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Articles 1 - 23 of 23
Full-Text Articles in Computer Engineering
Using Long Short-Term Memory Networks To Make And Train Neural Network Based Pseudo Random Number Generator, Aditya Harshvardhan
Using Long Short-Term Memory Networks To Make And Train Neural Network Based Pseudo Random Number Generator, Aditya Harshvardhan
Electronic Theses and Dissertations
Neural Networks have been used in many decision-making models and been employed in computer vision, and natural language processing. Several works have also used Neural Networks for developing Pseudo-Random Number Generators [2, 4, 5, 7, 8]. However, despite great performance in the National Institute of Standards and Technology (NIST) statistical test suite for randomness, they fail to discuss how the complexity of a neural network affects such statistical results. This work introduces: 1) a series of new Long Short- Term Memory Network (LSTM) based and Fully Connected Neural Network (FCNN – baseline [2] + variations) Pseudo Random Number Generators (PRNG) …
Study On Performance Of Pruned Cnn-Based Classification Models, Mengling Deng
Study On Performance Of Pruned Cnn-Based Classification Models, Mengling Deng
Electronic Theses and Dissertations
Convolutional Neural Network (CNN) is a neural network developed for processing image data. CNNs have been studied extensively and have been used in numerous computer vision tasks such as image classification and segmentation, object detection and recognition, etc. [1] Although, the CNNs-based approaches showed humanlevel performances in these tasks [2], they require heavy computation in both training and inference stages, and the models consist of millions of parameters. This hinders the development and deployment of CNN-based models for real world applications. Neural Network Pruning and Compression techniques have been proposed [3, 4] to reduce the computation complexity of trained CNNs …
Improved Secure And Low Computation Authentication Protocol For Wireless Body Area Network With Ecc And 2d Hash Chain, Soohyeon Choi
Improved Secure And Low Computation Authentication Protocol For Wireless Body Area Network With Ecc And 2d Hash Chain, Soohyeon Choi
Electronic Theses and Dissertations
Since technologies have been developing rapidly, Wireless Body Area Network (WBAN) has emerged as a promising technique for healthcare systems. People can monitor patients’ body condition and collect data remotely and continuously by using WBAN with small and compact wearable sensors. These sensors can be located in, on, and around the patient’s body and measure the patient’s health condition. Afterwards sensor nodes send the data via short-range wireless communication techniques to an intermediate node. The WBANs deal with critical health data, therefore, secure communication within the WBAN is important. There are important criteria in designing a security protocol for a …
Human Activity Recognition Based On Wearable Flex Sensor And Pulse Sensor, Xiaozhu Jin
Human Activity Recognition Based On Wearable Flex Sensor And Pulse Sensor, Xiaozhu Jin
Electronic Theses and Dissertations
In order to fulfill the needs of everyday monitoring for healthcare and emergency advice, many HAR systems have been designed [1]. Based on the healthcare purpose, these systems can be implanted into an astronaut’s spacesuit to provide necessary life movement monitoring and healthcare suggestions. Most of these systems use acceleration data-based data record as human activity representation [2,3]. But this data attribute approach has a limitation that makes it impossible to be used as an activity monitoring system for astronavigation. Because an accelerometer senses acceleration by distinguishing acceleration data based on the earth’s gravity offset [4], the accelerometer cannot read …
Past To Present (P2p): Road Thermal Image Colorization, Yuseong Park
Past To Present (P2p): Road Thermal Image Colorization, Yuseong Park
Electronic Theses and Dissertations
Thermal image colorization into realistic RGB image is a challenging task. Thermal cameras are easily to detect objects in particular situation (e.g. darkness and fog) that the human eyes cannot detect. However, it is difficult to interpret the thermal image with human eyes. Enhancing thermal image colorization is an important task to improve these areas. The results of the existing colorization method still have color ambiguities, distortion, and blurriness problems. This paper focused on thermal image colorization using pix2pix network architecture based on Generative Adversarial Net (GAN). Pix2pix is a model that transforms thermal image into RGB image, but our …
Semantic Segmentation Using Modified U-Net Architecture For Crack Detection, Michael Sun
Semantic Segmentation Using Modified U-Net Architecture For Crack Detection, Michael Sun
Electronic Theses and Dissertations
