Multiple Source Scenarios Research Articles

Globalising the study of diffusion: multiple sources and the East African Community

ABSTRACT A growing literature on diffusion shows that ideas, policies and institutions originating in the European Union shape decision-making in other regional organisations (ROs). However, a research programme which claims to examine processes of interdependent decision-making between ROs in general cannot treat a single RO as the ‘default’ source of this diffusion. This paper thus (1) discusses what we term the ‘multiple sources’ problem in diffusion research and (2) presents a model of institutional diffusion, which highlights the important role of local actors in the multiple sources scenario. Based on original data collected via extensive fieldwork in East Africa, we present a case study on the interdependent sources behind the East African Community (EAC)’s establishment in drawing on this model. We find that EAC policymakers selected between, combined and modified several external institutions. These findings highlight local actors’ capacity for institutional innovation.

Detection and Localization of Multiple Ships Using Acoustic Vector Sensors on Buoyancy Gliders: Practical Design Considerations and Experimental Verifications

Real-time detection of illegal activities in maritime areas, such as drug trafficking or illegal fishing, is a very challenging task. It generally requires important and expensive means (patrol planes, coastguard, or navy ships) to provide an efficient monitoring of large areas. As ships emit noise due to their propulsion (propeller and engines), passive acoustic monitoring can be an effective alternative to accomplish this task, at a reasonable cost. To be efficient, the detection process should be autonomous, run in real-time, and should transmit information within a short period. In this study, we demonstrate the potential of using gliders equipped with a tri-dimensional acoustic vector sensor (AVS) to run a survey mission in total autonomy. Based on the ability of the AVS to measure the direction of arrival of the incoming wave, we develop an embedded and real-time processing chain to detect ships and to cluster detections in a multiple-sources scenario. We also propose a receiver that runs in real time to measure the time difference of arrival (TDOA) if multipath propagation occurs. The sonar contacts are transmitted every 30 s to a command and control (C2) station using an acoustic modem, without interrupting the detection process. The C2 can process the sonar contacts with a tracker for target localization; the article also shows that it is possible to get a good estimate of the source distance by processing the TDOA from multipath propagation. The proposed methods are validated on data recorded during an at-sea experiment in shallow waters.

Effective Resource Allocation and Load Balancing in Hierarchical HetNets: Toward QoS-Aware Multi-Access Edge Computing

AbstractMobile edge computing (MEC) is a key feature of next-generation heterogeneous networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the user equipment. In this research, we investigated on connection management approaches in multi-access edge computing systems. This paper presents joint radio resource allocation and MEC optimization in a multi-layer NOMA HetNet in order to maximize system’s energy efficiency. The continues carrier allocation and handoff decision variables, in addition to the interference incorporated in the goal function, modifies the primary optimization problem to a mixed integer nonlinear programming. Network selection is done statically based on the Analytic Hierarchy Process, and station selection is done dynamically based on the Data Envelope Analysis method. Also, an effective feedback mechanism has been designed in collaboration with the server resource manager to solve a global optimization problem in order to load balancing and meet the users quality of service constraints simultaneously. To reduce the computational complexity and to achieve a locally optimal solution, we applied variable relaxation and majorization minimization approach in which offloading decision and multi-part Markov noise analysis have been developed to model users’ preferences without the need for explicit information from the users. Based on the simulations, the proposed approach not only results in a significant increase of system’s energy efficiency but also enhances the system throughput in multiple-source scenarios.

A feasibility study of gamma ray source finder development for multiple sources scenario based on a Monte Carlo simulation

In this research, a rapid and accurate gamma finder system which can find orphan gamma-ray sources has been designed based on NaI(Tl) detector. By proposing complementary methods to our previous researches, an attempt has been made to provide an approach to solve the problem of 3D localization in multiple orphan gamma-ray sources scenario. Based on our previous research, the new conceptual design has been proposed and simulated using Monte Carlo MCNPX2.6 code. Afterward, in order to identify its key characteristics and features, the proposed design has been tested in several different scenarios (multiple gamma sources with various activities in different distances).

