Mobile crowdsensing with mobile agents

We introduce mobile agents for mobile crowdsensing. Crowdsensing campaigns are designed through different roles that are implemented as mobile agents. The role-based tasks of mobile agents include collecting data, analyzing data and sharing data in the campaign. Mobile agents execute and control the campaign autonomously as a multi-agent system and migrate in the opportunistic network of participants’ devices. Mobile agents take into account the available resources in the devices and match participants’ privacy requirements to the campaign requirements. Sharing of task results in real-time facilitates cooperation towards the campaign goal while maintaining a selected global measure, such as energy efficiency. We discuss current challenges in crowdsensing and propose mobile agent based solutions for campaign execution and monitoring, addressing data collection and participant-related issues. We present a software framework for mobile agents-based crowdsensing that is seamlessly integrated into the Web. A set of simulations are conducted to compare mobile agent-based campaigns with existing crowdsensing approaches. We implemented and evaluated a small-scale real-world mobile agent based campaign for pedestrian flock detection. The simulation and evaluation results show that mobile agent based campaigns produce comparable results with less energy consumption when the number of agents is relatively small and enables in-network data processing with sharing of data and task results with insignificant overhead.     

Glare Generation Based on Wave Optics

This paper proposes a novel and general method of glare generation based on wave optics. A glare image is regarded as a result of Fraunhofer diffraction, which is equivalent to a 2D Fourier transform of the image of given apertures or obstacles. In conventional methods, the shapes of glare images are categorized according to their source apertures, such as pupils and eyelashes and their basic shapes (e.g. halos, coronas, or radial streaks) are manually generated as templates, mainly based on statistical observation. Realistic variations of these basic shapes often depend on the use of random numbers. Our proposed method computes glare images fully automatically from aperture images and can be applied universally to all kinds of apertures, including camera diaphragms. It can handle dynamic changes in the position of the aperture relative to the light source, which enables subtle movement or rotation of glare streaks. Spectra can also be simulated in the glare, since the intensity of diffraction depends on the wavelength of light. The resulting glare image is superimposed onto a given computer‐generated image containing high‐intensity light sources or reflections, aligning the center of the glare image to the high‐intensity areas. Our method is implemented as a multipass rendering software. By precomputing the dynamic glare image set and putting it into texture memory, the software runs at an interactive rate.     

Animated interface agent applying atms-based multimodal input interpretation

Two requirements should be met in order to develop a practical multimodal interface system , i . e ., ( 1 ) integration of delayed arrival of data and ( 2 ) elimination of ambiguity in recognition results of each modality . This paper presents an efficient and generic methodology for interpretation of multimodal input to satisfy these requirements . The proposed methodology can integrate delayed - arrival data satisfactorily and efficiently interpret multimodal input that contains ambiguity . In the input interpretation the multimodal interpretation process is regarded as hypothetical reasoning , and the control mechanismof interpretation is formalized by applying the assumption - based truth maintenance system ( ATMS ). The proposed method is applied to an interface agent system that accepts multimodal input consisting of voice and direct indication gesture on a touch display . The systemcommunicates to the user through a human - like interface agent's three - dimensional motion image with facial expressions , gestures , and a synthesized voice .     

慕课授课中的学生听课行为自动分析系统

为了解决在线课程(Massive open online course, MOOC)授课过程中,缺乏对于学生学习情况的跟踪与教学效果评估问题,本文依据视频信息对学生行为进行建模,提出了一种评判学生听课专心程度的行为自动分析算法.该算法能够有效跟踪学生的学习状态,提取学生的行为特征参数,并对这些参数进行D-S融合判决,以获得学生的听课专注度.经过多次实验的结果表明,本文采用的方法能够有效评判学生在授课期间的专心程度,在数据融合上,与贝叶斯推理方法相比,采用D-S融合方法能有效提高实验结果的准确性和可靠性.     

An incentive game based evolutionary model for crowd sensing networks

Crowd sensing networks can be used for large scale sensing of the physical world or other information service by leveraging the available sensors on the phones. The collector hopes to collect as much as sensed data at relatively low cost. However, the sensing participants want to earn much money at low cost. This paper examines the evolutionary process among participants sensing networks and proposes an evolutionary game model to depict collaborative game phenomenon in the crowd sensing networks based on the principles of game theory in economics. A effectively incentive mechanism is established through corrected the penalty function of the game model accordance with the cooperation rates of the participant, and corrected the game times in accordance with it’s payoff. The collector controls the process of game by adjusting the price function. We find that the proposed incentive game based evolutionary model can help decision makers simulate evolutionary process under various scenarios. The crowd sensing networks structure significantly influence cooperation ratio and the total number of participant involved in the game, and the distribution of population with different game strategy. Through evolutionary game model, the manager can select an optimal price to facilitate the system reach equilibrium state quickly, and get the number of participants involved in the game. The incentive game based evolutionary model in crowd sensing networks provides valuable decision-making support to managers.     

A Hu moment invariant as a shape circularity measure

In this paper we propose a new circularity measure which defines the degree to which a shape differs from a perfect circle. The new measure is easy to compute and, being area based, is robust—e.g., with respect to noise or narrow intrusions. Also, it satisfies the following desirable properties:

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it ranges over (0,1] and gives the measured circularity equal to 1 if and only if the measured shape is a circle;

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it is invariant with respect to translations, rotations and scaling.

Compared with the most standard circularity measure, which considers the relation between the shape area and the shape perimeter, the new measure performs better in the case of shapes with boundary defects (which lead to a large increase in perimeter) and in the case of compound shapes. In contrast to the standard circularity measure, the new measure depends on the mutual position of the components inside a compound shape.Also, the new measure performs consistently in the case of shapes with very small (i.e., close to zero) measured circularity. It turns out that such a property enables the new measure to measure the linearity of shapes.In addition, we propose a generalisation of the new measure so that shape circularity can be computed while controlling the impact of the relative position of points inside the shape. An additional advantage of the generalised measure is that it can be used for detecting small irregularities in nearly circular shapes damaged by noise or during an extraction process in a particular image processing task.