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A high-gain transmit–reflect-array (TRA) antenna with asymmetric bidirectional beams using sparse-array method is presented in this communication. The proposed element consists of three-layer hexagonal-ring-patch (HRP) structure, able to achieve both amplitude (0 or 1) and phase modulation. Based on this kind of element structure, a 125 mm $\times \,\, 125$ mm square-shaped TRA is designed and fabricated. A 45.3% of the radiation units operate in transmission mode while others in reflection mode. Spatially fed by a conical horn antenna, two well-defined beams are obtained on both sides of the TRA, pointing to the direction of $\theta = 0^{\circ }$ and 170° ( $\varphi =0^{\circ }$ ), respectively. The measured results show a maximum gain of 21.4 dBi with a 1 dB gain bandwidth of 6.7% for the transmitted beam, and a maximum gain of 24.4 dBi with a 1 dB gain bandwidth of 9.3% for the reflected beam. To the authors’ best knowledge, it is the first time that the sparse-array method is employed in spatially fed antenna design. The proposed method can conveniently manipulate the pointing directions and beampattern characteristics of the bidirectional beams, providing a promising candidate for bidirectional wireless communication applications.

Conventional transmit subarray partitioning schemes of phased-MIMO radar will cause several problems, the reduction of array aperture, the increase of feeding network complexity and optimization algorithms time cost. Focus on the problems above, this paper proposes a deep learning-based interleaved sparse transmit subarray partitioning method. Firstly, the training data is generated by phased-MIMO array manifold matrix. Secondly, a dimensionality reduction method for radar data is introduced to reduce the dimensionality of the sample data while minimizing information loss. Then, a lightweight convolutional neural network is constructed for training and the optimal array structures is selected by classification. Finally, linear and plane arrays experiment results show that our proposed method can achieve 97.95% classification accuracy, better than other conventional dimensionality reduction methods and neural networks; compared with the traditional SCP partitioning method, our proposed method has similar beampattern sidelobe level and DOA estimation accuracy, but the time cost is greatly reduced.

Sparse sensor array selection arises in many engineering applications, where it is imperative to obtain maximum spatial resolution from a limited number of array elements. Recent research shows that computational complexity of array selection is reduced by replacing the conventional optimization and greedy search methods with a deep learning network. However, in practice, sufficient and well-calibrated labeled training data are unavailable and, more so, for arbitrary array configurations. To address this, we adopt a deep transfer learning (TL) approach, wherein we train a deep convolutional neural network (CNN) with data of a source sensor array for which calibrated data are readily available and reuse this pre-trained CNN for a different, data-insufficient target array geometry to perform sparse array selection. Numerical experiments with uniform rectangular and circular arrays demonstrate enhanced performance of TL-CNN on the target model than the CNN trained with insufficient data from the same model. In particular, our TL framework provides approximately 20% higher sensor selection accuracy and 10% improvement in the direction-of-arrival estimation error., Accepted paper in IEEE Transactions on Cognitive Communications and Networking

In this article, we propose a novel type of a wideband 2-D array of cold-electron bolometers (CEBs) sensitive to both polarizations of the incoming optical signal. This array was developed for osservatorio per il lontano infrarosso e le microonde su pallone orientabile (OLIMPO) balloon telescope with a requirement on bandwidth more than 10%. Sensitivity to both polarization components was achieved by a special meander-type arrangement of orthogonal L/2 dipoles. We present theoretical estimations for the optimal arrangement of 2-D arrays of CEBs, which allows achieving maximum efficiency in a wide band with the lowest noise equivalent power (NEP). A pixel layout with an optimal density of CEBs, sensitive to dual polarizations, and having more than 10% FWHM bandwidth at around 350 GHz was proposed and numerically analyzed. The layout consists of a periodic sparse array of orthogonal dipoles sensitive to both polarization components of the incoming signal. The numerical analysis confirmed that the sparse array is the most promising for a combination of high absorbing efficiency and low noise of the system and satisfying the 10% bandwidth requirements for the OLIMPO instrument. Different combinations of series and parallel connections of bolometers were considered for better noise properties to achieve the photon noise-limited level.

This paper proposes simulated annealing (SA) assisted deep learning (DL) based sparse array selection approach. Conventional DL-based antenna selectors are primarily data-driven techniques. As a result, the required dataset is generated by listing all possible combinations of selecting $M$ sensors given $N$ uniform array, which is computationally expensive. A simulated annealing algorithm is proposed to assist dataset generation as an initializer to circumvent the above limitation. The SA algorithm sequentially samples and optimizes the subarrays that constitute the training data samples while retaining specific array characteristics. Hence, it simplifies the dataset annotation as most array configurations generated contain desired properties, thereby reducing the computation complexity of the overall data annotation processes. Therefore, the initializer reduces computation costs related to data generation considerably. Simulation examples show that using the dataset generated by the proposed method improves the DL-based array selector’s accuracy compared to the one generated by the conventional random sampler. Moreover, the realized sparse arrays show better sparse array configuration characteristics and enhanced DOA estimation performance.

