Active fault isolation of parametric faults in closed-loop MIMO systems are considered in this paper. The fault isolation consists of two steps. The first step is group-wise fault isolation. Here, a group of faults is...
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In light of the new capability to fork an already parallelized kernel on a GPU, this paper shows how the use of the parallelization capabilities of a PC's Graphics Processing Unit (GPU) make the solution of couple...
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ISBN:
(纸本)9781632666789
In light of the new capability to fork an already parallelized kernel on a GPU, this paper shows how the use of the parallelization capabilities of a PC's Graphics Processing Unit (GPU) make the solution of coupled inverse problems realistic in terms of solution time.
Detecting complex video events based on audio and visual modalities is still a largely unresolved issue. While the conventional video representation methods extract each modality ineffectively, we propose a regularize...
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Detecting complex video events based on audio and visual modalities is still a largely unresolved issue. While the conventional video representation methods extract each modality ineffectively, we propose a regularized multi-modality deep learning for video event detection. We first build an auto-encoder based on unconstrained minimization and adopt the conjugate gradient method with linear search for optimization. The learned auto-encoder can capture the relationship between the audio and visual modality corresponding to the same video event at each layer of the network. To make the network robust to local variance, we adopt the commonly used local contrast normalization and spatial maximum pooling to each modality for video representation. Compared with traditional methods using manually designed features, our method is more efficient. Experimental results on publicly available video event detection datasets demonstrate that the proposed method consistently outperforms the state-of-the-art video representation methods.
In this paper, our recently proposed mobile-conductance based analytical framework is extended to the sparse settings, thus offering a unified tool for analyzing information spreading in mobile networks. A penalty fac...
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In this paper, our recently proposed mobile-conductance based analytical framework is extended to the sparse settings, thus offering a unified tool for analyzing information spreading in mobile networks. A penalty factor is identified for information spreading in sparse networks as compared to the connected scenario, which is then intuitively interpreted and verified by simulations. With the analytical results obtained, the mobility-connectivity tradeoff is quantitatively analyzed to determine how much mobility may be exploited to make up for network connectivity deficiency.
There are diverse hardware realization for digital watermarking of multimedia proposed in the literature. This paper focuses on the design and implementation of a fast FPGA(Field Programmable Gate Array) based archite...
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There are diverse hardware realization for digital watermarking of multimedia proposed in the literature. This paper focuses on the design and implementation of a fast FPGA(Field Programmable Gate Array) based architecture using reversible contrast mapping (RCM) based image watermarking algorithm. The specialty of this architecture attracts to the fact of clock-less encoder design and implementation which makes the design faster. The encoder module response time is independent of clock frequency, so the embedding of the watermark is possible as soon as the input is fetched. The schematic based design and implementation of the VLSI architecture have been done with Xilinx 14.1 on Spartan 3E FPGA family. The encoder requires 528 4-input LUTs and 303 slices. On the contrary, the decoder requires 613 LUTs and 347 slices. The maximum clock frequency of the decoder is 45 MHz. The results show the viability of low cost, high speed realtime use of the proposed VLSI architecture.
To achieve higher coding efficiency, the latest video coding standard called High Efficiency Video Coding (HEVC) has adopted the mechanism of Advanced Motion Vector Prediction (AMVP) to further improve the accuracy of...
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ISBN:
(纸本)9781479928941
To achieve higher coding efficiency, the latest video coding standard called High Efficiency Video Coding (HEVC) has adopted the mechanism of Advanced Motion Vector Prediction (AMVP) to further improve the accuracy of motion vector predictor. However, the adoption of AMVP significantly increases the hardware realization overhead as well as the data access bandwidth requirements. In addition, the dependency between different coding units (CUs) or prediction units (PUs) for predicting AMVP also noticeably degrades the overall hardware coding throughput. To deal with this problem, this paper proposes an efficient motion vector prediction method for avoiding AMVP data dependency. By modeling the relationship between motion vector predictors of largest coding unit (LCU) and other small CU and PU sizes, the motion vectors of small CUs and PUs are estimated directly from the motion vectors of LCU. Furthermore, the predicted motion vectors of small CUs and PUs are also used to pre-fetch the corresponding reference data from external memory in advanced so that the data access time can be hided. Simulation results demonstrate that the proposed motion vector prediction method can achieve at least 53.8% coding throughput improvement with only 1.04% BD-rate increasing when compared to direct AMVP realization.
