Open Access. Powered by Scholars. Published by Universities.®

Computer Engineering Commons™

Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences

Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 5041 - 5070 of 13043

Full-Text Articles in Computer Engineering

A Comparative Study Of Text Summarization On E-Mail Data Using Unsupervised Learning Approaches, Tijo Thomas Jan 2020

A Comparative Study Of Text Summarization On E-Mail Data Using Unsupervised Learning Approaches, Tijo Thomas

Dissertations

Over the last few years, email has met with enormous popularity. People send and receive a lot of messages every day, connect with colleagues and friends, share files and information. Unfortunately, the email overload outbreak has developed into a personal trouble for users as well as a financial concerns for businesses. Accessing an ever-increasing number of lengthy emails in the present generation has become a major concern for many users. Email text summarization is a promising approach to resolve this challenge. Email messages are general domain text, unstructured and not always well developed syntactically. Such elements introduce challenges for study …

Go to article

Customer Churn Prediction, Deepshikha Wadikar Jan 2020

Customer Churn Prediction, Deepshikha Wadikar

Dissertations

Churned customers identification plays an essential role for the functioning and growth of any business. Identification of churned customers can help the business to know the reasons for the churn and they can plan their market strategies accordingly to enhance the growth of a business. This research is aimed at developing a machine learning model that can precisely predict the churned customers from the total customers of a Credit Union financial institution. A quantitative and deductive research strategies are employed to build a supervised machine learning model that addresses the class imbalance problem handled feature selection and efficiently predict the …

Go to article

Geoaware - A Simulation-Based Framework For Synthetic Trajectory Generation From Mobility Patterns, Jameson D. Morgan Jan 2020

Geoaware - A Simulation-Based Framework For Synthetic Trajectory Generation From Mobility Patterns, Jameson D. Morgan

Browse all Theses and Dissertations

Recent advances in location acquisition services have resulted in vast amounts of trajectory data; providing valuable insight into human mobility. The field of trajectory data mining has exploded as a result, with literature detailing algorithms for (pre)processing, map matching, pattern mining, and the like. Unfortunately, obtaining trajectory data for the design and evaluation of such algorithms is problematic due to privacy, ethical, dataset size, researcher access, and sampling frequency concerns. Synthetic trajectories provide a solution to such a problem as they are cheap to produce and are derived from a fully controllable generation procedure. Citing deficiencies in modern synthetic trajectory …

Go to article

Extracting Information From Subroutines Using Static Analysis Semantics, Luke A. Burnett Jan 2020

Extracting Information From Subroutines Using Static Analysis Semantics, Luke A. Burnett

Browse all Theses and Dissertations

Understanding how a system component can interact with other services can take an immeasurable amount of time. Reverse engineering embedded and large systems can rely on understanding how components interact with one another. This process is time consuming and can sometimes be generalized through certain behavior.We will be explaining two such complicated systems and highlighting similarities between them. We will show that through static analysis you can capture compiler behavior and apply it to the understanding of a function, reducing the total time required to understand a component of whichever system you are learning.

Go to article

Predicting Subjective Sleep Quality Using Objective Measurements In Older Adults, Reza Sadeghi Jan 2020

Predicting Subjective Sleep Quality Using Objective Measurements In Older Adults, Reza Sadeghi

Browse all Theses and Dissertations

Humans spend almost a third of their lives asleep. Sleep has a pivotal effect on job performance, memory, fatigue recovery, and both mental and physical health. Sleep quality (SQ) is a subjective experience and reported via patients’ self-reports. Predicting subjective SQ based on objective measurements can enhance diagnosis and treatment of SQ defects, especially in older adults who are subject to poor SQ. In this dissertation, we assessed enhancement of subjective SQ prediction using an easy-to-use E4 wearable device, machine learning techniques and identifying disease-specific risk factors of abnormal SQ in older adults. First, we designed a clinical decision support …

