Crop classification using airborne radar and LANDSAT data

Airborne radar data acquired with a 13.3 GHz scatterometer over a test-site near Colby, Kansas were used to investigate the statistical properties of the scattering coefficient of three types of vegetation cover and of bare soil. A statistical model for radar data was developed that incorporates signal-fading and natural within-field variabilities. Estimates of the within-field and between-field coefficients of variation were obtained for each cover-type and compared with similar quantities derived from LANDSAT images of the same fields. The classification accuracy provided by LANDSAT alone, radar alone, and both sensors combined was investigated. The results indicate that the addition of radar to LANDSAT improves the classification accuracy by about 10; percentage-points when the classification is performed on a pixel basis and by about 15 points when performed on a field-average basis

Performance Analysis on Text Steganalysis Method Using A Computational Intelligence Approach

In this paper, a critical view of the utilization ofcomputational intelligence approach from the text steganalysisperspective is presented. This paper proposes a formalization ofgenetic algorithm method in order to detect hidden message on ananalyzed text. Five metric parameters such as running time, fitnessvalue, average mean probability, variance probability, and standarddeviation probability were used to measure the detection performancebetween statistical methods and genetic algorithm methods.Experiments conducted using both methods showed that geneticalgorithm method performs much better than statistical method,especially in detecting short analyzed texts. Thus, the findings showedthat the genetic algorithm method on analyzed stego text is verypromising. For future work, several significant factors such as datasetenvironment, searching process and types of fitness values throughother intelligent methods of computational intelligence should beinvestigated

Rigidity-Based Surface Recognition for a Domestic Legged Robot

Although the infrared (IR) range and motor force sensors have been rarely applied to the surface recognition of mobile robots, they are fused in this paper with accelerometer and ground contact force sensors to distinguish six indoor surface types. Their sensor values are affected by the crawling gait period, therefore, certain components of the fast Fourier transform over these data are included in the feature vectors as well as remarkable discriminative power is observed for the same scalar statistics of different sensing modalities. The machine learning aspects are analyzed with random forests (RF) because of their stable performance and some inherent, beneficial properties for the model development process. The robustness is evaluated with unseen data after the model accuracy is estimated with cross-validation (CV), and regardless whether a Sony ERS-7 walks barefoot or wears socks, the forests achieve 94% accuracy. This result outperforms the state of the art techniques for indoor surfaces in the literature and the classification execution is real-time on the robot. The above mentioned model development process with RF is documented to create new models for other robots more quickly and efficiently

Use of Data from Smart Hospital Bed

Stále širší využití technologií ve zdravotnictví bude v budoucnu nevyhnutelné. Jednou z již běžně používaných technologií je inteligentní zdravotní lůžko. Tato práce prezentuje údaje získané z inteligentního zdravotní lůžka a navrhuje jejich využití pro detekci polohy těla metodami klasifikace vzorů. Byla testována řada klasifikátorů jak v intrapersonálním, tak v interpersonálním scénáři. V obou scénářích bylo testováno několik klasifikátorů. Pro oba byl nejlepším klasifikátorem naivní Bayesovský klasifikátor s průměrnou chybou 8.1\% pro intra-personální a 23.1 \% pro inter-personální klasifikaci. Nejhorší klasifikační chyby dosáhl rozhodovací strom s průměrnou chybou 19.8\% pro inter- a 33.4\% pro intra-personální klasifikaci. Tato práce také diskutuje možné budoucí rozšíření. Konkrétní třídy poloh jsou porovnávány podle jejich rozlišitelnosti a výsledky jsou popsány a shrnuty.The usage of technologies in medical field in future is indisputable. One of already commonly used technology is smart medical bed. This thesis presents and analyses the data obtained from smart medical bed and proposes their usage for posture detection using pattern classification methods. Two scenarios were examined. Intra-personal classifier is trained on the same person on which it will be used. Inter-personal classifier can classify data from person it has never seen before. Several classifiers were tested in both scenarios. For both the best classifier was naive Bayes with average error 8.1\% and 23.1\% for intra-personal classification and inter-personal classification, respectively. The worst classifier turned out to be decision tree with average error 19.8\% and 33.4\%, respectively. The particular classes of postures are compared according to their discriminability and all results are described and summarized

