We propose novel tree construction algorithms for multicast communication in photonic networks. Since multicast communications consume many more link resources than unicast communications, effective algorithms for route selection and wavelength assignment are required. We propose a novel tree construction algorithm, called the Weighted Steiner Tree (WST) algorithm and a variation of the WST algorithm, called the Composite Weighted Steiner Tree (CWST) algorithm. Because these algorithms are based on the Steiner Tree algorithm, link resources among source and destination pairs tend to be commonly used and link utilization ratios are improved. Because of this, these algorithms can accept many more multicast requests than other multicast tree construction algorithms based on the Dijkstra algorithm. However, under certain delay constraints, the blocking characteristics of the proposed Weighted Steiner Tree algorithm deteriorate since some light paths between source and destinations use many hops and cannot satisfy the delay constraint. In order to adapt the approach to the delay-sensitive environments, we have devised the Composite Weighted Steiner Tree algorithm comprising the Weighted Steiner Tree algorithm and the Dijkstra algorithm for use in a delay constrained environment such as an IPTV application. In this paper, we also give the results of simulation experiments which demonstrate the superiority of the proposed Composite Weighted Steiner Tree algorithm compared with the Distributed Minimum Hop Tree (DMHT) algorithm, from the viewpoint of the light-tree request blocking.

This paper presents a commercial shot classification scheme combining well-designed visual and textual features to automatically detect TV commercials. To identify the inherent difference between commercials and general programs, a special mid-level textual descriptor is proposed, aiming to capture the spatio-temporal properties of the video texts typical of commercials. In addition, we introduce an ensemble-learning based combination method, named Co-AdaBoost, to interactively exploit the intrinsic relations between the visual and textual features employed.

We describe a user scheduling scheme suitable for zero-forcing beamforming (ZFBF) downlink multiuser multiple-input multiple-output (MU-MIMO) orthogonal frequency-division multiplexing (OFDM) transmissions in time-division-duplex distributed antenna systems. This user scheduling scheme consists of inter-cell-interference mitigation scheduling by using fractional frequency reuse, proportional fair scheduling in the OFDM frequency domain, and high-capacity ZFBF-MU-MIMO scheduling by using zero-forcing with selection (ZFS). Simulation results demonstrate in a severe user-distribution condition that includes cell-edge users that the proposed user scheduling scheme achieves high average cell throughputs close to that provided by only ZFS and that it also achieves almost the same degree of user fairness as round-robin user scheduling.

This paper studies the performance of a coded convolutional spreading CDMA system with cyclic prefix (CS-CDMA/CP) combined with the zero correlation zone code generated from the M-sequence (M-ZCZ code) for downlink transmission over a multipath fast fading channel. In particular, we propose a new pilot-aided channel estimation scheme based on the shift property of the M-ZCZ code and show the robustness of the scheme against fast fading through comparison with the W-CDMA system empolying time-multiplexed pilot signals.

We have developed a macro model, which allows us to describe precise LDMOS DC/AC characteristics. Characterization of anomalous gate input capacitance is the key issue in the LDMOS model development. We have newly employed a T-type distributed RC scheme for gate overlapped LDMOS drift region. The bias dependent resistance and capacitance are modeled independently in Verilog-A as R-model and PMOS-capacitance. The dividing factor of the distributed R is introduced to reflect the shield effect of the gate overlap capacitance. Comparison between the new model and measurement results has proven that the developed macro model reproduces accurately not only the gate input capacitance, but also DC characteristics.

Intrusion Detection Systems (IDS) have been received considerable attention among the network security researchers as one of the most promising countermeasures to defend our crucial computer systems or networks against attackers on the Internet. Over the past few years, many machine learning techniques have been applied to IDSs so as to improve their performance and to construct them with low cost and effort. Especially, unsupervised anomaly detection techniques have a significant advantage in their capability to identify unforeseen attacks, i.e., 0-day attacks, and to build intrusion detection models without any labeled (i.e., pre-classified) training data in an automated manner. In this paper, we conduct a set of experiments to evaluate and analyze performance of the major unsupervised anomaly detection techniques using real traffic data which are obtained at our honeypots deployed inside and outside of the campus network of Kyoto University, and using various evaluation criteria, i.e., performance evaluation by similarity measurements and the size of training data, overall performance, detection ability for unknown attacks, and time complexity. Our experimental results give some practical and useful guidelines to IDS researchers and operators, so that they can acquire insight to apply these techniques to the area of intrusion detection, and devise more effective intrusion detection models.

