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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.

This paper analyzes the limits of FFT performance on FPGAs. For this purpose, a FFT generation tool has been developed. This tool is highly parameterizable and allows for generating FFTs with different FFT sizes and amount of parallelization. Experimental results for FFT sizes from 16 to 65536, and 4 to 64 parallel samples have been obtained. They show that even the largest FFT architectures fit well in today's FPGAs, achieving throughput rates from several GSamples/s to tens of GSamples/s.

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.

Following an “unwritten tradition” this paper offers a review of another decade of achievements in the area of Spectral Techniques. Topics like transforms of matrix-valued functions, transforms based on non-Abelian groups, latest advancements related to Linear Independent transforms, a space-efficient algorithm to calculate spectral transforms, will be discussed. A special mention will be given to other areas, like signal processing, and coding theory, where Spectral Techniques have been (and continue to be) used, even though with possibly different names.

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.

RST invarian watermarking scheme is proposed in this paper based on the SIFT feature points and improved pseudo-Zernike moment. Firstly, feature points are detected using SIFT, and some suitable points are selected to form localized feature regions which are scaled to a standard sized. After computing these regions by the improved pseudo-Zernike moments of these regions , the digital watermark are embedded into the host image by quantizing some low-lever moments. Experimental results show this scheme is robust against the rotation, shearing, JPEG compression, noise and filtering attack.

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)

Precise motor control requires high accuracy of the rotor position through the incremental encoder. The speed and accuracy of the acquisition equipment (microcontroller) play an important element in terms of cost and efficiency. In this paper, the author presents alternative methods for speed acquisition from an incremental encoder. In the first stage of research, the main performances of the STM32 microcontroller, connected with an incremental encoder, will be analyzed and compared with two different acquisition systems, i.e., ELVIS II and a Unidrive M701 power inverter. Using the LabVIEW graphical programming language, a user-friendly, convenient, and flexible human–machine interface is designed. Due to the advantages provided by the STM32 microcontroller in terms of processing power, cost, and programming interface, the obtained results are accurate and consistent. Through experimental testing and analysis, the speed acquisition is stable for both developed software algorithms used for ELVIS II and STM32 platforms. It is the aim of the paper to propose a useful speed acquisition tool in low-cost, high-accuracy prototyping applications.

In this paper, a novel method is proposed to achieve high range resolution imaging with orthogonal sub-band frequency diverse array. In the proposed system, the signals transmitted by adjacent subarray has a frequency increment which is close to signal bandwidth. When transmits narrow band signals, this system has the ability of Moving Target Indication (MTI). Meanwhile, this system is capable of high-resolution imaging by splicing spectrum technique. This paper focuses on the application of the proposed method in SAR imaging to obtain high range resolution imaging by spectrum splicing. Simulation results have verified the effectiveness of the proposed method.

The world is living a major shift from information era to artificial intelligence (AI) era. Machines are giving the ability to sense the surrounding world and to take decisions. Computer vision and especially multi-object tracking(MOT), which relies on Deep Learning, is at the heart of this shift. Indeed, with the growth of deep learning, the methods and algorithms that are tackling this problem have gained better performance from the integration of deep learning models. Deep Learning has been demonstrated as MOT, which tackles the challenges of in-and-out objects, unlabeled data, confusing appearance and occlusion. Deep learning, which relied on MOT techniques, has recently gained a fast ground from representation learning to modelling the networks thanks to the advancement of deep learning hypothesis and benchmark arrangement. This paper sums up and analyzes deep learning based MOT techniques which are at a highest level. The paper also offers a comprehensive review about the different techniques applied in MOT of deep learning based on different methods. Furthermore, this study analyzes the benefits and the constraints of current strategies, techniques and methods.

