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2020

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Articles 1231 - 1260 of 2675

Full-Text Articles in Computer Engineering

Improved Rsa Security Using Chinese Remainder Theorem And Multiple Keys, Hatem Abdulkader, Rasha Samir, Reda Hussien Jun 2020

Improved Rsa Security Using Chinese Remainder Theorem And Multiple Keys, Hatem Abdulkader, Rasha Samir, Reda Hussien

Future Computing and Informatics Journal

Now a days, have a great dependence on computer and network and the security of computer related to the whole world and everybody. Cryptography is the art and science of achieving security by encoding message to make them non readable, to secure data information transmits over the network, In this paper introduced modified RSA approach based on multi keys and Chinese remainder theorem (CRT), which RSA algorithm is asymmetric key encryption technique. The objective of this Technique is to provide secure transmission of data between any networks. Which is the Network security is an activity which is designed to provide …

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Evaluating And Improving The Seu Reliability Of Artificial Neural Networks Implemented In Sram-Based Fpgas With Tmr, Brittany Michelle Wilson Jun 2020

Evaluating And Improving The Seu Reliability Of Artificial Neural Networks Implemented In Sram-Based Fpgas With Tmr, Brittany Michelle Wilson

Theses and Dissertations

Artificial neural networks (ANNs) are used in many types of computing applications. Traditionally, ANNs have been implemented in software, executing on CPUs and even GPUs, which capitalize on the parallelizable nature of ANNs. More recently, FPGAs have become a target platform for ANN implementations due to their relatively low cost, low power, and flexibility. Some safety-critical applications could benefit from ANNs, but these applications require a certain level of reliability. SRAM-based FPGAs are sensitive to single-event upsets (SEUs), which can lead to faults and errors in execution. However there are techniques that can mask such SEUs and thereby improve the …

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Dynamic Reconfigurable Real-Time Video Processing Pipelines On Sram-Based Fpgas, Andrew Elbert Wilson Jun 2020

Dynamic Reconfigurable Real-Time Video Processing Pipelines On Sram-Based Fpgas, Andrew Elbert Wilson

Theses and Dissertations

For applications such as live video processing, there is a high demand for high performance and low latency solutions. The configurable logic in FPGAs allows for custom hardware to be tailored to a specific video application. These FPGA designs require technical expertise and lengthy implementation times by vendor tools for each unique solution. This thesis presents a dynamically configurable topology as an FPGA overlay to deploy custom hardware processing pipelines during run-time by utilizing dynamic partial reconfiguration. Within the FPGA overlay, a configurable topology with a routable switch allows video streams to be copied and mixed to create complex data …

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Modeling And Simulation Of A 20kv Ultra-Fast Dc Circuit Breaker For Electric Shipboard Applications, Trevor Arvin, Jiangbiao He, Nathan Weise, Tiefu Zhao Jun 2020

Modeling And Simulation Of A 20kv Ultra-Fast Dc Circuit Breaker For Electric Shipboard Applications, Trevor Arvin, Jiangbiao He, Nathan Weise, Tiefu Zhao

Electrical and Computer Engineering Faculty Research and Publications

A novel hybrid circuit breaker for medium voltage dc (MVDC) electric shipboard power systems is proposed. The breaker combines the benefits of the efficiency of a mechanical breaker and the interruption speed of a solid-state breaker. The proposed breaker utilizes a fast-ramping current source with a fast-actuating vacuum interrupter (VI) to provided ultra-fast response time and high on-state efficiency. During normal operation, nominal load current flows through the vacuum interrupter in the main conduction branch, providing a low-resistance path with negligible losses. During a fault, a current zero crossing is achieved by the use of a controllable resonant current source …

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Approximate Computing For Application Performance In Heterogenous Systems, Himadri Sekhar Paul, Arijit Mukherjee, Arpan Pal, Ansuman Banerjee Jun 2020

Approximate Computing For Application Performance In Heterogenous Systems, Himadri Sekhar Paul, Arijit Mukherjee, Arpan Pal, Ansuman Banerjee

