This paper proposes a graph-theory-based Euler number computing algorithm. According to the graph theory and the analysis of a mask's configuration, the Euler number of a binary image in our algorithm is calculated by counting four patterns of the mask. Unlike most conventional Euler number computing algorithms, we do not need to do any processing of the background pixels. Experimental results demonstrated that our algorithm is much more efficient than conventional Euler number computing algorithms.

In a multi-cell MIMO system, the rate of edge users is limited by the inter-cell co-channel interference. The CoMP scheme which includes Joint Process (JP) and Coordinated Scheduling/Beamforming (CS/CB) was developed to reduce the inter-cell interference and enhance the edge rate. Because CS/CB can alleviate the overhead of network, it gains attention recently. In this paper, a modified zero forcing beamforming (ZFBF) is applied to downlink transmission in a two-cell MIMO system. In order to enhance system sum rate, a novel coordinated user scheduling algorithm is proposed. Firstly, we select users with high correlation among cross-channels as candidates, and then group users from candidates with high orthogonality among direct-channels, and match user groups in different cells as the final scheduling group pair. Simulations show that the proposed algorithm can achieve a higher system sum rate with low complexity than traditional scheduling algorithms.

Forty Thieves is a solitaire game with two 52-card decks. The object is to move all cards from ten tableau piles of four cards to eight foundations. Each foundation is built up by suit from ace to king of the same suit, and each tableau pile is built down by suit. You may move the top card from any tableau pile to a tableau or foundation pile, and from the stock to a foundation pile. We prove that the generalized version of Forty Thieves is NP-complete.

Most recent work on cooperative spectrum sensing using cognitive radios has focused on issues involving the sensing channels and seemed to ignore those involving the reporting channels. Furthermore, no research has treated the effect of correlated composite Rayleigh-lognormal fading, also known as Suzuki fading, in cognitive radio. This paper proposes a technique for reuse of shadowed CRs, discarded during the sensing phase, as amplified-and-forward (AF) diversity relays for other surviving CRs to mitigate the effects of such fading in reporting channels. A thorough analysis of and a closed-form expression for the outage probability of the resulting cooperative AF diversity network in correlated composite Rayleigh-lognormal fading channels are presented in this paper. In particular, an efficient solution to the “PDF of sum-of-powers” of correlated Suzuki-distributed random variables using moment generating function (MGF) is proposed.

In several important applications, we often encounter with the computation of a Toeplitz matrix vector product (TMVP). In this work, we propose a k-way splitting method for a TMVP over any field F, which is a generalization of that over GF(2) presented by Hasan and Negre. Furthermore, as an application of the TMVP method over F, we present the first subquadratic space complexity multiplier over any finite field GF(pn) defined by an irreducible trinomial.

Network traffic load usually differs significantly at different times of a day due to users' different time-preference. Network congestion may happen in traffic peak times. In order to prevent this from happening, network service providers (NSPs) can either over-provision capacity for demand at peak times of the day, or use dynamic time-dependent pricing (TDP) scheme to reduce the demand at traffic peak times. Since over-provisioning network capacity is costly, many researchers have proposed TDP schemes to control congestion as well as to improve the revenue of NSPs. To the best of our knowledge, all the studies on TDP schemes consider only the monopoly or duopoly NSP case. In our previous work, the duopoly NSP case has been studied with the assumption that each NSP has complete information of quality of service (QoS) of the other NSP. In this paper, an oligopoly NSP case is studied. NSPs try to maximize their overall revenue by setting time-dependent price, while users choose NSPs by considering their own time preference, congestion status in the networks and the price set by the NSPs. The interactions among NSPs are modeled as an oligopoly Bertrand game. Firstly, assuming that each NSP has complete information of QoS of all NSPs, a unique Nash equilibrium of the game is established under the assumption that users' valuation of QoS is uniformly distributed. Secondly, the assumption of complete information of QoS of all NSPs is relaxed, and a learning algorithm is proposed for NSPs to achieve the Nash equilibrium of the game. Analytical and experimental results show that NSPs can benefit from TDP scheme, however, not only the competition effect but also the incomplete information among NSPs causes revenue loss for NSPs under the TDP scheme.

Speeded up robust features (SURF) can detect/describe scale- and rotation-invariant features at high speed by relying on integral images for image convolutions. However, the time taken for matching SURF descriptors is still long, and this has been an obstacle for use in real-time applications. In addition, the matching time further increases in proportion to the number of features and the dimensionality of the descriptor. Therefore, we propose a fast matching method that rearranges the elements of SURF descriptors based on their entropies, divides SURF descriptors into sub-descriptors, and sequentially and analytically matches them to each other. Our results show that the matching time could be reduced by about 75% at the expense of a small drop in accuracy.