Recent developments in electronics have pushed miniaturized satellites to the femto-scale, with masses between 10 and 100 g. Although femtosatellites have been proven as a feasible concept, most designs are limited in mission capacity and lifetime due to the lack of environmental protection and onboard propellant. In this article, a novel concept for femtosatellites for earth remote sensing is proposed. In particular, a swarm of femtosatellites are used as elements of a sparse array in orbit to receive radar echoes. They also feature active orbit control enabled by solar radiation pressure to extend their lifetime. A simple active orbit control algorithm has been demonstrated. A mission concept based on a sun-synchronous circular orbit is proposed to maximize the benefit for both earth remote sensing and active orbit control. A synthetic aperture radar mission has been used to characterize their performance.

In this paper, on the basis of multifunctional reconfigurable pixel antenna (RPA) elements, a novel linear sparse array with an attractive compound reconfigurability is presented. It has the potential advantages of its beam scanning with low gain fluctuation, low sidelobe in two orthogonal planes, and polarization reconfigurable performance. Specifically, an RPA with simultaneous polarization and pattern reconstruction capabilities, consisting of the driven patch and the parasitic pixels on the same layer of dielectric substrate, is firstly designed, which can work in several operation modes corresponding to steerable beam directions θ=0°;θxoz=25°, 45°;θyoz=15° with two circular polarizations in X-band. Cross-slot coupling feed is used to improve polarization reconstruction capability and reduce the complexity of hybrid reconstruction topology optimization. Then, those RPAs are integrated into the 1×8 linear sparse array to realize the reconfiguration of two circular polarizations and beam steering in xoz- and yoz-plane. Simulation results show that the gain fluctuation and sidelobe level of the array during beam scanning have significant advantages over the previous phased array, and the generation of antenna grating lobes is avoided. Moreover, both RPA element and RPA array prototypes have been fabricated and measured to testify the efficiency. The measured results agree well with the simulated ones, which indicates the application potential in the field of modern wireless communication system of the proposed linear sparse array.

Absorption of the solar radiation over a wide spectral range is of utmost importance to applications related to the harvesting of solar energy. We numerically demonstrate broadband solar absorption enhancement employing a metamaterial in the form of arrays composed of subwavelength silicon truncated inverted cones, henceforth referred to as light funnel (LF) arrays. We show that the broadband absorption efficiency of an unoptimized LF array is 36% greater compared with an optically-maximized NP array. We show that photon trapping in LF arrays is motivated by proximity effects related to the optical overlap between LFs. We make the distinction between two types of optical overlap: weak overlap in which the coupling between the sparse array modes and the impinging illumination increases with array densification, and strong overlap where the array densification introduces new highly absorbing modes. We show that in nanopillar (NP) arrays the optical intensity inside the NPs decreases upon densification and the overall increase in absorptivity is due to increase in filling ratio (as was also shown by others), while the densification of LF arrays increases the optical intensity inside the individual LF and with the concurrent increase in filling ratio concludes light trapping much superior to that of NP arrays. Light trapping governed by strong proximity effects was not reported to date, and we believe it is an important paradigm for miniaturized lab-on-chip technologies.

This article presents a double-layer four-corner-fed slotted waveguide sparse array antenna designed for the near-field-focused applications in the X-band. Generally, it is challenging to realize the ideal phase distribution of the near-field focused array antenna, especially for the near-field focusing array antenna with a focal distance less than $5\lambda $ . We propose a four-corner-feed structure associated with appropriate waveguide dimensions to realize approximately ideal phase distribution for a focus at $1.5\lambda $ . The sparse array based on the particle swarm optimization is adopted to suppress the sidelobes and the grating lobes on the focal plane caused by the residual phase errors. A test antenna is designed at the center frequency of 10 GHz, and is fabricated by Direct Metal Laser Sintering technique for demonstration. It is verified by the experiment that we achieve satisfying near-field focusing effects, especially in terms of the focal spot size and sidelobe levels on the focal plane 45 mm away from the antenna aperture.

As ultrasonic phased array total focus method (TFM) imaging technology can achieve full range of dynamic focusing with clear imaging and strong ability to characterize defects, TFM imaging algorithm has become the gold standard for testing other post-processing algorithms. However, due to the large amount of data and time-consuming calculation, the TFM imaging has limited the application in some industrial fields. With the reduction of the number of phased array transmitting elements, the imaging quality gets worse. To improve the imaging efficiency of the TFM algorithm and ensure the imaging quality, this paper proposes a method combining Siamese Convolutional Neural Network (SCNN) and Genetic Algorithm (GA) to obtain an optimal sparse array layout, to approximate the imaging effect of the full array with limited effective elements. After selection of an appropriate sparsity ratio, GA is used to optimize the sparse array layout. The sparse array elements emit ultrasonic waves, and the full array elements receive echo signals for imaging. SCNN is trained by a self-built industrial defect dataset, to output the similarity between the sparse-TFM image and the full-array TFM image. The similarity is used as an objective evaluation index to evaluate the imaging effect. The optimal sparse array layout is proposed by combining subjective evaluation with objective evaluation.

