Array Processing Research Articles

Analysis of error rates and capacity outage of wireless links under jamming and multipath signal fading

AbstractThe author addresses the question, under which conditions an active jammer will be successful in corrupting a wireless communication link, when both the desired link and the jamming link are subject to multipath signal fading. To this end, a simple closed‐form analytical expression is derived for the resulting block‐error probability (BLEP) under Nakagami‐ fading with in general unequal fading parameters for the desired and the jamming link. As a by‐product, a novel expression for the corresponding probability of a capacity outage is obtained. An asymptotic analysis and numerical evaluation of the BLEP expression show that in the single‐antenna case the jammer will indeed be successful in corrupting the wireless link, unless there is a rather high signal‐to‐noise ratio (SNR) advantage for the desired link. This even holds true, when signal fading for the jamming link is significantly more severe than for the desired link. Finally, the case of multiple antennas is considered and the benefits of array processing and diversity reception at the desired receiver are explored. It is found that receive array processing can significantly improve the resulting BLEP, provided that the desired link is characterized by favorable fading conditions. On the other hand, diversity reception can significantly improve the BLEP, if the desired link has favorable SNR conditions.

Multi-Objective Design and Optimization of Hardware-Friendly Grid-Based Sparse MIMO Arrays.

A comprehensive design framework is proposed for optimizing sparse MIMO (multiple-input, multiple-output) arrays to enhance multi-target detection. The framework emphasizes efficient utilization of antenna resources, including strategies for minimizing inter-element mutual coupling and exploring alternative grid-based sparse array (GBSA) configurations by efficiently separating interacting elements. Alternative strategies are explored to enhance angular beamforming metrics, including beamwidth (BW), peak-to-sidelobe ratio (PSLR), and grating lobe limited field of view. Additionally, a set of performance metrics is introduced to evaluate virtual aperture effectiveness and beamwidth loss factors. The framework explores optimization strategies for the partial sharing of antenna elements, specifically tailored for multi-mode radar applications, utilizing the desirability function to enhance performance across various operational modes. A novel machine learning initialization approach is introduced for rapid convergence. Key observations include the potential for peak-to-sidelobe ratio (PSLR) reduction in dense arrays and insights into GBSA feasibility and performance compared to uniform arrays. The study validates the efficacy of the proposed framework through simulated and measured results. The study emphasizes the importance of effective sparse array processing in multi-target scenarios and highlights the advantages of the proposed design framework. The proposed design framework for grid-spaced sparse arrays stands out for its superior efficiency and applicability in processing hardware compared to both uniform and non-uniform arrays.

Noncircular Distributed Source DOA Estimation with Nested Arrays via Reduced-Dimension MUSIC

This paper focuses on the direction-of-arrival (DOA) estimation for noncircular coherently distributed (CD) sources with nested arrays. Usually, for point sources, sparse arrays have the potential to improve the estimation performance of algorithms by obtaining more degrees of freedom. However, algorithms have to be reconsidered for CD sources with sparse arrays and many problems arise. One thorny problem is the disappearance of displacement invariance of the virtual array manifold constructed by the virtualization technique. To deal with this issue, a nested array processing method for CD sources transmitting noncircular signals is proposed in this paper. Firstly, we construct the virtual sum-and-difference co-array by leveraging the noncircular quality of signals with a nested array. Then, an approximation is made to degrade CD sources into point sources. In this way, spatial smoothing techniques can be applied to restore the rank. Finally, in order to reduce the complexity, we modify the reduced-dimension MUSIC to estimate DOAs through a one-dimensional peak-searching procedure. The simulation results prove the superiority of our algorithm against other competitors.

Estimation of the surface impedance based on the discrete complex image source method and array processing

This paper introduces a method for estimating the sound absorption coefficient of a large panel of absorbing material from measurements using a microphone array. The method relies upon describing the pressure field as the set of monopoles obtained when the reflection coefficient is represented as the Laplace Transform of an image source distribution. This formulation results in a well-behaved integral equation, which is discretized using Gaussian quadrature to yield an inverse problem. It is possible to determine the regularized solution to the inverse problem and reconstruct the surface impedance of the sample by conducting measurements of the sound pressure at multiple points of observation. This sound field representation is adequate for sound sources in the close range of the material and the array. The paper presents simulated and experimental investigations to delineate the efficacy of the proposed measurement technique against the well-known image source method, thereby highlighting its significant potential to advance acoustic characterization methodologies.

