Event cameras are bio-inspired vision sensors that asynchronously represent pixel-level brightness changes as event streams. Event-based monocular multi-view stereo (EMVS) is a technique that exploits the event streams to estimate semi-dense 3D structure with known trajectory. It is a critical task for event-based monocular SLAM. However, the required intensive computation workloads make it challenging for real-time deployment on embedded platforms. In this paper, Eventor is proposed as a fast and efficient EMVS accelerator by realizing the most critical and time-consuming stages including event back-projection and volumetric ray-counting on FPGA. Highly paralleled and fully pipelined processing elements are specially designed via FPGA and integrated with the embedded ARM as a heterogeneous system to improve the throughput and reduce the memory footprint. Meanwhile, the EMVS algorithm is reformulated to a more hardware-friendly manner by rescheduling, approximate computing and hybrid data quantization. Evaluation results on DAVIS dataset show that Eventor achieves up to 24X improvement in energy efficiency compared with Intel i5 CPU platform.

The methods in remote sensing image retrieval (RSIR) usually search the whole retrieval data set in the retrieval process, which takes much time and is unnecessary. To reduce the overall search time, this letter proposes a new retrieval scheme based on fuzzy rules. The proposed method calculates the fuzzy class membership of images using two ways. The first way predicts the fuzzy class membership by convolutional neural network (CNN). The other uses the image-to-class distance that is a distance between an image and each class on the training data set. The two fuzzy class memberships are used to measure the classification confidence, and a query image is classified into three fuzzy sets, namely, “low classification confidence,” “medium classification confidence,” and “high classification confidence,” based on the classification confidence. The fuzzy rules are built according to fuzzy classification to choose the search space for each fuzzy set. The final search space is determined by the two search spaces obtained by fuzzy rules. Moreover, the fuzzy distance between a query image and a retrieved image is used to improve the retrieval performance, which is calculated according to their fuzzy class memberships and the Euclidean distance between the two images. The experimental results on University of California, Merced data set (UCMD) and PatternNet databases show that our proposed method can not only enhance the retrieval performance but also reduce the search time in comparison to other state-of-the-art techniques.

Time-triggered Ethernet (TTE) technology introduces the concept of time-triggered on the basis of traditional Ethernet, so that it can achieve conflict-free and deterministic service forwarding without sacrificing compatibility. However, storage resources in industrial, aviation, aerospace and other equipment are limited. Therefore, it is important for TTEthernet to develop switching technologies with high storage efficiency and scalability. This paper proposes a dual plane switching (DPS) architecture for TTEthernet, which divides time-triggered services and event-triggered services into two planes for data forwarding. Experimental results show that using the TTE switch of this architecture has the advantages of high clock synchronization accuracy, high throughout, low transmission delay and small jitter of TTE service.

Efficiently retrieving synthetic aperture radar (SAR) image is an important yet challenging task in the remote sensing field. Due to the shortage of labeled SAR images for fine-tuning convolutional neural network (CNN) models, this letter presents an unsupervised domain adaptation model based on CNN to learn the domain-invariant feature between SAR images and optical aerial images for SAR image retrieving, which can alleviate the burden of manual labeling. We extend a deep CNN to a novel adversarial network by adding the domain discriminator and the pseudolabel predictor. We improve the adaptation capacity of the adversarial network by utilizing the class information of SAR training images, which is obtained by clustering. Compared with the other related methods, the proposed method can enhance retrieval performance with our SAR data set.

Neuron segmentation is an important task for automatic analyses of brain images that are of huge volume. Previous methods for neuron segmentation rely on handcrafted image features, and have difficulty in coping with high-resolution, low signal-to-noise-ratio brain images. Convolutional neural network (CNN) has achieved remarkable success in natural image segmentation, but CNN requires accurately labeled data for training that are difficult to achieve on brain images of huge volume. In this paper, we present a weakly supervised learning strategy to deal with the inaccurate training data problem, and thus adopt 3D CNN to perform automatic soma segmentation from brain images. We test our method on our own collected mouse brain images that are of single-neuron resolution, and results show that 3D CNN-based method outperforms the traditional methods by a significant margin.

