Gionata Massi

An adaptive genetic algorithm for large-size open stack problems

The problem of minimising the maximum number of open stacks arises in many contexts (production planning, cutting environments, very-large-scale-integration circuit design, etc.) and consists of finding a sequence of tasks (products, cutting patterns, circuit gates, etc.) that determines an efficient utilisation of resources (stacks). We propose a genetic approach that combines classical genetic operators (selection, order crossover and pairwise interchange mutation) with an adaptive search strategy, where intensification and diversification phases are obtained by neighbourhood search and by a composite and dynamic fitness function that suitably modifies the search landscape. Computational tests on random and real-world benchmarks show that the proposed approach is competitive with the state of the art for large-size problems, providing better results for some classes of instances.

Human activity analysis for in-home fall risk assessment

The aim of this work is to define a fall detection video system for indoor environments based on a RGB-D sensor and a low power and low cost embedded system that processes the sensor data in order to provide a description of human activities in the field of the Ambient Assisted Living. The RGB image is affected by a high luminescence sensibility, so the depth data have the aim to improve the human activity recognition. The system is usable in a sufficiently small room and it requires a RGB-D sensor located in the center of the ceiling and an embedded system connected on a computer network. The embedded system controls the RGB-D sensor and, in the mean time, classifies the images using computer vision algorithms based on the depth map. “Water Filling” algorithm or “Multi-Level Segmentation” algorithm are used to detect person. For each person, the system detects the position with respect to the room, estimating also the human posture. Among the features extracted we enumerate the height, the head size and the distance between the head and the shoulders. The system tracks a person through the frames starting from the first identification. Further, group interactions are monitored and analyzed. The posture detection algorithm takes into account the distance between the person head and the floor during the time. During the experimental phase, conducted in many domestic scenarios, the effectiveness of the proposed solution has been proved, that is fast, accurate and ables to provide a fall map in-home fall risk assessment.

A real-time reliability and durability testing framework

This paper presents a methodological framework for designing testing and measurement systems fully integrated with the enterprise information system. In comparison with the most common solutions for designing embedded testing platforms the proposed framework sets itself at a higher level of abstraction. The proposed framework allows getting different, programmable test benches that can run in parallel, and it does not restrict the choice of hardware, sensors and actuators, as it happens with commercial development systems for the same kind of machines. The framework is conceived to be used on embedded boards equipped with the GNU/Linux operating system and with at least one network interface. By using open data formats, the framework provides an easy way to exchange data with enterprise information systems, thus assuring interoperability with different IT solutions. The paper includes the description of a cooker hood testing system designed and implemented with this framework, and which highlights the advantages of the proposed development method.