Cloud computing is becoming increasingly popular. A large number of data are outsourced to the cloud by data owners motivated to access the large-scale computing resources and economic savings. To protect data privacy, the sensitive data should be encrypted by the data owner before outsourcing, which makes the traditional and efficient plaintext keyword search technique useless. So how to design an efficient, in the two aspects of accuracy and efficiency, searchable encryption scheme over encrypted cloud data is a very challenging task. In this paper, for the first time, we propose a practical, efficient, and flexible searchable encryption scheme which supports both multi-keyword ranked search and parallel search. To support multi-keyword search and result relevance ranking, we adopt Vector Space Model (VSM) to build the searchable index to achieve accurate search results. To improve search efficiency, we design a tree-based index structure which supports parallel search to take advantage of the powerful computing capacity and resources of the cloud server. With our designed parallel search algorithm, the search efficiency is well improved. We propose two secure searchable encryption schemes to meet different privacy requirements in two threat models. Extensive experiments on the real-world dataset validate our analysis and show that our proposed solution is very efficient and effective in supporting multi-keyword ranked parallel searches.

Secret sharing (SS) has been extensively studied as for both secure data storage and a fundamental building block for multiparty computation (MPC). Recently, Kikuchi et al. proposed a passively and unconditionally secure conversion protocol that converts from a share of a ramp scheme to another of homomorphic SS scheme. The share-size of the ramp scheme is small, and the homomorphic SS scheme is a class of SS schemes that includes Shamir's and replicated SS schemes, which are convenient for MPC. Therefore, their protocol is a conversion from an SS scheme whose share-size is small to MPC-friendly SS schemes, and can be applied to reduce the amount of data storage while maintaining extendibility to MPC. We propose five unconditionally and actively secure protocols in the honest majority. In this paper, we consider a privacy and correctness as security requirement and does not consider a robustness: A cheat caused by an active adversary must be detected. These protocols consist of two conversion protocols, two reveal protocols and a protocol generating specific randomness. Main protocols among them are two conversion protocols for bilateral conversion between a ramp scheme and linear SS scheme, and the others are building blocks of the main protocols. Linear SS scheme is a subset of homomorphic SS scheme but includes both Shamir's and replicated SS schemes. Therefore, these main protocols are conversions between an SS scheme whose share-size is small to MPC-friendly SS schemes. These main protocols are unconditionally and actively secure so if MPC protocols used after the conversion are actively secure, the whole system involving SS scheme, conversion, and MPC protocols can be unconditionally and actively secure by using our main protocols. One of our two main protocols is the first to convert from MPC-friendly SS schemes to the ramp scheme. This enhances applications, such as secure backup, of the conversion protocol. Other than the two main protocols, we propose a protocol for generating specific randomnesses and two reveal protocols as building blocks. The latter two reveal protocols are actively and unconditionally secure in the honest majority and requires O(n||F||)-bit communication per revealing, and we believe that it is independently interest.

Secret sharing scheme (SS) has been extensively studied since SSs are important not only for secure data storage but also as a fundamental building block for multiparty computation (MPC). For an application to secure data storage, the share size of SS is an important factor. For an application to a building block for MPC, the extendibility to MPC is needed. Computationally secure SSs and a ramp scheme have a small share size but there have been few studies concerning their MPC. In contrast, there have been many studies about MPC on Shamir's and replicated SSs while their share size is large. We consider an application scenario of SS such as applying SSs to secure data storage service with MPC. In this application, users store their data in servers through SS, and sometimes the servers perform MPC as an optional feature. In this case, the extendibility to MPC is needed and good code-efficiency is preferable. We propose a new computational SS, and show how to convert shares of our SS and a ramp SS to those of multiparty-friendly SS such as Shamir's and replicated SS. This enables one to secretly-share data compactly and extend secretly-shared data to MPC if needed.

This paper shows a method to find a linear transformation that reduces the number of variables to represent a given incompletely specified index generation function. It first generates the difference matrix, and then finds a minimal set of variables using a covering table. Linear transformations are used to modify the covering table to produce a smaller solution. Reduction of the difference matrix is also considered.

The maximization of non-Gaussianity is an effective approach to achieve the complex independent component analysis (ICA) problem. However, the traditional complex maximization of non-Gaussianity (CMN) algorithm does not consider the influence of noise. In this letter, a modification of the fixed-point algorithm is proposed for more practical occasions of the complex noisy ICA model. Simulations show that the proposed method demonstrates significantly improved performance over the traditional CMN algorithm in the noisy ICA model when the sample size is sufficient.