Go to article

Identifying Knowledge Gaps Using A Graph-Based Knowledge Representation, Daniel P. Schmidt Jan 2020

Identifying Knowledge Gaps Using A Graph-Based Knowledge Representation, Daniel P. Schmidt

Browse all Theses and Dissertations

Knowledge integration and knowledge bases are becoming more and more prevalent in the systems we use every day. When developing these knowledge bases, it is important to ensure the correctness of the information upon entry, as well as allow queries of all sorts; for this, understanding where the gaps in knowledge can arise is critical. This thesis proposes a descriptive taxonomy of knowledge gaps, along with a framework for automated detection and resolution of some of those gaps. Additionally, the effectiveness of this framework is evaluated in terms of successful responses to queries on a knowledge base constructed from a …

Go to article

Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management, Nathaniel C. Crossman Jan 2020

Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management, Nathaniel C. Crossman

Browse all Theses and Dissertations

In the last decade, the advent of social media and microblogging services have inevitably changed our world. These services produce vast amounts of streaming data, and one of the most important ways of analyzing and discovering interesting trends in the streaming data is through clustering. In clustering streaming data, it is desirable to perform a single pass over incoming data, such that we do not need to process old data again, and the clustering model should evolve over time not to lose any important feature statistics of the data. In this research, we have developed a new clustering system that …

Go to article

An Adversarial Framework For Deep 3d Target Template Generation, Walter E. Waldow Jan 2020

An Adversarial Framework For Deep 3d Target Template Generation, Walter E. Waldow

Browse all Theses and Dissertations

This paper presents a framework for the generation of 3D models. This is an important problem for many reasons. For example, 3D models are important for systems that are involved in target recognition. These systems use 3D models to train up accuracy on identifying real world object. Traditional means of gathering 3D models have limitations that the generation of 3D models can help overcome. The framework uses a novel generative adversarial network (GAN) that learns latent representations of two dimensional views of a model to bootstrap the network’s ability to learn to generate three dimensional objects. The novel architecture is …

Go to article

Quantitative Susceptibility Mapping (Qsm) Reconstruction From Mri Phase Data, Sara Gharabaghi Jan 2020

Quantitative Susceptibility Mapping (Qsm) Reconstruction From Mri Phase Data, Sara Gharabaghi

Browse all Theses and Dissertations

Quantitative susceptibility mapping (QSM) is a powerful technique that reveals changes in the underlying tissue susceptibility distribution. It can be used to measure the concentrations of iron and calcium in the brain both of which are linked with numerous neurodegenerative diseases. However, reconstructing the QSM image from the MRI phase data is an ill-posed inverse problem. Different methods have been proposed to overcome this difficulty. Still, the reconstructed QSM images suffer from streaking artifacts and underestimate the measured susceptibility of deep gray matter, veins, and other high susceptibility regions. This thesis proposes a structurally constrained Susceptibility Weighted Imaging and Mapping …

Go to article

Development Of Real-Time Systems For Supporting Collaborations In Distributed Human And Machine Teams, Aishwarya Bositty Jan 2020

Development Of Real-Time Systems For Supporting Collaborations In Distributed Human And Machine Teams, Aishwarya Bositty

Browse all Theses and Dissertations

Real-time distributed systems constitute computing nodes that are connected by a network and coordinate with one another to accomplish a cooperative task, combining the responsiveness, fault-tolerance and geographic independence to support time-constrained collaborative applications, including distributed Human-Machine Teaming. In this thesis research the viability of real-time distributed collaborative technologies is demonstrated through the design, development and validation of prototype systems that support two human-machine teaming scenarios namely, ACE-IMS (Affirmation Cue based Interruption Management Systems) and ReadMI (Real-time Assessment of Dialogue in Motivational Interview). ACE-IMS demonstrates how a combination of AI capabilities and the cloud and mobile computing infrastructure can be …

Go to article

Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management, Nathaniel C. Crossman Jan 2020

Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management, Nathaniel C. Crossman

Browse all Theses and Dissertations

In the last decade, the advent of social media and microblogging services have inevitably changed our world. These services produce vast amounts of streaming data, and one of the most important ways of analyzing and discovering interesting trends in the streaming data is through clustering. In clustering streaming data, it is desirable to perform a single pass over incoming data, such that we do not need to process old data again, and the clustering model should evolve over time not to lose any important feature statistics of the data. In this research, we have developed a new clustering system that …

Go to article

Quantum Computing: Principles And Applications, Yoshito Kanamori, Seong-Moo Yoo Jan 2020

Quantum Computing: Principles And Applications, Yoshito Kanamori, Seong-Moo Yoo

Journal of International Technology and Information Management

The development of quantum computers over the past few years is probably one of the significant advancements in the history of quantum computing. D-Wave quantum computer has been available for more than eight years. IBM has made its quantum computer accessible via its cloud service. Also, Microsoft, Google, Intel, and NASA have been heavily investing in the development of quantum computers and their applications. The quantum computer seems to be no longer just for physicists and computer scientists but also for information system researchers. This paper introduces the basic concepts of quantum computing and describes well-known quantum applications for non-physicists. …

Go to article

Comparison Of Object Detection And Patch-Based Classification Deep Learning Models On Mid- To Late-Season Weed Detection In Uav Imagery, Arun Narenthiran Veeranampalayam Sivakumar, Jiating Li, Stephen Scott, Eric T. Psota, Amit J. Jhala, Joe D. Luck, Yeyin Shi Jan 2020

Comparison Of Object Detection And Patch-Based Classification Deep Learning Models On Mid- To Late-Season Weed Detection In Uav Imagery, Arun Narenthiran Veeranampalayam Sivakumar, Jiating Li, Stephen Scott, Eric T. Psota, Amit J. Jhala, Joe D. Luck, Yeyin Shi

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Mid- to late-season weeds that escape from the routine early-season weed management threaten agricultural production by creating a large number of seeds for several future growing seasons. Rapid and accurate detection of weed patches in field is the first step of site-specific weed management. In this study, object detection-based convolutional neural network models were trained and evaluated over low-altitude unmanned aerial vehicle (UAV) imagery for mid- to late-season weed detection in soybean fields. The performance of two object detection models, Faster RCNN and the Single Shot Detector (SSD), were evaluated and compared in terms of weed detection performance using mean …

Go to article

Mac Protocols For Terahertz Communication: A Comprehensive Survey, Saim Ghafoor, Noureddine Boujnah, Mubashir Husain Rehmani, Alan Davy Jan 2020

Mac Protocols For Terahertz Communication: A Comprehensive Survey, Saim Ghafoor, Noureddine Boujnah, Mubashir Husain Rehmani, Alan Davy

Publications

Terahertz communication is emerging as a future technology to support Terabits per second link with highlighting features as high throughput and negligible latency. However, the unique features of the Terahertz band such as high path loss, scattering, and reflection pose new challenges and results in short communication distance. The antenna directionality, in turn, is required to enhance the communication distance and to overcome the high path loss. However, these features in combine negate the use of traditional medium access protocols (MAC). Therefore, novel MAC protocol designs are required to fully exploit their potential benefits including efficient channel access, control message …

Go to article

การวินิจฉัยโรคพาร์กินสันโดยใช้การเรียนรู้ของเครื่อง, หัสพล ธัมมิกรัตน์ Jan 2020

การวินิจฉัยโรคพาร์กินสันโดยใช้การเรียนรู้ของเครื่อง, หัสพล ธัมมิกรัตน์

Chulalongkorn University Theses and Dissertations (Chula ETD)

