The application of information hiding to image compression is investigated to improve compression efficiency for JPEG color images. In the proposed method, entropy-coded DCT coefficients of chrominance components are embedded into DCT coefficients of the luminance component. To recover an image in the face of the degradation caused by compression and embedding, an image restoration method is also applied. Experiments show that the use of both information hiding and image restoration is most effective to improve compression efficiency.

Searching for the shortest paths from a query point to several target points on a road network is an essential operation for several types of queries in location-based services. This search can be performed using Dijkstra's algorithm. Although the A* algorithm is faster than Dijkstra's algorithm for finding the shortest path from a query point to a target point, the A* algorithm is not so fast to find all paths between each point and the query point when several target points are given. In this case, the search areas on road network overlap for each search, and the total number of operations at each node is increased, especially when the number of query points increases. In the present paper, we propose the single-source multi-target A* (SSMTA*) algorithm, which is a multi-target version of the A* algorithm. The SSMTA* algorithm guarantees at most one operation for each road network node, and the searched area on road network is smaller than that of Dijkstra's algorithm. Deng et al. proposed the LBC approach with the same objective. However, several heaps are used to manage the search area on the road network and the contents in each heap must always be kept the same in their method. This operation requires much processing time. Since the proposed method uses only one heap, such content synchronization is not necessary. The present paper demonstrates through empirical evaluations that the proposed method outperforms other similar methods.

In this study, a novel forest method based on specific random trees (SRT) was proposed for a multiclass classification problem. The proposed SRT was built on one specific class, which decides whether a sample belongs to a certain class. The forest can make a final decision on classification by ensembling all the specific trees. Compared with the original random forest, our method has higher strength, but lower correlation and upper error bound. The experimental results based on 10 different public datasets demonstrated the efficiency of the proposed method.

This paper proposes an IP fast rerouting method which can be implemented in OpenFlow framework. While the current IP is robust, its reactive and global rerouting processes require the long recovery time against failure. On the other hand, IP fast rerouting provides a milliseconds-order recovery time by proactive and local restoration mechanism. Implementation of IP fast rerouting is not common in real systems, however; it requires the coordination of additional forwarding functions to a commercial hardware. We propose an IP fast rerouting mechanism using OpenFlow that separates control function from hardware implementation. Our mechanism does not require any extension of current forwarding hardware. On the contrary, increase of backup routes becomes main overhead of our proposal. We also embed the compression mechanism to our IP fast rerouting mechanism. We show the effectiveness of our IP fast rerouting in terms of the fast restoration and the backup routes compression effect through computer simulations.

We present a novel point of interest (POI) construction approach based on street-level imagery (SLI) such as Google StreetView. Our method consists of: (1) the creation of a conflation map between an SLI trace and a vector map; (2) the detection of the corresponding buildings between the SLI scene and the conflation map; and (3) POI name extraction from a signboard in the SLI scene by user-interactive text recognition. Finally, a POI is generated through a combination of the POI name and attributes of the building object on a vector map. The proposed method showed recall of 92.99% and precision of 97.10% for real-world POIs.

In this letter, the problem of feature quantization in robust hashing is studied from the perspective of approximate nearest neighbor (ANN). We model the features of perceptually identical media as ANNs in the feature set and show that ANN indexing can well meet the robustness and discrimination requirements of feature quantization. A feature quantization algorithm is then developed by exploiting the random-projection based ANN indexing. For performance study, the distortion tolerance and randomness of the quantizer are analytically derived. Experimental results demonstrate that the proposed work is superior to state-of-the-art quantizers, and its random nature can provide robust hashing with security against hash forgery.

As many digital terrestrial broadcasting stations have been installed and are now broadcasting, the problem of poor reception has become serious even though the receiving powers are high. Although we had developed a interference canceller for broadcast-wave relay stations, an adaptive array is desirable to be more robust against low-D/U multipath environment as a receiver for the service area. In this paper, we propose a weighting coefficient optimization algorithm for post-FFT adaptive array using the reciprocals of weighting coefficients. Numerical examples show the effectiveness of the proposed method.

In this paper, we propose a hardware solution for overcoming the problem of high computational demands in a nearest neighbor (NN) based multi-prototype learning system. The multiple prototypes are obtained by a high-speed K-means clustering algorithm utilizing a concept of software-hardware cooperation that takes advantage of the flexibility of the software and the efficiency of the hardware. The one nearest neighbor (1-NN) classifier is used to recognize an object by searching for the nearest Euclidean distance among the prototypes. The major deficiency in conventional implementations for both K-means and 1-NN is the high computational demand of the nearest neighbor searching. This deficiency is resolved by an FPGA-implemented coprocessor that is a VLSI circuit for searching the nearest Euclidean distance. The coprocessor requires 12.9% logic elements and 58% block memory bits of an Altera Stratix III E110 FPGA device. The hardware communicates with the software by a PCI Express (4) local-bus-compatible interface. We benchmark our learning system against the popular case of handwritten digit recognition in which abundant previous works for comparison are available. In the case of the MNIST database, we could attain the most efficient accuracy rate of 97.91% with 930 prototypes, the learning speed of 1.310-4 s/sample and the classification speed of 3.9410-8 s/character.

