Hossein Javidnia

Sensor Data Fusion by Support Vector Regression Methodology—A Comparative Study

Multisensor data fusion can be considered as a strong nonlinear system. A precise analytical solution is challenging to obtain, thus making it hard to dissect with routine diagnostic systems. Since tried-and-true logical systems are extremely difficult to undertake, soft computing methodologies are deemed having potential for such applications. This paper presents the support vector regression (SVR) methodology for sensor fusion to improve tracking ability. Radial basis function (RBF) and polynomial function are used as SVR kernel functions. The system combines Kalman filtering and soft computing principle, i.e., SVR, to structure an effective information combination method for the target framework. A radar-infrared system is proposed to adapt contextual changes and lessen the dubious unsettling influence of an information estimation from multisensory data. The experimental results show that an improvement in predictive accuracy and generalization capability can be achieved using the SVR with RBF kernel compared with the SVR with polynomial kernel approach.

Hybrid ANFIS-PSO approach for predicting optimum parameters of a protective spur dike

A protective spur dike is used to reduce scour depth around main spur dikes.A new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system (ANFIS-PSO) was used.Optimized parameters of the protective spur dike are presented. In this study a new approach was proposed to determine optimum parameters of a protective spur dike to mitigate scouring depth amount around existing main spur dikes. The studied parameters were angle of the protective spur dike relative to the flume wall, its length, and its distance from the main spur dikes, flow intensity, and the diameters of the sediment particles that were explored to find the optimum amounts. In prediction phase, a novel hybrid approach was developed, combining adaptive-network-based fuzzy inference system and particle swarm optimization (ANFIS-PSO) to predict protective spur dikes parameters in order to control scouring around a series of spur dikes. The results indicated that the accuracy of the proposed method is increased significantly compared to other approaches. In addition, the effectiveness of the developed method was confirmed using the available data.

TUBERCULOSIS DISEASE DIAGNOSIS USING ARTIFICIAL IMMUNE RECOGNITION SYSTEM

Background: There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. Objectives:This study is aimed at diagnosing TB using hybrid machine learning approaches. Materials and Methods: Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm. Results:Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.

Potential of support vector regression for optimization of lens system

Lens system design is an important factor in image quality. The main aspect of the lens system design methodology is the optimization procedure. Since optimization is a complex, non-linear task, soft computing optimization algorithms can be used. There are many tools that can be employed to measure optical performance, but the spot diagram is the most useful. The spot diagram gives an indication of the image of a point object. In this paper, the spot size radius is considered an optimization criterion. Intelligent soft computing scheme Support Vector Regression (SVR) is implemented. In this study, the polynomial and radial basis functions (RBF) are applied as the SVR kernel function to estimate the optimal lens system parameters. The performance of the proposed estimators is confirmed with the simulation results. The SVR results are then compared with other soft computing techniques. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR with polynomial basis function compared to other soft computing methodologies. The SVR coefficient of determination R 2 with the polynomial function was 0.9975 and with the radial basis function the R 2 was 0.964. The new optimization methods benefit from the soft computing capabilities of global optimization and multi-objective optimization rather than choosing a starting point by trial and error and combining multiple criteria into a single criterion in conventional lens design techniques. Lens system design represents a crucial factor for good image quality.Optimization procedure is the main part of the lens system design methodology.Soft computing methodologies optimization application.Adaptive neuro-fuzzy inference system (ANFIS) application.Support vector regression (SVR application).

Palmprint as a smartphone biometric

This paper focuses on smartphone based palmprint authentication. Various challenges in unconstrained acquisition and processing such as segmentation of palm images in a wide variety of backgrounds and illumination conditions are addressed. The main contribution of the paper is to eliminate the illumination using local illumination normalization algorithm.

Real-time automotive street-scene mapping through fusion of improved stereo depth and fast feature detection algorithms

The real-time tracking of street scenes as a vehicle is driving is a key enabling technology for autonomous vehicles. In this work we provide the basis for such a system through combining an improved advanced random walk with restart technique for stereo depth determination with fast, robust feature detection. The enables tracking and mapping of a wide range of scene structures which can be readily resolved into individual objects and scene elements. Thus it is practical to identify moving objects such as vehicles, pedestrians and fixed objects and structures such as buildings, trees and roadside kerb.

A review and comparative study of skin segmentation techniques for handheld imaging devices

A number of well-known algorithms for skin segmentation in unconstrained acquisition conditions are tested across many of the best known color spaces and some less well known ones. Although in theory each color space provides the exact same information, our results show that certain color spaces are more optimal where fast segmentation with limited resources is required. A comparative analysis and discussion of the results are presented and conclusions drawn as to the effectiveness of a range of different color spaces.

Latent space mapping for generation of object elements with corresponding data annotation

Abstract Deep neural generative models such as Variational Auto-Encoders (VAE) and Generative Adversarial Networks (GAN) give promising results in estimating the data distribution across a range of machine learning fields of application. Recent results have been especially impressive in image synthesis where learning the spatial appearance information is a key goal. This enables the generation of intermediate spatial data that corresponds to the original dataset. In the training stage, these models learn to decrease the distance of their output distribution to the actual data and, in the test phase, they map a latent space to the data space. Since these models have already learned their latent space mapping, one question is whether there is a function mapping the latent space to any aspect of the database for the given generator. In this work, it has been shown that this mapping is relatively straightforward using small neural network models and by minimizing the mean square error. As a demonstration of this technique, two example use cases have been implemented: firstly, the idea to generate facial images with corresponding landmark data and secondly, generation of low-quality iris images (as would be captured with a smartphone user-facing camera) with a corresponding ground-truth segmentation contour.

A Depth Map Post-Processing Approach Based on Adaptive Random Walk With Restart

Accurate depth estimation is still an important challenge after a decade, particularly from stereo images. The accuracy comes from a good depth level and preserved structure. For this purpose, a depth post-processing framework is proposed in this paper. The framework starts with the “Adaptive Random Walk with Restart (2015)” algorithm. To refine the depth map generated by this method, we introduced a form of median solver/filter based on the concept of the mutual structure, which refers to the structural information in both images. This filter is further enhanced by a joint filter. Next, a transformation in image domain is introduced to remove the artifacts that cause distortion in the image. The proposed post-processing method is then compared with the top eight algorithms in the Middlebury benchmark. To explore how well this method is able to compete with more widely known techniques, a comparison is performed with Googles new depth map estimation method. The experimental results demonstrate the accuracy and efficiency of the proposed post-processing method.