Shadow mapping is an efficient method to generate shadows in real time computer graphics and has broad variations from hard to soft shadow synthesis. Soft shadowing based on shadow mapping is a blurring technique on a shadow map or on screen space. Blurring on screen space has an advantage for efficient sampling on a shadow map, since the blurred target array has exactly the same coordinates as the screen. However, a previous blurring method on screen space has a drawback: the generated shadow is not correct when a view direction has a large angle to the normal of the shadowed plane. In this paper, we introduce a new screen space based method for soft shadowing that is fast and generates soft shadows more accurately than the previous screen space soft shadow mapping method. The resultant images show shadows produced by our method just stand in the same place, while shadows by the previous method change in terms of penumbra while the view moves. Surprisingly, although our method is more complex than the previous method, the measurement results of the calculation time show our method is almost the same performance. This is because it controls the blurring area more accurately and thus successfully reduces multiplications for blurring.

We propose a variant of OMP algorithm named BROMP for sparse solution. In our algorithm, the update rule of MP algorithm is employed to reduce the number of least square calculations and the refining strategy is introduced to further improve its performance. Simulations show that the proposed algorithm performs better than the OMP algorithm with significantly lower complexity.

Dubé and Beaudoin proposed a lossless data compression called compression via substring enumeration (CSE) in 2010. We evaluate an upper bound of the number of bits used by the CSE technique to encode any binary string from an unknown member of a known class of k-th order Markov processes. We compare the worst case maximum redundancy obtained by the CSE technique for any binary string with the least possible value of the worst case maximum redundancy obtained by the best fixed-to-variable length code that satisfies the Kraft inequality.

Complex-valued Hopfield associative memory (CHAM) is one of the most promising neural network models to deal with multilevel information. CHAM has an inherent property of rotational invariance. Rotational invariance is a factor that reduces a network's robustness to noise, which is a critical problem. Here, we proposed complex-valued bipartite auto-associative memory (CBAAM) to solve this reduction in noise robustness. CBAAM consists of two layers, a visible complex-valued layer and an invisible real-valued layer. The invisible real-valued layer prevents rotational invariance and the resulting reduction in noise robustness. In addition, CBAAM has high parallelism, unlike CHAM. By computer simulations, we show that CBAAM is superior to CHAM in noise robustness. The noise robustness of CHAM decreased as the resolution factor increased. On the other hand, CBAAM provided high noise robustness independent of the resolution factor.

Wireless patient monitoring is an active research area with the goal of ubiquitous health care services. This study presents a novel means of exploiting the distributed source coding (DSC) in low-complexity compression of ECG signals. We first convert the ECG data compression to an equivalent channel coding problem and exploit a linear channel code for the DSC construction. Performance is further enhanced by the use of a correlation channel that more precisely characterizes the statistical dependencies of ECG signals. Also proposed is a modified BCJR algorithm which performs symbol decoding of binary convolutional codes to better exploit the source's a priori information. Finally, a complete setup system for online ambulatory ECG monitoring via mobile cellular networks is presented. Experiments on the MIT-BIH arrhythmia database and real-time acquired ECG signals demonstrate that the proposed system outperforms other schemes in terms of encoder complexity and coding efficiency.

There have been many matching pursuit algorithms (MPAs) which handle the sparse signal recovery problem, called compressed sensing (CS). In the MPAs, the correlation step makes a dominant computational complexity. In this paper, we propose a new fast correlation method for the MPA when we use partial Fourier sensing matrices and partial Hadamard sensing matrices which are widely used as the sensing matrix in CS. The proposed correlation method can be applied to almost all MPAs without causing any degradation of their recovery performance. Also, the proposed correlation method can reduce the computational complexity of the MPAs well even though there are restrictions depending on a used MPA and parameters.

By exploiting the inherent sparsity of wireless propagation channels, the theory of compressive sensing (CS) provides us with novel technologies to estimate the channel state information (CSI) that require considerably fewer samples than traditional pilot-aided estimation methods. In this paper, we describe the block-sparse structure of the fast time-varying channel and apply the model-based CS (MCS) for channel estimation in orthogonal frequency division multiplexing (OFDM) systems. By exploiting the structured sparsity, the proposed MCS-based method can further compress the channel information, thereby allowing a more efficient and precise estimation of the CSI compared with conventional CS-based approaches. Furthermore, a specific pilot arrangement is tailored for the proposed estimation scheme. This so-called random grouped pilot pattern can not only effectively protect the measurements from the inter-carrier interference (ICI) caused by Doppler spreading but can also enable the measurement matrix to meet the conditions required for MCS with relatively high probability. Simulation results demonstrate that our method has good performance at high Doppler frequencies.

