Face To Face Proximity Estimation Using Bluetooth on Smartphones

ABSTRACT The availability of "always-on" communications has tremendous implications for how people interact socially. In particular, individuals within a certain distance? Moreover, the problem of proximity estimation is complicated by the fact that the measurement must be quite precise (1-1.5 m) and can cover a wide variety of environments. Existing approaches such as GPS and Wi-Fi triangulation are insufficient to meet the requirements of accuracy and flexibility. In contrast, Bluetooth, which is commonly available on most smartphones, provides a compelling alternative for proximity estimation. In this paper, we demonstrate through experimental studies the efficacy of Bluetooth for this exact purpose. We propose a proximity estimation model to determine the distance based on the RSSI values of Bluetooth and light sensor data in different environments. We present several real world scenarios and explore Bluetooth proximity estimation on Android with respect to accuracy and power consumption

Web Image Re-Ranking Using Query-Specific Semantic Signatures

Abstract: Image re-ranking, as an effective way to improve the results of web-based image search, has been adopted by current commercial search engines such as Bing and Google. Given a query keyword, a pool of images are first retrieved based on textual information. By asking the user to select a query image from the pool, the remaining images are re-ranked based on their visual similarities with the query image. A major challenge is that the similarities of visual features do not well correlate with images' semantic meanings which interpret users' search intention. In this paper, we propose a novel image re-ranking framework, which automatically offline learns different semantic spaces for different query keywords. The visual features of images are projected into their related semantic spaces to get semantic signatures. At the online stage, images are re-ranked by comparing their semantic signatures obtained from the semantic space specified by the query keyword. The proposed query-specific semantic signatures significantly improve both the accuracy and efficiency of image re-rankin

A Study of Effective Load Balancing Approaches in Cloud Computing

Cloud computing is the most recent technology in today’s world of computing and it overcomes deficiencies of traditional ways of computing. Cloud computing is a new way of providing the essential services to cloud users on “Pay As You Go ” basis. Cloud computing provides different features like on demand access, flexibility, instant response, pay per use etc. to customers. In order to provide all these features to cloud users, cloud computing systems must be structured and managed efficiently to provide the Quality of Services (QOS) to users. Various technological concepts such as abstraction and virtualization are used that hides the implementation details from an average cloud user. Cloud load balancing plays a very important role in providing all the cloud features to users which is the main topic of interest in our research. Different archictures apply altogether different load balancing algorithms. This paper includes the Study of different approaches of effective management of cloud systems. The study includes load balancing approaches in different system architectures like Centralized, Distributed and Cluster based architecture. Finally various algorithms have been compared based on the different parameters like response time, efficiency and throughput etc