Augmented Covariance Matrix Reconstruction for DOA Estimation Using Difference Coarray

As is well known, nonuniform linear arrays have significant advantages in array aperture and degrees of freedom over uniform linear arrays. Using their difference coarrays, subspace-based approaches can be utilized to perform underdetermined and high-resolution direction-of-arrival (DOA) estimation. However, the subspace-based approaches depend on the covariance matrix reconstruction in the coarray domain, which are not statistically efficient when the number of sources is more than one and less than the number of sensors. In this paper, to overcome this drawback, we devise an augmented covariance matrix reconstruction algorithm for DOA estimation in the coarray domain. The proposed algorithm recovers the complete augmented covariance matrix by solving a rank-minimization problem. But unlike the conventional schemes, it exploits the estimation error distribution of the incomplete augmented covariance matrix to derive the constraint condition of the rank-minimization problem. Based on the reconstructed augmented covariance matrix, we can enhance the DOA estimation performance for multiple source scenario at high signal-to-noise ratio. Although our algorithm is developed based on the non-consecutive coarray, it is also suitable for the consecutive coarray. Numerical results demonstrate the superiority of the proposed algorithm over several existing approaches.

Fast parallel factor decomposition technique for coherently distributed source localization

This paper links parallel factor (PARAFAC) analysis to the problem of nominal direction-of-arrival (DOA) estimation for coherently distributed (CD) sources and proposes a fast PARAFACbased algorithm by establishing the trilinear PARAFAC model. Relying on the uniqueness of the low-rank three-way array decomposition and the trilinear alternating least squares regression, the proposed algorithm achieves nominal DOA estimation and outperforms the conventional estimation of signal parameter via rotational technique CD (ESPRIT-CD) and propagator method CD (PM-CD) methods in terms of estimation accuracy. Furthermore, by means of the initialization via the propagator method, this paper accelerates the convergence procedure of the proposed algorithm with no estimation performance degradation. In addition, the proposed algorithm can be directly applied to the multiple-source scenario, where sources have different angular distribution shapes. Numerical simulation results corroborate the effectiveness and superiority of the proposed fast PARAFAC-based algorithm.

Sheaf cohomology on network codings: maxflow-mincut theorem

<p>Surveying briefly a novel algebraic topological application sheaf theory into directed network coding<br />problems, we obtain the weak duality in multiple source scenario by<br />using the idea of modified graph. Furthermore,we establish the<br />maxflow-mincut theorem with network coding sheaves in the case of multiple source.</p>

Augmented Intensity Vectors for Direction of Arrival Estimation in the Spherical Harmonic Domain

Pseudointensity vectors (PIVs) provide a means of direction of arrival (DOA) estimation for spherical microphone arrays using only the zeroth and the first-order spherical harmonics. An augmented intensity vector (AIV) is proposed which improves the accuracy of PIVs by exploiting higher order spherical harmonics. We compared DOA estimation using our proposed AIVs against PIVs, steered response power (SRP) and subspace methods where the number of sources, their angular separation, the reverberation time of the room and the sensor noise level are varied. The results show that the proposed approach outperforms the baseline methods and performs at least as accurately as the state-of-the-art method with strong robustness to reverberation, sensor noise, and number of sources. In the single and multiple source scenarios tested, which include realistic levels of reverberation and noise, the proposed method had average error of $1.5^{\circ }$ and $2^{\circ }$ , respectively.

Multi-frequency sparse Bayesian learning for matched field processing

Compressive sensing has been applied to underwater acoustic problems. Using multi-frequency sparse Bayesian learning (SBL), we present simulation and data results (SWellEx-96 Event S5) including mismatch. Mismatch is defined as a misalignment between the actual source field observed at the array and the modeled replica vector. Results for a multiple-source scenario indicate that SBL outperforms WNC and MUSIC when localizing a quiet source in the presence of a stronger source. Furthermore, simulations (including snapshots not corresponding exactly to replicas) and data results demonstrate that SBL offers robustness to mismatch including array-tilt. The array-tilt mismatch in the data varies over time and is especially pronounced at the closest point of approach, being 2 degrees. Because of its computational efficiency and performance, SBL is practical for real time applications requiring an adaptive and robust processor.

