Xu Mingyang

Bio: Xu Mingyang is an academic researcher. The author has contributed to research in topics: Feature recognition & Face (geometry). The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Face recognition algorithm

Abstract: The invention discloses a face recognition algorithm. According to the face recognition algorithm, face images under different states are selected; and a geometric feature recognition method is adopted to carry out face recognition. With the face recognition algorithm of the invention adopted, the accuracy of the geometric feature recognition method can be improved, and the advantage of high fast recognition speed of geometric feature recognition can be kept.

Spontaneous Proximity Clouds: Making Mobile Devices to Collaborate for Resource and Data Sharing

Abstract: The base motivation of Mobile Cloud Computing was empowering mobile devices by application offloading onto powerful cloud resources. However, this goal can’t entirely be reached because of the high offloading cost imposed by the long physical distance between the mobile device and the cloud. To address this issue, we propose an application offloading onto a nearby mobile cloud composed of the mobile devices in the vicinity - a Spontaneous Proximity Cloud. We introduce our proposed dynamic, ant-inspired, bi-objective offloading middleware - ACOMMA, and explain its extension to perform a close mobile application offloading. With the learning-based offloading decision-making process of ACOMMA, combined to the collaborative resource sharing, the mobile devices can cooperate for decision cache sharing. We evaluate the performance of ACOMMA in collaborative mode with real benchmarks - Face Recognition and Monte-Carlo algorithms - and achieve 50% execution time gain.

Spontaneous Proximity Clouds: Making Mobile Devices to Collaborate for Resource and Data Sharing

Abstract: The base motivation of Mobile Cloud Computing was empowering mobile devices by application offloading onto powerful cloud resources. However, this goal can't entirely be reached because of the high offloading cost imposed by the long physical distance between the mobile device and the cloud. To address this issue, we propose an application offloading onto a nearby mobile cloud composed of the mobile devices in the vicinity-a Spontaneous Proximity Cloud. We introduce our proposed dynamic, ant-inspired, bi-objective offloading middleware-ACOMMA, and explain its extension to perform a close mobile application offloading. With the learning-based offloading decision-making process of ACOMMA, combined to the collaborative resource sharing, the mobile devices can cooperate for decision cache sharing. We evaluate the performance of ACOMMA in collaborative mode with real benchmarks Face Recognition and Monte-Carlo algorithms-and achieve 50% execution time gain.

Automated application offloading through ant-inspired decision-making

Abstract: The explosive trend of smartphone usage as the most effective and convenient communication tools of human life in recent years make developers build ever more complex smartphone applications. Gaming, navigation, video editing, augmented reality, and speech recognition applications require considerable computational power and energy. Although smartphones have a wide range of capabilities — GPS, WiFi, cameras their inherent limitations — frequent disconnections, mobility — and significant constraints — size, lower weights, longer battery life make difficult to exploiting their full potential to run complex applications. Several research works have proposed solutions in application offloading domain, but few ones concerning the highly changing properties of the environment. To address these issues, we realize an automated application offloading middleware, ACOMMA, with dynamic and re-adaptable decision-making engine. The decision engine of ACOMMA is based on an ant-inspired algorithm.