The visual inspection of a concrete crack is essential to maintaining its good condition during the service life of the bridge. The visual inspection has been done manually by inspectors, but unfortunately, the results are subjective. On the other hand, automated visual inspection approaches are faster and less subjective. Concrete crack is an important deficiency type that is assessed by inspectors. Recently, various Convolutional Neural Networks (CNNs) have become a prominent strategy to spot concrete cracks mechanically. The CNNs outperforms the traditional image processing approaches in accuracy for the high-level recognition task. Of them, U-Net, a CNN based semantic segmentation …
Kernel-Controlled Dqn Based Cnn Pruning For Model Compression And Acceleration, Romancha Khatri
Kernel-Controlled Dqn Based Cnn Pruning For Model Compression And Acceleration, Romancha Khatri
Electronic Theses and Dissertations
Apart from the accuracy, the size of convolutional neural networks (CNN) models is another principal factor for facilitating the deployment of models on memory, power and budget constrained devices. However, conventional model compression techniques require human experts to setup parameters to explore the design space which is suboptimal and time consuming. Various pruning techniques are implemented to gain compression, trading off speed and accuracy. Given a CNN model [11], we propose an automated deep reinforcement learning [9] based model compression technique that can effectively turned off kernels on each layer by observing its significance on decision making. By observing accuracy, …
Development Of Enhanced Weed Detection System With Adaptive Thresholding, K-Means And Support Vector Machine, Dheeman Saha
Development Of Enhanced Weed Detection System With Adaptive Thresholding, K-Means And Support Vector Machine, Dheeman Saha
Electronic Theses and Dissertations
This paper proposes a sophisticated classification process to segment the leaves of carrots from weeds (mostly Chamomile). In the early stages, of the plants’ development, both weeds and carrot leaves are intermixed with each other and have similar color texture. This makes it difficult to identify without the help of the domain experts. Therefore, it is essential to remove the weed regions so that the carrot plants can grow without any interruptions. The process of identifying the weeds become more challenging when both plant and weed regions overlap (inter-leaves). The proposed system addresses this problem by creating a sophisticated means …
Development Of Semantic Scene Conversion Model For Image-Based Localization At Night, Dongyoun Kim
Development Of Semantic Scene Conversion Model For Image-Based Localization At Night, Dongyoun Kim
Electronic Theses and Dissertations
Developing an autonomous vehicle navigation system invariant to illumination change is one of the biggest challenges in vision-based localization field due to the fact that the appearance of an image becomes inconsistent under different light conditions even with the same location. In particular, the night scene images have greatest change in appearance compared to the according day scenes. Moreover, the night images do not have enough information in Image-based localization. To deal with illumination change, image conversion methods have been researched. However, these methods could lose the detail of objects and add fake objects into the output images. In this …
An Approach To Finding Parking Space Using The Csi-Based Wifi Technology, Yunfan Zhang
An Approach To Finding Parking Space Using The Csi-Based Wifi Technology, Yunfan Zhang
Electronic Theses and Dissertations
With ever-increasing number of vehicles and shortages of parking spaces, parking has always been a very important issue in transportation. It is necessary to use advanced intelligent technologies to help drivers find parking spaces, quickly. In this thesis, an approach to finding empty spaces in parking lots using the CSI-based WiFi technology is presented. First, the channel state information (CSI) of received WiFi signals is analyzed. The features of CSI data that are strongly correlated with the number of empty slots in parking lots are identified and extracted. A machine learning technique to perform multi-class classification that categorizes the input …
Chronic Risk And Disease Management Model Using Structured Query Language And Predictive Analysis, Mamata Ojha
Chronic Risk And Disease Management Model Using Structured Query Language And Predictive Analysis, Mamata Ojha
Electronic Theses and Dissertations
Individuals with chronic conditions are the ones who use health care most frequently and more than 50% of top ten causes of death are chronic diseases in United States and these members always have health high risk scores. In the field of population health management, identifying high risk members is very important in terms of patient health care, disease management and cost management. Disease management program is very effective way of monitoring and preventing chronic disease and health related complications and risk management allows physicians and healthcare companies to reduce patient’s health risk, help identifying members for care/disease management along …
Low Power Wide Area Networks (Lpwan): Technology Review And Experimental Study On Mobility Effect, Dhaval Patel
Low Power Wide Area Networks (Lpwan): Technology Review And Experimental Study On Mobility Effect, Dhaval Patel
Electronic Theses and Dissertations
