A number of battery-driven sensor nodes are deployed to operate a wireless sensor network, and many routing protocols have been proposed to reduce energy consumption for data communications in the networks. We have proposed a new routing policy which employs a nearest-neighbor forwarding based on hop progress. Our proposed routing method has a topology parameter named forwarding angle to determine which node to connect with as a next-hop, and is compared with other existing policies to clarify the best topology for energy efficiency. In this paper, we also formulate the energy budget for networks with the routing policy by means of stochastic-geometric analysis on hop-count distributions for random planar networks. The formulation enables us to tell how much energy is required for all nodes in the network to forward sensed data in a pre-deployment phase. Simulation results show that the optimal topology varies according to node density in the network. Direct communication to the sink is superior for a small-sized network, and the multihop routing is more effective as the network becomes sparser. Evaluation results also demonstrate that our energy formulation can well approximate the energy budget, especially for small networks with a small forwarding angle. Discussion on the error with a large forwarding angle is then made with a geographical metric. It is finally clarified that our analytical expressions can obtain the optimal forwarding angle which yields the best energy efficiency for the routing policy when the network is moderately dense.

Body biasing can be used to control the leakage power and performance by changing the threshold voltage of transistors after fabrication. Especially, a new process called Silicon-On-Thin Box (SOTB) CMOS can control their balance widely. When it is applied to a Coarse Grained Reconfigurable Array (CGRA), the leakage power can be much reduced by precise bias control with small domain size including a small number of PEs. On the other hand, the area overhead for separating power domain and delivering a lot of wires for body bias voltage supply increases. This paper explores the grain of domain size of an energy efficient CGRA called CMA (Cool Mega Array). By using Genetic Algorithm based body bias assignment method, the leakage reduction of various grain size was evaluated. As a result, a domain with 2x1 PEs achieved about 40% power reduction with a 6% area overhead. It has appeared that a combination of three body bias voltages; zero bias, weak reverse bias and strong reverse bias can achieve the optimal leakage reduction and area overhead balance in most cases.

On an inference-enabled Linked Open Data (LOD) endpoint, usually a query execution takes longer than on an LOD endpoint without inference engine due to its processing of reasoning. Although there are two separate kind of approaches, query modification approaches, and ontology modifications have been investigated on the different contexts, there have been discussions about how they can be chosen or combined for various settings. In this paper, for reducing query execution time on an inference-enabled LOD endpoint, we compare these two promising methods: query rewriting and ontology modification, as well as trying to combine them into a cluster of such systems. We employ an evolutionary approach to make such rewriting and modification of queries and ontologies based on the past-processed queries and their results. We show how those two approaches work well on implementing an inference-enabled LOD endpoint by a cluster of SPARQL endpoints.

This paper presents a method to segment single- and multiple-touching characters in offline handwritten Japanese text recognition with practical speed. Distortions due to handwriting and a mix of complex Chinese characters with simple phonetic and alphanumeric characters leave optical handwritten text recognition (OHTR) for Japanese still far from perfection. Segmentation of characters, which touch neighbors on multiple points, is a serious unsolved problem. Therefore, we propose a method to segment them which is made in two steps: coarse segmentation and fine segmentation. The coarse segmentation employs vertical projection, stroke-width estimation while the fine segmentation takes a graph-based approach for thinned text images, which employs a new bridge finding process and Voronoi diagrams with two improvements. Unlike previous methods, it locates character centers and seeks segmentation candidates between them. It draws vertical lines explicitly at estimated character centers in order to prevent vertically unconnected components from being left behind in the bridge finding. Multiple candidates of separation are produced by removing touching points combinatorially. SVM is applied to discard improbable segmentation boundaries. Then, ambiguities are finally solved by the text recognition employing linguistic context and geometric context to recognize segmented characters. The results of our experiments show that the proposed method can segment not only single-touching characters but also multiple-touching characters, and each component in our proposed method contributes to the improvement of segmentation and recognition rates.

The ability to efficiently process exponentially increasing data remains a challenging issue for computer platforms. In legacy computing platforms, large amounts of data can cause performance bottlenecks at the I/O interfaces between CPUs and storage devices. To overcome this problem, the in-storage computing (ISC) technique is introduced, which offloads some of the computations from the CPUs to the storage devices. In this paper, we propose DiSC, a distributed in-storage computing platform using cost-effective hardware. First, we designed a general-purpose ISC device, a so-called DiSC endpoint, by combining an inexpensive single-board computer (SBC) and a hard disk. Second, a Mesos-based resource manager is adapted into the DiSC platform to schedule the DiSC endpoint tasks. To draw comparisons to a general CPU-based platform, a DiSC testbed is constructed and experiments are carried out using essential applications. The experimental results show that DiSC attains cost-efficient performance advantages over a desktop, particularly for searching and filtering workloads.