Patents

A system and method for determining a configuration of a plurality of tasks to meet the specified deadline of a linear workflow of a real - time heterogeneous network . Often times , while meeting expected application performance in the heterogeneous network , it may possible to have graceful degradation of quality for ensuring timing constraints at the same time . In a multi - layered architecture , where each layer is equipped with multiple computational resources , the time optimization for each of the plurality of tasks can be achieved through approximate computing and analyzing all possible configurations of …

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Distributed Firewall For Iot, Ryan Lund, Anthony Fenzl, Chelsea Villanueva Jun 2020

Distributed Firewall For Iot, Ryan Lund, Anthony Fenzl, Chelsea Villanueva

Computer Science and Engineering Senior Theses

Minimal local resources, lack of consistency in low level protocols and market pressures contribute to IoT devices being more vulnerable than traditional computing devices. These devices not only have a wide variety of processors and implementations, but they often serve different purposes and generate unique network traffic. Current IoT network security solutions fail to account for and handle both the scale at which IoT devices can be deployed and the heterogeneous nature of the traffic they produce. In order to accommodate these differences and improve on current solutions, we propose the implementation of a microsegmented firewall for IoT networks. Unlike …

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Action Recognition Using The Motion Taxonomy, Maxat Alibayev Jun 2020

Action Recognition Using The Motion Taxonomy, Maxat Alibayev

USF Tampa Graduate Theses and Dissertations

In the last years, modern action recognition frameworks with deep architectures have achieved impressive results on the large-scale activity datasets. All state-of-the-art models share one common attribute: two-stream architectures. One deep model takes RGB frames, while the other model is fed with pre-computed optical flow vectors. The outputs of both models are combined to be used as a final probability distribution for the action classes. When comparing the results of individual models with the fused model, it is common to see that that latter method is more superior. Researchers explain that phenomena with the fact that optical flow vectors serve …

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Sustainability Action Tracker, Gladys Hilerio, Isabelle Termaat, Patricia Ornelas Jauregui Jun 2020

Sustainability Action Tracker, Gladys Hilerio, Isabelle Termaat, Patricia Ornelas Jauregui

Computer Science and Engineering Senior Theses

The Center for Sustainability at Santa Clara University is actively looking for ways to involve students in sustainable actions and accountability. With our help, they would like to create a site where students and faculty may track their sustainable behavior. This site will provide users with all the information they need to live a sustainable life, and include milestones in the form of progress bars and badges. The Center for Sustainability will be able to collect the data from this site to evaluate the progress of our university as well as the success of the site. Our motivation for this …

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Sdhome: Securing Fast Home Networks, Christopher Batula, Holden Gordon, Tianyi Zhao Jun 2020

Sdhome: Securing Fast Home Networks, Christopher Batula, Holden Gordon, Tianyi Zhao

Computer Science and Engineering Senior Theses

Distributed denial of service (DDoS) is a highly discussed network attack in Software Defined Networks. Attacks such as the Mirai Botnet threaten to compromise portion of large networks, including home users. Today, corporations secure their network using enterprise level software to protest their network from DDoS attacks . But their solutions are meant for large networks and depend on expensive hardware. There are few security solutions for home users and most are expensive or require a subscription for full protection. In this paper, we propose a new solution in the form of a plug and play device that will allow …

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Antlion Optimization And Boosting Classifier For Spam Email Detection, Amany A. Naem, Neveen I. Ghali Prof., Afaf A. Saleh Jun 2020

Antlion Optimization And Boosting Classifier For Spam Email Detection, Amany A. Naem, Neveen I. Ghali Prof., Afaf A. Saleh

Future Computing and Informatics Journal

Spam emails are not necessary, though they are harmful as they include viruses and spyware, so there is an emerging need for detecting spam emails. Several methods for detecting spam emails were suggested based on the methods of machine learning, which were submitted to reduce non relevant emails and get results of high precision for spam email classification. In this work, a new predictive method is submitted based on antlion optimization (ALO) and boosting termed as ALO-Boosting for solving spam emails problem. ALO is a computational model imitates the preying technicality of antlions to ants in the life cycle. Where …

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A New Online Scheduling Approach For Enhancing Qos In Cloud, Aida A. Nasr, Nirmeen A. El-Bahnasawy, Gamal Attiya, Ayman El-Sayed Jun 2020

