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We propose a novel interference alignment (IA) technique with receive antenna partitioning (RAP) for simultaneous wireless information and power transfer (SWIPT)-enabled downlink fog radio access networks (F-RANs). The basic idea of the proposed scheme is to apply a generalized downlink IA for each access point (AP) and to divide receive antennas at each user into two groups for information decoding (ID) and energy harvesting (EH), respectively. With the proposed scheme, an optimal receive antenna configuration at each user is obtained to maximize the weighted sum of achievable rate and the sum of harvested energy of the network. Also we develop an antenna configuration selection algorithm to obtain near-optimal performance with low computational complexity. Through extensive computer simulations, it is shown that the proposed scheme significantly improves a rate-energy region compared with existing techniques.

Recently, the correlation filter (CF) and Siamese network have become the two most popular frameworks in object tracking. Existing CF trackers, however, are limited by feature learning and context usage, making them sensitive to boundary effects. In contrast, Siamese trackers can easily suffer from the interference of semantic distractors. To address the above problems, we propose an end-to-end target-insight correlation network (TICNet) for object tracking, which aims at breaking the above limitations on top of a unified network. TICNet is an asymmetric dual-branch network involving a target-background awareness model (TBAM), a spatial-channel attention network (SCAN), and a distractor-aware filter (DAF) for end-to-end learning. Specifically, TBAM aims to distinguish a target from the background in the pixel level, yielding a target likelihood map based on color statistics to mine distractors for DAF learning. SCAN consists of a basic convolutional network, a channel-attention network, and a spatial-attention network, aiming to generate attentive weights to enhance the representation learning of the tracker. Especially, we formulate a differentiable DAF and employ it as a learnable layer in the network, thus helping suppress distracting regions in the background. During testing, DAF, together with TBAM, yields a response map for the final target estimation. Extensive experiments on seven benchmarks demonstrate that TICNet outperforms the state-of-the-art methods while running at real-time speed.

Wave run-up on surface-piercing columns is an important phenomenon in offshore engineering. Previous studies have mainly focused on a single column and circular column arrays. This study is concerned with the wave run-up ratio and the wave force on a fixed array of four rounded square columns. A series of regular wave tests were conducted, and the effects of wave periods and steepness were investigated. The nonlinearity of the wave run-up under incident waves with a small period is more sensitive to wave steepness. Utilizing empirical mode decomposition (EMD) and fast Fourier transform (FFT) methods, the measured signals were separated into three components representing characteristics of the incident wave component (IMF1), near-field interference (IMF2), and far-field low frequency reflected wave (IMF3), respectively. The proposed methods are helpful for studying the mechanism of near-field interference, and the experimental data are essential benchmarks for validating further numerical simulations of wave run-ups on rounded square columns.

Stiffened panels have been widely utilized in fuselages and wings as critical load-bearing components. These structures are prone to be damaged under long-term and extreme loads, and their health monitoring has been a common concern. The guided wave-based monitoring method is regarded as an efficient approach to detect the damage in stiffened plates because of its wide monitoring range and high sensitivity to micro-damage. Efficient simulation of wave propagation can theoretically demonstrate the detection mechanism of the method. In this study, a Time-Domain Spectral Finite Element Method (TD-SFEM) is adopted to study the wavefield in stiffened plates, where continuous Absorbing Layers with Increasing Damping (ALID) strategy is proposed to circumvent the disturbance of reflected waves on boundaries. After the convergence analysis, the developed TD-SFEM with ALID is validated by the finite element method first. Then, wave scattering and the influence of the stiffener are investigated in detail by comparing the results with the non-stiffened structure. Finally, the effects of the parameters of the stiffener, such as the height and width, on wave propagation are studied, respectively. The results illustrate that the proposed TD-SFEM with ALID is an efficient approach to study the wave propagation in the stiffened plate and can reveal the mechanism of influence of the stiffener. It is found that the height of the stiffener changes the interference of wavefield in the plate, while the effects of the width are mainly in wave scattering and mode conversion.

Conflict tasks have become one of the most dominant paradigms within cognitive psychology, with their key finding being the conflict effect: That participants are slower and less accurate when task-irrelevant information conflicts with task-relevant information (i.e., incompatible trials), compared to when these sources of information are consistent (i.e., compatible trials). However, the conflict effect can consist of two separate effects: Facilitation effects, which is the amount of benefit provided by consistent task-irrelevant information, and interference effects, which is the amount of impairment caused by conflicting task-irrelevant information. While previous studies have attempted to disentangle these effects using neutral trials, which contrast compatible and incompatible trials to trials that are designed to have neutral task-irrelevant information, these analyses rely on the assumptions of Donder's subtractive method, which are difficult to verify and may be violated in some circumstances. Here, we develop a model-based approach for disentangling facilitation and interference effects, which extends the existing diffusion model for conflict tasks (DMC) framework to allow for different levels of automatic activation in compatible and incompatible trials. Comprehensive parameter recovery assessments display the robust measurement properties of our model-based approach, which we apply to nine previous data sets from the flanker (6) and Simon (3) tasks. Our findings suggest asymmetric facilitation and interference effects, where interference effects appear to be present for most participants across most studies, whereas facilitation effects appear to be small or nonexistent. We believe that our novel model-based approach provides an important step forward for understanding how information processing operates in conflict tasks, allowing researchers to assess the convergence or divergence between experimental-based (i.e., neutral trials) and model-based approaches when investigating facilitation and interference effects. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Recently, reconfigurable intelligent surface (RIS) has attracted continuous attention in wireless communications. Because the normal incidence condition of electromagnetic waves cannot always be satisfied in practical applications, the angular insensitivity in RISs has become an important issue. The angular sensitivity in RISs may lead to the failure of RIS-assisted wireless communication networks that rely on the reciprocity of wireless channels. In this work, an angle-insensitive 3-bit RIS meta-atom is proposed. By introducing metallic vias in the substrate of programmable meta-atoms, the interference induced by multiple reflections in the multilayered structure is restrained, and the decoupling between adjacent meta-atoms makes the metasurface insensitive to the incident angle. Hence the angular insensitivity can be achieved. An RIS is fabricated using the proposed angleinsensitive programmable meta-atom with metallic vias, which is capable of maintaining stable phase and amplitude responses at oblique incidences. Both simulated and measured results show stable angle performance. A maximum of 3150 phase range and eight digital coding states with 450 stable interval are obtained for wide incidence angles from 00 to 600. Finally, numerical simulations and experimental results of anomalous reflections are performed to verify the angular reciprocity of the proposed RIS, which is important in wireless communications.

