Mohamed Ayadi

Effect of moderate spray drying conditions on functionality of dried egg white and whole egg.

Dried egg and egg-derived proteins have a range of applications in baking, dressings, and confectionery products. Egg powder was produced under high time-temperature scales (approximately 160 degrees C), which led to many changes in egg components, resulting in different functional properties of eggs after reconstitution. In this study, moderate operating conditions were selected to dry egg white and whole egg using a pilot-scale spray dryer. Functional properties changes were evaluated with an appropriate statistical technique. Major finding supports that spray drying of egg white at moderate conditions (air inlet temperature ranged from 110 to 125 degrees C) resulted in a product that enhanced considerably the water holding capacity of produced gels. Moreover, gel prepared with the dried samples was firmer than that of the fresh samples. Drying at a moderate scale allowed not only the increasing of the foaming capacity and the stability of foam but also an increase in their emulsifying capacity and stability of the emulsions.

Fuzzy MADM based vertical handover algorithm for enhancing network performances

One of the most challenging topics for next generation wireless networks is vertical handoff concept since several wireless technologies are assumed to cooperate in a single heterogeneous environment. Several parameters must be considered in vertical handoff process along with traditional received signal strength (RSS) information, they are related to user preferences, terminal capabilities and QoS, such as data rate, tolerated delay. In this context, Classical Multiple Attribute Decision Making (MADM) methods, like the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were used to make suitable handover decisions. Our approach called Fuzzy TOPSIS, is based on the combination of TOPSIS method with fuzzy logic in order to reduce the decision delays, blocking probability and the number of executed handoffs. Performance results of the proposed system are also compared with those of the classical TOPSIS method. The proposed algorithm is able to determine properly whether a handoff is necessary or not, and selects the best candidate radio access technology (RAT) considering the input parameters and fuzzy inference rules. Simulation results show an improvement of decision time by 40 % comparing to classical approach.

Whey Powder, ι-Carrageenan, and Fat Interactions and Their Influence on Instrumental Texture and Sensory Properties of Turkey Meat Sausage Using a Mixture Design Approach

A 10-point simplex lattice design was used to investigate the effect of varying the ratios of whey powder, ι-carrageenan, and fat, and their interactions on instrumental texture and sensory properties of mechanically separated turkey meat sausages. Regression models have been developed and contour plots were drawn in order to better understand the global tendency of measured responses. Whey powder had a more notable influence than that of ι-carrageenan on all textural parameters as whey powder proportion increased. At a higher proportion (8 g/100 g sausage), whey powder improved essentially hardness, gel stress, and chewiness of extra low-fat sausage (formulation 3, 4.2 ± 0.3 g of fat/100 g sausage). Environmental scanning electron microscopy of the corresponding sausage showed a compact microstructure characterized by large network connections. Sensory evaluation also indicated that whey powder increased the flavor, the firmness, and the sliceability scores of mechanically separated turkey meat sausage, in comparison to the high-fat standard sausage (formulation 1, 13.2 ± 0.2 g of fat/100 g sausage).

Cognitive radio networks management using an ANFIS approach with QoS/QoE mapping scheme

Future networks are characterized by a panoply of novel services based on multimedia services like gaming and real time video streaming. In addition, cognitive radio is considered as an emergent candidate of next generation (NextG) networks. Therefore, there is a need of some techniques that can ensure self-managed networks with learning capabilities. Our approach is based on adaptive neuro-fuzzy inference system (ANFIS) used for predicting the user video perception (e.g. MOS) and for managed decisions that can be achieved by a specific radio configuration (e.g. data rate, handover). The ANFIS model with Quality of Services/Quality of Experience (QoS/QoE) mapping is able to sense environment, decide, learn and optimize its decisions online by a learning algorithm that uses a set of experimental measurements. We used an implementation tool of the ANFIS model under MATLAB/SIMULINK environment supporting the development of real time scenarios.

