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Early paper manufacturing used sieve-like molds through which paper pulp was drained. Two pieces of paper are called moldmates if they were made using the same mold. When a large body of one artist?s work on paper exists, the identification of moldmates can help in establishing chronology, suggest paper preferences, and indicate periods of intense activity of the artist. Rembrandt is an especially good example. With several thousand prints in existence today, the study of Rembrandt?s prints has occupied scholars for over two centuries, and the study of his printing papers occupies a prominent place within this scholarship [1]. This article examines the feasibility of moldmate identification via chain-line pattern matching and conducts a series of experiments that demonstrate how accurately the measurements can be made, how straight and parallel the lines may be, and provides a rule of thumb for the number of chain lines required for accurate moldmate identification using a simplified model. The problem of identifying moldmates among Rembrandt?s prints is presented as a pair of image/signal processing tasks; our strategy is to provide basic solutions to these tasks and to then reveal the shortcomings of these solutions in the hopes of encouraging future work in the signal processing community. With the support of the Morgan Library & Museum and the Metropolitan Museum of Art, both of which are in New York, we have made high-resolution data available [2] to facilitate this quest.

Recently developed monitoring-systems show the need as also the possibilities for online- and offline-onsite-diagnostics. The limit in sensitivity for electrical pd-measurements according to IEC 60270 which is reached using filters for sinusoidal disturbances and compensation for corona impulses is still not satisfactory. The acoustic partial discharge measurement (pd-measurement) is also a well known and reliable method often used by transformer manufacturers in testing. In laboratory setups a gain in sensitivity of the acoustic in comparison to the electrical pd measurements could be established. This is reached by adapting the sensors to the propagation path, a modern but moderately priced acquisition hardware and the introduction of new signal processing algorithms. The developed sophisticated measuring systems show remarkable results in online acoustic pd-measurements onsite.

In this paper, we present a new fast Fourier transform (FFT) hardware architecture called constant multiplier (CM) FFT. Whereas rotators in previous architectures must rotate among several different angles, the CM FFT exploits the use of constant multipliers to calculate the rotations. The paper explores the 4-parallel and 8-parallel radix-2 CM FFT, which calculates the FFT entirely by using constant multipliers. Later, radices $2^{k}$ are presented, with emphasis in radix-24 and radix-25 as the best alternatives. Experimental results for a 1024-point radix-25 CM FFT show that the proposed approach reduces the number of block random-access memories (BRAM) and digital signal processing (DSP) slices with respect to previous approaches, while achieving the highest clock frequency for a 4-parallel FFT architecture on field-programmable gate arrays (FPGAs) reported so far.

Shannon's sampling theorem is one of the cornerstone topics that is well understood and explored, both mathematically and algorithmically. That said, practical realization of this theorem still suffers from a severe bottleneck due to the fundamental assumption that the samples can span an arbitrary range of amplitudes. In practice, the theorem is realized using so-called analog-to-digital converters (ADCs) which clip or saturate whenever the signal amplitude exceeds the maximum recordable ADC voltage thus leading to a significant information loss. In this paper, we develop an alternative paradigm for sensing and recovery, called the Unlimited Sampling Framework. It is based on the observation that when a signal is mapped to an appropriate bounded interval via a modulo operation before entering the ADC, the saturation problem no longer exists, but one rather encounters a different type of information loss due to the modulo operation. Such an alternative setup can be implemented, for example, via so-called folding or self-reset ADCs, as they have been proposed in various contexts in the circuit design literature. The key task that we need to accomplish in order to cope with this new type of information loss is to recover a bandlimited signal from its modulo samples. In this paper we derive conditions when perfect recovery is possible and complement them with a stable recovery algorithm. The sampling density required to guarantee recovery is independent of the maximum recordable ADC voltage and depends on the signal bandwidth only. Our recovery guarantees extend to measurements affected by bounded noise, which includes the case of round-off quantization. Numerical experiments validate our approach. Applications of the unlimited sampling paradigm can be found in a number of fields such as signal processing, communication and imaging., Comment: Accepted to IEEE Trans. on Sig. Proc. (10.1109/TSP.2020.3041955)

Time reversal (TR) relies on the reciprocity nature of the wave equation. Waves diverging away from a source can be time reversed and back-propagated to focus back at the source. The transmitting and receiving antenna arrays utilized for TR are collectively termed as the TR mirror (TRM). TR imaging in the microwave regime has shown its promise in nondestructive inspection and target localization. This paper deals with the design of a microwave TRM for the detection of breast tumors. The first part of this paper discusses TR simulations based on a signal processing algorithm, to obtain the tumor location from the total fields. The processed time-reversed fields can be back-propagated in a numerical model to focus the wave at the tumor locations. Simulation results aid in investigating the feasibility and the robustness of the method. The second part of this paper deals with experimental studies for the breast tumor detection, using a pulsed time-domain system and the back-propagation model. The successful detection of single and multiple tumors embedded in a liquid breast tissue-based phantom is shown using the microwave system.

