Signal Processing Commons^™

Source Localization With Machine Learning, Arjun Gupta

Electrical and Computer Engineering ETDs

Source localization with sensor arrays have found applications across domains beginning with radar and sonar, astronomy, acoustics, bio-medical devices and more recently in autonomous cars and adaptive communication systems. The knowledge of the spatial spectrum not only provide information about the source and interference but also assists in increasing signal integrity and avoid interference. This provides an added degree of freedom in the form of spatial diversity. This research investigates spatial spectrum estimation of waveforms from the signals sampled by arbitrarily distributed sensors. Conventional high resolution algorithms such as root-MuSiC fails to perform accurate source localization due to the reliance …

Time Series Data Analysis Using Machine Learning-(Ml) Approach, Mvv Prasad Kantipudi Dr., Pradeep Kumar N.S Dr., S.Sreenath Kashyap Dr., Ss Anusha Vemuri Ms

Library Philosophy and Practice (e-journal)

Healthcare benefits related to continuous monitoring of human movement and physical activity can potentially reduce the risk of accidents associated with elderly living alone at home. Based on the literature review, it is found that many studies focus on human activity recognition and are still active towards achieving practical solutions to support the elderly care system. The proposed system has introduced a joint approach of machine learning and signal processing technology for the recognition of human's physical movements using signal data generated by accelerometer sensors. The framework adopts the concept of DSP to select very descriptive feature sets and uses …

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 accurately …

Artificial Intelligence Aided Receiver Design For Wireless Communication Systems, Wenjie Xu

Theses, Dissertations and Capstones

Physical layer (PHY) design in the wireless communication field realizes gratifying achievements in the past few decades, especially in the emerging cellular communication systems starting from the first generation to the fifth generation (5G). With the gradual increase in technical requirements of large data processing and end-to-end system optimization, introducing artificial intelligence (AI) in PHY design has cautiously become a trend. A deep neural network (DNN), one of the population techniques of AI, enables the utilization of its ‘learnable’ feature to handle big data and establish a global system model. In this thesis, we exploited this characteristic of DNN as …

Matlab Modeling Of Ofdm Modulation Technique Across A 24 Khz, 48 Khz, And 3 Mhz Bandwidth In The High-Frequency Radio Band (3-30) Mhz, Josiah Myer, Tyler Collins, Sarah Taylor, Natalia Anglero

Faculty-Sponsored Student Research & Capstones

The goal of this project is to use MATLAB to model orthogonal frequency division multiplexing (OFDM) modulation technique across 24 kHz, 48 kHz, and 3 MHz bandwidths in the high frequency (HF) radio band (3-30 MHz). The purpose of our design is to make HF long distance communication faster and more reliable so that every part of the world, including the most remote parts, will have access to high speed, long distance wireless communication. Our MATLAB model will allow us to modify the bandwidth, carrier frequency, modulation type, signal to noise ratio (SNR), and image size to determine which combination …

Analog & Digital Remote Synthesizer, Adam Brunner, Andrew Cihon-Scott, Scott Grisso, Linus Wright

Williams Honors College, Honors Research Projects

The purpose of this project is to develop and design an analog synthesizer musical instrument that integrates embedded digital hardware into the design to enable control from a remote source. The use of digital hardware enables the potential for a wide range of convenient features such as sound profile saving and loading, output recording functionality, and the ability to accept digital input from another musical instrument utilizing the Musical Instrument Digital Interface (MIDI). In addition to the synthesizer itself, this project also includes the design of a companion application that can be hosted on a wide variety of consumer computing …

Fixed-Point Proximity Minimization: A Theoretical Review And Numerical Study, Daniel Weddle, Jianfeng Guo

OUR Journal: ODU Undergraduate Research Journal

This study examines the relatively recent development of a “fixed-point proximity” approach to one type of minimization problem, considers its application to image denoising, and explores convergence and divergence of the iterative algorithm beyond a (previously supplied) theoretically guaranteed convergence bound on one of the parameters (𝜆). While reviewing the fixed-point proximity approach and its application to image denoising, we aim to communicate the concepts and details in a way that will facilitate understanding for undergraduates and for scholars from other subfields. In the latter portion of our study, the numerical experiment provides thought-provoking data on the effects that parameters …

Interferometry In Fmcw Radars, Assid Nait, Theodore Grosch

The Kennesaw Journal of Undergraduate Research

interferometry is used in many fields using all frequencies of the electromagnetic spectrum and sound waves. In this study, data was collected from an FMCW radar is used at multiple heights above a flat surface on which sat a single retroreflector. These data were post-processed to discover the signal obtained from the target and then the phase form the radar at multiple locations was compared. Using the known geometry and measured phase, we find the interferometry is possible using a freerunning radar under certain geometric conditions.

