Vincent Havyarimana

A novel optimization framework for salient object detection

Visual saliency aims to locate the noticeable regions or objects in an image. In this paper, a coarse-to-fine measure is developed to model visual saliency. In the proposed approach, we firstly use the contrast and center bias to generate an initial prior map. Then, we weight the initial prior map with boundary contrast to obtain the coarse saliency map. Finally, a novel optimization framework that combines the coarse saliency map, the boundary contrast and the smoothness prior is introduced with the intention of refining the map. Experiments on three public datasets demonstrate the effectiveness of the proposed method.

A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment

In this paper, a novel nonlinear framework of smoothing method, non-Gaussian delayed particle smoother (nGDPS), is proposed, which enables vehicle state estimation (VSE) with high accuracy taking into account the non-Gaussianity of the measurement and process noises. Within the proposed method, the multivariate Student’s t-distribution is adopted in order to compute the probability distribution function (PDF) related to the process and measurement noises, which are assumed to be non-Gaussian distributed. A computation approach based on Ensemble Kalman Filter (EnKF) is designed to cope with the mean and the covariance matrix of the proposal non-Gaussian distribution. A delayed Gibbs sampling algorithm, which incorporates smoothing of the sampled trajectories over a fixed-delay, is proposed to deal with the sample degeneracy of particles. The performance is investigated based on the real-world data, which is collected by low-cost on-board vehicle sensors. The comparison study based on the real-world experiments and the statistical analysis demonstrates that the proposed nGDPS has significant improvement on the vehicle state accuracy and outperforms the existing filtering and smoothing methods.

Analytical Study on Multi-Tier 5G Heterogeneous Small Cell Networks: Coverage Performance and Energy Efficiency

In this paper, we investigate the coverage performance and energy efficiency of multi-tier heterogeneous cellular networks (HetNets) which are composed of macrocells and different types of small cells, i.e., picocells and femtocells. By virtue of stochastic geometry tools, we model the multi-tier HetNets based on a Poisson point process (PPP) and analyze the Signal to Interference Ratio (SIR) via studying the cumulative interference from pico-tier and femto-tier. We then derive the analytical expressions of coverage probabilities in order to evaluate coverage performance in different tiers and investigate how it varies with the small cells’ deployment density. By taking the fairness and user experience into consideration, we propose a disjoint channel allocation scheme and derive the system channel throughput for various tiers. Further, we formulate the energy efficiency optimization problem for multi-tier HetNets in terms of throughput performance and resource allocation fairness. To solve this problem, we devise a linear programming based approach to obtain the available area of the feasible solutions. System-level simulations demonstrate that the small cells’ deployment density has a significant effect on the coverage performance and energy efficiency. Simulation results also reveal that there exits an optimal small cell base station (SBS) density ratio between pico-tier and femto-tier which can be applied to maximize the energy efficiency and at the same time enhance the system performance. Our findings provide guidance for the design of multi-tier HetNets for improving the coverage performance as well as the energy efficiency.

A novel hybrid approach based-SRG model for vehicle position prediction in multi-GPS outage conditions

Abstract Trajectory prediction in autonomous driving system is an important aspect for preventing for instance the multi-vehicle collision. However, predicting accurately the future location of a vehicle is still a delicate task especially in intelligent transport systems. This paper proposes a hybrid approach of solving the position prediction problem of vehicle in multi-GPS outage conditions such as free and partial as well as short and long complete GPS outages. The proposed approach aggregates the advantages of both fuzzy inference system (FIS) and sparse random Gaussian (SRG) models, consequently named FIS-SRG, leading to a significant decrease in position prediction error of vehicle. The aforementioned outages are defined by adjusting the GPS propagation weight monitored by the Gaussian model and updated by fuzzy logic system. Experimental results based on data from GPS and INS and the comparison study with the existing prediction methods illustrate the good performance of the proposed approach, in all considered GPS outage conditions.