With the expiration of IEEE 754–2008 last 2018, a new standard IEEE 754–2019 has been made with some revisions from the previous standard specifically on the precision formats in which the square root calculations for digital signal processing conforms to this standard. This paper presents a new floating-point square root IP which allows configuration of different precision formats of IEEE 754–2019 - Half, Single, Single Extended, and Double Precisions - and undergoes full FPGA flow. The IP configurability of this study offers wider solutions from low-area applications to high-end processing. This paper uses modified non-restoring algorithm and employs pipelining to achieve a high-speed requirement of greater than 100 MHz for all formats. Synthesis and verification assessments are done in Lattice CrossLink-NX (LIFCL-40) FPGA using Lattice Radiant Software 2.1.

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.

The paper presents a discussion on the issue of possible acceleration of radiolocation signal processing algorithms in seekers using graphics processing units. A concept and implementation examples of algorithms performing digital data filtering on general purpose central and graphics processing units are introduced. The results of performance comparison of central and graphics processing units during computing discrete convolution are presented at the end of the paper.

The detection of the driver’s face is an important part of the active safety system for advanced driver assistance systems (ADAS) and this task becomes challenging and complex when normal visual cameras are used in poor illumination. This paper incorporates the adaptive attenuation quantification retinex (AAQR) method to improve the details of low and poorly illuminated images and videos with the Viola-Jones face detection algorithm. This paper also makes a comparative study of the implementation of the proposed solution and analyzes its performance on three different datasets: EBDD, 3MDAD IR2, and our self-generated validation dataset. The results have shown that when AAQR is combined with Viola-Jones, the face detection has been improved in the range of 11.18% to 24.21% for different illumination categories of EBDD, 16.71% for 3MDAD IR2 (night), and 46.79% for validation datasets.

Nowadays high-performance computing is gradually implementing Exa-scale computing, and the performance of single node has reached several T-flops. Communication problem has become one of the main concerns of parallel matrix multiplication algorithms. This paper surveys the research on PMM algorithms on supercomputers around the world. We first stress the significance of PMM (Parallel Matrix multiplication) algorithms in high performance scientific computing, followed by a background introduction on widely employed PMM algorithms. We introduce the researches on parallel techniques for PMM algorithms from two aspects: classical parallel matrix multiplication and Strassen’s fast matrix multiplication. Further, we present the detailed complexity analysis comparison of each algorithm. Finally, we summarize the paper and discuss potential directions of future work.

Ship detection methods which can be used in all kinds of complex weather is of great significance to the waterborne traffic. This paper uses the algorithm of dark channel estimation to simulate a data set under complex weather conditions naturally by small-scale dataset contain images mostly taken on sunny days. Besides, this paper compares several detectors based on CNN and different structure of feature extraction network. Finally, this paper gets a ship detection model that can achieve great performance under both original small-scale dataset and extended dataset with simulated complex weather condition images.

In this paper, we consider the downlink (DL) of a zero-forcing (ZF) precoded extra-large scale massive MIMO (XL-MIMO) system. The base-station (BS) operates with limited number of radio-frequency (RF) transceivers due to high cost, power consumption and interconnection bandwidth associated to the fully digital implementation. The BS, which is implemented with a subarray switching architecture, selects groups of active antennas inside each subarray to transmit the DL signal. This work proposes efficient resource allocation (RA) procedures to perform joint antenna selection (AS) and power allocation (PA) to maximize the DL spectral efficiency (SE) of an XL-MIMO system operating under different loading settings. Two metaheuristic RA procedures based on the genetic algorithm (GA) are assessed and compared in terms of performance, coordination data size and computational complexity. One algorithm is based on a quasi-distributed methodology while the other is based on the conventional centralized processing. Numerical results demonstrate that the quasi-distributed GA-based procedure results in a suitable trade-off between performance, complexity and exchanged coordination data. At the same time, it outperforms the centralized procedures with appropriate system operation settings., Comment: 31 pages, 8 figures, 4 tables, full research paper

Pipeline leak detection technology is one of the core issues in the field of pipeline safety research. Based on the classification of hardware-based and software-based methods, this paper summarizes the current mainstream pipeline leak detection technology at home and abroad, and expounds the mechanism, advantages and disadvantages and applicable conditions of each detection technology. This paper mainly introduces the pipeline leak detection technology based on DAS system, summarizes the current detection technology of this system at home and abroad, and proposes to apply VMD composite algorithm and deep learning neural network to pipeline leak detection. This paper also puts forward the design of pipeline leakage monitoring platform based on DAS system and the prospect of applying this system to pipeline monitoring in petrochemical wharf.

