Local Ambiguities Research Articles

Joint Source and Sensor Localization by Angles of Arrival

Joint source and sensor localization (JSSL) attempts to simultaneously locate the multiple sources and sensors by exploiting the measurements between them. The research of JSSL has been based on time of arrival (TOA) measurements that require synchronization of the sources and sensors. This paper explores the use of angle of arrival (AOA) observations instead for the JSSL problem that does not require synchronization. The investigation and development presented are valid for localization in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d\geq 2$</tex-math></inline-formula> dimensional space. We first look into the feasibility and the fundamental properties of JSSL using AOA in terms of localization ambiguity, the minimal source and sensor case and the Cramér-Rao lower bound (CRLB). By representing the AOA as the direction vector of arrival (DVOA), we next devise the rank property of DVOA matrix for the JSSL problem. The rank property leads us to develop the algorithms for the refinement of AOA observations, the estimation of TOAs between the sources and sensors and the closed-form solution of their positions. The optimality of the closed-form solution in reaching the global minimum of the cost function based on DVOA is established. Simulations confirm the theoretical developments, and support the performance of the proposed JSSL solution in reaching the CRLB accuracy under Gaussian noise when the number of sources and sensors is sufficient.

Gaussian Parameterized Information Aided Distributed Cooperative Underwater Positioning Algorithm

With the exploration of marine resources, the number of underwater nodes is increasing with each passing day, and the demand of mutual cooperative position between nodes is becoming extremely urgent. The existing underwater positioning technology is mainly realized by transplanting cooperative position technology from ground space, without taking into consideration the consequent problems of complex underwater acoustic channels, large signal propagation attenuation and the limited load of underwater nodes. According to the characteristics of the underwater cooperative position network, this paper proposes gaussian parameterized information aided distributed cooperative positioning algorithm (GPI-CP) with underwater environment. The algorithm utilized here is the form of Gauss parameter to calculate information and establish a confidence model. In order to reduce the computational complexity, the Taylor model was adopted to linear approximate European distance. Then distributed underwater node cooperative position is obtained by combing with the factor map theory. This algorithm combined with the sum-product theory and parameterized Gaussian information transmission, is promising to realize fast underwater cooperative position and reduce communication calculations. The proposed algorithm was simulated and analyzed in terms of location ambiguity, ranging error and the number of nodes, etc. When the proposed algorithm was compared with the existing underwater cooperative position method, it was noticed that the position performance had improved more than 10% and the communication calculation is less.

Route Deviation Algorithm with Location Ambiguity in Wireless Sensor Networks

Nowadays, the primary concern of geographic routing protocol lies in the fact of minimisation of energy dissipation during the transfer of each packet in a network. This paper proposes an energy-efficient real-time algorithm in sensor networks, i.e., Route deviation Algorithm. Route deviation algorithm combines the characteristics of both distance-based criteria and direction or angle based criteria. In this paper, we have shown a comparison amongst COMPASS algorithm, NFP, MER and Route deviation algorithm. The simulation model includes numerous parameters, namely, threshold energy, number of sensors, spread of the map, position of sensors and transmission time. The results obtained through the simulation model supports the fact that the Route deviation algorithm accomplishes the task of saving energy and adds to the life of the networks.

Localization of Acoustically Tagged Marine Animals in Under-Ranked Conditions

A key technology in the movement tracking of marine animals is localization using acoustic transmitters. These are attached to marine animals and are detected by an array of receivers. Then, offline localization is performed by multilateration. However, due to the transmitter's low power and environmental conditions, emissions may be detected by only a limited number of receivers, causing localization ambiguities to arise. This work proposes a solution for such localization ambiguities. The proposed method assumes that the position of acoustically-tagged marine animals follows a hidden Markov model, such that localization ambiguities can probabilistically be resolved using a Forward-Backward algorithm. Our method is able to extrapolate the positions in a data series, as long as one sample in that series is picked up by three receivers, or if the identity of the receivers changes during tracking. Performance analysis shows that the localization accuracy of our method approaches the Cramér-Rao lower bound. Furthermore, to demonstrate the suitability of our method in a real sea environment, we have established a testbed that operated for three months, demonstrating localization of 20 acoustically-tagged sandbar sharks. Compared to the available solutions, roughly 20 times more location estimates were made; thereby, significantly increasing the impact of the test-site.