1AbstractFor bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological sample’s complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptide’s monoisotopic mass, which is critical for the peptide’s identification.Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third algorithm compensates for saturation of the instrument’s detector thereby recovering lost dynamic range, and lastly, the fourth algorithm increases confidence of peptide identifications by simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. The software has been developed in Python using Numpy and Pandas and made freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). Data are available via ProteomeXchange with identifier PXD030706.2Author SummaryThe primary goal of mass spectrometry data processing pipelines in the proteomic analysis of complex biological samples is to identify peptides accurately and comprehensively with abundance across a broad dynamic range. It has been reported that detection of low-abundance peptides for early-disease biomarkers in complex fluids is limited by the sensitivity of biomarker discovery platforms (1), the dynamic range of plasma abundance, which can exceed ten orders of magnitude (2), and the fact that lower abundance proteins provide the most insight in disease processes (3). As mass spectrometry hardware improves, the corresponding increase in amounts of data for analysis pushes legacy software analysis methods out of their designed specification. Additionally, experimentation with new algorithms to analyse raw data produced by instruments such as the Bruker timsTOF Pro has been hampered by the paucity of modular, open-source software pipelines written in languages accessible by the large community of data scientists. Here we present several algorithms for simplifying MS1 and MS2 spectra that are written in Python. We show that these algorithms are effective to help improve the quality and accuracy of peptide identifications.

Ultrasound open platforms are ideal instruments to experiment innovative modalities in transmission and reception. So far, the high number of channels, involved in applications based on 2-D arrays, has been managed through complex combinations of scanners and personal computers, at the expense of ease of use, portability, and real-time performance. This work presents a new flexible architecture, based on the full (hardware and software) synchronization of multiple ULA-OP 256 scanners, in a Master-Slave architecture, and controlled by a single computer. For the hardware synchronization, a Master scanner provides two digital signals (clock and transmission start) to the Slave scanners, while the software synchronization is managed by two purposely developed MATLAB classes that allow the user to interface the scanners as a single system. To demonstrate the effectiveness of the architecture, a 512-channel prototype, composed of two ULA-OP 256 connected to a host PC and to a sparse array, was implemented and tested through applications that demonstrate the high real-time processing power.

In order to solve the problem that the wide range of damage monitoring for large-scale structures, a triangular sparse array-based damage monitoring is researched and validated in this paper. The typical Lamb wave structural responses is analyzed firstly. Multi characteristic parameters is extracted from the responses based on the varies of the response due to the damage. Using these multi characteristic parameters, the damage location in large-scale structure can be monitored by the damage identification model which is designed by using SVM (Support Vector Machine). Experimental validation shown that the multi characteristic parameters from triangular sparse array layout can be used to identify the specific orientation of damage in relative large-scale structure, and the trained SVM based damage identification model can also reveal the damage location. Therefore, the existing large-scale structural damage monitoring method is improved.

As a typical signal processing problem, direction-of-arrival (DOA) estimation has been adapted to a wide range of applications in radar-based systems. A high DOA resolution requires a large number of antenna elements which increases the overall cost. To minimise the cost, it is desirable to choose an optimum sub-array from a full array. To enable cognition, the subarrays are selected based on the present target scenario. By using deep learning (DL) based techniques, the authors show a cognitive sparse array selection technique. By using hardware simulations, they demonstrate the applicability of the deep learning (DL)-based sparse antenna selection network in direction-of-arrival (DOA) estimation problems. They show that the DL-based sub-arrays lead to a higher direction-of-arrival (DOA) estimation accuracy by 6 dB over random array selection.

This work evaluates various nonlinear ultrasound methods for in situ structural health monitoring of the loosened state of a four-bolt structure found on large-scale wind turbines. The aim was assessment of a four bolted structure with only two piezoelectric sensors, and determination of individual bolt loosened and the extent of loosening. Nonlinear ultrasound methods have been shown to have advantages over linear methods in terms of sensitivity, although the detection accuracy and robustness of these methods can be highly dependent on correct frequency selection. Thus, a frequency selection process based on the modal response of the structure is suggested for determination of bolt-specific frequencies, which was then used to evaluate the individual bolt loosened state. Two nonlinear ultrasound techniques were used to evaluate the bolted structure: the second- and third-order nonlinearity parameters and a nonlinear acoustic moment’s method. The modal response method used for frequency selection was able to determine specific bolt frequencies based on surface and bolt velocities. Nonlinear evaluation at these frequencies showed that specific frequencies related to individual bolts, and as the bolts loosened there was a clear increase in the production of nonlinearities. Thus, the loosened status of individual bolts could be tracked using specific pre-identified frequencies.

