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In recent years, various graph computing architectures have been proposed to process graph data that represent complex dependencies between different objects in the world. The designs of the processing element (PE) in traditional graph computing accelerators are often optimized for specific graph algorithms or tasks, which limits their flexibility in processing different types of graph algorithms, or the parallel configuration that can be supported by their PE arrays is inefficient. To achieve both flexibility and efficiency, this paper proposes Grapher, a reconfigurable graph computing accelerator based on an optimized PE array, efficiently supporting multiple graph algorithms, enhancing parallel computation, and improving adaptability and system performance through dynamic hardware resource configuration. To verify the performance of Grapher, this paper selected six datasets from the Stanford Network Analysis Project (SNAP) database for testing. Compared with the existing typical graph frameworks Ligra, Gemini, and GraphBIG, the processing time for the six datasets using the BFS, CC, and PR algorithms was reduced by up to 39.31%, 35.43%, and 27.67%, respectively. The energy efficiency has also been improved by 1.8× compared to Hitgraph and 4.7× compared to ThunderGP. [ABSTRACT FROM AUTHOR]

Inspired by the natural shape-morphing abilities of biological organisms, we introduce a strategy for creating energy-efficient dynamic 3D metasurfaces through spatiotemporal jamming of interleaved assemblies. Our approach, diverging from traditional shape-morphing techniques reliant on continuous energy inputs, utilizes strategically jammed, paper-based interleaved assemblies. By rapidly altering their stiffness at various spatial points and temporal phases during the relaxation of the soft substrate through jamming, we enable the formation of refreshable, intricate 3D shapes with a desirable load-bearing capability. This process, which does not require ongoing energy consumption, ensures energy-efficient and lasting shape displays. Our theoretical model, linking buckling deformation to residual pre-strain, underpins the inverse design process for an array of interleaved assemblies, facilitating the creation of diverse 3D configurations. This metasurface holds notable potential for tactile displays, particularly for the visually impaired, heralding possibilities in visual impaired education, haptic feedback, and virtual/augmented reality applications. This paper introduces a load-bearing 3D dynamic metasurface that alters the stiffness of interleaved assemblies at various spatial points and temporal phases through jamming. This approach does not require continuous energy input and was demonstrated as a tactile display for the visually impaired. [ABSTRACT FROM AUTHOR]

When the carrier of the Satcom-on-the-Move (SOTM) antenna is in a high maneuvering state of motion, the accuracy of the array antenna beam pointing towards the satellite may decrease, which may cause a decline in communication effectiveness for SOTM or even cause the antenna to deviate from the target satellite and lead to communication interruption. This paper proposes a multi-beam searching and capturing method for SOTM array antennas, which divides the array antenna into multiple parts to form multiple independent and controllable sub-beams. When the SOTM carrier is in a high maneuvering state of motion, multiple sub-beams are obtained by dividing the array antenna, thus obtaining multiple points in the radiation pattern. We designed an optimal pointing analyzer, which calculates the SOTM array antenna beam direction based on whether the SOTM antenna is pointing towards the satellite and utilizes different approaches for processing. Experimental verification was conducted for the proposed scheme. The experimental results show that if the antenna beam does not lose the satellite, the proposed scheme can obtain better communication quality compared to conventional schemes. If the SOTM antenna beam loses the satellite and causes communication interruption, the proposed scheme in this paper can capture the satellite faster than conventional schemes. The experimental results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In array signal processing, the direction of arrival (DOA) of the signal source has drawn broad research interests with its wide applications in fields such as sonar, radar, communications, medical detection, and electronic countermeasures. In recent years, the application of deep learning (DL) to DOA estimation has achieved great success. This study provides a systematic review of research on DOA estimation using deep neural network methods. We manually selected twenty-five papers related to this research from five prominent databases (SpringerLink, IEEE Xplore, ScienceDirect, Scopus, and Google Scholar) for exploration. Six questions describing the overall trend of DOA estimation using deep learning are put forward. Then, we answered these questions by reviewing the literature. This review is helpful for researchers in this field because it provides more specific and comprehensive information needed for future research. Specifically, we first analyzed the background of the selected papers, including the type of publication, the number of citations, and the country of origin. Then, the DL technology used in DOA estimation is systematically analyzed, including the purpose of using DL in DOA estimation, various DL models (convolutional neural network, deep neural network, and combination network), and various DOA estimation schemes. Finally, various evaluation criteria (root-mean-squared error, accuracy, and mean absolute error) are used to evaluate the DL technology in DOA estimation, and various factors (signal-to-noise ratio, number of snapshots, number of antennas, and number of signal sources) affecting DOA estimation are analyzed. Based on our findings, we believe that deep learning can perform DOA estimation well, and there is still room for improvement in deep learning technology. In this study, the factors affecting DOA estimation can be used as the direction for researchers to conduct in-depth research. [ABSTRACT FROM AUTHOR]

The direction estimation of the coherent source in a uniform circular array is an essential part of the signal processing area of the array, but the traditional uniform circular array algorithm has a low localization accuracy and a poor localization effect on the coherent source. To solve this problem, this paper proposes a two-dimensional direction of arrival (DOA) estimation for the coherent source in broadband. Firstly, the central frequency of the coherent sound source is estimated using the frequency estimation method of the delayed data, and a real-valued beamformer is constructed using the concept of the multiloop phase mode. Then, the cost function in the beam space is obtained. Finally, the cost function is searched in two dimensions to locate the sound source. In this paper, we simulate the DOA of the sound source at different frequencies and signal-to-noise ratios and analyze the resolution of the circular array. The simulation results show that the proposed algorithm can estimate the direction of arrival with high precision and achieve the desired results. [ABSTRACT FROM AUTHOR]