Infrared (IR) small target detection is one of the key technologies of infrared search and track (IRST) systems. Existing methods have some limitations in detection performance, especially when the target size is irregular or the background is complex. In this paper, we propose a pixel-level local contrast measure (PLLCM), which can subdivide small targets and backgrounds at pixel level simultaneously. With pixel-level segmentation, the difference between the target and the background becomes more obvious, which helps to improve the detection performance. First, we design a multiscale sliding window to quickly extract candidate target pixels. Then, a local window based on random walker (RW) is designed for pixel-level target segmentation. After that, PLLCM incorporating probability weights and scale constraints is proposed to accurately measure local contrast and suppress various types of background interference. Finally, an adaptive threshold operation is applied to separate the target from the PLLCM enhanced map. Experimental results show that the proposed method has a higher detection rate and a lower false alarm rate than the baseline algorithms, while achieving a high speed.

Memory representations can be stored in a passive state in a visual working memory (VWM) task. However, it remains unclear whether the representations stored in the passive state are prone to interference and decay. To explore this issue, we asked participants to successively remember two sets of memory items (M1 and M2) in three test manners: a combined test (both M1 and M2 are probed simultaneously), a backward test (probe M2 first and M1 second), or a forward test (probe M1 first and M2 second). We found that the contralateral delay activity (CDA) amplitude after the onset of M2 only tracked M2 independently of M1 in the two separate tests (Experiments 1-3), and the accuracy of M1 was well above chance. These results implied that the M1 representations had been transferred from the online state into the passive state after the onset of M2. Furthermore, the accuracy of M1 (two representations were transferred from the online state into the passive state and retrieved later) in the backward test was worse than M2 (2 representations in the online state throughout) in the backward test (Experiments 1-2), but was comparable to M1 (two representations were transferred from the online state into the passive state and retrieved first) in the forward test (Experiment 2). These results demonstrated that the memory representations were impaired during state switching. Importantly, once the representations had been stored in the passive state, they were robust with little memory loss during latent retention. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Identifying a spectral frequency band with abundant fault information from spectral coherence is essential for improved envelope spectrum-based bearing diagnosis. Both blind features and targeted features have been employed to distinguish informative spectral frequency band of spectral coherence. However, how to select appropriate feature to correctly discriminate the optimal frequency band of spectral coherence in different scenarios is problematic. In this study, a new targeted feature is presented to quantify the signal-to-noise ratio in narrow frequency bands of spectral coherence, and further a method based on the proposed feature is developed to distinguish an optimal spectral frequency band of spectral coherence for bearing diagnostics. The efficiency of the developed method, typical blind feature-based methods and typical targeted feature-based methods in identifying the defect-sensitive frequency band of spectral coherence and bearing fault diagnosis is validated and compared using simulated signals with different interference noises and bearing experimental signals. The advantages and limitations of typical blind and targeted feature-based methods in different scenarios are summarized to guide the application. The results demonstrate that the developed targeted feature can efficiently evaluate bearing failure information in the cyclic frequency domain, and the presented approach can accurately discriminate the failure-related spectral frequency band of spectral coherence and detect different bearing faults compared with the methods based on the state-of-the-art features.

Bushing plays a significant role in insulation and mechanical support for power transformers. Its working state is closely related to the reliable operation of power transformers. To overcome the electromagnetic interference and security risks during sensor installation, a Sagnac interference optical technique for partial discharge (PD) detection is proposed in this paper. By establishing a simulation model of the sensing structure, it is verified that output signal amplitude varies periodically with the length of sensing and delay fibers. Moreover, the output value decreases with increasing probe radius. The results show that a delay fiber of 1.5 km, a sensing fiber of 3 m, and a bending radius of 5 mm are optimal parameter combinations to detect PD in a 72.5 kV defective bushing. In addition, consistent detection waveforms induced by both internal and external defects are obtained by the optical sensor and high frequency current transformer (HFCT) simultaneously, then the effectiveness of the optical sensor on detecting PD signals for bushing is verified. It is of great help to carry out online monitoring of bushing insulation defects through optical non-destructive installation.

Signal time-frequency analysis is widely used in industrial fields, for example, machinery fault diagnosis. Current methods cannot well address the multi-components signals with crossing frequency trajectories. For example, the adaptive decomposition methods have mode mixing problem. The linear transforms have smear problems due to the mutual interference caused by crossing frequencies. To address these issues, this paper proposes an iterative approach, named adaptive linear chirplet transform (ALCT). It works by 1) calculating the signal linear chirplet transform (LCT) with a series of chirp rates as parameters; 2) iteratively estimating the characteristics of the signal mode with the highest amplitude in the residual LCT and updating the residual LCT by removing the LCT of the detected mode; 3) constructing the signal time-frequency representation (TFR) based on the estimated characteristics of the detected modes. The ALCT can clearly reveal the TFRs of signals with crossing frequency trajectories. It does not have the frequency estimation errors caused by mode mixing and avoids the mutual interference of the crossing frequencies. It is efficient, as it can obtain the LCT of the detected mode by directly weighting and shifting the LCT reference. The effectiveness of the ALCT is validated using simulations and real-world signals.