We present a novel imaging method for compact low-profile imagers in millimeter wave (mmW) and terahertz (THz) frequencies. The method borrows the principles of computerized tomography to generate 2-D or 3-D high-resolution images using simplified RF front ends. To perform imaging, the proposed scheme implements the projection-slice theorem. Specifically, it records the Radon transform (RT) of the desired field-of-view (FOV) and reconstructs the image using the Fourier slice theorem. To achieve the illumination of the FOV, the proposed imaging configuration uses a rotating linear antenna array that produces a highly directive fan beam. The antenna elements are further reduced using multistatic sparse array techniques without compromising the image reconstruction process. By dramatically reducing the number of active elements, the new method could allow high-resolution mmW/THz imaging systems to be embedded in low profile and lightweight platforms, including unmanned aerial vehicles or CubeSats. In this paper, we present the basic concept of the novel imaging scheme and simulation results verifying the proof of concept. Finally, we present a modified imaging antenna array that alleviates interference from objects and sources outside the FOV.

Sparse array (SA) is an approach to reduce the number of system channels. However, SA suffers from grating lobe (GL) artefacts due to the sparsity of array aperture resulting in degradation of the ultrasound image quality. Based on a given or known data sets of radio frequency (RF) echo acquired from active elements of an array, RF echo data in unknown and/or inactive elements of array can be created virtually and used to suppress the GL artefact in SA. This letter presents gap-filling (GF) approaches to generate channel data, which are not physically acquired. It is demonstrated that the proposed GF technique can reduce the artefacts of SA by filling the gaps in the array aperture. Simulation results show that the GF technique can suppress the GL level of SA by up to 16 dB. Also, the GF technique can improve the image quality of fully sampled arrays with small number of active elements.

A sparse substrate integrated waveguide (SIW) slot antenna array and its application on phase scanning are studied in this paper. The genetic algorithm is used to optimize the best arrangement for 8-element and 7-element sparse arrays over an aperture of 4.5λ0. Antenna arrays with feeding networks, for steering the main beam pointing to 0° and −15°, are demonstrated with the SIW technology. The comparison between the sparse array and the conventional uniformly spaced array with the same aperture are presented, which suggest that the same beam width can be obtained with the gain decreased by 0.5 or 1 dBi and the number of element reduced by 2 or 3, respectively. The sparse antenna array with beam scanning ability presented in this paper shows that, while the beam scanning in the range of ±15°, the gain fluctuation is less than 0.3 dBi and the side lobe level is lower than −10 dB.

Source number estimation is one of the key technologies of wireless location, and it is also the basis of parameter estimation and location solution. In order to improve the accuracy of existing non-circular source number estimation and realize underdetermined source number estimation, this paper presents a non-circular source number estimation method based on sparse array. This method firstly uses the non-circular characteristics of received sources to expand the array aperture, then combines the structural characteristics of sparse array to construct a virtual array model, and extracts the response parts of continuous virtual array elements for spatial smoothing processing. Finally, the minimum description length (MDL) method is used to realize the effective estimation of non-circular source number. The complexity analysis and simulation experiments are given, and the results of Monte Carlo experiments show that compared with the traditional source number estimation method, this method can not only improve the accuracy of the source number estimation but also realize the effective estimation of the source number in undetermined conditions.

This paper introduces the concept of the sparse array into the design of a wide-angle scanning phased array to obtain the low sidelobe level (SLL). The adopted array element is a half-mode substrate integrated waveguide slot array, which can radiate a high-gain and wide-beam pattern in the E-plane. A 16-element phased array with an optimized non-periodic array element arrangement, which is obtained by a semi-full-wave simulation-based synthesis method, is designed and studied. Compared with two periodic arrays with the same array element and element number, the sparse one exhibits the advantage of low SLL. Besides, the designed phased array has an ultra-low profile of only about 0.017λ0. A prototype is fabricated and measured, and measured results show that the proposed array can scan its main beam from -70° to +70° with a realized gain higher than 18.2 dBi and peak SLL lower than -15 dB.

A new method of creating microwave imagery by capturing the signals emitted by a small set of wireless WiFi transmitters is presented. The imaging technique leverages the fact that the signals emitted by separate WiFi transmitters are sufficiently statistically independent to create a radiation pattern that is spatially incoherent, enabling the use of spatial frequency sampling using a small set of receiving antennas in a sparse array. In contrast to traditional microwave imaging, this method requires no mechanical or electrical beam scanning and no coordination between transmitters and receivers. Furthermore, the WiFi imaging system requires far less receiver gain than passive microwave imagers and significantly less bandwidth. We experimentally demonstrate the 2-D image reconstruction of reflecting metal spheres and an X-shaped reflecting target using three transmitters emitting independent 16-level quadrature amplitude modulated signals at 5.5 GHz matching commercial WiFi protocols 802.11n/ac.

In this paper, a 60 GHz planar Fabry-Perot cavity (FPC) antenna with a circularly polarized (CP) radiation characteristic is proposed as a good candidate for millimeter-wave wireless systems. A sparse array design is employed to enhance the gain bandwidth (GBW) product of the antenna. Complete formulations for circular polarized radiation are developed to serve as a general guideline scalable to any frequencies. The theoretical results using ideal point sources are validated by numerical analysis using the finite elements method (FEM). In the full-wave simulations, the ideal sources are replaced by four annular-ring slot antennas to excite the FPC.

