As the energy consumption of embedded multiprocessor systems becomes increasingly prominent, the real-time energy-efficient scheduling in multiprocessor systems becomes an urgent problem to reduce the system energy co...
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As the energy consumption of embedded multiprocessor systems becomes increasingly prominent, the real-time energy-efficient scheduling in multiprocessor systems becomes an urgent problem to reduce the system energy consumption while meeting real-time constraints. For a multiprocessor with independent DVFS and DPM at each processor, this paper proposes an energy-efficient real-time scheduling algorithm named LRE-DVFS-EACH, based on LRE-TL which is an optimal real-time scheduling algorithm for sporadic tasks. LRE-DVFS-EACH utilizes the concept of TL plane and the idea of fluid scheduling to dynamically scale the voltage and frequency of processors at the initial time of each TL plane as well as the release time of a sporadic task in each TL plane. Consequently, LRE-DVFS-EACH can obtain a reasonable tradeoff between the real-time constraints and the energy saving. LRE-DVFS-EACH is also adaptive to the change of workload caused by the dynamic release of sporadic tasks, which can obtain more energy savings. The experimental results show that compared with existing algorithms, LRE-DVFS-EACH can not only guarantee the optimal feasibility of sporadic tasks, but also achieve more energy savings in all cases, especially in the case of high workloads.
Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practi...
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Nowadays, weakly hard real-time is attracted in the research of real-time system. Existing weakly hard real-time scheduling algorithms cannot guarantee the meeting ratio of executing sequence of which the length is la...
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Nowadays, weakly hard real-time is attracted in the research of real-time system. Existing weakly hard real-time scheduling algorithms cannot guarantee the meeting ratio of executing sequence of which the length is larger than fixed window-size. Therefore, this paper, based on the (m, p) constraint, proposes an algorithm which is named as CDBS (cut-down based scheduling). Since the discrimination of the satisfiability of (m, p) constraint needs to go over the whole executing sequence of the task, it's very difficult and infeasible. For this reason, this paper brings an efficient algorithm of cutting down the sequence, proves the correctness of the algorithm. We use proper data structures so that the complexity of judgment is not relevant to the length of sequence. Experiments show the efficiency. Furthermore, we compare CDBS with other classical algorithms, such as EDF, DBP, DWCS, and the results show its competence.
Image defogging (IDF) removes influences of fogs from an image to improve its quality. Since defogged images can significantly boost the performance of subsequent processing, IDF has attracted many attentions from the...
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Image defogging (IDF) removes influences of fogs from an image to improve its quality. Since defogged images can significantly boost the performance of subsequent processing, IDF has attracted many attentions from the computer vision community. However, existing IDF algorithms are built on the assumption that light is scattered once by a grain. Since such assumption is violated if images are contaminated by dense haze or heavy fog, traditional IDF algorithms often fail in this situation. In this paper, we propose a hybrid image defogging (HIDF) algorithm to overcome this deficiency. In particular, HIDF applies the single scattering physics model (SSPM) to pixels dominated by single scattering of light, and applies the multiple scattering physics model (MSPM) to remaining pixels. To distinguish two types of pixels, HIDF utilizes the optical thickness of corresponding pixels. If optical thickness is smaller than a threshold that determines whether the single scattering or the multiple scattering dominates, HIDF applies the SSPM, and HIDF applies the MSPM otherwise. Experimental results on several popular foggy images demonstrate that HIDF competes with the state-of-the-art algorithms, and show the promise of HIDF for defogging heavily foggy images.
In crowd counting, regression-based method shows better performance in extreme density scenes by introduces a density map. However, the regression-based method fails to locate the positions of each head, which signifi...
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ISBN:
(数字)9781728146447
ISBN:
(纸本)9781728146454
In crowd counting, regression-based method shows better performance in extreme density scenes by introduces a density map. However, the regression-based method fails to locate the positions of each head, which significantly restricts its applications. Detection-based method counts each head with their accurate locations but works only in middle or low-level density scenes. In this paper, a joint learning method, named Density-attentive Head Detector (DAHD) is developed to overcome their respective shortcomings via a multi-task training procedure. Specifically, to guarantee the sensitivity of the detector to small or partially occluded heads, we carefully equip the detector with a density map which learned from regression module. Moreover, a novel Dilated Feature Pyramid Network (DFPN) is introduced to our method to enlarge the receptive field of convolutional kernel, bringing confirmative additional benefits to identify small heads. Experiments on the popular ShanghaiTech and Mall datasets confirm the improved performance of DAHD compared with the current detection-based approaches, and a comparable performance to regression-based approaches in term of counting.
Collimated electron and proton beams from the interaction of an ultra-intense laser pulse with a rear hole target are studied by particle-in-cell (PIC) simulation. When an ultra-intense laser pulse irradiates on such ...
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ISBN:
(纸本)9789290833529
Collimated electron and proton beams from the interaction of an ultra-intense laser pulse with a rear hole target are studied by particle-in-cell (PIC) simulation. When an ultra-intense laser pulse irradiates on such a target, the hot electrons from the inner surfaces of the hole expand fast into the hole. However, the electrons at the corners expand slower and are compressed strongly. Then two intense electron jets as well as proton jets are sprayed out from the corners. The jets extend into the hole and focus along the laser propagation axis. The effects of the corner angle on the collimated proton beam are also investigated.
Recent studies on network traffic have shown that self-similar is very popular, and the character will not be changed during buffering, switching and transmitting. The character self-similar must be considered in netw...
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Recent studies on network traffic have shown that self-similar is very popular, and the character will not be changed during buffering, switching and transmitting. The character self-similar must be considered in network traffic prediction. This paper analyzed and summarized the research results of self-similar network traffic prediction from the fields of self-similar modeling, parameter computing and performance prediction. An equivalent bandwidth algorithm of self-similar traffic prediction based on measurement was put forward. Our analysis has shown that the algorithm can effectively reduce computing and realizing complexities.
One of the most significant challenges introduced by routing protocol in mobile networks is coping with the unpredictable motion and the unreliable behaviour of mobile nodes. In this paper, we present a hierarchical r...
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In this research paper, we integrate and implement High-Availability (HA) and Load-Balancing technologies to clusters of workstations, increasing both the availability and scalability of services and resources in thes...
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