A review of UAV Visual Detection and Tracking Methods

This paper presents a review of techniques used for the detection and tracking of UAVs or drones. There are different techniques that depend on collecting measurements of the position, velocity, and image of the UAV and then using them in detection and tracking. Hybrid detection techniques are also presented. The paper is a quick reference for a wide spectrum of methods that are used in the drone detection process.Comment: 10 page

Application Of Neural Generated Error Signal In Classification And Compression Of Ecg Beats

This paper presents a neural network architecture connected in cascade for ECG Holter system beat classification and compression. The system works in real time, on line, and is capable of recognizing and compressing up to 40 artificial and real QRS templates. The parallelism of neural network applied in this paper increases the efficiency of computations. An error signal derived from difference between predictor and testing signal is used in the classification and compression. This error signal is processed in two ways; firstly it is encoded with Hoffrnan sequence and saved as the compressed signal that may be used later for reconstructing the original ECG signal. Secondly the error signal is converted into primitive ternary signal and used in sharp and direct classification of the ECG beats. The proposed architecture consists of the following: a neural network used to generate linear predictions for signals. Another neural network generates error signals by calculating difference between predictions taken from first neural network and a testing signal. A third neural network does the classifications utilizing the error signals instead of complex raw signal. At first, Hermite functions are used in generating artificial ECG signals. Testing is done by adding noise to the original Hermite functions used in learning. The whole process is repeated for real ECG beats taken from MIT database. The results show clear success in classification besides additional success in compression

A Review of the Genetic Algorithm and JAYA Algorithm Applications

International audienceThis study throws the light on two metaheuristic algorithms and enable researchers to leverage the potential of adapting them in whatever applications they may have either in engineering, computer science, or business. The two algorithms are the GA and the JAYA. The JAYA algorithm is a modern population-based meta heuristic algorithm, its applications are presented in this work. The JA Y A algorithm integrates evolutionary algorithms' survival of the fittest concept with the productivity and richness of heuristic search methodologies. On the other a well-known and somewhat older evolutionary based method called the Genetic Algorithm with applications is also presented here. The recent two algorithms; the JA Y A and the GA have broad comparable applications in computer science and engineering applications

Web Based Online Hybrid Teaching Method of Network Music Course

Today, with the rapid development of online course teaching, the demand for online courses is increasing day by day, and the demand for online mixed teaching of online music courses is also increasing rapidly. In the context of big data, the lengthy personalized screening process of users has become one of the problems to be solved. Based on Web data mining, an improved algorithm of hybrid hierarchical recommendation algorithm and genetic algorithm is used in the experiment, and compared with the other two algorithms in the experiment. The experimental results show that the average accuracy of the improved algorithm is 79.63% in the limited training times, and has better adaptability. It can be applied to the online hybrid teaching recommendation and screening of online music courses in dynamic changes

Hybrid encryption technique: Integrating the neural network with distortion techniques

International audienceThis paper proposes a hybrid technique for data security. The computational model of the technique is grounded on both the non-linearity of neural network manipulations and the effective distortion operations. To accomplish this, a two-layer feedforward neural network is trained for each plaintext block. The first layer encodes the symbols of the input block, making the resulting ciphertext highly uncorrelated with the input block. The second layer reverses the impact of the first layer by generating weights that are used to restore the original plaintext block from the ciphered one. The distortion stage imposes further confusion on the ciphertext by applying a set of distortion and substitution operations whose functionality is fully controlled by random numbers generated by a key-based random number generator. This hybridization between these two stages (neural network stage and distortion stage) yields a very elusive technique that produces ciphertext with the maximum confusion. Furthermore, the proposed technique goes a step further by embedding a recurrent neural network that works in parallel with the first layer of the neural network to generate a digital signature for each input block. This signature is used to maintain the integrity of the block. The proposed method, therefore, not only ensures the confidentiality of the information but also equally maintains its integrity. The effectiveness of the proposed technique is proven through a set of rigorous randomness testing

Encryption technique based on chaotic neural network space shift and color-theory-induced distortion

International audienceAbstract Protecting information privacy is likely to promote trust in the digital world and increase its use. This trust may go a long way toward motivating a wider use of networks and the internet, making the vision of the semantic web and Internet of Things a reality. Many encryption techniques that purport to protect information against known attacks are available. However, since the security challenges are ever-growing, devising effective techniques that counter the emerging challenges seems a rational response to these challenges. This paper proffers an encryption technique with a unique computational model that inspires ideas from color theory and chaotic systems. This mix offers a novel computation model with effective operations that (1) highly confuse plaintext and (2) generate key-based enormously complicated codes to hide the resulting ciphertext. Experiments with the prototype implementation showed that the proposed technique is effective (passed rigorous NIST/ENT security tests) and fast

Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization

International audienceAn efficient optimization method is needed to address complicated problems and find optimal solutions. The gazelle optimization algorithm (GOA) is a global stochastic optimizer that is straightforward to comprehend and has powerful search capabilities. Nevertheless, the GOA is unsuitable for addressing multimodal, hybrid functions, and data mining problems. Therefore, the current paper proposes the orthogonal learning (OL) method with Rosenbrock’s direct rotation strategy to improve the GOA and sustain the solution variety (IGOA). We performed comprehensive experiments based on various functions, including 23 classical and IEEE CEC2017 problems. Moreover, eight data clustering problems taken from the UCI repository were tested to verify the proposed method’s performance further. The IGOA was compared with several other proposed meta-heuristic algorithms. Moreover, the Wilcoxon signed-rank test further assessed the experimental results to conduct more systematic data analyses. The IGOA surpassed other comparative optimizers in terms of convergence speed and precision. The empirical results show that the proposed IGOA achieved better outcomes than the basic GOA and other state-of-the-art methods and performed better in terms of solution quality