วิทยานิพนธ์นี้นำเสนอวิธีการวินิจฉัยโรคพาร์กินสันด้วยการใช้การเรียนรู้ของเครื่องสำหรับการตรวจพบโรคพาร์กินสันในระยะเริ่มต้น โดยใช้โครงข่ายประสาทเทียมแบบวนซ้ำชนิดพิเศษ Long Short-Term Memory กับข้อมูลโรคพาร์กินสันที่ได้รับจากผู้เชี่ยวชาญของโรงพยาบาลจุฬาลงกรณ์ โดยข้อมูลที่ใช้ประกอบไปด้วยข้อมูลจากเซ็นเซอร์และคีย์บอร์ดจากการเก็บข้อมูลจากผู้ร่วมทดสอบซึ่งมีทั้งกลุ่มควบคุมและผู้ป่วยจำนวนหนึ่งผ่านตัวควบคุมที่เก็บข้อมูลคีย์บอร์ดและเซ็นเซอร์ ซึ่งข้อมูลเซ็นเซอร์มีค่าตัวแปรความเร่งและมุม ข้อมูลคีย์บอร์ดคือการกดคีย์บอร์ดเป็นตัวอักษรพร้อมทั้งเวลาการกดคีย์บอร์ด การวิจัยนี้ทำเพื่อช่วยการวินิจฉัยแยกแยะระหว่างอาการสั่นหรือมีปัญหาทางการควบคุมการเครื่องไหวของผู้ป่วยโรคอื่นและผู้ป่วยโรคพาร์กินสัน การวิจัยนี้ได้ใช้การเรียนรู้ของเครื่องเพื่อคัดกรองผู้ป่วยเบื้องต้นแทนการใช้แพทย์ผู้เชี่ยวชาญทางโรคพาร์กินสันสำหรับแพทย์แผนกผู้ป่วยนอกในวินิจฉัยการคัดกรองผู้ป่วยที่มีอาการใกล้เคียงอย่างการเคลื่อนไหว และความผิดปกติของระบบประสาทและสมอง ผลการวินิจฉัยพบว่าการเรียนรู้เครื่องสามารถตรวจพบการวินิจฉัยโรคพาร์กินสัน ได้ร้อยละความถูกต้องที่ 88.78 เปอร์เซ็นต์

Go to article

Red Blood Cell Segmentation And Classification From Microscopic Images Using Machine Learning, Korranat Naruenatthanaset Jan 2020

Red Blood Cell Segmentation And Classification From Microscopic Images Using Machine Learning, Korranat Naruenatthanaset

Chulalongkorn University Theses and Dissertations (Chula ETD)

Red blood cell morphology analysis plays an essential role in diagnosing many diseases caused by RBC disorders. This manual inspection is a long process and requires practice and experience. Since recent computer vision and image processing in the medical imaging area can provide efficient tools, it can help hematologists to automatically analyze images from a microscope in a reduced time and cost. This research presents a new method to segment and classify RBCs from blood smear images. The process started from data collection, which a new application was created for precisely labeling. The normalization was done to reduce the color …

Go to article

Digital Platform Development For Performance Monitoring System In Oil And Gas Exploration And Production, Tanthai Poopaiboon Jan 2020

Digital Platform Development For Performance Monitoring System In Oil And Gas Exploration And Production, Tanthai Poopaiboon

Chulalongkorn University Theses and Dissertations (Chula ETD)

The paper provides a case study to enhance the Performance Management System for Oil and Gas Exploration and Production industry. Although the system was designed for the Oil and Gas Exploration and Production industry, the paper could be applied effectively for other industries because the modern organisation mainly utilised the Key Performance Indicator (KPI) to reflect its performance. So, the paper could be applied to most organisations with minor modifications. The Advanced Performance Management System was developed systematically powered by digital transformation according to research methodology framework, including research, analysis, project development, and result measurement. The research stage is studying …

