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.

Improvement of output and lifetime is a problem for biofuel cells. A structure was adopted in which gelation mixed with agarose and fuel (fructose) was sandwiched by electrodes made of graphene-coated carbon fiber. The electrode surface not contacting the gel was exposed to air. In addition, grooves were added to the gel surface to further increase the oxygen supply. The power density of the fuel cell was examined in terms of the electrode area exposed to air. The output increased almost in proportion to the area of the electrode exposed to air. Optimization of the concentration of fuel, gel, and the amount of enzyme at the cathode were also examined. The maximum power density in the proposed system was approximately 121μW/cm2, an enhancement of approximately 2.5 times that in the case of using liquid fuel. For the power density after 24h, the fuel gel was superior to the fuel liquid.

This paper proposes a novel distributed proximal minimization algorithm for constrained optimization problems over fixed strongly connected networks. At each iteration, each agent updates its own state by evaluating a proximal operator of its objective function under a constraint set and compensating the unbalancing due to unidirectional communications. We show that the states of all agents asymptotically converge to one of the optimal solutions. Numerical results are shown to confirm the validity of the proposed method.

Radar-absorbent materials (RAM) with various characteristics, such as broadband, oblique-incidence, and polarization characteristics, have been developed according to applications in recent years. This paper presents the optimized design method of two flat layers RAM with both broadband and oblique-incidence characteristics for the required RAM performance. The oblique-incidence characteristics mean that the RAM is possible to absorb radio waves continuously up to the maximum incidence angle. The index of the wave-absorption amount is 20dB, corresponding to an absorption rate of 99%. Because determination of the electrical material constant of each layer is the most important task with respect to the received frequency and the incidence angle, we optimized the values by using Non-dominated sorting genetic algorithm-II (NSGA-II). Two types of flat-layer RAM composed of dielectric and magnetic materials were designed and their characteristics were evaluated. Consequently, it was confirmed that oblique-incidence characteristics were better for the RAM composed of dielectric materials. The dielectric RAM achieved an incidence angle of up to 60° with broadband characteristics and a relative bandwidth of 77.01% at the transverse-magnetic (TM) wave incidence. In addition, the magnetic RAM could lower the minimum frequency of the system more than the dielectric RAM. The minimum frequency of the magnetic RAM was 1.38GHz with a relative bandwidth of 174.18% at TM-wave incidence and an incidence angle of 45°. We confirmed that it is possible to design RAM with broadband characteristics and continuous oblique-incidence characteristics by using the proposed method.

To optimize the performance of FIR filters that have low computation complexity, this paper proposes a hybrid design consisting of two optimization levels. The first optimization level is based on cyclic-shift synthesis, in which all possible sub filters (or windowed sub filters) with distinct cycle shifts are averaged to generate a synthesized filter. Due to the fact that the ripples of these sub filters' transfer curves can be individually compensated, this synthesized filter attains improved performance (besides two uprushes occur on the edges of a transition band) and thus this synthesis actually plays the role of ‘natural optimization’. Furthermore, this synthesis process can be equivalently summarized into a 3-step closed-form procedure, which converts the multi-variable optimization into a single-variable optimization. Hence, to suppress the uprushes, what the second optimization level (by Differential Evolution (DE) algorithm) needs to do is no more than searching for the optimum transition point which incurs only minimal complexity . Owning to the combination between the cyclic-shift synthesis and DE algorithm, unlike the regular evolutionary computing schemes, our hybrid design is more attractive due to its narrowed search space and higher convergence speed . Numerical results also show that the proposed design is superior to the conventional DE design in both filter performance and design efficiency, and it is comparable to the Remez design.

This paper proposes a consensus-based subgradient method under a common constraint set with switching undirected graphs. In the proposed method, each agent has a state and an auxiliary variable as the estimates of an optimal solution and accumulated information of past gradients of neighbor agents. We show that the states of all agents asymptotically converge to one of the optimal solutions of the convex optimization problem. The simulation results show that the proposed consensus-based algorithm with accumulated subgradient information achieves faster convergence than the standard subgradient algorithm.

In order to improve an imaging performance of a sparse array radar system we propose an optimization method to find a new antenna array layout. The method searches for a minimum of the cost function based on a 3D point spread function of the array. We found a solution for the simulated problem in a form of the new layout for the antenna array with more sparse middle-point distribution comparing with initial one.

