Visual Simulation of Turbulent Foams by Incorporating the Angular Momentum of Foam Particles into the Projective Framework

In this paper, we propose an angular momentum-based advection technique that can express the turbulent foam effect. The motion of foam particles, which are strongly bound to the motion of the underlying fluid, is viscous, and sometimes clumping problems occur. This problem is a decisive factor that makes it difficult to express realistic foam effects. Since foam particles, which are secondary effects, depend on the motion of the underlying water, in order to exaggerate the foam effects or express more lively foam effects, it is inevitable to tune the motion of the underlying water and then readjust the foam particles. Because of such a cumbersome process, the readjustment of the foam effects requires a change in the motion of the underlying water, and it is not easy to produce such a scene because the water and foam effects must change at the same time. In this paper, we present a method to maintain angular momentum-based force from water particles without tuning the motion of the underlying water. We can restore the lost turbulent flow by additional advection of foam particles based on this force. In addition, our method can be integrated with screen-space projection frameworks, allowing us to fully embrace all the advantages of this approach. In this paper, the turbulence of the foam particles was improved by minimizing the viscous motion of the foam particles without tuning the motion of the underlying water, and as a result, lively foam effects can be expressed.

Fusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs

It is well known that numerous long noncoding RNAs (lncRNAs) closely relate to the physiological and pathological processes of human diseases and can serves as potential biomarkers. Therefore, lncRNA-disease associations that are identified by computational methods as the targeted candidates reduce the cost of biological experiments focusing on deep study furtherly. However, inaccurate construction of similarity networks and inadequate numbers of observed known lncRNA–disease associations, such inherent problems make many mature computational methods that have been developed for many years still exit some limitations. It motivates us to explore a new computational method that was fused with KATZ measure and space projection to fast probing potential lncRNA-disease associations (namely KATZSP). KATZSP is comprised of following key steps: combining all the global information with which to change Boolean network of known lncRNA–disease associations into the weighted networks; changing the similarities calculation into counting the number of walks that connect lncRNA nodes and disease nodes in bipartite graphs; obtaining the space projection scores to refine the primary prediction scores. The process to fuse KATZ measure and space projection was simplified and uncomplicated with needing only one attenuation factor. The leave-one-out cross validation (LOOCV) experimental results showed that, compared with other state-of-the-art methods (NCPLDA, LDAI-ISPS and IIRWR), KATZSP had a higher predictive accuracy shown with area-under-the-curve (AUC) value on the three datasets built, while KATZSP well worked on inferring potential associations related to new lncRNAs (or isolated diseases). The results from real cases study (such as pancreas cancer, lung cancer and colorectal cancer) further confirmed that KATZSP is capable of superior predictive ability to be applied as a guide for traditional biological experiments.

Research on Environmental Art Design Based on Computer 3D Animation Technology

In order to improve the ability of environmental art design, a method of environmental art design based on computer three-dimensional animation technology is proposed, and a three-dimensional animation imaging model of environmental art design is constructed. Combining the RGB decomposition technology to extract the color components of the environmental art design three-dimensional animation image, use the color template space projection algorithm to perform the block fusion processing of the environmental art design three-dimensional animation image. The simulation results show that the three-dimensional recognition ability of environmental art design using this method is better, and the performance of feature reconstruction is better, which improves the three-dimensional visual presentation ability of environmental art design.

Numerical Quantification of Controllability in the Null Space for Redundant Manipulators

Redundant motion, which is possible when robotic manipulators are over-actuated, can be used to control robot arms for a wide range of tasks. One of the best known methods for controlling redundancy is the null space projection, which assigns a priority while executing desired tasks. However, when the manipulator is projected into null space, its motion would be limited, since the motion is only permitted in the direction that does not interfere with the primary task. In this study, we have analyzed the null space projector matrix to derive the appropriate direction of the redundant motion by quantifying the allowed motion in each direction. As a result, we have found an ellipsoidal boundary, in which the redundant motion is permitted to move. We have named this ellipsoidal boundary as ’null space quality’ in directions. The proposed null space quality shows similar aspects with that of the robot manipulability, but it reveals a decisively different value when the manipulator operates within the null space. The experimental results showed that the robotic manipulator tracked the sinusoidal input trajectory with reduced root mean square (RMS) error by 33.84%. Furthermore, we have demonstrated the obstacle avoidance of a robotic arm utilizing the null space projector while considering the null space quality.

