This paper investigates a frame aggregation (FA) technique in the medium access control (MAC) layer for downlink multi-user multiple input multiple output (MU-MIMO) channels in wireless local area networks (WLANs), and proposes a high-efficient FA scheme that ehances system performance: transmission performance and fairness in communication between mobile terminals (MTs). The proposed FA scheme employs novel criteria for selecting receiving MTs and wireless frame setting with a frame size adaptation mechanism for MU-MIMO transmissions. The proposed receiving MT selection gives higher priority to the MTs expecting higher throughput in the next MU-MIMO transmission and having large amount transmission data while reducing signaling overhead, leading to improvements in system throughput and fairness in communication. The proposed wireless frame setting, which employs hybrid A-MSDU/A-MPDU FA, achieves frame error rate (FER) better than the requirement from communication services by using A-MSDU frame size adaptation. Through system-level simulation, the effectiveness of the proposed scheme is validated for downlink MU-MIMO channels in WLANs.

In order to tackle a process-variation problem, we can define several scenarios, each of which corresponds to a particular LSI behavior, such as a typical-case scenario and a worst-case scenario. By designing a single LSI chip which realizes multiple scenarios simultaneously, we can have a process-variation-tolerant LSI chip. In this paper, we propose a multi-scenario high-level synthesis algorithm for variation-tolerant floorplan-driven design targeting new distributed-register architectures, called HDR architectures. We assume two scenarios, a typical-case scenario and a worst-case scenario, and realize them onto a single chip. We first schedule/bind each of the scenarios independently. After that, we commonize the scheduling/binding results for the typical-case and worst-case scenarios and thus generate a commonized area-minimized floorplan result. At that time, we can explicitly take into account interconnection delays by using distributed-register architectures. Experimental results show that our algorithm reduces the latency of the typical-case scenario by up to 50% without increasing the latency of the worst-case scenario, compared with several existing methods.

An efficient three-dimensional (3-D) fundamental locally one-dimensional finite-difference time-domain (FLOD-FDTD) method incorporated with memristor is presented. The FLOD-FDTD method achieves higher efficiency and simplicity with matrix-operator-free right-hand sides (RHS). The updating equations of memristor-incorporated FLOD-FDTD method are derived in detail. Numerical results are provided to show the trade-off between efficiency and accuracy.

SSDs consist of non-mechanical components (host interface, control core, DRAM, flash memory, etc.) whose integrated behavior is not well-known. This makes an SSD seem like a black-box to users. We analyzed power consumption of four SSDs with standard I/O operations. We find the following: (a) the power consumption of SSDs is not significantly lower than that of HDDs, (b) all SSDs we tested had similar power consumption patterns which, we assume, is a result of their internal parallelism. SSDs have a parallel architecture that connects flash memories by channel or by way. This parallel architecture improves performance of SSDs if the information is known to the file system. This paper proposes three SSD characterization algorithms to infer the characteristics of SSD, such as internal parallelism, I/O unit, and page allocation scheme, by measuring its power consumption with various sized workloads. These algorithms are applied to four real SSDs to find: (i) the internal parallelism to decide whether to perform I/Os in a concurrent or an interleaved manner, (ii) the I/O unit size that determines the maximum size that can be assigned to a flash memory, and (iii) a page allocation method to map the logical address of write operations, which are requested from the host to the physical address of flash memory. We developed a data sampling method to provide consistency in collecting power consumption patterns of each SSD. When we applied three algorithms to four real SSDs, we found flash memory configurations, I/O unit sizes, and page allocation schemes. We show that the performance of SSD can be improved by aligning the record size of file system with I/O unit of SSD, which we found by using our algorithm. We found that Q Pro has I/O unit of 32 KB, and by aligning the file system record size to 32 KB, the performance increased by 201% and energy consumption decreased by 85%, which compared to the record size of 4 KB.

We present a design method for miniaturizing double stub resonators that are potentially very useful for wide range of applications but have limited usage for MMICs due to their large footprint. The analytical design model, which we introduce in this paper, allows for determining the capacitances needed to achieve the targeted shrinking ratio while maintaining the original loaded-Q before miniaturization. To verify the model, 18-GHz stub resonators that are around 40% of the original sizes were designed and fabricated in GaAs MMIC technology. The effectiveness of the proposed technique is also demonstrated by a 9-GHz low phase-noise oscillator using the miniaturized resonator.

