Quantum computations have so far proved to be more powerful than classical computations, but quantum computers still have not been put into practical use due to several technical issues. One of the most serious problems for realizing quantum computers is decoherence that occurs inevitably since our apparatus are surrounded with environment and open systems. In this paper, we give some surveys on a variety of effects of decoherence in quantum algorithms such as Grover's database search and quantum walks, and we show how quantum algorithms work under decoherence, how sensitive they are against decoherence, and how to implement a robust quantum circuit.

Halftoning is an important process to convert a gray scale image into a binary image with black and white pixels. The Direct Binary Search (DBS) is one of the well-known halftoning methods that can generate high quality binary images for middle tone of original gray scale images. However, binary images generated by the DBS have clippings, that is, have no tone in highlights and shadows of original gray scale images. The first contribution of this paper is to show the reason why the DBS generates binary images with clippings, to clarify the range of tone in original images that may have clipping, and to present a clipping-free DBS-based halftoning algorithm. The key idea is to apply the ordered dither using a threshold array generated by DBS-based method, to highlights and shadows, and then use the DBS. The second contribution is to extend the DBS to generate L-level multitone images with each pixel taking one of the intensity levels , , ..., . However, clippings appear in highlights, middle tone, and shadows of generated L-level multitone images. The third contribution of this paper is to modify the multitone version of the DBS to generate a clipping-free L-level multitone images. The resulting multitone images are so good that they reproduce the tones and the details of the original gray scale images very well.

A scheduling algorithm aims to minimize the overall execution time of the program by properly allocating and arranging the execution order of the tasks on the core processors such that the precedence constraints among the tasks are preserved. In this paper, we present a new scheduling algorithm by using geometry analysis of the Task Precedence Graph (TPG) based on A* search technique and uses a computationally efficient cost function for guiding the search with reduced complexity and pruning techniques to produce an optimal solution for the allocation/scheduling problem of a parallel application to parallel and multiprocessor architecture. The main goal of this work is to significantly reduce the search space and achieve the optimality or near optimal solution. We implemented the algorithm on general task graph problems that are processed on most of related search work and obtain the optimal scheduling with a small number of states. The proposed algorithm reduced the exhaustive search by at least 50% of search space. The viability and potential of the proposed algorithm is demonstrated by an illustrative example.

Motion estimation (ME) is a computation and data intensive module in video coding system. The search window reuse methods play a critical role in bandwidth reduction by exploiting the data locality in video coding system. In this paper, a search window reuse method (Level C+) is proposed for MPEG-2 to H.264/AVC transcoding. The proposed method is designed for ultra-low bandwidth application, while the on-chip memory is not a main constraining factor. By loading search window for the motion estimation unit (MEU) and applying motion vector clipping processing, each MB in MEU can utilize both horizontal and vertical search reuse. A very low bandwidth level (Rα<2) can be achieved with an acceptable on-chip memory.

The core of the support vector machine (SVM) problem is a quadratic programming problem with a linear constraint and bounded variables. This problem can be transformed into the second order cone programming (SOCP) problems. An interior-point-method (IPM) can be designed for the SOCP problems in terms of storage requirements as well as computational complexity if the kernel matrix has low-rank. If the kernel matrix is not a low-rank matrix, it can be approximated by a low-rank positive semi-definite matrix, which in turn will be fed into the optimizer. In this paper we present two SOCP formulations for each SVM classification and regression problem. There are several search direction methods for implementing SOCPs. Our main goal is to find a better search direction for implementing the SOCP formulations of the SVM problems. Two popular search direction methods: HKM and AHO are tested analytically for the SVM problems, and efficiently implemented. The computational costs of each iteration of the HKM and AHO search direction methods are shown to be the same for the SVM problems. Thus, the training time depends on the number of IPM iterations. Our experimental results show that the HKM method converges faster than the AHO method. We also compare our results with the method proposed in Fine and Scheinberg (2001) that also exploits the low-rank of the kernel matrix, the state-of-the-art SVM optimization softwares SVMTorch and SVMlight. The proposed methods are also compared with Joachims 'Linear SVM' method on linear kernel.

This paper describes an improved local search method for synthesizing arbitrary Multiple-Valued Logic (MVL) function. In our approach, the MVL function is mapped from its algebraic presentation (sum-of-products form) on a multiple-layered network based on the functional completeness property. The output of the network is evaluated based on two metrics of correctness and optimality. A local search embedded with chaotic dynamics is utilized to train the network in order to minimize the MVL functions. With the characteristics of pseudo-randomness, ergodicity and irregularity, both the search sequence and solution neighbourhood generated by chaotic variables enables the system to avoid local minimum settling and improves the solution quality. Simulation results based on 2-variable 4-valued MVL functions and some other large instances also show that the improved local search learning algorithm outperforms the traditional methods in terms of the correctness and the average number of product terms required to realize a given MVL function.