Uncertainty is inevitable in the decision-making process of real applications. Quantum mechanics has become an interesting and popular topic in predicting and explaining human decision-making behaviors, especially regarding interference effects caused by uncertainty in the process of decision making, due to the limitations of Bayesian reasoning. In addition, complex evidence theory (CET), as a generalized Dempster-Shafer evidence theory, has been proposed to represent and handle uncertainty in the framework of the complex plane, and it is an effective tool in uncertainty reasoning. Particularly, the complex mass function, also known as a complex basic belief assignment in CET, is complex-value modeled, which is superior to the classical mass function in expressing uncertain information. CET is considered to have certain inherent connections with quantum mechanics since both are complex-value modeled and can be applied in handling uncertainty in decision-making problems. In this article, therefore, by bridging CET and quantum mechanics, we propose a new complex evidential quantum dynamical (CEQD) model to predict interference effects on human decision-making behaviors. In addition, uniform and weighted complex Pignistic belief transformation functions are proposed, which can be used effectively in the CEQD model to help explain interference effects. The experimental results and comparisons demonstrate the effectiveness of the proposed method. In summary, the proposed CEQD method provides a new perspective to study and explain the interference effects involved in human decision-making behaviors, which is significant for decision theory.

We develop a scheme for the computation of the full-counting statistics of transport described by Markovian master equations with an arbitrary time dependence. It is based on a hierarchy of generalized density operators, where the trace of each operator yields one cumulant. This direct relation offers a better numerical efficiency than the equivalent number-resolved master equation. The proposed method is particularly useful for conductors with an elaborate time-dependence stemming, e.g., from pulses or combinations of slow and fast parameter switching. As a test bench for the evaluation of the numerical stability, we consider time-independent problems for which the full-counting statistics can be computed by other means. As applications, we study cumulants of higher order for two time-dependent transport problems of recent interest, namely steady-state coherent transfer by adiabatic passage and Landau-Zener-St\"uckelberg-Majorana interference in an open double quantum dot., Comment: 7 pages, 5 figures

In daily pipeline inspection, it is significant to ensure good network communication and security. With the development of drone technology, it is possible to apply drones as air routers to collect information from pipeline networks and transmit it to pipeline inspectors. It is also crucial to achieve optimal drone deployment in pipeline networks. This article proposes a two-phase evolution optimal 3-D drone layout algorithm to deploy drones in pipeline networks. First, a 3-D pipeline graph model is designed to represent the possible projection position of drones, and the objective function is proposed for optimal drone deployment. Then, in the first phase, based on the features of the 3-D pipeline graph, the drone flight rules and constraint conditions are presented to calculate the number of drones and the initial layout sequence. In the second phase, according to the objective function and the above results, every drone is continuously moved in a small area to achieve a tradeoff between signal coverage and interference. Moreover, the key parameters of the objective function can be discussed to further optimize drone deployment. Simulation results are presented to illustrate the effectiveness and advantages of the proposed algorithm.

In this paper, the estimation problem of Toolface is investigated for the dynamic point-the-bit rotary steerable system (DPRSS), and it is essentially a nonlinear filtering problem under strong interference. Firstly, the DPRSS is modeled by a continuous-time nonlinear system which involves polynomial nonlinearities and strong interference whose amplitude is much larger than that of system states. By employing the Carleman approximation approach, a linear parameter varying system is derived from the polynomial nonlinear system, and a novel polynomial filter is constructed. Subsequently, several sufficient conditions in terms of parameter-dependent linear matrix inequalities (PDLMIs) are obtained to ensure the input-to-state stability of the filtering error system. Furthermore, the filter parameter can be calculated by solving PDLMIs through the sum-of-squares decomposition method. Finally, an experimental study on the DPRSS prototype is provided to show the effectiveness and applicability of the developed filtering scheme.

Seabed reflection and scattering measurements were conducted at the New England Mud Patch to better understand the acoustic properties of fine-grained (muddy) sediments. The measurement philosophy and the measurements themselves are summarized. In addition, geoacoustic information accessed directly from the data in the time and frequency domains is presented. The main result is the existence of an angle of intromission. This observation proves that the mud sound speed is less than that of the water and yields a sediment sound speed ratio 0.9865 with outer bounds {0.985 0.989}. Another result is the observation of strong scattered arrivals from within the mud volume at/near normal incidence but not at low grazing angles. These are likely due to anisotropic sediment heterogeneities with a large horizontal to vertical scale. Evidence is also presented for a highly heterogeneous mud–sand horizon with lateral variability down to scales of order meters. Finally, the reflection measurements successfully capture Bragg interference patterns. Their importance is their substantial geoacoustic information content, which can be accessed by several inversion methods.

Identification of transmit-antenna number is of importance in cognitive Internet of Things with multiple-input multiple-output (MIMO). Previous studies on transmit-antenna number detection only consider Gaussian noise and ignore impulsive interference. In the practical wireless communication, impulsive interference may exist due to low-frequency atmospheric noise, multiple access and electromagnetic disturbance. Such interference can usually be modeled as symmetric alpha stable (SαS), which cause the performance degradation of conventional algorithms based on Gaussian model. In this paper, we present a novel scheme to detect the transmit-antenna number for MIMO systems in cognitive Internet of Things, assuming that signals are corrupted by both SαS interference and Gaussian noise. We first introduce a new approach to characterize the generalized correlation matrix, and provide its bound with SαS interference. Then, the discriminating feature vector is constructed by utilizing the higher-order moments (HOM) of eigenvalues of the generalized correlation matrix. Finally, an advanced clustering algorithm is employed to detect the transmit-antenna number, using the cluster where the minimum eigenvalue is located. The proposed algorithm avoids the need for a priori information about the transmitted signals, such as coding mode, modulation type and pilot patterns. Simulation experiments demonstrate the feasibility of the proposed transmit-antenna number detection scheme in MIMO systems with Gaussian noise and SαS interference.