A Game Theoretical Approach for Topology Control in Wireless Ad Hoc Networks with Selfish Nodes

In ad hoc networks, a significant amount of energy available to devices is utilized in network management operations. Since devices have limited energy resources, therefore, they drop data packets of other nodes to reduce their energy consumption. This selfish behaviour increases number of retransmissions over the link which increases energy consumption of the source node, introduces time delays, and degrades throughput of the network. Although conventional distributed topology control solutions minimize energy utilization of the nodes by adjustment of transmission power, however, selfish behaviour by devices introduce additional complexity in design which make topology control a challenging task. In this paper, we proposed Energy Efficient Topology Control Algorithm (EETCA) using game theoretical approach, in which, utility of the node depends on selfishness of the neighbors, link traffic rate, and link length. In decision-making step, nodes remove the links with other nodes that have high drop rate under the condition that network remains connected. We show that Nash Equilibrium point of the proposed game results in Pareto optimal network topology. We compare results of EETCA with Optimum (OPT) and Minimum Least Power Path Tree (MLPT) algorithms presented in literature. We carried our simulations under multiple sources scenario which show that EETCA outperforms previous approaches when number of nodes in the network increases. Furthermore, we simulate the performance of Ad-hoc On-demand Distance Vector (AODV) routing protocol under EETCA topology and compare it with MLPT and OPT topologies. The results show that the ad hoc network constructed using proposed solution substantially improves throughput of AODV routing protocol as compared to MLPT and OPT topology control algorithms.

Robust multi-frequency sparse Bayesian learning: Data results

Compressive sensing based techniques have been applied successfully to underwater acoustics problems. We present data results using a robust multi-frequency sparse Bayesian learning (SBL) algorithm that can account for model mismatch leading to errors in the replica vector dictionary. We use the SWellEx-96 Event S5 data set to demonstrate SBL capabilities for the beamforming and matched field processing (MFP) application. Beamforming results using the entire 64 element array (design frequency of 400 Hz) and source frequencies ranging from 50 to 400 Hz indicate that multi-frequency SBL outperforms Bartlett and WNC processors in identifying multipath arrivals over the approximately 10 km source track. MFP results in a multiple-source scenario indicate that SBL offers a degree of robustness in the presence of data-replica mismatch when tracking a quiet source. The data-replica mismatch is especially pronounced at the closest point of approach due to array tilt of approximately 2 degrees. Data results further indicate that the two SBL tuning parameters (diagonal loading of the replica vector covariance matrix and number of sources for the algorithm’s noise estimate) do not require excessive calibration.

Multiple snapshot and multiple frequency compressive matched field processing

Matched field processing is a generalized beamforming method which matches received array data to a dictionary of replica vectors to locate and track a source. Its solution set generally is sparse since there are considerably fewer sources than replicas. The problem is also underdetermined since the number of sensors are less than the number of unique depth-range cells. Using compressive sensing (CS), the traditional spatial matched-filter problem is reformulated as a convex optimization problem subject to a row-sparsity constraint (RSC). The RSC selects the best match among the replica dictionary when using multiple snapshots. It is found that CS performance is equivalent to the Bartlett processor when comparing the sparse solution to the ambiguity surface’s peak for any number of snapshots in a single frequency—single source scenario. Results also indicate that CS performs similarly to the adaptive white noise constraint processor in a multiple source scenario. The RSC can further be exploited to select a common depth-range cell from snapshots corresponding to multiple frequencies to improve the source tracking in the presence of data-replica mismatch. Results are demonstrated using both simulated and SWellEx-96 experiment data.