Wideband digital phased array radar offers the advantages of high range resolution, which improves the recognition ability for multiple targets, group targets, and high-speed targets. Traditional wideband phased arrays use true time delay to compensate for aperture fill time; however, the cost increases significantly. In this paper, a wideband elemental digital array architecture based on the stretch processing method is proposed. By utilizing the time-domain and frequency-domain translation equivalence of the LFM (Linear Frequency Modulation) signal waveform, the equivalent aperture fill time is compensated for through frequency shift and phase shift after stretch processing. Compared to traditional wideband digital arrays, this method can dramatically reduce the required sampling rate and lower the requirements on antenna hardware, thereby reducing the manufacturing cost of system. A comprehensive analysis of the signal processing process and stretch processing method is provided. And an antenna array prototype is developed to verify the T/R channel compensation and wideband beamforming. Measured results show that the antenna is capable of ±60° scanning in azimuth plane and ±40° scanning in elevation plane, with a bandwidth of 500 MHz in S-band. The results demonstrate excellent wideband beam performance and accurate lobe scanning, which confirms the validity of the proposed wideband architecture for stretch processing, frequency shift, and phase shift. This method can be widely applied to the low-cost design and wideband performance improvement of wideband digital array radar. [ABSTRACT FROM AUTHOR]

Bit-serial neural network accelerators address the growing need for compact and energy-efficient deep learning tools. Traditional neural network accelerators, while effective, often grapple with issues of size, power consumption, and versatility in handling a variety of computational tasks. To counter these challenges, this paper introduces an approach that hinges on the integration of bit-serial processing with advanced dataflow techniques and architectural optimizations. Central to this approach is a column-buffering (CB) dataflow, which significantly reduces access and movement requirements for the input feature map (IFM), thereby enhancing efficiency. Moreover, a simplified quantization process effectively eliminates biases, streamlining the overall computation process. Furthermore, this paper presents a meticulously designed LeNet-5 accelerator leveraging a convolutional layer processing element array (CL PEA) architecture incorporating an improved bit-serial multiply–accumulate unit (MAC). Empirically, our work demonstrates superior performance in terms of frequency, chip area, and power consumption compared to current state-of-the-art ASIC designs. Specifically, our design utilizes fewer hardware resources to implement a complete accelerator, achieving a high performance of 7.87 GOPS on a Xilinx Kintex-7 FPGA with a brief processing time of 284.13 μs. The results affirm that our design is exceptionally suited for applications requiring compact, low-power, and real-time solutions. [ABSTRACT FROM AUTHOR]

In this paper, we examine the use of the Single-Sideband Transform (SSBT) for convolutive beamformers. We explore its unique properties and implications for beamformer design. Our study sheds light on the tradeoffs involved in using the SSBT in beamforming applications, offering insights into both its strengths and limitations. Despite the advantage of having real-valued coefficients, we show that the convolution handling of the transform presents challenges that impact fundamental beamforming principles. When compared to the Short-Time Fourier Transform (STFT), the SSBT displays lower robustness, especially in scenarios involving mismatch and modeling noise. Notably, we establish a direct equivalence between the SSBT and STFT when using identical transform parameters, enabling their seamless interchangeability and joint use in time–frequency signal enhancements. We validate our theoretical findings through realistic simulations using the Minimum-Power Distortionless Response beamformer. These simulations illustrate that although the STFT performs marginally better than the SSBT under optimal conditions, it outperforms significantly in non-ideal scenarios. [ABSTRACT FROM AUTHOR]

The purpose of the GLocal dataset is to enable research in international development that requires both global scope and local precision. Leveraging modern geospatial analysis tools, we process a diverse array of sources to provide researchers with a growing set of economic, demographic, ecological and socio-political variables for geographic units relevant to public policy. We provide separate data files for different levels of administrative and periodic aggregation, along with ad-hoc files with more detailed information on specific topics. In this data descriptor paper, we discuss both our data processing methodologies and validation pipelines, and provide a short case study to illustrate the research potential of the dataset. We also introduce a simple web app, glocal.streamlit.app, which offers a user-friendly interface for exploring and visualizing the dataset. Given the growing number of public and granular sources of relevance for international development research, we hope to continue adding features and expand the GLocal dataset in the future. [ABSTRACT FROM AUTHOR]

This paper presents a wideband signal (WS) beamforming method based on Inception convolutional neural network (ICNN), named as WS-ICNN algorithm. Firstly, an Inception module is constructed via some convolutional layers with feature maps of different sizes and a pooling layer. It can not only extract different scale information of covariance matrix, but also excavate the spatial correlation information about received wideband signals, so that the inception module can help neural network improve the beamforming output performance. On this basis, an ICNN model is established, which is suitable for wideband beamforming. Then, in order to obtain a good training label for the proposed ICNN model, a taper matrix and a second-order cone programming problem are introduced to calculate a wideband beamforming weight vector label. Based on this label, the training process of the proposed ICNN model is accomplished. Finally, the well-trained ICNN model accepts the input of the covariance matrix, and output the beamforming weight vector. Simulation results demonstrate the performance of the proposed algorithm in the cases of direction-of-arrival estimation error and sensor position error. [ABSTRACT FROM AUTHOR]

