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Abstract Localizing sources in the near-field is one of the emerging challenges for array signal processing, which has received a great deal of attention in recent years. The development of accurate localization algorithms requires the definition of a reliable model of the received signal that takes into account all wavefront characteristics, such as angle, range, and polarization, as well as electromagnetic effects, such as mutual coupling between antennas and the amplitude and phase behaviour of electromagnetic wavefronts. A system model that considers the electromagnetic-informed wave behaviour effects, independent of the type of receiver antennas, array structure, degree of correlation of sources signals and other electromagnetic effects, is considered an “ exact model ” in the literature. However, due to the mathematical complexity of this modeling approach, simplifications using several approximations are conventionally used. For instance, the phase of the exact model is approximated using the Fresnel approximation, while the magnitude of the exact model is simplified by assuming equal distances between the source and all elements in the array. In this work, we evaluate the accuracy of a localization algorithm, the multiple signal classification (MUSIC), using the exact and approximated models in the near-field region. Through a series of simulations, we demonstrate that the localization algorithm designed based on the electromagnetic-informed exact model outperforms the one designed using the approximated model. We also show that considering electromagnetic factors in the system model through the exact model results in a 13% improvement in the direction of arrival (DOA) root mean square error (RMSE) and a 57.7% improvement in range RMSE at signal-to-noise ratio (SNR) of 15 dB.

Presents corrections to the paper, (Corrections to “A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario”).

This work aims to simplify the characterization process of coded-apertures for computational imaging (CI) at microwave frequencies. A major benefit of the presented technique is the minimization of the processing time needed to calculate the system sensing matrix for microwave CI-based compressive sensing applications. To achieve this, a deep learning-based approach which is capable of generating the sensing matrix using features learned directly from the coded-aperture distribution is proposed. To avoid the vanishing gradient problem, the proposed deep learning network contains skip connections. Using a dataset of 1,000 testing samples, the average normalized mean-squared-error (NMSE) calculated between the sensing matrix generated by the conventional method and that predicted by the proposed network is 0.0036. Moreover, the average mean-squared-error (MSE) calculated between the images reconstructed using the conventional and the predicted sensing matrix is 0.00297. In addition to providing high-fidelity estimations with minimized error, we demonstrate that using the trained network, the prediction of the sensing matrix can be achieved in 0.212 s, corresponding to a 65% reduction in the computation time needed to calculate the sensing matrix. This has significant outcomes in achieving real-time operation of CI-based microwave imaging systems.

Direction-of-arrival (DOA) estimation plays a crucial role in array signal processing across various domains, including radar, sonar, wireless communications, and seismic exploration. However, traditional DOA techniques often assume either far-field (FF) or near-field (NF) propagation, limiting their applicability in scenarios involving mixed-field sources. DOA estimation and localization in scenarios involving mixed NF and FF sources is a complex and dynamic field that has garnered significant research attention in recent years. This multifaceted and evolving area holds promise for addressing challenges in radar, wireless communications, and acoustic sensing applications. This review paper provides a comprehensive overview of the methodologies, techniques, and advancements in this domain. We categorize existing methodologies, discussing their advantages and limitations. Furthermore, we delve into the mathematical modeling of mixed-field sources and essential signal processing techniques for parameter estimation. Special attention is given to technical issues such as aperture loss, computational complexity, and hardware considerations. The paper discusses the various sources of noise in the mentioned scenario and highlights the importance of modeling noise accurately for effective estimation. It also explores different scenarios and assumptions considered in the literature, ranging from non-Gaussian and non-stationary noise environments to scenarios involving multipath propagation and unknown mutual coupling effects. A detailed examination of the statistical approaches used in DOA estimation and localization reveals a diverse range of methods, including higher-order statistics and second-order statistics, each with its own advantages and applications. A comparative evaluation of various approaches highlights their performance in terms of estimation accuracy, resolution, aperture loss and computational efficiency. This provides insights into the trade-offs involved in choosing between different approaches. The review also identifies promising future research directions, such as the exploration of advanced signal processing techniques like compressive sensing and deep learning, exact NF modeling, estimation based on one-bit measurements, the integration of polarization diversity, employing metasurface antennas, tracking parameters, and the utilization of full-wave or experimental data for a more realistic representation of the challenges. By reviewing advances in methodologies and techniques, as well as outlining future research directions aimed at addressing the complexities of mixed-field scenarios, this paper paves the way for the development of more robust and reliable localization systems capable of handling real-world complexities.