Pre-Cantor bar, the one-dimensional fractal media, consists of two kinds of materials. Using the transmission-line theory we will explain the double-exponential behavior of the minimum of the transmittance as a function of the stage number n, and obtain formulae of two kinds of scaling behaviors of the transmittance. From numerical calculations for n=1 to 5 we will find that the maximum of field amplitudes of resonance which increases double-exponentially with n is well estimated by the theoretical upper bound. We will show that after sorting field amplitudes for resonance frequencies of the 5th stage their distribution is a staircase function of the index.

In this paper we propose a novel classification method for the multiple k-nearest neighbor (MkNN) classifier and show its practical application to medical image processing. The proposed method performs fine classification when a pair of the spatial coordinate of the observation data in the observation space and its corresponding feature vector in the feature space is provided. The proposed MkNN classifier uses the continuity of the distribution of features of the same class not only in the feature space but also in the observation space. In order to validate the performance of the present method, it is applied to the tissue characterization problem of coronary plaque. The quantitative and qualitative validity of the proposed MkNN classifier have been confirmed by actual experiments.

Visual tracking has been studied for several decades but continues to draw significant attention because of its critical role in many applications. Recent years have seen greater interest in the use of correlation filters in visual tracking systems, owing to their extremely compelling results in different competitions and benchmarks. However, there is still a need to improve the overall tracking capability to counter various tracking issues, including large scale variation, occlusion, and deformation. This paper presents an appealing tracker with robust scale estimation, which can handle the problem of fixed template size in Kernelized Correlation Filter (KCF) tracker with no significant decrease in the speed. We apply the discriminative correlation filter for scale estimation as an independent part after finding the optimal translation based on the KCF tracker. Compared to an exhaustive scale space search scheme, our approach provides improved performance while being computationally efficient. In order to reveal the effectiveness of our approach, we use benchmark sequences annotated with 11 attributes to evaluate how well the tracker handles different attributes. Numerous experiments demonstrate that the proposed algorithm performs favorably against several state-of-the-art algorithms. Appealing results both in accuracy and robustness are also achieved on all 51 benchmark sequences, which proves the efficiency of our tracker.

A method based on score level fusion using logistic regression has been developed that uses packet header information to classify Internet applications. Applications are classified not on the basis of the individual flows for each type of application but on the basis of all the flows for each type of application, i.e., the “overall traffic flow.” The overall traffic flow is divided into equal time slots, and the applications are classified using statistical information obtained for each time slot. Evaluation using overall traffic flow generated by five types of applications showed that its true and false positive rates are better than those of methods using feature level fusion.

In this paper, a convolution theorem which is analogous to the theorem for Fourier transform is shown among a certain type of polynomials. We establish a fast method of the multiplication in a special class of quotient rings of multivariate polynomials over q-element finite field GF(q). The polynomial which we treat is one of expressing forms of the multiple-valued logic function from the product of the semigroups in GF(q) to GF(q). Our results can be applied to the speedup of both software and hardware concerning multiple-valued Boolean logic.

Network virtualization (NV) provides a promising solution to overcome the resistance of the current Internet in aspects of architecture change, and virtual network embedding (VNE) has been recognized as a core component in NV. In this paper, the current advances in exploring model, methods and technologies for embedding the virtual network into the substrate network, are summarized. Furthermore, the future research trends are drawn. The main distinctive aspects of this survey with early ones include that it is mainly contributed to simplify the VNE problem on large networks, and that more recent publications in this field are introduced. In addition, the suggestions to the future investigation will concern some new terms of the VNE optimization.

In the development of intrabody communication systems, it is important to understand the effects of user's posture on the communication channels. In this study, dynamic measurements of intrabody communication channels were made and their dependences on the grounding conditions were investigated. Furthermore, the physical mechanism of the dynamic communication channels was discussed based on electrostatic simulations. According to the measured and the simulated results, the variations in the signal transmission characteristics depend not only on the distance between the Tx and the Rx but also on the shadowing by body parts.

Recent studies have obtained superior performance in image recognition tasks by using, as an image representation, the fully connected layer activations of Convolutional Neural Networks (CNN) trained with various kinds of images. However, the CNN representation is not very suitable for fine-grained image recognition tasks involving food image recognition. For improving performance of the CNN representation in food image recognition, we propose a novel image representation that is comprised of the covariances of convolutional layer feature maps. In the experiment on the ETHZ Food-101 dataset, our method achieved 58.65% averaged accuracy, which outperforms the previous methods such as the Bag-of-Visual-Words Histogram, the Improved Fisher Vector, and CNN-SVM.

