In recent years, encryption technology has been developed quickly and many image encryption methods have been put forward. Chaos based image encryption technique is a new encryption technique for images. It utilizes chaos random sequence to encrypt image, which is an efficient way to deal with the intractable problem of fast and highly secure image encryption. However, the Chaos based image encryption technique has some deficiencies, such as the limited accuracy problem. This paper researches on the chaotic encryption DES encryption and a combination of image encryption algorithm, and simulate these algorithms, through analysis of the algorithm to find the gaps. And on this basis, the algorithm has been improved. The new encryption scheme realizes the digital image encryption through the chaos and improving DES. Firstly, new encryption scheme uses the Logistic chaos sequencer to make the pseudo-random sequence, carries on the RGB with this sequence to the image chaotically, then makes double time encryptions with improvement DES, displays they respective merit. Theoretical analysis and the simulation indicate that this plan has the high starting value sensitivity, and enjoys high security and the encryption speed. In addition it also keeps the neighboring RGB relevance close to zero. The algorithm can be used in the actual image encryption.

Random access channel is incorporated in IEEE 802.16 WiMAX system to transport contention-based messages in the uplink, such as bandwidth requests generated by best effort applications, from mobile stations (MSs) to the base station (BS). Regarding the packet transmissions in the random access channel, delay, throughput and power consumption are usually considered as major performance metrics. We propose an enhanced power calibration protocol to optimize the system performance in terms of channel access delay, system throughput and MS transmit power consumption. Optimal selection of power levels is formulated in order to maximize the achievable throughput while being subject to power consumption constraint of mobile stations. Our scheme can substitute the random backoff mechanism in the current the WiMAX uplink random access channel, as it can accomplish a significantly improved access delay performance without an increment of design complexity. Simulations are conducted to validate our protocol and confirm its performance superiority as compared to the random channel access method used in the current WiMAX.

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This paper proposes an optimal structured deep convolutional neural network (DCNN) based on the marine predator algorithm (MPA) to construct a novel automatic diagnosis platform that may help radiologists identify COVID-19 and non-COVID-19 patients based on CT scan categorization and analysis. The goal is met with the help of three modifications based on the regular MPA. First, a novel encoding scheme based on Internet Protocol (IP) addresses is proposed, followed by introducing an Enfeebled layer to build a variable-length DCNN. Finally, the learning process divides big datasets into smaller chunks that are randomly evaluated. The proposed model is compared to the COVID-CT and SARS-CoV-2 datasets to undertake a complete evaluation. Following that, the performance of the developed model (DCNN-IPMPA) is compared to that of a typical DCNN and seven variable-length models using five well-known comparison metrics, as well as the receiver operating characteristic and precision-recall curves. The results show that the DCNN-IPMPA outperforms other benchmarks, with a final accuracy of 97.21% on the SARS-CoV-2 dataset and 97.94% on the COVID-CT dataset. Also, timing analysis indicates that the DCNN processing time is the best among all benchmarks as expected; however, DCNN-IPMPA represents a competitive result compared to the standard DCNN.

Random access channel is incorporated in IEEE 802.16 WiMAX system to transport contention-based messages in the uplink, such as bandwidth requests generated by best effort applications, from mobile stations (MSs) to the base station (BS). Regarding the packet transmissions in the random access channel, delay, throughput and power consumption are usually considered as major performance metrics. We propose an enhanced power calibration protocol to optimize the system performance in terms of channel access delay, system throughput and MS transmit power consumption. Optimal selection of power levels is formulated in order to maximize the achievable throughput while being subject to power consumption constraint of mobile stations. Our scheme can substitute the random backoff mechanism in the current the WiMAX uplink random access channel, as it can accomplish a better access delay performance without an increment of design complexity. Simulations are conducted to validate our protocol and confirm its performance superiority as compared to the random channel access method used in the current WiMAX.

Parkinson’s disease is the second most prevalent neurological disease, affecting millions of people globally. It is a condition that affects different regions of the brain in the basal ganglia, which is characterized by motor symptoms and postural instability. Currently, there is no cure available in order to completely eradicate the disease from the body. As a result, early diagnosis of Parkinson’s Disease (PD) is critical in combating the gradual loss of dopaminergic neurons in patients. Although much progress has been made in using medical images such as MRI and DaTScan for diagnosing the early stages of Parkinson’s Disease, the work remains difficult due to lack of properly labeled data, high error rates in clinical diagnosis and a lack of automatic detection and segmentation software. In this paper, we propose a software called PPDS (Parkinson’s Disease Diagnosis Software) for the detection and segmentation of deep brain structures from MRI and DaTScan images related to Parkinson’s disease. The proposed method utilizes state-of-the-art convolutional neural networks such as YOLO and UNET to correctly identify and segment regions of interest for Parkinson’s disease from both DatScan and MRI images, as well as deliver prediction results. The aim of this study is to evaluate the performance of deep convolutional networks in automating the task of identifying and segmenting the substantia nigra and striatum from T2-weighted MRI and DatScan images respectively, which are used to monitor the loss of dopaminergic neurons in these areas.

Medical image segmentation is essential for disease diagnosis and for support- ing medical decision systems. Automatic segmentation of brain tumors from Magnetic Resonance Imaging (MRI) is crucial for treatment planning and timely diagnosis. Due to the enormous amount of data that MRI provides as well as the variability in the location and size of the tumor, automatic seg- mentation is a difficult process. Consequently, a current outstanding problem in the field of deep learning-based medical image analysis is the development of an accurate and trustworthy way to separate the tumorous region from healthy tissues. In this paper, we propose a novel 3D Attention U-Net with dense encoder blocks and residual decoder blocks, which combines the bene- fits of both DenseNet and ResNet. Dense blocks with transition layers help to strengthen feature propagation, reduce vanishing gradient, and increase the receptive field. Because each layer receives feature maps from all previous layers, the network can be made thinner and more compact. To make predic- tions, it considers both low-level and high-level features at the same time. In addition, shortcut connections between the residual network are used to pre- serve low-level features at each level. As part of the proposed architecture, skip connections between dense and residual blocks are utilized along with an attention layer to speed up the training process. The proposed architecture was trained and validated using BraTS 2020 dataset, it showed promising results with dice scores of 0.866, 0.889, and 0.828 for the tumor core (TC), whole tumor (WT), and enhancing tumor (ET), respectively. In compar- ison to the original 3D U-Net, our approach performs better. According to the findings of our experiment, our approach is a competitive automatic brain tumor segmentation method when compared to some state-of-the-art techniques.

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