The mapping optimization problem in Network-on-Chip (NoC) is constraint and NP-hard, and the deterministic algorithms require considerable computation time to find an exact optimal mapping solution. Therefore, the metaheuristic algorithms (MAs) have attracted great interests of researchers. However, most MAs are designed for continuous problems and suffer from premature convergence. In this letter, a binary metaheuristic mapping algorithm (BMM) with a better exploration-exploitation balance is proposed to solve the mapping problem. The binary encoding is used to extend the MAs to the constraint problem and an adaptive strategy is introduced to combine Sine Cosine Algorithm (SCA) and Particle Swarm Algorithm (PSO). SCA is modified to explore the search space effectively, while the powerful exploitation ability of PSO is employed for the global optimum. A set of well-known applications and large-scale synthetic cores-graphs are used to test the performance of BMM. The results demonstrate that the proposed algorithm can improve the energy consumption more significantly than some other heuristic algorithms.

The 3D measurement is widely required in modern industries. In this letter, a method based on the RGBD saliency detection with depth range adjusting (RGBD-DRA) is proposed for 3D measurement. By using superpixels and prior maps, RGBD saliency detection is utilized to detect and measure the target object automatically Meanwhile, the proposed depth range adjusting is processing while measuring to prompt the measuring accuracy further. The experimental results demonstrate the proposed method automatic and accurate, with 3 mm and 3.77% maximum deviation value and rate, respectively.

Road generalization is a method for thinning out road networks to allow easy viewing according to the size of the map. Most conventional road generalization methods mainly focus on the length of a stroke, which is a chain of links with good continuity based on the principle of perceptual grouping applied to network data such as roads and rivers. However, in the case of facility search in a web map service, for example, a “restaurant guide map,” a road generalization mechanism can be more effective if it depends not only on the stroke length but also on the facility search results. Accordingly, in this study, we implement an on-demand road generalization method that adapts to both the facility search results and the stroke length. Moreover, a sufficiently fast response speed is achieved for practical use in web map services. In particular, this study proposes a fat-stroke model that links facility information to individual strokes and implements a road generalization method that uses this model to improve the response time. In addition, we develop a prototype based on the proposed system. The system evaluation results are based on three indicators, namely, response time of the road generalization system, connectivity between strokes, and connectivity between stroke and facilities. Our experimental results suggest that the proposed method can yield improved response times by a factor of 100 or more while affording higher connectivity.

This paper intends to reduce duration times in typical collective communications. We introduce logical addressing system apart from the physical one and, by rearranging the logical node addresses properly, we intend to reduce communication overheads so that ideal communication is performed. One of the key issues is rearrangement of the logical addressing system. We introduce genetic algorithm (GA) as meta-heuristic solution as well as the random search strategy. Our GA-based method achieves at most 2.50 times speedup in three-traffic-pattern cases.

Low-density chaotic binary sequences generated by Bernoulli map are discussed in this paper. We theoretically evaluate auto-correlation functions of the low-density chaotic binary sequences based on chaos theory.

As energy efficiency has become a major design constraint or objective, heterogeneous manycore architectures have emerged as mainstream target platforms not only in server systems but also in embedded systems. Manycore accelerators such as GPUs are getting also popular in embedded domains, as well as the heterogeneous CPU cores. However, as the number of cores in an embedded GPU is far less than that of a server GPU, it is important to utilize both heterogeneous multi-core CPUs and GPUs to achieve the desired throughput with the minimal energy consumption. In this paper, we present a case study of mapping LBP-based face detection onto a recent CPU-GPU heterogeneous embedded platform, which exploits both task parallelism and data parallelism to achieve maximal energy efficiency with a real-time constraint. We first present the parallelization technique of each task for the GPU execution, then we propose performance and energy models for both task-parallel and data-parallel executions on heterogeneous processors, which are used in design space exploration for the optimal mapping. The design space is huge since not only processor heterogeneity such as CPU-GPU and big.LITTLE, but also various data partitioning ratios for the data-parallel execution on these heterogeneous processors are considered. In our case study of LBP face detection on Exynos 5422, the estimation error of the proposed performance and energy models were on average -2.19% and -3.67% respectively. By systematically finding the optimal mappings with the proposed models, we could achieve 28.6% less energy consumption compared to the manual mapping, while still meeting the real-time constraint.

Recent researches on mobile robots show that convolutional neural network (CNN) has achieved impressive performance in visual place recognition especially for large-scale dynamic environment. However, CNN leads to the large space of image representation that cannot meet the real-time demand for robot navigation. Aiming at this problem, we evaluate the feature effectiveness of feature maps obtained from the layer of CNN by variance and propose a novel method that reserve salient feature maps and make adaptive binarization for them. Experimental results demonstrate the effectiveness and efficiency of our method. Compared with state of the art methods for visual place recognition, our method not only has no significant loss in precision, but also greatly reduces the space of image representation.

