Abstract Diagnosability is often considered as an important factor for measuring the self-diagnostic ability of network systems. However, classic system-level diagnosis focuses only on processor faults and ignores the objective reality of communication faults. Under real circumstances, missing edges and node failures usually occur simultaneously in multiprocessor systems (called hybrid fault circumstances). Therefore, it is important to study the diagnosability of multiprocessor systems under hybrid fault circumstances. In this paper, we propose several diagnosabilities of interconnection networks with missing edges and faulty nodes. By exploring some important relationships between diagnosability and the minimum degree of a network under hybrid fault circumstances, we present and prove the diagnosability of several classic interconnection networks, including BC (bijective connection) networks, star graphs, folded hypercubes, exchanged hypercubes, exchanged crossed cubes, k-ary n-cubes, bubble-sort star graphs and balanced hypercubes, with missing edges and broken-down nodes under the PMC (Preparata, Metze and Chien) and MM* (Maeng and Malek) models.

In cloud computing circumstance, users upload data to the cloud server and verify data integrity through a third-party audit (TPA). However, verifying data integrity is still a computationally intensive and time-consuming operation. If there are illegal users or unreliable cloud servers, it can only be known from the verification result, resulting in invalid computation and time overhead. In order to solve the above problems, RSA algorithm is used to verify the legitimacy of the user, and when the verification passes, Merkle hash tree is used to filter unreliable servers. To prevent replay attacks, data integrity is verified through the bilinear mapping feature. Finally, the simulation results show that the scheme not only can detect the legitimacy of users, but also filter out unreliable servers, and effectively reduce the computational and time overhead of verifying data integrity.

Proliferative Diabetic Retinopathy (PDR) is a seri-ous retinal disease threatening diabetic patients. Intense retinal neovascularization in the retinal image is the most important clinical symptom of PDR, leading to visual distortion if not controlled. Accurate and timely detection of neovascularization from retinal images allows patients to receive adequate treatment to avoid further vision loss. In this work, we propose a retinal neovascularization automatic segmentation model based on im-proved Pyramid Scene Parsing Network (PSP-Net). To improve the accuracy of the model, we introduce the proposed channel attention module into the model. The network is evaluated with color fundus images from practice. Evaluation results show the network is superior to FCN, SegNet, U-Net and PSP-Net in accuracy and sensitivity. The model could achieve accuracy, sensitivity, specificity, precision and Jaccard similarity score of 0.9832,0.9265,0.9897,0.9116 and 0.8501, respectively. This paper proves through plenty of experimental results that the network model is able to improve the accuracy of segmentation, relieve the workload of doctors, and is worthy of further clinical promotion.

The aerial light field (ALF) can render higher quality images of large-scale 3D scenes. In this paper, we apply the ALF technology to study the image captured and novel view rendering of unmanned aerial vehicle (UAV), which exists some problems, such as large scene and depth of field. First, we design an ALF sampling model using spectral analysis of Fourier theory. Based on the ALF sampling model, the exact expression of ALF spectrum is derived. By the spectral support of ALF, we analyze the influence of pitch angle on light field sampling and its bandwidth. Particularly, the bandwidth of ALF can be applied to determine the minimum sampling rate for UAV. Additionally, we design a reconstruction filter that is related to pitch angle to render novel views of UAV. Finally, our experiments show that our sampling and rendering methods can improve the rendering quality of UAV novel view rendering.

In the previous results, light field sampling is based on ideal assumptions (e.g., Lambertian and Non-occluded scene), and thus we would like to more precisely analyze the sparsity sampling of light field signal. We present a sparsity analysis of light field (SALF) method for optimizing light field sampling rate. The SALF method applies the Fourier projection-slice theorem to simplify the initialization of light field sampling. Furthermore, we use a voting scheme to select light field spectra in which the frequency coefficients are nonzero. These spectra include many scene information and their captured positions are approximately equal to camera positions in the frequency domain. If the camera is only placed in these selected camera positions, the sampling rate can be optimized and the rendering quality can be guaranteed. Finally, we compare SALF method with other light field sampling methods to verify the claimed performance. The reconstruction results show that the SALF method improves rendering quality of novel views and outperforms those of other comparison methods.

