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Contemporary navigation systems rely upon localisation accuracy and humongous spatial data for navigational assistance. Such spatial-data sources may have access restrictions or quality issues and require massive storage space. Affordable high-performance mobile consumer hardware and smart software have resulted in the popularity of AR and VR technologies. These technologies can help to develop sustainable devices for navigation. This paper introduces a robust, memory-efficient, augmented-reality-based navigation system for outdoor environments using crowdsourced spatial data, a device camera, and mapping algorithms. The proposed system unifies the basic map information, points of interest, and individual GPS trajectories of moving entities to generate and render the mapping information. This system can perform map localisation, pathfinding, and visualisation using a low-power mobile device. A case study was undertaken to evaluate the proposed system. It was observed that the proposed system resulted in a 29 percent decrease in CPU load and a 35 percent drop in memory requirements. As spatial information was stored as comma-separated values, it required almost negligible storage space compared to traditional spatial databases. The proposed navigation system attained a maximum accuracy of 99 percent with a root mean square error value of 0.113 and a minimum accuracy of 96 percent with a corresponding root mean square value of 0.17. [ABSTRACT FROM AUTHOR]

This paper studies the localization problem using noisy range-difference measurements, or equivalently time difference of arrival (TDOA) measurements. There is a reference sensor, and for each other sensor, the TDOA measurement is obtained with respect to the reference one. By minimizing the sum of squared errors, a nonconvex constrained least squares (CLS) problem is formulated. In this work, we focus on devising an algorithm to seek the global minimizer of the CLS problem, hoping that the numerical solution meets some precision requirement in terms of relative error. Based on the Lagrange multiplier method, we first branch the feasible Lagrange multiplier set into several subsets and develop a workflow in terms of if-then-else control structure to seek the global minimizer by searching for the optimal Lagrange multiplier. The execution order is carefully organized so that it is in line with the general principle of putting the flow that one normally understands to be executed first. We then dive into detailed searching methods in different cases and conduct computational error analysis, giving the error bound on the Lagrange multiplier, when we search for it, to meet the precision requirement on an approximate solution. Based on the above achievements, a programmable global minimizer seeking algorithm is proposed for the CLS problem. Simulations and experimental tests on a public dataset demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The UHF radio-frequency identification (RFID) has gained growing attention for tagged object localization in smart storage systems. Due to Non-Line-Of-Sight (NLOS) condition, it is challenging to accurately locate the position of tags inside closed spaces. In this paper, we propose a precise and cost-effective solution for tagged object localization in closed spaces, using only received signal strength (RSS) information. We establish a RSS profile for each tag and discover some important features of RSS profiles including uniqueness, time-variation, column-dependence and waveform-similarity. Based on these features, we propose a reference-free RSS-profile (RFRP) localization scheme. The advantage of our propose scheme is to accurately localize multiple tags in closed spaces by overcoming the challenges including the lack of pre-deployed reference tags, NLOS propagation, multi-path propagation and coupling effect. The RFRP scheme first roughly estimates tags’ coordinates based on Peak Asymmetry Factor, then acquires reference-tag substitutes through the similarity of RSS sequences. Subsequently, our scheme refines the relative positions of all tags by these substitutes. Finally all tags’ absolute positions are estimated through a RSS-ranging model. Extensive experiment results demonstrate that our approach can achieve high ordering accuracy and localization accuracy for the tags inside closed spaces. [ABSTRACT FROM AUTHOR]

Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded surfaces, and cluttered background of metal part images. Affected by these factors, bounding boxes generated by most existing methods could not locate low-contrast text areas very well. In this paper, we propose a refined feature-attentive network (RFN) to solve the inaccurate localization problem. Specifically, we first design a parallel feature integration mechanism to construct an adaptive feature representation from multi-resolution features, which enhances the perception of multi-scale texts at each scale-specific level to generate a high-quality attention map. Then, an attentive proposal refinement module is developed by the attention map to rectify the location deviation of candidate boxes. Besides, a re-scoring mechanism is designed to select text boxes with the best rectified location. To promote the research towards industrial scene text detection, we contribute two industrial scene text datasets, including a total of 102156 images and 1948809 text instances with various character structures and metal parts. Extensive experiments on our dataset and four public datasets demonstrate that our proposed method achieves the state-of-the-art performance. Both code and dataset are available at: https://github.com/TongkunGuan/RFN. [ABSTRACT FROM AUTHOR]

Temporal moment localization using natural language (TMLNL) is an emerging issue in computer vision for localizing a specific moment inside a long, untrimmed video. The goal of TMLNL is to obtain the video’s output moment, which is related to the input query in a substantial way. Previous research focused on the visual portion of TMLNL, such as objects, backdrops, and other visual attributes, but natural language processing (NLP) techniques were largely used for the textual portion. A long query requires sufficient context to properly localize moments within a long untrimmed video. Thus, as a consequence of not completely understanding how to handle queries, performances deteriorated, especially when the query was longer. In this paper, we treat the TMLNL challenge as a unique variation of VQA, which equally considers the visual elements by using our proposed VQA joint visual-textual framework (JVTF). However, we also manage complex and long input queries without employing natural language processing (NLP) by improving poorly graded to finely graded distinct granularity representations. Our suggested BCPN searches for insufficient context for long input queries using an approach called query handler (QH) and helps the JVTF find the most relevant moment. Previously, a recurrence of words was caused by increasing the number of encoding layers in transformers, LSTMs, and other NLP techniques; however, our QH ensured that repetition of word locations was reduced. The output of BCPN is combined with JVTF’s guided attention to further improve the end outcome. Therefore, we propose a novel bidirectional context predictor network (BCPN), in addition to a VQA joint visual-textual framework (JVTF), to address the equal importance of videos and queries. Through extensive experiments on three benchmark datasets, we show that the proposed BCPN outperforms the state-of-the-art methods by $IoU = 0.3 (2.65 \%) $ , $IoU = 0.5 (2.49 \%)$ , and $IoU = 0.7 (2.06 \%) $. [ABSTRACT FROM AUTHOR]