With the rapid development of non-geostationary orbit (NGSO) constellation networks, the co-frequency interference between geostationary orbit (GSO) and NGSO systems is becoming increasingly inevitable. Due to the characteristics of complex multi-mission planning and the large spatial and temporal scale of NGSO constellations, it is difficult to maximize the system performance under the constraints of GSO networks' interference protections. In this paper, based on an iterative greedy algorithm, a modified Q-learning approach in reinforcement learning (RL) is proposed to upgrade the system performance of NGSO network. Compared with the traditional RL algorithms, this modified approach has the advantages of improving the system performance and meeting the aggregate interference protections of GSO networks, by redefining the actions, rewards, and Q-tables. Simulation results show that by adopting the proposed algorithm, all NGSO links timely select and flexibly adapt their actions, including access objects, transmission powers, communication rates, as well as modulation and coding schemes, to achieve more global rewards with co-frequency interference constraints.

We study the propagation and distribution of information-carrying signals injected in dynamical systems serving as reservoir computers. Through different combinations of repeated input signals, a multivariate correlation analysis reveals measures known as the consistency spectrum and consistency capacity. These are high-dimensional portraits of the nonlinear functional dependence between input and reservoir state. For multiple inputs, a hierarchy of capacities characterizes the interference of signals from each source. For an individual input, the time-resolved capacities form a profile of the reservoir's nonlinear fading memory. We illustrate this methodology for a range of echo state networks.

The presence of array back lobe is a major source of interference, power loss, and degrades the signal-to-interference ratio (SIR) in wireless communication systems. In this paper, new distributed beamforming techniques based on an optimized elliptical arc geometry (EAG) are proposed for back lobe cancellation of linear antenna arrays (LAA). The optimized EAG allows for flexible control of the elements' locations and initial transmission phases of their radiations, allowing them to add constructively towards the main beam direction and add destructively towards the back lobe direction. Firstly, two EAG based beamforming techniques are introduced using either a single ellipse-EAG (SE-EAG) or double ellipses-EAGs (DE-EAG). In these techniques, the back lobes are minimized by optimizing both the minor axes of the elliptical arcs and the elements' angular distributions using the particle swarm optimization (PSO) technique. While the elements' transmission energies are kept the same as that of the original LAA. For further back lobe minimization, the double ellipses-EAG with optimized energies (DE-EAG-WOE) beamforming technique is introduced, where both elements' transmission energies and positions are optimized. To verify the superiority and the possibility of practical validation of the proposed techniques, the proposed distributed array configurations based on the DE-EAG-WOE are realized using the CST Microwave Studio software package using dipole elements.

Cognitive radio (CR) is an effective solution to improve the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share spectrum with primary users (PUs). Meanwhile, intelligent reflecting surface (IRS) has been recently proposed as a promising approach to enhance SE and energy efficiency (EE) of wireless communication systems through intelligently reconfiguring the channel environment. In this paper, we consider an IRS-assisted downlink CR system, in which a secondary access point (SAP) communicates with multiple SUs without affecting multiple PUs in the primary network and all nodes are equipped with multiple antennas. Our design objective is to maximize the achievable weighted sum rate (WSR) of SUs subject to the total transmit power constraint at the SAP and the interference constraints at PUs, by jointly optimizing the transmit precoding at the SAP and the reflecting coefficients at the IRS. To deal with the complex objective function, the problem is reformulated by employing the well-known weighted minimum mean-square error (WMMSE) method and an alternating optimization (AO)-based algorithm is proposed. Furthermore, a special scenario with only one PU is considered and AO algorithm is adopted again. It is worth mentioning that the proposed algorithm has a much lower computational complexity than the above algorithm without the performance loss. Finally, some numerical simulations have been provided to demonstrate that the proposed algorithm outperforms other benchmark schemes.

Steering vector and covariance matrix estimation mismatch along with moving jammer/platform, can affect the performance of null-steering beamformer. In this paper, by utilizing null-widening, null-deepening and diagonal loading shrinkage in space–time adaptive processing (STAP) structure, two methods for nullifying GNSS (global navigation satellite system) jamming/interference in cruise moving platforms are introduced. The enhanced performance of the proposed methods is investigated through numerical simulations. The results indicate improvement in the number of acquired GNSS satellites, in heavy jamming (GNSS denied) scenario, by reconstructing and acquiring at least 87% of satellites.

International audience; This paper presents a semi-analytical approximation of Symbol Error Rate (SER) for the well known LoRa Internet of Things (IoT) modulation scheme in the following two scenarios: 1) in multi-path frequency selective fading channel with Additive White Gaussian Noise (AW GN) and 2) in the presence of a second interfering LoRa user in flat-fading AW GN channel. Performances for both coherent and non-coherent cases are derived by considering the common Discrete Fourier transform (DF T) based detector on the received LoRa waveform. By considering these two scenarios, the detector exhibits parasitic peaks that severely degrade the performance of the LoRa receiver. We propose in that sense a theoretical expression for this result, from which a unified framework based on peak detection probabilities allows us to derive SER, which is validated by Monte Carlo simulations. Fast computation of the derived closedform SER allows to carry out deep performance analysis for these two scenarios.