An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix

An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.

Correcting MEG Artifacts Caused by Overt Speech

Recording brain activity during speech production using magnetoencephalography (MEG) can help us to understand the dynamics of speech production. However, these measurements are challenging due to the induced artifacts coming from several sources such as facial muscle activity, lower jaw and head movements. Here, we aimed to characterize speech-related artifacts, focusing on head movements, and subsequently present an approach to remove these artifacts from MEG data. We recorded MEG from 11 healthy participants while they pronounced various syllables in different loudness. Head positions/orientations were extracted during speech production to investigate its role in MEG distortions. Finally, we present an artifact rejection approach using the combination of regression analysis and signal space projection (SSP) in order to correct the induced artifact from MEG data. Our results show that louder speech leads to stronger head movements and stronger MEG distortions. Our proposed artifact rejection approach could successfully remove the speech-related artifact and retrieve the underlying neurophysiological signals. As the presented artifact rejection approach was shown to remove artifacts arising from head movements, induced by overt speech in the MEG, it will facilitate research addressing the neural basis of speech production with MEG.

An Automatic Identification Method for the Blink Artifacts in the Magnetoencephalography with Machine Learning

Magnetoencephalography (MEG) detects very weak magnetic fields originating from the neurons so as to study human brain functions. The original detected MEG data always include interference generated by blinks, which can be called blink artifacts. Blink artifacts could cover the MEG signal we are interested in, and therefore need to be removed. Commonly used artifact cleaning algorithms are signal space projection (SSP) and independent component analysis (ICA). These algorithms need to locate the blink artifacts, which is typically done with the identification of the blink signals in the electrooculogram (EOG). The EOG needs to be measured by electrodes placed near the eye. In this work, a new algorithm is proposed for automatic and on-the-fly identification of the blink artifacts from the original detected MEG data based on machine learning; specifically, the artificial neural network (ANN). Seven hundred and one blink artifacts contained in eight MEG signal data sets are harnessed to verify the effect of the proposed blink artifacts identification algorithm. The results show that the method can recognize the blink artifacts from the original detected MEG data, providing a feasible MEG data-processing approach that can potentially be implemented automatically and simultaneously with MEG data measurement.

Transcranial Evoked Potentials Can Be Reliably Recorded with Active Electrodes

Electroencephalographic (EEG) signals evoked by transcranial magnetic stimulation (TMS) are usually recorded with passive electrodes (PE). Active electrode (AE) systems have recently become widely available; compared to PE, they allow for easier electrode preparation and a higher-quality signal, due to the preamplification at the electrode stage, which reduces electrical line noise. The performance between the AE and PE can differ, especially with fast EEG voltage changes, which can easily occur with TMS-EEG; however, a systematic comparison in the TMS-EEG setting has not been made. Therefore, we recorded TMS-evoked EEG potentials (TEPs) in a group of healthy subjects in two sessions, one using PE and the other using AE. We stimulated the left primary motor cortex and right medial prefrontal cortex and used two different approaches to remove early TMS artefacts, Independent Component Analysis and Signal Space Projection—Source Informed Recovery. We assessed statistical differences in amplitude and topography of TEPs, and their similarity, by means of the concordance correlation coefficient (CCC). We also tested the capability of each system to approximate the final TEP waveform with a reduced number of trials. The results showed that TEPs recorded with AE and PE do not differ in amplitude and topography, and only few electrodes showed a lower-than-expected CCC between the two methods of amplification. We conclude that AE are a viable solution for TMS-EEG recording.