Given only video-level action categorical labels during training, weakly-supervised temporal action localization (WS-TAL) learns to detect action instances and locates their temporal boundaries in untrimmed videos. Compared to its fully supervised counterpart, WS-TAL is more cost-effective in data labeling and thus favorable in practical applications. However, the coarse video-level supervision inevitably incurs ambiguities in action localization, especially in untrimmed videos containing multiple action instances. To overcome this challenge, we observe that significant temporal contrasts among video snippets, e.g., caused by temporal discontinuities and sudden changes, often occur around true action boundaries. This motivates us to introduce a Contrast-based Localization EvaluAtioN Network (CleanNet), whose core is a new temporal action proposal evaluator, which provides fine-grained pseudo supervision by leveraging the temporal contrasts among snippet-level classification predictions. As a result, the uncertainty in locating action instances can be resolved via evaluating their temporal contrast scores. Moreover, the new action localization module is an integral part of CleanNet which enables end-to-end training. This is in contrast to many existing WS-TAL methods where action localization is merely a post-processing step. Besides, we also explore the usage of temporal contrast on temporal action proposal (TAP) generation task, which we believe is the first attempt with the weak supervision setting. Experiments on the THUMOS14, ActivityNet v1.2 and v1.3 datasets validate the efficacy of our method against existing state-of-the-art WS-TAL algorithms. [ABSTRACT FROM AUTHOR]

As a challenging task of high-level video understanding, weakly supervised temporal action localization has attracted more attention recently. With only video-level category labels, this task should indistinguishably identify the background and action categories frame by frame. However, it is non-trivial to achieve this in untrimmed videos, due to the unconstrained background, complex and multi-label actions. With the observation that these difficulties are mainly brought by the large variations within background and actions, we propose to address these challenges from the perspective of modeling variations. Moreover, it is desired to further reduce the variations, or learn compact features, so as to cast the problem of background identification as rejecting background and alleviate the contradiction between classification and detection. Accordingly, in this paper, we propose a two-branch relational prototypical network. The first branch, namely action-branch, adopts class-wise prototypes and mainly acts as an auxiliary to introduce priori knowledge about label dependencies and be a guide for the second branch. Meanwhile, the second branch, namely sub-branch, starts with multiple prototypes, namely sub-prototypes, to enable a powerful ability of modeling variations. As a further benefit, we elaborately design a multi-label clustering loss based on the sub-prototypes to learn compact features under the multi-label setting. The two branches are associated using the correspondences between two types of prototypes, leading to a special two-stage classifier in the s-branch, on the other hand, the two branches serve as regularization terms to each other, improving the final performance. Ablation studies find that the proposed model is capable of modeling classes with large variations and learning compact features. Extensive experimental evaluations on Thumos14, MultiThumos and ActivityNet datasets demonstrate the effectiveness of the proposed method and superior performance over state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

As an emerging and challenging problem in the computer vision community, weakly supervised object localization and detection plays an important role for developing new generation computer vision systems and has received significant attention in the past decade. As methods have been proposed, a comprehensive survey of these topics is of great importance. In this work, we review (1) classic models, (2) approaches with feature representations from off-the-shelf deep networks, (3) approaches solely based on deep learning, and (4) publicly available datasets and standard evaluation metrics that are widely used in this field. We also discuss the key challenges in this field, development history of this field, advantages/disadvantages of the methods in each category, the relationships between methods in different categories, applications of the weakly supervised object localization and detection methods, and potential future directions to further promote the development of this research field. [ABSTRACT FROM AUTHOR]

Recently, super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) has received much attention. However, ULM relies on low concentrations of microbubbles in the blood vessels, ultimately resulting in long acquisition times. Here, we present an alternative super-resolution approach, based on direct deconvolution of single-channel ultrasound radio-frequency (RF) signals with a one-dimensional dilated convolutional neural network (CNN). This work focuses on low-frequency ultrasound (1.7 MHz) for deep imaging (10 cm) of a dense cloud of monodisperse microbubbles (up to 1000 microbubbles in the measurement volume, corresponding to an average echo overlap of 94%). Data are generated with a simulator that uses a large range of acoustic pressures (5-250 kPa) and captures the full, nonlinear response of resonant, lipid-coated microbubbles. The network is trained with a novel dual-loss function, which features elements of both a classification loss and a regression loss and improves the detection-localization characteristics of the output. Whereas imposing a localization tolerance of 0 yields poor detection metrics, imposing a localization tolerance corresponding to 4% of the wavelength yields a precision and recall of both 0.90. Furthermore, the detection improves with increasing acoustic pressure and deteriorates with increasing microbubble density. The potential of the presented approach to super-resolution ultrasound imaging is demonstrated with a delay-and-sum reconstruction with deconvolved element data. The resulting image shows an order-of-magnitude gain in axial resolution compared to a delay-and-sum reconstruction with unprocessed element data. [ABSTRACT FROM AUTHOR]

