Improved anti-noise attack ability of image encryption algorithm using de-noising technique

Information security is considered as one of the important issues in the information age used to preserve the secret information through out transmissions in practical applications. With regard to image encryption, a lot of schemes related to information security were applied. Such approaches might be categorized into 2 domains; domain frequency and domain spatial. The presented work develops an encryption technique on the basis of conventional watermarking system with the use of singular value decomposition (SVD), discrete cosine transform (DCT), and discrete wavelet transform (DWT) together, the suggested DWT-DCT-SVD method has high robustness in comparison to the other conventional approaches and enhanced approach for having high robustness against Gaussian noise attacks with using denoising approach according to DWT. MSE in addition to the peak signal-to-noise ratio (PSNR) specified the performance measures which are the base of this study’s results, as they are showing that the algorithm utilized in this study has high robustness against Gaussian noise attacks

Underwater Image De-nosing using Discrete Wavelet Transform and Pre-Whitening Filter

Image denoising and improvement are essential processes in many underwater applications. Various scientific studies, including marine science and territorial defence, require underwater exploration. When it occurs underwater, noise power spectral density is inconsistent within a certain range of frequency, and the noise autocorrelation function is not a delta function. Therefore, underwater noise is characterised as coloured noise. In this study, a novel image denoising technique is proposed using discrete wavelet transform with different basis functions and a whitening filter, which converts coloured noise characteristics to white noise prior to the denoising process. Results of the proposed method depend on the following performance measures: peak signal-to-noise ratio (PSNR) and mean squared error. The results of different wavelet bases, such as Debauchies, biorthogonal and symlet, indicate that the denoising process that uses a pre-whitening filter produces more prominent images and better PSNR values than other methods

Experimental Investigation of Impact Load Effect on the Flexural Behavior of Reactive Powder Concrete Slabs with Different Thicknesses

Recently, buildings have been exposed to terrorist attacks that generate impact loads on their elements and affect their serviceability loads, which is the thing that inspired researchers to investigate the properties of reactive powder concrete, such as strength, serviceability loads, impact load, and the influences of impact loads on standard weight concrete separately. Therefore, the main goal of this paper is studying the flexural behavior of reactive powder concrete after applying impact load caused by the attacks. For this study, three reactive powder concrete slabs: the first one is with 80-mm thickness, the second one is with 60-mm, and the third one is with 40-mm thickness, were statically loaded after being subjected to impact load. Moreover, then a number of the three slabsꞌ properties were investigated, such as flexural load capacity, deflection, and number and widths of cracks. The laboratory tests have approved that the slab with the higher thickness and steel fiber has improved and provided the best serviceability loads after being dynamically loaded

Numerical analysis of the photovoltaic system inspection with active cooling

The use of solar energy may replace the present fossil fuel or gas to produce electricity. The goal of this study is to set up a simulation model to survey the performance of a photovoltaic thermal system (PV/T) based on the computational fluid dynamics (CFD) method. Ansys fluent software has been used for the simulation procedure. The electrical panel output and its efficiency were investigated numerically. In addition, the effect of variations in absorbed radiation on inlet fluid and absorber panel temperature on the system performance was investigated. The study was conducted for three cases, in a first case, where there is no refrigerant in the system and in the latter case, at constant fluid rate of the pump, whereas the third case with optimal pump operation. The numerical findings obtained from CFD simulators have been compared with the test records of the experimental results of the literature. The two results have a good agreement. From the obtained results, it can be noted that the system shows a good improvement for the electric net efficiency level of 3.52% with a lower reduction of the thermal system efficiency of 1.96% in comparison to the system when using the constantly high flow rate

