Signal Processing Commons^™

Review Of Data Mining Techniques For Detecting Churners In The Telecommunication Industry, Mahmoud Ewieda, Mohamed Ismail Roushdy, Essam Shaaban

Future Computing and Informatics Journal

The telecommunication sector has been developed rapidly and with large amounts of data obtained as a result of increasing in the number of subscribers, modern techniques, data-based applications, and services. As well as better awareness of customer requirements and excellent quality that meets their satisfaction. This satisfaction raises rivalry between firms to maintain the quality of their services and upgrade them. These data can be helpfully extracted for analysis and used for predicting churners. Researchers around the world have conducted important research to understand the uses of Data mining (DM) that can be used to predict customers' churn. This …

Precoder Design For Communication-Efficient Distributed Mimo Receivers With Controlled Peak-Average Power Ratio, Vinay A. Vaishampayan

Publications and Research

We consider the problem of communicating over a relay-assisted multiple-input multiple-output (MIMO) channel with additive noise, in which physically separated relays forward quantized information to a central decoder where the transmitted message is to be decoded. We assume that channel state information is available in the transmitter and show that the design of a rational-forcing precoder - a precoder which is matched to the quantizers used in the relays - is beneficial for reducing the symbol error probability. It turns out that for such rational-forcing precoder based systems, there is natural tradeoff between the peak to average power ratio in …

Efficiently Estimating Survival Signature And Two-Terminal Reliability Of Heterogeneous Networks Through Multi-Objective Optimization, Daniel Bruno Lopes Da Silva

Graduate Theses and Dissertations

The two-terminal reliability problem is a classical reliability problem with applications in wired and wireless communication networks, electronic circuit design, computer networks, and electrical power distribution, among other systems. However, the two-terminal reliability problem is among the hardest combinatorial problems and is intractable for large, complex networks. Several exact methods to solve the two-terminal reliability problem have been proposed since the 1960s, but they have exponential time complexity in general. Hence, practical studies involving large network-type systems resort to approximation methods to estimate the system's reliability. One attractive approach for quantifying the reliability of complex systems is to use signatures, …

Reference Design Of An Online Emulation And Hot-Patching Approach For Power Electronic Controller Validation, Estefano Soria Pearson

Graduate Theses and Dissertations

This thesis aims to develop a reference design of an online security system approach embedded in a power electronic controller for cybersecurity purposes. Cybersecurity in power electronics focuses on reducing vulnerabilities in the system, where most reside in the communication with the hardware devices. Although methods to secure communications lessen the probability and effects of cyber-attacks, discovering vulnerabilities is inevitable. This thesis attempts to provide a fail-safe approach to securing the system by targeting the safety of the power-electronic controller. This approach applies an additional security layer in case of a malicious or accidental controller firmware malfunction.

The online security …

Direct Torque Control For Silicon Carbide Motor Drives, Mohammad Hazzaz Mahmud

Graduate Theses and Dissertations

Direct torque control (DTC) is an extensively used control method for motor drives due to its unique advantages, e.g., the fast dynamic response and the robustness against motor parameters variations, uncertainties, and external disturbances. Using higher switching frequency is generally required by DTC to reduce the torque ripples and decrease stator current total harmonic distortion (THD), which however can lower the drive efficiency. Through the use of the emerging silicon carbide (SiC) devices, which have lower switching losses compared to their silicon counterparts, it is feasible to achieve high efficiency and low torque ripple simultaneously for DTC drives.

To overcome …

Comparative Study Of Nano-Rod And Nano-Sphere Based Localized Surface Plasmon Resonance Refractive Index Biosensors, Mariam M. Moussilli M. M. Moussilli, Abdul Rahman El Falou

BAU Journal - Science and Technology

Localized Surface Plasmon Resonance (LSPR) waves generated by the interaction of light with noble metal nanoparticles has been of great interest in recent years due to the high sensitivity of the extinction spectra of these nanoparticles to the medium's surrounding refractive index up to the atomic level.