Quartz material irradiated by femtosecond laser has increasingly attracted widespread attention for the micro-fabrication of photonic devices. Mechanism exploration is beneficial for accelerating the digital progress of laser processing. However, the mechanism between femtosecond laser and quartz is complicated and needs further theoretical investigation. This paper established the theoretical model based on the ionization model with the Drude equation to study the space–time evolution of free electron density and its influence on the absorption coefficient, reflectivity, and ablation depth. In addition, we achieved a 10 × 10 micro-holes array with a pore size less than 10 μm, cone angle less than 2° in a 0.25 mm thick quartz on the condition of a laser pulse energy Ep = 3 μJ, scanning velocity v = 0.1 mm/s, and defocusing distance Δf = −0.3 mm via the bottom-up femtosecond laser processing. The work gives a new insight into further understanding the ablation mechanism of transparent materials etching by the femtosecond laser. It provides a practical technical scheme for preparing commercial quartz photonic devices. [ABSTRACT FROM AUTHOR]

The ability to sense propagating electromagnetic plane waves based on their directions of arrival (DOAs) is fundamental to a range of radio frequency (RF) sensing, communications, and imaging applications. This paper introduces an algorithm for the wideband true time delay digital delay Vandermonde matrix (DVM), utilizing Thiran fractional delays that are useful for realizing RF sensors having multiple look DOA support. The digital DVM algorithm leverages sparse matrix factorization to yield multiple simultaneous RF beams for low-complexity sensing applications. Consequently, the proposed algorithm offers a reduction in circuit complexity for multi-beam digital wideband beamforming systems employing Thiran fractional delays. Unlike finite impulse response filter-based approaches which are wideband but of a high filter order, the Thiran filters trade usable bandwidth in favor of low-complexity circuits. The phase and group delay responses of Thiran filters with delays of a fractional sampling period will be demonstrated. Thiran filters show favorable results for sample delay blocks with a temporal oversampling factor of three. Thiran fractional delays of orders three and four are mapped to Xilinx FPGA RF-SoC technologies for evaluation. The preliminary results of the APF-based Thiran fractional delays on FPGA can potentially be used to realize DVM factorizations using application-specific integrated circuit (ASIC) technology. [ABSTRACT FROM AUTHOR]

Providing higher precision Direction of Arrival (DOA) estimation has become a hot topic in the field of array signal processing for parameter estimation in recent years. However, when the physical aperture of the actual array is small, its aperture limitation means that even with super-resolution estimation algorithms, the achievable estimation precision is limited. This paper takes a novel approach by constructing an optimization algorithm using the covariance fitting criterion based on the array output's covariance matrix to fit and obtain the covariance matrix of a large aperture virtual array, thereby providing high-precision angular resolution through virtual aperture expansion. The covariance fitting expansion analysis and discussion are unfolded for both uniform linear arrays (ULAs) and sparse linear arrays (SLAs) under four different scenarios. Theoretical analysis and simulation experiments demonstrate that these methods can enhance the effective performance of angle estimation, especially in low signal-to-noise ratios (SNRs) and at small angular intervals by fitting virtual extended aperture data. [ABSTRACT FROM AUTHOR]

The estimation of direction of arrival (DOA) is paramount in the realm of practical array signal processing systems. Nevertheless, traditional estimation methods often rely heavily on the Gaussian noise assumption, rendering them ineffective in achieving high-precision estimates in environments plagued by strong impulsive noise. To address this challenge, this paper introduces a novel DOA estimation algorithm that leverages mixed fractional lower-order correntropy (MFLOCR) in the context of Alpha-stable distributed impulsive noise. Correntropy is used as a measure of the similarity of the signals, using a Gaussian function to smooth extreme values and provide greater robustness against impulsive noise. By utilizing diverse kernel lengths to jointly regulate the kernel function, the concept of correntropy is expanded and implemented in the fractional lower-order moment (FLOM) algorithm for received signals. Subsequently, the MFLOCR is derived by adjusting the resulting form of correntropy. Finally, an enhanced DOA estimation algorithm is proposed that combines the MFLOCR operator with the MUSIC algorithm, specifically tailored for impulsive noise environments. Furthermore, a proof of boundedness is provided to validate the effectiveness of the proposed approach in such noisy conditions. Simulation experiments confirmed that the proposed method outperforms existing DOA estimation methods in the context of intense impulsive noise, a low generalized signal-to-noise ratio (GSNR), and a smaller number of snapshots. [ABSTRACT FROM AUTHOR]

The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE. [ABSTRACT FROM AUTHOR]

The compressive sensing method is an effective way of under-sampling array signal processing, and the measurement matrix is a key technology of compressive sensing processing, which is of decisive significance to improve the performance of under-sampling array signal processing. In this paper, an under-sampling array signal processing method based on an improved Hadamard measurement matrix was proposed. This method improved the construction of the measurement matrix by a compressing zero method capable of reducing the redundant data, enhancing the non-correlation between columns, improving the RIP condition, and improving the performance of the compressive sensing method. The performance was verified by simulation and real data from the lake, and the results showed that the method proposed in this paper has obvious performance advantages compared with the Toeplitz Circular Matrix, the m-sequence-based matrix, and the partial Hadamard matrix under the same conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning approach to the adaptive and sequential beamforming design problem for the initial access phase in a mmWave environment with a single-path channel. For a single-user scenario where the problem is equivalent to designing the sequence of sensing beamformers to learn the angle of arrival (AoA) of the dominant path, we propose a novel deep neural network (DNN) that designs the adaptive sensing vectors sequentially based on the available information so far at the base station (BS). By recognizing that the AoA posterior distribution is a sufficient statistic for solving the initial access problem, we use the posterior distribution as the input to the proposed DNN for designing the adaptive sensing strategy. However, computing the posterior distribution can be computationally challenging when the channel fading coefficient is unknown. To address this issue, this paper proposes to use an estimate of the fading coefficient to compute an approximation of the posterior distribution. Further, this paper shows that the proposed DNN can deal with practical beamforming constraints such as the constant modulus constraint. Numerical results demonstrate that compared to the existing adaptive and non-adaptive beamforming schemes, the proposed DNN-based adaptive sensing strategy achieves a significantly better AoA acquisition performance. [ABSTRACT FROM AUTHOR]