Novel Tools For Analysis Of Disordered Sleep And Motor Behavior In Preclinical Models Of Disease, Dillon M. Huffman

Theses and Dissertations--Biomedical Engineering

Subtle changes in sleep architecture can accompany and be symptomatic of various diseases or disorders. Understanding the complex interactions between sleep and health requires the ability to characterize sleep, probe its underlying mechanisms through perturbation, and quantify dependent physiological outcomes. Rodent models have come to be accepted as a valuable tool for preclinical investigations. However, experimental tools to accomplish such research typically rely on laborious methods that limit throughput and flexibility. Thus, research tools that minimize workload could be of great value to the research community and expedite investigation of the underlying mechanisms of sleep and further the development of …

Localization Of Stationary Source Of Floor Vibration Using The Steered Response Power Method, Mohammad Royvaran

Theses and Dissertations--Civil Engineering

If the generated vibration in a building exceeds the acceptable limit design for a floor system, it is necessary to identify the source of vibration, a process known as localization. The objective of this study is the localization of stationary vibration sources, and the approach used is the steered response power (SRP) method. This method has already been shown to work well for wireless and acoustical applications to locate transmitter and sound sources, respectively. To the writer’s knowledge, this study is the first application of the SRP method to locate vibration sources using floor vibration measurements. However, because waves behave …

Ecg Arrhythmia Classification Using Discrete Wavelet Transformation, Hannah E. Chookaszian, Nathan D. Diekema

Electrical Engineering

Cardiovascular diseases (CVDs) are the highest leading cause of death worldwide with an approximate 17.9 million related deaths every year according to the World Health Organization (WHO). Electrocardiographic (EKG or ECG) signals are electrical signals measured in the heart and are the main indicator for pre-existing cardiac conditions. The application of deep learning methods such as artificial neural networks (ANN) will assist in the automated detection and classification of ECG signals. The current methods for ECG analysis are lacking in accuracy and reliability considering the level of risk involved with CVDs and the importance of a correct diagnosis. Clinicians use …

Quality Assurance Of Lightweight Structures Via Phase-Based Motion Estimation, Ikenna E. Ifekaonwu

College of Graduate Studies: Theses & Dissertations

In recent years, lightweight structures have become mature and adopted in various applications. The importance of quality assurance cannot be overemphasized hence extensive research has been conducted to assess the quality of lightweight structures. This study investigates a novel process that exploits motion magnification to investigate the damage characteristics of lightweight mission-critical parts. The goal is to assure the structural integrity of 3D printed structures and composite structures by determining the inherent defects present in the part by exploiting their vibration characteristics. The minuscule vibration of the structure was recorded with the aid of a high-speed digital camera, and the …

Novel Machine Learning And Wearable Sensor Based Solutions For Smart Healthcare Monitoring, Rajdeep Kumar Nath

Theses and Dissertations--Electrical and Computer Engineering

The advent of IoT has enabled the design of connected and integrated smart health monitoring systems. These health monitoring systems can be utilized for monitoring the mental and physical wellbeing of a person. Stress, anxiety, and hypertension are the major elements responsible for the plethora of physical and mental illnesses. In this context, the older population demands special attention because of the several age-related complications that exacerbate the effects of stress, anxiety, and hypertension. Monitoring stress, anxiety, and blood pressure regularly can prevent long-term damage by initiating necessary intervention or clinical treatment beforehand. This will improve the quality of life …

Hard Hat Ambient Liability Observer (Halo), Hunter Hykes, Nathan Kish, Brian Thomson

Williams Honors College, Honors Research Projects

Capturing workplace incident information is a growing area of concern for most companies. To assist with this, the design team proposed the H.A.L.O. This design uses time-of-flight sensors connected to LEDs to create a proximity-based hazard warning system. It also records incident data using an accelerometer and micro-SD card. This helps workers avoid some of the most common workplace injuries, slips, trips, and falls and accidental collisions.

