Abstract. In this paper, we propose a novel gait authentication mechanism by mining sensor resources on mobile phone. Unlike previous works, both built-in accelerometer and magnetometer are used to handle mobile installation issues, including but not limited to disorientation, and misplacement errors. The authentication performance is improved by executing deep examination at pre-processing steps. A novel and effective segmentation algorithm is also provided to segment signal into separate gait cycles with perfect accuracy. Subsequently, features are then extracted on both time and frequency domains. We aim to construct a lightweight but high reliable model; hence feature subsets selection algorithms are applied to optimize the dimension of the feature vectors as well as the processing time of classification tasks. Afterward, the optimal feature vector is classified using SVM with RBF kernel. Since there is no public dataset in this field to evaluate fairly the effectiveness of our mechanism, a realistic dataset containing the influence of mobile installation errors and footgear is also constructed with the participation of 38 volunteers (28 males, 10 females). We achieved the accuracy approximately 94.93% under identification mode, the FMR, FNMR of 0%, 3.89% and processing time of less than 4 seconds under authentication mode.
Nowadays, recognizing human activities is an important subject; it is exploited widely and applied to many fields in real-life, especially in health care and context aware application. Research achievements are mainly focused on activities of daily living which are useful for suggesting advises to health care applications. Falling event is one of the biggest risks to the health and well-being of the elderly especially in independent living because falling accidents may be caused from heart attack. Recognizing this activity still remains in difficult research area. Many systems equipped wearable sensors have been proposed but they are not useful if users forget to wear the clothes or lack ability to adapt themselves to mobile systems without specific wearable sensors. In this paper, we develop a novel method based on analyzing the change of acceleration, orientation when the fall occurs and measure their similarity to featured fall patterns. In this study, we recruit five volunteers in our experiment including various fall categories. The results are effective for recognizing fall activity. Our system is implemented on G1 smart phone which are already plugged accelerometer and orientation sensors. The popular phone is used to get data from accelerometer and results showthe feasibility of our method and significant contribution to fall detection.
This study analyzes the efficiency of parallel computational applications with the adoption of recent graphics processing units (GPUs). We investigate the impacts of the additional resources of recent architecture on the popular benchmarks compared with previous architecture. Our simulation results demonstrate that Pascal GPU architecture improves the performance by 273% on average compared to old-fashioned Fermi architecture. To evaluate the performance improvement depending on specific hardware resources, we divide the hardware resources into two types: computing and memory resources. Computing resources have bigger impact on performance improvement than memory resources in most of benchmarks. For Hotspot and B+ tree, the architecture adopting only enhanced computing resources can achieve similar performance gains of the architecture adopting both computing and memory resources. We also evaluate the influence of the number of warp schedulers in the SM (Streaming Multiprocessor) to the GPU performance in relationship with barrier waiting time. Based on these analyses, we propose the development direction for the future generation of GPUs.
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