Underwater target detection and identification technology are currently two of the most important research directions in the information disciplines. Traditionally, underwater target detection technology has struggled to meet the needs of current engineering. However, due to the large manifold error of the underwater sonar array and the complexity of ensuring long-term signal stability, traditional high-resolution array signal processing methods are not ideal for practical underwater applications. In conventional beamforming methods, when the signal-to-noise ratio is lower than −43.05 dB, the general direction can only be vaguely identified in the general direction. To address the above challenges, this paper proposes a beamforming method based on a deep neural network. Through preprocessing, the space-time domain of the target sound signal is converted into two-dimensional data in the angle-time domain. Subsequently, we trained the network with enough sample datasets. Finally, high-resolution recognition and prediction of two-dimensional images are realized. The results of the test dataset in this paper demonstrate the effectiveness of the proposed method, with a minimum signal-to-noise ratio of −48 dB. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to theorizing and empirically investigating a phenomenon variously described by sociologists as internalized oppression or symbolic violence. Located at the intersection of internal worlds and external reality, the intrapsychic and the interpersonal and social, this object of inquiry—here termed self-negation—is crucial to many forms of societal domination. The paper explores its inner workings, analytically disaggregating it into an array of psychosocial processes drawn from the psychoanalytic theory of the defenses. Much of the work's originality consists in showing how these processes operate across multiple systems of domination and drive many and varied outward manifestations of the phenomenon. [ABSTRACT FROM AUTHOR]

The Ruscheweyh derivative operator is used in this paper to introduce and investigate interesting general subclasses of the function class Σ m of m-fold symmetric bi-univalent analytic functions. Estimates of the initial Taylor-Maclaurin coefficients | a m + 1 | and | a 2 m + 1 | are obtained for functions of the subclasses introduced in this study, and the consequences of the results are discussed. Additionally, the Fekete-Szegö inequalities for these classes are investigated. The results presented could generalize and improve some recent and earlier works. In some cases, our estimates are better than the existing coefficient bounds. Furthermore, within the engineering domain, the utilization of the Ruscheweyh derivative operator can encompass a broad spectrum of engineering applications, including the robotic manipulation control, optimizing optical systems, antenna array signal processing, image compression, and control system filter design. It emphasizes the potential for innovative solutions that can significantly enhance the reliability and effectiveness of engineering applications. [ABSTRACT FROM AUTHOR]

Due to its low cost and privacy protection, Channel-State-Information (CSI)-based activity detection has gained interest recently. However, to achieve high accuracy, which is challenging in practice, a significant number of training samples are required. To address the issues of the small sample size and cross-scenario in neural network training, this paper proposes a WiFi human activity-recognition system based on transfer learning using an antenna array: Wi-AR. First, the Intel5300 network card collects CSI signal measurements through an antenna array and processes them with a low-pass filter to reduce noise. Then, a threshold-based sliding window method is applied to extract the signal of independent activities, which is further transformed into time–frequency diagrams. Finally, the produced diagrams are used as input to a pretrained ResNet18 to recognize human activities. The proposed Wi-AR was evaluated using a dataset collected in three different room layouts. The testing results showed that the suggested Wi-AR recognizes human activities with a consistent accuracy of about 94%, outperforming the other conventional convolutional neural network approach. [ABSTRACT FROM AUTHOR]

Purpose: Technology-enabled business-to-business (B2B) services contribute the largest share to GDP growth and are fundamental for an economy's value creation. This article aims to identify key service- and digital technology-driven B2B innovation modes and proposes a research agenda for further exploration. Design/methodology/approach: This conceptual paper adopts a techno-demarcation view on service innovation, encompassing three core dimensions: service offering (the service product, or the "what"), service process (the "how") and service ecosystem (the "who/for whom"). It delineates the implications of three digital technologies – the internet-of-things (IoT), intelligent automation (IA) and digital platforms – for service innovation across these core dimensions in B2B markets. Findings: Digital technology has immense potential ramifications for value creation by reshaping all three core dimensions of service innovation. Specifically, IoT can transform physical resources into reconfigurable service products, IA can augment and automate a rapidly expanding array of service processes, while digital platforms provide the technical and organizational infrastructure for the integration of resources and stakeholders within service ecosystems. Originality/value: This study suggests an agenda with six themes for further research, each linked to one or more of the three service innovation dimensions. They are (1) new recurring revenue models, (2) service innovation in the metaverse, (3) scaling up service innovations, (4) ecosystem innovations, (5) power dependency and lock-in effects and (6) security and responsibility in digital domains. [ABSTRACT FROM AUTHOR]