Multiple-Source Time-Reversal Transmissions in Random Media

The ability of time-reversed signals in reproducing a given time-dependence of the electromagnetic field within random media is investigated. A general setup consisting of multiple sources cooperating in providing the best transmission is considered, where the constructive interference of their individual contributions is meant to improve the quality of the field generation with respect to a single-source setup. The medium response is described by means of tools from random-process theory, for the case of stationary media complex enough to ensure a large number of multi-path contributions. It is shown that even a very weak spatial coherence in the medium is sufficient to significantly hinder the improvement expected from the use of multiple-source scenarios. Experimental results obtained in a reverberation chamber support the validity of the proposed theory. Direct applications of these results can be found in recent proposals about the potential benefits of time-reversed signals used in wireless communications, imaging techniques, as well as in pulsed-field generation devices based on energy compression through dispersive media.

Detection of anomalies in radio tomography of asteroids: Source count and forward errors

The purpose of this study was to advance numerical methods for radio tomography in which asteroid׳s internal electric permittivity distribution is to be recovered from radio frequency data gathered by an orbiter. The focus was on signal generation via multiple sources (transponders) providing one potential, or even essential, scenario to be implemented in a challenging in situ measurement environment and within tight payload limits. As a novel feature, the effects of forward errors including noise and a priori uncertainty of the forward (data) simulation were examined through a combination of the iterative alternating sequential (IAS) inverse algorithm and finite-difference time-domain (FDTD) simulation of time evolution data. Single and multiple source scenarios were compared in two-dimensional localization of permittivity anomalies. Three different anomaly strengths and four levels of total noise were tested. Results suggest, among other things, that multiple sources can be necessary to obtain appropriate results, for example, to distinguish three separate anomalies with permittivity less or equal than half of the background value, relevant in recovery of internal cavities.

Binaural sound source localization using the frequency diversity of the head-related transfer function

The spectral localization cues contained in the head-related transfer function are known to play a contributory role in the sound source localization abilities of humans. However, existing localization techniques are unable to fully exploit this diversity to accurately localize a sound source. The availability of just two measured signals complicates matters further, and results in front to back confusions and poor performance distinguishing between the source locations in a vertical plane. This study evaluates the performance of a source location estimator that retains the frequency domain diversity of the head-related transfer function. First, a method for extracting the directional information in the subbands of a broadband signal is described, and a composite estimator based on signal subspace decomposition is introduced. The localization performance is experimentally evaluated for single and multiple source scenarios in the horizontal and vertical planes. The proposed estimator's ability to successfully localize a sound source and resolve the ambiguities in the vertical plane is demonstrated, and the impact of the source location, knowledge of the source and the effect of reverberation is discussed.

Tracking of Acoustic Sources Using Random Set Theory

In a passive acoustical tracking, sources are not expected to cooperate with the system. They may enter and leave the sensing area at various speed and directions. They may even stop vocalizing at any time. The standard solution divides this problem into two steps: estimating the number of sources, followed by tracking their geo-kinematic states. Instead, we propose using the random set theory to jointly estimate both the number of sources and their geo-kinematic states. The random set theory is a mathematical framework that gives a complete picture of the joint estimation problem. With this framework, one can analytically develop a tracking system that is robust against node failures or dynamic multiple source scenario. To cover the common wireless sensing deployment strategy, we investigate two topologies: a uniform grid, and a distributed sub-array. With each topology, we validate the proposed tracking system solution with simulations. For the distributed sub-array topology, we use results from a controlled field experiment and found this approach to be both practical and robust.

Scattering centers modeling of non-anechoic measurement environments

Abstract. Near-field measurements are established techniques to obtain the far-field radiation pattern of an Antenna Under Test via near-field measurements and subsequent near-field far-field transformation. For measurements acquired in echoic environments, additional post-processing is required to eliminate the effects of multipath signals in the resulting far-field pattern. One of such methods models the measurement environment as a multiple source scenario whereby the collected near-field data is attributed to the AUT and some scattering centers in the vicinity of the AUT. In this way, the contributions of the AUT at the probe can be separated from those of the disturbers during the near-field far-field transformation if the disturber locations are known. In this paper, we present ways of modeling the scattering centers on equivalent surfaces such that echo suppression is possible with only partial or no information about the geometry of the scatterers.

