5G Positioning and Mapping with Diffuse Multipath

5G mmWave communication is useful for positioning due to the geometric connection between the propagation channel and the propagation environment. Channel estimation methods can exploit the resulting sparsity to estimate parameters(delay and angles) of each propagation path, which in turn can be exploited for positioning and mapping. When paths exhibit significant spread in either angle or delay, these methods breakdown or lead to significant biases. We present a novel tensor-based method for channel estimation that allows estimation of mmWave channel parameters in a non-parametric form. The method is able to accurately estimate the channel, even in the absence of a specular component. This in turn enables positioning and mapping using only diffuse multipath. Simulation results are provided to demonstrate the efficacy of the proposed approach

Hybrid self-organizing feature map (SOM) for anomaly detection in cloud infrastructures using granular clustering based upon value-difference metrics

We have witnessed an increase in the availability of data from diverse sources over the past few years. Cloud computing, big data and Internet-of-Things (IoT) are distinctive cases of such an increase which demand novel approaches for data analytics in order to process and analyze huge volumes of data for security and business use. Cloud computing has been becoming popular for critical structure IT mainly due to cost savings and dynamic scalability. Current offerings, however, are not mature enough with respect to stringent security and resilience requirements. Mechanisms such as anomaly detection hybrid systems are required in order to protect against various challenges that include network based attacks, performance issues and operational anomalies. Such hybrid AI systems include Neural Networks, blackboard systems, belief (Bayesian) networks, case-based reasoning and rule-based systems and can be implemented in a variety of ways. Traffic in the cloud comes from multiple heterogeneous domains and changes rapidly due to the variety of operational characteristics of the tenants using the cloud and the elasticity of the provided services. The underlying detection mechanisms rely upon measurements drawn from multiple sources. However, the characteristics of the distribution of measurements within specific subspaces might be unknown. We argue in this paper that there is a need to cluster the observed data during normal network operation into multiple subspaces each one of them featuring specific local attributes, i.e. granules of information. Clustering is implemented by the inference engine of a model hybrid NN system. Several variations of the so-called value-difference metric (VDM) are investigated like local histograms and the Canberra distance for scalar attributes, the Jaccard distance for binary word attributes, rough sets as well as local histograms over an aggregate ordering distance and the Canberra measure for vectorial attributes. Low-dimensional subspace representations of each group of points (measurements) in the context of anomaly detection in critical cloud implementations is based upon VD metrics and can be either parametric or non-parametric. A novel application of a Self-Organizing-Feature Map (SOFM) of reduced/aggregate ordered sets of objects featuring VD metrics (as obtained from distributed network measurements) is proposed. Each node of the SOFM stands for a structured local distribution of such objects within the input space. The so-called Neighborhood-based Outlier Factor (NOOF) is defined for such reduced/aggregate ordered sets of objects as a value-difference metric of histogrammes. Measurements that do not belong to local distributions are detected as anomalies, i.e. outliers of the trained SOFM. Several methods of subspace clustering using Expectation-Maximization Gaussian Mixture Models (a parametric approach) as well as local data densities (a non-parametric approach) are outlined and compared against the proposed method using data that are obtained from our cloud testbed in emulated anomalous traffic conditions. The results—which are obtained from a model NN system—indicate that the proposed method performs well in comparison with conventional techniques

Neural Collaborative Subspace Clustering

We introduce the Neural Collaborative Subspace Clustering, a neural model that discovers clusters of data points drawn from a union of low-dimensional subspaces. In contrast to previous attempts, our model runs without the aid of spectral clustering. This makes our algorithm one of the kinds that can gracefully scale to large datasets. At its heart, our neural model benefits from a classifier which determines whether a pair of points lies on the same subspace or not. Essential to our model is the construction of two affinity matrices, one from the classifier and the other from a notion of subspace self-expressiveness, to supervise training in a collaborative scheme. We thoroughly assess and contrast the performance of our model against various state-of-the-art clustering algorithms including deep subspace-based ones.Comment: Accepted to ICML 201

A Sparsity-Inducing Optimization-Based Algorithm for Planar Patches Extraction from Noisy Point-Cloud Data

Currently, much of the manual labor needed to generate as-built Building Information Models (BIMs) of existing facilities is spent converting raw Point Cloud Datasets (PCDs) to BIMs descriptions. Automating the PCD conversion process can drastically reduce the cost of generating as-built BIMs. Due to the widespread existence of planar structures in civil infrastructures, detecting and extracting planar patches from raw PCDs is a fundamental step in the conversion pipeline from PCDs to BIMs. However, existing methods cannot effectively address both automatically detecting and extracting planar patches from infrastructure PCDs. The existing methods cannot resolve the problem due to the large scale and model complexity of civil infrastructure, or due to the requirements of extra constraints or known information. To address the problem, this paper presents a novel framework for automatically detecting and extracting planar patches from large-scale and noisy raw PCDs. The proposed method automatically detects planar structures, estimates the parametric plane models, and determines the boundaries of the planar patches. The first step recovers existing linear dependence relationships amongst points in the PCD by solving a group-sparsity inducing optimization problem. Next, a spectral clustering procedure based on the recovered linear dependence relationships segments the PCD. Then, for each segmented group, model parameters of the extracted planes are estimated via Singular Value Decomposition (SVD) and Maximum Likelihood Estimation Sample Consensus (MLESAC). Finally, the α-shape algorithm detects the boundaries of planar structures based on a projection of the data to the planar model. The proposed approach is evaluated comprehensively by experiments on two types of PCDs from real-world infrastructures, one captured directly by laser scanners and the other reconstructed from video using structure-from-motion techniques. In order to evaluate the performance comprehensively, five evaluation metrics are proposed which measure different aspects of performance. Experimental results reveal that the proposed method outperforms the existing methods, in the sense that the method automatically and accurately extracts planar patches from large-scaled raw PCDs without any extra constraints nor user assistance.This is the accepted manuscript. The final version is available from Wiley at http://onlinelibrary.wiley.com/doi/10.1111/mice.12063/abstract

Multi-View Face Recognition From Single RGBD Models of the Faces

This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks