Dynamic voltage scaling based energy-minimized partial task offloading in fog networks

Wireless Networks - With the dynamic voltage scaling (DVS) technology, the terminal node (TN) can dynamically adjust its computational speed, thus providing a new way to save energy during task...     

Graph-Theoretic Method on Topology Identification of Stochastic Multi-weighted Complex Networks with Time-Varying Delayed Coupling Based on Adaptive Synchronization

Neural Processing Letters - This paper is concerned with topology identification of stochastic multi-weighted complex networks based on adaptive synchronization. Different from previous work,...     

高光谱成像用高帧频CMOS图像传感器北大核心

针对高帧频、全局曝光和光谱平坦等成像应用需求,设计了一款高光谱成像用CMOS图像传感器。其光敏元采用PN型光电二极管,读出电路采用5T像素结构。采用列读出电路以及高速多通道模拟信号并行读出的设计方案来获得低像素固定图像噪声(FPN)和非均匀性抑制。芯片采用ASMC 0.35μm三层金属两层多晶硅标准CMOS工艺流片,为了抑制光电二极管的光谱干涉效应,后续进行了光谱平坦化VAE特殊工艺,并对器件的光电性能进行了测试评估。电路测试结果符合理论设计预期,成像效果良好,像素具备积分可调和全局快门功能,最终实现的像素规模为512×256,像元尺寸为30μm×30μm,最大满阱电子为400 ke^(-),FPN小于0.2%,动态范围为72 dB,帧频为450 f/s,相邻10 nm波段范围内量子效率相差小于10%,可满足高光谱成像系统对CMOS成像器件的要求。     

Rapid anti-solvent vapor-assisted synthesis of CsPbBr3/Cs4PbBr6 microcrystals with high brightness and stability of green light emission

Journal of Materials Science - The CsPbBr3/Cs4PbBr6 perovskite composite material has received wide attention due to its good stability and photoelectric properties, and it is promising to appear...     

泸县6.0级地震文化遗产震害调查与分析

2021年9月16日，四川省泸县发生6.0级地震，对当地的文化遗产造成了不同程度的损坏。为掌握文化遗产在此次地震中的破坏情况，对古建筑、古桥、渡槽、摩崖造像、古遗址以及可移动文物进行震害调查，分析破坏原因，提出不同文化遗产的震害等级，并统计其在此次地震中破坏占比。研究结果表明：古建筑中木结构的震害较轻，砖木结构和石木结构的损伤较为严重，表现为墙体开裂、倾斜、倒塌和屋面损毁等破坏；古桥的桥面板产生裂缝和起壳，桥头堡基石发生移位；摩崖造像发生开裂、石像脱落，部分岩石发生坠落；古遗址的原有缝隙增大，石砌墙歪闪或倾斜；可移动文物与陈列台连接的金属卡件在地震下崩落。根据震害调查结果，将文化遗产的震害划分为4个等级，并对古建筑的维修与加固、摩崖造像的一体化监测、古桥的维护与应急抢险、可移动文物的隔震保护提出几点建议，为我国文化遗产的抗震保护提供参考。     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.