To solve the problem in target acoustic signal processing and recognition when the target flies at a low altitude based on the acoustic positioning system, which is often affected by external interference and brings false information, this paper proposes a target signal processing and recognition algorithm for low-altitude target acoustic positioning based on variational modal decomposition and the test method of multiple five-element acoustic arrays. This algorithm uses VMD to decompose the target signal into modal components with different central frequencies and then performs wavelet threshold processing on the low-frequency part of the signal. After determining the remaining signal components and the low-frequency part's threshold, the residual component is reconstructed. Based on the test principle and calculation model of the five-element acoustic positioning system, following processing of the low-altitude target acoustic positioning signal using variational modal decomposition, the cross-correlation function method is introduced to perform correlation operations on the basic array of five acoustic sensors and then obtain the time value and time difference of the target acoustic information in each acoustic sensor, ultimately determining the spatial position of the target. Finally, we used the data fusion processing method for target coordinates in multi-acoustic basic arrays to determine the actual target position. By comparing the results obtained using the high-speed camera method with those of the proposed approach, it was found that the average error in the test area of 100 × 100 m was less than 1 m. [ABSTRACT FROM AUTHOR]

Sparse arrays are widely employed in array signal processing due to their obvious advantages in array element distribution and uniform degrees of freedom (uDOFs). In this paper, a generalized augmented multi-subarray nested array (GAMSNA-I) and its variant, GAMSNA-II are proposed, with the objective of increasing uDOFs and reducing mutual coupling. Based on two subarrays of the prototype nested array (NA), GAMSNA-I is constructed by reconfiguring the dense uniform linear array (ULA) and forward-shifting the sparse ULA. GAMSNA-II is obtained by sparsifying the dense part of GAMSNA-I, ensuring constant uDOFs while further reducing mutual coupling. Subsequently, the closed-form expression for the uDOFs of GAMSNA-I with an arbitrary number of sensors is derived, and the proof is provided that the uDOFs of GAMSNA-II remain unchanged relative to that of GAMSNA-I. Compared to some existing array configurations, both GAMSNA-I and GAMSNA-II exhibit improved uDOFs, with GAMSNA-II achieving lower mutual coupling. Simulation results show the superior performance of the proposed GAMSNA-I and GAMSNA-II. [ABSTRACT FROM AUTHOR]

Activation is an important component of the neural network, and the standard instructions of RISC-V are difficult to use to effectively handle the activation of the array. In this paper, we propose an optimized implementation of activation instruction based on RISC-V. Based on the opensource RISC-V processor Hummingbird E203, we designed a special instruction for the implementation of activation functions. A single instruction is chosen to implement the entire activation operation, including data loading, data arithmetic and data write-back. At the hardware level, we designed a method of alternate reading and writing that only needs a small hardware storage unit to meet the requirements of the activation operation for long arrays without affecting the activation efficiency. In addition, we added the length of the array as a new parameter to instruct our designed hardware to adapt to any length of arrays. Finally, the scheduling method of some instructions in the activation process was optimized in accordance with the law of instructions, which improves the execution efficiency of instructions. Considering an activation process with an array length of 15, our design demonstrates a 4.89-fold increase in speed compared to RISC-V standard instructions while consuming only 7.78% of the energy. [ABSTRACT FROM AUTHOR]

Blind gain and phase calibration (BGPC) is a bilinear inverse problem involving the determination of unknown gains and phases of the sensing system, and the unknown signal, jointly. BGPC arises in numerous applications, e.g., blind albedo estimation in inverse rendering, synthetic aperture radar autofocus, and sensor array auto-calibration. In some cases, sparse structure in the unknown signal alleviates the ill-posedness of BGPC. Recently, there has been renewed interest in solutions to BGPC with careful analysis of error bounds. In this paper, we formulate BGPC as an eigenvalue/eigenvector problem and propose to solve it via power iteration, or in the sparsity or joint sparsity case, via truncated power iteration. Under certain assumptions, the unknown gains, phases, and the unknown signal can be recovered simultaneously. Numerical experiments show that power iteration algorithms work not only in the regime predicted by our main results, but also in regimes where theoretical analysis is limited. We also show that our power iteration algorithms for BGPC compare favorably with competing algorithms in adversarial conditions, e.g., with noisy measurement or with a bad initial estimate. [ABSTRACT FROM AUTHOR]

A flexible PIN diode sensor array process has been successfully demonstrated. Device fabrication of the thin film transistor (TFT) backplane occurred below 200 °C on a polyethylene naphthalate (PEN) substrate using indium gallium zinc oxide (IGZO) with a saturation mobility of 13.5 cm2/V-s as the active semiconductor. The increased mobility enables the fabrication of higher speed and higher resolution sensor arrays in comparison to current amorphous silicon (a-Si) technology. [ABSTRACT FROM AUTHOR]

This paper proposes the generalized MUltiple SIgnal Classification (MUSIC)-like algorithm for robust MUSIC-like processing for underwater applications. The solution proposed in this paper is to generalize the noise correlation assumption and include a noise correlation model in its problem formulation. By doing so, the proposed generalized MUSIC-like algorithm is able to provide robust MUSIC-like performances in any noise condition, so long as the noise correlation property of the environment is known partially. Results from simulations and real data processing show that our proposed algorithm is able to suppress spurious peaks caused by mismatched noise assumptions in standard MUSIC-like algorithms. The bound of the controlling parameter denoted by $\beta $ for the proposed generalized MUSIC-like algorithm is also discussed in this paper. Performance study using Monte Carlo simulations shows that the proposed generalized MUSIC-like algorithm has the same resolving power as the MUSIC method but slightly poorer accuracy in direction-of-arrival (DOA) estimation. This paper also presents the results from real data processing by the generalized MUSIC-like algorithm and demonstrates better resolving power than the Capon and MUSIC algorithms used consistently in the experiment. [ABSTRACT FROM AUTHOR]