High-definition (HD) videos become more and more popular on portable devices these years. Due to the resolution mismatch between the HD video sources and the relative low-resolution screens of portable devices, the HD videos are usually fully decoded and then down-sampled (FDDS) for the displays, which not only increase the cost of both computational power and memory bandwidth, but also lose the details of video contents. In this paper, an encoder-unconstrained partial decoding scheme for H.264/AVC is presented to solve the problem by only decoding the object of interest (OOI) related region, which is defined by users. A simplified compression domain tracking method is utilized to ensure that the OOI locates in the center of the display area. The decoded partial area (DPA) adaptation, the reference block relocation (RBR) and co-located temporal Intra prediction (CTIP) methods are proposed to improve the visual quality for the DPA with low complexity. The simulation results show that the proposed partial decoding scheme provides an average of 50.16% decoding time reduction comparing to the fully decoding process. The displayed region also presents the original HD granularity of OOI. The proposed partial decoding scheme is especially useful for displaying HD video on the devices of which the battery life is a crucial factor.

This letter proposes a new image colorization algorithm based on the sparse optimization. Introducing some assumptions, a problem of recovering a color image from a grayscale image with the small number of known color pixels is formulated as a mixed l0/l1 norm minimization, and an iterative reweighted least squares (IRLS) algorithm is proposed. Numerical examples show that the proposed algorithm colorizes the grayscale image efficiently.

In this paper, we propose a novel in-network aggregation scheduling scheme for forest fire monitoring in a wireless sensor network. This adaptively configures both the timeout and the collecting period according to the potential level of a fire occurrence. At normal times, the proposed scheme decreases a timeout that is a wait time for packets sent from child nodes and makes the collecting period longer. That reduces the dissipated energy of the sensor node. Conversely, the proposed scheme increases the timeout and makes the collecting period shorter during fire occurrences in order to achieve more accurate data aggregation and early fire detection.

A packet network architecture called “functionally distributed transport networking” is being studied, where control elements (CEs) are separated from the forwarding elements (FEs) of all routers in a network, and a centralized CE manages the control functions for all FEs. A crucial issue to be addressed in this network architecture is the occurrence of bottlenecks in the CE performance, and rapid network restoration after failures is the main problem to be solved. Thus, we propose here a fast backup route determination method suitable for this network architecture, and we also show the practicality of this architecture. Most failures can be categorized as single-node or single-link failures. The proposed method prepares backup routes for all possible single-node failures in advance and computes backup routes for single-link failures after the failure occurs. The number of possible single-node failures is much less than that of possible single-link failures, and the preparation of backup routes for single-node failures is practical under the memory requirements. Two techniques are used in computing backup routes for single-link failures in order to reduce the computation time. One is to calculate only the routes affected by the link failure. The other is to use an algorithm to compute backup routes for single-link failures based on preplanned backup routes for single-node failures. To demonstrate the practicality of our method, we evaluated the amount of memory and computation time needed to prepare backup routes for all single-node failures, and we carried out simulations with various network topologies to evaluate the route computation time required for a single-link failure.

As ROADMs (Reconfigurable Optical Add/Drop Multiplexers) are becoming widely used in metro/core networks, distributed control of wavelength paths by extended GMPLS (Generalized MultiProtocol Label Switching) protocols has attracted much attention. For the automatic establishment of an arbitrary wavelength path satisfying dynamic traffic demands over a ROADM or WXC (Wavelength Cross Connect)-based network, precise determination of chromatic dispersion over the path and optimized assignment of dispersion compensation capabilities at related nodes are essential. This paper reports an experiment over in-field fibers where GMPLS-based control was applied for the automatic discovery of chromatic dispersion, path computation, and wavelength path establishment with dynamic adjustment of variable dispersion compensation. The GMPLS-based control scheme, which the authors called GMPLS-Plus, extended GMPLS's distributed control architecture with attributes for automatic discovery, advertisement, and signaling of chromatic dispersion. In this experiment, wavelength paths with distances of 24 km and 360 km were successfully established and error-free data transmission was verified. The experiment also confirmed path restoration with dynamic compensation adjustment upon fiber failure.

To improve the observability during the post-silicon validation, it is the key to select the limited trace signals effectively for the data acquisition. This paper proposes an automated trace signal selection algorithm, which uses the pruning-based strategy to reduce the exploration space. First, the restoration range is covered for each candidate signals. Second, the constraints are generated based on the conjunctive normal form (CNF) to avoid the conflict. Finally the candidates are selected through pruning-based enumeration. The experimental results indicate that the proposed algorithm can bring higher restoration ratios and is more effective compared to existing methods.