This paper considers an optimal stabilization problem of quantitative discrete event systems (DESs) under the influence of disturbances. We model a DES by a deterministic weighted automaton. The control cost is concerned with the sum of the weights along the generated trajectories reaching the target state. The region of weak attraction is the set of states of the system such that all trajectories starting from them can be controlled to reach a specified set of target states and stay there indefinitely. An optimal stabilizing controller is a controller that drives the states in this region to the set of target states with minimum control cost and keeps them there. We consider two control objectives: to minimize the worst-case control cost (1) subject to all enabled trajectories and (2) subject to the enabled trajectories starting by controllable events. Moreover, we consider the disturbances which are uncontrollable events that rarely occur in the real system but may degrade the control performance when they occur. We propose a linearithmic time algorithm for the synthesis of an optimal stabilizing controller which is robust to disturbances.

A striped inductor and its utilization of a voltage-controlled oscillator (VCO) are studied with the aim of suppressing phase noise degradation in K- and Ka-bands. The proposed striped inductor exhibits reduced series resistance in the high frequency region by increasing the cross-sectional peripheral length, as with the Litz wire, and the VCO of the striped inductor simultaneously exhibits a lower phase noise than that of the conventional inductor. Striped and conventional inductors and VCOs are designed and fabricated, and their use of K- and Ka-bands is measured. Results show that the Q factor and corner frequency of the striped inductor are approximately 1.3 and 1.6 times higher, respectively, than that of the conventional inductor. Moreover, the 1-MHz-offset phase noise of the striped inductor's VCO in the K- and Ka-bands was approximately 3.5 dB lower than that of the conventional inductor. In this study, a 65-nm standard CMOS process was used.

Linked data entity resolution is the detection of instances that reside in different repositories but co-describe the same topic. The quality of the resolution result depends on the appropriateness of the configuration, including the selected matching properties and the similarity measures. Because such configuration details are currently set differently across domains and repositories, a general resolution approach for every repository is necessary. In this paper, we present cLink, a system that can perform entity resolution on any input effectively by using a learning algorithm to find the optimal configuration. Experiments show that cLink achieves high performance even when being given only a small amount of training data. cLink also outperforms recent systems, including the ones that use the supervised learning approach.

Because the perceptual compressive sensing framework can achieve a much better performance than the legacy compressive sensing framework, it is very promising for the compressive sensing based image compression system. In this paper, we propose an innovative adaptive perceptual block compressive sensing scheme. Firstly, a new block-based statistical metric which can more appropriately measure each block's sparsity and perceptual sensibility is devised. Then, the approximated theoretical minimum measurement number for each block is derived from the new block-based metric and used as weight for adaptive measurements allocation. The obtained experimental results show that our scheme can significantly enhance both objective and subjective performance of a perceptual compressive sensing framework.

Connection Service Providers (CSP) are wishing to increase their Return on Investment (ROI) by utilizing the data assets generated by tracking subscriber behaviors. This results in the ability to apply personalized policies, monitor and control the service traffic to subscribers and gain more revenue through the usage of subscriber data with ad networks. In this paper, a system is proposed to monitor and analyze the Internet access of the subscribers of a regional SP in order to classify the subscribers into interest categories from the Interactive Advertising Bureau (IAB) categories. The study employs the categorization engine to build category vectors for all individuals using Internet services through the subscription. The proposal makes it easy to detect changes in the interests of individuals/subscribers over time.

Laplacian operator is a basic tool for image processing. For an image with regular pixels, the Laplacian operator can be represented as a stencil in which constant weights are arranged spatially to indicate which picture cells they apply to. However, in a discrete spherical image the image pixels are irregular; thus, a stencil with constant weights is not suitable. In this paper a spherical Laplacian operator is derived from Gauss's theorem; which is suitable to images with irregular pixels. The effectiveness of the proposed discrete spherical Laplacian operator is shown by the experimental results.

Based on the completeness of the real-valued discrete Gabor transform, a new biorthogonal relationship between analysis window and synthesis window is derived and a fast algorithm for computing the analysis window is presented for any given synthesis window. The new biorthogonal relationship can be expressed as a linear equation set, which can be separated into a certain number of independent sub-equation sets, where each of them can be fast and independently solved by using convolution operations and FFT to obtain the analysis window for any given synthesis window. Computational complexity analysis and comparison indicate that the proposed algorithm can save a considerable amount of computation and is more efficient than the existing algorithms.