An adaptive rate controller (ARC) based on an adaptive neural fuzzy inference system (ANFIS) is designed to autonomously adjust the data rate of a mobile heterogeneous network to adapt to the changing traffic load and the user speed for multimedia call services. The effect of user speed on the handoff rate is considered. Through simulations, it has been demonstrated that the ANFIS-ARC is able to maintain new call blocking probability and handoff failure probability of the mobile heterogeneous network below a prescribed low level over different user speeds and new call origination rates while optimizing the average throughput. It has also been shown that the mobile cognitive wireless network with the proposed CS-ANFIS-ARC protocol can support more traffic load than neural fuzzy call-admission and rate controller (NFCRC) protocol.

In Digital Library (DL) applications, digital book clustering is an important and urgent research task. However, it is difficult to conduct effectively because of the great length of digital books. To do the correct clustering for digital books, a novel method based on probabilistic topic model is proposed. Firstly, we build a topic model named LDAC. The main goal of LDAC topic modeling is to effectively extract topics from digital books. Subsequently, Gibbs sampling is applied for parameter inference. Once the model parameters are learned, each book is assigned to the cluster which maximizes the posterior probability. Experimental results demonstrate that our approach based on LDAC is able to achieve significant improvement as compared to the related methods.

An important and widely considered signal identification technique for cognitive radios is cyclostationarity-based feature detection because this method does not require time and frequency synchronization and prior information except for information concerning cyclic autocorrelation features of target signals. This paper presents the development and experimental evaluation of cyclostationarity-based signal identification equipment. A spatial channel emulator is used in conjunction with the equipment that provides an environment to evaluate realistic spectrum sharing scenarios. The results reveal the effectiveness of the cyclostationarity-based signal identification methodology in realistic spectrum sharing scenarios, especially in terms of the capability to identify weak signals.

Non-negative matrix factorization (NMF) is widely used for monaural musical sound source separation because of its efficiency and good performance. However, an additional clustering process is required because the musical sound mixture is separated into more signals than the number of musical tracks during NMF separation. In the conventional method, manual clustering or training-based clustering is performed with an additional learning process. Recently, a clustering algorithm based on the mel-frequency cepstrum coefficient (MFCC) was proposed for unsupervised clustering. However, MFCC clustering supplies limited information for clustering. In this paper, we propose various timbre features for unsupervised clustering and a clustering algorithm with these features. Simulation experiments are carried out using various musical sound mixtures. The results indicate that the proposed method improves clustering performance, as compared to conventional MFCC-based clustering.

We provide a theoretical analysis of the capacity achievable by an open/closed-access cognitive radio system, where the system uses spectrum resources primarily allocated to a macro cellular system. For spectrum sharing, we consider two methods based on listen-before-talk and adaptive transmit power control principles. Moreover, outdoor and indoor installations of CRS stations are investigated. We have also taken the effect of antenna heights into consideration. Numerical results reveal the capacities possible from CRS base stations installed within the coverage area of the macro cell system. We show numerical examples that compare the capacities achievable by open-access and closed access cognitive radio systems.

In this paper, we study the problem of distributed spectrum allocation under a vertical spectrum sharing scenario in a cognitive radio network. The secondary users share the spectrum licensed to the primary user by observing the activity statistics of the primary users, and regulate their transmission strategy in order to abide by the spectrum sharing etiquette. When the primary user is inactive in a subset of the available frequency bands, from the perspective of the secondary users the problem reduces to a distributed horizontal spectrum sharing. For a specific class of networks, the latter problem is addressed by the recently proposed GADIA algorithm [1]. In this paper, we present analytical and numerical results on the performance of the GADIA algorithm in conjunction with the above-mentioned vertical spectrum sharing scenario. These results reveal near-optimal performance guarantees for the overall vertical spectrum sharing scenario.

For large-scale sensor networks, multiple sinks are often deployed in order to reduce source-to-sink distance and thus cost of data delivery. However, having multiple sinks may work against cost reduction, because routes from sources can diverge towards different sinks which reduces the benefit of in-network data aggregation. In this letter we propose a self-clustering data aggregation protocol (SCAP) that can benefit from having multiple sinks as well as joint routes. In SCAP, nodes which detect the event communicate with each other to aggregate data between themselves, before sending the data to the sinks. The self-clustering extends network lifetime by reducing energy consumption of nodes near the sinks, because the number of paths in which the packets are delivered is reduced. A performance comparison with existing protocols L-PEDAP and LEO shows that SCAP can conserve energy and extend network lifetime significantly, in a multi-sink environment.

Despite the benefits of the Gustafson-Kessel (GK) clustering algorithm, it becomes computationally inefficient when applied to high-dimensional data. In this letter, a parallel implementation of the GK algorithm on the GPU with CUDA is proposed. Using an optimized matrix multiplication algorithm with fast access to shared memory, the CUDA version achieved a maximum 240-fold speedup over the single-CPU version.