The Importance of Monitoring Interval for Rockfall Magnitude‐Frequency Estimation

AbstractRockfalls commonly exhibit power law volume‐frequency distributions, where fewer large events are observed relative to more numerous small events. Within most inventories, the smallest rockfalls are the most difficult to detect and so may not be adequately represented. A primary challenge occurs when neighboring events within a single monitoring interval are recorded as one, producing ambiguity in event location, timing, volume, and frequency. Identifying measurement intervals that minimize these uncertainties is therefore essential. To address this, we use an hourly data set comprising 8,987 3‐D point clouds of a cliff that experiences frequent rockfalls. Multiple rockfall inventories are derived from this data set using change detections for the same 10‐month period, but over different monitoring intervals. The power law describing the probability distribution of rockfall volumes is highly sensitive to monitoring interval. The exponent,β, is stable for intervals &gt;12 hr but increases nonlinearly over progressively short timescales. This change is manifested as an increase in observed rockfall numbers, from 1.4 × 103(30 day intervals) to 1.4 × 104(1 hr intervals), and a threefold reduction in mean rockfall volume. When the monitoring interval exceeds 4 hr, the geometry of detected rockfalls becomes increasingly similar to that of blocks defined by rock mass structure. This behavior change reveals a time‐dependent component to rockfall occurrence, where smaller rockfalls (identifiable from more frequent monitoring) are more sensitive to progressive deformation of the rock mass. Acquiring complete inventories and attributing discrete controls over rockfall occurrence may therefore only be achievable with high‐frequency monitoring, dependent upon local lithology.

Interactive Structure-aware Blending of Diverse Edge Bundling Visualizations.

Many edge bundling techniques (i.e., data simplification as a support for data visualization and decision making) exist but they are not directly applicable to any kind of dataset and their parameters are often too abstract and difficult to set up. As a result, this hinders the user ability to create efficient aggregated visualizations. To address these issues, we investigated a novel way of handling visual aggregation with a task-driven and user-centered approach. Given a graph, our approach produces a decluttered view as follows: first, the user investigates different edge bundling results and specifies areas, where certain edge bundling techniques would provide user-desired results. Second, our system then computes a smooth and structural preserving transition between these specified areas. Lastly, the user can further fine-tune the global visualization with a direct manipulation technique to remove the local ambiguity and to apply different visual deformations. In this paper, we provide details for our design rationale and implementation. Also, we show how our algorithm gives more suitable results compared to current edge bundling techniques, and in the end, we provide concrete instances of usages, where the algorithm combines various edge bundling results to support diverse data exploration and visualizations.

Low Complexity, Hardware-Efficient Neighbor-Guided SGM Optical Flow for Low-Power Mobile Vision Applications

Accurate, low-latency, and energy-efficient optical flow estimation is a fundamental kernel function to enable several real-time vision applications on mobile platforms. This paper presents neighbor-guided semi-global matching (NG-fSGM), a new low-complexity optical flow algorithm tailored for low-power mobile applications. NG-fSGM obtains high accuracy optical flow by aggregating local matching costs over a semi-global region, successfully resolving local ambiguity in texture-less and occluded regions. The proposed NG-fSGM aggressively prunes the search space based on neighboring pixels’ information to significantly lower the algorithm complexity from the original fSGM. As a result, NG-fSGM achieves $17.9{\times}$ reduction in the number of computations and $8.37{\times}$ reduction in memory space compared to the original fSGM without compromising its algorithm accuracy. A multicore architecture for NG-fSGM is implemented in hardware to quantify algorithm complexity and power consumption. The proposed architecture realizes NG-fSGM with overlapping blocks processed in parallel to enhance throughput and to lower power consumption. The eight-core architecture achieves 20 M pixel/s (66 frames/s for VGA) throughput with 9.6 mm2 area at 679.2-mW power consumption in 28-nm node.

