This paper presents differential-based distinguishers against double-branch compression functions and applies them to ISO standard hash functions RIPEMD-128 and RIPEMD-160. A double-branch compression function computes two branch functions to update a chaining variable and then merges their outputs. For such a compression function, we observe that second-order differential paths will be constructed by finding a sub-path in each branch independently. This leads to 4-sum attacks on 47 steps (out of 64 steps) of RIPEMD-128 and 40 steps (out of 80 steps) of RIPEMD-160. Then new properties called a (partial) 2-dimension sum and a q-multi-second-order collision are considered. The partial 2-dimension sum is generated on 48 steps of RIPEMD-128 and 42 steps of RIPEMD-160, with complexities of 235 and 236, respectively. Theoretically, the 2-dimension sum is generated faster than the brute force attack up to 52 steps of RIPEMD-128 and 51 steps of RIPEMD-160, with complexities of 2101 and 2158, respectively. The results on RIPEMD-128 can also be viewed as q-multi-second-order collision attacks. The practical attacks have been implemented and examples are presented. We stress that our results do not impact to the security of full RIPEMD-128 and RIPEMD-160 hash functions.

Recently, various research efforts have been conducted to develop strategies for accelerating multi-dimensional query processing using the graphics processing units (GPUs). However, well-known multi-dimensional access methods such as the R-tree, B-tree, and their variants are hardly applicable to GPUs in practice, mainly due to the characteristics of a hierarchical index structure. More specifically, the hierarchical structure not only causes frequent transfers of small volumes of data but also provides limited opportunity to exploit the advanced data parallelism of GPUs. To address these problems, we propose an approach that uses GPUs as a buffer. The main idea is that object entries in recently visited leaf nodes are buffered in the global memory of GPUs and processed by massive parallel threads of the GPUs. Through extensive performance studies, we observed that the proposed approach achieved query performance up to five times higher than that of the original R-tree.

The main target of compressed sensing is recovery of one-dimensional signals, because signals more than two-dimension can also be treated as one-dimensional ones by raster scan, which makes the sensing matrix huge. This is unavoidable for general sensing processes. In separable cases like discrete Fourier transform (DFT) or standard wavelet transforms, however, the corresponding sensing process can be formulated using two matrices which are multiplied from both sides of the target two-dimensional signals. We propose an approximate message passing (AMP) algorithm for the separable sensing process. Typically, we suppose DFT for the sensing process, in which the measurements are complex numbers. Therefore, the formulation includes cases in which both target signal and measurements are complex. We show the effectiveness of the proposed algorithm by computer simulations.

To achieve high recording density in a bit-patterned media recording system, the spacing between data bit islands in both the along-track and the across-track directions must be decreased, thus leading to the increase of two-dimensional (2D) interference. One way to reduce the 2D interference is to apply a 2D coding scheme on a data sequence before recording; however, this method usually requires many redundant bits, thus lowering a code rate. Therefore, we propose a novel 2D coding scheme referred to as a recorded-bit patterning (RBP) scheme to mitigate the 2D interference, which requires no redundant bits at the expense of using more buffer memory. Specifically, an input data sequence is first split into three tracks in which will then be rotated to find the best 3-track data pattern based on a look-up table before recording, such that the shifted data tracks yield the least effect of 2D interference in the readback signal. Numerical results indicate that the proposed RBP scheme provides a significant performance improvement if compared to a conventional system (without 2D coding), especially when the recording density is high and/or the position jitter noise is large.

This paper reviews two simple numerical algorithms particularly useful in Computational ElectroMagnetics (CEM): the Weighted Averages (WA) algorithm and the Double Exponential (DE) quadrature. After a short historical introduction and an elementary description of the mathematical procedures underlying both techniques, they are applied to the evaluation of Sommerfeld integrals, where WA and DE combine together to provide a numerical tool of unprecedented quality. It is also shown that both algorithms have a much wider range of applications. A generalization of the WA algorithm, able to cope with integrands including products of Bessel and similar oscillatory functions, is described. Similarly, the original DE algorithm is adapted with exceptional results to the evaluation of the multidimensional singular integrals arising in the discretization of Integral-Equation based CEM formulations. The new possibilities of WA and DE algorithms are demonstrated through several practical numerical examples.

