5,339 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Analysis and Design of Non-Orthogonal Multiple Access (NOMA) Techniques for Next Generation Wireless Communication Systems

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    The current surge in wireless connectivity, anticipated to amplify significantly in future wireless technologies, brings a new wave of users. Given the impracticality of an endlessly expanding bandwidth, there’s a pressing need for communication techniques that efficiently serve this burgeoning user base with limited resources. Multiple Access (MA) techniques, notably Orthogonal Multiple Access (OMA), have long addressed bandwidth constraints. However, with escalating user numbers, OMA’s orthogonality becomes limiting for emerging wireless technologies. Non-Orthogonal Multiple Access (NOMA), employing superposition coding, serves more users within the same bandwidth as OMA by allocating different power levels to users whose signals can then be detected using the gap between them, thus offering superior spectral efficiency and massive connectivity. This thesis examines the integration of NOMA techniques with cooperative relaying, EXtrinsic Information Transfer (EXIT) chart analysis, and deep learning for enhancing 6G and beyond communication systems. The adopted methodology aims to optimize the systems’ performance, spanning from bit-error rate (BER) versus signal to noise ratio (SNR) to overall system efficiency and data rates. The primary focus of this thesis is the investigation of the integration of NOMA with cooperative relaying, EXIT chart analysis, and deep learning techniques. In the cooperative relaying context, NOMA notably improved diversity gains, thereby proving the superiority of combining NOMA with cooperative relaying over just NOMA. With EXIT chart analysis, NOMA achieved low BER at mid-range SNR as well as achieved optimal user fairness in the power allocation stage. Additionally, employing a trained neural network enhanced signal detection for NOMA in the deep learning scenario, thereby producing a simpler signal detection for NOMA which addresses NOMAs’ complex receiver problem

    Integrated Optical Fiber Sensor for Simultaneous Monitoring of Temperature, Vibration, and Strain in High Temperature Environment

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    Important high-temperature parts of an aero-engine, especially the power-related fuel system and rotor system, are directly related to the reliability and service life of the engine. The working environment of these parts is extremely harsh, usually overloaded with high temperature, vibration and strain which are the main factors leading to their failure. Therefore, the simultaneous measurement of high temperature, vibration, and strain is essential to monitor and ensure the safe operation of an aero-engine. In my thesis work, I have focused on the research and development of two new sensors for fuel and rotor systems of an aero-engine that need to withstand the same high temperature condition, typically at 900 °C or above, but with different requirements for vibration and strain measurement. Firstly, to meet the demand for high temperature operation, high vibration sensitivity, and high strain resolution in fuel systems, an integrated sensor based on two fiber Bragg gratings in series (Bi-FBG sensor) to simultaneously measure temperature, strain, and vibration is proposed and demonstrated. In this sensor, an L-shaped cantilever is introduced to improve the vibration sensitivity. By converting its free end displacement into a stress effect on the FBG, the sensitivity of the L-shaped cantilever is improved by about 400% compared with that of straight cantilevers. To compensate for the strain sensitivity of FBGs, a spring-beam strain sensitization structure is designed and the sensitivity is increased to 5.44 pm/με by concentrating strain deformation. A novel decoupling method ‘Steps Decoupling and Temperature Compensation (SDTC)’ is proposed to address the interference between temperature, vibration, and strain. A model of sensing characteristics and interference of different parameters is established to achieve accurate signal decoupling. Experimental tests have been performed and demonstrated the good performance of the sensor. Secondly, a sensor based on cascaded three fiber Fabry-Pérot interferometers in series (Tri-FFPI sensor) for multiparameter measurement is designed and demonstrated for engine rotor systems that require higher vibration frequencies and greater strain measurement requirements. In this sensor, the cascaded-FFPI structure is introduced to ensure high temperature and large strain simultaneous measurement. An FFPI with a cantilever for high vibration frequency measurement is designed with a miniaturized size and its geometric parameters optimization model is established to investigate the influencing factors of sensing characteristics. A cascaded-FFPI preparation method with chemical etching and offset fusion is proposed to maintain the flatness and high reflectivity of FFPIs’ surface, which contributes to the improvement of measurement accuracy. A new high-precision cavity length demodulation method is developed based on vector matching and clustering-competition particle swarm optimization (CCPSO) to improve the demodulation accuracy of cascaded-FFPI cavity lengths. By investigating the correlation relationship between the cascaded-FFPI spectral and multidimensional space, the cavity length demodulation is transformed into a search for the highest correlation value in space, solving the problem that the cavity length demodulation accuracy is limited by the resolution of spectral wavelengths. Different clustering and competition characteristics are designed in CCPSO to reduce the demodulation error by 87.2% compared with the commonly used particle swarm optimization method. Good performance and multiparameter decoupling have been successfully demonstrated in experimental tests