Students have created a design with engineering, and marketing requirements that accomplish this task. The proposed design allows for this monitoring and mitigation systems to be attached to hard hats. Team members developed …

Light Loaded Automated Guided Vehicle, Marcus Radtka, Nazar Paramashchuk, Lawrence Shevock

Williams Honors College, Honors Research Projects

The objective of the locomotion system was to design and implement the mechanical, electrical, and software related functions to ensure the LLAGV had the capability of maneuvering its surroundings. The LLAGV’s motors were represented in an open loop transfer function to utilize RPM feedback and a compensator when needed. The modeled compensator helped control the LLAGV’s speed and acceleration, enabling further control of the LLAGV. The internal circuitry has the means to properly distributed power to all components and allowed the user to control the LLAGV to their desire. The application software within the LLAGV locomotion system (LLAGV-LS) had consideration …

Vibro-Acoustic Codling Moth Larvae Infestation Detection In Apples, Chadwick A. Parrish

Theses and Dissertations--Electrical and Computer Engineering

Within recent years, the demand for organic produce has greatly increased due to many factors, including increasing knowledge about such things as dietary fiber and balanced gastrointestinal bacterial ecosystems. This increase in demand, coupled with the financial penalties for sending invasive species and pests across borders, presents a need for a scalable and accurate system to non-destructively detect infestation. The proposed work addresses this problem by testing the performance of a non-destructive vibro-acoustic method for detecting lava activity in apples. This involved 3 steps; design a mechanical data collection prototype for testing apples, a evaluate a set of features, and …

Machine Learning Approach For Vigilance State Classification In Mice, Anik Muhury

Theses and Dissertations--Electrical and Computer Engineering

Sleep has a significant impact on cognitive abilities such as memory, reaction time, productivity, and creative thinking; however, there are many aspects of this important activity that are not clearly understood. Over the last century, researchers have developed technology and animal models to assist in the study of sleep. Manual sleep scoring is time consuming, reduces productivity, and is impacted by human scorer subjectivity. On the other hand, automatic sleep stage categorization can enhance consistency and reliability, aiding professionals in identifying sleep related health problems.

In recent times various studies reported significant achievements for automatic vigilance detection and overcome the …

Weakly Supervised Learning For Multi-Image Synthesis, Muhammad Usman Rafique

Theses and Dissertations--Electrical and Computer Engineering

Machine learning-based approaches have been achieving state-of-the-art results on many computer vision tasks. While deep learning and convolutional networks have been incredibly popular, these approaches come at the expense of huge amounts of labeled data required for training. Manually annotating large amounts of data, often millions of images in a single dataset, is costly and time consuming. To deal with the problem of data annotation, the research community has been exploring approaches that require less amount of labelled data.

The central problem that we consider in this research is image synthesis without any manual labeling. Image synthesis is a classic …

Accelerometer-Based Vigilance State Classification In Dairy Cows, Evan King

Theses and Dissertations--Electrical and Computer Engineering

Globally, dairy farming is a $700 billion industry, with more than 9 million dairy cows in the United States alone. Depriving cows of required activities such as sleep has been shown to negatively impact reproductive efficiency, decrease the volume of milk produced, and increase the risk of culling. Overcrowded herds can decrease individual animal health, demanding the need for automatic behavior detection that would provide insight into their state of health.

Using electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG) to characterize the phases of sleep is a technique which has been used for decades. While these techniques are considered the …

Integration Of Deep Hashing And Channel Coding For Biometric Security And Biometric Retrieval, Veeru Talreja

Graduate Theses, Dissertations, and Problem Reports (ETD)

In the last few years, the research growth in many research and commercial fields are due to the adoption of state of the art deep learning techniques. The same applies to even biometrics and biometric security. Additionally, there has been a rise in the development of deep learning techniques used for approximate nearest neighbor (ANN) search for retrieval on multi-modal datasets. These deep learning techniques knows as deep hashing (DH) integrate feature learning and hash coding into an end-to-end trainable framework. Motivated by these factors, this dissertation considers the integration of deep hashing and channel coding for biometric security and …