Study on optimal velocity selection using velocity obstacle (OVVO) in dynamic and crowded environment

The velocity obstacle (VO) method is one of local path generation method considering a velocity of obstacles. By dividing an available velocity region into collision and collision-free area, a robot can avoid collisions using the VO. However, if there are numerous obstacles near a robot, the robot will have very few velocity candidates. In this paper, a method to choose an optimal velocity by introducing a cost function about safety of the velocity, and the cost function consists of a pass-time and a clearance. By latticizing available velocity map of a robot, each velocity can be evaluated from the cost function and a robot can select better velocity among collision-free velocity candidates. A performance of introduced method is compared to other VO method using simulation, and experiments are conducted to verify the results of simulation.     

On-chip whole blood plasma separator based on microfiltration,sedimentation and wetting contrast

Miniaturized on-chip blood separators have a great value for point-of-care diagnosis. In our work, a combined design strategy—microfiltration, sedimentation in a retarded flow, and wetting contrast—was taken to overcome the known limitations of on-chip blood separators. Our microfluidic chip consists of a polydimethylsiloxane micropillar array and an etched glass with microchannel branches. The red blood cells are significantly slowed and gradually settled down due to micropillars and enlarged dimension of a chamber. An etched glass microchannel allows the extraction of blood plasma exclusively due to the capillary effect. The fabricated microfluidic device can separate blood plasma from a whole blood sample without any external driving force or dilution. The measured plasma separation efficiency was close to 100 % from human whole blood. Autonomous on-chip separation and collection of blood plasma was demonstrated.     

Incidental Exposure,Selective Exposure,and Political Information Sharing: Integrating Online Exposure Patterns and Expression on Social Media

Political information sharing in social media offers citizens opportunities to engage with news and express their political views, but how do different patterns of online political information exposure, including both incidental and selective exposure, affect sharing? Using two‐wave panel survey data collected in the United States, we examine the relationship between incidental and selective exposure and their consequent links to political information sharing, across different levels of strength of political party affiliation. Our results demonstrate that incidental exposure to counter‐attitudinal information drives stronger partisans to more actively seek out like‐minded political content, which subsequently encourages political information sharing on social media. The results highlight the need to consider both types of political information exposure when modeling citizens' political behavior online.     

When brands come to life: experimental research on the vividness effect of Virtual Reality in transformational marketing communications

Mobile Virtual Reality provides a gateway for marketers to innovatively reach consumers. This study examines the impact of Virtual Reality in the context of transformational brand experience appeals, focussing specifically on the determining role of vividness. A three-dimensional conceptual framework is presented, offering a systematic review of the literature on vividness effects in marketing communications, revealing the major gap that most available studies only focus on informational messages. We conducted an experiment to address this gap and demonstrate in the context of a transformational ad that Virtual Reality generates higher perceptions of vividness and presence than a regular two-dimensional video, with vividness positively affecting attitude toward the ad, both directly and indirectly via presence. Our study also reveals that vividness in turn elicits a positive effect on brand attitudes which stimulates consumers’ purchase intentions. As such, the strategic potential of Virtual Reality for marketing communications is highlighted.     

Semi-automatic construction of a named entity dictionary for entity-based sentiment analysis in social media

To understand the user experience in social media or to facilitate the design of human-centric services by social media, users’ opinions about specific entities in text messages should be captured. A fine-grained named entity recognizer (NER) is an essential module for identifying opinion targets in text messages, and a named-entity (NE) dictionary is a major resource that affects the performance of an NER. However, it is not easy to construct an NE dictionary manually, because human annotation is time-consuming and labor-intensive. To reduce construction time and labor, we propose a semi-automatic system to construct an NE dictionary from the free online resource, Wikipedia. The proposed system constructs a pseudo-document for each Wikipedia NE by using an active-learning technique. It then classifies Wikipedia entries into NE classes based on similarities between the entries and pseudo-documents located in a vector space. In experiments, the proposed system classified 92.3 % of Wikipedia entries into 29 NE classes. It showed a high performance, with a macro-averaging F1-measure of 0.872 and micro-averaging F1-measure of 0.935.     

Design and implementation of a same-user identification system in invoked reality space

The objective of this study is to solve the problem of user data not being precisely received from sensors because of sensing region limitations in invoked reality (IR) space, distortion of colors or patterns by lighting, and blocking or overlapping of a user by other users. The sensing scope range is thus expanded using multiple sensors in the IR space. Moreover, user feature data are accurately identified by user sensing. Specifically, multiple sensors are employed when not all of user data are sensed because they overlap with data of other users. In the proposed approach, all clients share the user feature data from multiple sensors. Accordingly, each client recognizes that the user is the same individual on the basis of the shared data. Furthermore, the identification accuracy is improved by identifying the user features based on colors and patterns that are less affected by lighting. Therefore, accurate identification of the user feature data is enabled, even under lighting changes. The proposed system was implemented based on system performance analysis standards. The practicality and system performance in identifying the same person using the proposed method were verified through an experiment.     

Enhanced image similarity analysis system in digital pathology

Multimedia Tools and Applications - In digital pathology, image similarity algorithms are used to find cancer in tissue cells from medical images. However, it is very difficult to apply image...