A fast hybrid image cryptosystem based on random generator and modified logistic map

Multimedia Tools and Applications - This paper demonstrates an image encryption technique using a hybrid method. This method consists of two stages. The image is XORed with the Pseudo-Random Number...     

Simulation of ternary ammonia–water–salt absorption refrigeration cyclesSimulation des cycles frigorifiques à absorption à ammoniac / eau / sel

This paper proposes a new working fluid for refrigeration cycles utilizing low temperature heat sources. The proposed working fluid consists of the ammonia–water working fluid mixture and a salt. The salt is used to aid the removal of ammonia from the liquid solution. This effect is a manifestation of the well known “salting-out” effect. While the addition of salt improves the generator performance, it also has a detrimental effect on the absorber. The overall effects on the performance of three absorption cycles using the NH₃–H₂O–NaOH working fluid have been investigated using computer simulations. The results indicated that salting out can lower the generator operating temperature while simultaneously improving the cycle performance. Furthermore, limiting the salt to the generator suggests potential for further improvement in cycle performance.     

Assessment of progressive collapse potential in corner floor panels of reinforced concrete buildings

The Department of Defense (DoD) document, UFC 4-023-23, which provides technical guidance for mitigation of progressive collapse, classifies buildings based on the desired level of protection. Medium and high levels of protection categories require the use of the Alternate Path (AP) method to investigate the capability of the structural system to transfer loads safely from a notionally removed column to the remaining structural elements. Certain columns and structural elements at prescribed locations must be investigated to determine the structural bridging capabilities over the removed column. Transfer of loads from a notionally removed corner column to the adjacent structural elements can impose significant stress/deformation demand on structural elements supporting the corner panel. When the panel area exceeds the floor damage limits, the panel and its structural elements must be designed to support the additional load or the loads must be transferred to adjacent columns. This paper investigates the implementation of UFC 4-023-23 to protect against progressive collapse of corner floor panels when their dimensions exceed the damage limits. A case study of a reinforced concrete building is analyzed, designed, and investigated using the AP method.     

Non-destructive ultrasonic evaluation of CFRP–concrete specimens subjected to accelerated aging conditions

The objective of this work is to utilize surface acoustic waves (SAWs) for non-destructive structural health monitoring of concrete specimens externally bonded with carbon fiber-reinforced polymer (CFRP) composites and subjected to accelerated aging conditions. Both experimental testing and signal processing schemes of ultrasonic wave propagation through the CFRP substrate are described. The surface waves are generated and received at the external face of the CFRP using narrow-band transducers with a 110-kHz center frequency. The received signals are filtered and amplified then digitized and processed to extract various parameters in both time and frequency domains including average power (P_Avg), maximum amplitude (V_max), and maximum power–frequency ratio ((P/F)_max). Changes in these parameters due to water-immersion aging at different temperatures were monitored over 12 weeks. Results indicated a marked decrease in measured ultrasonic parameters over time, particularly after the first 2 weeks, indicating a possible debonding or deterioration in the samples. Ultrasonic results showed good agreement with the findings of a parallel destructive study on mode-II fracture loading of CFRP–concrete samples, tested to obtain fracture energy (G_f) and define traction–separation response under temperature and water-immersion aging effects. It was observed that all ultrasonic parameters exhibit good correlations (|r|>0.5, P<0.05) with the fracture energy at all temperatures. Moreover, when the measurements at all temperatures were incorporated and linear relationships between destructive and non-destructive parameters were assumed, correlations of r=0.84, 0.80, and 0.80 were found between G_f and P_Avg, V_max, and (P/F)_max, respectively. This study paves the way for developing a non-destructive testing protocol for structural health monitoring of bridges and concrete structures undergoing repair and rehabilitation with CFRP composites.     