A Novel Probabilistic Approach for Vehicle Position Prediction in Free, Partial, and Full GPS Outages

In this paper, a novel framework is developed with the intention of continuously predicting vehicle position even in the challenging environments such as partial and full GPS outages. To achieve this, the Bayesian-Sparse Random Gaussian Prediction (B-SRGP) approach is proposed where the sparse random Gaussian matrix which obeys the restricted isometry property with high probability is adopted to handle the measurement model. During the full GPS outages, the proposed method fuses all available INS measurements to improve the vehicle position prediction whereas in free outages only the GPS data are processed. Besides, the Bayesian inference is used to specifically deal with the vehicle position prediction in partial GPS outages where data from both GPS and INS are taken as inputs. In all cases, measurement noises are assumed to be non-Gaussian distributed and follow the generalized error distribution. The performance of B-SRGP is evaluated with respect to real-world data collected using Smartphone-based vehicular sensing model. The proposed method is tested when measurement noises are both Gaussian and non-Gaussian distributed and also compared with the existing prediction methods. Experimental results confirm that B-SRGP presents higher accuracy prediction and lower mean-squared prediction error for vehicle position when measurement noises are non-Gaussian distributed.

Online-SVR for vehicular position prediction during GPS outages using low-cost INS

Vehicle position prediction has become more and more critical for most applications in intelligent transportation systems (ITS). Prediction based INS/GPS integration provides continuous and reliable navigation solution when compared to standalone Inertial Navigation System (INS) or Global Positioning System (GPS). Although there have been several research works for fusing INS and GPS data to bridge navigation during GPS outages, most of them are offline methods and do not consider sensors data fluctuation due to traffic incident, inclement weather conditions or rush hour. This paper proposes a supervised statistical learning technique called Online Support Vector Machine for Regression (OL-SVR) for the prediction of vehicle position. During GPS availability, the OL-SVR models INS errors by fusing the INS and GPS data; meanwhile during outages, the trained OL-SVR method is utilized to predict accurate vehicle position. The proposed method is compared with two well-known prediction techniques including Partial Least Squares Regression (PLSR) and Artificial Neural Network (ANN). Experiments conducted at rush hour on real urban roads and simulation results prove that OL-SVR is more efficient and accurate in position prediction than PLSR and ANN, achieving an accuracy improvement of 20.3%-64.8%.

Two-level Tardos fingerprinting code

Digital fingerprinting is an emerging technology that offers proactive post-delivery protection of multimedia. It is used to trace back illegal users, where unique ID known as digital fingerprints is embedded into content before distribution. Tardos proposed binary codes for fingerprinting with a code length of theoretically minimum order, and the related works mainly focused on the reduction of the code length were presented. In this paper, for the reduction of computational costs required for the detection, a two-level Tardos code structure is introduced on the number of operations for calculating correlation scores. Furthermore, using the central limit theorem for correlation scores, we present a proper threshold for detecting colluders. The performance evaluation and analysis show that our proposed scheme is efficient.

Low-cost Sensors Aided Vehicular Position Prediction with Partial Least Squares Regression during GPS Outage

Vehicular position prediction is very important in intelligent transport systems (ITS), and the requirements of accuracy for position prediction have been significantly increasing in recent years. In this paper, we focus on designing a more low-cost and convenient method which can operate during GPS outages. In order to better deal with the position prediction during the lack of GPS signals, we introduce a windowed partial least squares regression (WPLSR) approach where vehicle position information from the low-cost sensors was used. Moreover, the window is adjustable and it reduces the step of regression in WPLSR algorithm. The sensor data outside the window that has nothing to do with the latest position prediction is eliminated. Therefore, the position accuracy can be improved significantly. Finally, the proposed method is evaluated by using road experiments from real urban areas. Compared with the conventional techniques such as PLSR and extended Kalman filter combined with an interactive multimodel (IMM-EKF), the results show that WPLSR presents the higher position accuracy especially during the GPS outages.