Ultrasound localization microscopy (ULM) based on microbubble (MB) localization was recently introduced to overcome the resolution limit of conventional ultrasound. However, ULM is currently challenged by the requirement for long data acquisition times to accumulate adequate MB events to fully reconstruct vasculature. In this study, we present a curvelet transform-based sparsity promoting (CTSP) algorithm that improves ULM imaging speed by recovering missing MB localization signal from data with very short acquisition times. CTSP was first validated in a simulated microvessel model, followed by the chicken embryo chorioallantoic membrane (CAM), and finally, in the mouse brain. In the simulated microvessel study, CTSP robustly recovered the vessel model to achieve an 86.94% vessel filling percentage from a corrupted image with only 4.78% of the true vessel pixels. In the chicken embryo CAM study, CTSP effectively recovered the missing MB signal within the vasculature, leading to marked improvement in ULM imaging quality with a very short data acquisition. Taking the optical image as reference, the vessel filling percentage increased from 2.7% to 42.2% using 50ms of data acquisition after applying CTSP. CTSP used 80% less time to achieve the same 90% maximum saturation level as compared with conventional MB localization. We also applied CTSP on the microvessel flow speed maps and found that CTSP was able to use only 1.6s of microbubble data to recover flow speed images that have similar qualities as those constructed using 33.6s of data. In the mouse brain study, CTSP was able to reconstruct the majority of the cerebral vasculature using 1-2s of data acquisition. Additionally, CTSP only needed 3.2s of microbubble data to generate flow velocity maps that are comparable to those using 129.6s of data. These results suggest that CTSP can facilitate fast and robust ULM imaging especially under the circumstances of inadequate microbubble localizations. [ABSTRACT FROM AUTHOR]

In the face of synthetic aperture radar (SAR) image object detection with different distributions of training and test data, traditional supervised learning methods cannot achieve good detection performance. Domain adaptation (DA) method has been shown to have the ability to solve this problem, but existing DA object detection algorithms all use adversarial DA theory for the detection task, which is ineffective in solving object regression localization in the detection task. In this article, to better solve the above problem, an automatic SAR image ship detection method based on feature decomposition crossing different satellites is proposed. The feature extraction layer of backbone network is divided into low level and high level, where domain-invariant feature (DIF) extractors are designed for the local features extracted from the low level and the global features extracted from the high level, respectively. We argue that the local and global features extracted from source domain and target domain contain domain-specific features (DSF) for adversarial DA and DIFs that contribute to object regression localization. Then, we decompose the local features and global features into DSF and DIF via vector decomposition method. For DSF counterpart, we introduce adversarial DA attention for feature alignment. DIF from the local features are fused into the backbone network for high-level global feature extraction. Finally, using region proposal network and adversarial domain classifier, we can get the accurate bounding box and object class of SAR image objects. Extensive experiments prove that the proposed method outperforms the state-of-the-art methods in terms of detection performance. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) online monitoring is an essential part of transformer insulation condition based maintenance and prognostics and health management in industrial power system. In order to realize online localization of multiple PD sources conveniently, an approach that uses single-channel mixed acoustic emission (AE) signal is proposed in this article. First, the combined method based on the singular spectrum analysis and the independent component analysis is used to calculate the number of PD sources and realize the separation of mixed AE signals. Next, a difference selection algorithm is proposed to solve the problem that the inherent disorder of signal blind source separation makes the time difference of arrival technology difficult to localize the multiple PD sources directly. Then, the simulation signals and measured signals have verified that the signal separation effect of the proposed single-channel-based method is no less than that of the multichannel-based method. Finally, the proposed approach is applied to a transformer. The disordered separated signals were successfully grouped to realize multisource PD localization. [ABSTRACT FROM AUTHOR]

This article considered the persistent real-time localization problem of dynamic multiagent systems with repetitive running characteristics under directed graph. The trajectory length of the agent is randomly varying caused by system constraints and external environment. A novel distributed iterative learning localization estimation method with full historical average data compensation is designed. The average value of all available historical data in the previous operations is used to compensate the incomplete trajectory. The designed diagnosis mechanism and search mechanism are used to determine whether the agent has stopped running and to screen out the available historical data. In order to reduce the computation burden, an improved distributed localization algorithm with receding horizon historical average data compensation is proposed. The asymptotic convergence of the estimation algorithms in the sense of mathematical expectation is derived through the rigorous analysis. Meanwhile, the influence of the incomplete repetitive trajectory of the agent on estimation error and convergence rate is also analyzed. The experimental result based on QBot-2e robot platform verifies the realizability of the proposed method. [ABSTRACT FROM AUTHOR]