A novel ultra-wideband wide-angle scanning sparse array antenna is proposed in this paper. The log periodic dipole antenna with exponential shape is selected as the unit of sparse array antenna due to its small size, ultra-wideband and non-frequency-dependent characteristics. After the unit design is completed, genetic algorithm is used to optimize the array layout efficiently, and the final number of units is determined to be 40. The aperture area of the sparse array is $\mathrm{300}\_\text{mm} \times \mathrm{300}\_\text{mm}$ . Compared to the conventional periodic array, the proposed sparse array has reduced about 190 antenna units. Through optimization of performance and position of the element, the sparse array can achieve active $\text{VSWR} and S11 < −10dB in the ultra-wideband of 3 – 12GHz (120%). Simulation results show that the beam pointing accuracy is better than 10 degrees, the azimuth scanning angle is plus or minus 45 degrees, and the pitching scanning angle is plus or minus 30 degrees. The gain at the center frequency 7GHz is about 22dBi.

A new cascading multiple input multiple output (MIMO) radar at V-band with 192 virtual elements is developed. The experiment for imaging indoor and outfield is performed with an angular resolution of 0.6°. In addition, sparse array with 108 virtual elements based on this platform is tested with genetic algorithm, the imaging results show that the sparse array imaging with a sparse rate of 55.1% agree well with full array.

Lens-less millimeter-wave (mmWave) imaging of moving objects using a sparse array relies on knowledge of the relative positions between the moving object and the imaging system to enable coherent image reconstruction. However, accurate object position information is rarely available in commercial applications where the moving object, e.g. a conveyor belt or a robot, is controlled independently of the imaging system, or where the imaged objects move autonomously. This poses a significant hurdle for many commercial mmWave imaging applications. We present a video-based motion extraction approach for active mmWave imaging. The object velocity is extracted in real time from motion vectors obtained from a compressed video. This information is combined with readouts from a distance sensor to infer the position of the object at each time instant. Leveraging video-derived motion vectors enables the offloading of computational complexity of 2-D spatial correlations to highly optimized algorithms operating on camera frames. We show experimentally that the image quality of a commercial high-throughput 3-D mmWave imaging system prototype is improved significantly by this approach when the velocity of the target is unknown and time-varying. We furthermore show that image quality is also improved compared to known average motion profiles of the imaged objects. Using a lab setup with known ground truth, we show that the RMS position error is 2.5 mm over a travel length of 0.52 m. This is better than 1/8 of the wavelength at K-band (24 GHz) along the trajectory and thus sufficient to achieve excellent image quality at K-band and longer wavelengths.

This paper proposes a simulated annealing (SA) assisted deep learning (DL) based 2D sparse array selection approach. A SA-based optimisation method is used to assist in generation and annotation of dataset. The antenna selection problem is formulated as a multi-classification problem, and a convolutional neural network (CNN) is used to solve it. The SA-based dataset generation method enables optimal sensor distribution and reduces computation complexity related with dataset generation. Numerical examples verify the effectiveness of the proposed method in terms of configuration of the realised sparse arrays and DOA estimation performance.

In this article, we propose a hybrid electromechanical scanning lens antenna array architecture suitable for the steering of highly directive beams at submillimeter wavelengths with field-of-views (FoV) of ±25°. The concept relies on combining electronic phase shifting of a sparse array with a mechanical translation of a lens array. The use of a sparse-phased array significantly simplifies the RF front-end (number of active components, routing, thermal problems), while the translation of a lens array steers the element patterns to angles off-broadside, reducing the impact of grating lobes over a wide FoV. The mechanical translation required for the lens array is also significantly reduced compared to a single large lens, leading to faster and low-power mechanical implementation. In order to achieve wide bandwidth and large steering angles, a novel leaky wave lens feed concept is also implemented. A 550-GHz prototype was fabricated and measured demonstrating the scanning capabilities of the embedded element pattern and the radiation performance of the leaky wave fed antenna.

Sparse arrays have grating lobes in the far field pattern due to the large spacing of elements residing in a rectangular or triangular grid. Random element spacing removes the grating lobes but produces large variations in element density across the aperture. In fact, some areas are so dense that the elements overlap. This paper introduces a low discrepancy sequence (LDS) for generating the element locations in sparse planar arrays without grating lobes. This nonrandom alternative finds an element layout that reduces the grating lobes while keeping the elements far enough apart for practical construction. Our studies consider uniform sparse LDS arrays with 86% less elements than a fully populated array, and numerical results are presented that show these sampling techniques are capable of completely removing the grating lobes of sparse arrays. We present the mathematical formulation for implementing an LDS generated element lattice for sparse planar arrays, and present numerical results on their performance. Multiple array configurations are studied, and we show that these LDS techniques are not impacted by the type/shape of the planar array. Moreover, in comparison between the LDS techniques, we show that the Poisson disk sampling technique outperforms all other approaches and is the recommended LDS technique for sparse arrays.