Bayesian Focusing for Coherent Wideband Beamforming

In this paper, we present and study a Bayesian focusing transformation (BFT) for coherent wideband array processing, which takes into account the uncertainty of the direction of arrivals (DOAs). The Bayesian focusing method minimizes the mean-square error of the transformation over the probability density functions (pdfs) of the DOAs, thus achieving improved focusing accuracy over the entire bandwidth. In order to solve the Bayesian focusing problem, we derive and utilize a weighted extension of the wavefield interpolated narrowband generated subspace (WINGS) focusing transformation. We provide a closed-form expression for the optimal BFT and extend it to the case of directional sensors. We then consider a numerical computation scheme of the BFT in the angular domain. We show that if an angular sampling condition is satisfied then the angle domain approximation yields the optimal BFT. We also treat the important issue of robust focused minimum variance distortionless response (MVDR) beamformer. We analyze the sensitivity of the focused MVDR to the focusing errors and show that the array gain (AG) is inversely proportional to the square of the signal-to-noise ratio (SNR) for large values of the SNR, and highly sensitive to the focusing errors. In order to reduce this sensitivity we generalize the popular narrowband diagonal loaded MVDR to the focused wideband case, referred to as the Q-loaded focused MVDR wideband beamformer. We derive a closed-form analytic expression for the AG of the Q-loaded focused MVDR beamformer which depends on the focusing transformations. A numerical performance evaluation and simulations demonstrate the advantage of the BFT over that of other focusing transformations, for multiple source scenarios.

Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: Depth localization and source separation for focal primary currents

The estimation of the activity-related ion currents by measuring the induced electromagnetic fields at the head surface is a challenging and severely ill-posed inverse problem. This is especially true in the recovery of brain networks involving deep-lying sources by means of EEG/MEG recordings which is still a challenging task for any inverse method. Recently, hierarchical Bayesian modeling (HBM) emerged as a unifying framework for current density reconstruction (CDR) approaches comprising most established methods as well as offering promising new methods. Our work examines the performance of fully-Bayesian inference methods for HBM for source configurations consisting of few, focal sources when used with realistic, high-resolution finite element (FE) head models. The main foci of interest are the correct depth localization, a well-known source of systematic error of many CDR methods, and the separation of single sources in multiple-source scenarios. Both aspects are very important in the analysis of neurophysiological data and in clinical applications. For these tasks, HBM provides a promising framework and is able to improve upon established CDR methods such as minimum norm estimation (MNE) or sLORETA in many aspects. For challenging multiple-source scenarios where the established methods show crucial errors, promising results are attained. Additionally, we introduce Wasserstein distances as performance measures for the validation of inverse methods in complex source scenarios.

Simultaneous recovery of origin time, hypocentre location and seismic moment tensor using sparse representation theory

SUMMARY Using a proper parametrization, the source displacement field of a seismic event can be efficiently reconstructed by a redundant dictionary of Green’s functions based on sparse representation theory. Then, by subjecting the pre-existing event records and pre-computed dictionary of Green’s functions into a sparsity-promoting algorithm, it is possible to simultaneously evaluate the origin time, hypocentre coordinates and seismic moment tensor. The proposed method is applicable to single- or multiple-source scenarios and, with minor adjustments, can be a valuable tool for real-time, automatic monitoring systems. This study demonstrates the effectiveness and accuracy of the dictionary-based approach via (1) detection of microseismic events produced during the hydraulic fracturing of oil and gas wells and (2) inversion of a small-magnitude, regional earthquake (2002 June 18 in Caborn, Indiana) data. Our experiments based on numerical simulations and earthquake observations underscore the largely untapped potential of dictionary-based approaches and sparse representation theory in continuous source parameter recovery.