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Copy Move Image Forgery Detection with Exact Match Block Based Technique 基于精确匹配块技术的复制移动图像伪造检测
Pub Date : 2019-10-04 DOI: 10.13005/ojcst12.03.07
Priyanka Arora, Derminder Singh
Digital images are a momentous part of today’s digital communication. It is very easy to manipulate digital images for hiding some useful information by image rendering tools such as Adobe Photoshop, Microsoft Paint etc. The common image forgery which is easy to carry out is copy-move in which some part of an image is copied and pasted on another part of the same image to hide the important information. In this paper we propose an algorithm to spot the copy-move forgery based on exact match block based technique. The algorithm works by matching the regions in image that are equivalent by matching the small blocks of size b b. The program is tested for 45 images of mixed image file formats by considering block sizes 2, 4, 6, 8, 10, 12, 14, and 16. It is observed from the experimental results that the proposed algorithm can detect copy-move image forgery in TIF, BMP and PNG image formats only. Results reveal that as the block size increases, execution time (time taken by CPU to display output) also increases but the number of detected forged images increases till block size 10 and attains saturation thereafter. Consequently block size should be set to 10 for getting good results in terms of less execution time.
数字图像是当今数字通信的重要组成部分。通过Adobe Photoshop、Microsoft Paint等图像渲染工具,可以很容易地处理数字图像,隐藏一些有用的信息。复制移动是一种常见的图像伪造方法,它是将图像的一部分复制粘贴到同一图像的另一部分上,以隐藏重要信息。本文提出了一种基于精确匹配块的复制移动伪造识别算法。该算法通过匹配大小为b b的小块来匹配图像中等效的区域。通过考虑块大小为2、4、6、8、10、12、14和16,对45张混合图像文件格式的图像进行了测试。实验结果表明,该算法仅能检测TIF、BMP和PNG格式的复制-移动图像伪造。结果表明,随着块大小的增加,执行时间(CPU显示输出的时间)也会增加,但检测到的伪造图像数量会增加,直到块大小为10并达到饱和。因此,块大小应该设置为10,以便在减少执行时间方面获得良好的结果。
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引用次数: 0
Architecture Selection in Neural Networks by Statistical and Machine Learning 基于统计和机器学习的神经网络结构选择
Pub Date : 2019-10-04 DOI: 10.13005/ojcst12.03.02
Ç. Aladag
One of the biggest problems in using artificial neural networks is to determine the best architecture. This is a crucial problem since there are no general rules to select the best architecture structure. Selection of the best architecture is to determine how many neurons should be used in the layers of a network. It is a well-known fact that using a proper architecture structure directly affect the performance of the method. Therefore, various approaches ranging from trial and error method to heuristic optimization algorithms have been suggested to solve this problem in the literature. Although there have been systematical approaches in the literature, trial and error method has been widely used in various applications to find a good architecture. This study propose a new architecture selection method based on statistical and machine learning. The proposed method utilizes regression analysis that is a supervised learning technique in machine learning. In this new architecture selection approach, it is aimed to combine statistical and machine learning to reach good architectures which has high performance. The proposed approach brings a new perspective since it is possible to perform statistical hypothesis tests and to statistically evaluate the obtained results when artificial neural networks are used. The best architecture structure can be statistically determined in the proposed approach. In addition to this, the proposed approach provides some important advantages. This is the first study using a statistical method to utilize statistical hypothesis tests in artificial neural networks. Using regression analysis is easy to use so applying the proposed method is also easy. And, the proposed approach saves time since the best architecture is determined by regression analysis. Furthermore, it is possible to make inference for architectures which is not examined. The proposed approach is applied to three real data sets to show the applicability of the approach. The obtained results show that the proposed method gives very satisfactory results for real data sets.