International audience; This paper discusses a novel conceptual formulation of the fractional-order variational framework for retinex, which is a fractional-order partial differential equation (FPDE) formulation of retinex for the multi-scale nonlocal contrast enhancement with texture preserving. The well-known shortcomings of traditional integer-order computation-based contrast-enhancement algorithms, such as ringing artefacts and staircase effects, are still in great need of special research attention. Fractional calculus has potentially received prominence in applications in the domain of signal processing and image processing mainly because of its strengths like long-term memory, nonlocality, and weak singularity, and because of the ability of a fractional differential to enhance the complex textural details of an image in a nonlinear manner. Therefore, in an attempt to address the aforementioned problems associated with traditional integer-order computation-based contrast-enhancement algorithms, we have studied here, as an interesting theoretical problem, whether it will be possible to hybridize the capabilities of preserving the edges and the textural details of fractional calculus with texture image multi-scale nonlocal contrast enhancement. Motivated by this need, in this paper, we introduce a novel conceptual formulation of the fractional-order variational framework for retinex. First, we implement the FPDE by means of the fractional-order steepest descent method. Second, we discuss the implementation of the restrictive fractional-order optimization algorithm and the fractional-order Courant-Friedrichs-Lewy condition. Third, we perform experiments to analyze the capability of the FPDE to preserve edges and textural details, while enhancing the contrast. The capability of the FPDE to preserve edges and textural details is a fundamental important advantage, which makes our proposed algorithm superior to the traditional integer-order computation-based contrast enhancement algorithms, especially for images rich in textural details.

Prognostics and health management is an emerging discipline to scientifically manage the health condition of engineering systems and their critical components. It mainly consists of three main aspects: construction of health indicators, remaining useful life prediction, and health management. Construction of health indicators aims to evaluate the system's current health condition and its critical components. Given the observations of a health indicator, prediction of the remaining useful life is used to infer the time when an engineering systems or a critical component will no longer perform its intended function. Health management involves planning the optimal maintenance schedule according to the system's current and future health condition, its critical components and the replacement costs. Construction of health indicators is the key to predicting the remaining useful life. Bearings and gears are the most common mechanical components in rotating machines, and their health conditions are of great concern in practice. Because it is difficult to measure and quantify the health conditions of bearings and gears in many cases, numerous vibration-based methods have been proposed to construct bearing and gear health indicators. This paper presents a thorough review of vibration-based bearing and gear health indicators constructed from mechanical signal processing, modeling, and machine learning. This review paper will be helpful for designing further advanced bearing and gear health indicators and provides a basis for predicting the remaining useful life of bearings and gears. Most of the bearing and gear health indicators reviewed in this paper are highly relevant to simulated and experimental run-to-failure data rather than artificially seeded bearing and gear fault data. Finally, some problems in the literature are highlighted and areas for future study are identified.

Total variation (TV) is a one of the most popular regularizers for stabilizing the solution of ill-posed inverse problems. This paper proposes a novel proximal-gradient algorithm for minimizing TV regularized least-squares cost functionals. Unlike traditional methods that require nested iterations for computing the proximal step of TV, our algorithm approximates the latter with several simple proximals that have closed form solutions. We theoretically prove that the proposed parallel proximal method achieves the TV solution with arbitrarily high precision at a global rate of converge that is equivalent to the fast proximal-gradient methods. The results in this paper have the potential to enhance the applicability of TV for solving very large-scale imaging inverse problems.

This special issue collects 9 peer-reviewed papers. The main topics deal with smart wearable device development (4 papers) and intelligent signal processing algorithms integrated into wearable devices (5 papers). These papers are based on specific applications on online monitoring or tracking of human physical activity (5 papers) and medical indices (EEG, ECG, PPG and blood glucose) (4 papers) but can also be extended to processing of more generalized scenarios.

We correct errors in step 6 of Algorithm 1 given in the paper ‘`Sparse-Group Lasso for Graph Learning from Multi-Attribute Data.’' Other parts of the paper including numerical results are unchanged as the algorithm was implemented correctly.

Next-generation phasor measurement units (PMU) are expected to be more accurate than existing ones, especially to address the stricter requirements of future active distribution grids. From this perspective, the influence of acquisition wideband noise (which includes multiple contributions both in amplitude and in phase) has to be carefully evaluated. In this context, the contribution of this paper is twofold. First, it provides a general framework to evaluate the effect of wideband noise on synchrophasor amplitude, phase, frequency, and rate of change of frequency (ROCOF) estimation. The results of this analysis show that the influence of wideband noise can become critical for compliance with the requirements reported in the IEEE standards C37.118.1-2011 and C37.118.1a-2014, especially for frequency and ROCOF estimation. In addition, this paper reports general guidelines to choose the PMU effective resolution and sampling rate for which the impact of wideband noise on both P Class and M Class PMUs is negligible.

