With a big success in data communication, wireless signals are now exploited for fine-grained contactless activity sensing including human respiration monitoring, finger gesture recognition, subtle chin movement tracking when speaking, etc. Different from coarsegrained body and limb movements, these fine-grained movements are in the scale of millimetres and are thus difficult to be sensed. While good sensing performance can be achieved at one location, the performance degrades dramatically at a very nearby location. In this paper, by revealing the effect of static multipaths in sensing, we propose a novel method to add man-made "virtual" multipath to significantly improve the sensing performance. With carefully designed "virtual" multipath, we are able to boost the sensing performance at each location purely in software without any extra hardware.

Device-free passive indoor localization is playing a critical role in many applications such as elderly care, intrusion detection, smart home, etc. However, existing device-free localization systems either suffer from labor-intensive offline training or require dedicated special-purpose devices. To address the challenges, we present our system named MaTrack, which is implemented on commodity off-the-shelf Intel 5300 Wi-Fi cards. MaTrack proposes a novel Dynamic-MUSIC method to detect the subtle reflection signals from human body and further differentiate them from those reflected signals from static objects (furniture, walls, etc.) to identify the human target's angle for localization. MaTrack does not require any offline training compared to existing signature-based systems and is insensitive to changes in environment. With just two receivers, MaTrack is able to achieve a median localization accuracy below 0.6 m when the human is walking, outperforming the state-of-the-art schemes.

Estimating the cost is important for cloud application developers to services in clouds, and becomes even important when it needs remaining a certain service level at the same time. Though currently much work has been down to predict cost and performance of cloud applications, most of them perform either before application design or after the application construction, which leads to either imprecise estimation or irreparable design fault. In this paper, we propose an approach to estimate the cost of running typical applications in Amazon Web Service (AWS) cloud during design phase. We propose an UML Activity-extended model (AeModel) to describe execution of application service and introduce an extraction algorithm to extract information contained in the AeModel automatically. We propose a cost model on AWS, which can help developers to estimate operating cost during design phase and satisfy performance needs, with an algorithm to produce suitable purchase solutions automatically. We perform case studies using a web-based business application to show effectiveness of our approach, and find that our approach can help developers lessen cost by adjusting application models.