Distributed or decentralized estimation of covariance, and distributed principal component analysis have been introduced and studied in the signal processing community in recent years, and applications in array processing have been indicated in some detail. Inspired by these, this paper provides a detailed development of several distributed algorithms for array processing. New distributed algorithms are proposed for DOA estimation methods like root-MUSIC, total least squares-ESPRIT, and FOCUSS. Other contributions include distributed design of the Capon beamformer from data, and distributed implementation of the spatial smoothing method for coherent sources. A distributed implementation of a recently proposed beamspace method called the convolutional beamspace (CBS) is also proposed. The proposed algorithms are fully distributed – an average consensus (AC) is used to avoid the need for a fusion center. The algorithms are based on a recently reported finite-time version of AC which converges to the exact solution in a finite number of iterations. Numerical examples are given throughout the paper to show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Adaptive antenna arrays are widely used to protect Global Navigation Satellite System (GNSS) receivers from interference. However, conventional blind anti-jamming array processing methods can cause satellite signal attenuation, which reduces signal tracking precision and measurement accuracy. To alleviate this problem, this paper proposes the noncoherent channel combining method for GNSS signal tracking with an adaptive antenna array. A two-stage processing architecture is applied in the proposed method, where the first stage implements interference suppression, and the second stage achieves noncoherent channel combining. The proposed method has the advantage of blind characteristics that can be implemented without any other auxiliary information. It also reveals a unified processing architecture for anti-jamming array processing methods. Thus, a detailed comparison and analysis of the proposed method with several typical blind anti-jamming methods is performed. Simulation results demonstrate the effectiveness of the proposed method and its superiority over existing methods in terms of tracking performance. [ABSTRACT FROM AUTHOR]

Object detection is a technique of computer vision whose primary intent is to detect objects. The objects can be detected from any image or video feeds. Now a day's object detection is extensively applied in video surveillance systems, human tracking, and self-driving cars. This paper presented a novel object detection approach that uses only wireframe-based features. The wireframe of the image is identified by using Cellular logical array processing. This technique can determine the visual and geometric features of the image. This paper focuses on a deep neural network framework to detect the target object in the image. Fast R-CNN is used for the detection of objects. The detection speed is fast because only the wireframe of the image is obtained first and then fed into the Fast RCNN model for detection and classification purposes. The performance of the proposed methodology is evaluated on PASCAL VOC, example-based synthesis dataset and real-time dataset. The proposed methodology gives mean average precision (mAP) 89.4%, 91.33% and 88.1% on PASCAL VOC, example-based and real-time dataset. The experimental analysis demonstrated that our proposed detection method achieves better results than the other state of art methods. The approach is helpful to detect the 2D and 3D objects as well. [ABSTRACT FROM AUTHOR]

In array signal processing, Estimating the quantity of signal sources represents a crucial area of investigation. In this paper, a comprehensive introduction and analysis of the estimation methods for determining the number of signal sources are presented, including the background and significance, and the significance of precise estimation of the quantity of signal sources. The influence of factors such as signal-to-noise ratio (SNR), noise background, and number of snapshots on the estimation algorithm is discussed in detail. At the same time, common array models are introduced. Then, different signal source number estimation algorithms are analyzed in detail, and their respective advantages and applicable conditions are pointed out. Finally, the performance of each algorithm in different situations is evaluated by comparing the performance of the algorithms under different SNRs, snapshot numbers, and array elements. The experimental results show that with the increase of the SNR and the number of array elements, the correct estimation probability of the algorithm also increases correspondingly, which provides a reliable experimental basis and performance evaluation for the estimation. [ABSTRACT FROM AUTHOR]

Because of the inhomogeneity and anisotropy of glass fibre reinforced plastics (GFRP), it is always a challenge to detect the real-time dynamic quality during manufacturing and service. In this paper, a virtual loading focusing enhancement technology (VLFET) based on time reversal (TR) theory is proposed to accurately locate the acoustic emission (AE) source in the GFRP. First, the focusing enhancement theoretical model with virtual reverse loading is deduced according to the TR theory. Then, after measuring the optimal detection range and the average acoustic velocity, the sensor array is positioned on the GFRP specimen. The excitation signals applied to simulate the AE source at some positions are produced by using lead breaking, and the detection signals are received by the sensor array. Finally, the signals received by the sensor array are processed according to the theoretical model, and the fluctuation image in the monitoring area is reconstructed with fluctuation amplitude of each pixel to evaluate the AE source position. The experimental results demonstrate that the excitation sources can be accurately evaluated with the VLFET. The location error with the proposed method is less than 3.1%, higher than that from the current AE instrument. The proposed technology can also be applied to the AE evaluation of other anisotropic materials. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS), which consists of a large number of tunable reflective elements, is capable of enhancing the wireless propagation environment in a cellular network by intelligently reflecting the electromagnetic waves from the base-station (BS) toward the users. The optimal tuning of the phase shifters at the IRS is, however, a challenging problem, because due to the passive nature of reflective elements, it is difficult to directly measure the channels between the IRS, the BS, and the users. Instead of following the traditional paradigm of first estimating the channels then optimizing the system parameters, this paper advocates a machine learning approach capable of directly optimizing both the beamformers at the BS and the reflective coefficients at the IRS based on a system objective. This is achieved by using a deep neural network to parameterize the mapping from the received pilots (plus any additional information, such as the user locations) to an optimized system configuration, and by adopting a permutation invariant/equivariant graph neural network (GNN) architecture to capture the interactions among the different users in the cellular network. Simulation results show that the proposed implicit channel estimation based approach is generalizable, can be interpreted, and can efficiently learn to maximize a sum-rate or minimum-rate objective from a much fewer number of pilots than the traditional explicit channel estimation based approaches. [ABSTRACT FROM AUTHOR]