Numerical Study of Inspection the Photovoltaic System with Active Cooling

-Electricity become a part of human modern life. It has many uses in our daily life and we cannot think of a word without electrical power. Solar source can replace the current fossil fuel or gas for generating the electric power. The aim of this study is to establish a simulation model to investigate the performance of a photovoltaic thermal system (PV/T) by using computational fluid dynamics method (CFD). The model includes a water conduct tube, absorber plate and system for convection heat transfer. The ANSYS FLUENT software has been used for simulation process. The panel electrical output and its efficiency were numerically investigated. In addition, the effect of the absorbed radiation changes on the inlet fluid temperature and absorbing plate on the system performance were investigated. A dynamic analysis of hybrid photovoltaic thermal system with a circulatory pump was given. A detailed mathematical model of the system is presented. The study was conducted for three cases, in the first case, when there is no coolant in the system and in the second case, at a constant fluid flow of the pump, while the third case with optimized operation of the pump. The obtained numerical results by CFD simulators were compared with the experimental results of the documentation. The both results have good agreement. From the obtained results, it can be seen that the system gives a good improvement for the net electrical efficiency of 3.52 % with a low reduction in thermal efficiency of the system by 1.96 % compared to the system when the consistently high flow is use

TFUZZY-OF: a new method for routing protocol for low-power and lossy networks load balancing using multi-criteria decision-making

The internet of things (IoT) based on a network layer perspective includes low-power and lossy networks (LLN) that are limited in terms of power consumption, memory, and energy usage. The routing protocol used in these networks is called routing over low-power and lossy networks (RPL). Therefore, the IoT networks include smart objects that need multiple routing for their interconnections which makes traffic load balancing techniques indispensable to RPL routing protocol. In this paper, we propose a method based on fuzzy logic and the technique for the order of prioritization by similarity to the ideal solution (TOPSIS) as a well-known multi-criteria decision-making method to solve the load balancing problem by routing metrics composition. For this purpose, a combination of both link and node routing metrics namely hop count, expected transmission count, and received signal strength indicator is used. The results of simulations show that this method can increase the quality of services in terms of packet delivery ratio and average end-to-end delay

MRI image segmentation using machine learning networks and level set approaches

The segmented brain tissues from magnetic resonance images (MRI) always pose substantive challenges to the clinical researcher community, especially while making precise estimation of such tissues. In the recent years, advancements in deep learning techniques, more specifically in fully convolution neural networks (FCN) have yielded path breaking results in segmenting brain tumour tissues with pin-point accuracy and precision, much to the relief of clinical physicians and researchers alike. A new hybrid deep learning architecture combining SegNet and U-Net techniques to segment brain tissue is proposed here. Here, a skip connection of the concerned U-Net network was suitably explored. The results indicated optimal multi-scale information generated from the SegNet, which was further exploited to obtain precise tissue boundaries from the brain images. Further, in order to ensure that the segmentation method performed better in conjunction with precisely delineated contours, the output is incorporated as the level set layer in the deep learning network. The proposed method primarily focused on analysing brain tumor segmentation (BraTS) 2017 and BraTS 2018, dedicated datasets dealing with MRI brain tumour. The results clearly indicate better performance in segmenting brain tumours than existing ones

Dynamic filtering of malicious records using machine learning integrated databases

Machine Learning, Deep Learning and Predictive Analytics are the key domains of research in assorted domains of implementations including engineering, finance, economics, real time imaging and many others. The researchers are working on different tools and technologies including open source and own developed frameworks so that the higher degree of accuracy can be achieved. The research reports from Market Research News US predicted that the global market size of machine learning based implementations will exceed 20 billion dollars in year 2024. Most of the government and social services are nowadays in process to be deployed with the advanced technologies of machine learning and deep learning so that the minimum error factor can be there. The key players in the industry include; Google, Facebook, IBM Watson, Baidu, Apple, Microsoft, Wipro, Amazon, Intel, Nuance and many others which are working on the advanced algorithms and implementation perspectives of machine learning

Comparison of speech enhancement algorithms for forensic applications

Speech enhancement algorithms play an essential role in forensic applications, and enhanced speech signals can be used in court as evidence in criminal cases. This paper compares the performance of single channel (spectral subtraction and level dependent wavelet threshold techniques) and multiple channel (independent component analysis or ICA) speech enhancement algorithms to remove real environmental noise from noisy audio recording signals. Experimental results demonstrate that ICA achieves a significant improvement in average signal to noise ratio (SNR) enhancement compared to single channel speech enhancement algorithms, when 100 sentences from a forensic voice comparison database were corrupted with a car, street and factory noise at input SNR (-10 to 10 dB)