Efficient Deep CNN Model for COVID-19 Classification

Coronavirus (COVID-19) infection was initially acknowledged as a global pandemic in Wuhan in China. World Health Organization (WHO) stated that the COVID-19 is an epidemic that causes a 3.4% death rate. Chest X-Ray (CXR) and Computerized Tomography (CT) screening of infected persons are essential in diagnosis applications. There are numerous ways to identify positive COVID-19 cases. One of the fundamental ways is radiology imaging through CXR, or CT images. The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality. Hence, automated classification techniques are required to facilitate the diagnosis process. Deep Learning (DL) is an effective tool that can be utilized for detection and classification this type of medical images. The deep Convolutional Neural Networks (CNNs) can learn and extract essential features from different medical image datasets. In this paper, a CNN architecture for automated COVID-19 detection from CXR and CT images is offered. Three activation functions as well as three optimizers are tested and compared for this task. The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it. The performance is tested and investigated on the CT and CXR datasets. Three activation functions: Tanh, Sigmoid, and ReLU are compared using a constant learning rate and different batch sizes. Different optimizers are studied with different batch sizes and a constant learning rate. Finally, a comparison between different combinations of activation functions and optimizers is presented, and the optimal configuration is determined. Hence, the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage. The proposed model achieves a classification accuracy of 91.67% on CXR image dataset, and a classification accuracy of 100% on CT dataset with training times of 58 min and 46 min on CXR and CT datasets, respectively. The best results are obtained using the ReLU activation function combined with the SGDM optimizer at a learning rate of 10⁻⁵ and a minibatch size of 16.     

Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional Magnetic Resonance Image (MRI) and Computed Tomography (CT) scans utilizing 3D U-Net Design and ResNet50, taken after by conventional classification strategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, and the 3D U-Net scored 97.99% among the different methods of deep learning. It is to be mentioned that traditional Convolutional Neural Network (CNN) gives 97.90% accuracy on top of the 3D MRI. In expansion, the image fusion approach combines the multimodal images and makes a fused image to extricate more highlights from the medical images. Other than that, we have identified the loss function by utilizing several dice measurements approach and received Dice Result on top of a specific test case. The average mean score of dice coefficient and soft dice loss for three test cases was 0.0980. At the same time, for two test cases, the sensitivity and specification were recorded to be 0.0211 and 0.5867 using patch level predictions. On the other hand, a software integration pipeline was integrated to deploy the concentrated model into the webserver for accessing it from the software system using the Representational state transfer (REST) API. Eventually, the suggested models were validated through the Area Under the Curve–Receiver Characteristic Operator (AUC–ROC) curve and Confusion Matrix and compared with the existing research articles to understand the underlying problem. Through Comparative Analysis, we have extracted meaningful insights regarding brain tumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imaging modalities.     

Embedded Coded Relay System for Molecular Communications

With the emergence of the COVID-19 pandemic, the World Health Organization (WHO) has urged scientists and industrialists to explore modern information and communication technology (ICT) as a means to reduce or even eliminate it. The World Health Organization recently reported that the virus may infect the organism through any organ in the living body, such as the respiratory, the immunity, the nervous, the digestive, or the cardiovascular system. Targeting the abovementioned goal, we envision an implanted nanosystem embedded in the intra living-body network. The main function of the nanosystem is either to perform diagnosis and mitigation of infectious diseases or to implement a targeted drug delivery system (i.e., delivery of the therapeutic drug to the diseased tissue or targeted cell). The communication among the nanomachines is accomplished via communication-based molecular diffusion. The control/interconnection of the nanosystem is accomplished through the utilization of Internet of bio-nano things (IoBNT). The proposed nanosystem is designed to employ a coded relay nanomachine disciplined by the decode and forward (DF) principle to ensure reliable drug delivery to the targeted cell. Notably, both the sensitivity of the drug dose and the phenomenon of drug molecules loss before delivery to the target cell site in long-distance due to the molecules diffusion process are taken into account. In this paper, a coded relay NM with conventional coding techniques such as RS and Turbo codes is selected to achieve minimum bit error rate (BER) performance and high signal-to-noise ratio (SNR), while the detection process is based on maximum likelihood (ML) probability and minimum error probability (MEP). The performance analysis of the proposed scheme is evaluated in terms of channel capacity and bit error rate by varying system parameters such as relay position, number of released molecules, relay and receiver size. Analysis results are validated through simulation and demonstrate that the proposed scheme can significantly improve delivery performance of the desirable drugs in the molecular communication system.