In this paper, we present a recently developed pattern search method called Genetic Pattern Search algorithm (GPSA) for the global optimization of cost function subject to simple bounds. GPSA is a combined global optimization method using genetic algorithm (GA) and Digital Pattern Search (DPS) method, which has the digital structure represented by binary strings and guarantees convergence to stationary points from arbitrary starting points. The performance of GPSA is validated through extensive numerical experiments on a number of well known functions and on robot walking application. The optimization results confirm that GPSA is a robust and efficient global optimization method.

The deployment of historical trajectories of moving objects has greatly increased for various applications in road networks. For instance, similar patterns of moving-object trajectories are very useful for designing the transportation network of a new city. In this paper, we define a spatio-temporal similarity measure based on a road network distance, rather than a Euclidean distance. We also propose a new similar trajectory search algorithm based on the spatio-temporal measure by using an efficient pruning mechanism. Finally, we show the efficiency of our algorithm, both in terms of retrieval accuracy and retrieval efficiency.

Resource search is a fundamental problem in large-scale and highly dynamic Peer-to-Peer (P2P) systems. Unstructured search approaches are widely used because of their flexibility and robustness. However, such approaches incur high communication cost. The index-dissemination-based search is a kind of efficient unstructured search approach. We investigate such approaches with respect to minimize the system communication cost. Based on a dynamic system model that peers continuously leave and join, we solve two problems. One problem is how to efficiently disseminate and maintain a given number of indices. Another is to determine the optimal number of indices for each resource object of a given popularity. Finally, we propose an optimized index dissemination scheme which is fully decentralized and self-adaptive. A remarkable advantage is that the scheme yields no additional communication cost to achieve the self-adaptive feature.

This paper proposes a new algorithm for evaluating the number of chaining variable conditions (CVCs) in the selecting step of a disturbance vector (DV) for the analysis of SHA-1 collision search. The algorithm is constructed by combining four strategies, that can evaluate the number of CVCs more strictly compared with the previous approach. By using our method, we found some DVs that have 57 (or 59) essential CVCs for 1st (or 2nd) block in the case if we assume that we can modify messages up to step 25, which we have not confirmed the practicability of the assumption.

In this paper, we propose an extension to GridFTP that optimizes its performance by dynamically adjusting the number of parallel TCP connections. GridFTP has been used as a data transfer protocol to effectively transfer a large volume of data in Grid computing. GridFTP supports a feature called parallel data transfer that improves throughput by establishing multiple TCP connections in parallel. However, for achieving high GridFTP throughput, the number of TCP connections should be optimized based on the network status. In this paper, we propose an automatic parallelism tuning mechanism called GridFTP-APT (GridFTP with Automatic Parallelism Tuning) that adjusts the number of parallel TCP connections according to information available to the Grid middleware. Through simulations, we demonstrate that GridFTP-APT significantly improves the performance of GridFTP in various network environments.

This letter proposes a fast carrier search method, the Carrier Search Step method (CSSM), to quickly detect the carrier frequency even when mobile stations have no knowledge of the carrier frequencies used [1]. CSSM consists of two operations: 1) mobile stations use the coarse-to-fine search to detect the synchronization channel (SCH), and 2) the center frequency of SCH is shifted within the channel bandwidth so that mobile stations can detect the SCH in an early step of the coarse-to-fine search. Compared with conventional methods, CSSM can reduce carrier search time by 90% when SCH bandwidth is 1.08 MHz and the channel bandwidth is 5 MHz. The reduction in carrier search time strengthens as the channel bandwidth increases.

A novel fast lock-in digitally controlled phase-locked loop (DCPLL) is proposed in this letter. This DCPLL adopts a novel frequency search algorithm to reduce the lock-in time. Furthermore, to reduce the power consumption, the frequency divider is reused as a frequency detector during the frequency acquisition, and reused as a time-to-digital converter module during the phase acquisition. To verify the proposed algorithm and architecture, a DCPLL design is implemented by SMIC 0.18 µm 1P6M CMOS technology. The Spice simulation results show that the DCPLL can achieve frequency acquisition in 3 reference cycles and complete phase acquisition in 11 reference cycles when locking to 200 MHz. The corresponding power consumption of DCPLL is 3.71 mW.

