图像中椒盐噪声去除算法研究

为了有效地去除数字图像中的椒盐噪声,提高图像质量,本文 在分析一些典型消除噪声方法的基础上,给出了一种新的椒盐噪声去除算法。首先,针对椒 盐噪声的特点,设计了一种基于动态窗口和邻域像素统计信息的噪声检测算法,有效地区分 了噪声点与非噪声,然后对检测出的噪声点,采用改进的自适性的中值滤波算法进行噪声滤 除,在滤波算法中加入了窗口大小自适应控制和滤波值调优策略。实验表明：该方法不仅能 去除图像中的椒盐噪声,而且能有效地保护图像的细节特征,对于高密度噪声的图像去 除噪声的效果比其他方法更优。     

地方本科院校计算机类专业人才培养模式的探索与实践

地方本科院校计算机类专业承担着为我国培养计算机应用型人才的重任,探索新时期下计算机应用型人才的培养模式,其根本出路就是坚持"以就业为导向、以服务为宗旨,走产学研结合之路"。在培养目标上,目前应分为以下4个方向:计算机硬件维护与应用、计算机网络安全技术、计算机程序开发技术、计算机信息管理。在理论教学上采取"基本素质模块"+"专业基础模块"+"专业主干模块"+"专业创新模块";在实践教学上采取"基本技能"+"专业技能"+"开发设计技能"+"岗位综合应用技能"。     

Your comments matter: incorporating viewers’ comments for ranking online video content using bibliometrics

ABSTRACT

The quality of user-generated content over World Wide Web media is a matter of serious concern for both creators and users. To measure the quality of content, webometric techniques are commonly used. In recent times, bibliometric techniques have been introduced to good effect for evaluation of the quality of user-generated content, which were originally used for scholarly data. However, the application of bibliometric techniques to evaluate the quality of YouTube content is limited to h-index and g-index considering only views. This paper advocates for and demonstrates the adaptation of existing Bibliometric indices including h-index, g-index and M-index exploiting both views and comments and proposes three indices h_vc, g_vc and m_vc for YouTube video channel ranking. The empirical results prove that the proposed indices using views along with the comments outperform the existing approaches on a real-world dataset of YouTube.     

基于领域类别信息C-value的多词串自动抽取

该本的多词串抽取是自然语言处理领域一项重要的研究内容。该文提出了一种多类别C-value(Multi-Class C-value)方法,利用多词串在不同领域的分布信息改善领域相关的多词串抽取的性能。在汽车、科技和旅行三个领域的数据上进行实验,评价多词串的准确率,在top-100级别上,较传统的C-value方法在三个领域中分别提高了12、12和13个百分点。实验结果验证了方法的有效性。     

网格工作流在军事信息网格中的应用研究

首先介绍了工作流和工作流管理系统的定义,阐述了网格工作流和军事信息网格的研究现状.其次研究了网格工作流的调度模型并分析了网格工作流调度过程,通过将工作流的思想应用到军事信息网格系统中,给出了基于网格工作流的军事信息网格系统总体设计架构,并对架构中的各个组成模块进行了研究.     

Petromyzontidae-Biomimetic Multimodal Microneedles-Integrated Bioelectronic Catheters for Theranostic Endoscopic Surgery

Current catheter devices in minimally invasive surgery still possess limited functional options, lacking multimodal integration of both sensing and therapy. Catheter devices usually operate outside the tissue, incapable to detect intra-tissue biochemical information for accurate localization and assessment of lesions during surgery. Inspired by the feature and functions of Petromyzontidae, here a multimodal core-shell microneedles-integrated bioelectronic catheter (MNIBC) for tissue-penetrating theranostics in endoscopic surgery is developed. The microneedle (MN) device possesses individually addressable functionality at single-MN tip resolution, enabling multiplex functions (a total of 11 functions distributed in three types of catheters) including biochemical sensing, myoelectric modulation, electroporation, and drug delivery in a submucosal environment. The MNIBC is prepared through hybrid fabrication and dimensionality reduction strategies, where the MN electrodes are functionalized with an MXene-carbon nanotube (MXene-CNT)-based electron mediator, addressing the challenge of reduced electrode sensitivity on ultra-small MN tip. The functionalities of MNIBC are demonstrated both ex vivo and in vivo on anesthetized rabbits via laparoscopy, simulated cystoscopy, and laparotomy. The MNIBC can effectively detect intra-tissue biochemical signals in the bladder, and offers localized electroporation and intra-tissue drug delivery for precise treatments of lesions. The versatile features of the MNIBC present a highly advanced platform for precise surgeries.     

