Discrete particle swarm optimization for combinatorial problems with innovative applications.

Master of Science in Computer Science. University of KwaZulu-Natal, Durban 2016.Abstract available in PDF file

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method

Preliminaries for distributed natural computing inspired by the slime mold Physarum Polycephalum

This doctoral thesis aims towards distributed natural computing inspired by the slime mold Physarum polycephalum. The vein networks formed by this organism presumably support efficient transport of protoplasmic fluid. Devising models which capture the natural efficiency of the organism and form a suitable basis for the development of natural computing algorithms is an interesting and challenging goal. We start working towards this goal by designing and executing wet-lab experi- ments geared towards producing a large number of images of the vein networks of P. polycephalum. Next, we turn the depicted vein networks into graphs using our own custom software called Nefi. This enables a detailed numerical study, yielding a catalogue of characterizing observables spanning a wide array of different graph properties. To share our results and data, i.e. raw experimental data, graphs and analysis results, we introduce a dedicated repository revolving around slime mold data, the Smgr. The purpose of this repository is to promote data reuse and to foster a practice of increased data sharing. Finally we present a model based on interacting electronic circuits including current controlled voltage sources, which mimics the emergent flow patterns observed in live P. polycephalum. The model is simple, distributed and robust to changes in the underlying network topology. Thus it constitutes a promising basis for the development of distributed natural computing algorithms.Diese Dissertation dient als Vorarbeit für den Entwurf von verteilten Algorithmen, inspiriert durch den Schleimpilz Physarum polycephalum. Es wird vermutet, dass die Venen-Netze dieses Organismus den effizienten Transport von protoplasmischer Flüssigkeit ermöglichen. Die Herleitung von Modellen, welche sowohl die natürliche Effizienz des Organismus widerspiegeln, als auch eine geeignete Basis für den Entwurf von Algorithmen bieten, gilt weiterhin als schwierig. Wir nähern uns diesem Ziel mittels Laborversuchen zur Produktion von zahlreichen Abbildungen von Venen-Netzwerken. Weiters führen wir die abgebildeten Netze in Graphen über. Hierfür verwenden wir unsere eigene Software, genannt Nefi. Diese ermöglicht eine numerische Studie der Graphen, welche einen Katalog von charakteristischen Grapheigenschaften liefert. Um die gewonnenen Erkenntnisse und Daten zu teilen, führen wir ein spezialisiertes Daten-Repository ein, genannt Smgr. Hiermit begünstigen wir die Wiederverwendung von Daten und fördern das Teilen derselben. Abschließend präsentieren wir ein Modell, basierend auf elektrischen Elementen, insbesondere stromabhängigen Spannungsquellen, welches die Flüsse von P. poly- cephalum nachahmt. Das Modell ist simpel, verteilt und robust gegenüber topolo- gischen änderungen. Aus diesen Gründen stellt es eine vielversprechende Basis für den Entwurf von verteilten Algorithmen dar

Journal of Telecommunications and Information Technology, 2019, nr 1

kwartalni

Toward a formal theory for computing machines made out of whatever physics offers: extended version

Approaching limitations of digital computing technologies have spurred research in neuromorphic and other unconventional approaches to computing. Here we argue that if we want to systematically engineer computing systems that are based on unconventional physical effects, we need guidance from a formal theory that is different from the symbolic-algorithmic theory of today's computer science textbooks. We propose a general strategy for developing such a theory, and within that general view, a specific approach that we call "fluent computing". In contrast to Turing, who modeled computing processes from a top-down perspective as symbolic reasoning, we adopt the scientific paradigm of physics and model physical computing systems bottom-up by formalizing what can ultimately be measured in any physical substrate. This leads to an understanding of computing as the structuring of processes, while classical models of computing systems describe the processing of structures.Comment: 76 pages. This is an extended version of a perspective article with the same title that will appear in Nature Communications soon after this manuscript goes public on arxi