Lazhar Khriji

Vector median-rational hybrid filters for multichannel image processing

In this letter, a new class of nonlinear filters called vector median-rational hybrid filters (VMRHFs) for multispectral image processing is introduced and applied to the color image filtering problem. These filters are based on rational functions (RFs) offering a number of advantages. First, a rational function is a universal approximator and a good extrapolator. Second, it can be trained by a linear adaptive algorithm. Third, it produces the best approximation (w.r.t. a given cost function) for some specific functions. The output is the result of a vector rational operation over the output of three subfilters, such as two vector median (VM) subfilters and one center weighted vector median filter (CWVMF). These filters exhibit desirable properties, such as edge and details preservation and accurate chromaticity estimation.

Adaptive fuzzy order statistics-rational hybrid filters for color image processing

In this paper, multichannel image processing using an adaptive approach is studied. The proposed approach is simpler and more appropriate than the traditional approaches that have been addressed by means of groupwise vector ordering information. These adaptive techniques are formed by a two-layer filter based on rational functions using fuzzy transformations of either the Euclidean or angular distances among the different vectors to adapt to local data in the color image. The output is the result of a vector rational operation taking into account three fuzzy sub-function outputs. Extensive simulation results illustrate that the new adaptive fuzzy filters are computationally attractive and achieve noise attenuation, chromaticity retention, and edges and details preservation.

High-resolution digital resampling using vector rational filters

Rational filters are extended to multichannel signal process- ing and applied to image interpolation. Two commonly used decimation schemes are considered: a rectangular grid and a quincunx grid. For each decimation lattice, we propose a number of adaptive resampling algorithms based on the vector rational filter (VRF). These algorithms exhibit desirable properties such as edge and detail preservation and accurate chromaticity estimation. In these approaches, color image pix- els are considered as three-component vectors in the color space. Therefore, the inherent correlation that exists between the different color components is not ignored. This leads to better image quality compared to that obtained by componentwise or marginal processing. Extensive simulations show that multichannel image processing with the proposed algorithms (VRFL) and (VRFd) based on l p-norm and directional pro- cessing, respectively; significantly outperform linear and some nonlinear techniques, e.g., vector FIR median hybrid filters (VFMH). Some images interpolated using VRFL and VRFd are presented for qualitative compari- son. These images are free from blockiness and jaggedness, confirming the quantitative results.

Median-rational hybrid filters

We introduce a new class of nonlinear filters called median-rational hybrid filters (MRHF) based on rational functions (RF). The filter output is the result of a rational operation taking into account three sub-functions, such as two FIR or median sub-filters and one center weighted median filter (CWMF). The proposed MRHF filters have the inherent property that on smooth areas they provide good noise attenuation whereas on changing areas the noise attenuation is traded for good response to the change. The performance of the proposed filter is compared against widely known nonlinear filters such as: morphological signal adaptive median filters, stack filters, rank-order morphological filters and simple rational filters. It is shown that a significant subjective improvement in the restored image quality as well as a consistent reduction in the objectively measured mean absolute error and mean square error is obtained.

Mobile Robot Navigation Based on Q-Learning Technique

This paper shows how Q-learning approach can be used in a successful way to deal with the problem of mobile robot navigation. In real situations where a large number of obstacles are involved, normal Q-learning approach would encounter two major problems due to excessively large state space. First, learning the Q-values in tabular form may be infeasible because of the excessive amount of memory needed to store the table. Second, rewards in the state space may be so sparse that with random exploration they will only be discovered extremely slowly. In this paper, we propose a navigation approach for mobile robot, in which the prior knowledge is used within Q-learning. We address the issue of individual behavior design using fuzzy logic. The strategy of behaviors based navigation reduces the complexity of the navigation problem by dividing them in small actions easier for design and implementation. The Q-Learning algorithm is applied to coordinate between these behaviors, which make a great reduction in learning convergence times. Simulation and experimental results confirm the convergence to the desired results in terms of saved time and computational resources.

Color image interpolation using vector rational filters

Rational filters are extended to multichannel signal processing and applied to the image interpolation problem. The proposed nonlinear interpolator exhibits desirable properties, such as, edge and details preservation. In this approach the pixels of the color image are considered as 3-component vectors in the color space. Therefore, the inherent correlation which exists between the different color components is not ignored; thus, leading to better image quality than those obtained by component-wise processing. Simulations show that the resulting edges obtained using vector rational filters (VRF) are free from blockiness and jaggedness, which are usually present in images interpolated using especially linear, but also some nonlinear techniques, e.g. vector median hybrid filters (VFMH).

A 3G/WiFi-enabled 6LoWPAN-based U-healthcare system for ubiquitous real-time monitoring and data logging

Ubiquitous healthcare (U-healthcare) systems are expected to offer flexible and resilient high-end technological solutions enabling remote monitoring of patients health status in real-time and provisioning of feedback and remote actions by healthcare providers. In this paper, we present a 6LowPAN based U-healthcare platform that contributes to the realization of the above expectation. The proposed system comprises two sensor nodes sending temperature data and ECG signals to a remote processing unit. These sensors are being assigned an IPv6 address to enable the Internet-of-Things (IoT) functionality. A 6LowPAN-enabled edge router, connected to a PC, is serving as a base station through a serial interface, to collect data from the sensor nodes. Furthermore, a program interfacing through a Serial-Line-Internet-Protocol (SLIP) and running on the PC provides a network interface that receives IPv6 packets from the edge router. The above system is enhanced by having the application save readings from the sensors into a file that can be downloaded by a remote server using a free Cloud service such as UbuntuOne. This enhancement makes the system robust against data loss especially for outdoor healthcare services, where the 3G/4G connectivity may get lost because of signal quality fluctuations. The system provided a proof of concept of successful remote U-healthcare monitoring illustrating the IoT functionality and involving 3G/4G connectivity while being enhanced by a cloud-based backup.

Wearable wireless medical sensors toward standards, safety and intelligence: a review

Using miniature wearable devices, it is possible to follow the health of individuals from outside the hospital in a convenient and easy way that does not restrict their movements or their daily activities. The wearable biomedical sensors or the Wireless Body Area Network (WBAN) systems is about to become an important tool to match the express way of life nowadays. This paper reviews the technological developments in wireless sensor nodes for biomedical applications during the last decade. The review covers the electronics hardware design, challenges, safety and ending by the standards required to implement those systems. The designer of the wearable medical sensor should be aware of the standards and the safety issues at the acquisition and storage level, wireless transmission protocol level, powering level, security and privacy level, and overall safety level. Finally, this paper suggests some of the recent technologies that can be used to enhance the future WBAN systems.

Wireless healthcare monitoring system with ZigBee communication link support

We present a framework for a wireless healthcare monitoring system with ZigBee communication link support. It has the capability to monitor vital signs from multiple biosensors. Biomedical signals are collected and processed using two-tier subsystems. The first stage is the mobile device carried on the body that runs a number of biosensors (internal subsystem). At the second stage, further processing is performed by a local base station (external subsystem) using the raw data transmitted on-request by the mobile device. The processed data as well as the analysis results are then monitored and diagnosed through a man-machine interface.

Old movie restoration using rational spatial interpolators

The restoration of old movies using the newly developed rational functions filters is considered. After localization of the stationary and random defects, a spatial rational interpolator scheme is proposed to reconstruct the missing data. The experimental results demonstrate the usefulness of the rational interpolator in an application involving the restoration of defects in old movies. The resulting edges obtained using the proposed interpolator are free from blockiness and jaggedness.