Extracting channel state information (CSI) from WiFi signals is of proved high-effectiveness in locating human locations in a device-free manner. However, existing localization/positioning systems are mainly trained and deployed in a fixed environment, and thus they are likely to suffer from substantial performance declines when immigrating to new environments. In this paper, we address the fundamental problem of WiFi-based cross-environment indoor localization using a semi-supervised approach, in which we only have access to the annotations of the source environment while the data in the target environments are un-annotated. This problem is of high practical values in enabling a well-trained system to be scalable to new environments without tedious human annotations. To this end, a deep neural forest is introduced which unifies the ensemble learning with the representation learning functionalities from deep neural networks in an end-to-end trainable fashion. On top of that, an adversarial training strategy is further employed to learn environment-invariant feature representations for facilitating more robust localization. Extensive experiments on real-world datasets demonstrate the superiority of the proposed methods over state-of-the-art baselines. Compared with the best-performing baseline, our model excels with an average 12.7% relative improvement on all six evaluation settings. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have recently attracted tremendous attentions in both industries and academic communities. Thanks to the high mobility and flexibility, UAVs can be deployed in many scenarios to provide various types of services. In these scenarios, the position of the UAVs must be timely and accurately acquired to avoid UAV collisions and realize millimeter-wave beamforming. Particle swarm optimization (PSO) is a potential approach to fulfill localization under GPS-denied environment. However, it has the drawbacks of high complexity and relative large localization error. In this article, we consider the UAV localization problem based on improved PSO, which aims at reducing complexity and localization error. We firstly analyze the performance metrics and performance bounds of conventional PSO in the considered UAV localization scenario. Then, the particle initialization process is reconsidered, where a particle and search space reduction method is introduced as the hierarchical PSO (HPSO). Next, the particle updating schemes are redesigned based on the particle number, where the reference best particle is introduced to deal with the limitations in conventional PSO, this is called reference PSO (RPSO). Lastly, the proposed HPSO and RPSO are validated in simulation results. It is shown that the proposed PSO method has both reduced complexity and localization error compared with conventional PSO and other reference methods. [ABSTRACT FROM AUTHOR]

This study is motivated by the fact that localization in millimeter wave(mmWave) Vehicle-to-Vehicle communication becomes a more critical problem because both the terminals of the communication link are in motion. The positional awareness merely based on GPS or local sensors has an error margin of around 10 meters, which can worsen in uncertain real-time conditions such as road topology and highway traffic. The paper analyses the relation between vehicle dynamics, beamforming, and beam alignment for highly directive antennas. When the subsystem models presented in this paper are taken into consideration, the joint vehicle dynamics-beamforming approach will optimize the link connectivity and SNR. The vehicle dynamics model is designed to be more realistic considering the non-linear acceleration based on the throttle-brake jerks due to induced noises. The Unscented Kalman Filter based prediction subsystem predicts the alignment values even during non-linear acceleration and jerks. The feasibility of antenna design for the beamforming strategies is supported by the requirements of phase shifters and the number of elements. [ABSTRACT FROM AUTHOR]

Falling down is a serious problem for health and has become one of the major etiologies of accidental death for the elderly living alone. In recent years, many efforts have been paid to fall recognition based on wearable sensors or standard vision sensors. However, the prior methods have the risk of privacy leaks, and almost all these methods are based on video clips, which cannot localize where the falls occurred in long videos. For these reasons, in this article, the bioinspired vision sensor-based falls temporal localization framework is proposed. The bioinspired vision sensors, such as dynamic and active-pixel vision sensor (DAVIS) camera applied in this work responds to pixels’ brightness change, and each pixel works independently and asynchronously compared to the standard vision sensors. This property makes it have a very high dynamic range and privacy preserving. First, to better represent event data, compared with the typical constant temporal window mechanism, an adaptive temporal window conversion mechanism is developed. The temporal localization framework follows a proven proposal and classification paradigm. Second, for the high-efficient and recall proposal generation, different from the traditional sliding window scheme, the event temporal density as the actionness score is set and the 1D-watershed algorithm to generate proposals is applied. In addition, we combine the temporal and spatial attention mechanism with our feature extraction network to temporally model the falls. Finally, to evaluate the performance of our framework, 30 volunteers are recruited to join the simulated fall experiments. According to the results of experiments, our framework can realize precise falls temporal localization and achieve the state-of-the-art performance. [ABSTRACT FROM AUTHOR]

In future cellular systems, wireless localization and sensing functions will be built-in for specific applications, e.g., navigation, transportation, and healthcare, and to support flexible and seamless connectivity. Driven by this trend, the need for fine-resolution sensing solutions and centimeter-level localization accuracy arises, while the accuracy of current wireless systems is limited by the quality of the propagation environment. Recently, with the development of new materials, reconfigurable intelligent surfaces (RISs) provide an opportunity to reshape and control the electromagnetic characteristics of the environment, which can be utilized to improve the performance of wireless sensing and localization. In this tutorial, we will first review the background and motivation for utilizing wireless signals for sensing and localization. Next, we will introduce how to incorporate RIS into applications of sensing and localization, including key challenges and enabling techniques, and then, some case studies will be presented. Finally, future research directions will also be discussed. [ABSTRACT FROM AUTHOR]