This paper presents a new hybrid A/D conversion principle based on the combination of an incremental conversion for the most significant part of the result and on a cyclic conversion for the least significant part. The proposed approach exhibits a resolution comparable to a multiorder sigma–delta A/D converter (ADC), but with a higher conversion rate (typically a factor of two) and/or a lower complexity, at the cost of a more stringent antialiasing filter requirement. After having presented the basic equations of the conversion principle, a theoretical analysis of the resolution limitations is given, based on nonidealities and noise considerations. Finally, a switched-capacitor implementation example is given with the corresponding simulations, consisting of a low-complexity 14-b ADC suited for applications requiring medium-speed and very compact ADC.

In this paper, discrete-time blind-source separation (BSS) of instantaneous mixtures is studied. Decorrelation-based sufficient criteria for BSS of stationary and nonstationary sources are derived based on nonstationarity and nonwhiteness. A gradient algorithm is proposed based on these criteria. A batch-data algorithm and an on-line algorithm are developed based on the corollaries of the BSS criteria. These algorithms are especially useful for the separation of nonstationary sources. They are robust to additive white noises if the time-delayed decorrelation and the nonstationarity of the sources are considered simultaneously in the algorithms. Experiment results show the effectiveness and performance of the proposed algorithms

As one of the biggest contributors to road accidents and fatalities, drink driving is worthy of significant research attention. However, most existing systems on detecting or preventing drink driving either require special hardware or require much effort from the user, making these systems inapplicable to continuous drink driving monitoring in a real driving environment. In this paper, we present DetectDUI, a contactless, non-invasive, real-time system that yields a relatively highly accurate drink driving monitoring by combining vital signs (heart rate and respiration rate) extracted from in-car WiFi system and driver's psychomotor coordination through steering wheel operations. The framework consists of a series of signal processing algorithms for extracting clean and informative vital signs and psychomotor coordination, and integrate the two data streams using a self-attention convolutional neural network (i.e., C-Attention). In safe laboratory experiments with 15 participants, DetectDUI achieves drink driving detection accuracy of 96.6% and BAC predictions with an average mean error of 2 ~ 5mg/dl. These promising results provide a highly encouraging case for continued development.

In this paper, we propose a general framework to provide a desirable trade-off between inference accuracy and privacy protection in the inference as service scenario (IAS). Instead of sending data directly to the server, the user will preprocess the data through a privacy-preserving mapping, which will increase privacy protection but reduce inference accuracy. To properly address the trade-off between privacy protection and inference accuracy, we formulate an optimization problem to find the privacy-preserving mapping. Even though the problem is non-convex in general, we characterize nice structures of the problem and develop an iterative algorithm to find the desired privacy-preserving mapping, with convergence analysis provided under certain assumptions. From numerical examples, we observe that the proposed method has better performance than gradient ascent method in the convergence speed, solution quality and algorithm stability.

This paper discusses the approach taken in conducting a real-time digital signal processing (DSP) design class for undergraduates. The most recent TMS320C5402 DSP Starter Kit (DSK) from Texas Instruments (TI) is used, and a set of courseware that exposes students to both theoretical understanding and hands-on exercises is developed. It leads the students from the conceptual stage in DSP design to the actual implementation stage. This paper discusses the lecturer and students' perspective in this real-time DSP course.

For representing stationary signals, several well-established methods are available. For nonstationary signals, however, these approaches can be used only with serious limitations. If the signal can be characterized as a sequence of stationary intervals, overcomplete signal representations help to handle such problems. This paper introduces the concept of recursive overcomplete representations using different recursive signal processing algorithms. The novelty of the paper is that an on-going set of signal transformations together with appropriate (e.g., L/sub 1/ norm) minimization procedures can provide optimal on-going representations, on-going signal segmentations into stationary intervals, and on-going feature extractions for immediate utilization in diagnostics, or other applications.

The 17 papers in this special issue focus on the young field of compressive sensing. Four main topics are covered by the paper, with many papers falling into multiple categories: applications; algorithms; theory; and new models.

Cell-free massive multiple-input-multiple-output (mMIMO) is an emerging technology for beyond 5G with its promising features such as higher spectral efficiency and superior spatial diversity as compared to conventional multiple-input-multiple-output (MIMO) technology. The main working principle of cell-free mMIMO is that many distributed access points (APs) cooperate simultaneously to serve all the users within the network without creating cell boundaries. This paper considers the uplink of a cell-free mMIMO system utilizing the radio stripe network architecture with a sequential fronthaul between the APs. A novel uplink sequential processing algorithm is developed, which is proved to be optimal in both the maximum spectral efficiency (SE) and the minimum mean square error (MSE) sense. A detailed quantitative analysis of the fronthaul requirement or signaling of the proposed algorithm and its comparison with competing sub-optimal algorithms is provided. Key conclusions and implications are summarized in the form of corollaries. Based on the analytical and numerical simulation results, we conclude that the proposed scheme can significantly reduce the fronthaul signaling, without compromising the communication performance. Funding Agencies|Swedish Research CouncilSwedish Research CouncilEuropean Commission [2019-05068]; Swedish Research Council (VR)Swedish Research Council [642-2013-7607]; ELLIIT; Knut and Alice Wallenberg (KAW) FoundationKnut & Alice Wallenberg Foundation