Since a radio frequency identification (RFID) transponder (tag) generates both location and time information when it enters and leaves a reader, the trajectory of a moving, tagged object can be traced. Due to the time intervals between entries to successive readers, during which tags are not tracked, accurate tracing of complete trajectories can be difficult. To overcome this problem, we propose a tag trajectory indexing scheme called TR-tree (R-tree-based tag trajectory index) that can trace tags by combining the local trajectories at each reader. In experiments, this scheme showed superior performance compared with other indices.

We propose a novel representation called Feature Interaction Descriptor (FIND) to capture high-level properties of object appearance by computing pairwise interactions of adjacent region-level features. In order to deal with pedestrian detection task, we employ localized oriented gradient histograms as region-level features and measure interactions between adjacent histogram elements with a suitable histogram-similarity function. The experimental results show that our descriptor improves upon HOG significantly and outperforms related high-level features such as GLAC and CoHOG.

In this paper we clarify for the boost and the buck-boost converter that the ripple effect is not ignorable for the frequency response, and reveal that it causes the unexpected characteristics where either the phase lag or the phase lead appears depending on the shape of waveform of the ramp generator in the PWM circuit. Eventually the phase margin for the stability drastically changes depending on the slope direction (normal or reverse) of the sawtooth waveform of the ramp generator even in the same circuit configuration. For the ripple effects we propose the general analysis model and analyze them of the boost and the buck-boost converters. As the result we identify that the ripple effects are caused mainly by the variation of the slope and the average of the ripple, and reveal that the both converters have the asymmetric characteristics for the slope direction of the sawtooth waveform of the ramp generator and there is more advantage for the stability in case of the reverse slope direction than in case of the normal one. It also clarified that the effect of ESR of the output capacitor of the converter on the frequency response is different according to the shape of the sawtooth waveforms. The proposed analysis method is validated by the experiments and simulations.

Similarities and differences of the thermal analysis issues between the intelligent and general AC contactors are analyzed. Heat source model of the magnet system is established according to the unique control mode of the intelligent AC contactor. Linking with the features common of the two kinds of contactors, the extension of the thermal analysis method of the general AC contactor to the intelligent AC contactor is demonstrated. Consequently, a comprehensive thermal analysis model considering heat sources of both main circuit and magnet system is constructed for the intelligent AC contactor. With this model, the steady-state temperature rise of the intelligent AC contactor is calculated and compared with the measurements of an actual intelligent AC contactor.

In this letter, we address the problem of downlink power allocation for the generalized distributed antenna system (DAS) with cooperative clusters. Considering practical applications, we assume that only the large-scale channel state information is available at the transmitter. The power allocation scheme is investigated with the target of ergodic achievable sum rate maximization. Based on some approximations and the Rayleigh Quotient Theory, the simple selective power allocation scheme is derived for the low SNR scenario and the high SNR scenario, respectively. The methods are applicable in practice due to their low complexity.

This paper proposes a new pattern matching architecture with multi-character processing for deep packet inspection. The proposed pattern matching architecture detects the start point of pattern matching from multi-character input using input text alignment. By eliminating duplicate hardware components using process element tree, hardware cost is greatly reduced in the proposed pattern matching architecture.

This paper proposes methods for accelerating DPA by using the CPU and the GPU in a parallel manner. The overhead of naive DPA evaluation software increases excessively as the number of points in a trace or the number of traces is enlarged due to the rapid increase of file I/O overhead. This paper presents some techniques, with respect to DPA-arithmetic and file handling, which can make the overhead of DPA software become not extreme but gradual as the increase of the amount of trace data to be processed. Through generic experiments, we show that the software, equipped with the proposed methods, using both CPU and GPU can shorten the time for evaluating the DPA resistance of devices by almost half.