Program comprehension using dynamic information is one of key tasks of software maintenance. Software visualization with sequence diagrams is a promising technique to help developer comprehend the behavior of object-oriented systems effectively. There are many tools that can support automatic generation of a sequence diagram from execution traces. However it is still difficult to understand the behavior because the size of automatically generated sequence diagrams from the massive amounts of execution traces tends to be beyond developer's capacity. In this paper, we propose an execution trace slicing and visualization method. Our proposed method is capable of slice calculation based on a behavior model which can treat dependencies based on static and dynamic analysis and supports for various programs including exceptions and multi-threading. We also introduce our tool that perform our proposed slice calculation on the Eclipse platform. We show the applicability of our proposed method by applying the tool to two Java programs as case studies. As a result, we confirm effectiveness of our proposed method for understanding the behavior of object-oriented systems.

The susceptibility of a static random access memory (SRAM) core against static and dynamic variation of power supply voltage is evaluated, by using on-chip diagnosis structures of memory built-in self testing (MBIST) and on-chip voltage waveform monitoring (OCM). The SRAM core of interest in this paper is a synthesizable version applicable to general systems-on-a-chip (SoC) design, and fabricated in a 90 nm CMOS technology. RF power injection to power supply networks is quantified by OCM. The number of resultant erroneous bits as well as their distribution in the cell array is given by MBIST. The frequency-dependent sensitivity reflects the highly capacitive nature of densely integrated SRAM cells.

Users recently find their interests by checking the contents published or mentioned by their immediate neighbors in social networking services. We propose semantics-based link navigation; links guide the active user to potential neighbors who may provide new interests. Our method first creates a graph that has users as nodes and shared interests as links. Then it divides the graph by link pruning to extract practical numbers, that the active user can navigate, of interest-sharing groups, i.e. communities of interests (COIs). It then attaches a different semantic tag to the link to each representative user, which best reflects the interests of COIs that they are included in, and to the link to each immediate neighbor of the active user. It finally calculates link attractiveness by analyzing the semantic tags on links. The active user can select the link to access by checking the semantic tags and link attractiveness. User interests extracted from large scale actual blog-entries are used to confirm the efficiency of our proposal. Results show that navigation based on link attractiveness and representative users allows the user to find new interests much more accurately than is otherwise possible.

The present status of superconducting terahertz emitter using the intrinsic Josephson junctions in high-Tc superconductor Bi2Sr2CaCu2O8+δ is reviewed. Fabrication methods of the emitting device, electrical and optical characteristics of them, synchronizing operation of two emitters and an example of applications to the terahertz imaging will be discussed. After the description of fabrication techniques by an Argon ion milling with photolithography or metal masks and by a focused ion beam, optical properties of radiation spectra, the line width, polarization and the spatial distribution of emission are presented with some discussion on the operation mechanism. For electrical properties, reversible and irreversible operations at high and low electrical currents, respectively, and electrical modulation of the radiation intensity for terahertz imaging are presented.

In the task of face recognition, a challenging issue is the one sample problem, namely, there is only one training sample per person. Principal component analysis (PCA) seeks a low-dimensional representation that maximizes the global scatter of the training samples, and thus is suitable for one sample problem. However, standard PCA is sensitive to the outliers and emphasizes more on the relatively distant sample pairs, which implies that the close samples belonging to different classes tend to be merged together. In this paper, we propose two-stage block-based whitened PCA (TS-BWPCA) to address this problem. For a specific probe image, in the first stage, we seek the K-Nearest Neighbors (K-NNs) in the whitened PCA space and thus exclude most of samples which are distant to the probe. In the second stage, we maximize the “local” scatter by performing whitened PCA on the K nearest samples, which could explore the most discriminative information for similar classes. Moreover, block-based scheme is incorporated to address the small sample problem. This two-stage process is actually a coarse-to-fine scheme that can maximize both global and local scatter, and thus overcomes the aforementioned shortcomings of PCA. Experimental results on FERET face database show that our proposed algorithm is better than several representative approaches.

We study the problem of reconfiguring one list edge-coloring of a graph into another list edge-coloring by changing only one edge color assignment at a time, while at all times maintaining a list edge-coloring, given a list of allowed colors for each edge. Ito, Kamiski and Demaine gave a sufficient condition so that any list edge-coloring of a tree can be transformed into any other. In this paper, we give a new sufficient condition which improves the known one. Our sufficient condition is best possible in some sense. The proof is constructive, and yields a polynomial-time algorithm that finds a transformation between two given list edge-colorings of a tree with n vertices via O(n2) recoloring steps. We remark that the upper bound O(n2) on the number of recoloring steps is tight, because there is an infinite family of instances on paths that satisfy our sufficient condition and whose reconfiguration requires Ω(n2) recoloring steps.

