Symmetry detection by generalized complex (GC) moments: aclose-form solution

This paper presents a unified method for detecting both reflection-symmetry and rotation-symmetry of 2D images based on an identical set of features, i.e., the first three nonzero generalized complex (GC) moments. This method is theoretically guaranteed to detect all the axes of symmetries of every 2D image, if more nonzero GC moments are used in the feature set. Furthermore, we establish the relationship between reflectional symmetry and rotational symmetry in an image, which can be used to check the correctness of symmetry detection. This method has been demonstrated experimentally using more than 200 images     

Segment-Based Map Building Using Enhanced Adaptive Fuzzy Clustering Algorithm for Mobile Robot Applications

In this paper, we present a technique for on-line segment-based map building in an unknown indoor environment from sonar sensor observations. The world model is represented with two-dimensional line segments. The information obtained by the ultrasonic sensors is updated instantaneously while the mobile robot is moving through the workspace. An Enhanced Adaptive Fuzzy Clustering Algorithm (EAFC) along with Noise Clustering (NC) is proposed to extract and classify the line segments in order to construct a complete map for an unknown environment. Furthermore, to alleviate the problem of extensive computation associated with the process of map building, the workplace of the mobile robot is divided into square cells. A compatible line segment merging technique is then suggested to combine the similar segments after the extraction of the line segment by EAFC along with NC algorithm. The performance of the algorithm is demonstrated by experimental results on a Pioneer II mobile robot.     

Constrained Nanoparticles Deliver siRNA and sgRNA to T Cells In Vivo without Targeting Ligands

T cells help regulate immunity, which makes them an important target for RNA therapies. While nanoparticles carrying RNA have been directed to T cells in vivo using protein‐ and aptamer‐based targeting ligands, systemic delivery to T cells without targeting ligands remains challenging. Given that T cells endocytose lipoprotein particles and enveloped viruses, two natural systems with structures that can be similar to lipid nanoparticles (LNPs), it is hypothesized that LNPs devoid of targeting ligands can deliver RNA to T cells in vivo. To test this hypothesis, the delivery of siRNA to 9 cell types in vivo by 168 nanoparticles using a novel siGFP‐based barcoding system and bioinformatics is quantified. It is found that nanomaterials containing conformationally constrained lipids form stable LNPs, herein named constrained lipid nanoparticles (cLNPs). cLNPs deliver siRNA and sgRNA to T cells at doses as low as 0.5 mg kg^?1 and, unlike previously reported LNPs, do not preferentially target hepatocytes. Delivery occurs via a chemical composition‐dependent, size‐independent mechanism. These data suggest that the degree to which lipids are constrained alters nanoparticle targeting, and also suggest that natural lipid trafficking pathways can promote T cell delivery, offering an alternative to active targeting approaches.     

Autonomous detection of cryospheric change with hyperion on-board Earth Observing-1

On-board detection of cryospheric change in sea ice, lake ice, and snow cover is being conducted as part of the Autonomous Sciencecraft Experiment (ASE), using classifiers developed for the Hyperion hyper-spectral visible/infrared spectrometer on-board the Earth Observing-1 (EO-1) spacecraft. This classifier development was done with consideration for the novel limitations of on-board processing, data calibration, spacecraft targeting error and the spectral range of the instrument. During on-board tests, these algorithms were used to measure the extent of cloud, snow, and ice cover at a global suite of targets. Coupled with baseline imaging, uploaded thresholds were used to detect cryospheric changes such as the freeze and thaw of lake ice and the formation and break-up of sea ice. These thresholds were used to autonomously trigger follow-up observations, demonstrating the capability of the technique for future planetary missions where downlink is a constrained resource and there is high interest in data covering dynamic events, including cryospheric change. Before upload classifier performance was assessed with an overall accuracy of 83.3% as measured against manual labeling of 134 scenes. Performance was further assessed against field mapping conducted at Lake Mendota, Wisconsin as well as with labeling of scenes that were classified during on-board tests.     