In this paper, the design of a computational microwave imaging (CMI) oriented frequency-diverse reflection metasurface antenna (FDRMA) is presented. Designing a FDRMA for CMI requires a careful synthesis framework, from the topology of metamaterial elements to the statistical analyses of the metasurface and the evaluation of its CMI performance. Consequently, we begin with an investigation of different metamaterial element topologies with the aim to choose the optimal one to constitute a desired reflection metasurface. The FDRMA is then designed by randomly distributing the metamaterial elements with diverse structural parameters. The orthogonality of the reflected field patterns is investigated by means of a spatial-correlation evaluation and a singular value decomposition. To mitigate random errors, each type of FDRMA is replicated 20 times, and the evaluation indexes are averaged. Finally, the CMI experiments are carried out through full-wave simulations using different FDRMAs to verify their computational imaging performance. Moreover, a prototype of the optimal FDRMA topology is fabricated and real CMI experiments are implemented to validate the proposed design method.

This paper presents an analysis of two W-band millimeter-wave guides: a new gliding walled multilayer waveguide and a traditional longitudinally corrugated waveguide. The gliding walled multilayer waveguide (GWMLW) is a novel waveguide architecture that uses walls along the multipath channel to prevent field leakage. Its construction, however, resembles the traditional longitudinal corrugated rectangular waveguide (LCRW). The paper elaborates on the creation process of the aluminum longitudinally corrugated prototype. Using the obtained simulation results, an LCR waveguide prototype similar to the GWMLW was fabricated. The measurement results of the LCR waveguide show a reflection coefficient below −40 dB with average transmission losses of −4 dB. Although the structures appear similar, there is a sharp difference in performance due to the manufacturing process. The differences between the simulated and measured performances of both types of waveguides are attributed to similar designs but different construction methods. Furthermore, the paper highlights the comparative differences in the design and manufacturing of these waveguides while offering perspectives on millimeter-wave transmission line technology.

Abstract This paper presents a framework for achieving machine learning (ML)‐assisted direction‐of‐arrival (DoA) accuracy enhancement using a millimetre‐wave (mmWave) dynamic aperture. The technique used for the enhanced DoA estimation accuracy leverages an over‐sized lens‐loaded cavity antenna connected to a single RF chain in the physical layer and a computational method in the computational layer of the framework. It is shown for the first time that by introducing a reconfigurable mode‐mixing mechanism inside the over‐sized lens‐loaded cavity hardware, a greater number of spatially orthogonal radiation modes can be achieved giving rise to many cavity states. If the best cavity state is determined and selected by means of design exploration using a contemporary ML‐assisted antenna optimisation method, the computational DoA estimation accuracy can be improved. The mode‐mixing mechanism in this work is a randomly oriented metallic scatterer located inside an over‐sized constant−ϵr lens‐loaded cavity, connected to a stepper motor that is electronically controlled by inputs from the computational layer of the presented framework. Measurement results in terms of near‐field radiation mode scans are included in this study to verify and validate that the proposed ML‐assisted framework enhances the DoA estimation accuracy. Moreover, this investigation simultaneously provides a simplification in the physical layer implementation of mmWave radio hardware, and DoA accuracy enhancement, which in turn lends itself favourably to the adoption of the proposed framework for channel sounding in mmWave communication systems.

Abstract This paper presents a millimeter-wave direction of arrival estimation (DoA) technique powered by dynamic aperture optimization. The frequency-diverse medium in this work is a lens-loaded oversized mmWave cavity that hosts quasi-random wave-chaotic radiation modes. The presence of the lens is shown to confine the radiation within the field of view and improve the gain of each radiation mode; hence, enhancing the accuracy of the DoA estimation. It is also shown, for the first time, that a lens loaded-cavity can be transformed into a lens-loaded dynamic aperture by introducing a mechanically controlled mode-mixing mechanism inside the cavity. This work also proposes a way of optimizing this lens-loaded dynamic aperture by exploiting the mode mixing mechanism governed by a machine learning-assisted evolutionary algorithm. The concept is verified by a series of extensive simulations of the dynamic aperture states obtained via the machine learning-assisted evolutionary optimization technique. The simulation results show a 25 $$\%$$ % improvement in the conditioning for the DoA estimation using the proposed technique.