A 1000FPS@360,000pixels mixed-signal sensing with computing macro featuring analog compression and maximum parallelism for objective detection tasks

Ubiquitous vision sensors continuously collect data from the environment, imposing a significant processing burden. Especially, high-frame-rate convolution processing for large-scale pixel arrays presents substantial challenges. This paper proposes a mixed-signal sensing with computing macro featuring analog compression and maximum parallelism for high-frame-rate object detection tasks. A customized neural network (NN) architecture is designed specifically for object detection. Key circuitry modules, including signal readout, dark current offset, normalization, first-layer convolution, and pooling, are implemented in the analog domain, leading to high-quality information extraction and substantial data compression. Maximum parallelism in the first-layer convolution is realized in our proposed processing flow, ensuring high-frame-rate convolution for large-scale pixel arrays. Additionally, the subsequent NN layers are efficiently deployed on SRAM-based in-memory digital computing. The macro was designed using a 55-nm foundry process. Results show that our macro can achieve 1000 fps@360,000 pixels with a 63.8 TOPS throughput under 4/8-bit hybrid precision, outperforming the state-of-the-art in frame rate by 2–10 times.

Двоїстий алгоритм пошуку простих чисел на відрізках великих розмірностей

A matrix model of subsequence of natural numbers with a multiplicative basis from the first prime numbers is proposed. The matrix model is a square matrix, where vector columns are arithmetic progressions with difference and number of progressions equal to product of base elements. By crossing out arithmetic progressions with first terms which are multiples of base elements, we obtain a symmetric sparse matrix that contains all the prime numbers of subsequence of natural numbers, except for the base ones, which increases the density of prime numbers in sparse matrices. Sparse matrices are not formed clearly, only the vector of first terms of arithmetic progressions is formed. Properties of sparse matrices have been proven. Formulas that speed up the calculation of compound numbers in arithmetic progressions have been derived, the structures of vector elements of first terms of arithmetic progressions have been determined, the connectivity of symmetric parts of sparse matrices has been investigated. With the expansion of the base the number of pairs of elements with the difference equal to the power of two («twins», «fours», etc.) increases in the vector of first terms. This is a necessary condition for the existence of constants for which linear equations of two variables can have an infinite set of solutions in prime numbers. The irregularity of distribution of prime numbers in subsequences of natural numbers is related to the structure of elements of the vector of first terms. An algorithm for finding prime numbers on segments of large dimensions with a parallel calculation process has been built. The proposed algorithm is binary to algorithms for sifting subsequences of natural numbers by prime divisors. In these algorithms, it is not possible to parallelize the calculation process, since screening procedure requires storing the numerical information from the preceding steps (vector model of array processing). The binary algorithm calculates compound numbers in each pair of arithmetic progressions with symmetric first terms simultaneously, using only vector of the first terms of arithmetic progressions, which makes possible processing of large-dimensional arrays

Position estimation of acoustic elements based on improved delay estimation algorithm

Array signal processing is extensively utilized in the field of underwater acoustics (UWA). The majority of existing array signal processing algorithms require precise array position information to optimize their functionality. However, the intricate nature of the UWA environment introduces challenges such as the influence of water flow, which may result in deviations from the predetermined array positions. Consequently, this can amplify errors in array processing algorithms. Therefore, a high-precision array positioning method is needed to estimate the actual position of the array. The effectiveness of current array localization algorithms employing delay differential matching depends significantly on the accuracy of delay estimation and the formulation of the ambiguity function. In response to these crucial factors, this paper presents an algorithm for estimating array element positions that leverages an improved approach to delay estimation. Firstly, we propose the ρ-PHAT algorithm enhanced by the artificial fish swarm algorithm (AFSA-PHAT), significantly improving delay estimation accuracy, particularly in low signal-to-noise ratio (SNR) conditions. Compared to the traditional ρ-PHAT algorithm, this approach achieves a 3 dB increase in precision and a reduction in the root-mean-square error (RMSE). Additionally, a novel method is introduced for constructing the ambiguity function, which focuses on minimizing the acoustic complexity to encompass only direct and surface-reflected sounds. This improvement makes it particularly suitable for hydrophone arrays deployed near the sea surface. Computer simulations and experimental results validate that the algorithm, incorporating the aforementioned improvements, achieves enhanced accuracy in position estimation, reduced RMSE, and increased robustness.

