Adaptive beamforming using uniform concentric circular arrays with frequency invariant characteristics

This paper proposes a new method for adaptive beamforming using uniform concentric circular array (UCCA) that has nearly frequency invariant (FI) characteristics. The basic principle of FI UCCA is to transform the received signals to the phase mode and compensate for the frequency dependency of the individual phase mode through the use of a digital beamforming network. The far field pattern of the array is then determined by a set of weights and it is approximately invariant over a wide range of frequencies. Therefore, the minimum variance beamforming (MVB) approach can be used to adapt the small set of weights, as if it is a narrowband array, Design examples and simulation are given to demonstrate the usefulness of the proposed FI UCCA in broadband DOA estimation and beamforming. © 2005 IEEE.published_or_final_versio

Frequency invariant uniform concentric circular arrays with directional elements

A new approach for designing frequency invariant (FI) uniform concentric circular arrays (UCCAs) with directional elements is proposed, and their applications to direction-of-arrival (DOA) estimation and adaptive beamforming are studied. By treating the sensors along the radial direction of the UCCA as linear subarrays and using appropriately designed beamformers, each subarray is transformed to a virtual element with appropriate directivity. Consequently, the whole UCCA can be viewed as a virtual uniform circular array (UCA) with desired element directivity for broadband processing. By extending the approach for designing FI-UCAs, the frequency dependency of the phase modes of the virtual UCA is compensated to facilitate broadband DOA and adaptive beamforming. Both the linear array beamformers (LABFs) and compensation filters can be designed separately using second- order cone programming (SOCP). Moreover, a new method to tackle the possible noise amplification problem in such large arrays by imposing additional norm constraints on the design of the compensation filters is proposed. The advantages of this decoupled approach are 1) the complicated design problem of large UCCAs can be decoupled into simpler problems of designing the LABFs and compensation filters, and 2) directional elements, which are frequently encountered, can be treated readily under the proposed framework. Numerical examples are provided to demonstrate the effectiveness and improvement of the proposed methods in DOA estimation, adaptive beamforming, and elevation control over the conventional FI-UCCA design method.published_or_final_versio

Advanced ultrawideband imaging algorithms for breast cancer detection

Ultrawideband (UWB) technology has received considerable attention in recent years as it is regarded to be able to revolutionise a wide range of applications. UWB imaging for breast cancer detection is particularly promising due to its appealing capabilities and advantages over existing techniques, which can serve as an early-stage screening tool, thereby saving millions of lives. Although a lot of progress has been made, several challenges still need to be overcome before it can be applied in practice. These challenges include accurate signal propagation modelling and breast phantom construction, artefact resistant imaging algorithms in realistic breast models, and low-complexity implementations. Under this context, novel solutions are proposed in this thesis to address these key bottlenecks. The thesis first proposes a versatile electromagnetic computational engine (VECE) for simulating the interaction between UWB signals and breast tissues. VECE provides the first implementation of its kind combining auxiliary differential equations (ADE) and convolutional perfectly matched layer (CPML) for describing Debye dispersive medium, and truncating computational domain, respectively. High accuracy and improved computational and memory storage efficiency are offered by VECE, which are validated via extensive analysis and simulations. VECE integrates the state-of-the-art realistic breast phantoms, enabling the modelling of signal propagation and evaluation of imaging algorithms. To mitigate the severe interference of artefacts in UWB breast cancer imaging, a robust and artefact resistant (RAR) algorithm based on neighbourhood pairwise correlation is proposed. RAR is fully investigated and evaluated in a variety of scenarios, and compared with four well-known algorithms. It has been shown to achieve improved tumour detection and robust artefact resistance over its counterparts in most cases, while maintaining high computational efficiency. Simulated tumours in both homogeneous and heterogeneous breast phantoms with mild to moderate densities, combined with an entropy-based artefact removal algorithm, are successfully identified and localised. To further improve the performance of algorithms, diverse and dynamic correlation weighting factors are investigated. Two new algorithms, local coherence exploration (LCE) and dynamic neighbourhood pairwise correlation (DNPC), are presented, which offer improved clutter suppression and image resolution. Moreover, a multiple spatial diversity (MSD) algorithm, which explores and exploits the richness of signals among different transmitter and receiver pairs, is proposed. It is shown to achieve enhanced tumour detection even in severely dense breasts. Finally, two accelerated image reconstruction mechanisms referred to as redundancy elimination (RE) and annulus predication (AP) are proposed. RE removes a huge number of repetitive operations, whereas AP employs a novel annulus prediction to calculate millions of time delays in a highly efficient batch mode. Their efficacy is demonstrated by extensive analysis and simulations. Compared with the non-accelerated method, RE increases the computation speed by two-fold without any performance loss, whereas AP can be 45 times faster with negligible performance degradation

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Design of Beam Steering Electronic Circuits for Medical Applications