A New Online Scheduling Approach For Enhancing Qos In Cloud, Aida A. Nasr, Nirmeen A. El-Bahnasawy, Gamal Attiya, Ayman El-Sayed

Future Computing and Informatics Journal

Quality-of-Services (QoS) is one of the most important requirements of cloud users. So, cloud providers continuously try to enhance cloud management tools to guarantee the required QoS and provide users the services with high quality. One of the most important management tools which play a vital role in enhancing QoS is scheduling. Scheduling is the process of assigning users’ tasks into available Virtual Machines (VMs). This paper presents a new task scheduling approach, called Online Potential Finish Time (OPFT), to enhance the cloud data-center broker, which is responsible for the scheduling process, and solve the QoS issue. The main idea …

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Automatic Labeling Of Hidden Web Data Using Multi-Heuristics Annotator, Umamageswari Baskaran, R. Kalpana Jun 2020

Automatic Labeling Of Hidden Web Data Using Multi-Heuristics Annotator, Umamageswari Baskaran, R. Kalpana

Future Computing and Informatics Journal

Hidden web contains huge amount of high quality data which are not indexed to search engines. Hidden web refers to web pages which are generated dynamically by embedding backend data matching the search keywords, in server-side templates. They are created for human consumption and makes automated processing cumbersome since structured data is embedded within unstructured HTML tags. In order to enable machine processing, structured data must be detected, extracted and annotated. Many heuristic based approaches DeLa [1], MSAA [2] are available in the literature to perform automatic annotation. Most of these techniques fail if data values didn't contain labels present …

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A Systematic Review For The Determination And Classification Of The Crm Critical Success Factors Supporting With Their Metrics, Mahmoud Abd Ellatif, Marwa Salah Farhan, Amira Hassan Abed Jun 2020

A Systematic Review For The Determination And Classification Of The Crm Critical Success Factors Supporting With Their Metrics, Mahmoud Abd Ellatif, Marwa Salah Farhan, Amira Hassan Abed

Future Computing and Informatics Journal

The successful implementation of customer relationship management (CRM) is not easy and seems to be a complex task. Almost about 70% of all CRM implementation projects fail to achieve their expected objectives. Therefore, most researchers and information systems developers concentrate on the critical success factors approach which can enhance the success of CRM implementation and turn the failure and drawbacks faced CRM into successful CRM systems adoption and implementation. In this paper, the number of the previous studies is reviewed to demonstrate the barriers behind this high failure rate. In addition, an extensive review is conducted in order to identify …

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Stage – Specific Predictive Models For Main Prognosis Measures Of Breast Cancer, Ahmed Attia Said, Laila A. Abd-Elmegid, Sherif Kholeif, Ayman Abdelsamie Gaber Jun 2020

Stage – Specific Predictive Models For Main Prognosis Measures Of Breast Cancer, Ahmed Attia Said, Laila A. Abd-Elmegid, Sherif Kholeif, Ayman Abdelsamie Gaber

Future Computing and Informatics Journal

Breast cancer is a malignant tumor that starts in the cells of the breast. A malignant tumor is a group of cancer cells that can grow into near tissues or invading the distant areas of the body. The disease occurs almost entirely in women, but men can get it, too. Survival rate, recurrence detection and disease-free survival rate (DFS) are the main patient's outcome and prognosis measures. Breast cancer outcomes are vary among different stages of the disease. There are five stages of breast cancer named as 0, 1, 2, 3, and 4. Prognosis helps doctors to save patients' lives …

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Applying Spatial Intelligence For Decision Support Systems, Amira Idrees, Mohamed H. Ibrahim Jun 2020

Applying Spatial Intelligence For Decision Support Systems, Amira Idrees, Mohamed H. Ibrahim

Future Computing and Informatics Journal

Data mining is one of the vital techniques that could be applied in different fields such as medical, educational and industrial fields. Extracting patterns from spatial data is very useful to be used for discovering the trends in the data. However, analyzing spatial data is exhaustive due to its details as it is related to locations with a special representation such as longitude and latitude. This paper aims at proposing an approach for applying data mining techniques over spatial data to find trends in the data for decision support. Basic information considering spatial data is presented with presenting the proposed …

