The Machine-to-Machine (M2M) service network platform accommodates M2M communications traffic efficiently by using tree-structured networks and the computation resources deployed on network nodes. In the M2M service network platform, program files required for controlling devices are placed on network nodes, which have different amounts of computation resources according to their position in the hierarchy. The program files must be dynamically repositioned in response to service quality requests from each device, such as computation power, link bandwidth, and latency. This paper proposes a Program File Placement (PFP) method for the M2M service network platform. First, the PFP problem is formulated in the Mixed-Integer Linear Programming (MILP) approach. We prove that the decision version of the PFP problem is NP-complete. Next, we present heuristic algorithms that attain sub-optimal but attractive solutions. Evaluations show that the heuristic algorithm based on the number of devices that share a program file reduces the total number of placed program files compared to the algorithm that moves program files based on their position.

On surfaces of tris-(8-hydroxyquinolate) aluminum (Alq) and tris(7-propyl-8-hydroxyquinolinato) aluminum (Al7p) thin-films, positive and negative polarization charges appear, respectively, owing to spontaneous orientation of these polar molecules. Alq is a typical electron transport material where electrons are injected from cathode. Because the polarization charge exists at the Alq/cathode interface, it is likely that it affects the electron injection process because of Coulomb interaction. In order to evaluate an impact of polarization charge on electron injection from cathode, electron only devices (EODs) composed of Alq or Al7p were prepared and evaluated by displacement current measurement. We found that Alq-EOD has lower resistance than Al7p-EOD, indicating that the positive polarization charge at Alq/cathode interface enhances the electron injection due to Coulomb attraction, while the electron injection is suppressed by the negative polarization charge at the Al7p/Al interface. These results clearly suggest that it is necessary to design organic semiconductor devices by taking polarization charge into account.

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.

Lasso regression based on the L1 regularization is one of the most popular sparse estimation methods. It is often required to set appropriately in advance the regularization parameter that determines the degree of regularization. Although the empirical Bayes approach provides an effective method to estimate the regularization parameter, its solution has yet to be fully investigated in the lasso regression model. In this study, we analyze the empirical Bayes estimator of the one-parameter model of lasso regression and show its uniqueness and its properties. Furthermore, we compare this estimator with that of the variational approximation, and its accuracy is evaluated.

Information-centric networking (ICN) has gained attention from network research communities due to its capability of efficient content dissemination. In-network caching function in ICN plays an important role to achieve the design motivation. However, many researchers on in-network caching due to its ability to efficiently disseminate content. The in-network caching function in ICN plays an important role in realizing the design goals. However, many in-network caching researchers have focused on where to cache rather than how to cache: the former is known as content deployment in the network and the latter is known as cache replacement in an ICN router. Although the cache replacement has been intensively researched in the context of web-caching and content delivery network previously, networks, the conventional approaches cannot be directly applied to ICN due to the fine granularity of chunks in ICN, which eventually changes the access patterns. In this paper, we argue that ICN requires a novel cache replacement algorithm to fulfill the requirements in the design of a high performance ICN router. Then, we propose a novel cache replacement algorithm to satisfy the requirements named Compact CLOCK with Adaptive Replacement (Compact CAR), which can reduce the consumption of cache memory to one-tenth compared to conventional approaches. In this paper, we argue that ICN requires a novel cache replacement algorithm to fulfill the requirements set for high performance ICN routers. Our solution, Compact CLOCK with Adaptive Replacement (Compact CAR), is a novel cache replacement algorithm that satisfies the requirements. The evaluation result shows that the consumption of cache memory required to achieve a desired performance can be reduced by 90% compared to conventional approaches such as FIFO and CLOCK.

Numerical inverse Laplace transform (NILT) methods are potential methods for time domain simulations, for instance the analysis of the transient phenomena in systems with lumped and/or distributed parameters. This paper proposes a numerical inverse Laplace transform method based originally on hyperbolic relations. The method is further enhanced by properly adapting several convergence acceleration techniques, namely, the epsilon algorithm of Wynn, the quotient-difference algorithm of Rutishauser and the Euler transform. The resulting accelerated models are compared as for their accuracy and computational efficiency. Moreover, an expansion to two dimensions is presented for the first time in the context of the accelerated hyperbolic NILT method, followed by the error analysis. The expansion is done by repeated application of one-dimensional partial numerical inverse Laplace transforms. A detailed static error analysis of the resulting 2D NILT is performed to prove the effectivness of the method. The work is followed by a practical application of the 2D NILT method to simulate voltage/current distributions along a transmission line. The method and application are programmed using the Matlab language.