This paper proposes a new wireless enabling technology for future smart radio environments. The approach aims to enhance signal coverage within the shadow region(s) of wireless networks with the aid of so-called ‘reconfigurable intelligent edges (RIEs)’. RIEs may be installed at the fringes of shadowing objects such as buildings, walls and other obstacles which obscure the optical signal path from a transmitter to a receiver. We investigate two approaches to illuminating the shadow region in wireless networks using RIEs that exploit refraction or diffraction, operating in passive or active mode. The operation of RIE-assisted communications are investigated, in particular the ways they can redirect electromagnetic energy towards regions with little or no wireless network coverage. Following from this, a number of variations of RIEs are tested in real-world scenarios which consider illuminating the shadow region behind high-rise buildings, first in a city center, and then along a shoreline. Refractive RIEs in particular, are shown to provide significant gains compared to the case when no RIEs are involved, enhancing signal reception in the shadow region at street level behind a high-rise building by as much as 12 dB. Summary gain statistics are provided so that they can be easily included in system-level analyses and simulations. Critically, it is shown that reconfigurable intelligent edges offer a low complexity and cost-effective solution for improving connectivity in shadowing-limited environments.

Abstract This paper presents a single-pixel polarimetric compressive sensing (CS)-based direction of arrival (DoA) estimation technique using a cavity backed programmable coding metasurface aperture. The single-pixel DoA retrieval technique relies on a dynamically modulated waveform diversity, enabling spatially incoherent radiation masks to encode the incoming plane waves on the radar aperture using a single channel. The polarimetric nature of the wave-chaotic coded metasurface ensures that the DOA estimation is sensitive to the polarization state of the incoming waves. We show that the polarimetric single-pixel DoA concept can be realized by encoding the polarization information of the incoming waves at the physical layer level within the antenna. A dynamically reconfigurable wave-chaotic metasurface, which possesses a structured sparsity of dual-polarized coded metamaterial elements, is proposed for the proof of concept. It is shown that by encoding and compressing the source generated far-field incident waves into a single channel, we can retrieve high fidelity polarimetric DoA information from compressed measurements.

Abstract Antenna arrays and multi-antenna systems are essential in beyond 5G wireless networks for providing wireless connectivity, especially in the context of Internet-of-Everything. To facilitate this requirement, beamforming technology is emerging as a key enabling solution for adaptive on-demand wireless coverage. Despite digital beamforming being the primary choice for adaptive wireless coverage, a set of applications rely on pure analogue beamforming approaches, e.g., in point-to-multi point and physical-layer secure communication links. In this work, we present a novel scalable analogue beamforming hardware architecture that is capable of adaptive 2.5-dimensional beam steering and beam shaping to fulfil the coverage requirements. Beamformer hardware comprises of a finite size Maxwell fisheye lens used as a scalable feed network solution for a semi-circular array of monopole antennas. This unique hardware architecture enables a flexibility of using 2 to 8 antenna elements. Beamformer development stages are presented while experimental beam steering and beam shaping results show good agreement with the estimated performance.

Abstract This paper investigates uni-/multi-cast and orbital angular momentum (OAM) mode data transmission in orthogonal directions by the utilization of a new circular antenna array, operating at 28 GHz. In the horizontal plane the proposed antenna array operates as multimode transmitter (i.e., it provides broad-, uni- and/or multi-cast communication), while in the vertical direction OAM transmission occurs (i.e., it is capable of generating up to 15 spatially orthogonal OAM modes). Antenna array is designed using twelve, low-complexity, electromagnetically coupled microstrip patch antennas with high radiation efficiency. Each of these can transmit power of equivalent order of magnitude in both horizontal (i.e., broadside radiation pattern) and vertical direction (i.e., endfire radiation pattern) over electromagnetic waves of orthogonal electric components. This property leads to the formation of uni-/multi-cast and OAM modes in the horizontal plane and vertical direction, respectively. Antenna was tested through full-wave electromagnetic analysis and measurements in terms of impedance matching, mutual coupling and radiation pattern: good agreement between simulated and measurement results was observed. Specifically, it presents up to 8.65 dBi and 6.48 dBi realized gain under the uni-cast (in the horizontal plane) and OAM mode (in the vertical plane), respectively. The proposed antenna array is perfect candidate for high spectral efficiency data transmission for 5G and beyond wireless applications, where orthogonality in communication links and OAM multiplexing is a requirement.

Imperceptible latency, uninterrupted communication, and the availability of inexhaustible bandwidth are conceptualized as essential milestones to revolutionize the modes by which societies generated, circulate, receive, and perceive information. The exponential increase in wireless data traffic has raised concerns to investigate suitable bands in the radio spectrum to satisfy the intensifying user's data rate requirements. Overall the wireless infrastructure needs development and exploitation to synchronize with the massive capacity and connectivity demands. The Terahertz (THz) frequency band (0.1-10 THz) is considered as a pivotal solution to fulfill the needs of applications and devices requiring the high speed transmission, and have received noticeable attention from the research community. Technologies in this spectrum are facing rapid development and hold high potentials in applications like ultra-fast short-range wireless communications, remote sensing, biological detection, and basic material research. The antenna is one of the critical components to support the THz systems and require a considerable attention in terms of precision. Compact high-gain antennas are desirable for low latency and high data rate THz wireless communication systems, specifically for applications having space limitation, for example, in the high speed interlink inside the high density wireless communication base station (BS). Nevertheless, there still exist many challenges, while designing the antenna for THz communications requiring innovative solutions. This paper serves an introductory guideline to address the challenges and opportunities, while designing a THz enabled antenna.