使用人工神经网络的最大问题之一是确定最佳架构。这是一个至关重要的问题,因为没有一般规则来选择最佳的体系结构结构。选择最佳的结构是决定在网络的各层中应该使用多少神经元。众所周知,使用合适的体系结构直接影响方法的性能。因此,文献中提出了从试错法到启发式优化算法的各种方法来解决这一问题。虽然在文献中已经有了系统的方法,但试错法在各种应用中被广泛用于寻找好的体系结构。本研究提出了一种基于统计和机器学习的新架构选择方法。提出的方法利用回归分析,这是机器学习中的一种监督学习技术。在这种新的架构选择方法中,它旨在将统计和机器学习相结合,以获得具有高性能的良好架构。所提出的方法带来了一个新的视角,因为当使用人工神经网络时,可以进行统计假设检验和统计评估所获得的结果。在本文提出的方法中,可以统计地确定最佳的体系结构结构。除此之外,该方法还提供了一些重要的优点。这是首次使用统计方法在人工神经网络中利用统计假设检验的研究。回归分析方法易于使用,因此应用该方法也很容易。而且,由于最佳体系结构是由回归分析确定的,因此所提出的方法节省了时间。此外,还可以对未被检查的体系结构进行推断。将该方法应用于三个实际数据集,验证了该方法的适用性。实验结果表明,该方法对实际数据集具有非常满意的效果。
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引用次数: 4
Suitability of OFDM in 5G Waveform – A Review OFDM在5G波形中的适用性综述
Pub Date : 2019-10-04 DOI: 10.13005/ojcst12.03.01
P. Lavanya, P. Satyanarayana, Afaq Ahmad
Systematic pursuits are being developed to set forth the framework for the Fifth Generation (5G) wireless standards. This paper emphases on the most extensively deployed technology - Orthogonal Frequency Division Multiplexing (OFDM) that has outpaced other waveform aspirants for Fourth Generation (4G) communication standards. Irrespective of the beneficial features, it does possess a number of significant limitations that mark it as an incompatible candidate for the upcoming 5G standard. This paper highlights on its major drawback i.e high Peak-to-Average Power Ratio (PAPR). Results state that PAPR does cause sudden upsurge to the output signal envelope causing further other damages. There exists a need for more flexible waveforms to replace the conventional OFDM in order to address the unprecedented challenges. The future research directions in the domain are presented.
为制定第五代(5G)无线标准的框架,正在制定系统的追求。本文重点介绍了最广泛部署的技术-正交频分复用(OFDM),它已经超过了第四代(4G)通信标准的其他波形抱负。抛开这些有益的功能不谈,它确实有一些明显的限制,使其成为即将到来的5G标准的不兼容候选者。本文重点介绍了其主要缺点,即峰值平均功率比(PAPR)高。结果表明,PAPR确实会引起输出信号包络的突然上升,从而造成进一步的其他损害。为了应对前所未有的挑战,需要更灵活的波形来取代传统的OFDM。展望了该领域未来的研究方向。
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引用次数: 4
Boolean Models Guide Intentionally Continuous Information and Computation Inside the Brain 布尔模型引导有意连续的信息和大脑内的计算
Pub Date : 2019-10-04 DOI: 10.13005/ojcst12.03.03
G. Resconi
In 1943 Machculloch and Pitts create the formal neuron where many input signals are linearly composed with different weights on the neuron soma. When the soma electrical signal goes over a specific threshold an output is produced. The main topic in this model is that the response is the same response as in a Boolean function used a lot for the digital computer. Logic functions can be simplified with the formal neuron. But there is the big problem for which not all logic functions, as XOR , cannot be designed in the formal neuron. After a long time the back propagation and many other neural models overcame the big problem in some cases but not in all cases creating a lot of uncertainty. The model proposed does not consider the formal neuron but the natural network controlled by a set of differential equations for neural channels that model the current and voltage on the neuron surface. The steady state of the probabilities is the activation state continuous function whose maximum and minimum are the values of the Boolean function associated with the activation time of spikes of the neuron. With this method the activation function can be designed when the Boolean functions are known. Moreover the neuron differential equation can be designed in order to realize the wanted Boolean function in the neuron itself. The activation function theory permits to compute the neural parameters in agreement with the intention.
1943年,Machculloch和Pitts创建了形式神经元,其中许多输入信号在神经元体上以不同的权重线性组成。当体细胞电信号超过特定阈值时,就会产生输出。该模型的主要主题是响应与数字计算机中大量使用的布尔函数中的响应相同。逻辑函数可以用形式神经元进行简化。但并不是所有的逻辑函数,如异或,都不能在形式神经元中设计,这是一个很大的问题。经过很长一段时间后,反向传播和许多其他神经模型在某些情况下克服了这个大问题,但并不是在所有情况下都克服了这个问题,造成了很多不确定性。该模型不考虑形式神经元,而是考虑由一组神经通道微分方程控制的自然网络,这些方程模拟神经元表面的电流和电压。概率的稳态是激活状态连续函数,其最大值和最小值是与神经元尖峰激活时间相关的布尔函数的值。利用该方法可以在布尔函数已知的情况下设计激活函数。此外,还可以设计神经元的微分方程,以实现神经元本身的布尔函数。激活函数理论允许计算与意图一致的神经参数。
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引用次数: 0
Real Time Depth Hole Filling using Kinect Sensor and Depth Extract from Stereo Images 基于Kinect传感器的实时深度洞填充和立体图像的深度提取
Pub Date : 2019-10-04 DOI: 10.13005/ojcst12.03.06
Kapil S. Raviya, V. Dwivedi, A. Kothari, Gunvantsinh Gohil
The researcher have suggested real time depth based on frequency domain hole filling. It get better quality of depth sequence generated by sensor. This method is capable to produce high feature depth video which can be quite useful in improving the performance of various applications of Microsoft Kinect such as obstacle detection and avoidance, facial tracking, gesture recognition, pose estimation and skeletal. For stereo matching approach images depth extraction is the hybrid (Combination of Morphological Operation) mathematical algorithm. There are few step like color conversion, block matching, guided filtering, minimum disparity assignment design, mathematical perimeter, zero depth assignment, combination of hole filling and permutation of morphological operator and last nonlinear spatial filtering. Our algorithm is produce smooth, reliable, noise less and efficient depth map. The evaluation parameter such as Structure Similarity Index Map (SSIM), Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) measure the results for proportional analysis.