Timeliness is an emerging requirement for many Internet of Things (IoT) applications. In IoT networks, where a large-number of nodes are distributed, severe interference may incur during the transmission phase which causes age of information (AoI) degradation. It is therefore important to study the performance limit of AoI as well as how to achieve such limit. In this paper, we aim to optimize the AoI in random access Poisson networks. By taking into account the spatio-temporal interactions amongst the transmitters, an expression of the peak AoI is derived, based on explicit expressions of the optimal peak AoI and the corresponding optimal system parameters including the packet arrival rate and the channel access probability are further derived. It is shown that with a given packet arrival rate (resp. a given channel access probability), the optimal channel access probability (resp. the optimal packet arrival rate), is equal to one under a small node deployment density, and decrease monotonically as the spatial deployment density increases due to the severe interference caused by spatio-temproal coupling between transmitters. When joint tuning of the packet arrival rate and channel access probability is performed, the optimal channel access probability is always set to be one. Moreover, with the sole tuning of the channel access probability, it is found that the optimal peak AoI performance can be improved with a smaller packet arrival rate only when the node deployment density is high, which is contrast to the case of the sole tuning of the packet arrival rate, where a higher channel access probability always leads to better optimal peak AoI regardless of the node deployment density. In all the cases of optimal tuning of system parameters, the optimal peak AoI linearly grows with the node deployment density as opposed to an exponential growth with fixed system parameters.

Targeting global EDF scheduling, this paper proposes an optimal algorithm for parallelizing tasks with parallelization freedom. For this, we extend the interference-based sufficient schedulability analysis and derive monotonic increasing properties of both tolerance and interference for the schedulability. Leveraging those properties, we propose a one-way search based optimal algorithm with polynomial time complexity. We present a formal proof of the optimality of the proposed algorithm. We first address the single-segment task model and then extend to the multi-segment task model. Our extensive experiments through both simulation and actual implementation show that our proposed approach can significantly improve the schedulability.

Estimating the location parameters of moving target is an important part of intelligent surveillance for Internet of Vehicles (IoV). Satellite has the potential to play a key role in many applications of space-air-ground integrated networks (SAGIN). In this paper, a novel passive location parameter estimator using multiple satellites for moving aerial target is proposed. In this estimator, the direct wave signals in reference channels are first filtered by a band-pass filter, followed by a sequence cancellation algorithm to suppress the direct-path interference and multi-path interference. Then, the fourth-order cyclic cumulant cross ambiguity function (FOCCCAF) of the signals in the reference channels and the four-weighted fractional Fourier transform fourth-order cyclic cumulant cross-ambiguity function (FWFRFT-FOCCCAF) of signals in the surveillance channels are derived. Using them, the time difference of arrival (TDOA) and the frequency difference of arrival (FDOA) are estimated and the distance between the target and the receiver and the velocity of the moving aerial target are estimated by using multiple satellites. Finally, the Cramer-Rao Lower Bounds of the proposed location parameter estimators are derived to benchmark the estimator. Simulation results show that the proposed method can effectively and precisely estimate the location parameters of the moving aerial target.

The existence of out-of-band interference (OBI) signals in power systems will dramatically degrade the performance of synchrophasor estimation. In order to alleviate the influence of OBI, a hybrid phasor model is proposed, in which the OBI phasor and the dynamic fundamental phasor are expressed by Taylor series, respectively. Then, the rough fundamental frequency and OBI frequency estimation are achieved via the rough fundamental component and OBI component estimated from discrete Fourier transform (DFT). For attaining the accurate phasor, the proposed algorithm can invoke the corresponding coefficient matrices calculated off-line according to whether the power signal contains OBI or not. Finally, field data and computer-generated signals are used to assess the proposed algorithm performance. The results obtained indicate that the proposed algorithm can enhance phasor estimation performance by keeping maximum TVEs below 0.001% and 1% under frequency deviation and power modulation tests with OBI, respectively.

Electromagnetic interference to pipelines and railways from AC sources has long been a cause for concern. Methods and standards have evolved to enable calculations of the voltages produced under different conditions. These take into account the AC frequency, the soil resistivity and the pipeline characteristics. However, the approximations presented in some standards fail to take into account the phase relationship of the currents in the AC conductors and how that affects the induced currents in the earth. This paper re-examines these issues by introducing a revised complex image method for 3-phase systems that provides a simple and accurate way to include the influence of induced currents in calculations of the induced emf in nearby conductors. Normal positive sequence 3-phase currents produce fields in neighbouring conductors that tend to cancel. For the associated image currents in the earth, the cancellation is so complete that they have no influence on the induced emf. A different situation occurs with the zero sequence currents that occur during fault conditions and triplen harmonics. These currents are in phase, so there is none of the cancelling effect and the contributions from each conductor add to give a larger induced emf in a neighbouring conductor.

We treat whistler mode radiation from a dipole antenna in a cold magnetized plasma based on the Fresnel zone construction theory. Radiated waves from the antenna with different propagation directions interfere in space. Regions of enhanced waves are formed when the interference is predominantly constructive at the observing point. The coherently enhanced wave propagates at the group velocity. If the antenna is perpendicular to the background magnetic field, the regions of strong waves form two back-to-back Fresnel zones that are parabolic-shaped cones along the background magnetic field from the antenna. The power within each cone drops with distance at a rate of 1/r much more slowly than the 1/r2 thinning of a spherical wave as in vacuum. The total radiation can be approximately derived by the total energy flux integrated over the Fresnel zone. The derived radiation resistance is much greater than that in vacuum. It is proportional to frequency as 1/f2 and proportional to the antenna length as d2. To radiate more power a longer antenna is preferred, a result that is opposite to some previous theoretical studies.

This paper proposes an Automatic Identification System (AIS) data aided Rayleigh constant false alarm rate (AIS-RCFAR) ship detection algorithm of multiple-target environment in synthetic aperture radar (SAR) images. This method aims to improve the detection performance in complex environment with the aid of AIS data. Traditional CFAR detectors generally use all the samples in the local background window for parameter estimation. However, in multiple-target environment, clutter edges and transition areas, due to the interference of the high-intensity outliers such as target pixels, ghosts, and other interfering pixels, the parameters are often overestimated, causing degradation of the detection performance. Aiming at solving this problem, AIS-RCFAR designs an adaptive-threshold based clutter trimming method with an adaptive-trimming-depth aided by AIS data to effectively eliminate the high-intensity outliers in the local background window while greatly sustaining the real sea clutter samples. Maximum-likelihood-estimator (MLE) with a closed-form solution is proposed to precisely estimate the parameters using the adaptively-trimmed clutter samples, the probability density function (PDF) of the sea clutter following Rayleigh distribution can be accurately modeled. AIS-RCFAR greatly enhances the detection rate (DR) in both homogeneous and non-homogeneous multiple-target environment, it also achieves a very low false alarm rate (FAR). In addition, the whole procedure of AIS-RCFAR is simple and efficient. Simulated data and real SAR images with corresponding matched AIS data are used for experiments to validate the superiority and feasibility of AIS-RCFAR.