Service providers should monitor the quality of experience of a communication service in real time to confirm its status. To do this, we previously proposed a packet-layer model that can be used for monitoring the average video quality of typical Internet protocol television content using parameters derived from transmitted packet headers. However, it is difficult to monitor the video quality per user using the average video quality because video quality depends on the video content. To accurately monitor the video quality per user, a model that can be used for estimating the video quality per video content rather than the average video quality should be developed. Therefore, to take into account the impact of video content on video quality, we propose a model that calculates the difference in video quality between the video quality of the estimation-target video and the average video quality estimated using a packet-layer model. We first conducted extensive subjective quality assessments for different codecs and video sequences. We then model their characteristics based on parameters related to compression and packet loss. Finally, we verify the performance of the proposed model by applying it to unknown data sets different from the training data sets used for developing the model.

The energy consumption of the information and communication technology (ICT) industry, which has become a serious problem, is mostly due to the network infrastructure rather than the mobile terminals. In this paper, we focus on reducing the energy consumption of base stations (BSs) by adjusting their working modes (active or sleep). Specifically, the objective is to minimize the energy consumption while satisfying quality of service (QoS, e.g., blocking probability) requirement and, at the same time, avoiding frequent mode switching to reduce signaling and delay overhead. The problem is modeled as a dynamic programming (DP) problem, which is NP-hard in general. Based on cooperation among neighboring BSs, a low-complexity algorithm is proposed to reduce the size of state space as well as that of action space. Simulations demonstrate that, with the proposed algorithm, the active BS pattern well meets the time variation and the non-uniform spatial distribution of system traffic. Moreover, the tradeoff between the energy saving from BS sleeping and the cost of switching is well balanced by the proposed scheme.

Multimedia applications such as music or video streaming, video teleconferencing and IP telephony are flourishing in packet-switched networks. Applications that generate such real-time data can have very diverse quality-of-service (QoS) requirements. In order to guarantee diverse QoS requirements, the combined use of a packet scheduling algorithm based on Generalized Processor Sharing (GPS) and leaky bucket traffic regulator is the most successful QoS mechanism. GPS can provide a minimum guaranteed service rate for each session and tight delay bounds for leaky bucket constrained sessions. However, the delay bounds for leaky bucket constrained sessions under GPS are unnecessarily large because each session is served according to its associated constant weight until the session buffer is empty. In order to solve this problem, a scheduling policy called Output Rate-Controlled Generalized Processor Sharing (ORC-GPS) was proposed in [17]. ORC-GPS is a rate-based scheduling like GPS, and controls the service rate in order to lower the delay bounds for leaky bucket constrained sessions. In this paper, we propose a call admission control (CAC) algorithm for ORC-GPS, for leaky-bucket constrained sessions with deterministic delay requirements. This CAC algorithm for ORC-GPS determines the optimal values of parameters of ORC-GPS from the deterministic delay requirements of the sessions. In numerical experiments, we compare the CAC algorithm for ORC-GPS with one for GPS in terms of schedulable region and computational complexity.

Photonic integrated circuits (PICs) produced by large-scale integration (LSI) on Si platforms have been intensively researched. Since thermal diffusion from the LSI logic layer is a serious obstacle to realizing a Si-based optical integrated circuit, we have proposed and realized athermal wavelength filters using Si slot waveguides embedded with benzocyclobutene (BCB). First, the athermal conditions were theoretically investigated by controlling the waveguide and gap width of the slot waveguides. In order to introduce the calculated waveguide structures to wavelength filters, the propagation losses and bending losses of the Si slot waveguides were evaluated. The propagation losses were measured to be 5.6 and 5.3 dB/cm for slot waveguide widths of 500 and 700 nm, respectively. Finally, athermal wavelength filters, a ring resonator, and a Mach-Zhender interferometer (MZI) with a slot waveguide width of 700 nm were designed and fabricated. Further, a temperature coefficient of -0.9 pm/K for the operating wavelength was achieved with the athermal MZI.

We demonstrate phase demodulation of 10-Gbps DPSK signals using a silicon micro-ring resonator with a radius of 10 µm and with various coupling gaps for light of ∼1550 nm in wavelength. Influence of the Q factors and transmissions of the resonators on the response speed and power balance of the two output ports is discussed. Furthermore, temperature sensitivity on resonance peak was measured and we discuss its effect on practical demodulation application.

In this paper, we propose a duplicate document detection model recognizing both partial duplicates and near duplicates. The proposed model can detect partial duplicates as well as exact duplicates by splitting a large document into many small sentence fingerprints. Furthermore, the proposed model can detect even near duplicates, the result of trivial revisions, by filtering the common words and reordering the word sequence.