In this paper, we present a gridless channel estimation algorithm for a hybrid millimeter wave (mmWave) MIMO-OFDM system assuming a frequency-selective channel. The proposed algorithm is based on the Tensor-ESPRIT in DFT beamspace algorithm framework. First, we derive the $R$ -dimensional ( $R$ -D) Standard/Unitary Tensor-ESPRIT in DFT beamspace framework and its analytic performance. We show that ESPRIT-type algorithms in a reduced-dimensional DFT beamspace can provide a significant performance gain over ESPRIT-type algorithms in full DFT beamspace and in element space under mild conditions. Afterwards, we develop a gridless channel estimation algorithm that is based on 3-D Tensor-ESPRIT in DFT beamspace algorithms. Numerical simulation results show that the proposed channel estimation algorithm can provide accurate channel estimates using only a few training resources.

This paper reviews the main features of acoustic emission technique. First, the characteristics of acoustic signals and their physics are addressed. Then, the structure of acoustic sensors employed for capturing signals is studied. In the next step, the acoustic method of PD measurement is compared with the standard electrical method. Afterward, the applications of AET in PD measurement in different equipment are summarized. All acoustic method and combined acoustic-electrical method for PD localization in transformers are discussed. Moreover, the applications of AET in the monitoring of GIS systems are explained, along with the acoustic behaviors of moving particles in these systems. Finally, the general considerations in the signal processing of the acoustic signals are reviewed.

In this paper, an enhanced sparse representation approach is proposed to estimate the 3D shapes of objects in 2D image sequences. In the proposed method, the unknown 3D shape is estimated via a two-stage scheme, namely the main 3D shape estimation stage and the compensatory 3D shape estimation stage. Moreover, a reweighted sparse representation model is constructed to extract the shape bases for each estimation stage. In the sparse model, a reweighted constraint is enforced to enhance the coefficient sparsity of the shape bases. Experimental results on the well-known CMU image sequences demonstrate the effectiveness and feasibility of the proposed approach.

Wavelet tight frames have been actively investigated for various image restoration problems. In this paper, we introduce an analysis-sparsity model via $\ell _2$ -relaxed truncated $\ell _0$ regularization and nonlocal estimation, and the resulted nonconvex minimization problem is tackled by a proximal alternating minimization strategy. Numerical experiments demonstrate that the proposed algorithm is superior to many popular methods in both objective and perceptual quality.

Recently there were research works on multi-task estimation. However, there exists an issue that nodes may suffer from malicious attacks when sending data in the adversarial multi-task network environment. To address this issue, we propose a secure distributed information sharing algorithm based on attack detection. In our work, via determining an adaptive threshold, a distributed detection algorithm based on the correlation between tasks and a safe multi-task diffusion least mean square (SM-DLMS) algorithm are proposed, which not only reduce communication costs but also effectively decrease the impact of attacks. We also theoretically analyze the stability performance of the proposed SM-DLMS algorithm. Meanwhile, the robustness and effectiveness of the proposed algorithm under network-attacks are illustrated through numerical simulations.

After decades of research in Direction of Arrival (DoA) estimation, today Maximum Likelihood (ML) algorithms still provide the best performance in terms of resolution capabilities. At the cost of a multidimensional search, ML algorithms achieve a significant reduction of the outlier production mechanism in the threshold region, where the number of snapshots per antenna and/or the signal to noise ratio (SNR) are low. The objective of this paper is to characterize the resolution capabilities of ML algorithms in the threshold region. Both conditional and unconditional versions of the ML algorithms are investigated in the asymptotic regime where both the number of antennas and the number of snapshots are large but comparable in magnitude. By using random matrix theory techniques, the finite dimensional distributions of both cost functions are shown to be Gaussian distributed in this asymptotic regime, and a closed form expression of the corresponding asymptotic covariance matrices is provided. These results allow to characterize the asymptotic behavior of the resolution probability, which is defined as the probability that the cost function evaluated at the true DoAs is smaller than the values that it takes at the positions of the other asymptotic local minima.