Imaging applications tailored towards ultrasound-based treatment, such as high intensity focused ultrasound (FUS), where higher power ultrasound generates a radiation force for ultrasound elasticity imaging or therapeutics/theranostics, are affected by interference from FUS. The artifact becomes more pronounced with intensity and power. To overcome this limitation, we propose FUS-net, a method that incorporates a CNN-based U-net autoencoder trained end-to-end on 'clean' and 'corrupted' RF data in Tensorflow 2.3 for FUS artifact removal. The network learns the representation of RF data and FUS artifacts in latent space so that the output of corrupted RF input is clean RF data. We find that FUS-net perform 15% better than stacked autoencoders (SAE) on evaluated test datasets. B-mode images beamformed from FUS-net RF shows superior speckle quality and better contrast-to-noise (CNR) than both notch-filtered and adaptive least means squares filtered RF data. Furthermore, FUS-net filtered images had lower errors and higher similarity to clean images collected from unseen scans at all pressure levels. Lastly, FUS-net RF can be used with existing cross-correlation speckle-tracking algorithms to generate displacement maps. FUS-net currently outperforms conventional filtering and SAEs for removing high pressure FUS interference from RF data, and hence may be applicable to all FUS-based imaging and therapeutic methods.

Using ideas from Chu and Bode/Fano theories, we characterize the maximum achievable rate over the single-input single-output wireless communication channels under a restriction on the antenna size at the receiver. By employing circuit-theoretic multiport models for radio communication systems, we derive the information-theoretic limits of compact antennas. We first describe an equivalent Chu's antenna circuit under the physical realizability conditions of its reflection coefficient. Such a design allows us to subsequently compute the achievable rate for a given receive antenna size thereby providing a physical bound on the system performance that we compare to the standard size-unconstrained Shannon capacity. We also determine the effective signal-to-noise ratio (SNR) which strongly depends on the antenna size and experiences an apparent finite-size performance degradation where only a fraction of Shannon capacity can be achieved. We further determine the optimal signaling bandwidth which shows that impedance matching is essential in both narrowband and broadband scenarios. We also examine the achievable rate in presence of interference showing that the size constraint is immaterial in interference-limited scenarios. Finally, our numerical results of the derived achievable rate as function of the antenna size and the SNR reveal new insights for the physically consistent design of radio systems., Comment: accepted to IEEE Transactions on Communications

With the development of multiprocessor technology, multiple processors are increasingly used in embedded real-time systems. The parallelism of multiple processors makes the Worst Case Response Time (WCRT) analysis of multiprocessor real-time systems complicated. In the widely used Global Fixed-Priority (GFP) scheduling, some effective approximate WCRT analysis methods have been proposed. These methods mainly focus on optimizing overall procedure or carry-in workload, and assume that the whole carry-out workload of a high-priority task cause interference on the analyzed job. However, through our careful observation of specific examples, partial carry-out workload of a high-priority task and the analyzed job may be executed in parallel. In other words, the part of parallel carry-out workload of a high-priority task could not cause interference on the analyzed job. In this paper, we propose an improved WCRT analysis method by optimizing carry-out interference estimation for GFP scheduling, resulting in a more accurate upper bound of WCRT than the recent advanced methods. Experimental results show that our proposed method is superior than the recent advanced methods in terms of the acceptance rate of the system.

To protect military broadcasting against both hostile interference and eavesdropping, an artificial noise (AN) sheltered frequency hopping (FH) broadcasting (ANS-FHB) architecture is proposed in this paper, and the secrecy capacity is employed to measure its secrecy performance. Besides, considering the inevitable frequency mismatch after synchronization will degrade system secrecy in practical applications, the optimal transmitting power allocation (PA) for the confidential information (CI) and AN is provided to maximize secrecy capacity in presence of frequency mismatch. Moreover, to be adapted to various application scenarios, the proposed optimal PA scheme is further simplified according to the frequency synchronization ability and relative channel quality. The analysis and simulations show that the proposed ANS-FHB system can achieve a non-negative secrecy rate even when existing multiple eavesdroppers. In addition, the optimal power ratio of AN to CI shows a downward trend from one to zero as frequency mismatch increases to mitigate the secrecy loss.

Intelligent reflecting surface (IRS) is envisioned to play a significant role in future wireless communication systems as an effective means of reconfiguring the radio signal propagation environment. In this paper, we study a new multi-IRS aided massive multiple-input multiple-output (MIMO) system, where a multi-antenna BS transmits independent messages to a set of remote single-antenna users using orthogonal beams that are subsequently reflected by different groups of IRSs via their respective multi-hop passive beamforming over pairwise line-of-sight (LoS) links. We aim to select optimal IRSs and their beam routing path for each of the users, along with the active/passive beamforming at the BS/IRSs, such that the minimum received signal power among all users is maximized. This problem is particularly difficult to solve due to a new type of path separation constraints for avoiding the IRS-reflected signal induced interference among different users. To tackle this difficulty, we first derive the optimal BS/IRS active/passive beamforming solutions based on their practical codebooks given the reflection paths. Then we show that the resultant multi-beam multi-hop routing problem can be recast as an equivalent graph-optimization problem, which is however NP-complete. To solve this challenging problem, we propose an efficient recursive algorithm to partially enumerate the feasible routing solutions, which is able to effectively balance the performance-complexity trade-off. Numerical results demonstrate that the proposed algorithm achieves near-optimal performance with low complexity and outperforms other benchmark schemes. Useful insights into the optimal multi-beam multi-hop routing design are also drawn under different setups of the multi-IRS aided massive MIMO network., Comment: 30 pages, 10 figures. Accepted for publication by IEEE TWC. Conference version (arXiv:2011.02354). Special case of single-beam multi-hop routing (arXiv:2010.13589)

