Signal Processing Commons^™

Improving Energy Efficiency In Open Ran Through Dynamic Cpu Scheduling, Saish Urumkar, Byrav Ramamurthy, Sachin Sharma

Conference papers

Open RAN is a promising cellular technology that is currently undergoing extensive research for future wireless radio access networks. Achieving optimal energy efficiency in Open RAN poses a significant challenge. This paper introduces a CPU scheduling algorithm that specifically targets this chal- lenge by optimizing energy consumption at the base station while maintaining optimal performance levels. With the goal of minimizing energy consumption, the proposed algorithm dynamically adjusts the CPU core states, seamlessly switching between active and sleep modes based on the load conditions. To evaluate the algorithm’s effectiveness in terms of energy saving and performance, experimental testing is conducted …

Design And Fabrication Of A Low Power 7.2 Terabit Transmitter For Exascale Computing, Scott Mcwilliams

Electrical Engineering Theses and Dissertations

Enhanced Coupled Strength (ECS) gratings fabricated into III-V based devices offer high reflected power per unit length and broad band reflectivity as compared to conventional 1st order gratings, desired qualities for short-haul high speed transmitters that can be implemented without the need for chip-level temperature control, contributing to the low power per transmitted bit. For commercial DBR lasers, the grating reflectivity results in an extremely narrow reflectivity spectrum, which is highly desired for most/many applications, but requires a power hungry thermo-electric cooler to maintain a fixed frequency. The proposed LEAM (laser electro-absorption modulator) requires a broad reflectivity spectrum, which, by …

Route Condition Estimation By Video Data Analysis, Sharafat Hossain

Theses and Dissertations

Catastrophic natural disasters have an impact on millions of individuals each year, whether directly or indirectly. A successful rescue operation can save a great deal of lives in the post-disaster phase, but researchers are still facing difficulties in carrying this out. Even while they are in peril, today's people frequently post updates about their whereabouts on well-known social media platforms, sometimes even asking for help. A prompt and appropriate response to these catastrophic events depends on developing an efficient and automated approach capable of retrieving real-time data from impacted locations and extracting essential elements. The research focuses on predicting flood …

Neural Networks For Improved Signal Source Enumeration And Localization With Unsteered Antenna Arrays, John T. Rogers Ii

Theses and Dissertations

Direction of Arrival estimation using unsteered antenna arrays, unlike mechanically scanned or phased arrays, requires complex algorithms which perform poorly with small aperture arrays or without a large number of observations, or snapshots. In general, these algorithms compute a sample covriance matrix to obtain the direction of arrival and some require a prior estimate of the number of signal sources. Herein, artificial neural network architectures are proposed which demonstrate improved estimation of the number of signal sources, the true signal covariance matrix, and the direction of arrival. The proposed number of source estimation network demonstrates robust performance in the case …

Traffic Light Detection And V2i Communications Of An Autonomous Vehicle With The Traffic Light For An Effective Intersection Navigation Using Mavs Simulation, Mahfuzur Rahman

Theses and Dissertations

Intersection Navigation plays a significant role in autonomous vehicle operation. This paper focuses on enhancing autonomous vehicle intersection navigation through advanced computer vision and Vehicle-to-Infrastructure (V2I) communication systems. The research unfolds in two phases. In the first phase, an approach utilizing YOLOv8s is proposed for precise traffic light detection and recognition, trained on the Small-Scale Traffic Light Dataset (S2TLD). The second phase establishes seamless connectivity between autonomous vehicles and traffic lights in a simulated Mississippi State University Autonomous Vehicle Simulation (MAVS) environment resembling a small city with multiple intersections. This V2I system enables the transmission of Signal Phase and Timing …

Resilient, Sustainable, And Secure Systems Support For Ultra-Low-Power Computational Things, Nicole Tobias

All Dissertations

Wireless battery-free and energy-harvesting devices are expanding the reach and vision of the Internet of Things, where trillions of embedded computational things interconnect ubiquitously around us and inform many different aspects of our everyday lives. Designing these systems without batteries and interconnecting wires lowers maintenance, environmental, and economic costs while also extending device lifetime and deployment opportunities. Over the last decade, research on these ultra-low-power embedded sensors and systems has dramatically increased — enabling new and exciting prospects in many different scientific fields, from smart building and health monitoring applications to animal and activity tracking.