© 2013 IEEE. Digital radio-over-fibre transmission has been studied extensively as a way of providing seamless last-mile wireless connectivity by carrying digitised radio frequency (RF) services over broadband optical infrastructures. With the growing demand on wireless capacity and the number of wireless services, a key challenge is the enormous scale of the digital data generated after the digitisation process. In turn, this leads to optical links needing to have very large capacity and hence, high capital expenditure (CAPEX). In this paper, we firstly present and then experimentally demonstrate a multiservice DRoF system for a neutral-host fronthaul link where both forward and reverse links use data compression, multiband multiplexing and synchronisation algorithms. The effect of a novel digital automatic gain control (DAGC) is comprehensively analysed to show an improved RF dynamic range alongside bit rate reduction. In this case, the system allows all cellular services from the three Chinese mobile network operators (MNOs) to be converged onto a single fiber infrastructure. We successfully demonstrate 14 wireless channels over a 10Gbps 20km optical link for indoor and outdoor wireless coverage, showing a minimum error value magnitude (EVM) of < 8% and >60dB RF dynamic range. It is believed that the technology provides an ideal solution for last-mile wireless coverage in 5G and beyond.

Molecular signatures of cancer, e.g., genes or microRNAs (miRNAs), have been recognized very important in predicting the occurrence of cancer. From gene-expression and miRNA-expression data, the challenge of identifying molecular signatures lies in the huge number of molecules compared to the small number of samples. To address this issue, in this paper, we propose a heuristic algorithm to identify molecular signatures, termed HAMS, for cancer diagnosis by modeling it as a multi-objective optimization problem. In the proposed HAMS, an elitist-guided individual update strategy is proposed to obtain a small number of molecular signatures, which are closely related with cancer and contain less redundant signatures. Experimental results demonstrate that the proposed HAMS achieves superior performance over seven state-of-the-art algorithms on both gene-expression and miRNA-expression datasets. We also validate the biological significance of the molecular signatures obtained by the proposed HAMS through biological analysis.

With the miniaturization of electronics, Global Navigation Satellite Systems (GNSS) receivers are getting more and more embedded into devices with harsh energy constraints. This process has led to new signal processing challenges due to the limited processing power on battery-operated devices and to challenging wireless environments, such as deep urban canyons, tunnels and bridges, forest canopies, increased jamming and spoofing. The latter is typically tackled via new GNSS constellations and modernization of the GNSS signals. However, the increase in signal complexity leads to higher computation requirements to recover the signals; thus, the trade-off between precision and energy should be evaluated for each application. This paper dives into low-power GNSS, focusing on the energy consumption of satellite-based positioning receivers used in battery-operated consumer devices and Internet of Things (IoT) sensors.We briefly overview the GNSS basics and the differences between legacy and modernized signals. Factors dominating the energy consumption of GNSS receivers are then reviewed, with special attention given to the complexity of the processing algorithms. Onboard and offloaded (Cloud/Edge) processing strategies are explored and compared. Finally, we highlight the current challenges of today’s research in low-power GNSS.

Following the Unlimited Sampling strategy to alleviate the omnipresent dynamic range barrier, we study the problem of recovering a bandlimited signal from point-wise modulo samples, aiming to connect theoretical guarantees with hardware implementation considerations. Our starting point is a class of non-idealities that we observe in prototyping an unlimited sampling based analog-to-digital converter. To address these non-idealities, we provide a new Fourier domain recovery algorithm. Our approach is validated both in theory and via extensive experiments on our prototype analog-to-digital converter, providing the first demonstration of unlimited sampling for data arising from real hardware, both for the current and previous approaches. Advantages of our algorithm include that it is agnostic to the modulo threshold and it can handle arbitrary folding times. We expect that the end-to-end realization studied in this paper will pave the path for exploring the unlimited sampling methodology in a number of real world applications., 10 figures. Manuscript submitted for possible publication. We report a Fourier domain algorithm that is validated on real experiments based on a modulo sampling ADC

One of the main issues restricting the practical efficiency of brain-computer interface (BCI) systems is the inevitable occurrence of physiological artifacts during electroencephalography (EEG) recordings. The effects of the artifacts are, however, mostly discarded in practical BCI systems, due to the time-consuming and complicated computational processes. This paper presents the influences of the artifacts and the efficiency of reducing these influences in a practical BCI. Ocular and muscular artifacts are considered due to the high-amplitude and frequent presence. The paradigm is designed based on the mental controlling of a radio-control (RC) car. Two motor imagery commands, containing the imagination of movement of left/right hand, are used to navigate the BCI-based RC car to turn left/right. The results indicate that the artifacts can highly affect the system performance; reducing their influence significantly improves the efficiency.

In a high-speed digital communication system, jitter performance plays a crucial role in bit-error rate. It is important to accurately derive each type of jitter as well as total jitter and to identify root causes of jitter by jitter decomposition as a key tool. In this paper, we extend our previously reported algorithm to decompose duty cycle distortion as well as other deterministic jitter components including periodic jitter (PJ), intersymbol interference (ISI), and random jitter (RJ), and describe more detail of this algorithm with extensively simulation and measurements to verify the algorithm. The proposed ISI model only requires the time invariant condition, which is true for almost all systems, while the traditional ISI model is valid only with linear time invariant assumption. Compared to the conventional methods, the proposed jitter decomposition method is able to obtain estimated individual jitter component values with good accuracy by using fewer samples of total jitter data. The efficiency and accuracy of the proposed method were demonstrated by simulation and hardware experiments.