Accurate high-temperature profile sensing with dense multipoint arrays of regenerated fiber Bragg gratings

A spectrally and spatially dense sensor array consisting of 15 regenerated fiber Bragg gratings (RFBGs) over a length of 30 mm is presented for precise and fast multipoint temperature sensing up to 700°C. For the first time, it could be shown that with a dense fiber Bragg grating (FBG)-based sensor array the accuracy requirements of Class 1 thermocouples could be achieved and even exceeded. This also represents the highest spatial density of FBG-based high-temperature multipoint sensing reported so far. The mitigation of broadband losses during the regeneration process was studied, revealing the advantages of the low broadband loss characteristics of the RFBGs, especially when larger numbers of measuring points are required. Low measurement uncertainties were achieved by a new, improved calibration methodology and by an analysis of interferences of an FBG with the side lobes of spectrally neighboring FBGs as well as their suppression by a suitable arrangement of the Bragg wavelengths within the array and corresponding data processing. The capabilities of this multipoint sensor technique were demonstrated by resolving the temperature profile within the calibration volume of a calibration furnace and by resolving the temporal and spatial temperature gradients within the flame of a Bunsen burner. The results are of great importance for fiber-optic sensing of high-temperature profiles in real-world applications.

Research on data processing technology based on 6LiF/ZnO:Ga fiber neutron detector array

The 6LiF/ZnO:Ga fiber neutron detector, consisting of a 6LiF/ZnO:Ga scintillator, optical fiber, and PMT, has distinct advantages such as compactness, strong real-time online measurement capabilities, and resistance to electromagnetic interference. These characteristics make it highly suitable for neutron detection in confined spaces with significant electromagnetic interference. However, its small size limits the neutron detection efficiency of a single detector, necessitating the use of multiple fibers to construct a fiber array and enhance sensitivity. The goal is to study data processing techniques for the 6LiF/ZnO:Ga fiber detector array. Inside the FPGA, three digital signal processing channels are used to extract multiple information from signal such as the amplitude, timestamp, and measurement channel ID. The fast channel adopts triangular shaping to provide real-time baselines and timestamps for the discrimination channel and the slow channel. The slow channel adopt cascade shaping method to extract the amplitude of nuclear signal. The discrimination channel method adopt cascade discrimination method to realize the real-time discrimination between gamma signals, neutron signals and interference signals. The method has been successfully applied to 6LiF/ZnO:Ga fiber neutron detector arrays with different mass ratios, providing new solution for digital data processing in fiber neutron detector arrays.

Deep learning models for regional phase detection on seismic stations in Northern Europe and the European Arctic

SUMMARY Seismic phase detection and classification using deep learning is so far poorly investigated for regional events since most studies focus on local events and short time windows as the input to the detection models. To evaluate deep learning on regional seismic records, we create a data set of events in Northern Europe and the European Arctic. This data set consists of about 151 000 three component event waveforms and corresponding phase arrival picks at stations in mainland Norway, Finland and Svalbard. We train several state-of-the-art and one newly developed deep learning model on this data set to pick P- and S-wave arrivals. The new method modifies the popular PhaseNet model with new convolutional blocks including transformers. This yields more accurate predictions on the long input time windows associated with regional events. Evaluated on event records not used for training, our new method improves the performance of the current state-of-the-art methods when it comes to recall, precision and pick time residuals. Finally, we test our new model for continuous mode processing on 4 d of single-station data from the ARCES array. Results show that our new method outperforms the existing array detector at ARCES. This opens up new opportunities to improve automatic array processing with deep learning detectors.

Optimal sensor placement for the spatial reconstruction of sound fields

The estimation sound fields over space is of interest in sound field control and analysis, spatial audio, room acoustics and virtual reality. Sound fields can be estimated from a number of measurements distributed over space yet this remains a challenging problem due to the large experimental effort required. In this work we investigate sensor distributions that are optimal to estimate sound fields. Such optimization is valuable as it can greatly reduce the number of measurements required. The sensor positions are optimized with respect to the parameters describing a sound field, or the pressure reconstructed at the area of interest, by finding the positions that minimize the Bayesian Cramér-Rao bound (BCRB). The optimized distributions are investigated in a numerical study as well as with measured room impulse responses. We observe a reduction in the number of measurements of approximately 50% when the sensor positions are optimized for reconstructing the sound field when compared with random distributions. The results indicate that optimizing the sensors positions is also valuable when the vector of parameters is sparse, specially compared with random sensor distributions, which are often adopted in sparse array processing in acoustics.

