Relative image similarity learning with contextual information for Internet cross-media retrieval

With the fast explosive rate of the amount of image data on the Internet, how to efficiently utilize them in the cross-media scenario becomes an urgent problem. Images are usually accompanied with contextual textual information. These two heterogeneous modalities are mutually reinforcing to make the Internet content more informative. In most cases, visual information can be regarded as an enhanced content of the textual document. To make image-to-image similarity being more consistent with document-to-document similarity, this paper proposes a method to learn image similarities according to the relations of the accompanied textual documents. More specifically, instead of using the static quantitative relations, rank-based learning procedure by employing structural SVM is adopted in this paper, and the ranking structure is established by comparing the relative relations of textual information. The learning results are in more accordance with the human’s recognition. The proposed method in this paper can be used not only for the image-to-image retrieval, but also for cross-modality multimedia, where a query expansion framework is proposed to get more satisfactory results. Extensive experimental evaluations on large scale Internet dataset validate the performance of the proposed methods.     

Evolutionary circuit design for fast FPGA-based classification of network application protocols

The evolutionary design can produce fast and efficient implementations of digital circuits. It is shown in this paper how evolved circuits, optimized for the latency and area, can increase the throughput of a manually designed classifier of application protocols. The classifier is intended for high speed networks operating at 100 Gbps. Because a very low latency is the main design constraint, the classifier is constructed as a combinational circuit in a field programmable gate array (FPGA). The classification is performed using the first packet carrying the application payload. The improvements in latency (and area) obtained by Cartesian genetic programming are validated using a professional FPGA design tool. The quality of classification is evaluated by means of real network data. All results are compared with commonly used classifiers based on regular expressions describing application protocols.     

Globally stable adaptive robust tracking control using RBF neural networks as feedforward compensators

In previous adaptive neural network control schemes, neural networks are usually used as feedback compensators. So, only semi-globally uniformly ultimate boundedness of closed-loop systems can be guaranteed, and no methods are given to determine the neural network approximation domain. However, in this paper, it is showed that if neural networks are used as feedforward compensators instead of feedback ones, then we can ensure the globally uniformly ultimate boundedness of closed-loop systems and determine the neural network approximation domain via the bound of known reference signals. It should be pointed out that this domain is very important for designing the neural network structure, for example, it directly determines the choice of the centers of radial basis function neural networks. Simulation examples are given to illustrate the effectiveness of the proposed control approaches.     

A novel image fusion framework for night-vision navigation and surveillance

The night-vision image fusion plays a critical role in detecting targets and obstructions in low light or total darkness, which has great importance for pedestrian recognition, vehicle navigation, surveillance and monitoring applications. The central idea is to fuse low-light visible and infrared imagery into a single output. In this paper, we describe a new fusion framework for spatially registered visual and infrared images. The proposed framework utilizes the properties of fractal dimension and phase congruency in the non-subsampled contourlet transform (NSCT) domain. The proposed framework applies multiscale NSCT on visual and IR images to get low- and high-frequency bands. The varied frequency bands of the transformed images are then fused while exploiting their characteristics. Finally, the inverse NSCT is performed to get the fused image. The performance of the proposed framework is validated by extensive experiments on different scene imaginary, where the definite advantages are demonstrated subjectively and objectively.     

A distributed data storage protocol for heterogeneous wireless sensor networks with mobile sinks

This paper presents ProFlex, a distributed data storage protocol for large-scale Heterogeneous Wireless Sensor Networks (HWSNs) with mobile sinks. ProFlex guarantees robustness in data collection by intelligently managing data replication among selected storage nodes in the network. Contrarily to related protocols in the literature, ProFlex considers the resource constraints of sensor nodes and constructs multiple data replication structures, which are managed by more powerful nodes. Additionally, ProFlex takes advantage of the higher communication range of such powerful nodes and uses the long-range links to improve data distribution by storage nodes. When compared with related protocols, we show through simulation that Proflex has an acceptable performance under message loss scenarios, decreases the overhead of transmitted messages, and decreases the occurrence of the energy hole problem. Moreover, we propose an improvement that allows the protocol to leverage the inherent data correlation and redundancy of wireless sensor networks in order to decrease even further the protocol’s overhead without affecting the quality of the data distribution by storage nodes.     