Peer-to-peer overlay networks can easily achieve a large-scale content sharing system on the Internet. Although unstructured peer-to-peer networks are suitable for finding entire partial-match content, flooding-based search is an inefficient way to obtain target content. When the shared content is semantically specified by a great number of attributes, it is difficult to derive the semantic similarity of peers beforehand. This means that content search methods relying on interest-based locality are more advantageous than those based on the semantic similarity of peers. Existing search methods that exploit interest-based locality organize multiple peer groups, in each of which peers with common interests are densely connected using short-cut links. However, content searches among multiple peer groups are still inefficient when the number of incident links at each peer is limited due to the capacity of the peer. This paper proposes a novel content search method that exploits interest-based locality. The proposed method can organize an efficient peer-to-peer network similar to the semantic small-world random graph, which can be organized by the existing methods based on the semantic similarity of peers. In the proposed method, topology transformation based on local link replacement maintains the numbers of incident links at all the peers. Simulation results confirm that the proposed method can achieve a significantly higher ratio of obtainable partial-match content than existing methods that organize peer groups.

Information-centric networking (ICN) has received increasing attention from all over the world. The novel aspects of ICN (e.g., the combination of caching, multicasting, and aggregating requests) is based on names that act as addresses for content. The communication with name has the potential to cope with the growing and complicating Internet technology, for example, Internet of Things, cloud computing, and a smart society. To realize ICN, router hardware must implement an innovative cache replacement algorithm that offers performance far superior to a simple policy-based algorithm while still operating with feasible computational and memory overhead. However, most previous studies on cache replacement policies in ICN have proposed policies that are too blunt to achieve significant performance improvement, such as first-in first-out (popularly, FIFO) and random policies, or impractical policies in a resource-restricted environment, such as least recently used (LRU). Thus, we propose CLOCK-Pro Using Switching Hash-tables (CUSH) as the suitable policy for network caching. CUSH can identify and keep popular content worth caching in a network environment. CUSH also employs CLOCK and hash-tables, which are low-overhead data structure, to satisfy the cost requirement. We numerically evaluate our proposed approach, showing that our proposal can achieve cache hits against the traffic traces that simple conventional algorithms hardly cause any hits.

Image sensor communication (ISC), derived from visible light communication (VLC) is an attractive solution for outdoor mobile environments, particularly for intelligent transport systems (ITS). In ITS-ISC, tracking a transmitter in the image plane is critical issue since vehicle vibrations make it difficult to selsct the correct pixels for data reception. Our goal in this study is to develop a precise tracking method. To accomplish this, vehicle vibration modeling and its parameters estimation, i.e., represetative frequencies and their amplitudes for inherent vehicle vibration, and the variance of the Gaussian random process represnting road surface irregularity, are required. In this paper, we measured actual vehicle vibration in a driving situation and determined parameters based on the frequency characteristics. Then, we demonstrate that vehicle vibration that induces transmitter displacement in an image plane can be modeled by only Gaussian random processes that represent road surface irregularity when a high frame rate (e.g., 1000fps) image sensor is used as an ISC receiver. The simplified vehicle vibration model and its parameters are evaluated by numerical analysis and experimental measurement and obtained result shows that the proposed model can reproduce the characteristics of the transmitter displacement sufficiently.

Several applications for 3-D visualization require dense detection of correspondence for displacement estimation among heterogeneous multi-view images. Due to differences in resolution or sampling density and field of view in the images, estimation of dense displacement is not straight forward. Therefore, we propose a scale invariant polynomial expansion method that can estimate dense displacement between two heterogeneous views. Evaluation on heterogeneous images verifies accuracy of our approach.

