Joint loss pattern characterization and unequal interleaved FEC protection for robust H.264 video distribution over wireless LAN

The recently adopted H.264 standard achieves efficient video encoding and bandwidth savings. Thus, designing communication protocols and QoS control mechanisms for H.264 video distribution over wireless IP networks is a topic of intense research interest. Delivering video streams to terminals via a wireless last hop is indeed a challenging task due to the varying nature of the wireless link. While a common approach suggests exploiting the variations of the wireless channel, an alternative is to exploit characteristics of the video stream to improve the transmission. In this paper, we combine both approaches through an efficient wireless loss characterization and a low-delay unequal interleaved FEC protection. Besides deriving new QoS metrics for FEC block allocation, the wireless loss characterization is as well used to adjust the interleaving level depending on the loss correlation exhibited by the wireless channel. This novel unequal interleaved FEC (UI-FEC) protocol allows graceful video quality degradation over error-prone wireless links while minimizing the overall bandwidth consumption and the end-to-end latency.     

Control schemes for stable teleoperation with communication delay based on IOS small gain theorem

The problem of stabilization of a force-reflecting telerobotic system in presence of time delay in the communication channel is addressed. We introduce an approach that is based on application of the input-to-output stability (IOS) small gain theorem for functional differential equations (FDEs). A version of the stabilization algorithm as well as its two adaptive extensions are proposed. For all these control schemes, the input-to-state stability (ISS) of the overall telerobotic system is guaranteed in the global, global practical, or semiglobal practical sense for any constant communication delay under the assumption that the environmental dynamics satisfy a weak form of finite-gain stability property. As an intermediate step, we formulate and prove a general IOS (ISS) small gain result for FDEs.     

Proprietes magnetiques de la phase Sr1,50La0,50MnO4

The crystallographic, electrical and magnetic properties of Sr_1,50La_0.50MnO₄ which has a K₂NiF₄-type structure show the existence of ferromagnetic clusters due to Mn³⁺Mn⁴⁺ short range ordering antiferromagnetically coupled.     

Analysis of two particular iterative learning control schemes in frequency and time domains

This paper deals with iterative learning control design for multiple-input multiple-output (MIMO), linear time-invariant (LTI) systems. Two particular ILC schemes are considered and analyzed in both frequency and time domains. Some remarks on the convergence, implementation, robustness with respect to disturbances and reinitialization errors, as well as positive realness issues related to both schemes are provided.     

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

Electrocardiogram (ECG) signal is a measure of the heart’s electrical activity. Recently, ECG detection and classification have benefited from the use of computer-aided systems by cardiologists. The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization (DTO) and Differential Evolution Algorithm (DEA) into a unified algorithm to optimize the hyperparameters of neural network (NN) for boosting the ECG classification accuracy. In addition, we proposed a new feature selection method for selecting the significant feature that can improve the overall performance. To prove the superiority of the proposed approach, several experiments were conducted to compare the results achieved by the proposed approach and other competing approaches. Moreover, statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests. Experimental results confirmed the superiority and effectiveness of the proposed approach. The classification accuracy achieved by the proposed approach is (99.98%).     

Q2ABR: QoE‐aware adaptive video bit rate solution

In this paper, we propose a new adaptive bit rate (ABR) streaming method. This method is based on estimating and monitoring users' video streaming experience, their quality of experience (QoE). This ensures a good user QoE and optimises bandwidth utilisation by monitoring video buffer fill rate to ensure minimal data traffic. First, we achieve a QoE evaluation model based on network bandwidth, video segment representation, and dropped video frame rate parameters. Second, following our QoE evaluation model, we formulate an ABR method using the reinforcement learning (RL) paradigm to select video representations and using a breakpoint detection mechanism to monitor end‐user QoE variation. The proposed ABR method is called “QoE‐aware adaptive bit rate (Q2ABR)” and is composed of three individual modules, one for QoE estimation using machine learning methods, one for QoE variation monitoring using the breakpoint detection mechanism, and one for video representation selection using reinforcement learning. The design objective of Q2ABR is to ensure the overall QoE of these users while maintaining a minimum variation in the standard deviation of the users' QoE values. Third, the performance of the Q2ABR method is evaluated and compared with several existing ABR approaches in the literature using real traces that we collect on different transport scenarios (such as bus and train, among others). Since this method considers the user's perception of video quality as a regulator for optimising the overall video distribution network, good results are ensured in terms of the user's experience and buffer fill rate.     

A fair comparison of SAC-OCDMA system configurations based on two dimensional cyclic shift code and spectral direct detection

This paper investigates shortcomings that limit the performance of optical code division multiple access (OCDMA) systems including the low cardinality and data rate as well as the high power at reception. The main drawback for such systems known as multiple access interference accompanying by phase induced intensity noise is also investigated to effeciencly propose a novel two dimensional cyclic shift (2D-CS) code to be implemented in non-coherent OCDMA systems. The developed code is based on a one dimensional cyclic shift (1D-CS) code previously provided by research works processing spectral amplitude coding for optical code division multiple access (SAC-OCDMA) systems. Numerical results obtained by this study are therefore compared to previous studies employing different codes like two dimensional extended double weight (2D-EDW), two dimensional flexible cross correlation/modified double weight (2D-FCC/MDW), two dimensional perfect difference (2D-PD), two dimensional diluted perfect difference (2D-DPD), two dimensional multi service (2D-MS) and two dimensional zero cross correlation/multi diagonal (2D-ZCC/MD) codes. Accordingly, it is demonstrated that the proposed 2D-CS code outperforms all codes given previously in terms of system capacity where the small increasing percentage is about 40% compared to 2D-ZCC/MD and 2D-MS. Systems using 2D-CS code can support until 203 simultaneous users with a total code length equal to 171. System performance investigation leads to a BER and Q-Factor closely to1.0E?12 and 1.0E?27, and 6.6 dB and 10.6 dB at 20 km of single mode fiber length using white light source and Laser, respectively. Furthermore, such a code can be easily adopted by OCDMA systems for a long distance up to approximately 55 and 100 km.

     

Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

Applications of internet-of-things (IoT) are increasingly being used in many facets of our daily life, which results in an enormous volume of data. Cloud computing and fog computing, two of the most common technologies used in IoT applications, have led to major security concerns. Cyberattacks are on the rise as a result of the usage of these technologies since present security measures are insufficient. Several artificial intelligence (AI) based security solutions, such as intrusion detection systems (IDS), have been proposed in recent years. Intelligent technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection (FS) techniques to increase classification accuracy by minimizing the number of features selected. On the other hand, metaheuristic optimization algorithms have been widely used in feature selection in recent decades. In this paper, we proposed a hybrid optimization algorithm for feature selection in IDS. The proposed algorithm is based on grey wolf (GW), and dipper throated optimization (DTO) algorithms and is referred to as GWDTO. The proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better performance. On the employed IoT-IDS dataset, the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in the literature to validate its superiority. In addition, a statistical analysis is performed to assess the stability and effectiveness of the proposed approach. Experimental results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.