    Technology for Low Resolution Space Based RSO Detection and Characterisation

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    Space Situational Awareness (SSA) refers to all activities to detect, identify and track objects in Earth orbit. SSA is critical to all current and future space activities and protect space assets by providing access control, conjunction warnings, and monitoring status of active satellites. Currently SSA methods and infrastructure are not sufficient to account for the proliferations of space debris. In response to the need for better SSA there has been many different areas of research looking to improve SSA most of the requiring dedicated ground or space-based infrastructure. In this thesis, a novel approach for the characterisation of RSO’s (Resident Space Objects) from passive low-resolution space-based sensors is presented with all the background work performed to enable this novel method. Low resolution space-based sensors are common on current satellites, with many of these sensors being in space using them passively to detect RSO’s can greatly augment SSA with out expensive infrastructure or long lead times. One of the largest hurtles to overcome with research in the area has to do with the lack of publicly available labelled data to test and confirm results with. To overcome this hurtle a simulation software, ORBITALS, was created. To verify and validate the ORBITALS simulator it was compared with the Fast Auroral Imager images, which is one of the only publicly available low-resolution space-based images found with auxiliary data. During the development of the ORBITALS simulator it was found that the generation of these simulated images are computationally intensive when propagating the entire space catalog. To overcome this an upgrade of the currently used propagation method, Specialised General Perturbation Method 4th order (SGP4), was performed to allow the algorithm to run in parallel reducing the computational time required to propagate entire catalogs of RSO’s. From the results it was found that the standard facet model with a particle swarm optimisation performed the best estimating an RSO’s attitude with a 0.66 degree RMSE accuracy across a sequence, and ~1% MAPE accuracy for the optical properties. This accomplished this thesis goal of demonstrating the feasibility of low-resolution passive RSO characterisation from space-based platforms in a simulated environment

    Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

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    The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

    Robust and Listening-Efficient Contention Resolution

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    This paper shows how to achieve contention resolution on a shared communication channel using only a small number of channel accesses -- both for listening and sending -- and the resulting algorithm is resistant to adversarial noise. The shared channel operates over a sequence of synchronized time slots, and in any slot agents may attempt to broadcast a packet. An agent's broadcast succeeds if no other agent broadcasts during that slot. If two or more agents broadcast in the same slot, then the broadcasts collide and both broadcasts fail. An agent listening on the channel during a slot receives ternary feedback, learning whether that slot had silence, a successful broadcast, or a collision. Agents are (adversarially) injected into the system over time. The goal is to coordinate the agents so that each is able to successfully broadcast its packet. A contention-resolution protocol is measured both in terms of its throughput and the number of slots during which an agent broadcasts or listens. Most prior work assumes that listening is free and only tries to minimize the number of broadcasts. This paper answers two foundational questions. First, is constant throughput achievable when using polylogarithmic channel accesses per agent, both for listening and broadcasting? Second, is constant throughput still achievable when an adversary jams some slots by broadcasting noise in them? Specifically, for NN packets arriving over time and JJ jammed slots, we give an algorithm that with high probability in N+JN+J guarantees Θ(1)\Theta(1) throughput and achieves on average O(polylog(N+J))O(\texttt{polylog}(N+J)) channel accesses against an adaptive adversary. We also have per-agent high-probability guarantees on the number of channel accesses -- either O(polylog(N+J))O(\texttt{polylog}(N+J)) or O((J+1)polylog(N))O((J+1) \texttt{polylog}(N)), depending on how quickly the adversary can react to what is being broadcast