Designing Unimodular Sequence With Low Peak of Sidelobe Level of Local Ambiguity Function

This paper considers the problem of designing unimodular sequence with low peak of sidelobe level of local ambiguity function. The optimization problem is difficult because of the mixed nonconvex unimodulus and highly nonlinear fourth-order polynomial-like constraints. To solve the problem, we propose a hybrid lagrange programming neural network-alternating direction method of multipliers (LPNN-ADMM) approach. To separately deal with the complex nonconvex and nonlinear constraints, we introduce auxiliary variables to transfer the fourth-order polynomials from the nonlinear constraints into the Lagrange function. As a result, the original optimization variables and auxiliary variables are updated alternately in the ADMM framework. Especially, the original optimization variables are updated from the newly formed nonlinear objective function together with the unimodular constraints via the LPNN method, in which an adaptive selection scheme of the penalty parameter is developed to minimize the corresponding Lagrange function while satisfying the constraints. The performance of the proposed method is demonstrated via numerical examples.

Borehole Microseismic Imaging of Hydraulic Fracturing: A Pilot Study on a Coal Bed Methane Reservoir in Indonesia

Over the last decade, microseismic monitoring has emerged as a considerable and capable technology for imaging stimulated hydraulic fractures in the development of unconventional hydrocarbon resources. In this study, pilot hydraulic-fracturing treatments were operated at a coal-bed methane (CBM) field in Indonesia to stimulate the flow and increase the reservoir's permeability while the monitoring system was set in a single near-vertical borehole. Locating event sources accurately is fundamental to investigating the induced fractures, but the geometry of a single downhole array is a challenging data processing task, especially to remove ambiguity of the source locations. The locating procedure was reviewed in 3 main steps: (i) accurate picking of P- and S-wave phases; (ii) inclusion of P-wave particle motion to estimate the back azimuth; (iii) guided inversion for hypocenter determination. Furthermore, the seismic-source moment magnitudes were calculated by employing Brune's model. Reliable solutions of locations were obtained as shown statistically by uncertainty ellipsoids and a small misfit. Based on our results, both induced and triggered seismicity could be observed during the treatments and therefore conducting intensive monitoring is important. The triggered seismicity is an undesired activity so disaster precautions need to be taken, in particular for preventing reactivation of pre-existing faults.

Illumination-Invariant Video Cut-Out Using Octagon Sensitive Optimization

This paper presents an effective video cut-out approach, which can be utilized to segment the moving object in video shots. We first introduce the Octagon-Sensitive-Filtering (OSF) and its illumination invariant feature (IIF), which is computed on each pixel of the image via adding contributions from neighboring pixels. We integrate our IIF into the variational model and obtain the seeds during preprocessing to help address large displacement and illumination changes. An effective seed update method based on tracking-then-refinement based on IIF is presented to compensate for location ambiguities, and the strategy is effective to deal with illumination variances and objects deformation. Furthermore, we apply the IIF-based graph-cut to deal with fuzzy boundaries. Multiple experiments on quantitative challenging datasets have shown the robustness, high-quality video cut-out and efficiency of our approach to acute variances of illumination and complex motion.