Commercial coherent receivers utilize balanced photodetectors (PDs) with high single-port rejection ratio (SPRR) to mitigate the signal-signal beat interference (SSBI) due to the square-law detection process. As the symbol rates of coherent transponders are increased to 100 Gbaud and beyond, maintaining a high SPRR in a cost-effective manner becomes more and more challenging. One potential approach for solving this problem is to leverage the concept of single-ended coherent receiver (SER) where single-ended PDs are used instead of the balanced PDs. In this case, the resulting SSBI should be mitigated in the digital domain. In this paper, we show that SSBI can be effectively mitigated using various low-complexity techniques, such as the direct filed reconstruction (DFR), clipped iterative SSBI cancellation (CIC) and gradient decent (GD). In addition, we present a self-calibration technique for SERs which can be extended for characterizing the optical-to-electrical (O/E) response of a conventional balanced coherent receiver (BR). Using the developed techniques, we then experimentally demonstrate a 90 Gbaud probabilistically constellation shaped 64-QAM (PCS-64QAM) transmission using a SER, achieving a net data rate of 882 Gb/s over 100 km of standard single mode fiber (SSMF). The sensitivity penalty compared to the BR is below 0.5 dB. We expect that when the symbol rate is increased further, a SER can potentially outperform a BR, especially when applied to cost-sensitive commercial pluggable coherent transceivers

Phase-generated carrier (PGC) demodulation technology has been widely used in fiber-optic interferometric sensors system in recent years. Nonlinear errors caused by interference noise and parasitic amplitude modulation (AM) are key factors that affect the reliability of traditional PGC demodulation system based on arctangent (PGC-Arctan) algorithm. In order to enhance the reliable performance of PGC demodulation system under the conditions of low signal-to-noise ratio (SNR) and high parasitic AM interference, an ellipse fitting algorithm based on the Gauss–Newton iteration (GNI) is proposed, which is called the PGC-Arctan-GNI demodulation algorithm. The proposed algorithm uses the Euclidean distance to accurately estimate the geometric parameters of ellipse, which can correct the nonlinear distortion of the demodulated signal. In addition, the PGC demodulation system designed based on GNI algorithm has the advantages of strong antinoise ability, good antiparasitic AM ability, and widely dynamic range of the input signal amplitude. Experimental results show that, compared with the direct demodulation algorithm and the ellipse fitting algorithm based on the least squares (LS) method, the SINAD value of the proposed algorithm is always greater than 30 dB and higher than the other two algorithms under the condition of low SNR. Under the condition that the parasitic AM index $m$ changes from 0 to 0.5, the signal-to-noise and distortion ratio (SINAD) curve of the proposed algorithm is more stable than the curves of the other two algorithms, and the fluctuation range of the SINAD value does not exceed 3 dB. This algorithm also has a wider input signal amplitude range than the LS algorithm. Especially, when the amplitude of the measured signal is less than $\pi$ /2, the relative amplitude error of the GNI algorithm is less than 2% and significantly smaller than the LS algorithm. Finally, the demodulation results using real measured data in the actual system show that the frequency and amplitude of the demodulated signal are same as the original signal. At the same time, the SNIAD value of this system reaches 67.40 dB, the spurious-free dynamic range value reaches 68.82 dB, and the total harmonic distortion value reaches -68.40 dB@1 KHz. In summary, the proposed PGC-Arctan-GNI algorithm can eliminate system nonlinear errors caused by the parasitic AM, harmonics, and noise interference by estimating the ellipse parameters, thereby effectively enhancing the stability and reliability of the PGC demodulation system.

Cognitive radio (CR) is a key technology that can enable opportunistic spectrum access, which enables secondary users (SUs) to dynamically exploit the under-utilized channels in the licensed spectrum, owned by primary radio networks (PRNs), referred to as dominant firms. Such sharing is subject to interference, SU QoS and cost constraints, in which SUs should not introduce harmful interference to PR users, achieve QoS rate demand and pay a price for using the licensed PR spectrum. The price of accessing idle PR channels depends on the level of channel utilization and price paid by PRs to access the channels, while the amount of needed spectrum to serve the rate demand of each SU heavily depends on the link-quality of the various channels. In this paper, the spectrum assignment problem in a CR network (CRN), referred to as follower firm, is investigated with the target of serving the largest possible number of SUs with the least possible total price paid to the PRNs (highest CRN profit) while being aware of the time-varying achieved transmission rate and level of utilization of the various PR channels. The problem is mathematically expressed as an optimization problem with the goal of maximizing the number of served SUs and the profit made by the CRN, which has been shown to be a binary linear programming (BLP) problem. Due to the high complexity of solving such optimization, we use the well-known sequential-fixing optimization method to obtain sub-optimal solutions. Simulation results indicate that our channel-assignment optimization significantly increases the CRN profit by reducing the price paid to the PRNs while achieving comparable performance offered by previous price-unaware protocols.

Recently, a metric called energy dispersion index (EDI) was proposed to qualitatively indicate the nonlinear interference (NLI) induced by correlated symbols during optical transmission. In this paper, we propose a new shaper architecture to decrease the EDI of transmitted symbols and thus, increase the signal-to-noise ratio (SNR). We call this shaper the list-encoding constant-composition distribution matcher (L-CCDM). L-CCDM consists of an additional EDI selecting module, which is compatible with standard probabilistic amplitude shaping (PAS) architecture. Numerical results obtained from a multi-span multi-channel system show that when compared to standard CCDM with 256-ary quadrature amplitude modulation (256QAM), the proposed architecture offers an effective SNR gain of 0.35 dB, an achievable information rate gain of 0.22 bit/4D-symbol, or equivalently an 8% reach extension.