Correlation power analysis (CPA) is a well-known attack against cryptographic modules with which an attacker evaluates the correlation between the power consumption and the sensitive data candidates calculated from a guessed sub-key and known data such as plaintexts and ciphertexts. This paper enhances CPA to propose a new general power analysis, built-in determined sub-key CPA (BS-CPA), which finds a new sub-key by using the previously determined sub-keys recursively to compute the sensitive data candidates and to increase the signal-to-noise ratio in its analysis. BS-CPA also reuses the power traces in the repetitions of finding sub-keys to decrease the total number of the required traces for determining the all sub-keys. BS-CPA is powerful and effective when the multiple sensitive data blocks such as sbox outputs are processed simultaneously as in the hardware implementation. We apply BS-CPA to the power traces provided at the DPA contest and succeed in finding a DES key using fewer traces than the original CPA does.

RFID (Radio Frequency Identification) technology is expected to be used as a localization tool. By the localization of RFID tags, a mobile robot equipped with an RFID reader can recognize the surrounding environment. In this paper, we propose a novel effective scheme called the communication range recognition (CRR) scheme for localizing RFID tags. In this scheme, an RFID reader determines the boundaries of the communication range when it is appropriately positioned by the robot. We evaluate the estimated position accuracy through numerous experiments. We show that the moving distance of the RFID reader in the proposed scheme is lower than that in conventional schemes.

A transceiver employing hierarchical constellation encodes two hierarchies with different levels of protection and selectively decodes one or both of them, resulting in constellation inconsistency of encoding and decoding. Therefore, a conventional ordered successive interference cancellation (OSIC) receiver, which restores the signals as they are transmitted, can not be compatible with the constellation inconsistency. To mitigate this problem, an OSIC detector with the individual received bit rate per data stream is first designed. To further improve the error performance, the proposed detector is modified, for which distinct criteria are used for demodulation and cancellation. It is shown that the proposed detector achieves spectrally efficient detection while guaranteeing reliable communication.

In a WSAN (Wireless Sensor and Actuator Network), most resources, including sensors and actuators, are designed for certain applications in a dedicated environment. Many researchers have proposed to use of gateways to infer and annotate heterogeneous data; however, such centralized methods produce a bottlenecking network and computation overhead on the gateways that causes longer response time in activity processing, worsening performance. This work proposes two distribution inference mechanisms: regionalized and sequential inference mechanisms to reduce the response time in activity processing. Finally, experimental results for the proposed inference mechanisms are presented, and it shows that our mechanisms outperform the traditional centralized inference mechanism.

In this paper, the robustness of the posterior-based confidence measures is improved by utilizing entropy information, which is calculated for speech-unit-level posteriors using only the best recognition result, without requiring a larger computational load than conventional methods. Using different normalization methods, two posterior-based entropy confidence measures are proposed. Practical details are discussed for two typical levels of hidden Markov model (HMM)-based posterior confidence measures, and both levels are compared in terms of their performances. Experiments show that the entropy information results in significant improvements in the posterior-based confidence measures. The absolute improvements of the out-of-vocabulary (OOV) rejection rate are more than 20% for both the phoneme-level confidence measures and the state-level confidence measures for our embedded test sets, without a significant decline of the in-vocabulary accuracy.

Cognitive radio has emerged as an efficient approach to reusing the licensed spectrums. How to appropriately set parameters of secondary user (SU) plays a rather important role in constructing cognitive radio networks. In this letter, we have analyzed the theoretical value of SUs' density, which provides a standard for controlling the number of SUs around one primary receiver, in order to guarantee that primary communication links do not experience excessive interference. The simulation result of secondary density well matches with the theoretical result derived from our analysis. Additionally, the achievable rate of secondary user under density control is also analyzed and simulated.

Interrupt service routines are a key technology for embedded systems. In this paper, we introduce the standard approach for using Generalized Stochastic Petri Nets (GSPNs) as a high-level model for generating CTMC Continuous-Time Markov Chains (CTMCs) and then use Markov Reward Models (MRMs) to compute the performance for embedded systems. This framework is employed to analyze two embedded controllers with low cost and high performance, ARM7 and Cortex-M3. Cortex-M3 is designed with a tail-chaining mechanism to improve the performance of ARM7 when a nested interrupt occurs on an embedded controller. The Platform Independent Petri net Editor 2 (PIPE2) tool is used to model and evaluate the controllers in terms of power consumption and interrupt overhead performance. Using numerical results, in spite of the power consumption or interrupt overhead, Cortex-M3 performs better than ARM7.