Accurate single-shot object detection is an extremely challenging task in real environments because of complex scenes, occlusion, ambiguities, blur, and shadow, i.e., these factors are called uncertainty problem. It leads to unreliable labeling of bounding box annotation and makes detectors arduous to learn bounding box localization. Previous methods viewed the ground truth box coordinates as a rigid distribution omitting localization uncertainty in real datasets. This article proposes a novel bounding box encoding algorithm integrated into the single-shot detector (BBENet) to consider the flexible distribution of bounding box localization. First, discretized ground truth labels are generated by decomposing each object’s boundary into multiple boundaries. The new representation of ground truth boxes is more arbitrary and flexible to cover any case of complex scenes. During training, the detector directly learns discretized box locations instead of continuous domain. Second, the bounding box encoding algorithm reorganizes bounding box predictions to be more accurate. Furthermore, another problem in existing methods is inconsistency in estimating detection quality. The single-shot detection consists of classification and localization tasks, but the popular detectors consider the classification score as the final detection quality. Thus, it lacks localization quality and hinders the overall performance because both tasks have a positive correlation. To overcome this problem, BBENet introduces detection quality by combining the localization and classification quality to rank detection during nonmaximum suppression. The localization quality is computed based on how uncertain the predicted boxes are, which is a new perspective in detection literature. The proposed BBENet is evaluated on three benchmark datasets, i.e., MS-COCO, Pascal VOC, and CrowdHuman. Without bells and whistles, BBENet outperforms the existing methods by a large margin with comparable speed, achieving the state-of-the-art single-shot detector. [ABSTRACT FROM AUTHOR]

Wireless techniques have progressed beyond two-dimensional (2D) applications; in particular, three-dimensional (3D) localization in wireless sensor networks has attracted increasing attention. While many research works in 3D localization focused on the accuracy enhancement of schemes based on the sensors’ measurements, limited research works addressed the design of 3D localization schemes considering the narrowband signal restriction of sensors. Against this background, we propose a cube-based multitarget three-dimensional (3D) localization solution by exploiting sensors’ time-difference-of-arrival (TDOA) measurements. In particular, unlike the traditional TDOA based scheme, our scheme works in an asynchronous network. Our contributions are twofold. First, the distributed TDOA–based sensor arrays placed with a predefined method create a cube-based location system in 3D space. Second, we propose a turbo expectation propagation (EP)-based decoding algorithm (TED). EP computation is an efficient tool in Bayesian machine learning. With the assistance of the iterative sensor reliability correction (ISRC), we propose an improved algorithm referred to as ISRC-TED. Specifically, the ISRC-TED algorithm outperforms the TED algorithm by utilizing the decisions of the sensor array at every iteration to improve the decoding accuracy further. A reduced-complexity tree search–based decoding strategy for TED and ISRC-TED is also proposed. In simulations, the proposed TED and ISRC-TED algorithms were highly effective, even when the partial sensor network was in power-saving mode. For example, the proposed ISRC-TED algorithm had almost no localization performance loss when 10% of the sensors were in sleep mode. [ABSTRACT FROM AUTHOR]

The idea of cooperation has been introduced to self-driving cars about a decade ago with the aim to reduce the occlusion caused by other users or the scene. More recently, the research efforts turned toward cooperative infrastructure bringing a new kind of the point of view as well as more processing power. This paper lies in this new field providing a survey that addresses the cooperative environment. We provide an overview of the architectures available to create such a system as well as the challenges introduced by the cooperation. Later, we review the main blocks involved in the perception: localization, object detection & tracking, map generation. Each block is reviewed under the prism of cooperation. We also provide a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of the cooperative perception as well as a list of related scenarios alongside experimentations. Finally, we list some related datasets before concluding our paper, underlining the perspectives for further works. [ABSTRACT FROM AUTHOR]

The steered response power phase transform (SRP-PHAT) is a well-known algorithm for acoustic source localization using microphone arrays. It consists in the computation of the generalized cross-correlation (GCC) between each microphone pair, and in the coherent summation of the GCC values in the grid search space. Several improvements based on the volumetric grid have been proposed in order to achieve spatial resolution scalability and to reduce the computational cost by using a coarser grid. In general, the problem of the volumetric based methods is that the noise and the reverberation are projected into the search space since all GCC information is used to build the acoustic map. It is hence proposed a volumetric grid SRP-PHAT algorithm based on the geometrically sampled grid (GSG) that incorporates a max-pooling (MP) operation in the volume accumulation of the GCC values in order to improve the localization performance. The MP is the solution of a minimization-maximization problem that aims at minimizing the deleterious effect of noise and reverberation and at maximizing the accuracy of the GCC values related to the target sound source. Simulations and real-world experiments demonstrate the efficiency of the proposed SRP-GSG-MP algorithm in adverse conditions. [ABSTRACT FROM AUTHOR]

Following the recent progress in terahertz (THz) signal generation and radiation methods, joint THz communications and sensing (CAS) applications are being proposed for future wireless systems. Toward this end, THz spectroscopy is expected to be carried over user equipment devices to identify material and gaseous components of interest. THz-specific signal processing techniques should complement this resurgent interest in THz sensing for efficient utilization of the THz band. In this article, we present an overview of these techniques, with an emphasis on signal preprocessing [standard normal variate (SNV) normalization, minimum–maximum normalization, and Savitzky–Golay (SG) filtering], feature extraction [principal component analysis (PCA), partial least squares (PLS), t-distributed stochastic neighbor embedding (t-SNE), and nonnegative matrix factorization (NMF)], and classification techniques [support vector machines (SVMs), the k-nearest neighbor (kNN), discriminant analysis (DA), and naive Bayes (NB)]. We also address the effectiveness of deep learning techniques by exploring their promising sensing and localization capabilities at the THz band. Finally, we investigate the performance and complexity tradeoffs of the studied methods in the context of joint CAS (JCAS). We thereby motivate corresponding use cases and present a handful of contextual future research directions. [ABSTRACT FROM AUTHOR]