Go to article

V-Slam And Sensor Fusion For Ground Robots, Ejup Hoxha Jan 2020

V-Slam And Sensor Fusion For Ground Robots, Ejup Hoxha

Dissertations and Theses

In underground, underwater and indoor environments, a robot has to rely solely on its on-board sensors to sense and understand its surroundings. This is the main reason why SLAM gained the popularity it has today. In recent years, we have seen excellent improvement on accuracy of localization using cameras and combinations of different sensors, especially camera-IMU (VIO) fusion. Incorporating more sensors leads to improvement of accuracy,but also robustness of SLAM. However, while testing SLAM in our ground robots, we have seen a decrease in performance quality when using the same algorithms on flying vehicles.We have an additional sensor for ground …

Go to article

Remote Communication In Wilderness Search And Rescue: Implications For The Design Of Emergency Distributed-Collaboration Tools For Network-Sparse Environments, Brennan Jones, Anthony Tang, Carman Neustaedter Jan 2020

Remote Communication In Wilderness Search And Rescue: Implications For The Design Of Emergency Distributed-Collaboration Tools For Network-Sparse Environments, Brennan Jones, Anthony Tang, Carman Neustaedter

Research Collection School Of Computing and Information Systems

Wilderness search and rescue (WSAR) requires careful communication between workers in different locations. To understand the contexts from which WSAR workers communicate and the challenges they face, we interviewed WSAR workers and observed a mock-WSAR scenario. Our findings illustrate that WSAR workers face challenges in maintaining a shared mental model. This is primarily done through distributed communication using two-way radios and cell phones for text and photo messaging; yet both implicit and explicit communication suffer. WSAR workers send messages for various reasons and share different types of information with varying levels of urgency. This warrants the use of multiple communication …

Go to article

A Probabilistic Machine Learning Framework For Cloud Resource Selection On The Cloud, Syeduzzaman Khan Jan 2020

A Probabilistic Machine Learning Framework For Cloud Resource Selection On The Cloud, Syeduzzaman Khan

University of the Pacific Theses and Dissertations

The execution of the scientific applications on the Cloud comes with great flexibility, scalability, cost-effectiveness, and substantial computing power. Market-leading Cloud service providers such as Amazon Web service (AWS), Azure, Google Cloud Platform (GCP) offer various general purposes, memory-intensive, and compute-intensive Cloud instances for the execution of scientific applications. The scientific community, especially small research institutions and undergraduate universities, face many hurdles while conducting high-performance computing research in the absence of large dedicated clusters. The Cloud provides a lucrative alternative to dedicated clusters, however a wide range of Cloud computing choices makes the instance selection for the end-users. This thesis …

Go to article

Scalable, Pluggable, And Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems, Michael Partin Jan 2020

Scalable, Pluggable, And Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems, Michael Partin

Browse all Theses and Dissertations

Features and attributes that describe an event (disasters, social movements, etc.) are heterogeneous in nature. For virtually all events that impact humans, technology enables us to capture a large amount and variety of data from many sources, including humans (i.e., social media) and sensors/internet of things (IoTs). The corresponding modalities of data include text, imagery, voice and video, along with structured data such as gazetteers (i.e., location-based data) and government and statistical data. However, even though there is often an abundance of information produced, this information is fragmented across the various modalities and sources. The DisasterRecord system aims to provide …

Go to article

Simulation And Analysis Of Wind Turbine Radar Echo Based On 3-D Scattering Point Model, Jiangong Zhang, Bin Hao, Bo Tang, Li Huang, Jiawei Yang Jan 2020

Simulation And Analysis Of Wind Turbine Radar Echo Based On 3-D Scattering Point Model, Jiangong Zhang, Bin Hao, Bo Tang, Li Huang, Jiawei Yang