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An Qos Based Multifaceted Matchmaking Framework For Web Services Discovery, G. Sambasivam Jun 2020

An Qos Based Multifaceted Matchmaking Framework For Web Services Discovery, G. Sambasivam

Future Computing and Informatics Journal

With the increasing demand, the web service has been the prominent technology for providing good solutions to the interoperability of different kind of systems. Web service supports mainly interoperability properties as it is the major usage of this promising technology. Although several technologies had been evolved before web service technology and this has more advantage of other technologies. This paper has concentrated mainly on the Multifaceted Matchmaking framework for Web Services Discovery using Quality of Services parameters. Traditionally web services have been discovered only with the functional properties like input, output, precondition and effect. Nowadays there is an increase in …

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Medical Image Retrieval Using Self-Organising Map On Texture Features, Shashwati Mishra Jun 2020

Medical Image Retrieval Using Self-Organising Map On Texture Features, Shashwati Mishra

Future Computing and Informatics Journal

The process of capturing, transfer and sharing of information in the form of digital images have become easier due to the use of advanced technologies. Retrieval of desired images from these huge collections of image databases is one of the popular research areas and has its applications in various fields. An image set consists of images containing objects of different colours, shapes, orientations and sizes. The surface texture of the object in an image may also vary from another object in a different image. These factors make the process of image retrieval a difficult one. In this paper, Self-Organising Map …

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Benign And Malignant Breast Cancer Segmentation Using Optimized Region Growing Technique, S. Punitha, A. Amuthan, K. Suresh Joseph Jun 2020

Benign And Malignant Breast Cancer Segmentation Using Optimized Region Growing Technique, S. Punitha, A. Amuthan, K. Suresh Joseph

Future Computing and Informatics Journal

Breast cancer is one of the dreadful diseases that affect women globally. The occurrences of breast masses in the breast region are the main cause for women to develop a breast cancer. Early detection of breast mass will increase the survival rate of women and hence developing an automated system for detection of the breast masses will support radiologists for accurate diagnosis. In the pre-processing step, the images are pre-processed using Gaussian filtering. An automated detection method of breast masses is proposed using an optimized region growing technique where the initial seed points and thresholds are optimally generated using a …

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A Genetic Algorithm For Service Flow Management With Budget Constraint In Heterogeneous Computing, Ahmed A. Abdulhamed, Medhat A. Tawfeek, Arabi E. Keshk Jun 2020

A Genetic Algorithm For Service Flow Management With Budget Constraint In Heterogeneous Computing, Ahmed A. Abdulhamed, Medhat A. Tawfeek, Arabi E. Keshk

Future Computing and Informatics Journal

Heterogeneous computing supply various and scalable resources for many applications requirements. Its structure is based on interconnecting machines with several processing capacity spread over networks. The scientific bioinformatics and many other applications demand service flow processing in which services have dependencies execution. The environments of this computing are suitable for huge computational needs that contains diverse groups of services. Managing and mapping services of service flow to the suitable candidates who provides the service is classified as NP-complete problem. The managing such interdependent services on heterogeneous environments also takes the Quality of Service (QoS) requirements from users into account. This …

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Time Series Forecasting Using Artificial Neural Networks Methodologies: A Systematic Review, Ahmed Tealab Jun 2020

Time Series Forecasting Using Artificial Neural Networks Methodologies: A Systematic Review, Ahmed Tealab

Future Computing and Informatics Journal

This paper studies the advances in time series forecasting models using artificial neural network methodologies in a systematic literature review. The systematic review has been done using a manual search of the published papers in the last 11 years (2006e2016) for the time series forecasting using new neural network models and the used methods are displayed. In the covered period in the study, the results obtained found 17 studies that meet all the requirements of the search criteria. Only three of the obtained proposals considered a process different to the autoregressive of a neural networks model. These results conclude that, …

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Fuzzy Clustering Based Transition Region Extraction For Image Segmentation, Priyadarsan Parida Jun 2020

Fuzzy Clustering Based Transition Region Extraction For Image Segmentation, Priyadarsan Parida