An ASIC crypto processor optimized for the 254-bit prime-field optimal-ate pairing over Barreto-Naehrig (BN) curve is proposed. The data path of the proposed crypto processor is designed to compute five Fp2 operations, a multiplication, three addition/subtractions, and an inversion, simultaneously. We further propose a design methodology to automate the instruction scheduling by using a combinatorial optimization solver, with which the total cycle count is reduced to 1/2 compared with ever reported. The proposed crypto processor is designed and fabricated by using a 65nm silicon-on-thin-box (SOTB) CMOS process. The chip measurement result shows that the fabricated chip successfully computes a pairing in 0.185ms when a typical operating voltage of 1.20V is applied, which corresponds to 2.8× speed up compared to the current state-of-the-art pairing implementation on ASIC platform.

Pose estimation is a basic requirement for the autonomous behavior of robots. In this article we present a robust and fast visual odometry method to obtain camera poses by using RGB-D images. We first propose a motion estimation method based on sparse geometric constraint and derive the analytic Jacobian of the geometric cost function to improve the convergence performance, then we use our motion estimation method to replace the tracking thread in ORB-SLAM for improving its runtime performance. Experimental results show that our method is twice faster than ORB-SLAM while keeping the similar accuracy.

Traffic matrix (TM) estimation has been extensively studied for decades. Although conventional estimation techniques assume that traffic volumes are unchanged between origins and destinations, packets are often lost on a path due to traffic burstiness, silent failures, etc. Counting every path at every link, we could easily get the traffic volumes with their change, but this approach significantly increases the measurement cost since counters are usually implemented using expensive memory structures like a SRAM. This paper proposes a mathematical model to estimate TMs including volume changes. The method is established on a Boolean fault localization technique; the technique requires fewer counters as it simply determines whether each link is lossy. This paper extends the Boolean technique so as to deal with traffic volumes with error bounds that requires only a few counters. In our method, the estimation errors can be controlled through parameter settings, while the minimum-cost counter placement is determined with submodular optimization. Numerical experiments are conducted with real network datasets to evaluate our method.

Smartphones for wireless communication typically consist of a large area frontal liquid crystal display (LCD), which incorporates a metal back plate, and a back cover chassis made from metal. Leveraging this structure a new approach to construct antennas for smartphones is proposed where the complete metal back cover chassis and LCD back plate are used as the radiating element and ground plane. In the design a feedline is connected between the metal back cover chassis and LCD back plate, along with shorts at various locations between the two metal plates, to control the resonance frequency of the resulting antenna. Multiple-band operation is possible without the need for any slots in the plates for radiation. Results show that antenna frequency reconfigurability can be achieved when switching function is added to the shorts so that several wireless communication bands can be covered. This approach is different from existing metallic frame antenna designs currently available in the market. A design example is provided which uses one PIN diode for the switching shorts and the target frequency bands are 740-780MHz and 900-1000MHz & 1700-1900MHz. The optimization of LC matchings and concerns of hand effects and metallic components between the chassis and LCD metal back plate are also addressed.

We propose an approach to allocate bandwidth for a satellite communications (SATCOM) system that includes the recent high-throughput satellite (HTS) with frequency flexibility. To efficiently operate the system, we manage the limited bandwidth resources available for SATCOM by employing a control method that allows the allocated bandwidths to exceed the communication demand of user terminals per HTS beam. To this end, we consider bandwidth allocation for SATCOM as an optimal control problem. Then, assuming that the model of communication requests is available, we propose an optimal control method by combining model predictive control and sparse optimization. The resulting control method enables the efficient use of the limited bandwidth and reduces the bandwidth loss and number of control actions for the HTS compared to a setup with conventional frequency allocation and no frequency flexibility. Furthermore, the proposed method allows to allocate bandwidth depending on various control objectives and beam priorities by tuning the corresponding weighting matrices. These findings were verified through numerical simulations by using a simple time variation model of the communication requests and predicted aircraft communication demand obtained from the analysis of actual flight tracking data.

In this paper, we apply extended regularized channel inversion precoding to address the multiuser multiantenna downlink transmission problem. Different from conventional regularized channel inversion precoding, extended RCI precoding considers non-homogeneous channels, adjusts more regularization parameters, and exploits the information gained by inverting the covariance matrix of the channel. Two ways of determining the regularization parameters are investigated. First, the parameters can be determined by solving a max-min SINR problem. The constraints of the problem can be transformed to the second-order cone (SOC) constraints. The optimal solution of the problem can be obtained by iteratively solving a second-order cone programming (SOCP) problem. In order to reduce the computational complexity, a one-shot algorithm is proposed. Second, the sum-rate maximization problem is discussed. The simple gradient-based method is used to solve the problem and get the regularization parameters. The simulation results indicate that the proposed algorithms exhibit improved max-min SINR performance and sum-rate performance over RCI precoding.