Ultrasound navigation based on minimally designed vehicle inspired by the bio-sonar strategy of bats

ABSTRACTIn this study, we conducted vehicle experiments and a numerical simulation based on a simple algorithm inspired by the bio-sonar system of bats to investigate how the behavioral strategy employed by bats contributes to acoustic navigation for minimal-design sensing. In particular, a double-pulse scanning method inspired from the echolocation behavior of bats was proposed, in which (1) the direction of ultrasound emission by a vehicle equipped with 1 transmitter and 2 receivers was alternately shifted between the movement direction of the vehicle and the direction of the nearest obstacle, and (2) the movement direction of the vehicle was calculated for every double-pulse emission based on integrated information from all echoes detected. As a result of 100 repeated drives in a practical course, the success rate of an obstacle-avoidance drive improved from 13% with the conventional single-pulse scanning method to 73% with the proposed method. Furthermore, the numerical simulation demonstrated that the proposed method achieves robust path planning by suppressing the localization ambiguity due to the interference of multiple echoes. The practical experiments and numerical simulation suggest that bats employ a simple behavioral solution in the operation of acoustic sensing for various problems occurring in the real world.

LiDAL: Light Detection and Localization

In this paper, we present the first indoor light-based detection and localization system that builds on concepts from radio detection and ranging (radar) making use of the expected growth in the use and adoption of visible light communication (VLC), which can provide the infrastructure for our Light Detection and Localization (LiDAL) system. Our system enables active detection, counting, and localization of people, in addition to being fully compatible with the existing VLC systems. In order to detect human (targets), LiDAL uses the visible light spectrum. It sends pulses using a VLC transmitter and analyses the reflected signal collected by a photodetector receiver. Although we examine the use of the visible spectrum here, LiDAL can be used in the infrared spectrum and other parts of the light spectrum. We introduce LiDAL with different transmitter-receiver configurations and optimum and sub-optimum detectors considering the fluctuation of the received reflected signal from the target in the presence of Gaussian noise. We design an efficient multiple input multiple output (MIMO) LiDAL system with a wide field of view (FOV) single photodetector receiver, and also design a multiple input single output (MISO) LiDAL system with an imaging receiver to eliminate the ambiguity in target detection and localization. We develop models for the human body and its reflections and consider the impact of the color and texture of the cloth used as well as the impact of target mobility. A number of detection and localization methods are developed for our LiDAL system, including cross correlation and a background subtraction method. These methods are considered to distinguish a mobile target from the ambient reflections due to background obstacles (furniture) in a realistic indoor environment.

Localization Based on MAP and PSO for Drifting-Restricted Underwater Acoustic Sensor Networks.

Localization is a critical issue for Underwater Acoustic Sensor Networks (UASNs). Existing localization algorithms mainly focus on localizing unknown nodes (location-unaware) by measuring their distances to beacon nodes (location-aware), whereas ignoring additional challenges posed by harsh underwater environments. Especially, underwater nodes move constantly with ocean currents and measurement noises vary with distances. In this paper, we consider a special drifting-restricted UASN and propose a novel beacon-free algorithm, called MAP-PSO. It consists of two steps: MAP estimation and PSO localization. In MAP estimation, we analyze nodes’ mobility patterns, which provide the priori knowledge for localization, and characterize distance measurements under the assumption of additive and multiplicative noises, which serve as the likelihood information for localization. Then the priori and likelihood information are fused to derive the localization objective function. In PSO localization, a swarm of particles are used to search the best location solution from local and global views simultaneously. Moreover, we eliminate the localization ambiguity using a novel reference selection mechanism and improve the convergence speed using a bound constraint mechanism. In the simulations, we evaluate the performance of the proposed algorithm under different settings and determine the optimal values for tunable parameters. The results show that our algorithm outperforms the benchmark method with high localization accuracy and low energy consumption.

A Soft Range Limited K-Nearest Neighbors Algorithm for Indoor Localization Enhancement

This paper proposes a soft range limited K nearest neighbours (SRL-KNN) localization fingerprinting algorithm. The conventional KNN determines the neighbours of a user by calculating and ranking the fingerprint distance measured at the unknown user location and the reference locations in the database. Different from that method, SRL-KNN scales the fingerprint distance by a range factor related to the physical distance between the user's previous position and the reference location in the database to reduce the spatial ambiguity in localization. Although utilizing the prior locations, SRL-KNN does not require knowledge of the exact moving speed and direction of the user. Moreover, to take into account of the temporal fluctuations of the received signal strength indicator (RSSI), RSSI histogram is incorporated into the distance calculation. Actual on-site experiments demonstrate that the new algorithm achieves an average localization error of $0.66$ m with $80\%$ of the errors under $0.89$ m, which outperforms conventional KNN algorithms by $45\%$ under the same test environment.