In this letter, we propose combinatorial and search construction methods of 2-D multi-weight optical orthogonal codes (OOCs) with autocorrelation 0 and crosscorrelation 1, called multi-weight single or no pulse per row (MSNPR) codes. An upper bound on the size of MSNPR codes is derived and the performance of MSNPR codes is compared to those of other OOCs in terms of the bit error rate (BER) and evaluated using blocking probability. It is also demonstrated that the MSNPR codes can be flexibly constructed for different applications, providing the scalability to optical CDMA networks.

In an RFID search protocol, a reader uses a designated query instead of an unspecified query commonly used in RFID authentication protocols. Due to this fundamental difference, techniques used in RFID authentication protocols may not be suitable for RFID search protocols. Tan et al.'s protocol, however, is based on techniques used in previous works such as using random values. In this paper, we propose two RFID search protocols, one based on static ID and the other based on dynamic ID, both which does not require additional measures to satisfy security requirements of RFID protocols. We achieve this by using counters.

This letter proposes a Cell-based Hybrid Index (CHI) for energy conserving k Nearest Neighbor search on air. The proposed CHI provides global knowledge on data distribution for fast decision of the search space and local knowledge for efficient pruning of data items. Simulations show that CHI outperforms the existing indexing schemes in terms of tuning time and energy efficiency. With respect to access time, it outperforms them except the distributed indexing scheme optimized for access time.

Point Pattern Matching (PPM) is an essential problem in many image analysis and computer vision tasks. This paper presents a two-stage algorithm for PPM problem using ellipse fitting and dual Hilbert scans. In the first matching stage, transformation parameters are coarsely estimated by using four node points of ellipses which are fitted by Weighted Least Square Fitting (WLSF). Then, Hilbert scans are used in two aspects of the second matching stage: it is applied to the similarity measure and it is also used for search space reduction. The similarity measure named Hilbert Scanning Distance (HSD) can be computed fast by converting the 2-D coordinates of 2-D points into 1-D space information using Hilbert scan. On the other hand, the N-D search space can be converted to a 1-D search space sequence by N-D Hilbert Scan and an efficient search strategy is proposed on the 1-D search space sequence. In the experiments, we use both simulated point set data and real fingerprint images to evaluate the performance of our algorithm, and our algorithm gives satisfying results both in accuracy and efficiency.

In this paper, we propose an image completion algorithm which takes advantage of the countless number of images available on Internet photo sharing sites to replace occlusions in an input image. The algorithm 1) automatically selects the most suitable images from a database of downloaded images and 2) seamlessly completes the input image using the selected images with minimal user intervention. Experimental results on input images captured at various locations and scene conditions demonstrate the effectiveness of the proposed technique in seamlessly reconstructing user-defined occlusions.

Programming the multiprocessor system-on-chip (MPSoC) requires partitioning the sequential reference programs onto multiple processors running in parallel. However, designers still need to partition the code manually due to the lack of automated partition techniques. To settle this issue, this paper proposes a partition exploration algorithm based on the search space smoothing techniques, and implements the proposed method using a commercial extensible processor (Xtensa LX2 processor from Tensilica Inc.). We have verified the feasibility of the algorithm by implementing the MPEG2 benchmark on the Xtensa-based two-processor system. The final experimental results indicate a performance improvement of at least 1.6 compared to the single-processor system.

Compared with previous standards, H.264/AVC adopts variable block size motion estimation (VBSME) and multiple reference frames (MRF) to improve the video quality. Full search motion estimation algorithm (FS), which calculates every search candidate in the search window for 7 block type with multiple reference frames, consumes massive computation power. Mathematical analysis reveals that the aliasing problem of subsampling algorithm comes from high frequency signal components. Moreover, high frequency signal components are also the main issues that make MRF algorithm essential. As we know, a picture being rich of texture must contain lots of high frequency signals. So based on these mathematical investigations, two fast VBSME algorithms are proposed in this paper, namely edge block detection based subsampling method and motion vector based MRF early termination algorithm. Experiments show that strong correlation exists among the motion vectors of those blocks belonging to the same macroblock. Through exploiting this feature, a dynamically adjustment of the search ranges of integer motion estimation is proposed in this paper. Combing our proposed algorithms with UMHS almost saves 96-98% Integer Motion Estimation (IME) time compared to the exhaustive search algorithm. The induced coding quality loss is less than 0.8% bitrate increase or 0.04 dB PSNR decline on average.