Dynamic Motion Modelling for Legged Robots

An accurate motion model is an important component in modern-day robotic systems, but building such a model for a complex system often requires an appreciable amount of manual effort. In this paper we present a motion model representation, the Dynamic Gaussian Mixture Model (DGMM), that alleviates the need to manually design the form of a motion model, and provides a direct means of incorporating auxiliary sensory data into the model. This representation and its accompanying algorithms are validated experimentally using an 8-legged kinematically complex robot, as well as a standard benchmark dataset. The presented method not only learns the robot's motion model, but also improves the model's accuracy by incorporating information about the terrain surrounding the robot

Evolution of Neural Networks Through Incremental Acquisition of Neural Structures

In this contribution we present a novel method, called Evolutionary Acquisition of Neural Topologies (EANT), of evolving the structures and weights of neural networks. The method introduces an efficient and compact genetic encoding of a neural network onto a linear genome that enables one to evaluate the network without decoding it. The method uses a meta-level evolutionary process where new structures are explored at larger time-scale and the existing structures are exploited at lower time-scale. This enables it to find minimal neural structures for solving a given learning task

Learning and Adaption: A Comparison of Methods in Case of Navigation in an Artificial Robot World

Neural networks, reinforcement learning systems and evolutionary algorithms are widely used to solve problems in real-world robotics. We investigate learning and adaptation capabilities of agents and show that the learning time required in continual learning is shorter than that of learning from scratch under various learning conditions. We argue that agents using appropriate hybridization of learning and evolutionary algorithms show better learning and adaptation capability as compared to agents using learning algorithms only. We support our argument with experiments, where agents learn optimal policies in an artificial robot worl

Towards a Unified Approach to Learning and Adaptation

The aim of this thesis is to develop a system that enables autonomous and situated agents to learn and adapt to the environment in which they live and operate. In doing so, the system exploits both adaptation through learning and evolution. A unified approach to learning and adaptation, which combines the principles of neural networks, reinforcement learning and evolutionary methods, is used as a basis for the development of the system. In this regard, a novel method, called Evolutionary Acquisition of Neural Topologies (EANT), of evolving the structures and weights of neural networks is developed. The method introduces an efficient and compact genetic encoding of a neural network onto a linear genome that encodes the topology of the neural network implicitly in the ordering of the elements of the linear genome. Moreover, it enables one to evaluate the neural network without decoding it. The presented genetic encoding is complete in that it can represent any type of neural network. In addition to this, it is closed under both structural mutation and a specially designed crossover operator which exploits the fact that structures originating from some initial structure have some common parts. For evolving the structure and weights of neural networks, the method uses a biologically inspired meta-level evolutionary process where new structures are explored at larger timescale and existing structures are exploited at smaller timescale. The evolutionary process starts with networks of minimal structures whose initial complexity is specified by the domain expert. The introduction of neural structures by structural mutation results in a gradual increase in the complexity of the neural networks along the evolution. The evolutionary process stops searching for the solution when a solution with the necessary minimum complexity is found. This enables EANT to find optimal neural structures for solving a given learning task. The efficiency of EANT is tested on couple of learning tasks and its performance is found to be very good in comparison to other systems tested on the same tasks

Inter simple sequence repeat (ISSR) analysis of Ethiopian white lupine (Lupinus albus L.)

