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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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.