In this article, we define and study a family of modified Baskakov type operators based on a parameter . This family is a generalization of the classical Baskakov sequence. First, we prove that it converges to the function being approximated. Then, we find a Voronovsky-type formula and obtain that the order of approximation of this family is . This order is better than the order of the classical Baskakov sequence  whenever . Finally, we apply our sequence to approximate two test functions and analyze the numerical results obtained.

Data hiding (Steganography) is a method used for data security purpose and to protect the data during its transmission. Steganography is used to hide the communication between two parties by embedding a secret message inside another cover (audio, text, image or video). In this paper a new text Steganography method is proposed that based on a parser and the ASCII of non-printed characters to hide the secret information in the English cover text after coding the secret message and compression it using modified Run Length Encoding method (RLE). The proposed method achieved a high capacity ratio for Steganography (five times more than the cover text length) when compared with other methods, and provides a 1.0 transparency by depending on som

The futuristic age requires progress in handwork or even sub-machine dependency and Brain-Computer Interface (BCI) provides the necessary BCI procession. As the article suggests, it is a pathway between the signals created by a human brain thinking and the computer, which can translate the signal transmitted into action. BCI-processed brain activity is typically measured using EEG. Throughout this article, further intend to provide an available and up-to-date review of EEG-based BCI, concentrating on its technical aspects. In specific, we present several essential neuroscience backgrounds that describe well how to build an EEG-based BCI, including evaluating which signal processing, software, and hardware techniques to use. Individu

    Computer vision is an emerging area with a huge number of applications. Identification of the fingertip is one of the major parts of those areas. Augmented reality and virtual reality are the most recent technological advancements that use fingertip identification. The interaction between computers and humans can be performed easily by this technique. Virtual reality, robotics, smart gaming are the main application domains of these fingertip detection techniques. Gesture recognition is one of the most fascinating fields of fingertip detection. Gestures are the easiest and productive methods of communication with regard to collaboration with the computer. This analysis examines the different studies done in the field of

Computer systems and networks are being used in almost every aspect of our daily life; as a result the security threats to computers and networks have also increased significantly. Traditionally, password-based user authentication is widely used to authenticate legitimate user in the current system0T but0T this method has many loop holes such as password sharing, shoulder surfing, brute force attack, dictionary attack, guessing, phishing and many more. The aim of this paper is to enhance the password authentication method by presenting a keystroke dynamics with back propagation neural network as a transparent layer of user authentication. Keystroke Dynamics is one of the famous and inexpensive behavioral biometric technologies, which identi

Metaheuristics under the swarm intelligence (SI) class have proven to be efficient and have become popular methods for solving different optimization problems. Based on the usage of memory, metaheuristics can be classified into algorithms with memory and without memory (memory-less). The absence of memory in some metaheuristics will lead to the loss of the information gained in previous iterations. The metaheuristics tend to divert from promising areas of solutions search spaces which will lead to non-optimal solutions. This paper aims to review memory usage and its effect on the performance of the main SI-based metaheuristics. Investigation has been performed on SI metaheuristics, memory usage and memory-less metaheuristics, memory char

Features is the description of the image contents which could be corner, blob or edge. Corners are one of the most important feature to describe image, therefore there are many algorithms to detect corners such as Harris, FAST, SUSAN, etc. Harris is a method for corner detection and it is an efficient and accurate feature detection method. Harris corner detection is rotation invariant but it isn’t scale invariant. This paper presents an efficient harris corner detector invariant to scale, this improvement done by using gaussian function with different scales. The experimental results illustrate that it is very useful to use Gaussian linear equation to deal with harris weakness.

A Multiple System Biometric System Based on ECG Data