In the past decade, we have witnessed explosive growth in the number of low-power embedded and Internet-connected devices, reinforcing the new paradigm, Internet of Things (IoT). IoT devices like smartphones, home security systems, smart electric meters, garage parking indicators, etc., have penetrated deeply into our daily lives. These IoT devices are increasingly attached and operated in mobile objects like unmanned vehicles, trains, airplanes, etc. The low power wide area network (LPWAN), due to its long-range, low-power and low-cost communication capability, is actively considered by academia and industry as the future wireless communication standard for IoT. However, despite the increasing popularity …
Design, Implementation And A Pilot Study Of Mobile Framework For Pedestrian Safety Using Smartphone Sensors, Aawesh Man Shrestha
Design, Implementation And A Pilot Study Of Mobile Framework For Pedestrian Safety Using Smartphone Sensors, Aawesh Man Shrestha
Electronic Theses and Dissertations
Pedestrian distraction from smartphones is a serious social problem that caused an ever increasing number of fatalities especially as virtual reality (VR) games have gained popularity recently. In this thesis, we present the design, implementation, and a pilot study of WiPedCross, a WiFi direct-based pedestrian safety system that senses and evaluates a risk, and alerts accordingly the user to prevent traffic accidents. In order to develop a non-intrusive, accurate, and energy-efficient pedestrian safety system, a number of technical challenges are addressed: to enhance the positioning accuracy of the user for precise risk assessment, a map-matching algorithm based on a Hidden …
Wi-Fi Finger-Printing Based Indoor Localization Using Nano-Scale Unmanned Aerial Vehicles, Appala Narasimha Raju Chekuri
Wi-Fi Finger-Printing Based Indoor Localization Using Nano-Scale Unmanned Aerial Vehicles, Appala Narasimha Raju Chekuri
Electronic Theses and Dissertations
Explosive growth in the number of mobile devices like smartphones, tablets, and smartwatches has escalated the demand for localization-based services, spurring development of numerous indoor localization techniques. Especially, widespread deployment of wireless LANs prompted ever increasing interests in WiFi-based indoor localization mechanisms. However, a critical shortcoming of such localization schemes is the intensive time and labor requirements for collecting and building the WiFi fingerprinting database, especially when the system needs to cover a large space. In this thesis, we propose to automate the WiFi fingerprint survey process using a group of nano-scale unmanned aerial vehicles (NAVs). The proposed system significantly …
Enabling Low Cost Wifi-Based Traffic Monitoring System Using Deep Learning, Sayan Sahu
Enabling Low Cost Wifi-Based Traffic Monitoring System Using Deep Learning, Sayan Sahu
Electronic Theses and Dissertations
A traffic monitoring system (TMS) is an integral part of Intelligent Transportation Systems (ITS) for traffic analysis and planning. However, covering huge miles of rural highways (119,247 miles in U.S.) with a large number of TMSs is a very challenging problem due to the cost issue. This paper aims to address the problem by developing a low-cost and portable TMS called DeepWiTraffic based on COTs WiFi devices. The proposed system enables accurate vehicle detection (counting) and classification by exploiting the unique WiFi Channel State Information (CSI) of passing vehicles. Spatial and temporal correlations of CSI amplitude and phase data are …
A Scale Space Local Binary Pattern (Sslbp) – Based Feature Extraction Framework To Detect Bones From Knee Mri Scans, Jinyeong Mun
A Scale Space Local Binary Pattern (Sslbp) – Based Feature Extraction Framework To Detect Bones From Knee Mri Scans, Jinyeong Mun
Electronic Theses and Dissertations
The medical industry is currently working on a fully autonomous surgical system, which is considered a novel modality to go beyond technical limitations of conventional surgery. In order to apply an autonomous surgical system to knees, one of the primarily responsible areas for supporting the total weight of human body, accurate segmentation of bones from knee Magnetic Resonance Imaging (MRI) scans plays a crucial role. In this paper, we propose employing the Scale Space Local Binary Pattern (SSLBP) feature extraction, a variant of local binary pattern extractions, for detecting bones from knee images. The proposed methods consist of two phases. …
Localization Of Microcalcification On The Mammogram Using Deep Convolutional Neural Network, Jieun Jhang
Localization Of Microcalcification On The Mammogram Using Deep Convolutional Neural Network, Jieun Jhang
Electronic Theses and Dissertations
Breast cancer is the most common cancer in women worldwide, and the mammogram is the most widely used screening technique for breast cancer. To make a diagnosis in the early stage of breast cancer, the appearance of masses and microcalcifications on the mammogram are two crucial indicators. Notably, the early detection of malignant microcalcifications can facilitate the diagnosis and the treatment of breast cancer at the appropriate time. Making an accurate evaluation on microcalcifications is a timeconsuming and challenging task for the radiologists due to the small size and the low contrast of microcalcification. Compared to the background and mammogram …