Transport is one of the pillars of the economy of any country: every year government and car/vehicle manufacturers spending a lot of money on road safety. But unfortunately, crashes on roads cause various problems for the government. The main factors that cause fatal crashes, grievous injuries, and deaths are of varied nature. The type of roads, the structure of ways, weather conditions, and behaviour of the driver are a few pertinent factors that are studied and relationship identified. This paper describes a device that is capable to locate crashes early. In case if a crash occurs, the device can recognize and send information to the nearest hospital and police station. There are two acoustic signals analysed, vehicle crash sound and human distress call (generated during crashes/accidents). For recognition of both the sounds, this paper describes an algorithm based on acoustic signal processing. The algorithm was tested in the lab and it is robust and the efficiency of the algorithm 94.7% to detect a collision.

Winograd’s algorithms are an effective tool for calculating the discrete Fourier transform (DFT). These algorithms described in well-known articles are traditionally represented either with the help of sets of recurrent relations or with the help of products of sparse matrices obtained on the basis of various methods of the DFT matrix factorization. Unfortunately, in the mentioned papers, it is not shown how the described relations were obtained or how the presented factorizations were found. In this paper, we use a simple, understandable and fairly unified approach to the derivation of the Winograd-type DFT algorithms for the cases N = 8, N = 16 and N = 32. It is easy to verify that algorithms for other lengths of sequences that are powers of two can be synthesized in a similar way.

This paper considers the resilient consensus in multi-dimensional spaces, where some of the agents might be misbehaving. Despite the influence of network misbehaviors, the normal agents aim to reach an agreement within the convex hull of their initial states. In particular, an Event-based Safe Kernel (E-SK) protocol is developed in this paper, which increases the network resilience while saves its communication costs. In the algorithm, each normal agent computes a "safe kernel" based on its neighboring information, and updates its state towards a point inside this kernel at every step. With the event-based mechanism, the updated state would be sent to its neighbors only when it sufficiently changes since the last transmission. Assuming the number of malicious nodes is upper bounded, we provide sufficient topological conditions for E-SK to guarantee the resilient consensus while reducing the unnecessary communications. Numerical examples are provided in the end to validate the theoretical results.

Wireless sensor network (WSN) can be applied for sensing, collecting, processing and transmission of information. Most of the applications of WSNs are a few issues of localization problem. To minimize the drawback of basic Distance Vector-Hop algorithm, a Particle Swarm Optimization (PSO) based Distance Vector-Hop localization algorithm is presented in this paper. The validation of results are complied by simulation. Error variance, error accuracy with localization error have analyzed with the performance of implemented in Distance Vector algorithm. In this paper, error localization and variance are decreased and accuracy is increased.

Mine detection techniques require extremely high detection rates. In this paper, enhancement in a signal processing algorithm is applied in the ground penetrating radar (GPR) in an ongoing research. In GPR microwave signals penetrate into the ground until a buried object reflects them back and the reflected signals are processed in order to extract information about the target. The main purpose of this paper is to apply an algorithm that provides a solution to the main problem of GPR application, which is the strong reflections from non-homogeneous clutter and other reflections from underground rocks and other materials. In this work the synthetic aperture radar (SAR) processing is applied to enhance resolution and SAR matrix is formed as GPR moves to the next position, followed by adaptive reference subtraction. Afterwards, a sand surface removal, reference subtraction and transition detection are applied in order to emphasize the weak reflections from the buried plastic landmine only and remove other reflections. The results obtained show that the algorithm worked well in the presence of high noise and non-homogenous clutter environment.

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.

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.

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.