Close approximations to the sound of a cochlear implant.

Cochlear implant (CI) systems differ in terms of electrode design and signal processing. It is likely that patients fit with different implant systems will experience different percepts when presented speech via their implant. The sound quality of speech can be evaluated by asking single-sided-deaf (SSD) listeners fit with a cochlear implant (CI) to modify clean signals presented to their typically hearing ear to match the sound quality of signals presented to their CI ear. In this paper, we describe very close matches to CI sound quality, i.e., similarity ratings of 9.5 to 10 on a 10-point scale, by ten patients fit with a 28 mm electrode array and MED EL signal processing. The modifications required to make close approximations to CI sound quality fell into two groups: One consisted of a restricted frequency bandwidth and spectral smearing while a second was characterized by a wide bandwidth and no spectral smearing. Both sets of modifications were different from those found for patients with shorter electrode arrays who chose upshifts in voice pitch and formant frequencies to match CI sound quality. The data from matching-based metrics of CI sound quality document that speech sound-quality differs for patients fit with different CIs and among patients fit with the same CI.

Preparation of brittle ITO microstructures using Laser-Induced forward transfer technology

The performance of transparent conductive microstructures plays a significant role in determining the device efficiency. The fabrication of ITO microstructures on the flexible optoelectronic devices remains challenging because of the high-temperature processing requirements and brittleness of the ceramic material. This paper highlights a three-dimensional microfabrication technique that combines sacrificial layer etching and laser-induced transfer. By studying the donor material thickness and the laser energy density, control of the sacrificial layer transfer thrust was achieved to yield an optimal microstructure performance. Different shapes of ITO transparent conductive microstructures were fabricated on polydimethylsiloxane (PDMS) surfaces, demonstrating structural integrity, visible light transmittances > 90 %, and resistivities of ≤ 8 × 10−4 Ω·cm. Finite element analysis was employed to simulate the impact transfer of the donor material onto the receiving substrate, thereby elucidating the energy-absorbing protective role of the flexible PDMS layer. Effective control of the transfer force was identified as a crucial factor in achieving the successful transfer of brittle films. This high-precision and rapid array processing approach meets the demands of automated batch manufacturing, indicating its potential for use in patterned processing on flexible substrates, as an alternative to lithographic techniques. This technique could be applied in the preparation of electronic skins and flexible devices. Additionally, for the first time, a 3 × 3 pixel resistive array sensor was integrated on a Ag nanowires/PDMS surface, demonstrating array pressure-sensing capabilities at pressures of 0–80 kPa. This study represents a significant step towards the development of multifunctional flexible optoelectronic devices.

Enhancing Computation-Efficiency of Deep Neural Network Processing on Edge Devices through Serial/Parallel Systolic Computing

In recent years, deep neural networks (DNNs) have addressed new applications with intelligent autonomy, often achieving higher accuracy than human experts. This capability comes at the expense of the ever-increasing complexity of emerging DNNs, causing enormous challenges while deploying on resource-limited edge devices. Improving the efficiency of DNN hardware accelerators by compression has been explored previously. Existing state-of-the-art studies applied approximate computing to enhance energy efficiency even at the expense of a little accuracy loss. In contrast, bit-serial processing has been used for improving the computational efficiency of neural processing without accuracy loss, exploiting a simple design, dynamic precision adjustment, and computation pruning. This research presents Serial/Parallel Systolic Array (SPSA) and Octet Serial/Parallel Systolic Array (OSPSA) processing elements for edge DNN acceleration, which exploit bit-serial processing on systolic array architecture for improving computational efficiency. For evaluation, all designs were described at the RTL level and synthesized in 28 nm technology. Post-synthesis cycle-accurate simulations of image classification over DNNs illustrated that, on average, a sample 16 × 16 systolic array indicated remarkable improvements of 17.6% and 50.6% in energy efficiency compared to the baseline, with no loss of accuracy.