Environmental sound classification (ESC) based on single-classifier-type multi-class deep neural network is gaining growing attention in current audio classification research. In order to improve the performance of multi-class deep neural networks, neural architecture search (NAS) can generally be used, but such methods often demand tremendous computational costs. This paper presents a novel multi-class framework based on local one-versus-rest (OVR) deep neural network, which can improve the classification performance of a pre-trained deep neural network at an affordable computational cost. The main idea of the framework is to first identify the weak classification category group (WeakClass group) of the pre-trained network for the actual sample using only the training data. This is achieved by using the training average confidence matrix of the pre-trained deep neural classification network. The next step is to build a sparse array of OVR subnetwork classifiers according to the WeakClass group. Afterwards, the OVR subnetwork classifiers are integrated into the original pre-trained network to form the final multi-class classifier. We apply this framework to ESC problem and the experimental results show that the proposed framework achieves a classification accuracy of 86.8% on the ESC-50 dataset, which is better than other related algorithms.

The paper considers receive beamforming for designing sparse array configurations. We consider to maximize signal-to-interference plus noise ratio (MaxSINR) for a desired point source in a narrowband interferening environment. The sparse array design methods can either be data driven or rely entirely on the prior knowledge of the interference DOAs and respective powers. In this paper, we propose a design which is essentially data-driven and conceived by training the Deep Neural Network (DNN). Towards this goal, the training scenarios are generated through enumeration to learn an effective representation that can perform well in a downstream task. The input to the DNN is the received correlation matrix and output is the corresponding sparse configuration with superior interference mitigation capability. The performance of the DNN is evaluated by the ability to learn the implementation of enumerated design. It is shown through design examples that DNN effectively learns the enumerated algorithm and, as such, paves the way for efficient real-time implementation.

High-resolution direction of arrival estimation requires a large number of antenna elements which increases the computational cost, hardware complexity, and power requirements. To balance between hardware complexity and resolution, recently, we proposed a cognitive, scalable, sparse array selection technique based on a submodular-greedy algorithm. In this demo, we present a design and implementation of a hardware prototype that demonstrate the proposed sparse antenna selection strategy. Through real-time experiments, we show that the proposed sparse selection method results in a 2 − 3 dB lower error compared to a typically employed random selection method.

Cavitation is involved in a number of therapeutic applications of ultrasound (US) [1] [2] [3]. Monitoring these treatments requires real-time imaging of the cavitation activity during treatment. For this reason, Passive Acoustic Mapping (PAM) of cavitation has drawn an increasing attention during the past years [4] [5] [6] [7] [8].

Sparse arrays are a topic of high interest within the ultrasound (US) imaging community, because of their promising ability to reduce costs, complexity, energy consumption, and data transfer requirements of US systems, thus addressing the main challenges of 3-D and portable 2-D systems. Undersampling a transducer array usually results in a significant increase in imaging artifacts, caused primarily by higher grating lobe (GL) levels. Thus, state-of-the-art sparse arrays design strategies focus on avoiding GLs, while compromising on the resulting image resolution and uniformity. In this work, we investigated the applicability of convolutional neural network (CNN)-based image reconstruction, having recently proven its potential in reducing GL artifacts, for reconstructing images from single unfocused acquisitions using uniformly undersampled linear array configurations on receive. The proposed reconstruction method consists of first computing a low-quality estimate from the undersampled single-shot acquisitions using a delay-and-sum (DAS) algorithm, followed by applying a real-time-capable CNN, trained specifically to reduce diffraction artifacts. Experiments were conducted within a simulation environment, in the context of plane wave imaging on a numerical test phantom dedicated to US image quality assessment. The proposed approach achieved an image comparable or better to that obtained from conventional DAS beamforming using the full array with uniformly undersampled arrays up to a factor of three, demonstrating a promising potential for sparse array imaging in general.

Pupil densification is a technique to improve contrast in a direct imaging interferometer. By enlarging the size of each aperture in a sparse array, the fraction of energy contained in the central point spread function peak can be increased. However, the effective field of view is reduced as the contrast improves. Our work investigates the optimal strategy for pupil densification in the context of extended source imaging. Analytical expressions are derived to optimize the signal-to-noise ratio at the edge of the field of view for a variety of hypertelescope configurations. Free-space beam transport and fiber-optic beam transport are both considered. The relative advantages and disadvantages between the considered hypertelescope configurations are discussed.

Subarray application for increasing the wide-angular scanning capability of a linear array is discussed in this paper. The original linear array is a sparse array where element locations fit in a $\lambda$/2 grid. Empty places in the grid allow for integrating small subarrays. There are two subarray configurations considered, in-line and cross-line subarrays. The in-line subarray is a 3 elements array with the extra element on the left and right sides of the center element. This subarray has been proven to compensate for the scan-loss and to suppress the sidelobes by optimizing the subarray pattern. In the optimum subarray, the phase relative to the center element equals $\Psi=\pm 140^{\circ}$ because this value gives the best trade-off between a high pattern at the direction of scanning and a null pattern in the other direction. The cross-line subarray utilizes extra elements on top and bottom of a center element in the dense part of a linear array. In the paper, we consider the case of a linear dense array with 41 elements. After sparsing and addition of phase shifters, inline subarray elements are positioned in empty places. The elements in the center part of the linear array are replaced by cross-line subarrays. When the antenna is scanned to large scan angles, this array exhibits 2.7 dB increase in directive gain, scan loss reduction of 1.3 dB and 3.9 dB lower peak sidelobe level (PSLL) compared to a uniform linear array (ULA) with the same length.