研究人员提出了基于频域填洞的实时深度。传感器生成的深度序列质量更好。该方法能够产生高特征深度的视频,这对于提高Microsoft Kinect的各种应用性能非常有用,例如障碍物检测和避免,面部跟踪,手势识别,姿势估计和骨骼。对于立体匹配方法,图像深度提取是混合(结合形态学运算)数学算法。其中包括颜色转换、分块匹配、引导滤波、最小视差分配设计、数学周长、零深度分配、补孔与形态算子置换组合、最后的非线性空间滤波等步骤。该算法可生成平滑、可靠、低噪、高效的深度图。结构相似指数图(SSIM)、峰值信噪比(PSNR)和均方误差(MSE)等评价参数衡量比例分析的结果。
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引用次数: 2
An Assessment of the Effectiveness of E-Learning in AMA Olongapo Campus AMA奥隆阿波校区电子学习效果评估
Pub Date : 2019-08-01 DOI: 10.13005/ojcst12.03.04
F. Mobo, Gesswein O. Sabado
E-learning system is designed to aid students build their comprehension towards their respected academic subjects to make their experience in learning more nourishing and engaging. The researchers decided to conduct this study to be au fait the benefits of online education and how the students accept the change this innovation gave. A survey questionnaire was distributed to the enrolled college students of AMA Computer College Year 2018-2019 to make the research more reliable, accurate and at the same time tackle the benefits of the said innovated education system. The said research was a success in defining what helps the students in their education as well as the improvement it can do in terms of catering different subjects and assessing the learners’ competency. The researchers recommend the future researchers to further expand and elaborate the topic in order to enhance the study. To further extend the scope and distribute further information regarding the tackled study.
电子学习系统旨在帮助学生建立对他们所尊重的学科的理解,使他们的学习经验更丰富,更有吸引力。研究人员决定进行这项研究,以了解在线教育的好处,以及学生如何接受这种创新带来的变化。为了使研究更加可靠、准确,同时也能反映出上述创新教育体系的好处,我们向AMA计算机学院2018-2019学年的在校生发放了一份调查问卷。上述研究成功地定义了什么有助于学生的教育,以及它在迎合不同学科和评估学习者能力方面所能做的改进。研究人员建议未来的研究人员进一步扩大和细化主题,以加强研究。进一步扩大研究范围,并分发有关已处理研究的进一步资料。
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引用次数: 6
A Review on Cyber Security and the Fifth Generation Cyberattacks 网络安全与第五代网络攻击研究综述
Pub Date : 2019-06-30 DOI: 10.13005/OJCST12.02.04
A. Saravanan, S. Bama
Cyber attacks have become quite common in this internet era. The cybercrimes are getting increased every year and the intensity of damage is also increasing. providing security against cyber-attacks becomes the most significant in this digital world. However, ensuring cyber security is an extremely intricate task as requires domain knowledge about the attacks and capability of analysing the possibility of threats. The main challenge of cybersecurity is the evolving nature of the attacks. This paper presents the significance of cyber security along with the various risks that are in the current digital era. The analysis made for cyber-attacks and their statistics shows the intensity of the attacks. Various cybersecurity threats are presented along with the machine learning algorithms that can be applied to cyber attacks detection. The need for the fifth generation cybersecurity architecture is discussed.
