This paper presents a novel algorithm to generate homogeneous superpixels from Markov random walks. We exploit Markov clustering (MCL) as the methodology, a generic graph clustering method based on stochastic flow circulation. In particular, we introduce a graph pruning strategy called compact pruning in order to capture intrinsic local image structure. The resulting superpixels are homogeneous, i.e. uniform in size and compact in shape. The original MCL algorithm does not scale well to a graph of an image due to the square computation of the Markov matrix which is necessary for circulating the flow. The proposed pruning scheme has the advantages of faster computation, smaller memory footprint, and straightforward parallel implementation. Through comparisons with other recent techniques, we show that the proposed algorithm achieves state-of-the-art performance.

High performance, low area multipliers are highly desired for modern and future DSP systems due to the increasing demand of high speed DSP applications. In this paper, we present an efficient architecture for an LUT-based truncated multiplier and its application in RGB to YCbCr color space conversion which can be used for digital TV, image and video processing systems. By employing an improved split LUT-based architecture and LUT optimization method, the proposed multiplier can reduce the value of area-delay product by up to 52% compared with other constant multiplier methods. The FPGA implementation of a color space conversion application employing the proposed multiplier also results in significant reduction of area-delay product of up to 48%.

Given a collection of k sets consisting of a total of n points in the plane, the distance from any point in the plane to each of the sets is defined to be the minimum among distances to each point in the set. The farthest-color Voronoi diagram is defined as a generalized Voronoi diagram of the k sets with respect to the distance functions for each of the k sets. The combinatorial complexity of the diagram is known to be Θ(kn) in the worst case. This paper initiates a study on farthest-color Voronoi diagrams having O(n) complexity. We introduce a realistic model, which defines a certain class of the diagrams with desirable geometric properties observed. We finally show that the farthest-color Voronoi diagrams under the model have linear complexity.

In this paper, a study on discrete-time coined quantum walks on the line is presented. Clear mathematical foundations are still lacking for this quantum walk model. As a step toward this objective, the following question is being addressed: Given a graph, what is the probability that a quantum walk arrives at a given vertex after some number of steps? This is a very natural question, and for random walks it can be answered by several different combinatorial arguments. For quantum walks this is a highly non-trivial task. Furthermore, this was only achieved before for one specific coin operator (Hadamard operator) for walks on the line. Even considering only walks on lines, generalizing these computations to a general SU(2) coin operator is a complex task. The main contribution is a closed-form formula for the amplitudes of the state of the walk (which includes the question above) for a general symmetric SU(2) operator for walks on the line. To this end, a coin operator with parameters that alters the phase of the state of the walk is defined. Then, closed-form solutions are computed by means of Fourier analysis and asymptotic approximation methods. We also present some basic properties of the walk which can be deducted using weak convergence theorems for quantum walks. In particular, the support of the induced probability distribution of the walk is calculated. Then, it is shown how changing the parameters in the coin operator affects the resulting probability distribution.

A method for efficiently representing the state equation in a class of max-plus linear systems is proposed. We introduce a construct referred to as 'cell' in which the list of possible longest paths is stored. By imposing interval constraints on the system parameters, we can reduce the complexity of the state equation. The proposed method would be useful in scheduling applications for systems with adjustable system parameters.

In multimedia communication, due to the limited computational capability of the personal information machine, a coder with low computational complexity is needed to integrate services from several media sources. This paper presents two efficient candidate schemes to simplify the most computationally demanding operation, the excitation codebook search procedure. For fast adaptive codebook search, we propose an algorithm that uses residual signals to predict the candidate gain-vectors of the adaptive codebook. For the fixed codebook, we propose a fast search algorithm using an energy function to predict the candidate pulses, and we redesign the codebook structure to twin multi-track positions architecture. Overall simulation results indicate that the average perceptual evaluation of speech quality (PESQ) score is degraded slightly, by 0.049, and our proposed methods can reduce total computational complexity by about 67% relative to the original G.723.1 encoder computation load, and with perceptually negligible degradation. Objective and subjective evaluations verify that the more efficient candidate schemes we propose can provide speech quality comparable to that using the original coder approach.