Underwater optical wireless communications (UOWCs) are promising and potential wireless carriers to envisage underwater phenomenal activities for various applications towards the futuristic 5G and beyond (5GB) wireless systems. The main challenges to deploy underwater applications are the physicochemical properties and strong turbulence channel conditions. In this regard, the end-to-end (E2E) performance analysis of a dual-hop mixed FSO/UVLC system under the intensity modulation/direct detection (IM/DD) technique in consideration of pulse amplitude modulation (PAM) scheme is investigated. Throughout this study, to tackle the issues of moderate-to-strong turbulence channel conditions, this work deploys the Gamma-Gamma (GG) distribution fading model and the links are designed by unifying plane wave models in the corresponding links, respectively. This investigation outperforms higher achievable data rate with minimal delay response and enhance network connectivity in real-time monitoring scenarios as compared with the traditional underwater wireless communication technologies. In more contrast, the probability distribution function (PDF), cumulative distribution function (CDF), and closed-form expression of the system are derived and presented in terms of Meijer-G function as well as Extended Generalized Bivariate Meijer-G Function (EGBMGF). The significant E2E performance metrics are obtained by employing the decode-and-forward (DF) relay protocol in hostile channel conditions. In aggregating this work, we combine the analytical expressions that present an efficient tool to depict the impact of channel parameters on the system. The simulation results are plausible of the system performance metrics as average BER (ABER) and outage probability $(P_{out})$ in the presence of pointing and without pointing error events. Finally, in this work, we use the Monte-Carlo approach for the best fitting curves and validate the numerical expression yields simulation results. [ABSTRACT FROM AUTHOR]

Low-Power Wide-Area Network (LPWAN) multi-Radio Access Technology (RAT) devices combine features of different low-power network technologies to flexibly manage heterogeneous requirements stemming from various Internet of Things (IoT) applications. IEEE 802.11ah is a novel technology that is envisioned to provide a “bridge” between Wi-Fi and s, thus it has been often considered as one of the supporting technologies in multi-RAT devices. In such multi- RAT scenarios, network discovery and handover procedures need to be utilized for determining the availability of a given technology and managing if the connection to the technology should be initiated. However, traditional discovery and handover procedures, such as beacon listening, have to be performed periodically and, therefore, consume substantial amount of energy, making them unsuitable for battery-powered IoT devices. To address this issue, we present a mechanism that is able to make more optimal discovery and handover decisions by leveraging the physical location information of the multi- RAT devices. We demonstrate how this approach is feasible in performing energy efficient handovers between a technology (NB-IoT) and IEEE 802.11ah based on estimated location. We do that by showing that the location-based procedure substantially reduces the energy consumption of the mobile device compared to the traditional discovery based on periodical listening for beacons, while maintaining comparable duration of the device’s association to IEEE 802.11ah. Moreover, we evaluate the energy and delay overheads caused by the localization service, showing only slight effects on the performance of the mechanism. [ABSTRACT FROM AUTHOR]

Nowadays the ability to locate people and objects is becoming an important and necessary aspect for the wide range of applications concerning localization-based services. Indeed, applications based on spatial and time awareness of people and objects within certain contexts are being developed. Some application examples in which the positioning problem must be considered and solved are: industrial automation, virtual reality, smart cities, smart health, robot navigation and remote surgery. Although in outdoor environments this does not represent a big issue thanks to the presence and support of Global Navigation Satellite Systems (GNSSs), there is a whole series of scenarios, such as indoors, underground, and underwater environments, in which GNSSs fail due to the complexity of the considered environments with respect to radio propagation. Nowadays, in the considered scenarios, the identification of a reliable method able to satisfy the positioning requirements is an open issue, for which the measurement community can provide a valuable contribution regarding the proposal of measurement methods and procedures, the evaluation of the major causes of uncertainty of the devices and the consideration of these features within positioning algorithms and data processing. [ABSTRACT FROM AUTHOR]

This article proposes a supervised learning approach for centralized localization of faults in microgrids with radial configuration that can operate in grid-connected or islanded mode. The key concept is to identify faults by learning and analyzing the features of voltage and current disturbances detected by a microgrid central protection unit. Two algorithms are developed that determine fault indices and transient features for simulation-based training, classification, and backup protection purposes. The proposed data-driven approach is effective under high penetration of distributed energy resources (DERs), and is robust under microgrid topology variations. Moreover, it requires few sensors (two for radial structure) and is easy to implement, yet provides high degree of reliability. The performance and accuracy of the developed approach are verified through extensive simulations of low-voltage microgrids that incorporate different types of DERs, including wind and solar photovoltaic systems. [ABSTRACT FROM AUTHOR]