Finding widespread events in a distributed network is crucial when detecting cyber-attacks or network malfunctions. We propose a new detection scheme for widespread events based on bitmaps that can succinctly record and deliver event information between monitoring agents and a central coordinator. Our proposed scheme reduces communication overhead as well as total number of rounds, and achieves even higher accuracy, compared with the current state of the art.

Stochastic resonance (SR) is a phenomenon in which signal response in a nonlinear system is enhanced by noise. Fluctuating activities in deterministic chaos are known to cause a phenomenon called chaotic resonance (CR), which is similar to SR. Most previous studies on CR showed that these signal responses were controlled by internal parameters. However, in several applications of CR, it is difficult to control these parameters externally, particularly in biological systems. In this study, to overcome this difficulty, we propose a method for controlling the signal response of CR by adjusting the strength of external feedback control. By using this method, we demonstrate the control of CR in a one-dimensional cubic map, where CR arises from chaos-chaos switching to a weak input signal.

The aim of this work is to develop a method for the simultaneous analysis of multiple groups and their members based on hierarchical tensor manifold modeling. The method is particularly designed to analyze multiple teams, such as sports teams and business teams. The proposed method represents members' data using a nonlinear manifold for each team, and then these manifolds are further modeled using another nonlinear manifold in the model space. For this purpose, the method estimates the role of each member in the team, and discovers correspondences between members that play similar roles in different teams. The proposed method was applied to basketball league data, and it demonstrated the ability of knowledge discovery from players' statistics. We also demonstrated that the method could be used as a general tool for multi-level multi-group analysis by applying it to marketing data.

We studied complicated superstable periodic orbits (SSPOs) in a spiking neuron model with a rectangular threshold signal. The neuron exhibited SSPOs with various periods that changed dramatically when we varied the parameter space. Using a one-dimensional return map defined by the spike phase, we evaluated period changes and showed its complicated distribution. Finally, we constructed a test circuit to confirm the typical phenomena displayed by the mathematical model.

Hazes with various properties spread widely across flat areas with depth continuities and corner areas with depth discontinuities. Removing haze from a single hazy image is difficult due to its ill-posed nature. To solve this problem, this study proposes a modified hybrid median filter that performs a median filter to preserve the edges of flat areas and a hybrid median filter to preserve depth discontinuity corners. Recovered scene radiance, which is obtained by removing hazy particles, restores image visibility using adaptive nonlinear curves for dynamic range expansion. Using comparative studies and quantitative evaluations, this study shows that the proposed method achieves similar or better results than those of other state-of-the-art methods.

This paper proposes a novel pseudo multi-exposure image fusion method based on a single image. Multi-exposure image fusion is used to produce images without saturation regions, by using photos with different exposures. However, it is difficult to take photos suited for the multi-exposure image fusion when we take a photo of dynamic scenes or record a video. In addition, the multi-exposure image fusion cannot be applied to existing images with a single exposure or videos. The proposed method enables us to produce pseudo multi-exposure images from a single image. To produce multi-exposure images, the proposed method utilizes the relationship between the exposure values and pixel values, which is obtained by assuming that a digital camera has a linear response function. Moreover, it is shown that the use of a local contrast enhancement method allows us to produce pseudo multi-exposure images with higher quality. Most of conventional multi-exposure image fusion methods are also applicable to the proposed multi-exposure images. Experimental results show the effectiveness of the proposed method by comparing the proposed one with conventional ones.

The present Letter proposes a design procedure for inducing synchronization in delayed-coupled one-dimensional map networks. We assume the practical situation where the connection delay, the detailed information about the network topology, and the number of the maps are unknown in advance. In such a situation, it is difficult to guarantee the stability of synchronization, since the local stability of a synchronized manifold is equivalent to that of a linear time-variant system. A sufficient condition in robust control theory helps us to derive a simple design procedure. The validity of our design procedure is numerically confirmed.

In this paper, we study self-dual cyclic codes of length n over the ring R=Z4[u]/, where n is an odd positive integer. We define a new Gray map φ from R to Z42. It is a bijective map and maintains the self-duality. Furthermore, we give the structures of the generators of cyclic codes and self-dual cyclic codes of odd length n over the ring R. As an application, some self-dual codes of length 2n over Z4 are obtained.