Remote sensing image retrieval (RSIR) is a fundamental task in remote sensing. Most content-based RSIR approaches take a simple distance as similarity criteria. A retrieval method based on weighted distance and basic features of convolutional neural network (CNN) is proposed in this letter. The method contains two stages. First, in offline stage, the pretrained CNN is fine-tuned by some labeled images from the target data set, then used to extract CNN features, and labeled the images in the retrieval data set. Second, in online stage, we use the fine-tuned CNN model to extract the CNN feature of the query image and calculate the weight of each image class and apply them to calculate the distance between the query image and the retrieved images. Experiments are conducted on two RSIR data sets. Compared with the state-of-the-art methods, the proposed method is simplified but efficient, significantly improving retrieval performance.

A novel H.264-based multiple description coding (MDC) framework, called data reuse MDC (DR-MDC), is proposed in this paper. The input video sequence is first down-sampled by a factor of two in both horizontal and vertical directions, respectively, on each frame to generate four sub-sequences, followed by grouping them into two descriptions via the quincunx manner. In each description, one sub-sequence is directly encoded by applying the H.264/AVC encoder, while the other is examined at each macroblock (MB) to determine whether the encoding of the current MB should be conducted or skipped completely based on the following criterion: If the MB is considered locating in a homogeneous or still background region, the encoding process will be skipped. Otherwise, the neighboring prediction algorithm will be used to predict the pixel values of this MB, and the resultant prediction errors will be further encoded and transmitted. Experimental results have shown that the proposed DR-MDC scheme is more error resilient and yields better reconstructed video than the existing state-of-the-art MDC methods.

Cloud-oriented service mashup can aggregate many services to provide personalized services for end-users on demand. However, how to securely aggregate mashup services becomes a bottleneck of hampering the development of cloud computing. In this paper, we present a secure cloud service mashup framework called SMEF to address this problem. In SMEF, we employ security entropy to measure the unascertained security degree of service mashup. The nonfunctional criteria of SMEF are aggregated as a single criterion by defining a utility function. Then the relatively optimal mashup services are selected to meet the user requirements. Finally, we have implemented a simulation of SMEF and conducted extensive experiments using simulations of different sizes of services and security factors. Experimental results show the feasibility and efficiency of the SMEF service mashup framework.

Abstract The principle of least privilege in role‐based access control is an important area of research. There are two crucial issues related to it: the specification and the enforcement. We believe that the existing least privilege specification schemes are not comprehensive enough and few of the enforcement methods are likely to scale well. In this paper, we formally define the basic principle of least privilege problem and present different variations, called the delta‐approx principle of least privilege problem and the minimizing‐approx principle of least privilege problem. Since there may be more than one result to enforce the same principle of least privilege, we introduce the notation about weights of permissions and roles to optimize the results. Then we prove that all least privilege problems are NP‐complete. As an important contribution of the paper, we show that the principle of least privilege problem can be reduced to minimal cost set covering (MCSC) problem. We can borrow the existing solutions of MCSC to solve the principle of least privilege problems. Finally, different algorithms are designed to solve the proposed least privilege problems. Experiments on performance study prove the superiority of our algorithms. Copyright © 2011 John Wiley & Sons, Ltd.

Traditional web application development often encounters tight coupling problem between access control logic and business logic. It is hard to configure and modify access control policies after a system has been deployed. In this demonstration, we present SecTag, a multi-policy supported secure web tag framework, to address this problem. We define a series of general-purpose secure attributes that meet the demand of fine-grained access control in web presentation layer. We also design a set of high interactive secure tags, which encapsulate secure features to provide reusable secure components for web development. A running example of SecTag is presented to demonstrate the effectiveness of the proposed framework.

This paper is concerned with the design problem of networked control systems with partly unknown transition probabilities. Firstly, a new time-delay switched linear system model is proposed for NCSs, in which both the network-induced delay and the packet dropouts are taken into account. A developed packet dropouts dependent Lyapunov functional is used to obtain the stability criteria, in which the method for treating the uncertainties of partly unknown transition probabilities is embedded. Furthermore, the state feedback controller can be designed. Moreover, because the partly unknown transition probabilities in our study do not need any bounds or structures of uncertainties, the obtained stability criteria can expand to the case that the time-delay switched linear system model under arbitrary switching. Finally, numerical example and simulation is used to illustrate the developed theory.

In this paper, a new delayed projection neural network with mixed delays is proposed for solving a class of quadratic programming (QP) problems. By the Lyapunov-Krasovskii theory and the linear matrix inequality (LMI) method, the proposed neural network is proved to be convergent to the optimal solution of the QP problems exponentially. The validity of the proposed neural network is verified by two simulation examples.