A number of research groups have proposed methods for ballistocardiogram (BCG) peak detection toward the identification of individual cardiac cycles. However, objective comparisons of these proposed methods are lacking. This paper, therefore, conducts a systematic and objective performance evaluation and comparison of several of these approaches. Five peak-detection methods (three replicated from the literature and two adapted from code provided by the methods' authors) are compared using data from 30 volunteers. A basic cross-correlation approach was also included as a sixth method. Two high-performing methods were identified: the method proposed by Sadek et al. and the method proposed by Brüser et al. The first achieved the highest average peak-detection rate of 94%, the lowest average false alarm rate of 0.0552 false alarms per second, and a relatively small mean absolute error between the real and detected peaks: 0.0175 seconds. The second method achieved the lowest mean absolute error of 0.0088 seconds between the real and detected peaks, an average peak-detection success rate of 89%, and 0.0766 false alarms per second. All metrics are averaged across participants.

L1-norm principal-component analysis (L1-PCA) is known to attain sturdy resistance against faulty points (outliers) among the processed data. However, computing the L1-PCA of large datasets, with high number of measurements and/or dimensions, may be computationally impractical; in such cases, incremental solutions could be preferred. At the same time, in many applications it is desired to track the signal subspace via principal component adaptation. In this paper, we present new methods for both incremental and adaptive L1-PCA. Our first algorithm computes L1-PCA incrementally, processing one measurement at a time and performing online rejection of possible outliers; due to its low computational and storage cost, this algorithm is appropriate for application to both large and streaming datasets. Our second algorithm combines the merits of the first one with the additional ability to track changes in the nominal signal subspace. The proposed algorithms are evaluated with experimental studies on subspace estimation/tracking, video surveillance, image conditioning, and direction-of-arrival estimation/tracking.

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. In this paper, we consider the problem of estimating and tracking an R-dimensional subspace with relevant information embedded in an N-dimensional ambient space, given that N>>R. We focus on a formulation of the signal subspace that interprets the problem as a least squares optimization. The approach we present relies on the geometrical concepts behind the Affine Projection Algorithms (APA) family to obtain the Affine Projection Subspace Tracking (APST) algorithm. This on-line solution possesses various desirable tracking capabilities, in addition to a high degree of configurability, making it suitable for a large range of applications with different convergence speed and computational complexity requirements. The APST provides a unified framework that generalises other well-known techniques, such as Oja’s rule and stochastic gradient based methods for subspace tracking. This algorithm is finally tested in a few synthetic scenarios against other classical adaptive methods. This work has been supported by the Spanish Ministry of Science and Innovation through project RODIN (PID2019-105717RB-C22 / AEI / 10.13039/501100011033) and by the Catalan Government (AGAUR) under grant 2017 SGR 578.

The non-homogeneous Poisson process provides a generalised framework for the modelling of random point data by allowing the intensity of point generation to vary across its domain of interest (time or space). The use of non-homogeneous Poisson processes have arisen in many areas of signal processing and machine learning, but application is still largely limited by its intractable likelihood function and the lack of computationally efficient inference schemes, although some methods do exist for the batch data case. In this paper, we propose for the first time a sequential framework for intensity inference which combines the non-homogeneous model of Poisson data with continuous-time state-space models for their time-varying intensity. This approach enables us to design efficient online inference schemes, for which we propose a novel sequential Markov chain Monte Carlo (SMCMC) algorithm, as is demanded by many applications where point data arrive sequentially and decisions need to be made with low latency. The proposed approach is compared with competing methods on synthetic datasets and tested with high-frequency financial order book data, showing in general improved performance and better computational efficiency than the main batch-based competitor algorithm, and better performance than a simple baseline kernel estimation scheme.

In this paper, we provide a detailed analysis of the global convergence properties of an extensively studied and extremely effective fixed-point algorithm for the Kullback-Leibler approximation of spectral densities, proposed by Pavon and Ferrante in [Pavon and Ferrante, 2006]. Our main result states that the algorithm globally converges to one of its fixed points., 16 pages, no figures. Corrected a few typos and changed title. To appear in IEEE Transactions on Automatic Control

Increasing penetration of distributed generators (DGs) and intelligent loads necessitates a transactive system, where a distribution system operator (DSO) is required to submit bidding curves for its net demand. This paper proposes a price-maker bidding approach for a DSO that captures distribution power flows, constraints, and uncertainties arising from DGs and loads. A neurodynamic algorithm is proposed to ensure that the bidding curve can be derived in real time. Simulation results justify the need for considering constraints and uncertainties in the bidding model and explain how these factors impact the bidding curve.