This thesis deals with the theory and design of a hemispherical antenna array circuit that is capable to operate in the intermediate zones. By doing that, this array can be used in Hyperthermia Treatment for Brain Cancer in which the aim is to noninvasively focus the fields at microwave frequencies to the location of the tumor cells in the brain. Another possible application of the array is to offer an alternative means of sustaining Deep Brain Stimulation other than using the traditional (surgical) approach. The new noninvasive technique is accomplished by the use of a hemispherical antenna array placed on the human's head. The array uses a new beamforming technique that achieves 3 dimensional beamforming or focusing of the magnetic field of antennas to desired points in the brain to achieve either cell death by temperature rise (Hyperthermia Application) or to cause brain stimulation and hopefully alleviate the affects of Parkinson's Disease (Deep Brain Stimulation). The main obstacle in this design was that the far field approximation that is usually used when designing antenna arrays does not apply in this case since the hemispherical array is in close proximity to where the magnetic field is desired to be focused. The antenna array problem is approached as a boundary-valued problem with the human head being modeled as a three layered hemisphere. The exact expressions for electromagnetic fields are derived. Health issues such as electric field exposure and specific absorption rate (SAR) are considered. After developing the main antenna and beamforming theory, a neural network is designed to accomplish the beamforming technique used. The radio-frequency (RF) transmitter was designed to transmit the fields at a frequency of 1.8 GHz. The antenna array can also be used as a receiver. The antenna and beamforming theory is presented. A new reception technique is shown which enables the array to receive multiple magnetic field sources from within the hemispherical surface. The receiver is designed to operate at 500 kHz with the RF receiver circuit designed to receive any signal from within the hemispherical surface at a frequency of 500 kHz

Large space structures and systems in the space station era: A bibliography with indexes (supplement 04)

Bibliographies and abstracts are listed for 1211 reports, articles, and other documents introduced into the NASA scientific and technical information system between 1 Jul. and 30 Dec. 1991. Its purpose is to provide helpful information to the researcher, manager, and designer in technology development and mission design according to system, interactive analysis and design, structural concepts and control systems, electronics, advanced materials, assembly concepts, propulsion, and solar power satellite systems

Reinforcement Learning in Self Organizing Cellular Networks

Self-organization is a key feature as cellular networks densify and become more heterogeneous, through the additional small cells such as pico and femtocells. Self- organizing networks (SONs) can perform self-configuration, self-optimization, and self-healing. These operations can cover basic tasks such as the configuration of a newly installed base station, resource management, and fault management in the network. In other words, SONs attempt to minimize human intervention where they use measurements from the network to minimize the cost of installation, configuration, and maintenance of the network. In fact, SONs aim to bring two main factors in play: intelligence and autonomous adaptability. One of the main requirements for achieving such goals is to learn from sensory data and signal measurements in networks. Therefore, machine learning techniques can play a major role in processing underutilized sensory data to enhance the performance of SONs. In the first part of this dissertation, we focus on reinforcement learning as a viable approach for learning from signal measurements. We develop a general framework in heterogeneous cellular networks agnostic to the learning approach. We design multiple reward functions and study different effects of the reward function, Markov state model, learning rate, and cooperation methods on the performance of reinforcement learning in cellular networks. Further, we look into the optimality of reinforcement learning solutions and provide insights into how to achieve optimal solutions. In the second part of the dissertation, we propose a novel architecture based on spatial indexing for system-evaluation of heterogeneous 5G cellular networks. We develop an open-source platform based on the proposed architecture that can be used to study large scale directional cellular networks. The proposed platform is used for generating training data sets of accurate signal-to-interference-plus-noise-ratio (SINR) values in millimeter-wave communications for machine learning purposes. Then, with taking advantage of the developed platform, we look into dense millimeter-wave networks as one of the key technologies in 5G cellular networks. We focus on topology management of millimeter-wave backhaul networks and study and provide multiple insights on the evaluation and selection of proper performance metrics in dense millimeter-wave networks. Finally, we finish this part by proposing a self-organizing solution to achieve k-connectivity via reinforcement learning in the topology management of wireless networks

Antenna Design for 5G and Beyond

With the rapid evolution of the wireless communications, fifth-generation (5G) communication has received much attention from both academia and industry, with many reported efforts and research outputs and significant improvements in different aspects, such as data rate speed and resolution, mobility, latency, etc. In some countries, the commercialization of 5G communication has already started as well as initial research of beyond technologies such as 6G.MIMO technology with multiple antennas is a promising technology to obtain the requirements of 5G/6G communications. It can significantly enhance the system capacity and resist multipath fading, and has become a hot spot in the field of wireless communications. This technology is a key component and probably the most established to truly reach the promised transfer data rates of future communication systems. In MIMO systems, multiple antennas are deployed at both the transmitter and receiver sides. The greater number of antennas can make the system more resistant to intentional jamming and interference. Massive MIMO with an especially high number of antennas can reduce energy consumption by targeting signals to individual users utilizing beamforming.Apart from sub-6 GHz frequency bands, 5G/6G devices are also expected to cover millimeter-wave (mmWave) and terahertz (THz) spectra. However, moving to higher bands will bring new challenges and will certainly require careful consideration of the antenna design for smart devices. Compact antennas arranged as conformal, planar, and linear arrays can be employed at different portions of base stations and user equipment to form phased arrays with high gain and directional radiation beams. The objective of this Special Issue is to cover all aspects of antenna designs used in existing or future wireless communication systems. The aim is to highlight recent advances, current trends, and possible future developments of 5G/6G antennas