The maturation of inhibitory transmission through γ-aminobutyric acid (GABA) is required to induce ocular dominance (OD) plasticity in the visual cortex. However, only circuits that are mediated by specific GABAA receptors can selectively elicit OD plasticity, implying a role of local circuits involved in GABA inhibition in this process. In this study, in order to theoretically examine the effects of such local pathways associated with cortical inhibition on the induction of OD plasticity, we compared synaptic modification dynamics regulated by feedforward inhibition and those regulated by feedback inhibition. Feedforward inhibition facilitated competitive interactions between different groups of inputs conveying correlated activities, which were required for the emergence of experience-dependent plasticity. Conversely, feedback inhibition suppressed competitive interactions and prevented synapses from reflecting past sensory experience. Our results suggest that the balance between feedforward and feedback inhibition regulates the timing and level of cortical plasticity by modulating competition among synapses. This result suggests an importance of activity-dependent competition in experience-dependent OD plasticity, which is in line with the results of previous experiments.

The last decade has witnessed an explosion of interest in research on human emotion modeling for generating intelligent virtual agents. This paper proposes a novel personality model based on the Revised NEO Personality Inventory (NEO PI-R). Compared to the popular Big-Five-Personality Factors (Big5) model, our proposed model is more capable than Big5 on describing a variety of personalities. Combining with emotion models it helps to produce more reasonable emotional reactions to external stimuli. A novel Resistant formulation is also proposed to effectively simulate the complicated negative emotions. Emotional reactions towards multiple stimuli are also effectively simulated with the proposed personality model.

We discuss a new high performance computing service (HPCS) platform that has been developed to provide domain-neutral computing service under the governmental support from “EDucation-research Integration through Simulation On the Net” (EDISON) project. With a first focus on technical features, we not only present in-depth explanations of the implementation details, but also describe the strengths of the EDISON platform against the successful nanoHUB.org gateway. To validate the performance and utility of the platform, we provide benchmarking results for the resource virtualization framework, and prove the stability and promptness of the EDISON platform in processing simulation requests by analyzing several statistical datasets obtained from a three-month trial service in the initiative area of computational nanoelectronics. We firmly believe that this work provides a good opportunity for understanding the science gateway project ongoing for the first time in Republic of Korea, and that the technical details presented here can be served as an useful guideline for any potential designs of HPCS platforms.

In this paper, the linear complexity and minimal polynomials of Legendre sequences over Fq have been calculated, where q = pm and p is a prime number. Our results show that Legendre sequences have high linear complexity over Fq for a large part of prime power number q so that they can resist the linear attack method.

This letter proposes an efficient Two-stage Resource scheduling algorithm for cloud based Live Media Streaming system (TRLMS). It transforms the cloud-based resource scheduling problem to a min-cost flow problem in a graph, and solves it by an improved Successive Short Path (SSP) algorithm. Simulation results show that TRLMS can enhance user demand satisfaction by 17.1% than mean-based method, and its time complexity is much lower than original SSP algorithm.

Based on the non-standard generalized Boolean functions (GBFs) over Z4, we propose a new method to convert those functions into the 16-QAM Golay complementary sequences (CSs). The resultant 16-QAM Golay CSs have the upper bound of peak-to-mean envelope power ratio (PMEPR) as low as 2. In addition, we obtain multiple 16-QAM Golay CSs for a given quadrature phase shift keying (QPSK) Golay CS.

Detailed high capacity vector maps must be compressed effectively for transmission or storage in Web GIS (geographic information system) and mobile GIS applications. In this paper, we present a polyline compression method that consists of polyline feature-based hybrid simplification and second derivative-based data compression. The polyline hybrid simplification function detects the feature points from a polyline using DP, SF, and TF algorithms, and divides the polyline into sectors using these feature points. It then simplifies the sectors using an algorithm to determine the minimum area difference among the DP, SF, and TF results. The polyline data compression method segments the second derivatives of the simplified polylines into integer and fractional parts. The integer parts are compressed using the minimum bounding box of the layer to determine the broad position of the object. The fractional parts are compressed using hierarchical precision levels. Experimental results verify that our method has higher simplification and compression efficiency than conventional methods and produces good quality compressed maps.