Histogram sequences represent high-dimensional time-series converted from images by space filling curves (SFCs). To overcome the high-dimensionality nature of histogram sequences (e.g., 106 dimensions for a 1024×1024 image), we often use lower-dimensional transformations, but the tightness of their lower-bounds is highly affected by the types of SFCs. In this paper we attack a challenging problem of evaluating which SFC shows the better performance when we apply the lower-dimensional transformation to histogram sequences. For this, we first present a concept of spatial locality and propose spatial locality preservation metric (SLPM in short). We then evaluate five well-known SFCs from the perspective of SLPM and verify that the evaluation result concurs with the actual transformation performance. Finally, we empirically validate the accuracy of SLPM by providing that the Hilbert-order with the highest SLPM also shows the best performance in k-NN (k-nearest neighbors) search.

To enhance the throughput while satisfying the quality of service (QoS) requirements of wireless local area networks (WLANs), this paper proposes a distributed coordination function-based (DCF-based) medium access control (MAC) protocol that realizes centralized radio resource management (RRM) for a basic service set. In the proposed protocol, an access point (AP) acts as a master to organize the associated stations and attempts to reserve the radio resource in a conventional DCF-manner. Once the radio resource is successfully reserved, the AP controls the access of each station by an orthogonal frequency division multiple access (OFDMA) scheme. Because the AP assigns radio resources to the stations through the opportunistic two-dimensional scheduling based on the QoS requirements and the channel condition of each station, the transmission opportunities can be granted to the appropriate stations. In order to reduce the signaling overhead caused by centralized RRM, the proposed protocol introduces a station-grouping scheme which groups the associated stations into clusters. Moreover, this paper proposes a heuristic resource allocation algorithm designed for the DCF-based MAC protocol. Numerical results confirm that the proposed protocol enhances the throughput of WLANs while satisfying the QoS requirements with high probability.

In this paper, we propose a new class of two dimensional (2D) M-channel (M-ch) non-separable filter banks (FBs) based on cosine modulated filter banks (CMFBs) via a new diagonally modulation scheme. Until now, many researchers have proposed 2D non-separable CMFBs. Nevertheless, efficient direction-selective CMFBs have not been yet. Thanks to our new modulations with diagonal shifts, proposed CMFBs have several frequency supports including direction-selective ones which cannot be realized by conventional ones. In a simulation, we show design examples of proposed CMFBs and their various directional frequency supports.

Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to select features. However, feature interaction, potentially a key characteristic in real-world problems, has not received much attention. As an attempt to take feature interaction into account, we propose 1-LSMI, an 1-regularization based algorithm that maximizes a squared-loss variant of mutual information between selected features and outputs. Numerical results show that 1-LSMI performs well in handling redundancy, detecting non-linear dependency, and considering feature interaction.

H.264 video codec system requires big capacity and high bandwidth of Frame Store (FS) for buffering reference frames. The up-to-date three dimensional (3D) stacked Phase change Random Access Memory (PRAM) is the promising approach for on-chip caching the reference signals, as 3D stacking offers high memory bandwidth, while PRAM possesses the advantages in terms of high density and low leakage power. However, the write endurance problem, that is a PRAM cell can only tolerant limited number of write operations, becomes the main barrier in practical applications. This paper studies the wear reduction techniques of PRAM based FS in H.264 codec system. On the basis of rate-distortion theory, the content oriented selective writing mechanisms are proposed to reduce bit updates in the reference frame buffers. With the proposed control parameter a, our methods make the quantitative trade off between the quality degradation and the PRAM lifetime prolongation. Specifically, taking a in the range of [0.2,2], experimental results demonstrate that, our methods averagely save 29.9–35.5% bit-wise write operations and reduce 52–57% power, at the cost of 12.95–20.57% BDBR bit-rate increase accordingly.

Multihigh-dimensional chaotic systems were reduced to low-dimensional space embedded equations (SEEs), and their macroscopic and statistical properties were investigated using eigen analysis of the moment vector equation (MVE) of the SEE. First, the state space of the target system was discretized into a finite discrete space. Next, an embedding from the discrete space to a low-dimensional discrete space was defined. The SEE of the target system was derived using the embedding. Finally, eigen analysis was applied to the MVE of the SEE to derive the properties of the target system. The geometric increase in the dimension of the MVE with the dimension of the target system was avoided by using the SEE. The pdfs of arbitrary elements in the target nonlinear system were derived without a reduction in accuracy due to dimension reduction. Moreover, since the dynamics of the system were expressed by the eigenvalues of the MVE, it was possible to identify multiple steady states that cannot be done using numerical simulation. This approach can thus be used to analyze the macroscopic and statistical properties of multi-dimensional chaotic systems.

