Cipher security is becoming an important step when transmitting important information through networks. The algorithms of cryptography play major roles in providing security and avoiding hacker attacks. In this work two hybrid cryptosystems have been proposed, that combine a modification of the symmetric cryptosystem Playfair cipher called the modified Playfair cipher and two modifications of the asymmetric cryptosystem RSA called the square of RSA technique and the square RSA with Chinese remainder theorem technique. The proposed hybrid cryptosystems have two layers of encryption and decryption. In the first layer the plaintext is encrypted using modified Playfair to get the cipher text, this cipher text will be encrypted using squared RSA to get the final cipher text. This algorithm achieved higher security to data but suffers from a long computational time. So Chinese remainder theorem has been used in the second hybrid cryptosystem to obtain less encryption and decryption time. The simulation results indicated that using the modified Playfair with the proposed square RSA has improved security. Moreover, using the Chinese remainder theorem achieved less encryption and decryption time in comparison to our first proposed and the standard algorithms.
Consider the (p,q) simple connected graph . The sum absolute values of the spectrum of quotient matrix of a graph make up the graph's quotient energy. The objective of this study is to examine the quotient energy of identity graphs and zero-divisor graphs of commutative rings using group theory, graph theory, and applications. In this study, the identity graphs derived from the group and a few classes of zero-divisor graphs of the commutative ring R are examined.
Internet of Things (IoT) is one of the newest matters in both industry and academia of the communication engineering world. On the other hand, wireless mesh networks, a network topology that has been debate for decades that haven’t been put into use in great scale, can make a transformation when it arises to the network in the IoT world nowadays. A Mesh IoT network is a local network architecture in which linked devices cooperate and route data using a specified protocol. Typically, IoT devices exchange sensor data by connecting to an IoT gateway. However, there are certain limitations if it involves to large number of sensors and the data that should be received is difficult to analyze. The aim of the work here is to implement a self-
... Show MoreInternet paths sharing the same congested link can be identified using several shared congestion detection techniques. The new detection technique which is proposed in this paper depends on the previous novel technique (delay correlation with wavelet denoising (DCW) with new denoising method called Discrete Multiwavelet Transform (DMWT) as signal denoising to separate between queuing delay caused by network congestion and delay caused by various other delay variations. The new detection technique provides faster convergence (3 to 5 seconds less than previous novel technique) while using fewer probe packets approximately half numbers than the previous novel technique, so it will reduce the overload on the network caused by probe packets.
... Show MoreToday many people suffering from health problems like dysfunction in lungs and cardiac. These problems often require surveillance and follow up to save a patient's health, besides control diseases before progression. For that, this work has been proposed to design and developed a remote patient surveillance system, which deals with 4 medical signs (temperature, SPO2, heart rate, and Electrocardiogram ECG. An adaptive filter has been used to remove any noise from the signal, also, a simple and fast search algorithm has been designed to find the features of ECG signal such as Q,R,S, and T waves. The system performs analysis for medical signs that are used to detected abnormal values. Besides, it sends data to the Base-Stati
... Show MoreStroke is the second largest cause of death worldwide and one of the most common causes of disability. However, several approaches have been proposed to deal with stroke patient rehabilitation like robotic devices and virtual reality systems, researchers have found that the brain-computer interfaces (BCI) approaches can provide better results. In this study, the electroencephalography (EEG) dataset from post-stroke patients were investigated to identify the effects of the motor imagery (MI)-based BCI therapy by investigating sensorimotor areas using frequency and time-domain features and to select particular methods that help in enhancing the MI-based BCI systems for stroke patients using EEG signal processing. Therefore, to detect
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