These systems are not without …

Ism-Band Energy Harvesting Wireless Sensor Node, Fnu Naveed

Graduate Theses and Dissertations

In recent years, the interest in remote wireless sensor networks has grown significantly, particularly with the rapid advancements in Internet of Things (IoT) technology. These networks find diverse applications, from inventory tracking to environmental monitoring. In remote areas where grid access is unavailable, wireless sensors are commonly powered by batteries, which imposes a constraint on their lifespan. However, with the emergence of wireless energy harvesting technologies, there is a transformative potential in addressing the power challenges faced by these sensors. By harnessing energy from the surrounding environment, such as solar, thermal, vibrational, or RF sources, these sensors can potentially operate …

Electronic Note-String Detector, Gavin Garcia-Rossi, Tommy Smail

Electrical Engineering

As the virtual space has become a dominant part of everyone’s day-to-day lives, many normal face-to-face interactions and services have not yet been facilitated by adapting technology. One of these prevailing areas is music lessons. Over Zoom meetings, or other virtual platforms, it is tremendously challenging to teach students. These challenges include recognizing student mistakes audibly and visually, and being able to give confident feedback on the incorrect notes played by learning musicians. Without having to delve into improving the complex systems that would be required to improve audio, video, and connection quality of these connections, we have another solution …

Low-Power, Event-Driven System On A Chip For Charge Pulse Processing Applications, Joseph A. Schmitz

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

This dissertation presents an electronic architecture and methodology capable of processing charge pulses generated by a range of sensors, including radiation detectors and tactile synthetic skin. These sensors output a charge signal proportional to the input stimulus, which is processed electronically in both the analog and digital domains. The presented work implements this functionality using an event-driven methodology, which greatly reduces power consumption compared to standard implementations. This enables new application areas that require a long operating time or compact physical dimensions, which would not otherwise be possible. The architecture is designed, fabricated, and tested in the aforementioned applications to …

4-Band Personal Eq Device, Carson Kellerman

Electrical Engineering

The device is a 4-band EQ with 3.5mm audio input and output jacks. The device has 4 adjustable EQ bands. Each of the 4 bands are adjustable via knob potentiometers, and are able to boost or cut their respective center frequencies of 150Hz, 400Hz, 2500Hz, and 10kHz from -15dB to +15dB. The device is battery powered off of 1 9V battery and is able to function for 10 hours before needing to replace the batteries. The device has a Total Harmonic Distortion of less than 1% and a Signal to Noise Ratio greater than 60dB. Overall, this device allows music …

Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad

Theses and Dissertations

Running computer vision algorithms requires complex devices with lots of computing power, these types of devices are not well suited for space deployment. The harsh radiation environment and limited power budgets have hindered the ability of running advanced computer vision algorithms in space. This problem makes running an on-orbit servicing detection algorithm very difficult. This work proposes using a low powered FPGA to accelerate the computer vision algorithms that enable satellite component feature extraction. This work uses AMD/Xilinx’s Zynq SoC and DPU IP to run model inference. Experiments in this work centered around improving model post processing by creating implementations …

Improving Cellphone-Based Bio-Imaging Technique For Fluorescence Detection, Erteza T. Efaz

LSU Master's Theses

The research presented in this thesis focuses on the design, development, and evaluation of a fluorescence detection system. The system is implemented primarily as an Android application, Auto Camera, which leverages smartphone cameras to capture and analyze fluorescent images. The application provides a user-friendly interface with some configurable features like exposure time, ISO speed, and storage limit; as well as defining detection thresholds and setting acquisition intervals. This study begins with the architectural framework of the Android application, which is written in Java using Android Studio. The API compatibility is set to version 33, and users are prompted to grant …

System-Level Noise Performance Of Coherent Imaging Systems, Derek J. Burrell, Joshua H. Follansbee, Mark F. Spencer, Ronald G. Driggers

Faculty Publications

We provide an in-depth analysis of noise considerations in coherent imaging, accounting for speckle and scintillation in addition to “conventional” image noise. Specifically, we formulate closed-form expressions for total effective noise in the presence of speckle only, scintillation only, and speckle combined with scintillation. We find analytically that photon shot noise is uncorrelated with both speckle and weak-to-moderate scintillation, despite their shared dependence on the mean signal. Furthermore, unmitigated speckle and scintillation noise tends to dominate coherent-imaging performance due to a squared mean-signal dependence. Strong coupling occurs between speckle and scintillation when both are present, and we characterize this behavior …

Study Of Improved Sorting Weighting Cfar Detectors For Gaussian Environment, Souad Chabbi, Khadidja Belhi, M'Hamed Hamadouche