在这个互联网时代,网络攻击已经变得相当普遍。网络犯罪每年都在增加,破坏的强度也在增加。在这个数字世界中,提供针对网络攻击的安全措施变得最为重要。然而,确保网络安全是一项极其复杂的任务,因为需要有关攻击的领域知识和分析威胁可能性的能力。网络安全的主要挑战是攻击的演变性质。本文介绍了网络安全的重要性以及当前数字时代的各种风险。对网络攻击的分析及其统计数据显示了攻击的强度。介绍了各种网络安全威胁以及可应用于网络攻击检测的机器学习算法。讨论了对第五代网络安全架构的需求。
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引用次数: 14
An Edge Computing Tutorial 边缘计算教程
Pub Date : 2019-06-30 DOI: 10.13005/OJCST12.02.02
Inés Sittón-Candanedo, J. Corchado
Edge Computing (EC) is an emerging technology that has made it possible to process the large volume of data generated by devices connected to the Internet, through the Internet of objects (IO). The article provides an introduction to EC and its definition. The integration of EC in those contexts would imply an optimisation of the processes that are normally executed in a cloud computing environment, bringing considerable advantages. The main contribution of EC is a better pre-processing of the data collected through devices before they are sent to a central server or the cloud.
边缘计算(EC)是一项新兴技术,它使通过物联网(IO)处理连接到互联网的设备产生的大量数据成为可能。本文介绍了电子商务及其定义。在这些环境中集成电子商务意味着对通常在云计算环境中执行的流程进行优化,从而带来相当大的优势。EC的主要贡献是在将设备收集的数据发送到中央服务器或云之前对其进行更好的预处理。
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引用次数: 6
Controlling the Speed of Conveyor Belt using Python – Raspberry Pi 3B+ 用Python -树莓派3B+控制输送带的速度
Pub Date : 2019-06-30 DOI: 10.13005/OJCST12.02.05
M. Kamalakannan, K. Devadharshini
In food processing industry, there arises a need to control a conveyor belt. currently industries are very necessary to use material handling system for to move materials from one place to another place continuously and to minimize operations time. Stepper Motor is suitable for controlling conveyor because of its high accuracy positioning over a short distance and provide high torque even at low speeds and it is also offer very low vibration and a wide range of features. This paper is focused on controlling the speed of the conveyor belt through the speed of stepper motor using the micro processor namely, Raspberry Pi 3B+’s (RP 3B+) GPIOs (General Purpose Input Output) and it can be generate sequence of control signals on the GPIO pins of RP 3B+. Interfacing the stepper motor with RP 3B+ using python programming language. The method is explained with the results of changing the weights the speed level is reduced through time variation on the conveyor belt and the model of working Conveyor belt with Stepper motor controlled by python and RP 3B+ with Easy Driver(A3967).
在食品加工业中,需要控制传送带。目前的工业非常需要使用物料搬运系统来将物料从一个地方连续地移动到另一个地方,并最大限度地减少操作时间。步进电机适用于控制输送机,因为它的高精度定位在短距离和提供高扭矩,即使在低速下,它也提供了非常低的振动和广泛的特点。本文主要研究利用树莓派3B+ (RP 3B+)的GPIO(通用输入输出)微处理器,通过步进电机的速度来控制输送带的速度,并在RP 3B+的GPIO引脚上产生一系列控制信号。用python编程语言实现步进电机与RP 3B+的接口。通过改变输送带的权值,通过输送带的时间变化来降低输送带的速度水平,并给出了由python控制的步进电机和带有Easy Driver(A3967)的RP 3B+工作输送带的模型。
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引用次数: 2
Justifying IT Investment: Extension of a Model using a Case Study from Jordan 证明IT投资:使用约旦案例研究的模型扩展
Pub Date : 2019-06-30 DOI: 10.13005/OJCST12.02.03
E. Abu-Shanab, Qais Hammouri, Mai Al-Sebae
Investing in information technology is a requirement for enterprises to sustain their competitive advantage in a market that is described as changing and global. IT is a very important resource for enterprises to improve their organizational performance, but requires some justification for its costs and burdens. This study utilized an existing model and applied it on a case in Jordan by analyzing and exploring the implications of investing in IT projects. The case used is the Japan Tobacco International, where a survey was used to collect response from JTI personnel and the documents available on their portal. Two models are proposed to improve our understanding of topic and set the stage for future research. The detailed results of this study are reported with conclusions at the end.
投资信息技术是企业在不断变化和全球化的市场中保持竞争优势的必要条件。IT是企业提高其组织绩效的非常重要的资源,但需要对其成本和负担进行一些解释。本研究利用现有模型,并通过分析和探索投资it项目的影响,将其应用于约旦的一个案例。使用的案例是日本烟草国际公司(Japan Tobacco International),在那里使用了一项调查来收集JTI人员的答复及其门户网站上提供的文件。提出了两个模型,以提高我们对主题的理解,并为未来的研究奠定基础。最后报告了本研究的详细结果和结论。
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引用次数: 1
期刊
Oriental journal of computer science and technology
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