Petri net is a powerful modeling tool for concurrent systems. Subclasses of Petri net are suggested to model certain realistic applications with less computational cost. Structurally weakly persistent net (SWPN) is one of such subclasses where liveness is verified in deterministic polynomial time. This paper studies the computational complexity to verify whether a give net is SWPN. 3UNSAT problem is reduced to the problem to verify whether a net is not SWPN. This implies co-NP completeness of verification problem of SWPN.

In this paper, we propose an approach to obtaining enhanced performance of the Linpack benchmark on a GPU-accelerated PC cluster connected via relatively slow inter-node connections. For one node with a quad-core Intel Xeon W3520 processor and a NVIDIA Tesla C1060 GPU card, we implement a CPU–GPU parallel double-precision general matrix–matrix multiplication (dgemm) operation, and achieve a performance improvement of 34% compared with the GPU-only case and 64% compared with the CPU-only case. For an entire 16-node cluster, each node of which is the same as the above and is connected with two gigabit Ethernet links, we use a computation-communication overlap scheme with GPU acceleration for the Linpack benchmark, and achieve a performance improvement of 28% compared with the GPU-accelerated high-performance Linpack benchmark (HPL) without overlapping. Our overlap GPU acceleration solution uses overlaps in which the main inter-node communication and data transfer to the GPU device memory are overlapped with the main computation task on the CPU cores. These overlaps use multi-core processors, which almost all of today's high-performance computers use. In particular, as well as using a CPU core for communication tasks, we also simultaneously use other CPU cores and the GPU for computation tasks. In order to enable overlap between inter-node communication and computation tasks, we eliminate their close dependence by breaking the main computation task into smaller tasks and rescheduling. Based on a scheme in which part of the CPU computation power is simultaneously used for tasks other than computation tasks, we experimentally find the optimal computation ratio for CPUs; this ratio differs from the case of parallel dgemm operation of one node.

This paper proposes likelihood smoothing techniques to improve decision tree-based acoustic models, where decision trees are used as replacements for Gaussian mixture models to compute the observation likelihoods for a given HMM state in a speech recognition system. Decision trees have a number of advantageous properties, such as not imposing restrictions on the number or types of features, and automatically performing feature selection. This paper describes basic configurations of decision tree-based acoustic models and proposes two methods to improve the robustness of the basic model: DT mixture models and soft decisions for continuous features. Experimental results for the Aurora 2 speech database show that a system using decision trees offers state-of-the-art performance, even without taking advantage of its full potential and soft decisions improve the performance of DT-based acoustic models with 16.8% relative error rate reduction over hard decisions.

In this letter, we propose an effective method to retrieve images from a 2D shape image database using discrete shock graphs combined with an adaptive selection algorithm. Experimental results show that our method is more accurate and fast than conventional approaches and reduces computational complexity.

A non-audible murmur (NAM), a very weak whisper sound produced without vocal fold vibration, has been researched in the development of a silent-speech communication tool for functional speech disorders as well as human-to-human/machine interfaces with inaudible voice input. The NAM can be detected using a specially designed microphone, called a NAM microphone, attached to the neck. However, the detected NAM signal has a low signal-to-noise ratio and severely suppressed high-frequency component. To improve NAM clarity, the mechanism of a NAM production must be clarified. In this work, an air flow through a glottis in the vocal tract was numerically simulated using computational fluid dynamics and vocal tract shape models that are obtained by a magnetic resonance imaging (MRI) scan for whispered voice production with various strengths, i.e. strong, weak, and very weak. For a very weak whispering during the MRI scan, subjects were trained, just before the scanning, to produce the very weak whispered voice, or the NAM. The numerical results show that a weak vorticity flow occurs in the supraglottal region even during a very weak whisper production; such vorticity flow provide aeroacoustic sources for a very weak whispering, i.e. NAM, as in an ordinary whispering.

