Digital Commons Network^™

Investigation Of Topological Phonons In Discrete Mechanical Metamaterials, Kai Qian

Dissertations

The study of topological mechanical metamaterials is a new emerging field that focuses on the topological properties of artificial mechanical structures. Inspired by topological insulators, topological mechanism has attracted intensive attention in condensed matter physics and successfully connected the quantum mechanical descriptions of electrons with the classical descriptions of phonons. It has led to experiments of mechanical metamaterials possessing topological characteristics, such as topologically protected conducting edges or surfaces without back-scattering. This dissertation presents a new experimental approach for mechanically engineering topological metamaterials based on patterning magnetically coupled spinners in order to localize the propagation of vibrations and evaluate different …

Towards Improving The Security Of The Software Supply Chain, Hammad Afzali

Dissertations

A software supply chain comprises a series of steps performed to develop and distribute a software product. History has shown that each of these steps is vulnerable to attacks that can have serious repercussions and can affect many users at once. To address the attacks against the software supply chain, end users must be provided with verifiable guarantees about the individual steps of the chain and with assurances that the steps are securely chained together.

In this dissertation, the security of several individual steps in the software supply chain is enhanced. The first step of the chain, managing the source …

Improving Multi-Threaded Qos In Clouds, Weiwei Jia

Dissertations

Multi-threading and resource sharing are pervasive and critical in clouds and data-centers. In order to ease management, save energy and improve resource utilization, multi-threaded applications from different tenants are often encapsulated in virtual machines (VMs) and consolidated on to the same servers. Unfortunately, despite much effort, it is still extremely challenging to maintain high quality of service (QoS) for multi-threaded applications of different tenants in clouds, and these applications often suffer severe performance degradation, poor scalability, unfair resource allocation, and so on.

The dissertation identifies the causes of the QoS problems and improves the QoS of multi-threaded execution with three …

Mechanisms Of Oscillations And Polyglot Entrainment In Neuronal And Circadian Models, Emel Khan

Dissertations

Entrainment is a type of synchronization in which the period of an endogenous oscillator matches the period of an external forcing signal and a stable phase relationship is maintained between them. Entrainment patterns are described in terms of the number of input oscillations (N) that are phase-locked to a number of output oscillations (M), referred to as N:M patterns. Arnold tongue diagrams are used to depict the regions of N:M entrainment patterns in the input period-amplitude parameter space. Although the entrainment of self-sustained oscillators by periodic forcing are well investigated is a well-studied problem, entrainment of damped oscillators has been …

Modeling And Analysis Of Intracellular Signaling Networks And Cellular Decisions, Mustafa Ozen

Dissertations

Developing molecular network analysis methods is important due to their applications on complex biological systems such as target discovery, development of drugs, discovering drug effects, and finding treatments for many complex diseases, e.g., cancer, autoimmune, and mental disorders. An example of analysis techniques is the fault diagnosis analysis, in which the purpose is to quantify how much vulnerable the entire network is to dysfunction of one or multiple molecules. Such analysis can be done after proper network models are implemented, trained, and tested against the experimental data. In this dissertation, a Boolean modeling framework is implemented and methods to train …

Rm-Net: Rasterizing Markov Signals To Images For Deep Learning, Kajal Gupta

Theses

Statistical machine learning approaches are quite famous for processing Markov signal data. They can model unobserved states and learn certain characteristics particular to a signal with good accuracy. However, with the advent of Deep learning the novice ways of solving a problem has shifted towards this more sophisticated algorithm, which is much better, powerful and more accurate. Specifically, Convolutional Neural Nets (CNN) have shown many promising results on images and videos. Here we illustrate how CNN can be applied to a 1D numeric signal using signal rasterization technique. We start by rasterizing a 1D numeric Markov signal into an image …

Iii-Nitride Ultraviolet Light-Emitting Diodes: Approaches For The Enhanced Efficiency, Moulik Patel

Theses

III-nitride ultraviolet (UV) light-emitting diodes (LEDs) offer marvelous potential for a wide range of applications, including air/water purification, surface disinfection, biochemical sensing, cancer cell elimination, and many more. III-nitride semiconductor alloys, especially AlGaN and AlInN, have drawn significant attention due to their significant advantages that include environmental-friendly material composition, compact in size, longer lifetime, low power consumption, and tunable optical emission. However, the performance of III-nitride UV LEDs in terms of efficiency and output power is still deficient. The presence of high defects and dislocations due to lattice-mismatched epilayers, polarization induced quantum-confined Stark effect, electron overflow with low hole injection …

Short Term Temperature Forecasting Using Lstms, And Cnn, Darshan Shah

Theses

Weather forecasting is a vital application in present times. We can use the predictions to minimize the weather related loss. Use of machine learning and deep learning algorithms for forecasting, can eliminate or reduce the necessity of big data and high computation dependent process of parameterization. Long Short-Term Memory (LSTM) is a widely used deep learning architecture for time series forecasting. In this paper, we aim to predict one day ahead average temperature using a 2-layer neural network consisting of one layer of LSTM and one layer of 1D convolution. The input is pre-processed using a smoothing technique and output …

Finite Element Modeling Of Underwater Acoustic Environments And Domain Decomposition Methods, General Ozochiawaeze

Theses

Underwater acoustic scattering problems have several important applications ranging from sonar imaging in target detection to providing information for sediment classification and geoacoustic inversion. This work presents numerical methods for time-harmonic acoustic scattering problems, specifically, finite element methods for the Helmholtz equation. Furthermore, an iterative domain decomposition formulation is introduced for acoustic scattering problems where the physical domain consists of multiple layers of different materials.

