A study on the accuracy of minimum width transistor area in estimating FPGA layout area

Integrating reconfigurable fabrics in SOCs requires an accurate estimation of the layout area of the reconfigurable fabrics in order to properly optimize the architectural-level design of the fabrics and accommodate early floor-planning. This work examines the accuracy of using minimum width transistor area, a widely-used area model in many previous FPGA architectural studies, in accurately predicting layout area. In particular, the layout areas of LUT multiplexers are used as a case study. We found that compared to the minimum width transistor area, the traditional metal area based stick diagrams can provide much more accurate layout area estimations. In particular, minimum width transistor area can underestimate the layout area of LUT multiplexers by as much as a factor of 2–3 while stick diagrams can achieve over 90% accuracy in layout area estimation while remaining IC-process independent.     

Association between objective and subjective assessments of environmental ergonomic factors in manufacturing plants

The association between objective and subjective assessments of environmental ergonomic factors including noise, lighting and heat were conducted in a field study in three manufacturing plants. Data were collected from 130 workstations using questionnaire and physical measurements of the noise (noise dosimetry), lighting (task area illuminance) and heat (wet bulb globe temperature – WBGT) levels. The recommended noise, illuminance and WBGT levels were not met in about half of the workstations surveyed, which was in agreement with low satisfaction levels with the environmental factors in the workplace. A considerable effect of the environmental factors was found on perceived workers' job performance, safety and health. The results from contingency coefficient analysis indicated a relatively good agreement between the measured noise, illuminance and WBGT levels and the workers' perception of these factors. The results suggest that quantitative physical measurements should be supplemented by qualitative subjective assessments to provide more specific and additional details about the environmental conditions in each workplace and consequently to improve workers' satisfaction, job performance, safety and health.Relevance to industryThe findings highlight the importance of environmental ergonomics and have implications for improvements in the design of the workplace to enhance workers' satisfaction, job performance, safety and health on areas where the environmental factors are not favourable. A better understanding of the environmental conditions and their effects in each working environment has the potential for a notable impact on productivity and workers' quality of life.     

Verifiable database supporting keyword searches with forward security

The primitive of verifiable database (VDB) enables the integrity of database when a resource-limited client outsources a huge dynamic database on an untrusted cloud server. Meanwhile, the client could perform various types of queries on the database such as index-based queries and keyword-based queries. In the real-world applications, it is more suitable to consider VDB schemes supporting full updating operations (i.e., replacement, insertion, and deletion). Inherently, forward/backward-secure searchable encryption schemes should be adopted to incorporate VDB schemes. In this paper, we first attempt to address the problem of how to perform forward-secure keyword searching on VDB schemes. Moreover, we present a concrete VDB construction that can support forward-secure keyword search and full updating operations. Security and efficiency analysis demonstrate that the proposed VDB schemes can achieve the desired security properties with high efficiency.     

Bayesian network model of maritime safety management

This paper presents a model of maritime safety management and its subareas. Furthermore, the paper links the safety management to the maritime traffic safety indicated by accident involvement, incidents reported by Vessel Traffic Service and the results from Port State Control inspections. Bayesian belief networks are applied as the modeling technique and the model parameters are based on expert elicitation and learning from historical data. The results from this new application domain of a Bayesian network based expert system suggest that, although several its subareas are functioning properly, the current status of the safety management on vessels navigating in the Finnish waters has room for improvement; the probability of zero poor safety management subareas is only 0.13. Furthermore, according to the model a good IT system for the safety management is the strongest safety-management related signal of an adequate overall safety management level. If no deficiencies have been discovered during a Port State Control inspection, the adequacy of the safety management is almost twice as probable as without knowledge on the inspection history. The resulted model could be applied to performing several safety management related queries and it thus provides support for maritime safety related decision making.     

Threshold public key encryption scheme resilient against continual leakage without random oracles

Threshold public key encryption allows a set of servers to decrypt a ciphertext if a given threshold of authorized servers cooperate. In the setting of threshold public key encryption, we consider the question of how to correctly decrypt a ciphertext where all servers continually leak information about their secret keys to an external attacker. Dodis et al. and Akavia et al. show two concrete schemes on how to store secrets on continually leaky servers. However, their construc- tions are only interactive between two servers. To achieve continual leakage security among more than two servers, we give the first threshold public key encryption scheme against adaptively chosen ciphertext attack in the continual leak- age model under three static assumptions. In our model, the servers update their keys individually and asynchronously, without any communication between two servers. Moreover, the update procedure is re-randomized and the randomness can leak as well.     