This paper deals with a problem to decide whether a given graph structure appears as a pattern in the structure of a given graph. A graph pattern is a triple p=(V,E,H), where (V,E) is a graph and H is a set of variables, which are ordered lists of vertices in V. A variable can be replaced with an arbitrary connected graph by a kind of hyperedge replacements. A substitution is a collection of such replacements. The graph pattern matching problem (GPMP) is the computational problem to decide whether or not a given graph G is obtained from a given graph pattern p by a substitution. In this paper, we show that GPMP for a graph pattern p and a graph G is solvable in polynomial time if the length of every variable in p is 2, p is of bounded treewidth, and G is connected.

Peer-to-peer (P2P) overlay networks are widely employed in distributed systems. The number of hops required by a node to locate an object is the fundamental search cost of a P2P network. Creating replicas can efficiently reduce the cost of object search, so how to deploy replicas to reduce the cost as much as possible is a critical problem of P2P networks. In the literature, most existing replica placement strategies arrange replicas at nodes near the one containing the considered object. In this paper, we formally demonstrate that for a complete Chord P2P network and many non-complete Chord ones, due to their deterministic structures, we can allocate replicas to nodes closest to the target in the identifier space to maximize the reduction in the total number of hops required by all nodes to reach a copy of the object during the search heading to the target node.

We propose a cache-aware method to accelerate texture-based volume rendering on a graphics processing unit (GPU) that is compatible with the compute unified device architecture. The proposed method extends a previous method such that it can maximize the average rendering performance while rotating the viewing direction around a volume. To realize this, the proposed method performs in-place rotation of volume data, which rearranges the order of voxels to allow consecutive threads (warps) to refer to voxels with the minimum access strides. Experiments indicate that the proposed method replaces the worst texture cache (TC) hit rate of 42% with the best TC hit rate of 93% for a 10243-voxel volume. Thus, the average frame rate increases by a factor of 1.6 in the proposed method compared with that in the previous method. Although the overhead of in-place rotation slightly decreases the frame rate from 2.0 frames per second (fps) to 1.9 fps, this slowdown occurs only with a few viewing directions.

The maintenance of a system on a ship has limitations when the ship is engaged in a voyage because of limited maintenance resources. When a system fails, it is either repaired instantly on ship with probability p or remains unrepaired during the voyage with probability 1-p owing to the lack of maintenance resources. In the latter case, the system is repaired after the voyage. We propose two management policies for the overhaul interval of an IFR system: one manages the overhaul interval by number of voyages and the other manages it by the total voyage time. Our goal is to determine the optimal policy that ensures the required availability of the system and minimizes the expected cost rate.

The write operations on emerging Non-Volatile Memory (NVM), such as NAND Flash and Phase Change Memory (PCM), usually incur high access latency, and are required to be optimized. In this paper, we propose Asymmetric Read-Write (ARW) policies to minimize the write traffic sent to NVM. ARW policies exploit the asymmetry costs of read and write operations, and make adjustments on the insertion policy and hit-promotion policy of the replacement algorithm. ARW can reduce the write traffic to NVM by preventing dirty data blocks from frequent evictions. We evaluate ARW policies on systems with PCM as main memory and NAND Flash as disk. Simulation results on an 8-core multicore show that ARW adopted on the last-level cache (LLC) can reduce write traffic by more than 15% on average compared to LRU baseline. When used on both LLC and DRAM cache, ARW policies achieve an impressive reduction of 40% in write traffic without system performance degradation. When employed on the on-disk buffer of the Solid State Drive (SSD), ARW demonstrates significant reductions in both write traffic and overall access latency. Moreover, ARW policies are lightweight, easy to implement, and incur negligible storage and runtime overhead.