Two-dimensional (2-D) codes for optical code-division multiple access (O-CDMA) systems can increase the number of subscribers and simultaneous users as compared to one-dimensional time-spreading codes. Multiple-wavelength optical orthogonal code (MWOOC), which is one of the 2-D codes, uses prime sequences as a wavelength-hopping code and an optical orthogonal code (OOC) as a time-spreading code. MWOOCs have some advantages over other 2-D codes especially in high bit-rate O-CDMA systems. The only drawback of MWOOC is that the performance degrades significantly when the number of wavelengths is not prime. Recently a generalized class of modified prime sequence codes (MPSCs), which includes the class of original MPSCs as its subclass, was presented. An important property of generalized MPSCs is that the codes can be constructed over not only prime fields but also extension fields. It has been shown that the correlation property of generalized MPSCs is the same as that of the original MPSCs. This paper investigates MWOOC with generalized prime sequences, which can be obtained in the process of generating the generalized MPSCs, as a wavelength-hopping code. Use of the generalized prime sequences can solve the nonprime problem of MWOOCs. The average error probability of the proposed MWOOCs is formulated theoretically and numerical results are compared with that of the original schemes. It is shown that nonprime numbers, such as 2m, 3m and 5m, can be also taken as the number of wavelengths without degrading the system performance in the proposed systems.

Accurate modeling of sea clutter and detection of low observable targets within sea clutter are the major goals of radar signal processing applications. Recently, fractal geometry has been applied to the analysis of high range resolution radar sea clutters. The box-counting method is widely used to estimate fractal dimension but it has some drawbacks. We explain the drawbacks and propose a new fractal dimension based detector to increase detection performance in comparison with traditional detectors. Both statistically generated and real data samples are used to compare detector performance.

In this paper we consider the two-class classification problem with high-dimensional data. It is important to find a class of distributions such that we cannot expect good performance in classification for any classifier. In this paper, when two population variance-covariance matrices are different, we give a reasonable sufficient condition for distributions such that the misclassification rate converges to the worst value as the dimension of data tends to infinity for any classifier. Our results can give guidelines to decide whether or not an experiment is worth performing in many fields such as bioinformatics.

Link prediction is the task of inferring the existence or absence of certain relationships among data objects such as identity, interaction, and collaboration. Link prediction is found in various applications in the fields of information integration, recommender systems, bioinformatics, and social network analysis. The increasing interest in dynamically changing networks has led to growing interest in a more general link prediction problem called temporal link prediction in the data mining and machine learning communities. However, only links among nodes at the same time point are considered in temporal link prediction. We propose a new link prediction problem called cross-temporal link prediction in which the links among nodes at different time points are inferred. A typical example of cross-temporal link prediction is cross-temporal entity resolution to determine the identity of real entities represented by data objects observed in different time periods. In dynamic environments, the features of data change over time, making it difficult to identify cross-temporal links by directly comparing observed data. Other examples of cross-temporal links are asynchronous communications in social networks such as Facebook and Twitter, where a message is posted in reply to a previous message. We adopt a dimension reduction approach to cross-temporal link prediction; that is, data objects in different time frames are mapped into a common low-dimensional latent feature space, and the links are identified on the basis of the distance between the data objects. The proposed method uses different low-dimensional feature projections in different time frames, enabling it to adapt to changes in the latent features over time. Using multi-task learning, it jointly learns a set of feature projection matrices from the training data, given the assumption of temporal smoothness of the projections. The optimal solutions are obtained by solving a single generalized eigenvalue problem. Experiments using a real-world set of bibliographic data for cross-temporal entity resolution and a real-world set of emails for unobserved asynchronous communication inference showed that introducing time-dependent feature projections improved the accuracy of link prediction.