Blind multichannel identification is a challenging problem in many domains. The normalized multichannel frequency-domain least-mean-square (NMCFLMS) algorithm was developed to blindly identify a single-input multiple-output acoustic system, which can yield good performance in noise-free environments. However, the robustness of this algorithm to noise has been shown to be problematic. One way to improve the robustness is by applying a constraint on the spectral flatness of the channel impulse responses, which led to the development of the so-called robust normalized multichannel frequency-domain least-mean-square (RNMCFLMS) algorithm. This spectral flatness constraint, however, may not be always proper or reasonable in realistic acoustic environments. In this paper, we develop an $\ell _p$ -norm constraint based robust normalized multichannel frequency-domain least-mean-square ( $\ell _p$ -RNMCFLMS) algorithm. The $\ell _p$ -norm constraint is introduced into the NMCFLMS algorithm to control the effect of different $\ell _p$ -norm penalties on the adaptive filter for the impulse responses with different degrees of sparseness. Numerical and realistic experiments justify the effectiveness of the proposed $\ell _p$ -RNMCFLMS algorithm.

Nowadays, the interest in low-cost and increasingly accurate phasor measurement units (PMUs) for active distribution systems is steadily growing. In this paper, an algorithm for synchrophasor, fundamental frequency, and rate of change of frequency estimation tailored for processing platforms with limited computational resources is described and characterized extensively in terms of both accuracy and processing time. The proposed solution harnesses the main advantages of two state-of-the-art algorithms, i.e., the interpolated discrete Fourier transform and the Taylor-Fourier transform. Such algorithms are combined and implemented in a computationally efficient manner to reduce processing time as much as possible, while ensuring good accuracy in the main testing conditions specified in the IEEE Standard C37.118.1-2011 and its Amendment C37.118.1a-2014. Estimation accuracy has been evaluated not only through simulations but also experimentally. The good consistency between simulation-based and experimental results provides clear evidence that the uncertainty contributions due to transducers, acquisition, and synchronization systems can be reasonably kept under control. The processing times of the algorithm, implemented on an embedded platform suitable for PMU prototyping, are compliant with the mandatory reporting rates of Class M PMUs.

An emerging way to deal with high-dimensional noneuclidean data is to assume that the underlying structure can be captured by a graph. Recently, ideas have begun to emerge related to the analysis of time-varying graph signals. This paper aims to elevate the notion of joint harmonic analysis to a full-fledged framework denoted as time-vertex signal processing, that links together the time-domain signal processing techniques with the new tools of graph signal processing. This entails three main contributions: a) We provide a formal motivation for harmonic time-vertex analysis as an analysis tool for the state evolution of simple partial differential equations on graphs; b) we improve the accuracy of joint filtering operators by up-to two orders of magnitude; c) using our joint filters, we construct time-vertex dictionaries analyzing the different scales and the local time-frequency content of a signal. The utility of our tools is illustrated in numerous applications and datasets, such as dynamic mesh denoising and classification, still-video inpainting, and source localization in seismic events. Our results suggest that joint analysis of time-vertex signals can bring benefits to regression and learning.

In the above paper, Bi and Yu 1 proposed radix algorithms for the type II and III discrete cosine transforms (DCT) of composite sequence lengths. Some corrections in this interesting paper are required, and certain factors of 2 should be introduced or removed. They are derived from (3) to (8) in the paper by utilizing trigonometric identities. a) In equations (9) and (10) of the paper, the in the denominator of the last term should be replaced by . This factor of 2 can be cancelled with the 2 in the numerator of (9).

The paper is concerned with stabilizing the family of adaptive filtering algorithms based on minimizing the $2Lth$ moment of the estimation error, with $L$ being an integer greater than 1. Stabilization is attained via a proposed normalization of the algorithm. Mean square stability of the normalized algorithm is proved for a Markov plant for all $L > 1$ . Transient and steady-state performances of the algorithm are analyzed for a time-invariant plant. Expressions are derived for the steady-state misadjustment and convergence time. Tradeoff between the transient and steady-state performances is evaluated. Dependence of this tradeoff on the value of L is studied. This tradeoff is compared with the tradeoff of the NLMS algorithm. The proposed algorithm is dramatically superior to the NLMS algorithm for sub-Gaussian noise. The superiority increases with L , initial mean-square deviation and signal-to-noise ratio. Analytical results are supported by simulations.

In this paper, the problem of pattern synthesis with antenna arrays is addressed, and new approaches based on the recently developed accurate array response control ( $ {\textrm {A}}^{2}\textrm {RC} $ ) algorithm are presented. It is shown that the array weight vector obtained by the $ {\textrm {A}}^{2}\textrm {RC} $ algorithm to control the normalized response at a single direction in each step belongs to a specific set. Thus, an appropriate weight vector chosen from the intersection of weight vector sets corresponding to the desired responses at multiple directions is capable of simultaneously controlling those responses. This results in the so-called multipoint accurate array response control ( $ {\textrm {MA}}^{2}\textrm {RC} $ ) algorithm. Moreover, in order to avoid possible beam axis shift in pattern synthesis, a modified $ {\textrm {MA}}^{2}\textrm {RC} $ ( $ {\textrm {M}}^{2}{\textrm {A}}^{2}\textrm {RC} $ ) algorithm is proposed by imposing a derivative constraint on the direction of beam axis. Representative numerical examples are provided to demonstrate the effectiveness of the proposed $ {\textrm {MA}}^{2}\textrm {RC} $ and $ {\textrm {M}}^{2}{\textrm {A}}^{2}\textrm {RC} $ algorithms for multipoint responses control and pattern synthesis.

