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Clustering technique plays a critical role in data mining, and has received great success to solve application problems like community analysis, image retrieval, personalized recommendation, activity prediction, etc. This paper first reviews the traditional clustering and the emerging multiple clustering methods, respectively. Although the existing methods have superior performance on some small or certain datasets, they fall short when clustering is performed on CPSS big data because of the high cost of computation and storage. With the powerful cloud computing, this challenge can be effectively addressed, but it brings enormous threat to individual or company’s privacy. Currently, privacy preserving data mining has attracted widespread attention in academia. Compared to other reviews, this paper focuses on privacy preserving clustering technique, guiding a detailed overview and discussion. Specifically, we introduce a novel privacy-preserving tensor-based multiple clustering, propose a privacy-preserving tensor-based multiple clustering analytic and service framework, and give an illustrated case study on the public transportation dataset. Furthermore, we indicate the remaining challenges of privacy preserving clustering and discuss the future significant research in this area.

Background: Grayscale medical image segmentation is the key step in clinical computer-aided diagnosis. Model-driven and data-driven image segmentation methods are widely used for their less computational complexity and more accurate feature extraction. However, model-driven methods like thresholding usually suffer from wrong segmentation and noises regions because different grayscale images have distinct intensity distribution property thus pre-processing is always demanded. While data-driven methods with deep learning like encoder-decoder networks always are always accompanied by complex architectures which require amounts of training data. Methods: Combining thresholding method and deep learning, this paper presents a novel method by using 2D&3D object detection technologies. First, interest regions contain segmented object are determined with fine-tuning 2D object detection network. Then, pixels in cropped images are turned as point cloud according to their positions and grayscale values. Finally, 3D object detection network is applied to obtain bounding boxes with target points and boxes’ bottoms and tops represent thresholding values for segmentation. After projecting to 2D images, these target points could composite the segmented object. Results: Three groups of grayscale medical images are used to evaluate the proposed image segmentation method. We obtain the IoU (DSC) scores of 0.92 (0.96), 0.88 (0.94) and 0.94 (0.94) for segmentation accuracy on different datasets respectively. Also, compared with five state of the arts and clinically performed well models, our method achieves higher scores and better performance.Conclusions: The prominent segmentation results demonstrate that the built method based on 2D&3D object detection with deep learning is workable and promising for segmentation task of grayscale medical images.

The electronic nose is the olfactory organ of the robot, which is composed of a large number of sensors to perceive the smell of objects through free diffusion. Traditionally, it is difficult to realize the active perception function, and it is difficult to meet the requirements of small size, low cost, and quick response that robots require. In order to address these issues, a novel electronic nose with active perception was designed and an ensemble learning method was proposed to distinguish the smell of different objects. An array of three MQ303 semiconductor gas sensors and an electrochemical sensor DART-2-Fe5 were used to construct the novel electronic nose, and the proposed ensemble learning method with four algorithms realized the active odor perception function. The experiment results verified that the accuracy of the active odor perception can reach more than 90%, even though it used 30% training data. The novel electronic nose with active perception based on the ensemble learning method can improve the efficiency and accuracy of odor data collection and olfactory perception.

We report a method for quantifying scanning thermal microscopy (SThM)\ud probe-sample thermal interactions in-air using a novel temperature calibration device.\ud This new device has been designed, fabricated and characterized using SThM to provide\ud an accurate and spatially variable temperature distribution that can be used as a\ud temperature reference due to its unique design. The device was characterized by means\ud of a microfabricated SThM probe operating in passive mode. This data was interpreted\ud using a heat transfer model, built to describe the thermal interactions during a SThM\ud thermal scan. This permitted the thermal contact resistance between the SThM tip and\ud the device to be determined as 8.33 × 105 K/W. It also permitted the probe-sample\ud contact radius to be clarified as being the same size as the probe’s tip radius of curvature.\ud Finally, the data was used in the construction of a lumped-system steady state model\ud for the SThM probe and its potential applications were addressed.

A novel fabrication technique is described for the production of multimaterial, lithographically defined, topography-free samples for use in experiments to investigate the nature of contrast in scanning probe microscopy (SPM). The approach uses a flat sacrificial substrate as the base for fabrication, which is deleted in the final step. This leaves an exposed, flat surface with patterns of materials contrast defined during the lithography stages. In the example application presented, these are designed to challenge the detection ability of a scanning thermal microscopy (SThM) probe, although many other applications can be envisioned. There are many instances in SPM where images can exhibit topographically induced artifacts. In SThM, these can result in a change of the thermal signal which can easily be misinterpreted as changes in the sample thermal conductivity or temperature. The elimination of these artifacts through postprocessing requires a knowledge of how the probe responds thermal features of differing sizes. The complete sample fabrication process, followed by successful topographic/thermal scanning is demonstrated, showing sub-1.5 nm topography with a clear artifact-free thermal signal from sub-100 nm gold wires. The thermal spatial resolution is determined for the sample materials and probe used in this study to be in the range of 35–75 nm.

The influence diagram is a graph model for solving complex decision problems based on uncertain information. In order to deal with various decision problems, many new types of influence diagram have been developed. In this paper, we extend GeNIe, a development environment for building graphical decision-theoretic models, by using the library of functions (SMILE) for graphical probabilistic and decision-theoretic models, and thus GeNIe can model and evaluate the qualitative influence diagram with weights and the influence diagram with interval parameters.