A Multi-Stage Secure IoT Authentication Protocol

The Internet of Things (IoT) is a network of heterogeneous and smart devices that can make decisions without human intervention. It can connect millions of devices across the universe. Their ability to collect information, perform analysis, and even come to meaningful conclusions without human capital intervention matters. Such circumstances require stringent security measures and, in particular, the extent of authentication. Systems applied in the IoT paradigm point out high-interest levels since enormous damage will occur if a malicious, wrongly authenticated device finds its way into the IoT system. This research provides a clear and updated view of the trends in the IoT authentication area. Among the issues covered include a series of authentication protocols that have remained research gaps in various studies. This study applies a comparative evaluation of authentication protocols, including their strengths and weaknesses. Thus, it forms the foundation in the IoT authentication field of study. In that direction, a multi authentication architecture that involves secured means is proposed for protocol authentication. Informal analysis can affect the security of the protocols. Burrows-Abadi-Needham (BAN) logic provides proof of the attainment of mutual authentication. NS3 simulator tool is used to compare the performance of the proposed protocol to verify the formal security offered by the BAN logic.     

Efficient Hardware Implementation Architectures for Long Integer Modular Multiplication over General Solinas Prime

Journal of Signal Processing Systems - Modular multiplication of long integers is a key component of elliptic curve cryptography and homomorphic encryption. The multiplication complexity can be...     

Evaluation of rutting potential for asphalt concrete mixes containing copper slag

Copper slag (CS) is a by-product of the copper extraction process, which can be used as coarse and/or fine aggregate in hot mix asphalt (HMA) pavements. This study used CS as a replacement of the fine aggregate with a percentage of up to 40% by total aggregate weight. The objective of this study was to evaluate the effect of CS on the rutting potential of the asphalt concrete mix using two methods. One method is based on the Dynamic modulus |E*| testing result. Actual pavement temperature data from a test section were used with the developed |E*| master curves. EverStressFE finite element program was used to perform a linear elastic load-deformation analysis for a pavement section and to determine the vertical resilient strain in a 40-mm HMA surface layer. The M-E PDG permanent deformation model was used with and Excel Visual Basic for Applications code to predict the accumulated rutting for different CS mixes for 10 million ESALs. The other method used the data from the flow number (FN) test. Based on the |E*| approach, the results indicated that adding 5% CS in the mix increased the predicted rutting from 0.59 to 0.98 mm at 10 million ESALs (increase by 68%). When 40% CS was used, rutting increased by more than 700% compared with the control mix. After analysing the FN results with the Francken model, the results indicated a decrease in FN as CS content is increased, indicating higher rutting potential. The decrease in FN ranged from 9% for 5% CS to 95% for 40% CS. The mixes containing up to 10% CS satisfied the minimum FN criteria for rutting. A calibration process for the M-E PDG distress prediction models that allows the use of waste and by-product materials such as CS should be considered in the future.     

Region-based surveillance video retrieval with effective object representation

Retrieving the most relevant video frames that contain the object specified in a given query (query-by-region) remains a challenging task. Two common challenges of region-based retrieval approaches are to accurately extract or segment object(s) and select a proper matching strategy. This paper addresses these problems by proposing a retrieval approach that uses a new region-based matching technique equipped with an effective object representation method. In the first stage, the proposed approach selects the most informative instances of each object that appeared in the video by utilizing an adapted clustering algorithm over the extracted features. In the retrieval stage, the new matching technique returns the most relevant sequences of video by mapping a given region with those identified representative instances of objects based on their similarity scores. The proposed approach is evaluated on standard datasets and the results demonstrate a 31% improvement in the retrieval performance compared to other state-of-the-art methods.