Future Computing and Informatics Journal

Transition region based approaches are recent hybrid segmentation techniques well known for its simplicity and effectiveness. Here, the segmentation effectiveness depends on robust extraction of transition regions. So, we have proposed clustering approach based transition region extraction method for image segmentation. The proposed method initially uses the local variance of the input image to get the variance feature image. Fuzzy C-means clustering is applied to the variance feature image to separate the transitional features from the feature image. Further, Otsu thresholding is applied to the transitional feature image to extract the transition region. For extracting the exact edge image, morphological …

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A Low Cost Autonomous Unmanned Ground Vehicle, Leckraj Nagowah Jun 2020

A Low Cost Autonomous Unmanned Ground Vehicle, Leckraj Nagowah

Future Computing and Informatics Journal

The aim of this project is to design and implement a low cost Autonomous Unmanned Ground Vehicle (AUGV), a vehicle that can be controlled remotely without an onboard human presence. The AUGV is also able to move autonomously while automatically detecting and avoiding obstacles. The vehicle also reads directions from QR codes, calculates the shortest path to its destination and autonomous move towards its final destination. A Raspberry Pi 3 has been used as the brain of the vehicle together with other components such as DC and Servo motors, Ultrasonic and Infrared sensors, webcam, batteries, power bank, motor controller and …

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Distributed Processing Of Location Based Spatial Query Through Vantage Point Transformation, M. Priya, R. Kalpana Jun 2020

Distributed Processing Of Location Based Spatial Query Through Vantage Point Transformation, M. Priya, R. Kalpana

Future Computing and Informatics Journal

Location Based Services is the popular and geo sensitive service implicated over the smart phone by internet. Nowadays these system find its own enhancement, as they are using device‘s real time geographical information to provide information and entertainment. It allows the user to get the response to the query based on their current location there by location becomes the most basic context for the user. For example these services are used to check in restaurants, coffee shops to get the business reward from the nearest shop or to track the location of a person. The user of the smart phone …

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A Proposed Hybrid Model For Adopting Cloud Computing In E-Government, Kh. E. Ali, Sh. A. Mazen, E. E. Hassanein Jun 2020

A Proposed Hybrid Model For Adopting Cloud Computing In E-Government, Kh. E. Ali, Sh. A. Mazen, E. E. Hassanein

Future Computing and Informatics Journal

Many developing countries are now experiencing revolution in e-government to deliver fluent and simple services for their citizens. However, governmental sectors face many challenges in using its e-governments’ services and its infrastructure, improving current services or developing new services; as data and applications increasingly inflating, IT budget costs, software licensing and support and difficulties in migration, integration and management for software and hardware. These challenges may lead to failure of e-governments’ projects. Therefore, there is a need for a solution to overcome these challenges. Cloud Computing plays a vital role to solve these problems. This paper demonstrates egovernment's obstacles and …

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Non-Sequential Partitioning Approaches To Decision Tree Classifier, Shankru Guggari, Vijayakumar Kadappa, V. Umadevi Jun 2020

Non-Sequential Partitioning Approaches To Decision Tree Classifier, Shankru Guggari, Vijayakumar Kadappa, V. Umadevi

Future Computing and Informatics Journal

Decision tree is a well-known classifier which is widely used in real-world applications. It is easy to interpret, however it suffers from instability and lower classification performance for high-dimensionality datasets due to curse of dimensionality. Feature set partitioning is a novel concept to address the higher dimensionality problem by dividing the feature set into subsets (blocks). Many of the existing partitioning based decision tree approaches are sequential in nature, which lack logical relationships amongst the features. In this work, we propose novel non-sequential feature set partitioning methods by exploiting the ideas of Ferrer Diagram and Bell Triangle to create feature …

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Feature Based Transition Region Extraction For Image Segmentation: Application To Worm Separation From Leaves, Priyadarsan Parida, Nilamani Bhoi Jun 2020

Feature Based Transition Region Extraction For Image Segmentation: Application To Worm Separation From Leaves, Priyadarsan Parida, Nilamani Bhoi