Dai-Yuan (DY) conjugate gradient method is an effective method for solving large-scale unconstrained optimization problems. In this paper, a new DY method, possessing a spectral conjugate parameter βk, is presented. An attractive property of the proposed method is that the search direction generated at each iteration is descent, which is independent of the line search. Global convergence of the proposed method is also established when strong Wolfe conditions are employed. Finally, comparison experiments on impulse noise removal are reported to demonstrate the effectiveness of the proposed method.

On-line object tracking (OLOT) has been a core technology in computer vision, and its importance has been increasing rapidly. Because this technology is utilized for battery-operated products, energy consumption must be minimized. This paper describes a method of adaptive frame-rate optimization to satisfy that requirement. An energy trade-off occurs between image capturing and object tracking. Therefore, the method optimizes the frame-rate based on always changed object speed for minimizing the total energy while taking into account the trade-off. Simulation results show a maximum energy reduction of 50.0%, and an average reduction of 35.9% without serious tracking accuracy degradation.

When trying to estimate time-varying multipath channels by applying a basis expansion model (BEM) in orthogonal frequency division multiplexing (OFDM) systems, pilot clusters are contaminated by inter-carrier interference (ICI). The pilot cluster ICI (PC-ICI) degrades the estimation accuracy of BEM coefficients, which degrades system performance. In this paper, a PC-ICI suppression scheme is proposed, in which two coded symbols defined as weighted sums of data symbols are inserted on both sides of each pilot cluster. Under the assumption that the channel has Flat Doppler spectrum, the optimized weight coefficients are obtained by an alternating iterative optimization algorithm, so that the sum of the PC-ICI generated by the encoded symbols and the data symbols is minimized. By approximating the optimized weight coefficients, they are independent of the channel tap power. Furthermore, it is verified that the proposed scheme is robust to the estimation error of the normalized Doppler frequency offset and can be applied to channels with other types of Doppler spectra. Numerical simulation results show that, compared with the conventional schemes, the proposed scheme achieves significant improvements in the performance of PC-ICI suppression, channel estimation and system bit-error-ratio (BER).

This paper reports a novel 3D topology optimization method based on the finite difference time domain (FDTD) method for a dielectric lens antenna. To obtain an optimal lens with smooth boundary, we apply normalized Gaussian networks (NGnet) to 3D topology optimization. Using the proposed method, the dielectric lens with desired radiation characteristics can be designed. As an example of the optimization using the proposed method, the width of the main beam is minimized assuming spatial symmetry. In the optimization, the lens is assumed to be loaded on the aperture of a waveguide slot antenna and is smaller compared with the wavelength. It is shown that the optimized lens has narrower beamwidth of the main beam than that of the conventional lens.

A free-viewpoint application has been developed that yields an immersive user experience. One of the simple free-viewpoint approaches called “billboard methods” is suitable for displaying a synthesized 3D view in a mobile device, but it suffers from the limitation that a billboard should be positioned in only one position in the world. This fact gives users an unacceptable impression in the case where an object being shot is situated at multiple points. To solve this problem, we propose optimal deformation of the billboard. The deformation is designed as a mapping of grid points in the input billboard silhouette to produce an optimal silhouette from an accurate voxel model of the object. We formulate and solve this procedure as a nonlinear optimization problem based on a grid-point constraint and some a priori information. Our results show that the proposed method generates a synthesized virtual image having a natural appearance and better objective score in terms of the silhouette and structural similarity.

We consider combinatorial online prediction problems and propose a new construction method of efficient algorithms for the problems. One of the previous approaches to the problem is to apply online prediction method, in which two external procedures the projection and the metarounding are assumed to be implemented. In this work, we generalize the projection to multiple projections. As an application of our framework, we show an algorithm for an online job scheduling problem with a single machine with precedence constraints.

In recent years, deep learning based image recognition, speech recognition, text translation and other related applications have brought great convenience to people's lives. With the advent of the era of internet of everything, how to run a computationally intensive deep learning algorithm on a limited resources edge device is a major challenge. For an edge oriented computing vector processor, combined with a specific neural network model, a new data layout method for putting the input feature maps in DDR, rearrangement of the convolutional kernel parameters in the nuclear memory bank is proposed. Aiming at the difficulty of parallelism of two-dimensional matrix convolution, a method of parallelizing the matrix convolution calculation in the third dimension is proposed, by setting the vector register with zero as the initial value of the max pooling to fuse the rectified linear unit (ReLU) activation function and pooling operations to reduce the repeated access to intermediate data. On the basis of single core implementation, a multi-core implementation scheme of Inception structure is proposed. Finally, based on the proposed vectorization method, we realize five kinds of neural network models, namely, AlexNet, VGG16, VGG19, GoogLeNet, ResNet18, and performance statistics and analysis based on CPU, gtx1080TI and FT2000 are presented. Experimental results show that the vector processor has better computing advantages than CPU and GPU, and can calculate large-scale neural network model in real time.