Location ambiguity resolution and tracking method of human targets in wireless infrared sensor network

Human tracking has attracted extensive attention by using low-cost pyroelectric infrared sensor network in recent years. This paper presents a location ambiguity resolution and tracking method for human targets in wireless, distributed and binary infrared sensor network. The tracking system can detect the human targets in the detection space, and activate the sensor detection lines dynamically. A bearing-crossing location method is designed. The intersections of all activated detection lines are called primary measurement points for human location, and some of them are false measurement points. The ambiguity of this bearing-crossing location method is discussed and a two-level bearing-crossing algorithm is proposed based on quartic K-means clustering and joint cost function. For the first level, an anti-logic algorithm is designed to get the initial effective measurement points, then these points are assigned to different targets using K-means clustering. For the second level, the final effective points are obtained by using a special joint cost function, and they are assigned to different targets using K-means clustering once again to get the final locating results. The cost value is used as a weight to adjust the covariance parameter in Kalman filter for target tracking as well. The experimental results show that the average tracking error of human targets is less than 0.8 m in a 10 m × 10 m space, which verify the proposed location ambiguity resolution and tracking method.

COMPLEX ODESSA SEA PORT ESTIMATION ON THE SUSTAINABLE DEVELOPMENT CONCEPT BASIS

The publication is devoted to the problem of choosing a strategic direction for further development of the Odessa seaport territories. According to some urban planning experts, the main problem of these territories is that the development of a cargo port, handling of bulk cargoes and increasing the traffic load in the city center of Odessa inevitably leads to the degradation of the city-port system. Therefore, they raise the issue of the cargo port remove outside the city to develop these territories as a recreational facilities. But their opponents believe that the port is not only noise and dust, it is also thousands of jobs for inhabitants and the source of state revenue refill and taxes to the local budget. Sea ports are also plays an important role in state interests as a gate for export of Ukrainian products. So the aim of the study is a comprehensive assessment of the port activity based on the sustainable deve-lopment concept, which considers the sustainable development of complex systems of human settlements, states or civilization as a whole by ensuring balanced development of the social, economic and environmental spheres of these systems. The article provides an integrated analysis of the Odessa seaport activities in the economic, social, environmental and urban planning fields. The role of this enterprise and the strategic directions of its further development in the context of transportation, the ecology of the urban environment and urban planning activity are defined. The analysis showed the complexity and ambiguity of the Odessa seaport location and its further deve-lopment in residential area in the historic center of the Odessa city. In order to reduce the environmental and traffic negative impact of the port to residential buildings, it is necessary to modernize the port and ensure the development of appropriate infrastructure, as well as eliminate impossible activities from an urban planning point of view at the port's location. As the main private investment attractor for the seaport development can be used some areas freed up after the optimization of the port activity, which will be provided under certain investment conditions for building recreational facilities. The development of a model for the comprehensive assessment of similar development projects is a promising direction for further research in this field.

Height-from-Polarisation with Unknown Lighting or Albedo.

We present a method for estimating surface height directly from a single polarisation image simply by solving a large, sparse system of linear equations. To do so, we show how to express polarisation constraints as equations that are linear in the unknown height. The local ambiguity in the surface normal azimuth angle is resolved globally when the optimal surface height is reconstructed. Our method is applicable to dielectric objects exhibiting diffuse and specular reflectance, though lighting and albedo must be known. We relax this requirement by showing that either spatially varying albedo or illumination can be estimated from the polarisation image alone using nonlinear methods. In the case of illumination, the estimate can only be made up to a binary ambiguity which we show is a generalised Bas-relief transformation corresponding to the convex/concave ambiguity. We believe that our method is the first passive, monocular shape-from-x technique that enables well-posed height estimation with only a single, uncalibrated illumination condition. We present results on real world data, including in uncontrolled, outdoor illumination.