Hierarchical Radiosity Model for the Prediction of Propagation in Micro-Cellular Urban Environment

As mobile networks are growing fast in size and complexity, radio network planning needs to be optimized to cope with both increased traffic demand and spectrum scarcity. During this process, these objectives are achieved by an accurate propagation models and precise analysis of mobile users behavior in terms of mobility and traffic. Nowadays, we can find in the literature many propagation models for radio network planning. These models can be classified in two different categories. The first one, called empirical models are deduced from statistics based on measurements, and express the mean attenuation as function of many parameters like distance, antenna height, frequency, etc... The major drawback of this kind of model is the lack of accuracy especially in micro-cellular environment where the presence of many obstacles is the dominant characteristic and accuracy of coverage required. The second category of models is qualified as exact; it has been introduced to recover the drawbacks of the empirical models, like radiosity. Our previous study done on this model has shown that this kind of model, although it is more accurate than empirical one, it requires a huge memory space and takes long processing time to generate results. In this paper, we focus our attention on the optimization of radiosity algorithm by introducing the concept of hierarchical radiosity. An implementation of this method is done to evaluate its performance and to compare it with progressive refinement radiosity. After, we compare the generated result for the two models with real measurements to determine their accuracy.

BER simulation for WLAN networks in realistic environment

In this paper, we focus our attention on the BER computing for WLAN network under realistic conditions. On one hand, the propagation models provide the received power at given location, on the other hand, the mobility models describe the movement behaviour of nodes. Our approach consists on the combination of the propagation and mobility models in order to extract the BER for real nodes movements.

A novel cognitive architecture for QoS/QoE management in NextG Networks based on Q-learning and R-MLP approaches

Next Generation Networks (NGN) principle or 5G technology, is based on the integration of heterogeneous wireless systems and cognitive networks. NGN is expected to have Quality of Service (QoS) as the major challenging issue. More specifically, NGN aims at providing guaranteed QoS levels and Quality of Experience (QoE), especially for multimedia services. However, with the increase in number of connections, services and data amounts, it becomes crucial to ensure an intelligent network management to ensure target goals. This paper proposes a novel cognitive radio network architecture for the management of cognitive networks and 5G technology. The goal of the proposed architecture consists on using artificial intelligence with learning capabilities to optimize network management decisions. The proposed architecture consists of 4 main states: sensing, decision management, action, and learning. It provides the functionality to manage cognitive components and facilitate the interfaces between the network and its surrounding environment. In order to demonstrate the functionality of the proposed architecture, we present simulation results and comparison with existing load balancing method in terms of throughput, latency and QoE.

A novel approach for indoor wave propagation modeling

Indoor radio propagation prediction modeling has been for a long time an important area of interest of research and development. The accuracy of developed model depends on the number of considered phenomena and their formulations. In general proposed formulation can be deduced from either experimental measurements, and then obtained model can be classified as empirical one. Or it can be deduced from physical lows of wave propagation, and hence, the developed model can be considered as exact or deterministic model. In this paper we are interesting to develop a new empirical indoor propagation model in 2100MHz band based on neural network theory. The new model has the advantage of combining many phenomena and parameters such as wall and floor attenuation and their number. To accomplish this work, a large number of Continuous Wave (CW) experimental measurements are collected and divided into two random set, the first one serves for model training while the second one serves for model validation. The new model shows high performances in term of accuracy.

A novel approach for mobile network QoS evaluation

Nowadays, with growth the number of mobile data network subscriber in very competitive environment, operators are invited constantly to assess and improve the quality of their network. They can use many tools to accomplish this task like drive test to detect coverage problem or passive probe or OSS (Operating Support System) counter to have an overview on all network at any moment. The major problem of these methods is the huge volume of collected row data that need specific treatment. In fact, during one day, we can collect many Gigabytes of data and millions of records. Traditional data base management system could not be used to extract KPI (Key Performance Indicator) which can reflect the network QoS (Quality of Service). In general, operator use specific tools and spend long time to compute desired indicators. In this work, we aim to use intelligent method as decisional support system to help to evaluate the quality of service of a mobile data network. The used method is the self-organizing maps. It helps to extract the most important parameters to compute KPI and hence reducing the huge volume of data and time processing. To accomplish this task, we have used collected data during one week from a Tunisian GPRS (General Packet Radio Service) network operator.

New Method for Cellular Network Traces Exploitation

In the idea to create a realistic model of mobility and traffic for the networks of 3rd and 4th generation, we have studied many techniques of modeling and planning. Our choice was for the real network traces exploitation techniques, mainly because compared to other techniques this technique is very adapted to the real modeling. Although this technique is most convenient, it has many disadvantages. From this context, was born the aim of this work which consists in the creation of a new method which can investigate on mobile network traces based on Data Mining. These new methods permit us to investigate on network traces and clear a very significant population for the study, modeling and planning existent and new network.