The purpose of this study was to identify the key variables that determine the quality of the auditory environment, for the purposes of workplace auditory design and assessment. To this end, we characterized changes in oscillatory neural activity in electroencephalographic (EEG) data recorded from subjects who performed an intellectual activity while exposed to fluctuating ambient noise. Seven healthy men participated in the study. Subjects performed a verbal and spatial task that used the 3-back task paradigm to study working memory. During the task, subjects were presented with auditory stimuli grouped by increasing high-frequency content: (1) a sound with frequencies similar to Brownian noise and no modulation; (2) an amplitude-modulated sound with frequencies similar to white noise; (3) amplitude-modulated pink noise; and (4) amplitude-modulated Brownian noise. Upon presentation, we observed a characteristic change in three EEG bands: theta (4-8Hz), alpha (8-13Hz), and beta (13-30Hz). In particular, a frequency-dependent enhancement and reduction of power was observed in the theta and beta bands, respectively.

This paper presents a novel method for unsupervised segmentation of objects with large displacements in high speed video sequences. Our general framework introduces a new foreground object predicting method that finds object hypotheses by encoding both spatial and temporal features via a semantic motion signature scheme. More specifically, temporal cues of object hypotheses are captured by the motion signature proposed in this paper, which is derived from sparse saliency representation imposed on magnitude of optical flow field. We integrate semantic scores derived from deep networks with location priors that allows us to directly estimate appearance potentials of foreground hypotheses. A unified MRF energy functional is proposed to simultaneously incorporate the information from the motion signature and semantic prediction features. The functional enforces both spatial and temporal consistency and impose appearance constancy and spatio-temporal smoothness constraints directly on the object hypotheses. It inherently handles the challenges of segmenting ambiguous objects with large displacements in high speed videos. Our experiments on video object segmentation benchmarks demonstrate the effectiveness of the proposed method for segmenting high speed objects despite the complicated scene dynamics and large displacements.

In this paper, we propose a novel placement algorithm for mixed-grained reconfigurable architectures (MGRAs). MGRA consists of coarse-grained and fine-grained clusters, in order to implement a combined digital systems of high-speed data paths with multi-bit operands and random logic circuits for state machines and bit-wise operations. For accelerating simulated annealing based FPGA placement algorithm, range limiter has been proposed to control the distance of two blocks to be interchanged. However, it is not applicable to MGRAs due to the heterogeneous structure of MGRAs. Proposed range limiter using connection bounding box effectively keeps the size of range limiter to encourage moves across fine-grain blocks in non-adjacent clusters. From experimental results, the proposed method achieved 47.8% reduction of cost in the best case compared with conventional methods.

To efficiently utilize storage resources, the in-network caching system of Information-Centric Networking has to deal with the popularity of huge content chunks which could cause large memory consumption. This paper presents a Popularity Monitoring based Gain-aware caching scheme, called PMG, which is an integrated design of cache placement and popularity monitoring. In PMG, by taking into account both the chunk popularity and the consumption saving of single cache hit, the cache placement process is transformed into a weighted popularity comparison, while the chunks with high cache gain are placed on the node closer to the content consumer. A Bloom Filter based sliding window algorithm, which is self-adaptive to the dynamic request rate, is proposed to capture the chunks with higher caching gain by Inter-Reference Gap (IRG) detection. Analysis shows that PMG can drastically reduce the memory consumption of popularity monitoring, and the simulation results confirm that our scheme can achieve popularity based cache placement and get better performance in terms of bandwidth saving and cache hit ratio when content popularity changes dynamically.

Recently in an SDN/NFV-enabled network, a consolidated middlebox is proposed in which middlebox functions required by a network flow are provided at a single machine in a virtualized manner. With the promising advantages such as simplifying network traffic routing and saving resources of switches and machines, consolidated middleboxes are going to replace traditional middleboxes in the near future. However, the location of consolidated middleboxes may affect the performance of an SDN/NFV network significantly. Accordingly, the consolidated middlebox positioning problem in an SDN/NFV-enabled network must be addressed adequately with service chain constraints (a flow must visit a specific type of consolidated middlebox), resource constraints (switch memory and processing power of the machine), and performance requirements (end-to-end delay and bandwidth consumption). In this paper, we propose a novel solution of the consolidated middlebox positioning problem in an SDN/NFV-enabled network based on flow clustering to improve the performance of service chain flows and utilization of a consolidated middlebox. Via extensive simulations, we show that our solution significantly reduces the number of routing rules per switch, the end-to-end delay and bandwidth consumption of service flows while meeting service chain and resource constraints.