This work aims to develop a secure lightweight cipher algorithm for constrained devices. A secure communication among constrained devices is a critical issue during the data transmission from the client to the server devices. Lightweight cipher algorithms are defined as a secure solution for constrained devices that require low computational functions and small memory. In contrast, most lightweight algorithms suffer from the trade-off between complexity and speed in order to produce robust cipher algorithm. The PRESENT cipher has been successfully experimented on as a lightweight cryptography algorithm, which transcends other ciphers in terms of its computational processing that required low complexity operations. The mathematical model of the present algorithm is simple, and the running operations required small execution time for encryption-decryption sensing data. Hence, a developed algorithm called DPRESENT was introduced to improve the complexity of the cipher text based on the PRESENT algorithm and DNA cryptography technique for developing a lightweight cipher algorithm. The NIST suite showed that the proposed algorithm tests presented high level of randomness and complexity. The execution time for the proposed algorithm was kept minimal as the current cipher algorithm. The developed algorithm is a new trend that can be applied for different lightweight cryptosystems to achieve the trade-off among complexity and speed as a robust cipher algorithm.
A novel series of chitosan derivatives were synthesized via reaction of chitosan with carbonyl compounds and grafted it’s by with different amine compounds substituted hydrogen. The produced polymers were characterized by different analyses FTIR, 1HCNMR, XRD, DSC and TGA. Solubility in water as well as many solvent was investigated, antibacterial activity of chitosan and its derivatives against two types of bacteria E. coli and S. aureus was also investigated. The results showed that derivatives sort of have antibacterial activities against Esherichia coli (Gram negative) better than chitosan whilst compound IX has better antibacterial against Staphylococcus aureus (Gram positive). SEM analysis showed that increase of surface roughness wi
... Show MoreDisease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature
... Show MoreThe aim of this study is to compare the effects of three methods: problem-based learning (PBL), PBL with lecture method, and conventional teaching on self-directed learning skills among physics undergraduates. The actual sample size comprises of 122 students, who were selected randomly from the Physics Department, College of Education in Iraq. In this study, the pre- and post-test were done and the instruments were administered to the students for data collection. The data was analyzed and statistical results rejected null hypothesis of this study. This study revealed that there are no signifigant differences between PBL and PBL with lecture method, thus the PBL without or with lecture method enhances the self-directed learning skills bette
... Show MoreThis paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength. This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.
Moreover, the proposed controller i
... Show MoreThis paper describes the digital chaotic signal with ship map design. The robust digital implementation eliminates the variation tolerance and electronics noise problems common in analog chaotic circuits. Generation of good non-repeatable and nonpredictable random sequences is of increasing importance in security applications. The use of 1-D chaotic signal to mask useful information and to mask it unrecognizable by the receiver is a field of research in full expansion. The piece-wise 1-D map such as ship map is used for this paper. The main advantages of chaos are the increased security of the transmission and ease of generation of a great number of distinct sequences. As consequence, the number of users in the systems can be increased. Rec
... Show MoreThe work includes synthesis and characterization of some new heterocyclic compounds, as flow: The compound (3) (5-(4-chlorophenyl) -2-hydrazinyl-1,3,4-oxadiazole was synthesized by using two methods; the first method includes the direct reaction between hydrazine hydrate 80% and 5-(4-chlorophenyl)-2- (ethylthio) 1,3,4-oxadiazole (1), the second method involves converting 5-(4-chlorophenyl)-1,3,4-oxadiazol-2-amine (2) to diazonium salt then reducing this salt to compound (3) by stannous chloride. Compound (3) was used as starting material for synthesizing several fused heterocyclic compounds. The compound 6-(4-chlorophenyl)[1,2.4] triazolo [3,4,b][1,3,4] oxadiazole-3-(2H) thione (compound 4) was synthesized from the reaction of compound (3)
... Show MoreCorrect grading of apple slices can help ensure quality and improve the marketability of the final product, which can impact the overall development of the apple slice industry post-harvest. The study intends to employ the convolutional neural network (CNN) architectures of ResNet-18 and DenseNet-201 and classical machine learning (ML) classifiers such as Wide Neural Networks (WNN), Naïve Bayes (NB), and two kernels of support vector machines (SVM) to classify apple slices into different hardness classes based on their RGB values. Our research data showed that the DenseNet-201 features classified by the SVM-Cubic kernel had the highest accuracy and lowest standard deviation (SD) among all the methods we tested, at 89.51 % 1.66 %. This
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