A Dynamic Scaling Methodology For Improving Performance Of Big Data Systems, Nashmiah Alhamdawi
A Dynamic Scaling Methodology For Improving Performance Of Big Data Systems, Nashmiah Alhamdawi
Electronic Theses and Dissertations
The continuous growth of data volume in various fields such as, healthcare, sciences, economics, and business has caused an overwhelming flow of data in the last decade. The overwhelming flow of data has raised challenges in processing, analyzing, and storing data, which lead many systems to face an issue in performance. Poor performance of systems creates negative impact such as delays, unprocessed data, and increasing response time. Processing huge amounts of data demands a powerful computational infrastructure to ensure that data processing and analysis success [7]. However, the architectures of these systems are not suitable to process that quantity of …
Sickle Blood Cell Detection Based On Image Segmentation, Kholoud Alotaibi
Sickle Blood Cell Detection Based On Image Segmentation, Kholoud Alotaibi
Electronic Theses and Dissertations
Red blood cells have a vital role in human health. Red blood cells have a circular shape and a concave surface and exchange the gasses between the inside and outside of the body. However, at times, these normally round cells become sickle shaped, which is an indication of sickle cell disease. This paper introduces a unique approach to detect sickle blood cells in blood samples using image segmentation and shape detection. This method is based on calculating the max axis and min axis of the cell. The form factor is computed using these properties to determine whether the cell is …
Product Authentication Using Hash Chains And Printed Qr Codes, Harshith R. Keni
Product Authentication Using Hash Chains And Printed Qr Codes, Harshith R. Keni
Electronic Theses and Dissertations
This thesis explores the usage of simple printed tags for authenticating products. Printed tags are a cheap alternative to RFID and other tag based systems and do not require specialized equipment. Due to the simplistic nature of such printed codes, many security issues like tag impersonation, server impersonation, reader impersonation, replay attacks and denial of service present in RFID based solutions need to be handled differently. An algorithm that utilizes hash chains to secure such simple tags while still keeping cost low is discussed. The security characteristics of this scheme as well as other product authentication schemes that use RFID …
Data Center Load Forecast Using Dependent Mixture Model, Md Riaz Ahmed Khan
Data Center Load Forecast Using Dependent Mixture Model, Md Riaz Ahmed Khan
Electronic Theses and Dissertations
The dependency on cloud computing is increasing day by day. With the boom of data centers, the cost is also increasing, which forces industries to come up with techniques and methodologies to reduce the data center energy use. Load forecasting plays a vital role in both efficient scheduling and operating a data center as a virtual power plant. In this thesis work a stochastic method, based on dependent mixtures is developed to model the data center load and is used for day-ahead forecast. The method is validated using three data sets from National Renewable Energy Laboratory (NREL) and one other …
Breast Cancer Classification Of Mammographic Masses Using Circularity Max Metric, A New Method, Tae Keun Heo
Breast Cancer Classification Of Mammographic Masses Using Circularity Max Metric, A New Method, Tae Keun Heo
Electronic Theses and Dissertations
Breast cancer classification can be divided into two categories. The first category is a benign tumor, and the other is a malignant tumor. The main purpose of breast cancer classification is to classify abnormalities into benign or malignant classes and thus help physicians with further analysis by minimizing potential errors that can be made by fatigued or inexperienced physicians. This paper proposes a new shape metric based on the area ratio of a circle to classify mammographic images into benign and malignant class. Support Vector Machine is used as a machine learning tool for training and classification purposes. The improved …
Game Theoretical Approach For Joint Relay Selection And Resource Allocation In Mobile Device Networks, Runan Yao
Electronic Theses and Dissertations
With the improvement of hardware, more and more multimedia applications are allowed to run in the mobile device. However, due to the limited radio bandwidth, wireless network performance becomes a critical issue. Common mobile solutions are based on the centralized structure, which require an access point to handle all the communication requirement in the work area. The transmission performance of centralized framework relies on the density of access points. But increasing the number of access points will cost lot of money and the interference between access point will reduce the transmission quality. Thanks to the wireless sensor network implementations, the …