This paper precisely characterizes secret sharing schemes based on arbitrary linear codes by using the relative dimension/length profile (RDLP) and the relative generalized Hamming weight (RGHW). We first describe the equivocation Δm of the secret vector =[s1,...,sl] given m shares in terms of the RDLP of linear codes. We also characterize two thresholds t1 and t2 in the secret sharing schemes by the RGHW of linear codes. One shows that any set of at most t1 shares leaks no information about , and the other shows that any set of at least t2 shares uniquely determines . It is clarified that both characterizations for t1 and t2 are better than Chen et al.'s ones derived by the regular minimum Hamming weight. Moreover, this paper characterizes the strong security in secret sharing schemes based on linear codes, by generalizing the definition of strongly-secure threshold ramp schemes. We define a secret sharing scheme achieving the α-strong security as the one such that the mutual information between any r elements of (s1,...,sl) and any α-r+1 shares is always zero. Then, it is clarified that secret sharing schemes based on linear codes can always achieve the α-strong security where the value α is precisely characterized by the RGHW.

Linear Discriminant Analysis (LDA) is one of the most popular dimensionality reduction techniques in existing handwritten Chinese character (HCC) recognition systems. However, when used for unconstrained handwritten Chinese character recognition, the traditional LDA algorithm is prone to two problems, namely, the class separation problem and multimodal sample distributions. To deal with these problems,we propose a new locally linear discriminant analysis (LLDA) method for handwritten Chinese character recognition.Our algorithm operates as follows. (1) Using the clustering algorithm, find clusters for the samples of each class. (2) Find the nearest neighboring clusters from the remaining classes for each cluster of one class. Then, use the corresponding cluster means to compute the between-class scatter matrix in LDA while keeping the within-class scatter matrix unchanged. (3) Finally, apply feature vector normalization to further improve the class separation problem. A series of experiments on both the HCL2000 and CASIA Chinese character handwriting databases show that our method can effectively improve recognition performance, with a reduction in error rate of 28.7% (HCL2000) and 16.7% (CASIA) compared with the traditional LDA method.Our algorithm also outperforms DLA (Discriminative Locality Alignment,one of the representative manifold learning-based dimensionality reduction algorithms proposed recently). Large-set handwritten Chinese character recognition experiments also verified the effectiveness of our proposed approach.

Internet threats caused by botnets/worms are one of the most important security issues to be addressed. Darknet, also called a dark IP address space, is one of the best solutions for monitoring anomalous packets sent by malicious software. However, since darknet is deployed only on an inactive IP address space, it is an inefficient way for monitoring a working network that has a considerable number of active IP addresses. The present paper addresses this problem. We propose a scalable, light-weight malicious packet monitoring system based on a multi-dimensional IP/port analysis. Our system significantly extends the monitoring scope of darknet. In order to extend the capacity of darknet, our approach leverages the active IP address space without affecting legitimate traffic. Multi-dimensional monitoring enables the monitoring of TCP ports with firewalls enabled on each of the IP addresses. We focus on delays of TCP syn/ack responses in the traffic. We locate syn/ack delayed packets and forward them to sensors or honeypots for further analysis. We also propose a policy-based flow classification and forwarding mechanism and develop a prototype of a monitoring system that implements our proposed architecture. We deploy our system on a campus network and perform several experiments for the evaluation of our system. We verify that our system can cover 89% of the IP addresses while darknet-based monitoring only covers 46%. On our campus network, our system monitors twice as many IP addresses as darknet.

We propose a new three-dimensional (3D) NAND flash memory array having Tied Bit-line and Ground Select Transistor (TiGer) [1]. Channels are stacked in the vertical direction to increase the memory density without the device size scaling. To distinguish stacked channels, a novel operation scheme is introduced instead of adding supplementary control gates. The stacked layers are selected by using ground select line (GSL) and common source line (CSL). Device structure and fabrication process are described. Operation scheme and simulation results for program inhibition are also discussed.

Cognitive radios are intelligent communications, and are expected to more efficiently utilize the radio channel. Modulation identification is one of the key issues in the cognitive radios. Many works were devoted to the classification of symbol-by-symbol modulations, however, few papers on block modulations have been published. In this paper, an identification error analysis is presented for block orthogonal modulations using General Orthogonal Modulation~(GOM). A symbol error probability is derived for the identified block orthogonal modulation. Numerical results of 4-dimensional block orthogonal modulation are presented with simulation results.