The inspection of some substances usually includes two levels. One is the detection of the physical properties of the substance, which can be carried out through a series of physical detection methods and corresponding physical experiments. In the process of chemical detection, the color change after a chemical reaction is an extremely identifying optical property feature. In today's increasingly mature Internet technology and related computer technology, the combination of this important identification chemical reaction and the former makes the chemical detection method visualized. The biggest difficulty in the application of this technology is to divide the color units produced by the chemical reaction in the contrast color sensor, which directly affects the identification process of the chemical reaction in the subsequent process. In order to better solve this problem, this paper will use a deep convolutional neural network to process the segmentation process of color units. And it is realized by image semantic processing of colorimetric sensor array and deep convolutional neural network processing of imaging. And through the experimental experiments based on convolutional neural network image segmentation processing, the results show that the efficiency of extracting features corresponding to different layers in the convolutional neural network is that the extraction efficiency of feature 1 and feature 2 is higher in the processing of 4 layers. They achieve 79.11%, 76.13%, 77.61%, 91.11% 92.31%, 91.05%, 91.03%, and 91.03%, respectively, and the extraction rate for feature 3 at layer 4 reaches 96.19%. It can be known from the above results that the segmented part of the image generated by the colorimetric sensor array processed by the deep convolutional neural network will be more conducive to the final color unit identification. [ABSTRACT FROM AUTHOR]

Recently, research on the design of relay structure and optimal precoding matrix in massive multiple-input multiple-output (MIMO) relay systems has been proposed in many works assuming that partial or all of the channel state information (CSI) is known. However, in the practical communication systems, the CSI is unknown and needs to be estimated. In particular, channel estimation is more difficult when the relay adopts a hybrid precoding structure. In this paper, we propose an efficient channel estimation scheme based on Tucker-2 tensor model for a multi-way massive MIMO hybrid relay system. The proposed algorithm provides all the CSI involved in the communication links at each user end. Compared with traditional channel estimation algorithms, the proposed algorithm has better estimation performance and requires few iterations to achieve convergence. In addition, we derive the analytical expression of the Cramér-Rao lower bound (CRB) for reference. Simulation results verify the effectiveness of the proposed Tucker-2-based channel estimation approach. [ABSTRACT FROM AUTHOR]

A coprime antenna array consists of two or more sparse subarrays featuring enhanced degrees of freedom (DOF) and reduced mutual coupling. This paper introduces a new class of planar coprime arrays, based on the theory of ideal lattices. In quadratic number fields, a splitting prime $p$ can be decomposed into the product of two distinct prime ideals, which give rise to the two sparse subarrays. Their virtual difference coarray enjoys a quadratic gain in DOF, thanks to the generalized Chinese remainder theorem (CRT). To enlarge the contiguous aperture of the coarray, we present hole-free symmetric CRT arrays with simple closed-form expressions. The ring of Gaussian integers and the ring of Eisenstein integers are considered as examples to demonstrate the procedure of designing coprime arrays. With Eisenstein integers, our design yields a difference coarray that is a subset of the hexagonal lattice, offering a significant gain in DOF over the rectangular lattice, given the same physical areas. Maximization of CRT arrays in the aspect of essentialness and the superior performance in the context of angle estimation will be presented in the companion paper (Part II). [ABSTRACT FROM AUTHOR]

This paper presents a multi-standard fully connected hybrid beamforming (FC-HBF) receiver that can be reconfigured between two fully connected two-stream multi-input-multi-output (MIMO) modes at 28- or 37-GHz band, and an inter-band carrier-aggregation (CA) mode enabling concurrent single-stream operation at 28 and 37 GHz. A new image-reject (IR) heterodyne beamforming architecture is introduced that facilitates easy reconfiguration. Concurrent dual-band operation of the beamformer front end is achieved by using an inherently wideband Cartesian-combining-based RF-domain complex-weighting architecture, which is implemented using coupled-resonator-based dual-band gain stages, and current-mode dual-band active combiners. The downconversion stage comprises a cascade of complex-quadrature mixing stages that combines Cartesian complex-weighting operation with image rejection. A new quadrature error detection and calibration scheme is also introduced to improve the image rejection of the proposed architecture. A minimum mean-square error (MMSE) beam adaptation technique, introduced for the first time in an RF or hybrid beamformer, breaks the need for individual access to the beamformer inputs needed in a traditional least-mean-square (LMS) scheme and allows both main lobe and null adaptation. A 28-/37-GHz hybrid beamforming receiver with four antenna inputs and two baseband output streams is designed in 65-nm CMOS that demonstrates the above features. In each antenna path, the receiver achieves 33-dB (26.5 dB) peak conversion gain, 2.75-GHz (3.75 GHz) RF bandwidth, and 5.7-dB (8.5 dB) NF at 28 GHz (37 GHz). The entire receiver consumes 310 mW and occupies 2.2 mm2 core area. Extensive characterization results for single-element and several multi-element scenarios are presented including concurrent dual-band receiving (>35-dB image rejection), null-steering (>25-dB peak to null for two elements), and multi-element CA (~50-dB inter-carrier interference rejection). [ABSTRACT FROM AUTHOR]