In this article, we simulate the extinction spectra of noble metal sphere and rod nanoparticles in order to study the effect of the geometrical shape and size of the nanoparticle on the sensitivity and detection accuracy performance parameters of the extinction spectra. We also simulated the response of the sphere and rod nanoparticle's …

An Analytical Method Of Switching Waveform Design For Harmonic Elimination, Sraddhanjoli Bhadra

Theses and Dissertations

In this dissertation, we have investigated an analytical approach for solving a set of transcendental equations commonly encountered in Selective Harmonic Elimination (SHE). Generally, numerical solutions are used to solve the transcendental equations, which require guessing of an initial value and a valid value of modulation index m. Invalid initial values and an invalid m affect convergence. Such difficulties can be avoided with analytical solutions. The solution set will also determine the possible range of m valid for that modulation. Due to the complexity of solution that often requires computer algorithm to find a solution, we plan to develop design …

The Effects Of The Transient And Performance Loss Rates On Pv Output Performance, Chibuisi Chinasaokwu Okorieimoh, Brian Norton, Michael Conlon

Conference papers

Solar photovoltaic (PV) panels experience long-term performance degradation as compared to their initial performance, resulting in lower like-per-like efficiencies and performance ratios. Manufacturers of solar photovoltaic modules normally guarantee a lifespan of more than 20 years. To meet such commitments, it is important to monitor and mitigate PV module degradation during this period, as well as beyond, to recognize maintenance and repair needs. Solar PV modules degrade over time, becoming less effective, less reliable, and eventually unusable. The effects of transient and performance loss rates on the output performance of polycrystalline silicon (p-Si) solar PV modules are the focus of …

Extending Instantaneous De-Mixing Algorithms To Anechoic Mixtures, Swarnadeep Bagchi, Ruairí De Fréin

Conference papers

The AdRess algorithm separates sources that are mixed using stereo, pan-mixing in a computationally efficient way. Pan-mixing gives the sources a location in the stereo field by introducing a relative attenuation between the versions of the sources that appear on each channel. AdRess achieves separation by constructing only a frequency-attenuation matrix. We introduce a new algorithm called Delayed-AdRess (D-AdRess), where, in addition to the frequency-attenuation matrix, two other matrices namely, frequency-delay and time-delay are used to separate sources from anechoic mixtures. By anechoic mixtures, we mean mixing scenarios where both attenuation and delays are experienced by the source signals.

Comparison Of Hilbert Transform And Derivative Methods For Converting Ecg Data Into Cardioid Plots To Detect Heart Abnormalities, Robert George Goldie

Master's Theses

Electrocardiogram (ECG) time-domain signals contain important information about the heart. Several techniques have been proposed for creating a two-dimensional visualization of an ECG, called a Cardioid, that can be used to detect heart abnormalities with computer algorithms. The derivative method is the prevailing technique, which is popular for its low complexity, but it can introduce distortion into the Cardioid plot without additional signal processing. The Hilbert transform is an alternative method which has unity gain and phase shifts the ECG signal by 90 degrees to create the Cardioid plot. However, the Hilbert transform is seldom used and has historically been …

Medical Signal Recognition System Based On Machine Learning, Deep Learning, And Internet Of Things, Arwa Hameed Mohammed Taqi

Theses and Dissertations

Internet of Things (IoT) technology is becoming increasingly common in the healthcare industry. The significant benefits of IoT are decreased operational costs, improved outcomes of the treatment through virtual infrastructures and accessibility of real-time information for making informed decisions, improved disease management, reduced errors, and enhanced patient experience. This enables hospitals to address the demands of the ever-growing population in this world. On the other hand, medical signal classification systems are systems that recognize normal from abnormal medical signals. These systems depend on two modes, training mode in which the system learns about the dominant features of the signals, and …

Sar Collection Planning And Data Quality Assessment, Jacob M. Brumfield

Theses and Dissertations

Radar resource management is an important research topic in the radar community. Identifying the performance of a synthetic aperture radar image early into a data processing chain can improve intelligence collection mission performance. To achieve that goal, separate flags can be presented to a radar technician along a data processing chain to identify various errors within a data collection. Toward the end, this thesis analyzes he radar image processing chain and identifies data quality checks that could be implemented. The first quality check is to identify canonical targets and the necessary Nyquist-Shannon sampling requirements. Then, observations can be made to …

Synthetic Aperture Radar Image Recognition Of Armored Vehicles, Christopher Szul, Torrey J. Wagner, Brent T. Langhals

Faculty Publications

Synthetic Aperture Radar (SAR) imagery is not affected by weather and allows for day-and-night observations, however it can be difficult to interpret. This work applies classical and neural network machine learning techniques to perform image classification of SAR imagery. The Moving and Stationary Target Acquisition and Recognition dataset from the Air Force Research Laboratory was used, which contained 2,987 total observations of the BMP-2, BTR-70, and T-72 vehicles. Using a 75%/25% train/test split, the classical model achieved an average multi-class image recognition accuracy of 70%, while a convolutional neural network was able to achieve a 97% accuracy with lower model …

Design And Fabrication Of Zinc Oxide Optofluidic Laser Elements, Kyle T. Bodily

Theses and Dissertations

This thesis systematically goes through the derivation, simulation, and experimentation of Zinc Oxide optofluidic micro laser elements. Single and coupled ring resonators were simulated to show single mode transmission as well as enhanced coupling capabilities when surrounded by high refractive index liquid. Devices with diameters ranging from 100-500 µm were successfully fabricated inexpensively through standard cleanroom procedures. The devices were tested using two different pump laser systems. Testing included such factors as high pump intensity, various angles of excitation, and low temperature. In all cases PL emission was observed and recorded.

