Biblio

The transition effect ring oscillator (TERO) based true random number generator (TRNG) was proposed by Varchola and Drutarovsky in 2010. There were several stochastic models for this advanced TRNG based on ring oscillator. This paper proposed an improved TERO based TRNG and implements both on Altera Cyclone series FPGA platform and on a 0.13um CMOS ASIC process. FPGA experimental results show that this balanced TERO TRNG is in good performance as the experimental data results past the national institute of standards and technology (NIST) test in 1M bit/s. The TRNG is feasible for a security SoC.

Information-Centric Network (ICN) is one of the most promising network architecture to handle the problem of rapid increase of data traffic because it allows in-network cache. ICNs with Linear Network Coding (LNC) can greatly improve the performance of content caching and delivery. In this paper, we propose a Secure Content Caching and Routing (SCCR) framework based on Software Defined Network (SDN) to find the optimal cache management and routing for secure content delivery, which aims to firstly minimize the total cost of cache and bandwidth consumption and then minimize the usage of random chunks to guarantee information theoretical security (ITS). Specifically, we firstly propose the SCCR problem and then introduce the main ideas of the SCCR framework. Next, we formulate the SCCR problem to two Linear Programming (LP) formulations and design the SCCR algorithm based on them to optimally solve the SCCR problem. Finally, extensive simulations are conducted to evaluate the proposed SCCR framework and algorithms.

Transferring artistic styles onto everyday photographs has become an extremely popular task in both academia and industry. Recently, offline training has replaced online iterative optimization, enabling nearly real-time stylization. When those stylization networks are applied directly to high-resolution images, however, the style of localized regions often appears less similar to the desired artistic style. This is because the transfer process fails to capture small, intricate textures and maintain correct texture scales of the artworks. Here we propose a multimodal convolutional neural network that takes into consideration faithful representations of both color and luminance channels, and performs stylization hierarchically with multiple losses of increasing scales. Compared to state-of-the-art networks, our network can also perform style transfer in nearly real-time by performing much more sophisticated training offline. By properly handling style and texture cues at multiple scales using several modalities, we can transfer not just large-scale, obvious style cues but also subtle, exquisite ones. That is, our scheme can generate results that are visually pleasing and more similar to multiple desired artistic styles with color and texture cues at multiple scales.

Ideally, minimizing the flow completion time (FCT) requires millions of priorities supported by the underlying network so that each flow has its unique priority. However, in production datacenters, the available switch priority queues for flow scheduling are very limited (merely 2 or 3). This practical constraint seriously degrades the performance of previous approaches. In this paper, we introduce Explicit Priority Notification (EPN), a novel scheduling mechanism which emulates fine-grained priorities (i.e., desired priorities or DP) using only two switch priority queues. EPN can support various flow scheduling disciplines with or without flow size information. We have implemented EPN on commodity switches and evaluated its performance with both testbed experiments and extensive simulations. Our results show that, with flow size information, EPN achieves comparable FCT as pFabric that requires clean-slate switch hardware. And EPN also outperforms TCP by up to 60.5% if it bins the traffic into two priority queues according to flow size. In information-agnostic setting, EPN outperforms PIAS with two priority queues by up to 37.7%. To the best of our knowledge, EPN is the first system that provides millions of priorities for flow scheduling with commodity switches.

Security issues in vehicular communication have become a huge concern to safeguard increasing applications. A group signature is one of the popular authentication approaches for VANETs (Vehicular ad hoc networks) which can be implemented to secure the vehicular communication. However, securely distributing group keys to fast-moving vehicular nodes is still a challenging problem. In this paper, we propose an efficient key management protocol for group signature based authentication, where a group is extended to a domain with multiple road side units. Our scheme not only provides a secure way to deliver group keys to vehicular nodes, but also ensures security features. The experiment results show that our key distribution scheme is a scalable, efficient and secure solution to vehicular networking.

A number of blind Image Quality Evaluation Metrics (IQEMs) for Unmanned Aerial Vehicle (UAV) photograph application are presented. Nowadays, the visible light camera is widely used for UAV photograph application because of its vivid imaging effect; however, the outdoor environment light will produce great negative influences on its imaging output unfortunately. In this paper, to conquer this problem above, we design and reuse a series of blind IQEMs to analyze the imaging quality of UAV application. The Human Visual System (HVS) based IQEMs, including the image brightness level, the image contrast level, the image noise level, the image edge blur level, the image texture intensity level, the image jitter level, and the image flicker level, are all considered in our application. Once these IQEMs are calculated, they can be utilized to provide a computational reference for the following image processing application, such as image understanding and recognition. Some preliminary experiments for image enhancement have proved the correctness and validity of our proposed technique.

The paper presents a joint optimization algorithm for coverage and capacity in heterogeneous cellular networks. A joint optimization objective related to capacity loss considering both coverage hole and overlap area based on power density distribution is proposed. The optimization object is a NP problem due to that the adjusting parameters are mixed with discrete and continuous, so the bacterial foraging (BF) algorithm is improved based on network performance analysis result to find a more effective direction than randomly selected. The results of simulation show that the optimization object is feasible gains a better effect than traditional method.