Combination of the biologically inspired coupled system and high‐frequency surface wave radar at signal level

AbstractVirtual aperture extension of small aperture array has attracted wide attention in high‐frequency surface wave radar (HFSWR). A biologically inspired coupled (BIC) system is employed to virtually extend the array aperture. However, the existing researches on BIC only consider the array signal processing model and do not combine it with actual radar signal principle. To indeed apply the BIC system to HFSWR, two detailed methods which combine the BIC and HFSWR at signal level are proposed. A three‐dimensional signal model of HFSWR considering array processing was established and the entire signal processing was derived. Then, two combination methods, namely fast‐time domain (FTD)‐BIC and slow‐time domain (STD)‐BIC are proposed. The former implements the BIC before fast‐time processing, while the latter implements the BIC before slow‐time processing. The authors demonstrate that they can virtually extend the array aperture without affecting the target detection. Meanwhile, their capabilities in multi‐target scenarios are analysed and satisfactory conclusions are obtained. By numerical simulations and experiments, the array aperture and range‐Doppler (RD) spectrum of the standard HFSWR and BIC‐HFSWR are compared. The results show that while the performance of their RD spectrum is almost the same, BIC‐HFSWR has an enlarged virtual aperture than standard HFSWR.

The expression of mantle seismic anisotropy in the global seismic wavefield

SUMMARY The dependence of seismic wave speeds on propagation or polarization direction, called seismic anisotropy, is a relatively direct indicator of mantle deformation and flow. Mantle seismic anisotropy is often inferred from measurements of shear-wave splitting. A number of standard techniques to measure shear-wave splitting have been applied globally; for example, *KS splitting is often used to measure upper mantle anisotropy. In order to obtain robust constraints on anisotropic geometry, it is necessary to sample seismic anisotropy from different directions, ideally using different seismic phases with different incidence angles. However, many standard analysis techniques can only be applied for certain epicentral distances and source–receiver geometries. To search for new ways to detect mantle anisotropy, instead of focusing on the sensitivity of individual phases, we investigate the wavefield as a whole: we apply a ‘wavefield differencing’ approach to (systematically) understand what parts of the seismic wavefield are most affected by splitting due to seismic anisotropy in the mantle. We analyze differences between synthetic global wavefields calculated for isotropic and anisotropic input models, incorporating seismic anisotropy at different depths. Our results confirm that the seismic phases that are commonly used in splitting techniques are indeed strongly influenced by mantle anisotropy. However, we also identify less commonly used phases whose waveforms reflect the effects of anisotropy. For example, PS is strongly affected by splitting due to seismic anisotropy in the upper mantle. We show that PS can be used to fill in gaps in global coverage in shear-wave splitting data sets (for example, beneath ocean basins). We find that PcS is also a promising phase, and present a proof-of-concept example of PcS splitting analysis across the contiguous United States using an array processing approach. Because PcS is recorded at much shorter distances than *KS phases, PcS splitting can therefore fill in gaps in backazimuthal coverage. Our wavefield differencing results further hint at additional potential novel methods to detect and characterize splitting due to mantle seismic anisotropy.

Secure MIMO NOMA transmission with energy harvesting-aided full-duplex jammer under erroneous channel information

NOMA allows for the simultaneous transmission of multiple user signals and full-duplex (FD) communication enables concurrent transmit-and-receive operations, both improving spectral efficiency. Additionally, NOMA networks utilize energy harvesting (EH) to efficiently harvest energy from radio frequency sources, contributing to improved energy efficiency. In addition, deploying multiple antennas at NOMA users facilitates energy transfer and harvesting, as well as reliable information transmission through antenna array processing. Moreover, jamming is presented as a useful method to secure wireless transmission. Therefore, multiple-input multiple-output (MIMO) NOMA transmission with EH-aided FD jammer (MMnOehFD) is envisioned to acquire high performance like energy efficiency, security, reliability, and throughput. The paper numerically assesses the reliability and security of MMnOehFD where evaluation metrics include energy efficiency, throughput, and outage probability. Further, practical factors such as erroneous channel information (ECI) and multi-antenna users are taken into account in the evaluation. Results indicate that the ECI significantly degrades system performance, and this degradation is influenced by multiple parameters. Also, the proposed MMnOehFD can avoid complete outage and attain optimal performance with appropriate system configuration. The performance of the MMnOehFD is notably improved with an increasing number of antennas. Further, the MMnOehFD outperforms two baseline schemes (MIMO OMA transmission with EH-aided FD jammer and MIMO NOMA transmission without jammer), highlighting the effectiveness of combining jammer and NOMA.