Smartphone-based WiFi ranging using fine time measurement (FTM) is severely impacted by Non-line-of-sight (NLoS) environments, which causes significant positioning errors. To address this problem, we propose a novel WiFi FTM positioning (WFP) approach based on the geomagnetism and enhanced genetic algorithm (EGA), which can simultaneously execute WiFi localization and ranging compensation. Based on the distribution of the ranging error in NLoS environments, a semiparametric error model-based ranging compensation method is proposed. To construct the EGA searching model, geomagnetism-based positioning is adopted and fed to the EGA together with the measured WiFi ranging data and the ranging compensation method. During online localization, the EGA model dynamically compensates for the erroneous ranging data until it finds the optimal position. Experimental results show that the ranging and localization accuracy of this EGA-based WFP are 1.33 m and 1.64 m, being an improvement of 30.7% and 56.5% compared to the uncompensated ranging data and the trilateration algorithm using the weighted least square (WLS) method, respectively. [ABSTRACT FROM AUTHOR]

Millimeter-wave ($mm$ -wave) multiple-input single-output (MISO) systems are expected to be extremely advantageous for the fifth generation (5G) cellular systems. In fact, these systems are considered key enablers of centimeter-level localization accuracy, even in the case of a single base station (BS). However, there are still fundamental issues that need to be addressed when applying $mm$ -wave MISO systems to practical scenarios, namely the effects of hardware impairments (HWIs). In this study, the 3D localization accuracy of $mm$ -wave MISO OFDM systems is investigated when there are HWIs at both the BS and the mobile station (MS). The localization is performed by estimating the downlink channel parameters of the line-of-sight (LOS) path using only a maximum likelihood (ML) estimator at the MS. We then transform these channel parameters into ones for localization. The Fisher information matrix (FIM) is employed to assess the accuracy of the estimation processes, considering also any non-LOS (NLOS) paths. The limit of localization is calculated in terms of the position error bound (PEB). Computer simulations demonstrate the destructive impacts of HWIs on the localization process. Moreover, it was proven that the effect of NLOS paths from unknown scatters on the localization process is related to the ratio between LOS and NLOS path gains. [ABSTRACT FROM AUTHOR]

In the GNSS-denied localization scenario, only received signals and corresponding distance measurements are available. Based on that, in this letter, we construct a multistage clustering-based model for UAV swarm. We characterize the proposed model as a coalition formation game (CFG) and provide corresponding preference criteria for algorithm design. A tree-like multistage clustering mechanism is adopted based on a coalitional graph game (CGG). Each cluster (local map) performs localization calculation and then merges through neighboring drones (NDs) stage by stage. The simulation results show that the proposed scheme can achieve better localization accuracy than the comparison algorithms and is more robust for irregular network topologies. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have been successfully applied in the computer-aided ultrasound diagnosis for breast cancer. Up to now, several CNN-based methods have been proposed. However, most of them consider tumor localization and classification as two separate steps, rather than performing them simultaneously. Besides, they suffer from the limited diagnosis information in the B-mode ultrasound (BUS) images. In this study, we develop a novel network ResNet-GAP that incorporates both localization and classification into a unified procedure. To enhance the performance of ResNet-GAP, we leverage stiffness information in the elastography ultrasound (EUS) modality by collaborative learning in the training stage. Specifically, a dual-channel ResNet-GAP network is developed, one channel for BUS and the other for EUS. In each channel, multiple class activity maps (CAMs) are generated using a series of convolutional kernels of different sizes. The multi-scale consistency of the CAMs in both channels are further considered in network optimization. Experiments on 264 patients in this study show that the newly developed ResNet-GAP achieves an accuracy of 88.6%, a sensitivity of 95.3%, a specificity of 84.6%, and an AUC of 93.6% on the classification task, and a 1.0NLF of 87.9% on the localization task, which is better than some state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Natural disasters affect millions of people every year. Finding missing persons in the shortest possible time is of crucial importance to reduce the death toll. This task is especially challenging when victims are sparsely distributed in large and/or difficult-to-reach areas and cellular networks are down. In this paper we present SARDO, a drone-based search and rescue solution that leverages the high penetration rate of mobile phones in the society to localize missing people. SARDO is an autonomous, all-in-one drone-based mobile network solution that does not require infrastructure support or mobile phones modifications. It builds on novel concepts such as pseudo-trilateration combined with machine-learning techniques to efficiently locate mobile phones in a given area. Our results, with a prototype implementation in a field-[1], show that SARDO rapidly determines the location of mobile phones ($\sim \!3$ ∼ 3 min/UE) in a given area with an accuracy of few tens of meters and at a low battery consumption cost ($\sim \!5\%$ ∼ 5 % ). State-of-the-art localization solutions for disaster scenarios rely either on mobile infrastructure support or exploit onboard cameras for human/computer vision, IR, thermal-based localization. To the best of our knowledge, SARDO is the first drone-based cellular search-and-rescue solution able to accurately localize missing victims through mobile phones. [ABSTRACT FROM AUTHOR]