Turkish Journal of Electrical Engineering and Computer Sciences

Wind turbine (WT) arrays in wind farms can cause serious interference on nearby radar stations. This interference could be filtered out if wind turbine radar echo (WTRE) can be obtained accurately. Considering the singleness of in-field experiments, numerical simulation became the majority among such works, but few of them reached necessary accuracy. Therefore, we propose a solution method of WTRE based on three-dimensional (3-D) scattering point model. Firstly, we use the nonuniform rational B-spline to build the 3-D model of WT. Secondly, based on the method of moments (MoM), the Rao-Wilton-Gisson (RWG) basis function is adopted to discretize the integral …

Go to article

A Hybrid Model Based On The Convolutional Neural Network Model And Artificial Bee Colony Or Particle Swarm Optimization-Based Iterative Thresholding For The Detection Of Bruised Apples, Mahmut Heki̇m, Onur Cömert, Kemal Adem Jan 2020

A Hybrid Model Based On The Convolutional Neural Network Model And Artificial Bee Colony Or Particle Swarm Optimization-Based Iterative Thresholding For The Detection Of Bruised Apples, Mahmut Heki̇m, Onur Cömert, Kemal Adem

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, apple images taken with near-infrared (NIR) cameras were classified as bruised and healthy objects using iterative thresholding approaches based on artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms supported by a convolutional neural network (CNN) deep learning model. The proposed model includes the following stages: image acquisition, image preprocessing, the segmentation of anatomical regions (stem-calyx regions) to be discarded, the detection of bruised areas on the apple images, and their classification. For this aim, by using the image acquisition platform with a NIR camera, a total of 1200 images at 6 different angles were taken …

Go to article

Time Series Forecasting On Multivariate Solar Radiation Data Using Deep Learning (Lstm), Murat Ci̇han Sorkun, Özlem Durmaz İncel, Christophe Paoli Jan 2020

Time Series Forecasting On Multivariate Solar Radiation Data Using Deep Learning (Lstm), Murat Ci̇han Sorkun, Özlem Durmaz İncel, Christophe Paoli

Turkish Journal of Electrical Engineering and Computer Sciences

Energy management is an emerging problem nowadays and utilization of renewable energy sources is an efficient solution. Solar radiation is an important source for electricity generation. For effective utilization, it is important to know precisely the amount from different sources and at different horizons: minutes, hours, and days. Depending on the horizon, two main classes of methods can be used to forecast the solar radiation: statistical time series forecasting methods for short to midterm horizons and numerical weather prediction methods for medium- to long-term horizons. Although statistical time series forecasting methods are utilized in the literature, there are a limited …

Go to article

Nonlocal Means Estimation Of Intrinsic Mode Functions For Speech Enhancement, Sagar Reddy Vumanthala, Bikshalu K Jan 2020

Nonlocal Means Estimation Of Intrinsic Mode Functions For Speech Enhancement, Sagar Reddy Vumanthala, Bikshalu K

Turkish Journal of Electrical Engineering and Computer Sciences

The main aim of this paper is to introduce a new approach to enhance speech signals by exploring the advantages of nonlocal means (NLM) estimation and empirical mode decomposition. NLM, a patch-based denoising method, is extensively used for two-dimensional signals like images. However, its use for one-dimensional signals has been attracting more attention recently. The NLM-based approach is quite useful for removing low-frequency noises based on nonlocal similarities present among samples of the signal. However, there is an issue of under averaging in the high-frequency regions. The temporal and spectral characteristics of the speech signal are changing markedly over time. …

Go to article

A Symmetric-Based Framework For Securing Cloud Data At Rest, Mohammed Mohammed, Fadhil Abed Jan 2020

A Symmetric-Based Framework For Securing Cloud Data At Rest, Mohammed Mohammed, Fadhil Abed

Turkish Journal of Electrical Engineering and Computer Sciences

Cloud computing is the umbrella term for delivering services via the Internet. It enables enterprises and individuals to access services such as virtual machines, storage, or applications on demand. It allows them to achieve more by paying less, and it removes the barrier of installing physical infrastructure. However, due to its openness and availability over the Internet, the issue of ensuring security and privacy arises. This requires careful consideration from enterprises and individuals before the adoption of cloud computing. In order to overcome security issues, cloud service providers are required to use strong security measures to secure their storage and …