Bibliometric Review Of Predictive Maintenance Using Vibration Analysis, Aashna Midha Ms., Ishita Maheshwari Ms., Kaushik Ojha Mr., Kritika Gupta Ms., Shripad V. Deshpande Mr.

Library Philosophy and Practice (e-journal)

Every day the world is depending more and more on machines in almost every aspect of life. With the increasing use of machines, there also needs to be an evolution in the maintenance of these machines. Predictive maintenance is a process used to monitor the equipment and machinery during its operation to detect any damages and/or deteriorations and enable the required maintenance plan in advance, resulting in reduced operational costs and full utilization of tools and parts. The fundamental goal of this bibliometric review paper is a comprehension of the extent and sources of the literature available for predictive maintenance …

An Lpc Pole Processing Method For Enhancing The Identification Of Dominant Spectral Features, Jin Xu, Mark Davis, Ruairí De Fréin

Articles

This paper proposes a new time-resolved spectral analysis method based on a modification to the linear predictive coding (LPC) method for enhancing the identification of the dominant frequencies of a signal. The method described here is based on a z-plane analysis of the LPC poles. These poles are used to produce a series of reduced order filter transfer functions which can accurately identify and estimate the frequency of the dominant spectral features. The standard LPC method has been shown to suffer from a sensitivity to noise and its performance is dependent on the filter order. The proposed method can …

Framework For Collecting Data From Iot Device, Md Saiful Islam

Symposium of Student Scholars

The Internet of Things (IoT) is the most significant and blooming technology in the 21st century. IoT has rapidly developed by covering hundreds of applications in the civil, health, military, and agriculture areas. IoT is based on the collection of sensor data through an embedded system, and this embedded system uploads the data on the internet. Devices and sensor technologies connected over a network can monitor and measure data in real-time. The main challenge is to collect data from IoT devices, transmit them to store in the Cloud, and later retrieve them at any time for visualization and data analysis. …

Data Analysis Methods For Health Monitoring Sensors, Shahriar Sobhan

Symposium of Student Scholars

Innovations in health monitoring systems are fundamental for the continuous improvement of remote healthcare. With the current presence of SARS-CoV-2, better known as COVID-19, in people’s daily lives, solutions for monitoring heart and especially respiration and pulmonary functions are more needed than ever. Besides, health monitoring systems are widely used for patients who need isolated care, unconscious patients who cannot get medical attention for themselves. As it is well-known, monitoring systems rely on sensor technologies. Currently, there are multiple research studies for remote monitoring using different types of sensors. In this effort, we survey the current approaches that utilize the …

Multilayer Perceptron With Auto Encoder Enabled Deep Learning Model For Recommender Systems, Subhashini Narayan

Future Computing and Informatics Journal

In this modern world of ever-increasing one-click purchases, movie bookings, music, health- care, fashion, the need for recommendations have increased the more. Google, Netflix, Spotify, Amazon and other tech giants use recommendations to customize and tailor their search engines to suit the user’s interests. Many of the existing systems are based on older algorithms which although have decent accuracies, require large training and testing datasets and with the emergence of deep learning, the accuracy of algorithms has further improved, and error rates have reduced due to the use of multiple layers. The need for large datasets has declined as well. …

Deep Feature Learning For Fog Episodes Prediction In Patients With Pd, Hadeer Elziaat, Nashwa El-Bendary, Ramdan Mowad

Future Computing and Informatics Journal

A common symptom of Parkinson's Disease is Freezing of Gait (FoG) that causes an interrupt of the forward progression of the patient’s feet while walking. Therefore, Freezing of Gait episodes is always engaged to the patient's falls. This paper proposes a model for Freezing of Gait episodes detection and prediction in patients with Parkinson's Disease. Predicting Freezing of Gait in this paper considers as a multi-class classification problem with 3 classes namely, FoG, pre-FoG, and walking episodes. In this paper, the extracted feature scheme applied for the detection and the prediction of FoG is Convolutional Neural Network (CNN) spectrogram time-frequency …