The power consumption of server platforms has been increasing as the amount of hardware resources equipped on them is increased. Especially, the capacity of DRAM continues to grow, and it is not rare that DRAM consumes higher power than processors on modern servers. Therefore, a reduction in the DRAM energy consumption is a critical challenge to reduce the system-level energy consumption. Although it is well known that improving row buffer locality(RBL) and bank-level parallelism (BLP) is effective to reduce the DRAM energy consumption, our preliminary evaluation on a real server demonstrates that RBL is generally low across 15 multithreaded benchmarks. In this paper, we investigate the memory access patterns of these benchmarks using a simulator and observe that cache line-grained channel interleaving schemes, which are widely applied to modern servers including multiple memory channels, hurt the RBL each of the benchmarks potentially possesses. In order to address this problem, we focus on a row-grained channel interleaving scheme and compare it with three cache line-grained schemes. Our evaluation shows that it reduces the DRAM energy consumption by 16.7%, 12.3%, and 5.5% on average (up to 34.7%, 28.2%, and 12.0%) compared to the other schemes, respectively.

In parallel processing applications, a few worker nodes called “stragglers”, which execute their tasks significantly slower than other tasks, increase the execution time of the job. In this paper, we propose a network switch based straggler handling system to mitigate the burden of the compute nodes. We also propose how to offload detecting stragglers and computing their results in the network switch with no additional communications between worker nodes. We introduce some approximate techniques for the proxy computation and response at the switch; thus our switch is called “ApproxSW.” As a result of a simulation experiment, the proposed approximation based on task similarity achieves the best accuracy in terms of quality of generated Map outputs. We also analyze how to suppress unnecessary proxy computation by the ApproxSW. We implement ApproxSW on NetFPGA-SUME board that has four 10Gbit Ethernet (10GbE) interfaces and a Virtex-7 FPGA. Experimental results shows that the ApproxSW functions do not degrade the original 10GbE switch performance.

Collaborative learning is an active teaching and learning strategy, in which learners who give each other elaborated explanations can learn most. However, it is difficult for learners to explain their own understanding elaborately in collaborative learning. In this study, we propose a collaborative use of a Kit-Build concept map (KB map) called “Reciprocal KB map”. In a Reciprocal KB map for a pair discussion, at first, the two participants make their own concept maps expressing their comprehension. Then, they exchange the components of their maps and request each other to reconstruct their maps by using the components. The differences between the original map and the reconstructed map are diagnosed automatically as an advantage of the KB map. Reciprocal KB map is expected to encourage pair discussion to recognize the understanding of each other and to create an effective discussion. In an experiment reported in this paper, Reciprocal KB map was used for supporting a pair discussion and was compared with a pair discussion which was supported by a traditional concept map. Nineteen pairs of university students were requested to use the traditional concept map in their discussion, while 20 pairs of university students used Reciprocal KB map for discussing the same topic. The results of the experiment were analyzed using three metrics: a discussion score, a similarity score, and questionnaires. The discussion score, which investigates the value of talk in discussion, demonstrates that Reciprocal KB map can promote more effective discussion between the partners compared to the traditional concept map. The similarity score, which evaluates the similarity of the concept maps, demonstrates that Reciprocal KB map can encourage the pair of partners to understand each other better compared to the traditional concept map. Last, the questionnaires illustrate that Reciprocal KB map can support the pair of partners to collaborate in the discussion smoothly and that the participants accepted this method for sharing their understanding with each other. These results suggest that Reciprocal KB map is a promising approach for encouraging pairs of partners to understand each other and to promote the effective discussions.

Image quality assessment (IQA) is an inherent problem in the field of image processing. Recently, deep learning-based image quality assessment has attracted increased attention, owing to its high prediction accuracy. In this paper, we propose a fully-blind and fast image quality predictor (FFIQP) using convolutional neural networks including two strategies. First, we propose a distortion clustering strategy based on the distribution function of intermediate-layer results in the convolutional neural network (CNN) to make IQA fully blind. Second, by analyzing the relationship between image saliency information and CNN prediction error, we utilize a pre-saliency map to skip the non-salient patches for IQA acceleration. Experimental results verify that our method can achieve the high accuracy (0.978) with subjective quality scores, outperforming existing IQA methods. Moreover, the proposed method is highly computationally appealing, achieving flexible complexity performance by assigning different thresholds in the saliency map.

We present a system to improve the robustness of real-time 3D surface reconstruction by utilizing non-inertial localization sensor. Benefiting from such sensor, our easy-to-build system can effectively avoid tracking drift and lost comparing with conventional dense tracking and mapping systems. To best fusing the sensor, we first adopt a hand-eye calibration and performance analysis for our setup and then propose a novel optimization framework based on adaptive criterion function to improve the robustness as well as accuracy. We apply our system to several challenging reconstruction tasks, which show significant improvement in scanning robustness and reconstruction quality.