Various types of classifiers and feature extraction methods for acoustic scene classification have been recently proposed in the IEEE Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 Challenge Task 1. The results of the final evaluation, however, have shown that even top 10 ranked teams, showed extremely low accuracy performance in particular class pairs with similar sounds. Due to such sound classes being difficult to distinguish even by human ears, the conventional deep learning based feature extraction methods, as used by most DCASE participating teams, are considered facing performance limitations. To address the low performance problem in similar class pair cases, this letter proposes to employ a recurrent neural network (RNN) based source separation for each class prior to the classification step. Based on the fact that the system can effectively extract trained sound components using the RNN structure, the mid-layer of the RNN can be considered to capture discriminative information of the trained class. Therefore, this letter proposes to use this mid-layer information as novel discriminative features. The proposed feature shows an average classification rate improvement of 2.3% compared to the conventional method, which uses additional classifiers for the similar class pair issue.

This work presents a new approach which derives a learned data representation method through matrix factorization on the complex domain. In particular, we introduce an encoding matrix-a new representation of data-that satisfies the simplicial constraint of the projective basis matrix on the field of complex numbers. A complex optimization framework is provided. It employs the gradient descent method and computes the derivative of the cost function based on Wirtinger's calculus.

Multiport SRAMs are frequently installed in network and/or telecommunication VLSIs to implement smart functions. This paper presents a high speed and low-power dual-port (i.e., 1W+1R two-port) SRAM macro customized for serial access operations. To reduce the wasted power dissipation due to subthreshold leakage currents, the supply voltage for 10T memory cells is lowered to 1 V and a power switch is prepared for every 64 word drivers. The switch is activated with look-ahead decoder-segment activation logic, so there is no penalty when selecting a wordline. The data I/O circuitry with a new column-based configuration makes it possible to hide the bitline precharge operation with the sensing operation in the read cycle ahead of it; that is, we have successfully reduced the read latency by a half clock cycle, resulting in a pure two-stage pipeline. The SRAM macro installed in a 4K-entry × 33-bit FIFO memory, fabricated with a 0.3-µm fully-depleted-SOI CMOS process, achieved a 500-MHz operation in the typical conditions of 2- and 1-V power supplies, and 25°C. The power consumption during the standby time was less than 1.0 mW, and that at a practical operating frequency of 400 MHz was in a range of 47-57 mW, depending on the bit-stream data pattern.

There is increasing interest in quantum computing, because of its enormous computing potential. A small number of powerful quantum algorithms have been proposed to date; however, the development of new quantum algorithms for practical use remains essential. Parallel computing with a neural network has successfully realized certain unique functions such as learning and recognition; therefore, the introduction of certain neural computing techniques into quantum computing to enlarge the quantum computing application field is worthwhile. In this paper, a novel quantum associative memory (QuAM) is proposed, which is achieved with a quantum neural network by employing adiabatic Hamiltonian evolution. The memorization and retrieval procedures are inspired by the concept of associative memory realized with an artificial neural network. To study the detailed dynamics of our QuAM, we examine two types of Hamiltonians for pattern memorization. The first is a Hamiltonian having diagonal elements, which is known as an Ising Hamiltonian and which is similar to the cost function of a Hopfield network. The second is a Hamiltonian having non-diagonal elements, which is known as a neuro-inspired Hamiltonian and which is based on interactions between qubits. Numerical simulations indicate that the proposed methods for pattern memorization and retrieval work well with both types of Hamiltonians. Further, both Hamiltonians yield almost identical performance, although their retrieval properties differ. The QuAM exhibits new and unique features, such as a large memory capacity, which differs from a conventional neural associative memory.

This paper presents an interactive locomotion controller using motion capture data and an inverted pendulum model (IPM). The motion data of a character is decomposed into those of upper and lower bodies, which are then dimension-reduced via what we call hierarchical Gaussian process dynamical model (H-GPDM). The locomotion controller receives the desired walking direction from the user. It is integrated into the IPM to determine the pose of the center of mass and the stance-foot position of the character. They are input to the H-GPDM, which interpolates the low-dimensional data to synthesise a redirected motion sequence on an uneven surface. The locomotion controller allows the upper and lower bodies to be independently controlled and helps us generate natural locomotion. It can be used in various real-time applications such as games.