The development of high data rate wireless communications systems using Multiple Input — Multiple Output (MIMO) antenna techniques requires detectors with reduced complexity and good Bit Error Rate (BER) performance. In this paper, we present the Semi-fixed Complexity Sphere Decoder (SCSD), which executes the process of detection in MIMO systems with a significantly lower computation cost than the high-performance/reduced-complexity detectors: Sphere Decoder (SD), K-best, Fixed Complexity Sphere Decoder (FSD) and Adaptive Set Partitioning (ASP). Simulation results show that when the Signal-to-Noise Ratio (SNR) is less than 15dB, the SCSD reduces the complexity by up to 90% with respect to SD, up to 60% with respect to K-best or ASP and by up to 90% with respect to FSD. In the proposed algorithm, the BER performance does not show significant degradation and therefore, can be considered as a complexity reduction scheme suitable for implementing in MIMO detectors.

This paper presents an efficient method using Hadoop MapReduce for constructing a K-nearest neighbor graph (K-NNG) from a large-scale data set. K-NNG has been utilized as a data structure for data analysis techniques in various applications. If we are to apply the techniques to a large-scale data set, it is desirable that we develop an efficient K-NNG construction method. We focus on NN-Descent, which is a recently proposed method that efficiently constructs an approximate K-NNG. NN-Descent is implemented on a shared-memory system with OpenMP-based parallelization, and its extension for the Hadoop MapReduce framework is implied for a larger data set such that the shared-memory system is difficult to deal with. However, a simple extension for the Hadoop MapReduce framework is impractical since it requires extremely high system performance because of the high memory consumption and the low data transmission efficiency of MapReduce jobs. The proposed method relaxes the requirement by improving the MapReduce jobs, which employs an appropriate key-value pair format and an efficient sampling strategy. Experiments on large-scale data sets demonstrate that the proposed method both works efficiently and is scalable in terms of a data size, the number of machine nodes, and the graph structural parameter K.

Chronic liver disease is a major worldwide health problem. Diagnosis and staging of chronic liver diseases is an important issue. In this paper, we propose a quantitative method of analyzing local morphological changes for accurate and practical computer-aided diagnosis of cirrhosis. Our method is based on sparse and low-rank matrix decomposition, since the matrix of the liver shapes can be decomposed into two parts: a low-rank matrix, which can be considered similar to that of a normal liver, and a sparse error term that represents the local deformation. Compared with the previous global morphological analysis strategy based on the statistical shape model (SSM), our proposed method improves the accuracy of both normal and abnormal classifications. We also propose using the norm of the sparse error term as a simple measure for classification as normal or abnormal. The experimental results of the proposed method are better than those of the state-of-the-art SSM-based methods.

This paper proposes an efficient two-scan labeling algorithm for binary hexagonal images. Unlike conventional labeling algorithms, which process pixels one by one in the first scan, our algorithm processes pixels two by two. We show that using our algorithm, we can check a smaller number of pixels. Experimental results demonstrated that our method is more efficient than the algorithm extended straightly from the corresponding labeling algorithm for rectangle binary images.

This paper presents a framework for reducing the energy consumption of embedded real-time systems. We implemented the presented framework as both an optimization toolchain and an energy-aware real-time operating system. The framework consists of the integration of multiple techniques to optimize the energy consumption. The main idea behind our approach is to utilize trade-offs between the energy consumption and the performance of different processor configurations during task checkpoints, and to maintain memory allocation during task context switches. In our framework, a target application is statically analyzed at both intra-task and inter-task levels. Based on these analyzed results, runtime optimization is performed in response to the behavior of the application. A case study shows that our toolchain and real-time operating systems have achieved energy reduction while satisfying the real-time performance. The toolchain has also been successfully applied to a practical application.

The mobile cloud computing (MCC) paradigm is aimed at integrating mobile devices with cloud computing. In the client-server architecture of MCC, mobile devices offload tasks to the cloud to utilize the computation and storage resources of data centers. However, due to the rapid increase in the traffic demand and complexity of mobile applications, service providers have to continuously upgrade their infrastructures at great expense. At the same time, modern mobile devices have greater resources (communication, computation, and sensing), and these resources are not always fully utilized by device users. Therefore, mobile devices, from time to time, encounter other devices that could provide resources to them. Because the amount of such resources has increased with the number of mobile devices, researchers have begun to consider making use of these resources, located at the “edge” of mobile networks, to increase the scalability of future information networks. This has led to a cooperation based architecture of MCC. This paper reports the concept and design of an resource sharing mechanism that utilize resources in mobile devices through opportunistic contacts between them. Theoretical models and formal definitions of problems are presented. The efficiency of the proposed mechanism is validated through formal proofs and extensive simulation.

For compressed sensing, to address problems which do not involve reconstruction, a correlation analysis between measurements and the transform coefficients is proposed. It is shown that there is a linear relationship between them, which indicates that we can abstract the inner property of images directly in the measurement domain.

The stability theory of stream ciphers plays an important role in designing good stream cipher systems. Two algorithms are presented, to determine the optimal shift and the minimum linear complexity of the sequence, that differs from a given sequence over Fq with period qn-1 by one digit. We also describe how the linear complexity changes with respect to one digit differing from a given sequence.