Assembly And Detection Of 3-D Qr Codes Through Additive Manufacturing And Terahertz Imaging, Patrick Dunn

Theses

Automatic identification and data capture, or AIDC, plays a substantial role in contemporary business, advertising, and military needs. The purpose of this study is to generate a potential alternative to current AIDC approaches by constructing three-dimensional plastic tags (or ‘3D QR codes’) using additive manufacturing techniques and interrogate them using Terahertz radiation. 3D Quick Response (QR) codes are designed in 3D computer-aided design software. The QR codes are 3D structures embedded in the printed plastic in which an air gap in the plastic (or an air gap filled in with another type of plastic) indicates a bit of information. Information …

Stationary Probability Distributions Of Stochastic Gradient Descent And The Success And Failure Of The Diffusion Approximation, William Joseph Mccann

Theses

In this thesis, Stochastic Gradient Descent (SGD), an optimization method originally popular due to its computational efficiency, is analyzed using Markov chain methods. We compute both numerically, and in some cases analytically, the stationary probability distributions (invariant measures) for the SGD Markov operator over all step sizes or learning rates. The stationary probability distributions provide insight into how the long-time behavior of SGD samples the objective function minimum.

A key focus of this thesis is to provide a systematic study in one dimension comparing the exact SGD stationary distributions to the Fokker-Planck diffusion approximation equations —which are commonly used in …

Time Series Forecasting With Applications To Finance, Viswapriya Misra

Theses

In finance, many phenomena are modeled as time series. This thesis investigates time series forecasting problems in finance, precisely the stock price prediction problem. We employ and compare traditional statistical algorithms like MA, ARIMA, and ARMA-GARCH with newly developed deep learning-based algorithms such RNNs, LSTMs, GRUs, TCNs, and bidirectional LSTMs and GRUs for predicting stock prices. We perform a comprehensive study and present all the experimental results on different datasets. We find that ARIMA and GRU perform better for single-step stock price prediction than other deep learning architectures. Adding market and economic indicators do not improve the performance of the …

Land Cover Image Segmentation Based On Individual Class Binary Segmentation, Sathyanarayanan Somasunder

Theses

Remote sensing techniques have been developed over the past decades to acquire data without being in contact of the target object or data source. Their application on land-cover image segmentation has attracted significant attention in recent years. With the help of satellites, scientists and researchers can collect and store high resolution image data that can be further processed, segmented, and classified. However, these research results have not yet been synthesized to provide coherent guidance on the effect of variant land-cover segmentation processes. In this paper, we present a novel model that augments segmentation using smaller networks to segment individual classes. …

Njit Library Annual Report 2019-2020, Njit Libraries

Library Annual Reports

No abstract provided.

Che 489-002: Process Dynamics And Control, Richard Cimino

Chemical and Materials Engineering Syllabi

No abstract provided.

Che 260-002: Fluid Flow, Ecevit Bilgili

Chemical and Materials Engineering Syllabi

No abstract provided.

Che 495-002: Chemical Engineering Lab I, Rees Rankin

Chemical and Materials Engineering Syllabi

No abstract provided.

Che 472-Hm2: Process And Plant Design, Nellone Reid

Chemical and Materials Engineering Syllabi

No abstract provided.

Che 472-002: Process And Plant Design, Nellone Reid

Chemical and Materials Engineering Syllabi

No abstract provided.

Me 231-004: Kinematics Of Machinery, Anthony Glick

Mechanical and Industrial Engineering Syllabi

No abstract provided.

Ie 334-002: Engineering Economy & Capital Investment Analysis, Athanassios Bladikas

Mechanical and Industrial Engineering Syllabi

No abstract provided.

Ie 335-452: Engineering Cost Analysis & Control, Dielanda Durnay

Mechanical and Industrial Engineering Syllabi

No abstract provided.

Ie 335-454: Engineering Cost Analysis & Control, Dielanda Durnay

Mechanical and Industrial Engineering Syllabi

No abstract provided.

Ie 466-002: Materials Management & Distribution Logistics, George Abdou

Mechanical and Industrial Engineering Syllabi

No abstract provided.

Ie 339-002: Work Measurement And Standards, George Abdou

Mechanical and Industrial Engineering Syllabi

No abstract provided.

Ie 492-104: Engineering Management, George Abdou

Mechanical and Industrial Engineering Syllabi

No abstract provided.

Me 430-002: Introduction To Computer Aided Design, Jaskirat Sodhi

Mechanical and Industrial Engineering Syllabi

No abstract provided.

Phys 121-104: Physics Ii, Ken Chin

Physics Syllabi

No abstract provided.

Phys 102-006: General Physics, N. Ravindra

Physics Syllabi

No abstract provided.

Phys 102-004: General Physics, N. Ravindra

Physics Syllabi

No abstract provided.