Mobile Edge Computing (MEC) can migrate traditionally deployed high-energy-consumption and high-complex computing tasks to nearby edge servers, providing users with faster services and better network performance. There is a security problem of malicious access by eavesdroppers when offloading computing tasks. In this article, we propose an access point (AP) and wireless devices (WDs) MEC system. WDs use a binary offloading strategy to calculate tasks. Taking the average time security and the stability of equipment buffer queue as constraints, an online computing offloading method based on Lyapunov is proposed. This method transforms the computing process into an online computing offloading problem with the goal of minimizing the total energy consumption of WDs, so as to decouple the CPU cycle frequency, transmission power and task offloading strategy in different time periods. Simulation results show that this method can achieve better performance in terms of energy consumption compared with other benchmark methods.

Occlusions are common phenomena in light field rendering (LFR) technology applications. The 3-D spatial structures of some features may be missing or incorrect when capturing some samples due to occlusion discontinuities. Most prior works on LFR, however, have neglected occlusions from other objects in 3-D scenes that do not participate in the capturing and rendering of the light field. To improve rendering quality, this report proposes an occlusion probability learning framework (OPLF) based on a deep Boltzmann machine (DBM) to compensate for the occluded information. In the OPLF, an occlusion probability density model is applied to calculate the visibility scores, which are modeled as hidden variables. Additionally, the probability of occlusion is related to the visibility, the camera configuration (i.e., position and direction), and the relationship between the occlusion object and occluded object. Furthermore, a deep probability model based on the OPLF is used for learning the occlusion relationship between the camera and object in multiple layers. The proposed OPLF can optimize the LFR quality. Finally, to verify the claimed performance, we also compare the OPLF with the most advanced occlusion theory and light field reconstruction algorithms. The experimental results show that the proposed OPLF outperforms other known occlusion quantization schemes.

Occlusions will reduce the performance of systems in many computer vision applications with discontinuous surfaces of 3D scenes. We explore a signal-processing framework of occlusions based on the light ray visibility to improve the rendering quality of views. An occlusion field (OCF) theory is derived by calculating the relationship between the occluded light rays and the nonoccluded light rays to quantify the occlusion degree (OCD). The OCF framework can describe the various in-scene information captured by the changes in the camera configuration (i.e., position and direction) through a quantitative description of the occlusion information. From a spectral analysis of the OCF, we mathematically derive analytical functions to determine the changing relationship between the scene and the camera configuration. A reconstruction filter can be designed to achieve interference cancellation and compensate for the missing information caused by the occlusions. Our measurements of different occlusions using this OCF framework included both synthetic and actual scenes. The experimental results show that the proposed OCF framework can improves the rendering quality of views and outperforms other known occlusion quantization schemes in a complex scene.

Occlusion as a common phenomenon in object surface can seriously affect information collection of light field. To visualize light field data-set, occlusions are usually idealized and neglected for most prior light field rendering (LFR) algorithms. However, the 3D spatial structure of some features may be missing to capture some incorrect samples caused by occlusion discontinuities. To solve this problem, we propose an occlusion probability (OCP) model to improve the capturing information and the rendering quality of views with occlusion for the LFR. In this OCP model, a probability density model is applied to obtain the scores of visibility are modeled as hidden variables. The occlusion probability is calculated by the visibility, position and orientation of camera. We compare different capturing/reconstruction techniques to visualize/manipulate our OCP model.

We consider a control problem in wireless multi-hop networks in which source-destination pairs desire to secure communication. Specifically, a control algorithm is proposed based on the stochastic network optimization to maximize a global utility function, subject to end-to-end secrecy transmission and network stability. To achieve secure communication, we firstly exploit an independent randomization encoding strategy to guarantee the multi-hops secrecy transmission. Then, the control algorithm is decomposed into flow control, routing and resource allocation. Based on the control algorithm, each node makes decisions on the arrival confidential data as well as the users and links. The numerical analysis illustrates that the proposed algorithm can achieve a utility result, arbitrarily close to optimal value.

In this paper, we investigate the achievable throughput capacity of three-dimensional (3D) inhomogeneous wireless ad hoc networks. In particular, we consider nodes placed according to Shot Noise Cox process, which allows to model the clustering behavior usually recognized in large-scale systems. For this class of networks, we partition the transmission into two parts: the intra-cluster transmission and the inter-cluster transmission. For the intra-cluster transmission, we firstly construct a spheroidal percolation model in which the highway system exists. Different from regular 2D percolation model, the highways in our model are in the radial direction and around the cluster. Based on our model, we propose a novel routing strategy with five phases and derive the per-node rate for the intra-cluster transmission. As for the inter-cluster transmission, we build some "information pipes'' to connect the clusters and then get the inter-cluster transmission rate. Finally, we take the transmission of the whole networks into account and obtain the lower bound of the throughput capacity of 3D inhomogeneous networks.