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

The variable nature of wind power and the associated ramp events poses a number of operational challenges to grid operators, especially under high penetration of wind energy. These challenges typically relate to system stability, frequency control and dispatch. The adverse impacts of wind power ramps can be mitigated in practice through optimal scheduling and dispatch of flexible reserves and rapid response ancillary services. This, however, requires appropriate ramp detection algorithms, together with accurate ramp forecasting. This paper proposes a novel multi-parameter segmentation algorithm for the detection of wind power ramps. Ramp detection results are presented for a utility-scale wind farm, and the performance of the proposed algorithm is compared with existing algorithms, including the L1-ramp detect with sliding window and the optimized swinging door algorithm. The results show that the proposed algorithm is superior, particularly with reference to criteria such as ramp detection accuracy, computational expedience and ramp start- and end-point accuracy.

This paper presents an integrated 90-nm CMOS low-energy dual-channel multiplier based on the serial/parallel scheme. Moreover, a comparative study of different multipliers is accomplished at 90 nm CMOS technology. Due to the tendency of the market to minimize devices' size to make them portable, small implementation area is considered by using the dual-channel multiplier. In this paper, a new implementation of multiplication process comes to exceed the battery lifetime by decreasing the power consumption by up to 93%. Furthermore, there is a reduction in the area by up to 67% as compared with previous work which makes it more appropriate for low power handheld devices applications. Also, great saving in energy is attained by using the proposed scheme when performing complex multiplications task.

The Internet of Things (IoT) has been widely used in care facilities such as hospitals and nursing homes. These facilities make efficiently health management of its patient or the elderly by monitoring vital signs using the IoT. The non-contact vital sensing is particularly interesting as a measuring method for monitoring since it does not need electrodes. It enables us to release patients from constraints by cables and electrodes. The Doppler sensor is a mean of the non-contact vital sensing. We can detect respirations and heart beats by the electro-magnetic wave. Considering the non-contact vital sensing using the Doppler sensor, there are two problems. First, vital signs obtained by the Doppler sensor is often noisy. Therefore, the signal processing algorithm must be robust against noisy vital signs. Second, the frequency of vital signs is typically very low such like less than 1 Hz. It is well-known that the fast Fourier transform (FFT) suffer from low resolution in such very low frequency band. In this paper, we propose a new signal parameter estimation algorithm which is robust against the noise while it achieves high resolution in the low frequency band. The performance of the proposed method is provided in this paper.

This paper proposes the novel imaging algorithm based on the range points migration (RPM) for the terahertz (THz) subsurface three-dimensional imaging. The inherent problem in the optical lens-based THz imaging system is the depth dependency for the azimuth resolution, and it is fatal, if the depth of object is not available in such as subsurface imaging issue. This paper newly introduces the range points migration (RPM) based auto-focusing algorithm, which is suitable for general THz-time domain spectroscopic (THz-TDS) measurement. The effectiveness of the proposed method has been validated through the THz-TDS measured data.

In this paper, FMICW radars and processing of the signals from these systems are described. Main part of the paper is a system for automatic analysis of the signals. An algorithm is applied on the two-dimensional Fast Fourier Transform. In paper, the complication caused by the signal processing and their removal are described.

When unbalances happen among the cells of a cascaded H-bridge (CHB) converter, variable angle phase-shifting (VAPS) pulse-width modulation (PWM) methods can be employed to improve the total harmonic distortion (THD) performance. However, as conventional methods are based on optimization algorithms which require a huge amount of evolutionary computations, the optimal carrier phase-shifting (PS) angles cannot be timely updated with a common standard digital signal processor (DSP) or microcontroller. During the period when the optimization is not finished, the THD of the total output voltage may significantly increase, bringing about higher power losses. Thus, a computation-efficient VAPS PWM method is proposed in this paper. With multiple calculation units (CUs) implemented in field programmable gate arrays (FPGAs), multiple calculations can be executed in parallel, significantly improving the optimization speed of the VAPS PWM method. Experimental results on a 4-cell CHB converter have demonstrated the effectiveness of the proposed method.

