Abstract-We consider time-of-arrival based robust geolocation in harsh line-of-sight/non-line-of-sight environments. Herein, we assume the probability density function (PDF) of the measurement error to be completely unknown and develop an iterative algorithm for robust position estimation. The iterative algorithm alternates between a PDF estimation step, which approximates the exact measurement error PDF (albeit unknown) under the current parameter estimate via adaptive kernel density estimation, and a parameter estimation step, which resolves a position estimate from the approximate log-likelihood function via a quasi-Newton method. Unless the convergence condition is satisfied, the resolved position estimate is then used to refine the PDF estimation in the next iteration. We also present the best achievable geolocation accuracy in terms of the Cramér-Rao lower bound. Various simulations have been conducted in both real-world and simulated scenarios. When the number of received range measurements is large, the new proposed position estimator attains the performance of the maximum likelihood estimator (MLE). When the number of range measurements is small, it deviates from the MLE, but still outperforms several salient robust estimators in terms of geolocation accuracy, which comes at the cost of higher computational complexity.Index Terms-Adaptive kernel density estimation (AKDE), Cramér-Rao lower bound (CRLB), non-line-of-sight (NLOS) mitigation, robust geolocation, time-of-arrival (TOA).

The cloud radio access network (C-RAN) is a promising paradigm to meet the stringent requirements of the fifth generation (5G) wireless systems. Meanwhile, wireless traffic prediction is a key enabler for C-RANs to improve both the spectrum efficiency and energy efficiency through load-aware network managements. This paper proposes a scalable Gaussian process (GP) framework as a promising solution to achieve large-scale wireless traffic prediction in a cost-efficient manner. Our contribution is three-fold. First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale executions.Second, in the prediction phase, we fuse local predictions obtained from the BBUs via a cross-validation based optimal strategy, which demonstrates itself to be reliable and robust for general regression tasks. Moreover, such a cross-validation based optimal fusion strategy is built upon a well acknowledged probabilistic model to retain the valuable closed-form GP inference properties. Third, we propose a C-RAN based scalable wireless prediction architecture, where the prediction accuracy and the time consumption can be balanced by tuning the number of the BBUs according to the real-time system demands. Experimental results show that our proposed scalable GP model can outperform the state-of-the-art approaches considerably, in terms of wireless traffic prediction performance. Index TermsC-RANs, Gaussian processes, parallel processing, ADMM, cross-validation, machine learning, wireless traffic solution to reach such ambitious goals is the adoption of cloud radio access networks (C-RANs) [2], which have attracted intense research interests from both academia and industry in recent years [3]. A C-RAN is composed of two parts: the distributed remote radio heads (RRHs) with basic radio functionalities to provide coverage over a large area, and the centralized baseband units (BBUs) pool with parallel BBUs to support joint processing and cooperative network management. The BBUs can perform dynamic resource allocation in accordance with realtime network demands based on the virtualized resources in cloud computing. One major feature for the C-RANs to enable high energy-efficient services is the fast adaptability to non-uniform traffic variations [1]-[4], e.g., the tidal effects. Consequently, wireless traffic prediction techniques stand out as the key enabler to realize such loadaware management and proactive control in C-RANs, e.g., the load-aware RRH on/off operation [4]. However, the adoption of wireless traffic prediction techniques in C-RANs must satisfy the requirements on prediction accuracy, cost-efficiency, implementability, and scalability for large-scale executions. A. Related WorksIn the literature, many statistical ti...

Commercially available extra virgin olive oils are often adulterated with some other cheaper edible oils with similar chemical compositions. A set of extra virgin olive oil samples adulterated with soybean oil, corn oil and sunflower seed oil were characterized by Raman spectra in the region 1000-1800 cm −1 . Based on the intensity of the Raman spectra with vibrational bands normalized by the band at 1441 cm −1 (CH 2 ), external standard method (ESM) was employed for the quantitative analysis, which was compared with the results achieved by support vector machine (SVM) methods. By plotting the adulterant content of extra virgin olive oil versus its corresponding band intensity in the Raman spectrum at 1265 cm −1 , the calibration curve was obtained. Coefficient of determination (R 2 ) of each curve was 0.9956, 0.9915 and 0.9905 for extra virgin olive oil samples adulterated with soybean oil, corn oil and sunflower seed oil, respectively. The mean absolute relative errors were calculated as 7.41, 7.78 and 9.45%, respectively, with ESM, while they were 5.10, 6.96 and 4.55, in the SVM model, respectively. The prediction accuracy shows that the ESM based on Raman spectroscopy is a promising technique for the authentication of extra virgin olive oil. The method also has the advantages of simplicity, time savings and non-requirement of sample preprocessing; especially, a portable Raman system is suitable for on-site testing and quality control in field applications.