A new scheme to learn a kernel in regularization networks

In this paper, we propose a new scheme to learn a kernel function from the convex combination of finite given kernels in regularization networks. We show that the corresponding variational problem is convex and under certain conditions, the variational problem can be approximated by a semidefinite programming problem which coincides with the Micchelli and Pontil's (MP's) Model (Micchelli and Pontil, 2005 [10]).     

Adaptive energy-efficient scheduling for real-time tasks on DVS-enabled heterogeneous clusters

Developing energy-efficient clusters not only can reduce power electricity cost but also can improve system reliability. Existing scheduling strategies developed for energy-efficient clusters conserve energy at the cost of performance. The performance problem becomes especially apparent when cluster computing systems are heavily loaded. To address this issue, we propose in this paper a novel scheduling strategy–adaptive energy-efficient scheduling or AEES–for aperiodic and independent real-time tasks on heterogeneous clusters with dynamic voltage scaling. The AEES scheme aims to adaptively adjust voltages according to the workload conditions of a cluster, thereby making the best trade-offs between energy conservation and schedulability. When the cluster is heavily loaded, AEES considers voltage levels of both new tasks and running tasks to meet tasks’ deadlines. Under light load, AEES aggressively reduces the voltage levels to conserve energy while maintaining higher guarantee ratios. We conducted extensive experiments to compare AEES with an existing algorithm–MEG, as well as two baseline algorithms–MELV, MEHV. Experimental results show that AEES significantly improves the scheduling quality of MELV, MEHV and MEG.     

基于时间平滑和Toeplitz矩阵重构的复相干信号DoA估计

针对相干多径环境,分别利用时间平滑和Toeplitz矩阵重构算法实现了复相干信号的波达方向(DoA)估计,并进一步通过数学分析和计算机仿真对这两种算法的性能进行了对比,得出结论：时间平滑算法和Toeplitz矩阵重构算法对复相干信号的DoA估计均具有效性,Toeplitz矩阵重构算法性能相对较好,但有一定阵列孔径损失;而基于时间平滑算法无阵列孔径损失,可估计M-1个多径信号(M为阵元数),但计算量偏大。     

基于双分支网络的图像修复取证方法

图像修复是一项利用图像已知区域的信息来修复图像中缺失或损坏区域的技术。人们借助以此为基础的图像编辑软件无须任何专业基础就可以轻松地编辑和修改数字图像内容,一旦图像修复技术被用于恶意移除图像的内容,会给真实的图像带来信任危机。目前图像修复取证的研究只能有效地检测某一种类型的图像修复。针对这一问题,提出了一种基于双分支网络的图像修复被动取证方法。双分支中的高通滤波卷积网络先使用一组高通滤波器来削弱图像中的低频分量,然后使用4个残差块提取特征,再进行两次4倍上采样的转置卷积对特征图进行放大,此后使用一个5×5的卷积来减弱转置卷积带来的棋盘伪影,生成图像高频分量上的鉴别特征图。双分支中的双注意力特征融合分支先使用预处理模块为图像增添局部二值模式特征图。然后使用双注意力卷积块自适应地集成图像局部特征和全局依赖,捕获图像修复区域和原始区域在内容及纹理上的差异,再对双注意力卷积块提取的特征进行融合。最后对特征图进行相同的上采样,生成图像内容和纹理上的鉴别特征图。实验结果表明该方法在检测移除对象的修复区域上,针对样本块修复方法上检测的F1分数较排名第二的方法提高了2.05%,交并比上提高了3.53%;...     

融合BTM和图论的微博检索模型

微博数据量庞大且微博文本的字符数少、特征稀疏,为提高检索精度,提出一种融合BTM和图论的微博检索模型,通过词汇语义相关度计算微博文本中带有标签的特征相关度,构建bi-term主题模型,用JSD距离计算映射到该模型中短文本的词对相关度,抽取CN-DBpedia中实体及图结构,再使用SimRank算法计算图结构中实体间的相关度。综上3种相关度为该模型最终相关度。最后使用新浪微博数据集进行检索实验,实验结果表明:对比于融合隐含狄利克雷分布算法与图论的检索模型和基于开放数据关联和图论方法系统模型,新模型在MAP、准确率和召回率上性能有明显提高,说明该模型具有较优的检索性能。