Unsaturated soils are prevalent in nature and significantly influence the performance of geotechnical infrastructure (e.g., highway embankments, landfills, shallow foundations, trench excavations, natural slopes, etc.) and issues of national defense and security. Infrastructure failures resulting from unsaturated soil issues result in significant economic damages and are becoming an increasing concern as infrastructure ages world-wide. Unsaturated soils are not only critical for geotechnical engineering problems but are also encountered in other geo-related scientific fields, e.g., geophysics, geoenvironmental and geochemical soil sciences. The understanding and theoretical development of unsaturated soil mechanics and impacts thereof is a very complex problem involving coupled water/air flows and solid skeleton deformations. Experimental techniques required to validate unsaturated soil constitutive models, signal processing algorithms, and approaches to measure thermo-hydro-mechanical behavior create the foundational basis for much of the scientific exploration. Often the physical testing of unsaturated soils is highly complex, requiring high quality physical validation that replicates in situ boundary conditions while controlling and measuring unsaturated soil parameters. Moreover, high quality experimentation can take months to complete a single test, with comprehensive datasets taking a year or longer to compile. A significant scientific focus over the last two decades has been in understanding the coupled thermo-hydro-chemo-bio-mechanical behavior of unsaturated soils. However, there has yet to be a comprehensive review and evaluation of the limitations and advantages of unsaturated laboratory equipment, experimental techniques, collections of high-quality unsaturated soil data, and analytical techniques used for data interpretation. As unsaturated soil mechanics is applied to evermore complex problems, the need for collection and critical review of such information has become a necessity. This special issue of ASTM International’s Geotechnical Testing Journal brings together 18 papers that illustrate recent significant advancements in physical equipment and experimental methodologies used to measure the thermo-hydro-chemo-bio-mechanical properties of unsaturated soils. This collection of papers covers four main topics: thermally controlled unsaturated soil testing, improved methods for measuring mechanical behavior, dynamic response and testing, and osmotic and diffusive testing. Advances described in this issue in thermally controlled experimentation include new methods to determine the critical soil temperature where coupled heat and moisture outflow is balanced by inward moisture flow induced by the suction gradient, as well as modified environmental chambers to investigate higher total suction limits and low stress environments. Papers on improved methods for measuring hydro-mechanical behavior focus on improved unsaturated triaxial testing and novel means to decrease the overall testing time without inducing an experimental bias within the data. Papers investigating the dynamic response and testing of unsaturated soils highlight advances in cyclic loading, characterization of seismic compression, and microbial induced desaturation for improved dynamic properties. Papers that address the nature of osmotic and diffusive testing of unsaturated soils illustrate improvements to vapor equilibrium testing, methane hydrate detection, and permeable clay lining experimentation. The editors of this special issue would like to gratefully acknowledge the efforts of all the authors and peer reviewers of these papers, and the dedication of the publication staff in producing this special issue.

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.

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.

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 introduces a new signal processing algorithm for vibration-based fault detection and diagnosis of roller bearings. The methodology proposed in this paper is based on the combination of two data-adaptive techniques which are further enhanced through the use of an automatic feature identification mechanism. The new technique, introduced as empirical mode envelope with minimum entropy, combines elements from the empirical mode decomposition (EMD) and minimum entropy deconvolution (MED) approaches with an energy moment technique to improve the feature selection stage of the EMD algorithm. This improvement allows the processing chain to identify early stage roller bearing faults in noisier signals. The energy moment technique is used to automatically identify the most appropriate intrinsic mode function from the EMD process prior to the MED algorithm being applied. This is in contrast to conventional approaches which tend to use the first mode or make selections based on traditional energy techniques. The combination of the adaptive techniques of EMD and MED allows the development of an improved technique for fault detection and diagnosis of signals. Combining these techniques with the energy moment approach allows further improved fault detection in complex non-stationary conditions. The processing chain has been tested using data obtained during laboratory testing. From the experimental results, it is shown that the new technique is capable of the detection of early stage (minor) roller and outer race defects found in tapered-roller-bearings rotating at a variety of speeds and noise scenarios.

Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise. Thus, random error processing is essential for proper elimination of artifact signals and improvement of the accuracy and reliability from such sensors. In this paper, a systematic review is carried out by investigating different random error signal processing models that have been recently developed for MEMS inertial sensor precision improvement. For this purpose, an in-depth literature search was performed on several databases viz., Web of Science, IEEE Xplore, Science Direct, and Association for Computing Machinery Digital Library. Forty-nine representative papers that focused on the processing of signals from MEMS accelerometers, MEMS gyroscopes, and MEMS inertial measuring units, published in journal or conference formats, and indexed on the databases within the last 10 years, were downloaded and carefully reviewed. From this literature overview, 30 mainstream algorithms were extracted and categorized into seven groups, which were analyzed to present the contributions, strengths, and weaknesses of the literature. Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. Finally, the development trend of MEMS inertial sensor technology and its application prospects were presented.

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.

The “cocktail party problem” refers to the ability of human listeners to separate the acoustic signal reaching their ears into its individual components, corresponding to individual sound sources in the environment. Despite this phenomenon appearing trivial for humans, solving the cocktail party problem computationally remains an ambitious challenge. The approach used in this paper takes inspiration from human strategies for separating an acoustic environment into distinct perceptual auditory streams. A series of time-frequency-based features, analogous to those thought to emerge at various stages in the human auditory processing pathway, are derived from biaural auditory inputs. These feature vectors are used as inputs to an unsupervised cluster analysis used to group feature values that are assumed to correspond to the same object. Reconstructed auditory streams are then correlated to the original components used to create the auditory scene. Our model is capable of reconstructing streams that correlate to the original components (r = 0.3-0.7) used to create the complex auditory scene. The success of the reconstructions is largely dependent on the signal-to-noise ratio of the components of the auditory scene.

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.

Co-evolutionary path optimization (CEPO) was recently proposed to calculate optimal actual traveling trajectory in a given dynamic routing environment, and it was originally focused on the 1st shortest path. This paper, by taking advantage of a relatively new nature-inspired method, i.e., ripple-spreading algorithm (RSA), attempts to develop an effective algorithm to calculate the k shortest paths for CEPO in a given dynamic routing environment, with a theoretical guarantee of optimality. Existing methods for static k shortest paths problem (k-SPP) often need to reconstruct route networks or sweep the original network for k times. Existing methods for the 1st shortest path in a given dynamic routing environment usually need a memory-expensive hyper-graph to guarantee optimality. This paper needs none of above measures, but by just a single run of ripple relay race on an original-sized network, can well resolve the k-SPP of CEPO. Experimental results demonstrate that the reported RSA can effectively address the k-SPP for CEPO.

This paper considers passive multistatic radar with Global Navigation Satellite System (GNSS) transmissions (such as GPS and Galileo) for target localization. The paper briefly describes the appropriate signal processing algorithm, which is essentially a multi-lateration technique. However, particular emphasis is given on the experimental confirmation of the system, obtained through a proof-of-concept experimental campaign where a marine target was simultaneously detected by 11 transmitters and a single receiver.

This paper proposes a new algorithm to estimate a disparity map. This map contains depth information from stereo matching process. Generally, this process is sensitive to low texture areas and high noise on images with high different brightness and contrast. To get over these problems, the proposed algorithm utilizes the RGB channels at the matching stage and edge-preserving filter at the second and final stage. The filter is known as Guided Filter (GF). The GF kernel well-recovered low texture areas which is able to reduce noise and sharpen the images. Additionally, GF is strong against the distortions due to high brightness and contrast. The propose algorithm produces accurate results on the disparity map for the low textured regions. The proposed work in this paper produces accurate results and performs much better compared to some established algorithms based on the quantitative and qualitative measurements using standard stereo benchmarking evaluation from the Middlebury.

This paper presents a novel emulated photovoltaic (PV) source that was constructed by using an indoor solar panel and an external current and voltage source. The mathematical model and electrical characteristics of the emulated PV source were defined and analyzed in detail in the paper. Measures for eliminating the errors of the emulated PV source was also proposed and demonstrated by experiment. The constructed PV source has the advantages of high bandwidth over the switching circuit-based PV simulators. The constructed PV source has used for testing various power electronics converters and various control techniques effectively in laboratory environments for researchers and university students.