—Face recognition is a very active domain in computer vision and in Biometrics. It is a biometric modality that has attracted huge interest in the automatic processing of digital images and videos in many applications, including biometric... more
—Face recognition is a very active domain in computer vision and in Biometrics. It is a biometric modality that has attracted huge interest in the automatic processing of digital images and videos in many applications, including biometric identification, video-surveillance, human-computer interaction and multimedia data management. Face recognition usually involves three key processes in its treatment: face detection, feature extraction and classification. In this article, we focus on the study and synthesis of the classification methods most widely used in face recognition, namely: metric distances, neural networks and Supports Vectors Machines (SVM).
—Face recognition is a very active domain in computer vision and in Biometrics. It is a biometric modality that has attracted huge interest in the automatic processing of digital images and videos in many applications, including biometric... more
—Face recognition is a very active domain in computer vision and in Biometrics. It is a biometric modality that has attracted huge interest in the automatic processing of digital images and videos in many applications, including biometric identification, video-surveillance, human-computer interaction and multimedia data management. Face recognition usually involves three key processes in its treatment: face detection, feature extraction and classification. In this article, we focus on the study and synthesis of the classification methods most widely used in face recognition, namely: metric distances, neural networks and Supports Vectors Machines (SVM).
ABSTRACT In this paper we are focused on the MC-CDMA equalization problem. The equal- ization is performed using the MMSE and ZF equalizer based on the identi�ed parameters of the BRAN A and BRAN E channels. These channels are normalized... more
ABSTRACT In this paper we are focused on the MC-CDMA equalization problem. The equal- ization is performed using the MMSE and ZF equalizer based on the identi�ed parameters of the BRAN A and BRAN E channels. These channels are normalized for fourth-generation mobile communication systems. However, for such high-speed data transmissions, the channel is severely frequency-selective due to the presence of many interfering paths with di�erent time delays. In this paper we discusses the identi�cation problem of the normalized channel for 4th generation mobile communication, representing the indoor scenario (European Telecommunica- tions Standards Institute Broadband Radio Access Networks (ETSI BRAN A) channel model) and outdoor scenario (ETSI BRAN E channel model). The identi�cation problem is performed using the Least Mean Squares (LMS) algorithm and the Takagi-Sugueno (TS) fuzzy system. The comparison between these techniques, for the channel identi�cation, will be made for di�erent Signal to Noise Ratios (SNR).
The modern telecommunication systems require very high transmission rates, in this context, the problem of channels identification is a challenge major. The use of blind techniques is a great interest to have the best compromise between a... more
The modern telecommunication systems require very high transmission rates, in this context, the problem of channels identification is a challenge major. The use of blind techniques is a great interest to have the best compromise between a suitable bit rate and quality of the information retrieved. In this paper, we are interested to learn the algorithms for blind channel identification. We propose a hybrid method that performs a trade-off between two existing methods in order to improve the channel estimation.
Levenberg-Marquardt (LM) Optimization is a virtual standard in nonlinear optimization. It is a pseudosecond order method which means that it works with only function evaluations and gradient information but it estimates the Hessian matrix... more
Levenberg-Marquardt (LM) Optimization is a virtual standard in nonlinear optimization. It is a pseudosecond order method which means that it works with only function evaluations and gradient information but it estimates the Hessian matrix using the sum of outer products of the gradients. In this word, we will applythe Levenberg- Marquardt method for learning and recognition pattern concerned Tifinaghcharacters; this last will be compared to other methods such as Windrow-Hoff (WH). This note reviews the application for Levenberg-Marquardt for network neuronal and also details the algorithm.
The modern telecommunication systems require very high transmission rates, in this context, the problem of channels identification is a challenge major. The use of blind techniques is a great interest to have the best compromise between a... more
The modern telecommunication systems require very high transmission rates, in this context, the problem of channels identification is a challenge major. The use of blind techniques is a great interest to have the best compromise between a suitable bit rate and quality of the information retrieved. In this paper, we are interested to learn the algorithms for blind channel identification. We propose a hybrid method that performs a trade-off between two existing methods in order to improve the channel estimation.
Over the last years, wavelet theory has been used with great success in a wide range of applications as signal de-noising and image compression. An ideal image compression system must yield high-quality compressed image with high... more
Over the last years, wavelet theory has been used with great success in a wide range of applications as signal de-noising and image compression. An ideal image compression system must yield high-quality compressed image with high compression ratio. This paper attempts to find the most useful wavelet function to compress an image among the existing members of wavelet families. Our idea is that a backpropagation neural network is trained to select the suitable wavelet function between the two families: orthogonal (Haar) and biorthogonal (bior4.4), to be used to compress an image efficiently and accurately with an ideal and optimum compression ratio. The simulation results indicated that the proposed technique can achieve good compressed images in terms of peak signal to noise ratio (PSNR) and compression ratio (t) in comparison with random selection of the mother wavelet.
Nowadays, digital images compression requires more and more significant attention of researchers. Even when high data rates are available, image compression is necessary in order to reduce the memory used, as well the transmission cost.... more
Nowadays, digital images compression requires more and more significant attention of researchers. Even when high data rates are available, image compression is necessary in order to reduce the memory used, as well the transmission cost. An ideal image compression system must yield high-quality compressed image with high compression ratio. In this article, a neural network is implemented for image compression using the feature of wavelet transform. The idea is that a back-propagation neural network can be trained to relate the image contents to its ideal compression method between two different wavelet transforms: orthogonal (Haar) and biorthogonal (bior4.4).
Covid 19 has dramatically changed people’s lives around the world. It has shut down schools, companies and workplaces, forcing individuals to stay at home and comply to quarantine orders. Thus, individuals have resorted to the Internet as... more
Covid 19 has dramatically changed people’s lives around the world. It has shut down schools, companies and workplaces, forcing individuals to stay at home and comply to quarantine orders. Thus, individuals have resorted to the Internet as a means for communicating and sharing information in different domains. Unfortunately, some communities are still unserved by commercial service providers. Mobile Adhoc Network (MANET) can be used to fill this gap. One of the core issues in MANET is the authentication of the participating nodes. This mechanism is a fundamental requirement for implementing access control to network resources by confirming a user’s identity. In recent years, security experts worldwide proposed distributed authentication for MANET due to the lack of a central authority to register and authenticate nodes. In this article, decentralized authentication based on the technology of fog computing and the concept of the blockchain is proposed. The evaluation of this mechanism...
There are considerable obstacles in the transport sector of developing countries, including poor road conditions, poor road maintenance and congestion. The dire impacts of these challenges could be extremely damaging to both human lives... more
There are considerable obstacles in the transport sector of developing countries, including poor road conditions, poor road maintenance and congestion. The dire impacts of these challenges could be extremely damaging to both human lives and the economies of the countries involved. Intelligent Transportation Systems (ITSs) integrate modern technologies into existing transportation systems to monitor traffic. Adopting Vehicular Adhoc Network (VANET) into the road transport system is one of the most ITS developments demonstrating its benefits in reducing incidents, traffic congestion, fuel consumption, waiting times and pollution. However, this type of network is vulnerable to many problems that can affect the availability of services. This article uses a Fuzzy Bayesian approach that combines Bayesian Networks (BN) and Fuzzy Logic (FL) for predicting the risks affecting the quality of service in VANET. The implementation of this model can be used for different types of predictions in t...