Design of a Circular Micro-strip Patch Antenna for Improved Directivity and Gain of Mobile Communication Base Station

This study presents the design of Circular Micro-strip Patch Antenna (CMPA) for improved directivity and gain of mobile communication base station. The design incorporates a Rogger/5880 substrate material with a permittivity of 2.2, tangent angle of 0.00009 and a substrate height (thickness) of 0.98μm at a frequency of 1.65THz. Diversity Antenna Array Processing (DAAP) and Chebyshev Antenna Array (CAA) methods were used in the design. AutoCAD software and 2D data plotter were used for the diagrams and charts while Maple software was used for simulation. For angles of 0o, 30o, 60o, 90o, 120o, 150o and 180o, the array factors are 7.84dB, 7.85dB, 7.87dB, 7.88dB, 7.87dB, 7.85dB and 7.84db respectively. The results from the design demonstrated a remarkably low side lobe level of 0.01dB, indicating excellent suppression of unwanted radiation in undesired directions. Additionally, the antenna exhibits a Voltage Standing Wave Ratio (VSWR) of unity, ensuring maximum power transfer efficiency from the source to the load. Furthermore, the antenna shows a high directivity and gain of approximately 9dB, which enhances the signal strength and coverage range. The Half Power Beam-width (HPBW) and First Null Beam-width (FNBW) was measured as 300 and 600 respectively. These parameters depict the angular width of the main radiation beam and the sharpness of the beam pattern’s nulls. In all, the designed CMPA with its CAA based antenna array holds great potential for communication networks and for mobile communication base station, offering enhanced performance in terms of radiation characteristics and signal quality.

Computer‐aided experimental studies for adaptive beamforming algorithms and array processing in engineering education

AbstractSmart antenna and digital antenna systems are considered as a crucial technology for current and future communication systems supporting 5G/6G and nonterrestrial‐networks communications. Based on the information of the received signal/s, smart antenna systems toward the radiation pattern to the desired direction/s. This technique is known as adaptive beamforming. The theories and the applications of adaptive beamforming are taught in engineering educational courses. This paper proposes a set of real‐time interactive experiments for adaptive beamforming algorithms in antenna array processing subject. The proposed experiments can be performed with the computer‐aided experimental studies tool prepared in the MATLAB® environment. The users can easily make comparisons and can perform trade‐offs between different types of adaptive beamforming algorithms, scenarios, and antenna architecture. This is used as a teaching tool for a graduate course on adaptive beamforming algorithms.

Multiplexed in-situ mutagenesis driven by a dCas12a-based dual-function base editor.

Mutagenesis driving genetic diversity is vital for understanding and engineering biological systems. However, the lack of effective methods to generate in-situ mutagenesis in multiple genomic loci combinatorially limits the study of complex biological functions. Here, we design and construct MultiduBE, a dCas12a-based multiplexed dual-function base editor, in an all-in-one plasmid for performing combinatorial in-situ mutagenesis. Two synthetic effectors, duBE-1a and duBE-2b, are created by amalgamating the functionalities of cytosine deaminase (fromhAPOBEC3A orhAID*Δ ), adenine deaminase (fromTadA9), and crRNA array processing (fromdCas12a). Furthermore, introducing the synthetic separator Sp4 minimizes interference in the crRNA array, thereby facilitating multiplexed in-situ mutagenesis in both Escherichia coli and Bacillus subtilis. Guided by the corresponding crRNA arrays, MultiduBE is successfully employed for cell physiology reprogramming and metabolic regulation. A novel mutation conferring streptomycin resistance has been identified in B. subtilis and incorporated into the mutant strains with multiple antibiotic resistance. Moreover, surfactin and riboflavin titers of the combinatorially mutant strains improved by 42% and 15-fold, respectively, compared with the control strains with single gene mutation. Overall, MultiduBE provides a convenient and efficient way to perform multiplexed in-situ mutagenesis.