A translucent wavelength switched optical network (WSON) is a cost-efficient infrastructure between opaque networks and transparent optical networks, which aims at seeking a graceful balance between network cost and service provisioning performance. In this paper, we experimentally present a resilient translucent WSON with the control of an enhanced path computation element (PCE) and extended generalized multi-protocol label switching (GMPLS) controllers. An adaptive routing and wavelength assignment scheme with the consideration of accumulated physical impairments, wavelength availabilities and regenerator allocation is experimentally demonstrated and evaluated for dynamic provisioning of lightpaths. By using two different network scenarios, we experimentally verify the feasibility of the proposed solutions in support of translucent WSON, and quantitatively evaluate the path computation latency, network blocking probability and service disruption time during end-to-end lightpath restoration. We also deeply analyze the experimental results and discuss the synchronization between the PCE and the network status. To the best of our knowledge, the most significant progress and contribution of this paper is that, for the first time, all the proposed methodologies in support of PCE/GMPLS controlled translucent WSON, including protocol extensions and related algorithms, are implemented in a network testbed and experimentally evaluated in detail, which allows verifying their feasibility and effectiveness when being potentially deployed into real translucent WSON.

This paper presents a simplifying method of the two previous fault attacks to pairing and the Miller algorithms based on a practical fault assumption. Our experimental result shows that the assumption is feasible and easy to implement.

Motion blur in TFT-LCD is caused by sample and hold characteristic, slow response time of liquid crystal, and the inconsistency between object tracking of the human eye and the actual object location. In order to solve this problem, a high frame rate driving method based on motion estimation and motion compensation has been applied to LCD products. However, as the required processing time of motion estimation increases in LCD TV and monitor systems, real-time video image processing becomes more difficult. Frame interpolation through the large macro block (MB) size has limitations to detect small objects. So, this paper proposes the efficient motion estimator architecture which uses seven kinds of macro blocks to enhance the accuracy of motion estimation and combines the parallel processing with pre-computation technology and hardware optimization for high-speed processing. Also, for increased efficiency in the hardware architecture, we employed an I2C (Inter Integrated Circuit) communication unit to control the key parameters easily through the personnel computer. Simulation results show that the critical path at the motion estimator is reduced by about 27.47% compared to the conventional structure. As a result, the proposed motion estimator will be applicable for the high-speed frame interpolation of variable video.

We study problems in computational geometry from the viewpoint of adaptive algorithms. Adaptive algorithms have been extensively studied for the sorting problem, and in this paper we generalize the framework to geometric problems. To this end, we think of geometric problems as permutation (or rearrangement) problems of arrays, and define the "presortedness" as a distance from the input array to the desired output array. We call an algorithm adaptive if it runs faster when a given input array is closer to the desired output, and furthermore it does not make use of any information of the presortedness. As a case study, we look into the planar convex hull problem for which we discover two natural formulations as permutation problems. An interesting phenomenon that we prove is that for one formulation the problem can be solved adaptively, but for the other formulation no adaptive algorithm can be better than an optimal output-sensitive algorithm for the planar convex hull problem. To further pursue the possibility of adaptive computational geometry, we also consider constructing a kd-tree.

An interactive support system for image quality enhancement to adjust display equipments according to the user's own subjectivity is developed. Interactive support system for image quality enhancement enable the parameters based on user's preference to be derived by only selecting user's preference images without adjusting image quality parameters directly. In the interactive support system for image quality enhancement, the more the number of parameters is, the more effective this system is. In this paper, lightness, color and sharpness are used as the image quality parameters and the images are enhanced by increasing the number of parameters. Shape of tone curve is controlled by two image quality adjustment parameters for lightness enhancement. Images are enhanced using two image quality adjustment parameters for color enhancement. The two parameters are controlled in L* a* b* color space. Degree and coarseness of image sharpness enhancement are adjusted by controlling a radius of mask of smoothing filter and weight of adding. To confirm the effectiveness of the proposed method, the image quality and derivation time of the proposed method are compared with a manual adjustment method.

