1,721,332 research outputs found
Compact multibeam antenna designs for millimeter wave wireless communication applications
No full text*CONFIDENTIAL*</p
Phaseless inverse scattering techniques for indoor imaging using Wi-Fi signals
No full textElectromagnetic inverse scattering problems (ISPs) have led to many ground-breaking imaging technologies in a wide range of fields, including medical imaging, remote sensing, nondestructive testing of mechanical structures, security scanners, sub-atomic microscopy, and astronomical imaging. Conventional techniques to solve electromagnetic ISPs are limited in terms of the size (in terms of wavelengths) and relative permittivity of objects that can be reconstructed. In addition, these techniques need both magnitude and phase measurements of the wave field scattered by the target objects. However, it is often difficult to obtain accurate phase measurements, especially in high-frequency imaging applications. For such applications, ISPs need to be solved with phaseless data, which results in a highly non-linear, non-convex, and severely ill-posed inverse problem. As a result, existing phaseless inverse scattering techniques have not found practical applications in large-scale microwave imaging applications such as indoor imaging, where scattering can be extremely strong, and the collection of accurate phase data is not practically feasible. This thesis presents new linear phaseless inverse scattering techniques that have a range of validity (in terms of object size and permittivity) far beyond existing techniques. These techniques are also implementable in terms of computation, measurement collection, and handling of experimental errors and are therefore extremely useful in many practical ISP settings. These techniques are based on the well-known Rytov Approximation (RA), which is a linear approximation to the underlying non-linear inverse problem and can be used with phaseless data. However, RA has a small validity range and fails under strong scattering conditions. To increase the validity range, crucial corrections to RA are derived using a high-frequency theory of inhomogeneous wave propagation in strongly scattering, lossy media. This corrected RA is denoted as the extended phaseless Rytov approximation for lossy media (xPRA-LM), and it is the basis for the phaseless inverse scattering techniques proposed in this work. This thesis is divided into six chapters. Chapter 1 provides a literature survey on existing inverse scattering techniques and also on the existing Wi-Fi-based indoor imaging techniques. Chapter 2 provides mathematical and physical preliminaries and ISP formulation in the context of a Wi-Fi-based indoor imaging setup. The proposed corrections to RA and derivation of the xPRA-LM model are provided in Chapter 3, along with the demonstration of imaging accuracy of xPRA-LM in the indoor environment. Chapter 4 extends the inverse xPRA-LM model to formulate a new non-iterative linear technique to solve the forward scattering problem. Chapter 5 incorporates the well-known distorted wave iterative framework with xPRA-LM model to achieve improved performance. Finally, in Chapter 6, the proposed techniques are further verified for 1D fault imaging in transmission lines, followed by the conclusions. Using extensive simulations and experiments for the use case of indoor imaging (using phaseless Wi-Fi signals), the proposed techniques are shown to surpass the state-of-the-art validity range by a significant margin. The proposed techniques are shown to provide an accurate reconstruction of objects up to relative permittivity values of 15 + 1.5j for object sizes greater than 30 wavelengths. Even at higher relative permittivity values of up to ϵr = 77+7j, object shape reconstruction remains accurate; however, the reconstruction amplitude is less accurate. To the best of our knowledge, no other existing phaseless inverse scattering techniques work under such extremely strong scattering conditions. Therefore, the proposed linear phaseless techniques can pave the way for using the theory of phaseless inverse scattering in practical microwave and radio imaging applications which was not possible before.</p
Reconfigurable intelligent surface design for wireless communications
No full text5th-generation (5G) networks, with their enhanced data throughput and connectivity, have transformed wireless communication. This improvement is due to technologies such as multiple-input multiple-output (MIMO) and millimeter waves. Reconfigurable intelligent surfaces (RISs) are now being explored to further boost spectral and energy efficiency. RISs adjust scattering characteristics to optimize wireless communication performance and are expected to significantly impact future 6th-generation (6G) networks. Despite the focus on system-level development of RIS-aided communication systems, there’s a lack of work on RIS design, implementation, and prototyping. To fulfill this research gap, this thesis proposes several RIS designs, including single-band, dual-band, and active RISs. In the design of single-band passive RIS, we propose a novel RIS with highly reconfigurable elements for passive beamforming. The RIS element consists of 5×5 sub-elements and 4 RF switches, striking a balance between complexity and reconfigurability. We optimize the RIS element geometry using phase entropy, a metric that evaluates phase diversity. The optimization considers incident angles to enable operation across a wide range. We also develop an efficient analytical method for calculating scattered waves and phase entropy. Experimental results of RIS with 4×4 element demonstrate wide-angle operation and reconfigurable reflection beamforming. For the dual-band passive RIS, we propose a novel dual-band independent RIS (DBI-RIS) design that combines mmWave and sub-6 GHz functionalities in a single aperture, bridging the gap between single-band RISs and future multi-band wireless systems. The design incorporates a double-layer patch antenna with a 1-bit phase shifter for the mmWave element, and selectively interconnected 8×8 mmWave arrays for the sub-6 GHz element. To optimize the design, we propose a suspended electromagnetic band gap (EBG) structure and a planar spiral inductor (PSI). Prototypes are fabricated and experimentally verified, demonstrating successful beam steering for both the sub-6 GHz and mmWave elements, aligning well with simulated results. In terms of active RIS, we present a design for an active RIS with beam-scanning and amplification capabilities. The active RIS consists of active elements, including a two-layer patch antenna and a phase-reconfigurable reflection amplifier. Theoretical analysis and numerical examples quantify the tradeoffs between the amplifier’s gain and the phase shifter’s loss. The reflection amplifier is designed with a gain of approximately 13 dB, while the phase shifter has 1.9 dB insertion loss and 25 dB return loss. We also propose an analytical method to calculate the scattered pattern by the active RIS with active devices. A fabricated 2x2 element prototype demonstrates effective beam steering with an 8.5 dB gain compared to the passive version. The scanning range depends on the RIS size. The advantages of amplification and reconfigurable phases position the proposed active RIS as a promising solution for the "double fading" problem in future 6G communication networks. These designs offer improved performance, wider functionality, making them valuable contributions to the field of RIS technology and enabling RIS as a promising candidate for future 6G communication networks.</p