Doppler distortion degrades the performance of the reiterative minimum mean-squared-error (RMMSE) adaptive pulse compression (APC) algorithm. This paper presents a new approach for robust RMMSE Doppler compensation through the use of covariance matrix tapers (RMMSE-CMT). RMMSE-CMT delivers performance comparable to the existing approaches such as Doppler-compensated APC (DC-APC). RMMSE-CMT is simple to implement and offers considerable computational savings over DC-APC. RMMSE-CMT also provides improved Doppler robustness in the fast-APC algorithm.

This paper presents a versatile low-cost procedure for complex permittivity measurements of liquids from 400 MHz to 5 GHz. Our procedure uses handheld instrumentation and custom software for data acquisition and postprocessing. The purpose is to replace the benchtop vector network analyzer (VNA) generally used in such applications with a portable and cheaper handheld spectrum analyzer used in the VNA mode. Postprocessing software based on homomorphic deconvolution is used to remove possible inaccuracies in the permittivity spectra coming from the reduced performance of the handheld VNA with respect to benchtop models. Our measurements are evaluated by comparison with those of the National Physical Laboratory (NPL). The results of the real and imaginary parts of the permittivity spectra of two well characterized organic compounds, ethanediol and 2-propanol, are in very good agreement with the NPL reference standards, showing a relative root-mean-square error (RRMSE) always less than 5%. Low values of the RRMSE, together with an expanded uncertainty less than 3%, ensure that our permittivity measurements are repeatable and accurate. Thanks to its versatility, portability, and at least half of the cost of commercial models, our system is suitable for on-site measurements in different applications, including food quality monitoring and control of medical treatments and biological procedures.

This paper presents a window-based three-dimens-ional (3-D) aggregation technique, which can approximate the surfaces of all kinds of objects, in stereo matching. The 3-D aggregation, which means to aggregate in 3-D surfaces, is implemented by decomposing the adaptive support window into horizontal segments; we allow the disparity to change smoothly in or between segments. Compared to traditional local stereo methods, the 3-D aggregation greatly improves the accuracy of results in slanted surfaces and occlusion areas while keeping excellent performance near depth discontinuities. We also propose an acceleration method that surprisingly improves the accuracy at the same time. The evaluation experiments confirm our achievements.

This paper presents optimal parameter selection and preconditioning of the alternating direction method of multipliers (ADMM) algorithm for a class of distributed quadratic problems, which can be formulated as equality-constrained quadratic programming problems. The parameter selection focuses on the ADMM step-size and relaxation parameter, while the preconditioning corresponds to selecting the edge weights of the underlying communication graph. We optimize these parameters to yield the smallest convergence factor of the iterates. Explicit expressions are derived for the step-size and relaxation parameter, as well as for the corresponding convergence factor. Numerical simulations justify our results and highlight the benefits of optimal parameter selection and preconditioning for the ADMM algorithm.

Radio astronomical observations are increasingly contaminated by RF interference. Assuming an array of telescopes, a previous technique considered spatial filtering based on projecting out the interferer array signature vector. A disadvantage is that this effectively reduces the array by one (expensive) telescope. In this paper, we consider extending the astronomical array with a reference antenna array, and develop spatial filtering algorithms for this situation. The information from the reference antennas improves the quality of the interferer signature vector estimation, hence more of the interference can be projected out. Moreover, since only the covariance data of the astronomical array has to be reconstructed, the conditioning of the problem improves as well. The algorithms are tested both on simulated and experimental data.

In a previous paper, Took et. Al. proposed a quaternion least mean square algorithm for adaptive filters. The gradients can be obtained in two different solutions from the same cost function. The derivation of the gradient is incorrect because the quaternion product is non-commutative. We study the quaternion gradient update rules and derive the right quaternion gradient. The obtained gradient in the correction has only one solution.

Implementation of the least mean squares (LMS) algorithm in finite precision (FP) arithmetic introduces degradations in performance compared to the infinite precision (IP) implementation. This paper derives a new and more accurate analytical result for the expected misadjustment in the LMS weight vector due to FP effects. Whereas previous results have been applicable only during steady state, the results of this present paper are applicable to both the transient adaptation period and steady state. Experimental data are then presented which show the new analytical results to be valid for approximating the weight misadjustment during the transient adaptation period over a wide range of filter and data values.