Emirates Journal for Engineering Research

The goal of this paper is to improve the detection performance and the false alarm regulation of the conventional order statistics Constant False Alarm Rate (OS-CFAR) detectors in a non-homogeneous Gaussian environment. To this end, we design and study the New Sorting Weighting (NSW-) and the Modified Sorting Weighting (MSW-) CFAR detectors. We find closed forms of the detection ( ) and the false alarm ( ) probabilities for both detectors. Moreover, we identify the optimum pairs of weights that maximize the and ensure a constant . Finally, we prove through Monte Carlo simulations that these detectors provide better detection …

Spoken Language Processing And Modeling For Aviation Communications, Aaron Van De Brook

Doctoral Dissertations and Master's Theses

With recent advances in machine learning and deep learning technologies and the creation of larger aviation-specific corpora, applying natural language processing technologies, especially those based on transformer neural networks, to aviation communications is becoming increasingly feasible. Previous work has focused on machine learning applications to natural language processing, such as N-grams and word lattices. This thesis experiments with a process for pretraining transformer-based language models on aviation English corpora and compare the effectiveness and performance of language models transfer learned from pretrained checkpoints and those trained from their base weight initializations (trained from scratch). The results suggest that transformer language …

Conservative Estimation Of Inertial Sensor Errors Using Allan Variance Data, Kyle A. Lethander, Clark N. Taylor

Faculty Publications

To understand the error sources present in inertial sensors, both the white (time-invariant) and correlated noise sources must be properly characterized. To understand both sources, the standard approach (IEEE standards 647-2006, 952-2020) is to compute the Allan variance of the noise and then use human-based interpretation of linear trends to estimate the separate noise sources present in a sensor. Recent work has sought to overcome the graphical nature and visual-inspection basis of this approach leading to more accurate noise estimates. However, when using noise characterization in a filter, it is important that the noise estimates be not only accurate but …

Trumpet Directivity From A Rotating Semicircular Array, Samuel D. Bellows, Joseph E. Avila, Timothy W. Leishman

Directivity

The directivity function of a played musical instrument describes the angular dependence of its acoustic radiation and diffraction about the instrument, musician, and musician’s chair. Directivity influences sound in rehearsal, performance, and recording environments and signals in audio systems. Because high-resolution, spherically comprehensive measurements of played musical instruments have been unavailable in the past, the authors have undertaken research to produce and share such data for studies of musical instruments, simulations of acoustical environments, optimizations of microphone placements, and other applications. The authors acquired the data from repeated chromatic scales produced by a trumpet played at mezzo-forte in an anechoic …

Comparison Of A Bottom-Up Gnss Radio Occultation Method To Measure D- And E-Region Electron Densities With Ionosondes And Firi, Dylan J. Shaver, Dong L. Wu, Nimalan Swarnalingam, Anthony L. Franz, Eugene V. Dao, Daniel J. Emmons

Faculty Publications

High-frequency skywave propagation can be heavily impacted by D- and E-region dynamics requiring accurate global measurements to optimize performance. A standard measurement technique is to use ionosondes, but they are unable to measure below 1 MHz and are only available at a limited number of land-locked sites around the globe. In contrast, the Global Navigation Satellite System radio occultation (GNSS-RO) bottom-up method is a new approach specifically designed to generate electron density profiles in the D- and E- region ionosphere. It takes advantage of satellite constellations that currently provide over 20,000 daily measurements and global coverage. In this paper, GNSS-RO …

Quantifying Atmospheric Turbulence Effects On Image Quality Using A Deconvolution Algorithm, Ahmad M. Almalki

Theses and Dissertations

Imaging through turbulence is affected by several factors including imaging system specifications, imaging system setup and more importantly the atmospheric turbulence as it is uncontrollable. One important parameter which is used to quantify the atmospheric turbulence severity is the atmospheric coherence diameter (��₀ ), known as Fried's parameter. This thesis explores ways to characterize the atmospheric turbulence effects on image quality using simulated and laboratory generated turbulence where ��₀ is estimated using a maximum a posteriori (MAP) estimator and frequency domain analysis algorithms. Furthermore, image quality metrics such as Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM) …

On Explainability Of Neural Networks, Cem Benar

Dissertations

It is widely reported that deep neural networks outperform most competitors for a range of applications. The state-of-the-art neural networks have built-in inductive bias of architectural choices, regularizations, optimizer types, and initialization methods. Using inductive bias is intuitive to enhance the model approximation. Deep neural networks are mostly dense and heavily overparameterized. They tend to be biased towards low-rank solutions to reduce complexity and improve generalization performance, known as implicit regularization. The implicit regularization as observed in specific architectures and various real-world data sets suggests to overparameterize neural networks judiciously and learn compressed representations (lower rank approximation) with improved performance. …