Purpose – When using a machine vision inspection system for rail surface defect detection, many complex factors such as illumination changes, reflection inequality, shadows, stains and rust might inevitably deform the scanned rail surface image. This paper aims to reduce the influence of these factors, a pipeline of image processing algorithms for robust defect detection is developed. Design/methodology/approach – First, a new inverse Perona-Malik (P-M) diffusion model is presented for image enhancement, which takes the reciprocal of gradient as feature to adjust the diffusion coefficients, and a distinct nearest-neighbor difference scheme is introduced to select proper defect boundaries during discretized implementation. As a result, the defect regions are sufficiently smoothened, whereas the faultless background remains unchanged. Then, by subtracting the diffused image from the original image, the defect features will be highlighted in the difference image. Subsequently, an adaptive threshold binarization, followed by an attribute opening like filter, can easily eliminate the noisy interferences and find out the desired defects. Findings – Using data from our developed inspection apparatus, the experiments show that the proposed method can attain a detection and measurement precisions as high as 93.6 and 85.9 per cent, respectively, while the recovery accuracy remains 93 per cent. Additionally, the proposed method is computationally efficient and can perform robustly even under complex environments. Originality/value – A pipeline of algorithms for rail surface detection is proposed. Particularly, an inverse P-M diffusion model with a distinct discretization scheme is introduced to enhance the defect boundaries and suppress noises. The performance of the proposed method has been verified with real images from our own developed system.

Abstract-We consider robust geolocation in mixed line-ofsight (LOS)/non-LOS (NLOS) environments in cellular radio

Abstract-We study cooperative sensor network localization in a realistic scenario where (1) the underlying measurement errors more probably follow a non-Gaussian distribution; (2) the measurement error distribution is unknown without conducting massive offline calibrations; and (3) non-line-of-sight identification is not performed due to the complexity constraint and/or storage limitation. The underlying measurement error distribution is approximated parametrically by a Gaussian mixture with finite number of components, and the expectation-conditional maximization (ECM) criterion is adopted to approximate the maximum-likelihood estimator of the unknown sensor positions and an extra set of Gaussian mixture model parameters. The resulting centralized ECM algorithms lead to easier inference tasks and meanwhile retain several convergence properties with a proof of the "space filling" condition. To meet the scalability requirement, we further develop two distributed ECM algorithms where an average consensus algorithm plays an important role for updating the Gaussian mixture model parameters locally. The proposed algorithms are analyzed systematically in terms of computational complexity and communication overhead. Various computer based tests are also conducted with both simulation and experimental data. The results pin down that the proposed distributed algorithms can provide overall good performance for the assumed scenario even under model mismatch, while the existing competing algorithms either cannot work without the prior knowledge of the measurement error statistics or merely provide degraded localization performance when the measurement error is clearly non-Gaussian.Index Terms-Centralized and distributed algorithms, cooperative localization, expectation-conditional maximization (ECM), Gaussian mixture, wireless sensor network (WSN).

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In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big data methods. We first review (1) state-of-the-art algorithms in the context of federated learning, (2) two widely used learning models, namely the deep neural network model and the Gaussian process model, and (3) various distributed model hyper-parameter optimization schemes. Then, we demonstrate various practical use cases that are summarized from a mixture of standard, newly published, and unpublished works, which cover a broad range of location services, including collaborative static localization/fingerprinting, indoor target tracking, outdoor navigation using low-sampling GPS, and spatio-temporal wireless traffic data modeling and prediction. Experimental results show that near centralized data fitting-and prediction performance can be achieved by a set of collaborative mobile users running distributed algorithms. All the surveyed use cases fall under our newly proposed Federated Localization (FedLoc) framework, which targets on collaboratively building accurate location services without sacrificing user privacy, in particular, sensitive information related to their geographical trajectories. Future research directions are also discussed at the end of this paper.