Reconfigurable topology synthesis for application-specific NoC on partially dynamically reconfigurable systems

In this paper, a four-stage method for synthesizing reconfigurable ASNoC topology is proposed for partially dynamically reconfigurable systems, where the topology is reconfigured dynamically at run-time along with the application's execution. Firstly, a simulated annealing based topology-aware integrated optimization framework is proposed to generate the proper schedule and floorplan of task modules. Secondly, based on the schedule and floorplan of task modules, an Integer Linear Programming (ILP)-based method and a heuristic method, are proposed to partition the communication requirements of the application into $T$ time intervals. Thirdly, we explore the proper positions of switches in the floorplan for global communications. Finally, considering the reconfiguration costs between adjacent time intervals, the routing path allocation problem is solved for time intervals in an iterative procedure to generate fine-grained dynamically reconfigurable ASNoC topologies. Experimental results show that, compared to the random partition of communication requirements, the proposed heuristic method and ILP-based method can achieve 5.4% and 10.0% power consumption improvement, respectively. And, the reconfigurable ASNoC can achieve 31.6% power consumption improvement when compared with static ASNoC.     

Hierarchical power management of a system with autonomously power-managed components using reinforcement learning

This paper presents a hierarchical dynamic power management (DPM) framework based on reinforcement learning (RL) technique, which aims at power savings in a computer system with multiple I/O devices running a number of heterogeneous applications. The proposed framework interacts with the CPU scheduler to perform effective application-level scheduling, thereby enabling further power savings. Moreover, it considers non-stationary workloads and differentiates between the service request generation rates of various software application. The online adaptive DPM technique consists of two layers: component-level local power manager and system-level global power manager. The component-level PM policy is pre-specified and fixed whereas the system-level PM employs temporal difference learning on semi-Markov decision process as the model-free RL technique, and it is specifically optimized for a heterogeneous application pool. Experiments show that the proposed approach considerably enhances power savings while maintaining good performance levels. In comparison with other reference systems, the proposed RL-based DPM approach, further enhances power savings, performs well under various workloads, can simultaneously consider power and performance, and achieves wide and deep power-performance tradeoff curves. Experiments conducted with multiple service providers confirm that up to 63% maximum energy saving per service provider can be achieved.     

A review of topic modeling methods

Topic modeling is a popular analytical tool for evaluating data. Numerous methods of topic modeling have been developed which consider many kinds of relationships and restrictions within datasets; however, these methods are not frequently employed. Instead many researchers gravitate to Latent Dirichlet Analysis, which although flexible and adaptive, is not always suited for modeling more complex data relationships. We present different topic modeling approaches capable of dealing with correlation between topics, the changes of topics over time, as well as the ability to handle short texts such as encountered in social media or sparse text data. We also briefly review the algorithms which are used to optimize and infer parameters in topic modeling, which is essential to producing meaningful results regardless of method. We believe this review will encourage more diversity when performing topic modeling and help determine what topic modeling method best suits the user needs.     

地下储气库注采井不同井段水泥环密封性能实验

针对我国西南地区首座地下储气库——相国寺储气库投产后注采井出现较大比例的B环空异常带压问题,采用室内实验的方法,根据注采井100 m、900 m、2 000 m垂深处温度和压力差异,设计了不同的水泥石养护温度和加载围压,并采用小尺寸的水泥环完整性测试装置基于应力等效原理测试了从井底到井口水泥环在试压和注采时的密封能力。实验结果表明:①井筒不同深度段水泥候凝温度和围压差异较大,同一种水泥浆体系在实际固井后沿井筒深度具有不同的机械性能,导致不同井段水泥环密封性能存在着巨大的差异;②交变载荷会导致水泥环的压实和累积损伤,加剧水泥环的密封失效;③注采井2 000 m处水泥环在试压和注采工况条件下,密封性完好,没有发生泄漏,1 000 m处水泥环在试压工况下能保持完好,在循环交变载荷下存在着水泥环密封失效的现象,井口附近100 m处水泥环在试压工况和注采工况时均出现了泄漏;④实验结果揭示,在试压和注采工况下,井筒中上部水泥环密封失效是造成B环空异常带压的根本原因。结论认为,该研究成果可以为提高地下储气库注采井固井水泥环密封的可靠性提供实验数据支撑。     

Failure analysis of a machine support for fique fibre processing

Replacement cast steel supports for a machine consisting of bars that straighten out fique fibres have presented recurrent fracture, beyond 10⁶ cycles of service and their failure surfaces show features typical of fatigue under alternating bending conditions. Differences between original supports and replacement supports related to geometry roughness, hardness, chemical composition and microstructure were identified. To obtain a longer service life selected modifications of the geometry and use of different material were proposed based on load state analysis, finite element analysis, and fatigue strength calculations.     

Content adaptive video denoising based on human visual perception

In this paper, we propose content adaptive denoising in highly corrupted videos based on human visual perception. We introduce the human visual perception in video denoising to achieve good performance. In general, smooth regions corrupted by noise are much more annoying to human observers than complex regions. Moreover, human eyes are more interested in complex regions with image details and more sensitive to luminance than chrominance. Based on the human visual perception, we perform perceptual video denoising to effectively preserve image details and remove annoying noise. To successfully remove noise and recover the image details, we extend nonlocal mean filtering to the spatiotemporal domain. With the guidance of content adaptive segmentation and motion detection, we conduct content adaptive filtering in the YUV color space to consider context in images and obtain perceptually pleasant results. Extensive experiments on various video sequences demonstrate that the proposed method reconstructs natural-looking results even in highly corrupted images and achieves good performance in terms of both visual quality and quantitative measures.     

Temporal information based GoP adaptation for linear video delivery schemes

An original wireless video transmission scheme called SoftCast has been recently proposed to deal with the issues encountered in conventional wireless video broadcasting systems (e.g. cliff effect). In this paper, we evaluate and optimize the performance of the SoftCast scheme according to the transmitted video content. Precisely, an adaptive coding mechanism based on GoP-size adaptation, which takes into account the temporal information fluctuations of the video, is proposed. This extension denoted Adaptive GoP-size mechanism based on Content and Cut detection for SoftCast (AGCC-SoftCast) significantly improves the performance of the SoftCast scheme. It consists in modifying the GoP-size according to the shot changes and the spatio-temporal characteristics of the transmitted video. When hardware capacities, such as buffer or processor performance are limited, an alternative method based only on the shot changes detection (AGCut-SoftCast) is also proposed. Improvements up to 16 dB for the PSNR and up to 0.55 for the SSIM are observed with the proposed solutions at the cut boundaries. In addition, temporal visual quality fluctuations are reduced under 1dB in average, showing the effectiveness of the proposed methods.