Distributed video coding (DVC) is a new paradigm of coding that makes very interest to itself in the past decade. It's usually based on temporal correlations between successive frames which are called key frames. ...
Distributed video coding (DVC) is a new paradigm of coding that makes very interest to itself in the past decade. It's usually based on temporal correlations between successive frames which are called key frames. But this approach failed for high object motion in a scene and need very complex decoder to cover it. In this paper we propose a novel technique to use spatial correlation instead of temporal correlation in a frame by dividing a single frame to two sub frames which one of them is used as a key frame for generate side information at the decoder. Simulation results show a significant improvement on rate distortion parameter in DVC schema.
Scientific computing has become one of the key players in the advance of modern science and technologies. In the meantime, due to the success of developments in processor fabrication, the computing power of Personal C...
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Scientific computing has become one of the key players in the advance of modern science and technologies. In the meantime, due to the success of developments in processor fabrication, the computing power of Personal computer (PC) is not to be ignored as well. Lots of high throughput type of applications can be satisfied by using the current desktop PCs, especially for those in computerized classrooms, and leave the supercomputers for the demands from large scale highperformance parallel computations. The goal of this work is to develop an automated mechanism for cluster computing to utilize the computing power such as resides in computerized classroom. The PCs in computerized classroom are usually setup for education and training purpose during the daytime, and shut down at night. After well deployment, these PCs can be transformed into a pre-configured cluster computing resource immediately without touching the existing education/training environment installed on these PCs. Thus, the training activities will not be affected by this additional activity to harvest idle computing cycles. To echo today's energy saving issues, a dynamic power management is also developed to minimize energy cost. This development not only greatly reduces the management efforts and time to build a cluster, but also implies the reduction of the power consumption by such a mechanism.
This paper studies the stator currents collected from several inverter-fed laboratory induction motors and proposes a new feature based frequency domain analysis method for performing the detection of induction motor ...
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This paper studies the stator currents collected from several inverter-fed laboratory induction motors and proposes a new feature based frequency domain analysis method for performing the detection of induction motor faults, such as the broken rotor-bar or bearing fault. The mathematical formulation is presented to calculate the features, which are called FFT-ICA features in this paper. The obtained FFT-ICA features are normalized by using healthy motor as benchmarks to establish a feature database for fault detection. Compare with conventional frequency-domain analysis method, no prior knowledge of the motor parameters or other measurements are required for calculating features. Only one phase stator current waveforms are enough to provide consistent diagnosis of inverter-fed induction motors at different frequencies. The proposed method also outperforms our previous time domain analysis method.
A supervisory strategy is proposed for improving the performance of an evolutionary-algorithm-based system-maintenance optimizer developed in our previous work for offshore power systems. The system-maintenance optimi...
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A supervisory strategy is proposed for improving the performance of an evolutionary-algorithm-based system-maintenance optimizer developed in our previous work for offshore power systems. The system-maintenance optimizer generates a set of initial maintenance plans, and exports them to an intelligent maintenance advisor connected to it for implementation. The proposed supervisory strategy uses a set of intelligent rules for adjusting the crossover and mutation rates of the present evolutionary algorithm. A mechanism is developed for refining and generalizing the supervisory rules according to the user's experience. The proposed supervisory strategy aims to improve the search ability and efficiency of the present evolutionary algorithm. Merits of the proposed supervisory strategy are demonstrated in case studies using our system-maintenance optimizer.
This paper proposes an online fault detection method for induction motors using frequency-domain independent component analysis. Frequency-domain results, which are obtained by applying Fast Fourier Transform (FFT) to...
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This paper proposes an online fault detection method for induction motors using frequency-domain independent component analysis. Frequency-domain results, which are obtained by applying Fast Fourier Transform (FFT) to measured stator current time-domain waveforms, are analyzed with the aim of extracting frequency signatures of healthy and faulty motors with broken rotor-bar or bearing problem. Independent components analysis (ICA) is applied for such an aim to the FFT results. The obtained independent components as well as the FFT results are then used to obtain the combined fault signatures. The proposed method overcomes problems occurring in many existing FFT-based methods. Results using laboratory-collected data demonstrate the robustness of the proposed method, as well as its immunity against measurement noises and motor parameters.
The wide variety of smart embedded computing devices and their increasing number of applications in our daily life have created new op- portunities to acquire knowledge from the physical world anytime and anywhere, wh...
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The wide variety of smart embedded computing devices and their increasing number of applications in our daily life have created new op- portunities to acquire knowledge from the physical world anytime and anywhere, which is envisioned as the"Internet of Things" (IoT). Since a huge number of heterogeneous resources are brought in- to IoT, one of the main challenges is how to effi- ciently manage the increasing complexity of IoT in a scalable, flexNle, and autonomic way. Further- more, the emerging IoT applications will require collaborations among loosely coupled devices, which may reside in various locations of the Inter- net. In this paper, we propose a new IoT network management architecture based on cognitive net- work management technology and Service-Orien- ted Architecture to provide effective and efficient network management of loT.
Collection, processing, storage and maintenance of samples to facilitate long-term cohort studies in biobanks, requires a system to manage samples in an effective way to prevent sample mix up and loss. Sample identifi...
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Distance matrix calculation used in phylogeny analysis is computational intensive. The growing sequences data sets necessitate fast computation method. This paper accelerate Felsenstein's DNADIST program by using ...
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Antimalware applications represent one of the most important research topic in the area of information security threat. Indeed, most computer network issues have malwares as their underlying cause. As a consequence, e...
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Antimalware applications represent one of the most important research topic in the area of information security threat. Indeed, most computer network issues have malwares as their underlying cause. As a consequence, enhanced systems for analyzing the behavior of malwares are needed in order to try to predict their malicious actions and minimize eventual computer damages. However, because the environments where malwares operate are characterized by high levels of imprecision and vagueness, the conventional data analysis tools lack to deal with these computer safety applications. This work tries to bridge this gap by integrating semantic technologies and computational intelligence methods, such as the Fuzzy Ontologies and Fuzzy Markup Language (FML), in order to propose an advanced semantic decision making system that, as shown by experimental results, achieves good performances in terms of malicious programs identification.
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