Future Computing and Informatics Journal

Transition region based approaches are recent hybrid segmentation techniques well known for its simplicity and effectiveness. Here, the segmentation effectiveness depends on robust extraction of transition regions. So, we have proposed transition region extraction method for image segmentation. The proposed method initially decomposes the gray image in wavelet domain. Local standard deviation filtering and thresholding operation is used to extract transition region feature matrix. Using this feature matrix, the corresponding prominent wavelet coefficients of different bands are found. The inverse wavelet transform is then applied to the modified coefficients to get edge image with more than one-pixel width. Global thresholding …

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Partition Based Clustering Of Large Datasets Using Mapreduce Framework: An Analysis Of Recent Themes And Directions, Tanvir Habib Sardar, Zahid Ansari Jun 2020

Partition Based Clustering Of Large Datasets Using Mapreduce Framework: An Analysis Of Recent Themes And Directions, Tanvir Habib Sardar, Zahid Ansari

Future Computing and Informatics Journal

Data clustering is one of the fundamental techniques in scientific analysis and data mining, which describes a dataset according to similarities among its objects. Partition based clustering algorithms are the most popular and widely used clustering technique. In this information era, due to the digitization of every field, the huge volume of data is available to data analysts. The quick growth of such datasets makes decade old computing platforms, programming paradigms, and clustering algorithms become inadequate to obtain knowledge from these datasets. To cluster such large datasets, Hadoop distributed platform, MapReduce programming paradigm and modified clustering algorithms are being used …

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Bio-Inspired Computing: Algorithms Review, Deep Analysis, And The Scope Of Applications, Ashraf Darwish Prof. Jun 2020

Bio-Inspired Computing: Algorithms Review, Deep Analysis, And The Scope Of Applications, Ashraf Darwish Prof.

Future Computing and Informatics Journal

Bio-inspired computing represents the umbrella of different studies of computer science, mathematics, and biology in the last years. Bioinspired computing optimization algorithms is an emerging approach which is based on the principles and inspiration of the biological evolution of nature to develop new and robust competing techniques. In the last years, the bio-inspired optimization algorithms are recognized in machine learning to address the optimal solutions of complex problems in science and engineering. However, these problems are usually nonlinear and restricted to multiple nonlinear constraints which propose many problems such as time requirements and high dimensionality to find the optimal solution. …

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Task Schedul Ing For Cloud Computing Using Multi-Objective Hybrid Bacteria Foraging Algorithm, Sobhanayak Srichandan, Turuk Ashok Kumar, Sahoo Bibhudatta Jun 2020

Task Schedul Ing For Cloud Computing Using Multi-Objective Hybrid Bacteria Foraging Algorithm, Sobhanayak Srichandan, Turuk Ashok Kumar, Sahoo Bibhudatta

Future Computing and Informatics Journal

Cloud computing is the delivery of computing services over the internet. Cloud services allow individuals and other businesses organization to use data that are managed by third parties or another person at remote locations. Most Cloud providers support services under constraints of Service Level Agreement (SLA) definitions. The SLAs are composed of different quality of service (QoS) rules promised by the provider. A cloud environment can be classified into two types: computing clouds and data clouds. In computing cloud, task scheduling plays a vital role in maintaining the quality of service and SLA. Efficient task scheduling is one of the …

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An Analysis Of Mapreduce Efficiency In Document Clustering Using Parallel K-Means Algorithm, Tanvir Habib Sardar, Zahid Ansari Jun 2020

An Analysis Of Mapreduce Efficiency In Document Clustering Using Parallel K-Means Algorithm, Tanvir Habib Sardar, Zahid Ansari

Future Computing and Informatics Journal

One of the significant data mining techniques is clustering. Due to expansion and digitalization of each field, large datasets are being generated rapidly. Such large dataset clustering is a challenge for traditional sequential clustering algorithms due to huge processing time. Distributed parallel architectures and algorithms are thus helpful to achieve performance and scalability requirement of clustering large datasets. In this study, we design and experiment a parallel k-means algorithm using MapReduce programming model and compared the result with sequential k-means for clustering varying size of document dataset. The result demonstrates that proposed k-means obtains higher performance and outperformed sequential k-means …

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