A System-of-Systems (SoS) is a complex, dynamic system whose Constituent Systems (CSs) are not known precisely at design time, and the environment in which they operate is uncertain. SoS behavior is unpredictable due to underlying architectural characteristics such as autonomy and independence. Although the stochastic composition of CSs is vital to achieving SoS missions, their unknown behaviors and impact on system properties are unavoidable. Moreover, unknown conditions and volatility have significant effects on crucial Quality Attributes (QAs) such as performance, reliability and security. Hence, the structure and behavior of a SoS must be modeled and validated quantitatively to foresee any potential impact on the properties critical for achieving the missions. Current modeling approaches lack the essential syntax and semantics required to model and verify SoS behaviors at design time and cannot offer alternative design choices for better design decisions. Therefore, the majority of existing techniques fail to provide qualitative and quantitative verification of SoS architecture models. Consequently, we have proposed an approach to model and verify Non-Deterministic (ND) SoS in advance by extending the current algebraic notations for the formal models as a hybrid stochastic formalism to specify and reason architectural elements with the required semantics. A formal stochastic model is developed using a hybrid approach for architectural descriptions of SoS with behavioral constraints. Through a model-driven approach, stochastic models are then translated into PRISM using formal verification rules. The effectiveness of the approach has been tested with an end-to-end case study design of an emergency response SoS for dealing with a fire situation. Architectural analysis is conducted on the stochastic model, using various qualitative and quantitative measures for SoS missions. Experimental results reveal critical aspects of SoS architecture model that facilitate better achievement of missions and QAs with improved design, using the proposed approach.

In this paper, we study an additional spatial diversity mechanism added to a cavity-backed frequency-diverse aperture for computational imaging applications at X-band frequencies. It is shown that the dynamic variation of cavity modes achieved by means of a simple, multi-port excitation mechanism can significantly simplify the diversity constraints on the frequency-diversity technique for computational imaging and can substantially improve the fidelity of the reconstructed microwave images. Leveraging the proposed technique, we present that, for the selected number of measurement modes, the condition number of the studied computational imaging problem is improved by 8.5 times in comparison to using the frequency-diversity as the only diversity mechanism. The reconstructed X-band images of various targets, including the letters “Q”, “U” and “B” reflect on this improvement and exhibit a higher reconstruction quality, significantly assisting in the identification of the imaged objects. We also present that the resolution limits of the frequency-diverse computational imaging system synthesized using the proposed multi-port cavity are in excellent agreement with the theoretical resolution limits.

In this paper, we present the application of a resonant electric based metamaterial element and its two-dimensional metasurface implementation for a variety of emerging wireless applications. Metasurface apertures developed in this work are synthesized using sub-wavelength sampled resonant electric-based unit-cell structures and can achieve electromagnetic wave manipulation at microwave frequencies. The presented surfaces are implemented in a variety of forms, from absorption surfaces for energy harvesting and wireless power transfer to wave-chaotic surfaces for compressive sensing based single-pixel direction of arrival estimation and reflecting surfaces. It is shown that the resonant electric-synthesized metasurface concept offers a significant potential for these applications with high fidelity absorption, transmission and reflection characteristics within the microwave frequency spectrum.

Continuous practices, i.e., continuous integration, delivery, and deployment, are the software development industry practices that enable organizations to frequently and reliably release new features and products. With the increasing interest in the literature on continuous practices, it is important to systematically review and synthesize the approaches, tools, challenges, and practices reported for adopting and implementing continuous practices. This paper aimed at systematically reviewing the state of the art of continuous practices to classify approaches and tools, identify challenges and practices in this regard, and identify the gaps for future research. We used the systematic literature review method for reviewing the peer-reviewed papers on continuous practices published between 2004 and June 1, 2016. We applied the thematic analysis method for analyzing the data extracted from reviewing 69 papers selected using predefined criteria. We have identified 30 approaches and associated tools, which facilitate the implementation of continuous practices in the following ways: (1) reducing build and test time in continuous integration (CI); (2) increasing visibility and awareness on build and test results in CI; (3) supporting (semi-) automated continuous testing; (4) detecting violations, flaws, and faults in CI; (5) addressing security and scalability issues in deployment pipeline; and (6) improving dependability and reliability of deployment process. We have also determined a list of critical factors, such as testing (effort and time), team awareness and transparency, good design principles, customer, highly skilled and motivated team, application domain, and appropriate infrastructure that should be carefully considered when introducing continuous practices in a given organization. The majority of the reviewed papers were validation (34.7%) and evaluation (36.2%) research types. This paper also reveals that continuous practices have been successfully applied to both greenfield and maintenance projects. Continuous practices have become an important area of software engineering research and practice. While the reported approaches, tools, and practices are addressing a wide range of challenges, there are several challenges and gaps, which require future research work for improving the capturing and reporting of contextual information in the studies reporting different aspects of continuous practices; gaining a deep understanding of how software-intensive systems should be (re-) architected to support continuous practices; and addressing the lack of knowledge and tools for engineering processes of designing and running secure deployment pipelines.