Go to article

A Compact Wideband Series Linear Dielectric Resonator Array Antenna, Yazeed Qasayneh, Abdullulah Almuhaisen, Talha Yazdani Jan 2020

A Compact Wideband Series Linear Dielectric Resonator Array Antenna, Yazeed Qasayneh, Abdullulah Almuhaisen, Talha Yazdani

Turkish Journal of Electrical Engineering and Computer Sciences

This communication presents a miniaturised series linear wideband array of notched rectangular dielectric resonator antennas that operate in the band IEEE 802.11a. Three dielectric resonators (DRs) were excited through the aperture slots coupled with a microstrip feed. To improve the array gain, the aperture slots were placed based on the attributes related to the standing-wave ratio on a short-ended microstrip feeder to obtain optimal joint power for the DRs, while the bandwidth was improved using the notched rectangular DRs. An equivalent impedance model of the proposed array was postulated to provide physical insight into the array resonance behaviour. The impedance …

Go to article

A Low Dropout Voltage Regulator With A Transient Voltage Spikes Reducer And Improved Figure Of Merit, Guru Prasad, Kumara Shama Jan 2020

A Low Dropout Voltage Regulator With A Transient Voltage Spikes Reducer And Improved Figure Of Merit, Guru Prasad, Kumara Shama

Turkish Journal of Electrical Engineering and Computer Sciences

An area efficient output capacitor-free low dropout [LDO] voltage regulator with an improved figure of merit is presented in this paper. The proposed LDO regulator consists of a novel, dynamically biased error amplifier that reduces overshoot and undershoot voltage spikes arising from abrupt load changes. Source bulk modulation is employed to enhance the current driving capability of the pass transistor. An adaptive biasing scheme is also used along with dynamic biasing to improve the current efficiency of the system. The on-chip capacitor required for proper working of the LDO regulator is only 35 pF. The proposed LDO regulator is designed …

Go to article

Pulse Width Modulation Control Of Fifteen-Switch Inverter For Four Ac Loads, Gaurav Goyal, Mohan Aware Jan 2020

Pulse Width Modulation Control Of Fifteen-Switch Inverter For Four Ac Loads, Gaurav Goyal, Mohan Aware

Turkish Journal of Electrical Engineering and Computer Sciences

In many studies in the literature, various topologies with reduced switch count are proposed. With the use of these topologies, a lower number of semiconductor switches are required to produce a desired set of voltage. This in turn reduces the size and cost of the inverter. This paper proposes a new reduced switch count topology named "fifteen-switch inverter (FSI)" which is experimentally verified. The FSI has five switches in one leg and have three legs for three phases. It is capable of controlling four three-phase ac loads. In this proposed inverter topology, fifteen switches are used against the twenty four …

Go to article

A Priority-Based Queuing Model Approach Using Destination Parameters Forreal-Time Applications On Ipv6 Networks, Sadetti̇n Demi̇r, İbrahi̇m Özçeli̇k Jan 2020

A Priority-Based Queuing Model Approach Using Destination Parameters Forreal-Time Applications On Ipv6 Networks, Sadetti̇n Demi̇r, İbrahi̇m Özçeli̇k

Turkish Journal of Electrical Engineering and Computer Sciences

In the early days of the Internet architecture, the most important aim is to transmit data over packet switched networks. The traditional Internet architecture used in these networks lacks quality of service. However, today, as realtime applications increase, it is needed. There are approaches to improving the quality of service using the flow label field in the Internet Protocol version 6 header. In this study, a novel algorithm that uses destination network parameters to reduce queuing and end-to-end delay is created. A round-robin-based time-aware priority queue new model is used within this algorithm. Data packets using this proposed queue are …

Go to article

First Prev Next Last