White lupine (Lupinus albus L.) collected from two zones (West Gojjam and Awi) of Amhara region and one zone (Metekel) of Benishangul - Gumuz regional state of Ethiopia were studied using inter simple sequence repeat (ISSR) markers in an attempt to assess the genetic diversity. Four ISSR primers of which three were dinucleotide repeats and one, a penta nucleotide repeat amplified a total of 39 clear and reproducible bands. Both unweighted pair- group method with arithmetic average (UPGMA) phenograms and a neighbor joining (NJ) trees were constructed for the individuals and populations using Jaccard’s similarity coefficient. The dendrogram clearly indicated four distinct groups/populations based on the area of origin. The principal coordinates (PCO) analysis also recovered UPGMA and neighbor joining tree groups, although Amhara region white lupine were intermixed with each other. The genetic diversity among white lupine population considered in the present study indicated that Merawi was the highest (0.223) followed by Addis Kidam, Sekela and Wembera with genetic diversity of 0.198, 0.189 and 0.167, respectively. Generally, Amhara region white lupine (0.203) population shows higher genetic diversity than white lupine population of B-Gumuz region (0.167). Analysis of molecular variance (AMOVA) in both grouping and without grouping revealed larger genetic diversity within the populations (74.6%) than among populations (25.4%). Shannon’s diversity index also confirmed the existence of higher genetic diversity in Amhara region lupine populations than in Benishangul-Gumuz. Furthermore AMOVA demonstrated highly significant (P = 0.00) genetic differences among populations within groups, among groups and within populations. Of the total variation, 64.64% was attributable to within populations, 27.23% to among groups and the least, 8.13% to among populations within groups. Generally, on the basis of samples of 39 bands in the four populations, ISSR was able to reveal moderate to high levels of genetic diversity within and among Ethiopian white lupine population.Keywords: Amhara, Benishangul - Gumuz, Ethiopia, genetic diversity, ISSR, white lupine.Abbreviation: ISSR, Inter simple sequence repeats; UPGMA, unweighted pair- group method with arithmetic average; NJ, neighbor joining; PCO, principal coordinates; AMOVA, analysis of molecular variance; RAPD, random amplified polymorphic DNA; AFLP, amplified fragment length polymorphism

Prevalence and factors associated with trachoma among children aged 1–9 years in Zala district, Gamo Gofa Zone, Southern Ethiopia

The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.Background: Trachoma is the leading cause of preventable blindness worldwide. It is common in areas where people are socioeconomically deprived. Globally, approximately 1.2 billion people live in trachoma-endemic areas, in which, 40.6 million individuals have active trachoma and 8.2 million have trichiasis. According to the World Health Organization’s 2007 report, globally close to 1.3 million people are blind due to trachoma, while approximately 84 million suffer from active trachoma. The National Survey (2007) of Ethiopia showed a prevalence of 40.1% active trachoma among children aged 1–9 years. Trachoma is still endemic in most parts of Ethiopia. Objective: To assess prevalence of trachoma and factors associated with it among children aged 1–9 years in Zala district, Gamo Gofa Zone, Southern Nations, Nationalities, and Peoples’ Region. Methods: A community-based cross-sectional study was conducted in Zala district from February 28 to March 26, 2014. A total of 611 children were examined for trachoma based on the simplified World Health Organization 1983 classification. A multistage stratified sampling technique with a systematic random sampling technique was used to select study participants. Data were collected by using a semistructured pretested questionnaire and clinical eye examination. The data were entered using EpiData version 3.1 and analyzed using SPSS version 16. Multivariable logistic regression analysis was used to identify independently associated factors. Results: The overall prevalence of active trachoma cases was 224 (36.7%) consisting of 207 (92.4%) trachomatous follicles, eight (3.6%) trachomatous intense, and nine (4.0%) combination of trachomatous follicle and trachomatous intense. Inadequate knowledge of family head about trachoma (adjusted odds ratio [AOR] =2.8 [95% CI: 1.9, 4.2]); #10 m latrine distance (AOR =1.6 [95% confidence interval {CI}: 1.09, 2.4]); presence of above two preschool children (AOR =2.2 [95% CI: 1.3, 3.7]), flies on the face (AOR =6.3 [95% CI: 2.7, 14.7]), and unclean face (AOR =2.4 [95% CI: 1.5, 3.9]) were found to be independently associated with trachoma. Conclusion: Trachoma among children in Zala district is a disease of public health importance. Factors like inadequate knowledge about trachoma by the head of the family, #10 m latrine distance, presence of above two preschool children, flies on the face, and an unclean face were independently associated with trachoma among children. So strengthening of antibiotic use, face washing, and environmental improvement strategy implementation is mandatory