Enhancement of Probabilistic Grid-based Map for Mobile Robot Applications

In this paper, a novel approach for fine-tuning of the grid-based map-building algorithm is reported. The traditional occupancy grid-based map-building algorithm uses a fixed probability distribution function of the sonar readings and disregards the information from the environment. In our approach, the probability distribution function is tuned by fuzzy rules formed from the information obtained from the environment at each sonar data scan. A Bayesian update rule is then used to update the occupancy probabilities of the grid cells. The proposed map-building algorithm is compared with other grid-based map-building methods through simulations and experiments. The simulation and experimental studies suggest that sharp grid maps can be obtained by incorporating fuzzy rules during the grid-based map generation. In comparison with other algorithms, improved convergence has also been noted.     

Optimization of system reliability in multi-factory production networks by maintenance approach

Maintaining the reliability of a system is one of the most critical and challenging tasks for factories during production. A reliable system is not only significant for improving a system’s productivity and products quality, but worth even more in multi-factory production because the failure of one entity may induce a vigorous chain reaction to the others. Maintaining the reliability in an acceptable level requires an optimal maintenance strategy and planning for each entity in the network. The objective of this paper is to propose a double tier genetic algorithm approach for multi-factory production networks, aiming to keep the system’s reliability in a defined acceptable level, and minimize the makespan of the jobs. The optimization algorithm simultaneously schedules perfect and imperfect maintenance during the process of distributed scheduling. The optimization reliability of the proposed algorithm is demonstrated through three numerical examples, including its ability to maintain the system’s reliability in a defined acceptable level, the relationship of the acceptance level to production scheduling, and that of the machine age reduction factors to production scheduling.     

A multi-resolution statistical deformable model (MISTO) for soft-tissue organ reconstruction

While active shape model (ASM) has been increasingly adopted in the medical domain, there are issues that need to be addressed for it to be applicable in practice. Among them, the small sample size problem and how to represent the variation of the clutter of surroundings are two of the challenges. In this paper, to overcome these problems, we propose a novel multi-resolution statistical deformable model and the associated techniques for the reconstruction of soft-tissue organs such as livers. To address the small sample size problem, we define a multi-resolution integrated model for soft-tissue organs called MISTO that is able to capture the most significant deformations from a small training set as well as to generate representative variation modes of the organ shapes. To deal with the complex surroundings of the model surface or landmark points in the underlying medical images during model deformation, we propose to apply multi-resolution appearance models which allows the surrounding visual context of the model surface points to be learnt and characterized automatically from the training samples. By combining the powerful shape models and the resulting context constraints, the object segmentation and reconstruction process can be carried out very robustly. Furthermore, to avoid the local minima during model optimization, we develop an adaptive deformation strategy such that the more stable parts of the surface are moved prior to the rest of the model surface. The experimental and validation results verify that our proposed approaches can be successfully and robustly applied to the reconstruction of the soft-tissue organs such as the human liver. The major contributions of our approaches are that we extend the traditional ASM to address open problems associated with reconstructing significantly deformable three-dimensional anatomies in cluttered surrounding, and we propose effective ways to formulate the perceptual knowledge of the anatomies and make use of it in the process of model construction and deformation for medical reconstruction.     

Semantic Provenance for eScience: Managing the Deluge of Scientific Data

Provenance information in eScience is metadata that's critical to effectively manage the exponentially increasing volumes of scientific data from industrial-scale experiment protocols. Semantic provenance, based on domain-specific provenance ontologies, lets software applications unambiguously interpret data in the correct context. The semantic provenance framework for eScience data comprises expressive provenance information and domain-specific provenance ontologies and applies this information to data management. The authors' "two degrees of separation" approach advocates the creation of high-quality provenance information using specialized services. In contrast to workflow engines generating provenance information as a core functionality, the specialized provenance services are integrated into a scientific workflow on demand. This article describes an implementation of the semantic provenance framework for glycoproteomics.