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This paper aims to establish the leaf morphological visualization model based on the rice's growth external environment. Firstly, the support vector machine is adopted to establish the prediction models of leaf's physical attributes; secondly, the piecewise cubic Hermite spline curves are applied to visualize this model. This study, based on the quadratic equation simulation on the main leaf vein,...
Animals' decision-making behaviors are widely studied in two-alternative forced-choice tasks. Many models have been proposed to model the decision-making process and explain results of behavioral experiments. These models can fit the data of behavioral experiments well. However, the process of learning the correct decision with reward or punishment feedback is ignored in these models. Learning with...
Biological interpretation and understanding of machine learning based predictive models are highly desirable in healthcare analytics. Predicting Adverse Drug Reactions (ADRs) is extremely important for safe and precision medicine. There are various machine learning based approaches to predict adverse reactions for drugs. These models, though effective, lack biological interpretation and are treated...
This paper presents preliminary results for motion behavior analysis of Madagascar hissing cockroach biobots subject to stochastic and periodic neurostimulation pulses corresponding to randomly applied right and left turn, and move forward commands. We present our experimental setup and propose an unguided search strategy based stimulation profile designed for exploration of unknown environments....
DNA strand displacement is an emerging method in the field of biological computation in recent years. It is an enzyme-free technique, which does not require any other auxiliary materials in the process of the reaction. Strand displacement reaction performs computation based on the principle of complementary base pairing. We use strand displacement systems to generated large-scale reaction network,...
The increasing trend of systematic collection of medical data (diagnoses, hospital admission emergencies, blood test results, scans etc) by health care providers offers an unprecedented opportunity for the application of modern data mining, pattern recognition, and machine learning algorithms. The ultimate aim is invariably that of improving outcomes, be it directly or indirectly. Notwithstanding...
This project investigates the impact of a virtual zero gravity experience on the human gravity model. In the planned experiment, subjects are immersed with HMD and full body motion capture in a virtual world exhibiting either normal gravity or the apparent absence of gravity (i.e. body and objects floating in space). The study evaluates changes in the subjects' gravity model by observing changes on...
It is well established that learners have differentlearning preferences. There are multiple ways ofcharacterising learners based on their learning preferencesavailable in the literature. Even though there are efforts todesign pedagogic practices considering the learningpreferences of learners, it still demands furtherinvestigation. Further, what factors influence learningpreferences of learners is...
Visual odometry is a core component of many visual navigation systems like visual simultaneous localization and mapping (SLAM). Grid cells have been found as part of the path integration system in the rat's entorhinal cortex, and they provide inputs for place cells in the rat's hippocampus. Together with other cells, they constitute a positioning system in the brain. Some computational models of grid...
Several conventional methods have been implemented in pattern recognition, but few of them have biological plausibility. This paper mimics the hierarchical visual system and uses the precise-spike-driven (PSD) synaptic plasticity rule to learn. The well-known HMAX model imitates the visual cortex and uses Gabor filter and max pooling to extract features. Compared with the traditional HMAX model, our...
A 3D realistic tongue system is proposed. Firstly, the muscle geometry and fiber arrangement are specified after a tongue mesh model is constructed from medical data. Secondly, with the target of the efficiency and realism of animation, the tongue tissues, including tongue muscles, are described by combining a fast parametric model and a precise anatomical model to simulate the active and passive...
Target characterization of a biological network identifies characteristics that distinguish targets (nodes that can serve as molecular targets of drugs) from other nodes. In this demonstration, we present TENET (Target charactErization using NEtwork Topology), a software that facilitates topological features-based characterization of known targets in signaling networks modelling dynamic interactions...
The biological characteristics of human visual processing can be investigated through the study of optical illusions and their perception, giving rise to intuitions that may improve computer vision to match human performance. Geometric illusions are a specific subfamily in which orientations and angles are misperceived. This paper reports quantifiable predictions of the degree of tilt for a typical...
L-system is a prevailing modeling method for generating fractals, especially self-similar patterns such as plants. However it's too hard to design an appropriate L-system to get the desired visual models of plants. In order to generate a favorable plant model, usually we need to deduce backwards or guess the production rules of the L-system and then try to modify some control parameters over and over...
In order to understand the hydrologie changes in the watersheds due to climate changes, the EPSCoR jurisdictions of Idaho, Nevada, and New Mexico have collaborated to create the Western Consortium for Watershed Analysis, Visualization and Exploration (WC-WAVE). WC-WAVE will create a Virtual Watershed Platform (VWP) framework for assisting watershed scientists in their research. This software environment...
We report on an evaluation of the Customer Journey Modelling Language (CJML) for documenting and visualizing a service process from the customer's perspective. The target group is employees in service organizations. We present a modelling toolkit and a scenario-based procedure that was used during the experiment with 48 target users. The purpose was to assess the applicability of CJML when introduced...
The movement towards cyberphysical systems and Industry 4.0 promises to imbue each and every stage of production with a myriad of sensors. The open question is how people are to comprehend and interact with data originating from industrial machinery. We propose a metaphor that compares machines with natural beings that appeal to people by representing machine states with patterns occurring in nature...
Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate...
Recognition of traffic signs is vary important in many applications such as in self-driving car/driverless car, traffic mapping and traffic surveillance. Recently, deep learning models demonstrated prominent representation capacity, and achieved outstanding performance in traffic sign recognition. In this paper, we propose a traffic sign recognition system by applying convolutional neural network...
Visual attention plays an important role in human visual system, helping people to attend to things even before recognition. By its functionality, it can be treated as three kinds: SBA (spatial-based attention), OBA (object-based attention), and FBA (feature-based attention). Early works that model the mechanism of visual saliency mainly concern only one aspect of saliency. Recently, it has been shown...
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