary encoder is used to measure the flux density magnitude as function of the angular machine position. The system is designed for permanent magnet motors with an air gap of at least 1 mm, but can also be used for other types of machines. The miniaturized sensor array with the flux sensors is 0.7 mm thick, and measures the distribution of the air-gap flux density magnitude in a range of ±2 T along the rotor axis. The design of the measurement system is described and tested on a prototype of an electronically commutated permanent magnet DC motor. The obtained measurement results are compared with the FEM simulation results of the prototype motor. A good match between the simulated and measured flux density magnitude is shown. The conclusions presented in this study, are used to further optimize the simulation model and the prototype motor.