Modern smart phones are equipped with a wide variety of sensors including GPS, accelerometer, gyroscope, or even biometric sensor. Many intelligent context-aware applications are thus also become popular. In particula...
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ISBN:
(纸本)9781479959686
Modern smart phones are equipped with a wide variety of sensors including GPS, accelerometer, gyroscope, or even biometric sensor. Many intelligent context-aware applications are thus also become popular. In particular, by using GPS data, many Location Based Services (LBSs) have been developed and deployed. In the literature, analysis based on the collection of GPS data from a large amount of users has also been widely studied, such as user classification, location prediction, personalized travel route recommendation, etc. However, analyzing the swarm behavior of a group of users based on their GPS data has received little attention. In this paper, we perform both quantitative and qualitative analysis on the swarm behavior of a group of users based on the Boids model proposed by Craig Reynolds. Specifically, we are interested in discovering the features of Boids model by analyzing the GPS trajectories of a group of people. Our results confirm that the moving of a group of people exhibits flocking behavior and can be explained by the features of Boids model.
The main aim of Wireless sensor networks for the current approaches is mostly focused on sensor communication and service strength. Nevertheless, the applications of Wireless Sensor Networks (WSNs) are dignified to ma...
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The main aim of Wireless sensor networks for the current approaches is mostly focused on sensor communication and service strength. Nevertheless, the applications of Wireless Sensor Networks (WSNs) are dignified to make revolution in cooperation with different goals. These methods, mostly deficient for various uses that restricted developers to control systems necessities for supply power consumption, bandwidth operation, transmission expectancy and reliability. The motivation this paper is to improve dynamic network sustenance for the above critical issues, and it is an illustration for the energy control mechanism by proposing Simultaneous Attentive Energy Routing Protocol (SAERP). The adaptation of this protocol is to practically addresses on communication energy for every packet, based on its limitation that can handle power saving and least usage without omitting any packet limitations and covers the scalability, bandwidth and memory limitation constraints.
In recent years, the researches on computation offloading have received significant attention. However, due to the long transmission distance, it could increase the network latency and energy consumption. Thus, severa...
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In recent years, the researches on computation offloading have received significant attention. However, due to the long transmission distance, it could increase the network latency and energy consumption. Thus, several researchers proposed the computation offloading in mobile networks. That is, a mobile device could offload a time or energy consuming computation to other nearby mobile devices. However, the security issue in mobile networks becomes more difficult because there is no trust information between any two mobile devices. In this paper, we propose a secure computation offloading approach based on social trust in mobile networks. Moreover, the secure computation offloading based on social trust (SCOST) system is proposed to realize and evaluate the proposed approach.
Global warming causes increasing natural disasters and gradually threatens human life and property safety. Under such an uncertain environment, efficiency and effectiveness in the energy management are an important an...
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Global warming causes increasing natural disasters and gradually threatens human life and property safety. Under such an uncertain environment, efficiency and effectiveness in the energy management are an important and a difficult question. This paper aims to provide an approach for energy management that optimizes the relationship between different variables such as time, areas, countries, users, seasons, evaluation methods, and various different energy productions such as nuclear, water, biomass, wind, solar, and thermal. To achieve this goal, this paper combines the technologies of ontology and fuzzy markup language (FML) with theories about uncertainty to evaluate the applicability of the energy production based on technological innovation, economic development, social safety, environmental protection, regional characteristics, and time series. The simulation results show that the proposed approach is feasible to provide an alternative for energy management through the viewpoints of people, governments, and enterprises. It is hoped to provide the optimized energy management decision model for different decision makers and users as a reference in the future.
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