One of the key objectives of 5G is to evolve the current mobile network architecture from “one-fit-all” design model to a more customized and dynamically scaling one that enables the deployment of parallel systems, tailored to the service requirements on top of a shared infrastructure. Indeed, the envisioned 5G services may require different needs in terms of capacity, latency, bandwidth, reliability and security, which cannot be efficiently sustained by the same network infrastructure. Coming to address these customization challenges, network softwarization expressed through Software Defined Networking (SDN) programmable network infrastructures, Network Function Virtualization (NFV) running network functions as software and cloud computing flexibility paradigms, is seen as a possible panacea to addressing the variations in the network requirements posed by the 5G use cases. This will enable network flexibility and programmability, allow the creation and lifecycle management of virtual network slices tailored to the needs of 5G verticals expressed in the form of Mobile Virtual Network Operators (MVNOs) for automotive, eHealth, massive IoT, massive multimedia broadband. In this vein, this paper introduces a potential 5G architecture that enables the orchestration, instantiation and management of end-to-end network slices over multiple administrative and technological domains. The architecture is described from both the management and the service perspective, underlining the common functionality as well as how the response to the diversified service requirements can be achieved through proper software network components development.

Depth estimation for a lense-array type light field camera is a challenging problem because of the sensor noise and the radiometric distortion which is a global brightness change among sub-aperture images caused by a vignetting effect of the micro-lenses. We propose a depth map estimation method which has robustness against sensor noise and radiometric distortion. Our method first binarizes sub-aperture images by applying the census transform. Next, the binarized images are matched by computing the majority operations between corresponding bits and summing up the Hamming distance. An initial depth obtained by matching has ambiguity caused by extremely short baselines among sub-aperture images. After an initial depth estimation process, we refine the result with following refinement steps. Our refinement steps first approximate the initial depth as a set of depth planes. Next, we optimize the result of plane fitting with an edge-preserving smoothness term. Experiments show that our method outperforms the conventional methods.

The objectives of this survey are to provide an in-depth coverage of a few selected research papers that have made significant contributions to the development of Network Function Virtualization (NFV), and to provide readers insights into the key advantages and disadvantages of NFV and Software Defined Networks (SDN) when compared to traditional networks. The research papers covered are classified into four categories: NFV Infrastructure (NFVI), Network Functions (NFs), Management And Network Orchestration (MANO), and service chaining. The NFVI papers describe “framework” software that implement common functions, such as dynamic scaling and load balancing, required by NF developers. Papers on NFs are classified as offering solutions for software switches or middleboxes. MANO papers covered in this survey are primarily on resource allocation (virtual network embedding), which is an orchestrator function. Finally, service chaining papers that offer examples and extensions are reviewed. Our conclusions are that with the current level of investment in NFV from cloud and Internet service providers, the promised cost savings are likely to be realized, though many challenges remain.

We proposed the simulation method of reconstructed holographic images in considering phase distribution in the small pixels of liquid crystal spatial light modulator (LC-SLM) and clarified zero-order diffraction appeared on the reconstructed images when the phase distribution in a single pixel is non-uniform. These results are useful for design of fine LC-SLM for realizing wide-viewing-angle holographic displays.

We developed flexible liquid crystal devices using ultra-thin polyimide substrates and bonding polymer spacers, and discussed the effects of polymer spacer structure on the cell thickness uniformity of flexible LCDs. We clarified that the lattice-shaped polymer spacer is effective to stabilize the cell thickness by suppressing the flow of the liquid crystal during bending process.

For ternary system, both anionic carboxylate ligand, namely, 4,4'-oxybis(benzoic acid)(H2oba) and different auxiliary ligand, namely, 1,10-phenanthroline(Phen), pyrazino[2,3-f][1,10]phenanthroline (dpq) and 1H-imidazole[2,3-f][1,10]phenanthroline(IP) have been designed and employed for the construction of a series of lanthanide compounds (Tb3+, Eu3+). The results of photoluminescence spectra of the compounds show the different optimal excitation spectra that make it closer to UV/Blue range.

The aim of this letter is to guarantee the ability of low probability of intercept (LPI) and anti-jamming (AJ) by maximizing the energy efficiency (EE) to improve wireless communication survivability and sustain wireless communication in jamming environments. We studied a scenario based on one transceiver pair with a partial-band noise jammer in a Rician fading channel and proposed an EE optimization algorithm to solve the optimization problem. With the proposed EE optimization algorithm, the LPI and AJ can be simultaneously guaranteed while satisfying the constraint of the maximum signal-to-jamming-and-noise ratio and combinatorial subchannel allocation condition, respectively. The results of the simulation indicate that the proposed algorithm is more energy-efficient than those of the baseline schemes and guarantees the LPI and AJ performance in a jamming environment.

In this letter, we propose a novel and effective haze removal method by using the structure-aware atmospheric veil. More specifically, the initial atmospheric veil is first estimated based on dark channel prior and morphological operator. Furthermore, an energy optimization function considering the structure feature of the input image is constructed to refine the initial atmospheric veil. At last, the haze-free image can be restored by inverting the atmospheric scattering model. Additionally, brightness adjustment is also performed for preventing the dehazing result too dark. Experimental results on hazy images reveal that the proposed method can effectively remove the haze and yield dehazing results with vivid color and high scene visibility.