In this paper, we propose a downlink multiple-input single-output (MISO) transmission scheme, which is assisted by an intelligent reflecting surface (IRS) consisting of a large number of passive reflecting elements. In the literature, it has been proved that nonorthogonal multiple access (NOMA) can achieve the same performance as computationally complex dirty paper coding, where the quasi-degradation condition is satisfied, conditioned on the users’ channels fall in the quasi-degradation region. However, in a conventional communication scenario, it is difficult to guarantee the quasi-degradation, because the channels are determined by the propagation environments and cannot be reconfigured. To overcome this difficulty, we focus on an IRS-assisted MISO NOMA system, where the wireless channels can be effectively tuned. We optimize the beamforming vectors and the IRS phase shift matrix for minimizing transmission power. Furthermore, we propose an improved quasi-degradation condition by using IRS, which can ensure that NOMA achieves the capacity region with high possibility. For a comparison, we study zero-forcing beamforming (ZFBF) as well, where the beamforming vectors and the IRS phase shift matrix are also jointly optimized. Comparing NOMA with ZFBF, it is shown that, with the same IRS phase shift matrix and the improved quasi-degradation condition, NOMA always outperforms ZFBF. At the same time, we identify the condition under which ZFBF outperforms NOMA, which motivates the proposed hybrid NOMA transmission. Simulation results show that the proposed IRS-assisted MISO system outperforms the MISO case without IRS, and the hybrid NOMA transmission scheme always achieves better performance than orthogonal multiple access. [ABSTRACT FROM AUTHOR]

This paper presents a comparative benchmarking of different beamforming techniques for robust multipath and spoofing mitigation in handheld receivers equipped with a small number of antenna elements. In the field of Global Navigation Satellite Systems (GNSSs), multipath and spoofing have become major sources of signal degradation, whereby the presence of closely spaced replicas of the signal of interest hampers the receiver's performance. Spatial filtering through the use of antenna arrays is one appealing approach to combat these effects. However, the upward trend in device miniaturization poses a serious concern for spatial filtering in handheld devices such as tablets or smartphones, particularly for the case of decimeter-wave signals such as in GNSSs. The terminal size constraints lead to deploying arrays with very few antennas, thus degrading the mitigation performance of the above impairments. This work explores the feasibility of performing robust multipath and spoofing mitigation in GNSS receivers with antenna arrays of very small sizes. Simulation results on four-antenna rectangular and two-antenna linear array distributions are provided, showing that the reduction in array size can be counteracted by the use of advanced beamformers. [ABSTRACT FROM AUTHOR]

Recently, many robust adaptive beamforming (RAB) algorithms have been proposed to improve beamforming performance when model mismatches occur. For a uniform linear array, a larger aperture array can obtain higher array gain for beamforming when the inter-sensor spacing is fixed. However, only the small aperture array can be used in the equipment limited by platform installation space, significantly weakening beamforming output performance. This paper proposes two beamforming methods for improving beamforming output in small aperture sensor arrays. The first method employs an integration algorithm that combines angular sector and gradient vector search to reconstruct the interference covariance matrix (ICM). Then, the interference-plus-noise covariance matrix (INCM) is reconstructed combined with the estimated noise power. The INCM and ICM are used to optimize the desired signal steering vector (SV) by solving a quadratically constrained quadratic programming (QCQP) problem. The second method proposes a beamforming algorithm based on a virtual extended array to increase the degree of freedom of the beamformer. First, the virtual conjugated array element is designed based on the structural characteristics of a uniform linear array, and received data at the virtual array element are obtained using a linear prediction method. Then, the extended INCM is reconstructed, and the desired signal SV is optimized using an algorithm similar to the actual array. The simulation results demonstrate the effectiveness of the proposed methods under different conditions. [ABSTRACT FROM AUTHOR]

Carbon nanotube (CNT) sensors provide a versatile chemical platform for ambient monitoring of ozone (O3) and nitrogen dioxide (NO2), two important airborne pollutants known to cause acute respiratory and cardiovascular health problems. CNTs have shown great potential for use as sensing layers due to their unique properties, including high surface to volume ratio, numerous active sites and crystal facets with high surface reactivity, and high thermal and electrical conductivity. With operational advantages such as compactness, low-power operation, and easy integration with electronics devices, nanotechnology is expected to have a significant impact on portable low-cost environmental sensors. Enhanced sensitivity is feasible by functionalizing the CNTs with polymers, metals, and metal oxides. This paper focuses on the design and performance of a two-element array of O3 and NO2 sensors comprising single-walled CNTs functionalized by covalent modification with organic functional groups. Unlike the conventional chemiresistor in which the change in DC resistance across the sensor terminals is measured, we characterize the sensor array response by measuring both the magnitude and phase of the AC impedance. Multivariate response provides higher degrees of freedom in sensor array data processing. The complex impedance of each sensor is measured at 5 kHz in a controlled gas-flow chamber using gas mixtures with O3 in the 60–120 ppb range and NO2 between 20 and 80 ppb. The measured data reveal response change in the 26–36% range for the O3 sensor and 5–31% for the NO2 sensor. Multivariate optimization is used to fit the laboratory measurements to a response surface mathematical model, from which sensitivity and selectivity are calculated. The ozone sensor exhibits high sensitivity (e.g., 5 to 6 MΩ/ppb for the impedance magnitude) and high selectivity (0.8 to 0.9) for interferent (NO2) levels below 30 ppb. However, the NO2 sensor is not selective. [ABSTRACT FROM AUTHOR]