Upper bounds on position error of a single location estimate in wireless sensor networks

This paper studies upper bounds on the position error for a single estimate of an unknown target node position based on distance estimates in wireless sensor networks. In this study, we investigate a number of approaches to confine the target node position to bounded sets for different scenarios. Firstly, if at least one distance estimate error is positive, we derive a simple, but potentially loose upper bound, which is always valid. In addition assuming that the probability density of measurement noise is nonzero for positive values and a sufficiently large number of distance estimates are available, we propose an upper bound, which is valid with high probability. Secondly, if a reasonable lower bound on negative measurement errors is known a priori, we manipulate the distance estimates to obtain a new set with positive measurement errors. In general, we formulate bounds as nonconvex optimization problems. To solve the problems, we employ a relaxation technique and obtain semidefinite programs. We also propose a simple approach to find the bounds in closed forms. Simulation results show reasonable tightness for different bounds in various situations. © 2014 Gholami et al.; licensee Springer

Image embedding and user multi-preference modeling for data collection sampling

This work proposes an end-to-end user-centric sampling method aimed at selecting the images from an image collection that are able to maximize the information perceived by a given user. As main contributions, we first introduce novel metrics that assess the amount of perceived information retained by the user when experiencing a set of images. Given the actual information present in a set of images, which is the volume spanned by the set in the corresponding latent space, we show how to take into account the user’s preferences in such a volume calculation to build a user-centric metric for the perceived information. Finally, we propose a sampling strategy seeking the minimum set of images that maximize the information perceived by a given user. Experiments using the coco dataset show the ability of the proposed approach to accurately integrate user preference while keeping a reasonable diversity in the sampled image set

Distributed Adaptive Learning of Graph Signals

The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables distributed reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of sampled observations taken from a subset of vertices. A detailed mean square analysis is carried out and illustrates the role played by the sampling strategy on the performance of the proposed method. Finally, some useful strategies for distributed selection of the sampling set are provided. Several numerical results validate our theoretical findings, and illustrate the performance of the proposed method for distributed adaptive learning of signals defined over graphs.Comment: To appear in IEEE Transactions on Signal Processing, 201

A novel algorithm and hardware architecture for fast video-based shape reconstruction of space debris

In order to enable the non-cooperative rendezvous, capture, and removal of large space debris, automatic recognition of the target is needed. Video-based techniques are the most suitable in the strict context of space missions, where low-energy consumption is fundamental, and sensors should be passive in order to avoid any possible damage to external objects as well as to the chaser satellite. This paper presents a novel fast shape-from-shading (SfS) algorithm and a field-programmable gate array (FPGA)-based system hardware architecture for video-based shape reconstruction of space debris. The FPGA-based architecture, equipped with a pair of cameras, includes a fast image pre-processing module, a core implementing a feature-based stereo-vision approach, and a processor that executes the novel SfS algorithm. Experimental results show the limited amount of logic resources needed to implement the proposed architecture, and the timing improvements with respect to other state-of-the-art SfS methods. The remaining resources available in the FPGA device can be exploited to integrate other vision-based techniques to improve the comprehension of debris model, allowing a fast evaluation of associated kinematics in order to select the most appropriate approach for capture of the target space debris

Detection, 3-D positioning, and sizing of small pore defects using digital radiography and tracking

This article presents an algorithm that handles the detection, positioning, and sizing of submillimeter-sized pores in welds using radiographic inspection and tracking. The possibility to detect, position, and size pores which have a low contrast-to-noise ratio increases the value of the nondestructive evaluation of welds by facilitating fatigue life predictions with lower uncertainty. In this article, a multiple hypothesis tracker with an extended Kalman filter is used to track an unknown number of pore indications in a sequence of radiographs as an object is rotated. Each pore is not required to be detected in all radiographs. In addition, in the tracking step, three-dimensional (3-D) positions of pore defects are calculated. To optimize, set up, and pre-evaluate the algorithm, the article explores a design of experimental approach in combination with synthetic radiographs of titanium laser welds containing pore defects. The pre-evaluation on synthetic radiographs at industrially reasonable contrast-to-noise ratios indicate less than 1% false detection rates at high detection rates and less than 0.1 mm of positioning errors for more than 90% of the pores. A comparison between experimental results of the presented algorithm and a computerized tomography reference measurement shows qualitatively good agreement in the 3-D positions of approximately 0.1-mm diameter pores in 5-mm-thick Ti-6242

On Spectral Coexistence of CP-OFDM and FB-MC Waveforms in 5G Networks

Future 5G networks will serve a variety of applications that will coexist on the same spectral band and geographical area, in an uncoordinated and asynchronous manner. It is widely accepted that using CP-OFDM, the waveform used by most current communication systems, will make it difficult to achieve this paradigm. Especially, CP-OFDM is not adapted for spectral coexistence because of its poor spectral localization. Therefore, it has been widely suggested to use filter bank based multi carrier (FB-MC) waveforms with enhanced spectral localization to replace CP-OFDM. Especially, FB-MC waveforms are expected to facilitate coexistence with legacy CP-OFDM based systems. However, this idea is based on the observation of the PSD of FB-MC waveforms only. In this paper, we demonstrate that this approach is flawed and show what metric should be used to rate interference between FB-MC and CP-OFDM systems. Finally, our results show that using FB-MC waveforms does not facilitate coexistence with CP-OFDM based systems to a high extent.Comment: Manuscript submitted for review to IEEE Transactions on Wireless Communication

Time-frequency represetation of radar signals using Doppler-Lag block searching Wigner-Ville distribution

Radar signals are time-varying signals where the signal parameters change over time. For these signals, Quadratic Time-Frequency Distribution (QTFD) offers advantages over classical spectrum estimation in terms of frequency and time resolution but it suffers heavily from cross-terms. In generating accurate Time-Frequency Representation (TFR), a kernel function must be able to suppress cross-terms while maintaining auto-terms energy especially in a non-cooperative environment where the parameters of the actual signal are unknown. Thus, a new signal-dependent QTFD is proposed that adaptively estimates the kernel parameters for a wide class of radar signals. The adaptive procedure, Doppler-Lag Block Searching (DLBS) kernel estimation was developed to serve this purpose. Accurate TFRs produced for all simulated radar signals with Instantaneous Frequency (IF) estimation performance are verified using Monte Carlo simulation meeting the requirements of the Cramer-Rao Lower Bound (CRLB) at SNR > 6 dB