The selection and effective utilization of peak-fitting software for conventional gamma-ray spectrum analysis is significant for accurate determination of the mass fraction of elements, particularly in complex peak regions. Majority of the peak-fitting programs can derive similar peak characteristics for singlet peaks, but very few programs can deconvolute multi-peaks in a complex region. The deconvolution of multi-peaks requires special peak-fitting functions, such as left and right-skew distributions. In the this study, 843.76 keV (27Mg) peak area from the complex region (840 keV–850 keV) determined and compared using four different peak-fitting programs, namely, GammaVision, Genie2000, HyperLab, and HyperGam. The 843.76 keV peak interfered with 841.63 keV (152mEu) and 846.81 keV (56Mn). The total Mg concentration was determined through k0-instrumental neutron activation analysis by applying the isotopic interference correction factor 27Al(n,p)27Mg through the simultaneous determination of Al concentration. HyperLab and HyperGam peak-fitting programs reported consistent peak areas, and resultant concentrations agreed with the certified values of matrix-certified reference materials.

In the fields of optics and photonics, phase-only spatial light modulators (SLMs) play an increasingly important role in wave-front engineering. However, the SLMs are subject to wavefront distortion arising from the imperfection in the birefringence effect and physical structure of modulators. This paper presents a simple self-interference phase calibration method applicable to liquid-crystal SLM. We build a interferometric imaging system basing on the Pancharatnam-phase-shifting to measure the phase distribution of a light beam coming from SLMs. Two types of phase modulation errors of SLMs can be characterized in the measurement process: the erroneous gamma curve and shape aberration. The former belongs to dynamic phase distortion and is measured through a four-step Pancharatnam phase shifting interference, which allows a one-shot recording of interference pattern via a polarization camera; the latter represents static phase distortion and is extracted from the interference between light waves coming from different regions of the SLM panel by using Zernike polynomial fitting. Our method has the advantages of high-precision pixel-wise phase correction and robustness against environmental disturbance, and thus can facilitate the applications of SLM in the optical field manipulation.

This brief discusses the limitation of previously published statistical decision feedback equalizer (DFE) models when residual inter-symbol interference (ISI) is present and proposes a method that overcomes this limitation. The paper presents an accurate closed-form expression for the DFE with residual ISI and additive white Gaussian noise (AWGN). The analysis is extended to 4-level pulse amplitude modulation (PAM-4) to cover serial links of current interest. The results verify that the proposed model estimates an accurate bit error rate (BER).

In this paper, an environment-robust polarization-based phase-shift dynamic demodulation method for optical fiber acoustic sensor is proposed. Through optical lever effect of polarization low-coherence interference and amplitude scaling differential cross multiplication (AS-DCM) algorithm, acoustic-source induced interference phase of the acoustic sensor can be demodulated in real-time with high precision. The maximum relative phase demodulation error was only 0.694% when the cavity length had an offset ∼4 μm. The maximum cavity length demodulation error was keeping less than 3.988 nm under the optical power reduced ten times, which demonstrated an ultra-stable demodulation for the largest permitted optical power attenuation range, to our best knowledge. The proposed method has a capability of tolerating large optical power attenuation and large cavity length random offset, providing an environment-robust way for optical fiber acoustic sensor working in extreme environments.

International audience; Full Duplex (FD) in cellular networks is expected to increase the cell spectral efficiency. However, while the downlink (DL) spectral efficiency (SE) increases with FD, the uplink (UL) SE decreases because of the Base Station to Base Station (BS) interference. In this paper, assuming a three-node model, we propose a method based on Successive Interference Cancellation (SIC) to reduce the BS-to-BS interference present in FD cellular networks. The approach consists in coordinating BSs to enable the decoding and the suppression of undesired signals that impair uplink transmissions. We analyze both distributed and Centralized Radio Access Networks (CRAN) architectures. Stochastic geometry is used to derive the coverage probability and mean data rate of the proposed scheme. In the distributed scenario, the FD UL average data rate is increased by 25% with our solution compared to a classical FD network, while our FD scheme still outperforms Half-Duplex (HD) on the DL. In the centralized scenario, our solution outperforms HD by 10% and classical FD by 78% on the UL, while preserving classical FD gains on the DL.

In this brief, an improved maximum correntropy criterion subband adaptive filter (MCC-SAF) algorithm is presented, which has excellent performance for alleviating the effect of impulsive noise and noisy input. Though the MCC-SAF algorithm performs well in the face of impulsive interference due to the advantage of the correntropy-based cost function, it yields estimated error when the system input is disturbed by noise. Profiting from the property of unbiased criterion, we propose a bias-compensated MCC-SAF algorithm (BC-MCC-SAF) by introducing a biased compensated term into the MCC-SAF algorithm. Besides, the computational complexity of the BC-MCC-SAF is investigated. Simulation results for system identification under various input signals have illustrated that the presented algorithm both retains robust achievement for the impulsive interference circumstance and achieves lower stable state error and brilliant tracking ability for noisy input.

The buck converter is frequently introduced in the industry to maintain the stability of the voltage output when having the interference suddenly. In order to promote the robustness of the buck converter, a novel hybrid H2 model following control (HHMFC) is developed based on digital redesign sliding mode control in this article. First, we design H2 model following SMC in the continue-time domain. Then, by using the digital redesign approach, the continuous SMC is redesigned to a corresponding discrete-time SMC and the proposed HHMFC is achieved. Finally, we apply the developed HHMFC to realize a robust buck converter. With both the simulation and experiment results, we can conclude that the proposed HHMFC can easily and successfully apply to the practical realization for the buck converter with better robustness performance.