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    The Active CryoCubeSat Technology: Active Thermal Control for Small Satellites

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    Modern CubeSats and Small Satellites have advanced in capability to tackle science and technology missions that would usually be reserved for more traditional, large satellites. However, this rapid growth in capability is only possible through the fast-to-production, low-cost, and advanced technology approach used by modern small satellite engineers. Advanced technologies in power generation, energy storage, and high-power density electronics have naturally led to a thermal bottleneck, where CubeSats and Small Satellites can generate more power than they can easily reject. The Active CryoCubeSat (ACCS) is an advanced active thermal control technology (ATC) for Small Satellites and CubeSats, which hopes to help solve this thermal problem. The ACCS technology is based on a two-stage design. An integrated miniature cryocooler forms the first stage, and a single-phase mechanically pumped fluid loop heat exchanger the second. The ACCS leverages advanced 3D manufacturing techniques to integrate the ATC directly into the satellite structure, which helps to improve the performance while simultaneously miniaturizing and simplifying the system. The ACCS system can easily be scaled to mission requirements and can control zonal temperature, bulk thermal rejection, and dynamic heat transfer within a satellite structure. The integrated cryocooler supports cryogenic science payloads such as advanced LWIR electro-optical detectors. The ACCS hopes to enable future advanced CubeSat and Small Satellite missions in earth science, heliophysics, and deep space operations. This dissertation will detail the design, development, and testing of the ACCS system technology

    Image Diversification via Deep Learning based Generative Models

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    Machine learning driven pattern recognition from imagery such as object detection has been prevalenting among society due to the high demand for autonomy and the recent remarkable advances in such technology. The machine learning technologies acquire the abstraction of the existing data and enable inference of the pattern of the future inputs. However, such technologies require a sheer amount of images as a training dataset which well covers the distribution of the future inputs in order to predict the proper patterns whereas it is impracticable to prepare enough variety of images in many cases. To address this problem, this thesis pursues to discover the method to diversify image datasets for fully enabling the capability of machine learning driven applications. Focusing on the plausible image synthesis ability of generative models, we investigate a number of approaches to expand the variety of the output images using image-to-image translation, mixup and diffusion models along with the technique to enable a computation and training dataset efficient diffusion approach. First, we propose the combined use of unpaired image-to-image translation and mixup for data augmentation on limited non-visible imagery. Second, we propose diffusion image-to-image translation that generates greater quality images than other previous adversarial training based translation methods. Third, we propose a patch-wise and discrete conditional training of diffusion method enabling the reduction of the computation and the robustness on small training datasets. Subsequently, we discuss a remaining open challenge about evaluation and the direction of future work. Lastly, we make an overall conclusion after stating social impact of this research field

    BCEdge: SLO-Aware DNN Inference Services with Adaptive Batching on Edge Platforms

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    As deep neural networks (DNNs) are being applied to a wide range of edge intelligent applications, it is critical for edge inference platforms to have both high-throughput and low-latency at the same time. Such edge platforms with multiple DNN models pose new challenges for scheduler designs. First, each request may have different service level objectives (SLOs) to improve quality of service (QoS). Second, the edge platforms should be able to efficiently schedule multiple heterogeneous DNN models so that system utilization can be improved. To meet these two goals, this paper proposes BCEdge, a novel learning-based scheduling framework that takes adaptive batching and concurrent execution of DNN inference services on edge platforms. We define a utility function to evaluate the trade-off between throughput and latency. The scheduler in BCEdge leverages maximum entropy-based deep reinforcement learning (DRL) to maximize utility by 1) co-optimizing batch size and 2) the number of concurrent models automatically. Our prototype implemented on different edge platforms shows that the proposed BCEdge enhances utility by up to 37.6% on average, compared to state-of-the-art solutions, while satisfying SLOs
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