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Articles 1 - 9 of 9
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 …
Instance Segmentation And Object Detection In Road Scenes Using Inverse Perspective Mapping Of 3d Point Clouds And 2d Images, Chungyup Lee
Instance Segmentation And Object Detection In Road Scenes Using Inverse Perspective Mapping Of 3d Point Clouds And 2d Images, Chungyup Lee
Electronic Theses and Dissertations
The instance segmentation and object detection are important tasks in smart car applications. Recently, a variety of neural network-based approaches have been proposed. One of the challenges is that there are various scales of objects in a scene, and it requires the neural network to have a large receptive field to deal with the scale variations. In other words, the neural network must have deep architectures which slow down computation. In smart car applications, the accuracy of detection and segmentation of vehicle and pedestrian is hugely critical. Besides, 2D images do not have distance information but enough visual appearance. On …
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 …
Optimized Multilayer Perceptron With Dynamic Learning Rate To Classify Breast Microwave Tomography Image, Chulwoo Pack
Optimized Multilayer Perceptron With Dynamic Learning Rate To Classify Breast Microwave Tomography Image, Chulwoo Pack
Electronic Theses and Dissertations
Most recently developed Computer Aided Diagnosis (CAD) systems and their related research is based on medical images that are usually obtained through conventional imaging techniques such as Magnetic Resonance Imaging (MRI), x-ray mammography, and ultrasound. With the development of a new imaging technology called Microwave Tomography Imaging (MTI), it has become inevitable to develop a CAD system that can show promising performance using new format of data. The platform can have a flexibility on its input by adopting Artificial Neural Network (ANN) as a classifier. Among the various phases of CAD system, we have focused on optimizing the classification phase …
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 …
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 …