Multi-factor cheating prevention in visual secret sharing by hybrid codebooks

Visual secret sharing, or the so-called visual cryptography, is a well-known scheme that encrypts a secret image into several meaningless share images, usually printed on transparencies, and decrypts as stacking some or all share images by the human visual system. More and more researches about visual secret sharing and its applications have been recently proposed. Unfortunately, the cheating attack in which malicious participants cheat the honest one(s) by forging a fake share image has existed. Since 2006, some cheating prevention schemes have been proposed but suffered from one or more disadvantages as follows: (1) maintaining extra share images used to verify the integrity of a share image prior to stacking, (2) introducing extra pixel expansion, (3) raising heavy computation cost, and (4) giving ambiguous cheating detection. In this paper, a multi-factor cheating–preventing scheme, aiming at exploiting the hybrid codebook to hide the additional verification images into the share images, has been proposed without suffering the above-mentioned deficiencies. Two-factor cheating–detection exploits the design of verification to both share images and stacked results to deter attackers’ cheating. The experimental results demonstrate the proposed scheme is feasible.     

Video denoising via spatially adaptive coefficient shrinkage and threshold adjustment in Surfacelet transform domain

Many video denoising methods originated from methods designed for processing static two-dimensional images. Videos would be processed frame by frame, a process with a relatively high computational complexity, without taking into account the correlation information between frames. In this paper, a video denoising method using coefficient shrinkage and threshold adjustment based on Surfacelet transform (CSTA-ST) is proposed, which processes multiple frames of a video as an ensemble. Spatial correlation is used to define a weighted spatial energy. Each Surfacelet transform (ST) coefficient has a corresponding estimated energy value, in which the ST coefficients are grouped by. The similarity of the ST coefficients in a group determines the threshold of each ST coefficient. In addition, according to the neighborhood information of ST coefficients, the threshold is adjusted by a threshold adjustment factor. The coefficient shrinkage parameter is determined based on the adjusted threshold, and the ST coefficients are shrunk. Finally, the denoised video is obtained by the inverse ST using the shrunk coefficients. In experiments, video sequences with noise are tested, and the denoised results of the proposed method are compared with that of current denoising methods. The experimental results show that the proposed method significantly improves the peak signal-to- noise ratio (PSNR) and the structural similarity (SSIM) for various levels of noise and motion, and the ideal denoised visual effect is obtained.     

Novel intra prediction via position-dependent filtering

This paper presents a novel intra prediction algorithm, named position-dependent filtering (PDF), to improve the intra prediction accuracy. Different from the existing schemes where the samples along one prediction direction are predicted with the same set of filtering coefficients, in the proposed PDF, position-dependent filtering coefficients are employed, i.e., different sets of filtering coefficients are pre-defined for samples with different coordinates in one coding block. For each intra prediction mode, the set of linear filtering coefficients for each position within one block is obtained from off-line training using the least square method. Moreover, to further reduce the algorithm complexity, a simplified PDF (sPDF) is proposed. In sPDF, only a subset of reference samples are used for prediction and the others are discarded because of the minor contribution to intra prediction. The proposed algorithm has been implemented in the latest ITU-T VCEG KTA software. Experimental results demonstrate that, compared with the original KTA with new intra coding tool enabled, up to 0.53 dB of average coding gain is achieved by the proposed method, while applicable computational complexity is retained for practical video codecs.     

基于Linux和MiniGUI的数据传输测试仪表监控终端系统的研究

本文提出了一种基于Linux和MiniGUI平台的数据传输测试仪表监控终端系统的设计方案,重点介绍了该方案所采用的图形支持系统MiniGUI,并详细阐述了MiniGUI到基于ARM/Linux平台的SBC-2410X开发板的移植过程及方法.最后给出了实现该终端系统应用程序所涉及的若干关键问题,包括系统业务逻辑的划分和层次结构、基于皮肤技术的个性化界面实现方案以及串口通信实现细节.