Multi-objective problems with two or more conflicting objectives are very common in every engineering fields, also for antenna optimization. Evolutionary optimization Algorithms are important tools due to their effectiveness, flexibility and applicability especially for multi-objective problems because they can provide directly the non-dominated set. Among Evolutionary Algorithms, Social Network optimization (SNO) shows very good optimization performance.In this paper three different approaches for solving a multiobjective problem are tested with SNO: the first one is the weighted sum method, the second is the epsilon-constrained method and the third one is the simultaneous search with a multiobjective implementation of SNO. The analysed application is the design of a sparse-array antenna.

This paper presents our study on a planar wideband aperiodic sparse phased array and the effect of element mutual coupling. The planar aperiodic sparse array in a circular disk with a diameter of 10$\lambda$0 was optimized by using covariance matrix adaption evolutionary strategy (CMA-ES) together with Danzer tiling to generate the peak SLL(Side Lobe Level) of array factor less than -13dB without the main beam steered and -9.5dB with the main beam steered to 60 away from the broadside in a band ranging from f 0 to 10f 0 . Then, the array using the rectangular patch antenna as the element has been simulated by using CST Microwave Studio to study the effect of mutual coupling when the elements are separated by $\lambda 0/2$ at f 0 . Basically, the peak gain degrades a little bit, while peak SLL has large degradation in some specific directions, resulting from the mutual coupling between the array elements.

Combined with confocal imaging, Fluorescence lifetime imaging microscopy (FLIM) can achieve 3-dimensional optical sectional capability with sub-nanosecond lifetime information. As confocal FLIM acquires multi-dimensional data 4D (3D space + time), it is inherently slow. Recent developments in lock-in pixel imagers with time gated pixels show such detectors are capable of collecting as many as 8-time gates in a single pixel cycle. We present a multiplexed confocal FLIM microscope, equipped with a 4-taps time-gated lock-in pixel imager. The multiplexing setup allows the use of the sparse array with sub-nanosecond time-gating to achieve high throughput FLIM acquisition.

A household sound event classification system consisting of an audio localization and enhancement front-end cascaded with an intelligent classification back-end is presented. The front-end is composed of a sparsely deployed microphone array and a preprocessing unit to localize the source and extract the associated signal. In the front-end, a two-stage method and a direct method are compared for localization. The two-stage method introduces a subspace algorithm to estimate the time difference of arrival, followed by a constrained least squares algorithm to determine the source location. The direct localization methods, the delay-and-sum beamformer, the minimum power distortionless response beamformer, and the multiple signal classification algorithm are compared in terms of localization performance for sparse array configuration. A modified particle swarm optimization algorithm enabled an efficient grid-search. A minimum variance distortionless response beamformer in conjunction with a minimum-mean-square-error postfilter is exploited to extract the source signals for sound event classification tasks that follow. The back-end of the system is a sound event classifier that is based on convolutional neural networks (CNNs), and convolutional long short-term memory networks Mel-spectrograms are used as the input features to the CNNs. Simulations and experiments conducted in a live room have demonstrated the strength and weakness of the direct and two-stage methods. Signal quality enhancement using the array-based front-end proves beneficial for improved classification accuracy over a single microphone.

Circulating power within a multijunction launcher is both the system’s greatest strength and a fundamental weakness. Although destructive interference of power reflected from the plasma interface at the multijunction results in low return loss to the RF source, the resulting standing wave within the launcher increases peak electric field, thereby placing fundamental limitations on maximum power handling due to the onset of multipactor breakdown. Passive active multijunctions improve impedance matching at low edge densities but suffer reduction in power density and non-optimal directivity due to the sparse array theorem as half of the waveguides are passive elements. An alternative technique of impedance matching a fully active lower hybrid grill to low edge density conditions with internal matching structures within the waveguide apertures is presented. This method of impedance matching reduces circulating power, sensitivity to edge density conditions and ponderomotive effects, and improves both power density and array directivity.

The Extreme Energy Events (EEE) Project is an experiment aimed at the detection of secondary cosmic ray muons. It consists of a sparse array of about 60 telescopes, based on Multigap Resistive Plate Chambers (MRPCs), mostly distributed throughout the Italian territory, mainly in high schools locations, and at CERN. The telescopes are now operational and taking data since more than ten years with a high duty cycle and detection efficiency. The analysis activity is currently in progress and focused on several items, including the detailed study of the muon ux under dfferent conditions, its connection with atmospheric and solar events, the detection of extensive air showers and the search for long distance correlations between different extensive air showers. In this paper an overall description of the experiment will be given, together with its educational fallout. The operation of the whole array is also discussed by showing the most recent results obtained from the analysis of the collected data.