3-D localization of wireless sensor nodes using near-field magnetic-induction communications

This paper proposes two localization methods for wireless sensor nodes that utilize an arbitrarily oriented tri-directional coil as both magnetic induction (MI) transmitter and receiver for wireless communications. Taking advantage of magnetic field measurements of a tri-directional coil antenna in the near-field, the two localization algorithms use only two anchor nodes to locate a sensor node in the 3-D space. Assuming each anchor node transmits the communication signals by three coils sequentially, which are received by the three coils at a sensor node simultaneously, this paper derives closed-form formulas for estimating the transmission distance and the polar angles to yield eight possible location points based on the signals of each anchor node. Then a rotation matrix (RM)-based method derives the orientation rotation matrix between the transmitter and receiver to find two possible location vectors with opposite directions in each anchor node. Then, we use maximum likelihood to estimate the location with two anchor nodes assisted. Another method called the distance-based method, taking into account the locations of the two anchor nodes and the two sets of eight possible location estimates of the sensor node, estimates the location by minimizing the distance. The RM-based method can achieve high localization accuracy while the distance-based method has less computational complexity. However, the distance-based method may encounter location ambiguity when the orientations of the two anchor nodes are the same. Simulations were performed to compare these two algorithms and the existing localization algorithm in this scenario. The results show that the proposed two localization algorithms and the derived closed-form formula of distance achieve good accuracy under large measurement errors.

Extraction of Aortic Knuckle Contour in Chest Radiographs Using Deep Learning.

In this paper, we aim to extract the aortic knuckle (AK) contour in chest radiographs, an anatomical structure rarely being addressed in the literature. Since the AK structure is small and thin, simply adopting the deep network methods that are successful for large organ segmentation is inadequate for achieving good pixel-level accuracy and resolving local ambiguities. To address this challenge, we propose a new coarse-to-fine segmentation approach which focuses on global and local information contexts, respectively. Two convolutional networks are used. For the coarse segmentation, we use FasterRCNN; for the fine segmentation, we use U-Net. Our evaluation uses the publicly available JSRT dataset; the results are promising. Besides presenting these results, we analyze issues such as the imprecision of manual contour marking, and automatic generation of the coarse segmentation ground-truth mask used for deep network training. Our approach is general and can be applied to extract other curve-like objects-of-interest.

Muver, a computational framework for accurately calling accumulated mutations

BackgroundIdentification of mutations from next-generation sequencing data typically requires a balance between sensitivity and accuracy. This is particularly true of DNA insertions and deletions (indels), that can impart significant phenotypic consequences on cells but are harder to call than substitution mutations from whole genome mutation accumulation experiments. To overcome these difficulties, we present muver, a computational framework that integrates established bioinformatics tools with novel analytical methods to generate mutation calls with the extremely low false positive rates and high sensitivity required for accurate mutation rate determination and comparison.ResultsMuver uses statistical comparison of ancestral and descendant allelic frequencies to identify variant loci and assigns genotypes with models that include per-sample assessments of sequencing errors by mutation type and repeat context. Muver identifies maximally parsimonious mutation pathways that connect these genotypes, differentiating potential allelic conversion events and delineating ambiguities in mutation location, type, and size. Benchmarking with a human gold standard father-son pair demonstrates muver’s sensitivity and low false positive rates. In DNA mismatch repair (MMR) deficient Saccharomyces cerevisiae, muver detects multi-base deletions in homopolymers longer than the replicative polymerase footprint at rates greater than predicted for sequential single-base deletions, implying a novel multi-repeat-unit slippage mechanism.ConclusionsBenchmarking results demonstrate the high accuracy and sensitivity achieved with muver, particularly for indels, relative to available tools. Applied to an MMR-deficient Saccharomyces cerevisiae system, muver mutation calls facilitate mechanistic insights into DNA replication fidelity.