Collaborative Artificial Intelligence Development for Social Robots

The main aim of this doctoral thesis was to investigate on how to involve a community for collaborative artificial intelligence (AI) development of a social robot. The work was initiated by the author’s personal interest in developing the Sony AIBO robots that have been unavailable on the retail markets, however, user communities with special interests in these robots remained on the internet. At first, to attract people’s attention, the author developed three specific features for the robot. These consisted of teaching the robot 1) sound event recognition in order to react to environmental audio stimuli, 2) a method to detect the underlying surface under the robot, and 3) of how to recognize its own body states. As this AI development proved to be very challenging, the author decided to start a community project for artificial intelligence development. Community involvement has a long history in open-source software projects and some robotics companies tried to benefit from their userbase in product development. An active online community of Sony AIBO owners was approached to investigate factors to engage its members in the creative processes. For this purpose, 78 Sony AIBO owners were recruited online to fill a questionnaire and their data were analyzed with respect to age, gender, culture, length of ownership, user contribution, and model preference. The results revealed the motives to own these robots for many years and how these heavy users perceived their social robots after a long period in the robot acceptance phase. For example, female participants tended to have more emotional relation to their robots than male who had more technically oriented long-term engagement motivation. The user expectations were also explored by analyzing the answers to this questionnaire to discover the key needs of this user group. The results revealed that the most-wanted skills were the interaction with humans and the autonomous operation. The integration with the AI agents and Internet services was important, but the long-term memory and learning capabilities were not so relevant for the participants. The diverse preferences for robot skills led to creating a prioritized recommendation list to complement the design guidelines for social robots in the literature. In sum, the findings of this thesis showed that developing AI features for an outdated robot is possible but takes a lot of time and shared community efforts. To involve a specific community, one needs first to build up trust by working with and for the community. Also, the trust for the long-term endurance of the development project was found as a precondition for the community commitment. The discoveries of this thesis can be applied to similar types of collaborative AI developments in the future. There are significant contributions in this dissertation to robotics. First, the long-term robot usage was not studied on a years-long scale before and the most extended human-robot interactions analyzed test subjects for only a few months. A questionnaire investigated the robot owners with 1-10+ years-long ownership in this work and their attitude towards robot acceptance. The survey results helped to understand the viable strategies to engage users for a long time. Second, innovative ways were explored to involve online communities in robotics development. The past approaches introduced the community ideas and opinions into product design and innovation iterations. The community in this dissertation tested the developed AI engine, provided inputs for further development directions, created content for the actual AI and gave their feedback about product quality. These contributions advance the social robotics field

Towards systems pharmacology models of druggable targets and disease mechanisms

The development of essential medicines is being slowed by a lack of efficiency in drug development as ninety per cent of drugs fail at some stage during clinical evaluation. This attrition in drug development is seen not because of a reduction in pharmaceutical research expenditure nor is it caused by a declining understanding of biology, if anything, these are both increasing. Instead, drugs are failing because we are unable to effectively predict how they will work before they are given to patients. This is due to limitations of the current methods used to evaluate a drug’s toxicity and efficacy prior to its development. Quite simply, these methods do not account for the full complexity of biology in humans. Systems pharmacology models are a likely candidate for increasing the efficiency of drug discovery as they seek to comprehensively model the fundamental biology of disease mechanisms in a quantit- ative manner. They are computational models, designed and hailed as a strategy for making well-informed and cost effective decisions on drug viability and target druggability and therefore attempt to reduce this time-consuming and costly attrition. Using text mining and text classification I present a growing landscape of systems pharmacology models in literature growing from humble roots because of step-wise increases in our understanding of biology. Furthermore, I develop a case for the capability of systems pharmacology models in making predictions by constructing a model of interleukin-6 signalling for rheumatoid arthritis. This model shows that druggable target selection is not necessarily an intuitive task as it results in an emergent but unanswered hypothesis for safety concerns in a monoclonal antibody. Finally, I show that predictive classification models can also be used to explore gene expression data in a novel work flow by attempting to predict patient response classes to an influenza vaccine.Funded by the BBSRC and GlaxoSmithKline as part of an industrial CASE studentship

Bilateral asymmetry identification for the early detection of breast cancer

Breast cancer is the second most common cancer overall and the leading cause of cancer deaths in women. Mammography is, at present, the only viable method for detecting most of tumors early enough for effective treatment. The secret of setting up the accurate diagnosis is to detect and understand the most subtle signs of breast lesions. Analysis of asymmetry between the left and right mammograms can provide clues about the presence of early signs of tumors. In this work we present an automated procedure for bilateral asymmetry detection composed of the following steps: (1) mammography density analysis and fibro-glandular disc detection through adaptive clustering techniques, (2) analysis and implementation of bilateral asymmetries detection algorithms based on Gabor filters analysis, (3) use of a linear Bayes classifier with the leave-one-out method to asses the asymmetry degree of the two breasts, (4) metrological evaluation of the whole system through random and systematic measurement uncertainty contributions modeling