In the task of face recognition, a challenging issue is the one sample problem, namely, there is only one training sample per person. Principal component analysis (PCA) seeks a low-dimensional representation that maximizes the global scatter of the training samples, and thus is suitable for one sample problem. However, standard PCA is sensitive to the outliers and emphasizes more on the relatively distant sample pairs, which implies that the close samples belonging to different classes tend to be merged together. In this paper, we propose two-stage block-based whitened PCA (TS-BWPCA) to address this problem. For a specific probe image, in the first stage, we seek the K-Nearest Neighbors (K-NNs) in the whitened PCA space and thus exclude most of samples which are distant to the probe. In the second stage, we maximize the “local” scatter by performing whitened PCA on the K nearest samples, which could explore the most discriminative information for similar classes. Moreover, block-based scheme is incorporated to address the small sample problem. This two-stage process is actually a coarse-to-fine scheme that can maximize both global and local scatter, and thus overcomes the aforementioned shortcomings of PCA. Experimental results on FERET face database show that our proposed algorithm is better than several representative approaches.

In this paper, we propose N-Dimensional (ND) Tensor Supervised Neighborhood Embedding (ND TSNE) for discriminant feature representation, which is used for view-based object recognition. ND TSNE uses a general Nth order tensor discriminant and neighborhood-embedding analysis approach for object representation. The benefits of ND TSNE include: (1) a natural way of representing data without losing structure information, i.e., the information about the relative positions of pixels or regions; (2) a reduction in the small sample size problem, which occurs in conventional supervised learning because the number of training samples is much less than the dimensionality of the feature space; (3) preserving a neighborhood structure in tensor feature space for object recognition and a good convergence property in training procedure. With Tensor-subspace features, the random forests is used as a multi-way classifier for object recognition, which is much easier for training and testing compared with multi-way SVM. We demonstrate the performance advantages of our proposed approach over existing techniques using experiments on the COIL-100 and the ETH-80 datasets.

This paper presents a restoration method using several degraded observed images obtained through a technique known as microscanning. It is shown that microscanning provides sufficient spatial information for image restoration with minimal information about the original image and without knowing the interference function that causes degradation.

The aim of this study is to improve the accuracy of flicker parameters estimation in old film sequences in which moving objects are present. Conventional methods tend to fail in flicker parameters estimation due to the effects of moving objects. Our proposed method firstly utilizes an adaptive Gaussian mixture model (GMM)-based method to detect the moving objects in the film sequences, and combines the detected results with the histogram-matched frames to generate reference frames for flicker parameters estimation. Then, on the basis of a linear flicker model, the proposed method uses an M-estimator with the reference frames to estimate the flicker parameters. Experimental results show that the proposed method can effectively improve the accuracy of flicker parameters estimation when the moving objects are present in the film sequences.

Let (X,Y) be a Rd R-valued random vector. In regression analysis one wants to estimate the regression function m(x):=E(Y|X=x) from a data set. In this paper we consider the convergence rate of the error for the k nearest neighbor estimators in case that m is (p,C)-smooth. It is known that the minimax rate is unachievable by any k nearest neighbor estimator for p > 1.5 and d=1. We generalize this result to any d ≥ 1. Throughout this paper, we assume that the data is independent and identically distributed and as an error criterion we use the expected L2 error.

In MANET (Mobile Ad-hoc NETworks), there are two kinds of routing methods: proactive and reactive. Each has different characteristics and advantages. The latter generally employs the flooding technique to finding a routing path to the destination. However, flooding has big overheads caused by broadcasting RREQ packets to the entire network. Therefore, reducing this overhead is really needed to enable several network efficiencies. Previous studies introduced many approaches which are mainly concerned with the restriction of flooding. However, they usually configure the detailed routing path in the forward flooding procedure and ignore the factors causing the flooding overheads. In this paper, we propose the FSRS (First Search and Reverse Setting) routing protocol which is a new approach in flooding techniques and a new paradigm shift. FSRS is based on cluster topology and is composed of two main mechanisms: inter-cluster and intra-cluster flooding. Inter-cluster routing floods RREQ packets between cluster units and sets a cluster path. When the destination node receives the RREQ packet, it floods RREP packets to an intra-cluster destination which is a gateway to relay the RREP packet to a previous cluster. This is called intra-cluster routing. So to speak, a specific routing path configuration progresses in the RREP process through the reverse cluster path. Consequently, FSRS is a new kind of hybrid protocol well adapted to wireless ad-hoc networks. This suggests a basic wireless networking architecture to make a dynamic cluster topology in future work. In the simulation using NS-2, we compare it to several other protocols and verify that FSRS is a powerful protocol. In the result of the simulation, FSRS conserves energy by a maximum of 12% compared to HCR.