In three-dimensional (3D) medical ultrasound imaging with two-dimensional (2D) arrays, sparse 2D arrays have been studied to reduce the number of active channels. Among them, sparse 2D arrays with regular or uniform arrangements of elements have advantages of low side lobe energy and uniform field responses over the entire field of view. This paper presents two uniform sparse array models: sparse rectangular arrays (SRAs) on a rectangular grid and sparse spiral arrays (SSAs) on a sunflower grid. Both arrays can be easily implemented on the commercially available or the custom-made arrays. To suppress the overall grating lobe levels, the transmit (Tx) and receive (Rx) array pairs of both the array models are designed not to have grating lobes at the same locations in the Tx/Rx beam patterns, for which the theoretical design rules are also proposed. Computer simulation results indicate that the proposed array pairs for both the SRAs and the SSAs achieve peak grating lobe levels below &ndash, 40 dB using about a quarter of the number of elements in the dense rectangular array while maintaining similar beam widths to that of the dense array pair.

In this study, a thorough analysis of image reconstruction in an active millimetre-wave (MMW) cylindrical imaging system is performed. To improve the system's performance in terms of the total cost as well as the coupling effect, sparse multi-static arrays are desired. Nevertheless, the existing reconstruction methods fail to operate in sparse multi-static antenna-array systems. Therefore, to address these shortcomings, in this study, at first, the existing MMW mono-static cylindrical image reconstruction methods are modified in both single-frequency and wideband reconstruction platforms. Moreover, in order to further improve the image quality and the system cost, a sparse multi-static one-dimensional antenna array has been proposed. To use this sparse array in the cylindrical MMW imaging system, a novel multi-static cylindrical image reconstruction method is also proposed. The performances of the proposed reconstruction methods for both mono-static and sparse multi-static cylindrical imaging structures are validated through numerical simulations. Through the obtained results, 83% reduction in antenna array cost along with a 25 dB decrease in coupling effect is achieved at the cost of a minor reduction in the reconstructed image quality.

Radio frequency interference (RFI) is a significant limiting factor in the retrieval of geophysical parameters measured by microwave radiometers. RFI localization is crucial to mitigate or remove the RFI impacts. In this paper, a novel RFI localization approach using covariance matrix augmentation in synthetic aperture interferometric radiometry (SAIR) is proposed. It utilizes the property of the sparse array configuration, which is commonly used in SAIR, where the sparse array can be viewed as a virtual filled array with much larger number of antenna elements. The approach can be applied in SAIR with a sparse array configuration, such as the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. Results on real SMOS data show that, compared with the previous approach, the presented approach has an improved performance of RFI localization with comparable accuracy of localization, such as improved spatial resolution, lower sidelobes, and larger identifiable number of RFIs.

We present the design and experimental demonstration of a cavity-backed, holographic metasurface capable of focusing microwaves in the Fresnel zone. The circular cavity consists of two stacked plates: microwaves are injected into the bottom plate via a coaxial connector, which forms the feed layer, and are coupled to the top holographic metasurface layer via an annular ring on the periphery of the cavity. This coupling results in an inward traveling cylindrical wave in the top layer, which serves as the reference wave for the hologram. A sparse array of slots, patterned into the upper plate, constitutes the hologram that produces the focal spot. To mitigate high sidelobe levels and improve performance, a tapered design, facilitated by varying the slot size, is also introduced. The proposed designs-which have a 10-cm diameter, operate at 20 GHz, and form a focal spot at a distance of 10 cm-are validated using full-wave simulations as well as measurements of fabricated samples. The proposed focusing metasurface may find application as a compact source for Fresnel zone wireless power transfer and remote sensing schemes.

A novel joint sparse representation based multi-source localization method is presented in this work. With the proposed approach, the direction of arrival (DOA) of each source may be formulated as a sparse reconstruction problem using spatial array covariance matrix, and the corresponding source locations can be formulated as a joint sparse representation of array covariance matrices (JSRACM) for multiple phase arrays. The proposed approach transforms the source location estimation problem into a spatial sparse signal representation (SSSR) optimization problem, which avoids the conventional DOA association in the case of multiple sources. To mitigate the high computation complexity of the JSRACM approach, a novel binary sparse indicative vector (SIV) is introduced to represent the support of joint SSSR of array covariance matrices. As such, the multiple source locations may be estimated by solving an unconstrained optimization problem of the SIV vector using existing FOCUSS-like algorithms. The resulting SIV-JSRACM algorithm does not require prior information of the number of sources nor initial source location estimates. Simulation results demonstrate the advantages of the proposed approach.

The growing demands for wireless communications in tunnel environments are driven by requests for maintaining uninterrupted internet access for users in tunnels as well as wireless connections for wireless sensors, security, and control networks. Nevertheless, wireless networks in tunnel environments are associated with technical challenges related to elongated wireless coverage in two opposite near-end-fire directions. Here, we introduce a low-profile bidirectional antenna that can be mounted on a large ground plane and that has a dual-magnetic-current mode exhibiting radiation patterns with 3-dB beamwidth coverage at near-end-fire angles. Furthermore, we realized a planar array with eight such bidirectional antennas that were configured as a sparse array in order to reduce the commonplace issues of mutual coupling and grating lobes. The radiation beams of the proposed antenna array can scan in the end-fire area (60°