In this paper, we propose two authenticated key exchange(AKE) protocols and prove their security in the extended Canetti-Krawczyk model. The first protocol, called NAXOS+, is obtained by slightly modifying the NAXOS protocol proposed by LaMacchia, Lauter and Mityagin [15]. We prove its security under the Computational Diffie-Hellman (CDH) assumption by using the trapdoor test introduced in [6]. To the authors' knowledge, this is the first AKE protocol which is secure under the CDH assumption in the eCK model. The second protocol, called NETS, enjoys a simple and tight security reduction compared to existing schemes including HMQV and CMQV without using the Forking Lemma. Since each session of the NETS protocol requires only three exponentiations per party, its efficiency is also comparable to MQV, HMQV and CMQV.

This paper proposes approaches to perform HW/SW (Hardware/Software) partition and parallelization of computing-intensive tasks of the H.264 HiP (High Profile) decoding algorithm on an embedded coarse-grained reconfigurable multimedia system, called REMUS (REconfigurable MUltimedia System). Several techniques, such as MB (Macro-Block) based parallelization, unfixed sub-block operation etc., are utilized to speed up the decoding process, satisfying the requirements of real-time and high quality H.264 applications. Tests show that the execution performance of MC (Motion Compensation), deblocking, and IDCT-IQ (Inverse Discrete Cosine Transform-Inverse Quantization) on REMUS is improved by 60%, 73%, 88.5% in the typical case and 60%, 69%, 88.5% in the worst case, respectively compared with that on XPP PACT (a commercial reconfigurable processor). Compared with ASIC solutions, the performance of MC is improved by 70%, 74% in the typical and in the worst case, respectively, while those of Deblocking remain the same. As for IDCT_IQ, the performance is improved by 17% no matter in the typical or worst case. Relying on the proposed techniques, 1080p@30 fps of H.264 HiP@ Level 4 decoding could be achieved on REMUS when utilizing a 200 MHz working frequency.

A disentanglement puzzle consists of mechanically interlinked pieces, and the puzzle is solved by disentangling one piece from another set of pieces. A cast puzzle is a type of disentanglement puzzle, where each piece is a zinc die-casting alloy. In this paper, we consider the generalized cast puzzle problem whose input is the layout of a finite number of pieces (polyhedrons) in the 3-dimensional Euclidean space. For every integer k ≥ 0, we present a polynomial-time transformation from an arbitrary k-exponential-space Turing machine M and its input x to a cast puzzle c1 of size k-exponential in |x| such that M accepts x if and only if c1 is solvable. Here, the layout of c1 is encoded as a string of length polynomial (even if c1 has size k-exponential). Therefore, the cast puzzle problem of size k-exponential is k-EXPSPACE-hard for every integer k ≥ 0. We also present a polynomial-time transformation from an arbitrary instance f of the SAT problem to a cast puzzle c2 such that f is satisfiable if and only if c2 is solvable.

In this paper, we propose two new chosen-ciphertext (CCA) secure schemes from the computational Diffie-Hellman (CDH) and bilinear computational Diffie-Hellman (BCDH) assumptions. Our first scheme from the CDH assumption is constructed by extending Cash-Kiltz-Shoup scheme. This scheme yields the same ciphertext as that of Hanaoka-Kurosawa scheme (and thus Cramer-Shoup scheme) with cheaper computational cost for encryption. However, key size is still the same as that of Hanaoka-Kurosawa scheme. Our second scheme from the BCDH assumption is constructed by extending Boyen-Mei-Waters scheme. Though this scheme requires a stronger underlying assumption than the CDH assumption, it yields significantly shorter key size for both public and secret keys. Furthermore, ciphertext length of our second scheme is the same as that of the original Boyen-Mei-Waters scheme.