Model-based deep learning techniques for inverse scattering problems in indoor imaging
No full textElectromagnetic inverse scattering problems (ISPs) involve accurately modeling the scattering of waves to infer the shape and constitution of objects. These problems can be found in various forms in a wide range of research and commercial applications including medical imaging, remote sensing, microscopy, security scanners, non-destructive evaluation and many more. Such inverse problems can be highly nonlinear and severely ill-posed under strong scattering conditions such as when the target objects have very high permittivity or are electrically large. The extent of non-linearity and ill-posedness can increase further if the measurements of scattered waves do not contain phase information. State-of-the-art model-based approaches to solve such ISPs rely on the underlying physics-based formulations and non-linear optimization techniques. However, such model-based techniques have a limited validity range due to the high non-linearity and severe ill-posedness of ISPs and do not provide accurate reconstructions for objects with large size and high permittivity. In recent years, deep learning-based methods have been employed to solve such problems. The success of these methods relies on the ability of deep networks to learn highly non-linear functions and intricate relationships, which is not possible with traditional model-based methods. However, these methods rely on the use of large amounts of training data to obtain accurate solutions, without which they fail to generalize successfully. This dependence on a large amount of training data makes purely deep learning-based methods impractical for use in a lot of scenarios where collecting data is difficult and expensive. The interpretability of deep learning methods has also not been fully understood. This thesis presents two model-based deep learning frameworks, which combine physics model-based techniques with deep learning in order to overcome the drawbacks of each of these and extend the validity range of both. Such frameworks exploit the domain knowledge available through physics-based models, while at the same time leveraging the deep networks to learn additional information from the training data available. Using physics-based models in the framework reduces the reliance of deep networks on training data, and the resulting model-based deep learning frameworks need much less data to obtain accurate solutions to ISPs as compared to purely deep learning-based methods. The performance of these frameworks is demonstrated on the use case of indoor imaging using simulations and experiment results in actual indoor environments using 2.4 GHz Wi-Fi phaseless measurements. Using model-based deep learning frameworks leads to a remarkable increase in the validity range over existing state-of-the-art linear and non-linear model-based methods, and accurate reconstructions are obtained for objects with very high relative permittivity (│∈r│≤77) and electrical size almost 20 times larger than the probing wavelength. This thesis is divided into five chapters. Chapter 1 provides the background on electromagnetic inverse scattering problems and on the different types of deep learning techniques used to solve such inverse problems. Chapter 2 provides the formulation of ISPs in the context of a Wi-Fi-based indoor imaging setup. Chapters 3 and 4 demonstrate the use of two different model-based deep learning frameworks to solve the ISPs for the indoor imaging use case, followed by the conclusion in Chapter 5.</p
Employing novel pixel and mesh surface structures to antenna design for IoT wireless applications
No full textIn this thesis, three new antenna design approaches are introduced and investigated, for enhancing the performance of Internet-of-Things (IoT) systems. The approaches are based on novel pixel and mesh surface structures and form the core contributions of these thesis research. In the first contribution, a new approach for designing compact MIMO antennas is proposed that is based on a pixel element surface which can be used in compact MIMO IoT receiver design for improving capacity. It is shown that optimizing the geometry of the pixelated surface can increase channel capacity by 13% and energy efficiency by 19.9% compared to the system without optimization. The importance of the approach is that it provides a direct link between communication and electromagnetic formulations of MIMO antenna systems. In the second contribution, a new approach for designing highly pattern-reconfigurable antennas is proposed based on a pixel surface. The antenna features of highly pattern-reconfigurable, 360° single and multi-beam steering ability, full 3D space scanning, planar geometry and compatibility with an FPGA controller makes it useful in various IoT applications such as wireless power transfer, RF sensing and analog precoding. In the third contribution, a new approach for designing hybrid radio frequency (RF) and solar energy harvesting systems utilizing a transparent multiport antenna with a mesh surface structure is proposed. A key advantage of this approach is that the surface area of the solar cell is fully reused for the multiport antenna saving space. This work demonstrates the potential usefulness of increasing energy diversity in hybrid energy harvester for powering indoor IoT devices.</p
An FPGA-based real-time RF imaging system
No full textThis thesis focuses on the development and evaluation of a novel RF imaging system that utilizes received RF signals for real-time and accurate image reconstruction. Unlike traditional imaging technologies that rely on capturing visible light, RF imaging operates by analyzing signal propagation within an environment, offering the unique advantage of seeing through obstacles and enabling monitoring applications. The proliferation of wireless network devices in indoor environments has paved the way for leveraging the abundance of wireless data to image and analyze environments. This thesis presents a detailed exploration of the RF imaging system, specifically implementing the extended phaseless Rytov approximation for low-loss media (xPRA-LM) algorithm. The xPRA-LM algorithm enables the reconstruction of images with permittivity distributions, providing valuable insights into object characteristics. The proposed RF imaging system, powered by FPGA-based transceivers, demonstrates its capability to accurately reconstruct images and visualize the results in real-time. Through comprehensive experiments and evaluations, the system's effectiveness in capturing object characteristics within the imaged environment is illustrated.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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