Investigation of contact deformation and wear characteristics of discrete track recording media

The even semester 2014/2015 Technical Information Engineering University of Semarang (USM) has been running the Competency Based Curriculum (CBC) in the management of learning. Conversions that occur in some subjects at an increase in scheduled meetings in the classroom or in the laboratory. Computer Networks is one of the subjects who experienced a conversion. In the curriculum in 2008, Computer Networking has a number of credits 3. From the 2 credits 3 credits are for credits 1 credits for theory and practical credits. While at the CBC in 2013, Computer Networking has 4 credits, with details of 2 credits 2 credits theory and practicum. As lecture and instructor Computer Network, researchers interested in studying the effect of applying the CBC in 2013 in the subje ct of Computer Network. Does the addition of meeting practical and theoretical material renewal in accordance with the expected competencies?. Researchers tried applying the CBC in 2013 by conducting action research. Implementation of the research was conducted during an ongoing lecture that even semester 2015/2016. The results of the study during the first half of researchers will compare with the achievements that never existed when the old curriculum still in use. The goals of this research is, subjects in the Computer Network has always been one of the subjects that the content of the material and its application in the lab was able to follow the needs of the workforc

Collective Motion and Phase Transitions of Symmetric Camphor Boats

The motion of several self-propelled boats in a narrow channel displays spontaneous pattern formation and kinetic phase transitions. In contrast with previous studies on self-propelled particles, this model does not require stochastic fluctuations and it is experimentally accessible. By varying the viscosity in the system, it is possible to form either a stationary state, correlated or uncorrelated oscillations, or unidirectional flow. Here, we describe and analyze these self organized patterns and their transitions.Comment: 6 pages, 6 figure

Brain-derived proteins in the CSF, do they correlate with brain pathology in CJD?

BACKGROUND: Brain derived proteins such as 14-3-3, neuron-specific enolase (NSE), S 100b, tau, phosphorylated tau and Aβ(1–42 )were found to be altered in the cerebrospinal fluid (CSF) in Creutzfeldt-Jakob disease (CJD) patients. The pathogenic mechanisms leading to these abnormalities are not known, but a relation to rapid neuronal damage is assumed. No systematic analysis on brain-derived proteins in the CSF and neuropathological lesion profiles has been performed. METHODS: CSF protein levels of brain-derived proteins and the degree of spongiform changes, neuronal loss and gliosis in various brain areas were analyzed in 57 CJD patients. RESULTS: We observed three different patterns of CSF alteration associated with the degree of cortical and subcortical changes. NSE levels increased with lesion severity of subcortical areas. Tau and 14-3-3 levels increased with minor pathological changes, a negative correlation was observed with severity of cortical lesions. Levels of the physiological form of the prion protein (PrP(c)) and Aβ(1–42 )levels correlated negatively with cortical pathology, most clearly with temporal and occipital lesions. CONCLUSION: Our results indicate that the alteration of levels of brain-derived proteins in the CSF does not only reflect the degree of neuronal damage, but it is also modified by the localization on the brain pathology. Brain specific lesion patterns have to be considered when analyzing CSF neuronal proteins

POINT-CLOUD COMPRESSION FOR VEHICLE-BASED MOBILE MAPPING SYSTEMS USING PORTABLE NETWORK GRAPHICS

A mobile mapping system is effective for capturing dense point-clouds of roads and roadside objects．Point-clouds of urban areas, residential areas, and arterial roads are useful for maintenance of infrastructure, map creation, and automatic driving. However, the data size of point-clouds measured in large areas is enormously large. A large storage capacity is required to store such point-clouds, and heavy loads will be taken on network if point-clouds are transferred through the network. Therefore, it is desirable to reduce data sizes of point-clouds without deterioration of quality. In this research, we propose a novel point-cloud compression method for vehicle-based mobile mapping systems. In our compression method, point-clouds are mapped onto 2D pixels using GPS time and the parameters of the laser scanner. Then, the images are encoded in the Portable Networking Graphics (PNG) format and compressed using the PNG algorithm. In our experiments, our method could efficiently compress point-clouds without deteriorating the quality

CLASSIFICATION OF POLE-LIKE OBJECTS USING POINT CLOUDS AND IMAGES CAPTURED BY MOBILE MAPPING SYSTEMS

The vehicle-based mobile mapping system (MMS) is effective for capturing 3D shapes and images of roadside objects. The laser scanner and cameras on the MMS capture point-clouds and sequential digital images synchronously during driving. In this paper, we propose a method for detecting and classifying pole-like objects using both point-clouds and images captured using the MMS. In our method, pole-like objects are detected from point-clouds, and then target objects, which are objects attached to poles, are extracted for identifying the types of pole-like objects. For associating each target object with images, the points of the target object are projected onto images, and the image of the target object is cropped. Each pole-like object is represented as a feature vector, which are calculated from point-clouds and images. The feature values of a point-cloud are calculated by point processing, and the ones of the cropped image are calculated using a convolutional neural network. The feature values of point-clouds and images are unified, and they are used as the input to machine learning. In experiments, we classified pole-like objects using three methods. The first method used only point-clouds, the second used only images, and the third used both point-clouds and images. The experimental results showed that the third method could most accurately classify pole-like objects