This paper aimed to improve MIMO detector's performance in both throughput and cost. Thus, it presents a FPGA architecture implementation for the SQRD detection in a 4′4 16-QAM MIMO wireless communication systems...
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This paper aimed to improve MIMO detector's performance in both throughput and cost. Thus, it presents a FPGA architecture implementation for the SQRD detection in a 4′4 16-QAM MIMO wireless communication systems. The exploitation of fine-grained parallelism and coarse-grained parallelism strategies are responsible for bettering the performance of the implementation. Besides, this paper proposes a method to ensure the correctness of the implementation of time-sharing modules, which is general and applicable to any MIMO detector implementation. The work results in a real-time FPGA-based implementation delivering 32 MSQRD/s with 5.2 us latency and lowering more than 50\% cost in hardware resources on a Xilinx Virtex6.
Improving the channel utilization is a significant issue to enhance the performance in WLAN. This paper presents, Pillow Talks MAC (PT-MAC), a novel spectrum sharing mechanism to create an extra channel (pt-channel) f...
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Nowadays GPS embedded in mobile device such as smartphones can easily identify people's physical locations. However, in daily life people are more concerned about semantic locations (such as dormitories, laborator...
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ISBN:
(纸本)9781467372121
Nowadays GPS embedded in mobile device such as smartphones can easily identify people's physical locations. However, in daily life people are more concerned about semantic locations (such as dormitories, laboratories, shopping malls, etc.). Usually GPS positioning uses continuous sampling method, which results in a lot of semantically independent sample points. We call these points outliers. How to remove outliers from GPS data and thereby cluster meaningful semantic places is a research challenge in current field of pervasive computing. Aiming at the characteristics of this problem, we first propose a novel approach to add semantic annotations to newly discovered places every day. We use an unsupervised method to discover semantic places, which ensures accuracy of the results and reduces the amount of calculation. Secondly, we discuss the concept of outliers in GPS data collected in daily life, and then eliminate outliers using a density-based method. Moreover, we perform experiments to validate its effectiveness. Thirdly by taking advantage of rule-based inference and reverse geocoding we proposed an approach to calculate the probable semantic labels, which can help user annotate places and reduce the burden on users. Finally, we develop a local System Annotating Semantic Label of Location(SASLL) and by carrying out experiments we demonstrate the validity of our research.
Data clustering is usually time-consuming since it by default needs to iteratively aggregate and process large volume of data. Approximate aggregation based on sample provides fast and quality ensured results. In this...
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ISBN:
(纸本)9781467365994
Data clustering is usually time-consuming since it by default needs to iteratively aggregate and process large volume of data. Approximate aggregation based on sample provides fast and quality ensured results. In this paper, we propose to leverage approximation techniques to data clustering to obtain the trade-off between clustering efficiency and result quality, along with online accuracy estimation. The proposed method is based on the bootstrap trials. We implemented this method as an Intelligent Bootstrap Library (IBL) on Spark to support efficient data clustering. Intensive evaluations show that IBL can provide a 2x speed-up over the state of art solution with the same error bound.
A deep belief network (DBN) is an important branch of deep learning models and has been successfully applied in many machine learning and pattern recognition fields such as computer vision and speech recognition. Howe...
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ISBN:
(纸本)9781479919611
A deep belief network (DBN) is an important branch of deep learning models and has been successfully applied in many machine learning and pattern recognition fields such as computer vision and speech recognition. However, the training of billions of parameters in DBN is computationally challenging for modern central processing units (CPUs). Many studies have reported the efficient implementations of the pre-training process of DBNs for graphics processing units (GPUs), but few studies have mentioned the fine-tuning process of DBNs. In this paper, we describe an efficient DBN implementation on the GPU, including the pre-training and fine-tuning processes. Experimental results show that our proposed method on the GPU (NVIDIA Tesla K40c) achieves up to 22 speedups on the pre-training process and 33 speedups on the fine-tuning processes compared with conventional CPU (Intel Core i7-4790K) implementations. Moreover, the performance of our algorithm is superior to that of the OpenBLAS library on the CPU and the CUBLAS library on the GPU.
This paper aimed to improve MIMO detector's performance in both throughput and cost. Thus, it presents a FPGA architecture implementation for the SQRD detection in a 4 × 4 16-QAM MIMO wireless communication s...
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This paper aimed to improve MIMO detector's performance in both throughput and cost. Thus, it presents a FPGA architecture implementation for the SQRD detection in a 4 × 4 16-QAM MIMO wireless communication systems. The exploitation of fine-grained parallelism and coarse-grained parallelism strategies are responsible for bettering the performance of the implementation. Besides, this paper proposes a method to ensure the correctness of the implementation of time-sharing modules, which is general and applicable to any MIMO detector implementation. The work results in a real-time FPGA-based implementation delivering 32 MSQRD/s with 5.2 us latency and lowering more than 50\% cost in hardware resources on a Xilinx Virtex6.
The contribution of parasitic bipolar amplification to SETs is experimentally verified using two P-hit target chains in the normal layout and in the special layout. For PMOSs in the normal layout, the single-event cha...
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The contribution of parasitic bipolar amplification to SETs is experimentally verified using two P-hit target chains in the normal layout and in the special layout. For PMOSs in the normal layout, the single-event charge collection is composed of diffusion, drift, and the parasitic bipolar effect, while for PMOSs in the special layout, the parasitic bipolar junction transistor cannot turn on. Heavy ion experimental results show that PMOSs without parasitic bipolar amplification have a 21.4% decrease in the average SET pulse width and roughly a 40.2% reduction in the SET cross-section.
As the de facto Internet inter-domain routing protocol, BGP protocol has a number of vulnerabilities and weakness. Monitoring BGP is an effective way to improve the security of inter-domain routing. This paper present...
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In this paper, we study about the problem of how to recognize the user emotion based on smartphone data more really. With single data used in the previous studies, it cannot make a comprehensive response of user behav...
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In this paper, we study about the problem of how to recognize the user emotion based on smartphone data more really. With single data used in the previous studies, it cannot make a comprehensive response of user behavior patterns. So we collected fine-grained sensing data which could reflect user daily behavior fully from multiple dimensions based on smartphone, and then used multidimensional data feature fusion method and six classification methods such as Support Vector Machine (SVM) and Random Forests. Finally, we carried out contrast experiment with twelve volunteers' hybrid data and personal data respectively to recognized user emotion based on discrete emotion model and circumplex emotion model. The results show that the multidimensional data feature fusion method we mentioned which could reflect user behavior comprehensively present high accuracy. The initial use of the hybrid data train only have 72.73% accuracy rate, but after personal data training the accuracy rate can reach 79.78%. In the experimental of different emotion model, circumplex emotion model is better than discrete emotion model.
Due to the uncertainty and unpredictability of environment changes, it is a great challenge to develop self-adaptive systems in open environment. First, it is difficult for developers to clearly predict various enviro...
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