Nonlinearity compensation algorithm and soft-decision forward error correction (FEC) are considered as key technologies for future high-capacity and long-haul optical transmission system. In this report, we experimentally demonstrate the following three benefits brought by low complexity perturbation back-propagation nonlinear compensation algorithm in 224Gb/s DP-16QAM transmission over large-Aeff pure silica core fiber; (1) improvement of pre-FEC bit error ratio, (2) reshaping noise distribution to more Gaussian, and (3) reduction of cycle slip probability.

This paper proposes a so called quasi-linear support vector machine (SVM), which is an SVM with a composite quasi-linear kernel. In the quasi-linear SVM model, the nonlinear separation hyperplane is approximated by multiple local linear models with interpolation. Instead of building multiple local SVM models separately, the quasi-linear SVM realizes the multi local linear model approach in the kernel level. That is, it is built exactly in the same way as a single SVM model, by composing a quasi-linear kernel. A guided partitioning method is proposed to obtain the local partitions for the composition of quasi-linear kernel function. Experiment results on artificial data and benchmark datasets show that the proposed method is effective and improves classification performances.

The k-error linear complexity of periodic sequences is an important security index of stream cipher systems. By using an interesting decomposing approach, we investigate the intrinsic structure for the set of 2n-periodic binary sequences with fixed complexity measures. For k ≤ 4, we construct the complete set of error vectors that give the k-error linear complexity. As auxiliary results we obtain the counting functions of the k-error linear complexity of 2n-periodic binary sequences for k ≤ 4, as well as the expectations of the k-error linear complexity of a random sequence for k ≤ 3. Moreover, we study the 2t-error linear complexity of the set of 2n-periodic binary sequences with some fixed linear complexity L, where t < n-1 and the Hamming weight of the binary representation of 2n-L is t. Also, we extend some results to pn-periodic sequences over Fp. Finally, we discuss some potential applications.

This paper presents a novel test data compression scheme for SoCs based on block merging and compatibility. The technique exploits the properties of compatibility and inverse compatibility between consecutive blocks, consecutive merged blocks, and two halves of the encoding merged block itself to encode the pre-computed test data. The decompression circuit is simple to be implemented and has advantage of test-independent. In addition, the proposed scheme is applicable for IP cores in SoCs since it compresses the test data without requiring any structural information of the circuit under test. Experimental results demonstrate that the proposed technique can achieve an average compression ratio up to 68.02% with significant low test application time.

Now that the subject of green computing is receiving a lot of attention, the energy consumption of datacenters has emerged as a significant issue. Consolidation of Virtual Machines (VMs) reduces the energy consumption since VM live migration not only optimizes VM placement, but also switches idle nodes to sleep mode. However, VM migration may negatively impact the performance of the system and lead to violations in SLA (Service Level Agreement) requirements between end users and cloud providers. In this study, we propose a VM consolidation mechanism that reduces the energy consumption of datacenters, eliminates unnecessary migrations, and minimizes the SLA violations. Compared to previous studies, the proposed policy shows a reduction of 2% to 3% in energy consumption, 13% to 41% in VM migration frequency, and 15% to 50% in SLA violations.

In conventional networks, service control function and network control function work independently. Therefore, stereotypical services are provided via fixed routes or selected routes in advance. Recently, advanced network services have been provided by assortment of distributed components at low cost. Furthermore, service platform, which unifies componentized network control and service control in order to provide advanced services with flexibility and stability, has attracted attention. In near future, network management system (NMS) is promising, which replies an answer quickly for such advanced service platforms when route setting is requested with some parameters: quality of service (QoS), source and destination addresses, cost (money) and so on. In addition, the NMS is required to provide routes exploiting functions such as path computation element (PCE) actually. This paper proposes scalable network architecture that can quickly reply an answer by pre-computing candidate routes when route setting is requested to a control unit like an Autonomous System (AS). Proposed architecture can manage network resources scalably, and answer the availability of the requested QoS-aware path settings instantaneously for the forthcoming service platform that finds an adequate combination of a server and a route. In the proposed method, hierarchical databases are established to manage the information related to optical network solution and their data are updated at fewer times by discretized states and their boundaries with some margin. Moreover, with multiple and overlapped overlay, it pre-computes multiple candidate routes with different characteristics like available bandwidth and the number of hops, latency, BER (bit error rate), before route set-up request comes. We present simulation results to verify the benefits of our proposed system. Then, we implement its prototype using OpenFlow, and evaluate its effectiveness in the experimental environment.