Computational imaging using coded apertures offers all-electronic operation with a substantially reduced hardware complexity for data acquisition. At the core of this technique is the single-pixel coded aperture modality, which produces spatio-temporarily varying, quasi-random bases to encode the back-scattered radar data replacing the conventional pixel-by-pixel raster scanning requirement of conventional imaging techniques. For a frequency-diverse computational imaging radar, the coded aperture is of significant importance, governing key imaging metrics such as the orthogonality of the information encoded from the scene as the frequency is swept, and hence the conditioning of the imaging problem, directly impacting the fidelity of the reconstructed images. In this paper, we present dielectric lens loading of coded apertures as an effective way to increase the information coding capacity of frequency-diverse antennas for computational imaging problems. We show that by lens loading the coded aperture for the presented imaging problem, the number of effective measurement modes can be increased by 32% while the conditioning of the imaging problem is improved by a factor of greater than two times.

Delays often occur during some activities in software development projects. Without handling of project delays effectively, many software development projects fail to meet their deadlines. If extra employees with same or similar skills and domain knowledge can be rescheduled for the remaining activities of the delayed projects, it can be possible to reduce or even eliminate existing delays in concurrent software development projects of similar nature. However, it is evident that employee rescheduling may result in delaying other activities, which may lead to the problem of delay propagation. Hence, it is important to investigate how to reduce or even eliminate the delay in one project without impacting other projects. By nature this is an NP-hard problem. Therefore, we propose a novel generic rescheduling strategy based on adaptive ant colony optimization algorithm to provide decision support for software project managers to select appropriate employees to deal with project delays. We have carried out a set of comprehensive experiments to evaluate the performance of the proposed strategy. In addition, three real world software project instances are also utilized to evaluate our strategy. The results show that our strategy is effective, efficient and able to outperform its representative counterparts significantly.

The use of a tunable power splitter (PS) as a constituent component of a simultaneous wireless information and power transfer (SWIPT) system is discussed. Two varactor diodes are used to achieve a tunable output power ratio P2 : P3 varying from 1 : 1 to 1 : 10 under good matching conditions. The SWIPT system that operates at 2.4 GHz consists of a typical patch antenna, cascaded with the tunable PS, and a voltage doubler rectifier. The constituent components were implemented and tested as stand-alone devices and were subsequently combined in a measurement system using interconnectors. The effect of the tunable PS was explored with respect to the SNR measurements on the port that is intended for the information decoding receiver and the DC voltage measurements on the termination load of the rectifier that is connected directly on the energy harvesting port of the tunable PS. A spectrum analyzer is used for the SNR measurements while the input power is controlled using a signal generator. Both wireless power transmission and on-board measurements verify that the harvested energy can be maximized by using the minimum SNR at the information decoding branch at the expense of DC power consumption required for the biasing of the varactor diodes.

Three planar, CPW-fed, UWB antennas with increasingly reduced size are presented and the miniaturization method is discussed. The first antenna is a CPW-fed elliptical slot with an uneven U-shaped tuning stub, the second antenna is a cactus shaped monopole, and the third one is a miniaturized version of the cactus shaped monopole antenna. All presented antennas have a simulated and measured return loss below −10 dB over the 3.1 to 10.6 GHz UWB frequency range and mostly omnidirectional radiation patterns. The proposed antennas are fabricated on liquid crystal polymer (LCP). The CPW-fed slot antenna requires an overall board dimension of 38 mm × 40 mm, and the evolved cactus monopole is confined in a 28 mm × 32 mm board, while the final miniaturized cactus monopole is printed on 28 mm × 20 mm board, resulting in a 41% and 63% size reduction, respectively. Using both simulations and measurements, the paper analyzes the response of all three antennas and discusses and demonstrates the effectiveness of the implemented miniaturization method.