This paper explores the use of variants of tf-idf-based descriptors, namely length-normalized-tf-idf and log-normalized-tf-idf, combined with a segmentation technique, for efficient modeling of variable-length protein sequences. The proposed solution, ProtVecGen-Ensemble, is an ensemble of three models trained on differently segmented datasets constructed from an input dataset containing complete protein sequences. Evaluations using biological process (BP) and molecular function (MF) datasets demonstrate that the proposed feature set is not only superior to its contemporaries but also produces more consistent results with respect to variation in sequence lengths. Improvements of +6.07% (BP) and +7.56% (MF) over state-of-the-art tf-idf-based MLDA feature set were obtained. The best results were achieved when ProtVecGen-Ensemble was combined with ProtVecGen-Plus — the state-of-the-art method for protein function prediction — resulting in improvements of +8.90% (BP) and +11.28% (MF) over MLDA and +1.49% (BP) and +2.07% (MF) over ProtVecGen-Plus+MLDA. To capture the performance consistency with respect to sequence lengths, we have defined a variance-based metric, with lower values indicating better performance. On this metric, the proposed ProtVecGen-Ensemble+ProtVecGen-Plus framework resulted in reductions of 56.85 percent (BP) and 56.08 percent (MF) over MLDA and 10.37 percent (BP) and 26.48 percent (MF) over ProtVecGenPlus+MLDA. [ABSTRACT FROM AUTHOR]

Automatic program repair has drawn more and more attention since software quality is facing increasing challenges. In existing approaches, the unlimited search space is considered to be the main limitation in finding the correct patch. So how to reduce the search space to improve the efficiency of automatic program repair remains a problem to be solved. In this work, we represent a similarity-based and location-awareness-based automatic program repair (SLARepair). SLARepair takes the similarity between codes as important search information. The search space is further subdivided by the location-awareness strategy to improve search efficiency. In addition, to better guide the search process, a new fitness function is designed for genetic programming, which brings notable improvements. Moreover, the patch verification time is further reduced by utilizing the test case prioritization approach combined with test case filtering. Extensive experiments demonstrate that our SLARepair outperforms the state-of-the-art approaches on the Defects4J benchmark and achieves competitive performances.

In this article, semidefinite programming (SDP) solutions are proposed for the elliptic localization problem in asynchronous radar networks, where the transmitters are subject to clock offsets. Relaxing the nonconvex problem into different convex forms, we design tight SDP (TSDP) and global SDP (GSDP) solutions for this problem. The TSDP solution includes several SDP cones to solve the problem, and the correlation is not considered. Hence, the TSDP solution performs poorly in the presence of correlated noise. We further put forward the GSDP solution to improve the performance by introducing a new GSDP form, which includes only one SDP cone to handle the correlated noise. We also theoretically prove that the GSDP problem is tight enough so that its performance is able to sufficiently approach the Cramér–Rao lower bound (CRLB) accuracy. The simulated results show that the TSDP solution can provide the comparable performance with the GSDP in the absence of related noise. The performance of the GSDP solution can almost attain the CRLB accuracy using less receivers. [ABSTRACT FROM AUTHOR]

The article proposes a visual-inertial navigation method to support the autonomous operation of a quadrotor UAV during the building façade inspection tasks, where state-of-the-art vision-based localization methods may fail due to texture point feature insufficiency. Considering the appearance characteristics of the building façades, we additionally fuse line features and their corresponding structure prior information to the sliding-window-based estimator to improve localization reliability and accuracy. The contribution of the proposed method lies mainly in two aspects. First, we develop an informative feature selection mechanism according to the façade patterns and the inspection trajectory patterns to make a balance between localization accuracy and computation overheads. Second, we further utilize structure prior information, which is defined as line-to-line and point-to-line relationships, as another source of high-fidelity measurement to restrain the localization drifts. The proposed method is tested not only on public datasets, but also on an actual flight data package recorded in a building inspection task. Experimental results show that the proposed method can serve as a practical tool for navigating a robot in a building inspection task, with improved localization reliability and accuracy. [ABSTRACT FROM AUTHOR]

Light detection and ranging (Lidar) has become the core device in a simultaneous localization and mapping system, which has attracted much attention in recent years. However, the point clouds acquired by Lidar are sparse and mutually uncorrelated, making it difficult to calculate correspondences between consecutive frames and resulting in data drifting. In this situation, we propose a tightly coupled Lidar + IMU fusion system with loop optimization to address the above-mentioned problems. First, the ground Lidar points are segmented and removed. Second, under our framework, an inertial measurement unit is used to deskew the motion distortion of Lidar. Third, a voxel grid filtering process is implemented to further eliminate the redundant points, and feature matching is performed by identifying lines and planes. Finally, loop closure detection is realized to correct pose estimation and localization error. Experimental results on both public dataset and field test demonstrate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Emitter localization is an active research topic in electronic reconnaissance. Recently, passive synthetic aperture (PSA) has been used to determine the azimuth and range distance of the emitter. However, the existing methods approximate the distance equation of the spaceborne model through the airborne model, resulting in a large positioning error. In this article, an emitter localization algorithm based on PSA in the spaceborne model is proposed. First, the phase of the received signal is accumulated by using the symmetry of the Doppler history of the received signal in long synthetic aperture time, improving the azimuth positioning accuracy. The effective velocity is then used to establish the nonlinear equations of range distance. A modulus function is used to solve the equations, correcting the range positioning error in the spaceborne model. Finally, the influence of noise, satellite position error, and satellite speed error on the positioning results is analyzed. The error matrix of the proposed method in wide swath is obtained. Simulation results show that the positioning accuracy of this method is one order of magnitude higher than that of traditional single-satellite positioning methods. The effectiveness of the proposed method is verified through actual satellite experiments. [ABSTRACT FROM AUTHOR]