In time-varying fading environments, the performance of multiple-input multiple-output (MIMO) systems applying an eigenbeam-space division multiplexing (E-SDM) technique may be degraded due to a channel change during the time interval between the transmit weight matrix determination and the actual data transmission. To compensate for the channel change, we have proposed some channel prediction methods. Simulation results based on computer-generated channel data showed that better performance can be obtained when using the prediction methods in Rayleigh fading environments assuming the Jakes model with rich scatterers. However, actual MIMO systems may be used in line-of-sight (LOS) environments, and even in a non-LOS case, scatterers may not be uniformly distributed around a receiver and/or a transmitter. In addition, mutual coupling between antennas at both the transmitter and the receiver cannot be ignored as it affects the system performance in actual implementation. We conducted MIMO channel measurement campaigns at a 5.2 GHz frequency band to evaluate the channel prediction techniques. In this paper, we present the experiment and simulation results using the measured channel data. The results show that robust bit-error rate performance is obtained when using the channel prediction methods and that the methods can be used in both Rayleigh and Rician fading environments, and do not need to know the maximum Doppler frequency.

This paper presents a real-time decision support system (RDSS) based on artificial intelligence (AI) for voltage collapse avoidance (VCA) in power supply networks. The RDSS scheme employs a fuzzy hyperrectangular composite neural network (FHRCNN) to carry out voltage risk identification (VRI). In the event that a threat to the security of the power supply network is detected, an evolutionary programming (EP)-based algorithm is triggered to determine the operational settings required to restore the power supply network to a secure condition. The effectiveness of the RDSS methodology is demonstrated through its application to the American Electric Power Provider System (AEP, 30-bus system) under various heavy load conditions and contingency scenarios. In general, the numerical results confirm the ability of the RDSS scheme to minimize the risk of voltage collapse in power supply networks. In other words, RDSS provides Power Provider Enterprises (PPEs) with a viable tool for performing on-line voltage risk assessment and power system security enhancement functions.

Multi-user MIMO (Multiple Input Multiple Output) systems, in which multiple Mobile Stations (MSs) equipped with multiple antennas simultaneously communicate with a Base Station (BS) equipped with multiple antennas, at the same frequency, are attracting attention because of their potential for improved transmission performance in wireless communications. In the uplink of Space Division Multiplexing based multi-user MIMO (multi-user MIMO/SDM) systems that do not require full Channel State Information (CSI) at the transmitters, selecting active MS antennas, which corresponds to scheduling transmit antennas, is an effective technique. The Full search Selection Algorithm based on exhaustive search (FSA) has been studied as an optimal active MS antenna selection algorithm for multi-user MIMO systems. Unfortunately, FSA suffers from extreme computational complexity given large numbers of MSs. To solve this problem, this paper introduces the Gram-Schmidt orthogonalization based Selection Algorithm (GSSA) to uplink multi-user MIMO/SDM systems. GSSA is a suboptimal active MS antenna selection algorithm that offers lower computational complexity than the optimal algorithm. This paper evaluates the transmission performance improvement of GSSA in uplink multi-user MIMO/SDM systems under realistic propagation conditions such as spatially correlated BS antennas and clarifies the effectiveness of GSSA.

A phase-retrieval method is applied to the quasioptical feed system of the offset Cassegrain antenna of the Superconducting Submillimeter-Wave Limb-Emission Sounder (JEM/SMILES) to be aboard the International Space Station for evaluating the beam alignment by estimating the phase pattern from the beam amplitude pattern measurements. As the result, the application of the phase retrieval method is demonstrated to be effective for measuring and evaluating the quasioptical antenna feed system. It is also demonstrated that the far-field radiation pattern of the antenna main reflector can be estimated from the phase-retrieved beam pattern of the feed system.

A framework for generating facial expressions from emotional states in daily conversation is described. It provides a mapping between emotional states and facial expressions, where the former is represented by vectors with psychologically-defined abstract dimensions, and the latter is coded by the Facial Action Coding System. In order to obtain the mapping, parallel data with rated emotional states and facial expressions were collected for utterances of a female speaker, and a neural network was trained with the data. The effectiveness of proposed method is verified by a subjective evaluation test. As the result, the Mean Opinion Score with respect to the suitability of generated facial expression was 3.86 for the speaker, which was close to that of hand-made facial expressions.

While significant work has been dedicated to analyzing the performance of antennas for multiple-input multiple-output (MIMO) and diversity systems, little has appeared on synthesizing optimal antennas for these systems. This paper explores optimal antenna characteristics given understanding about the average structure of the propagation environment, making the results applicable for time-varying channels created by mobile nodes or scatterer motion. Specifically, it examines optimal antenna designs for the cases where 1) the antennas reside in a fixed aperture or 2) the number of antenna elements is fixed (under fast-fading conditions with spatially correlated signals).

In this letter, we analyze the performance of limited feedback beamforming in a distributed antenna system. We propose a novel codebook design scheme to maximize a lower bound of the averaged effective signal-to-noise ratio (SNR), which is a function of the power of the signal and noise, the number of antennas, and the number of total feedback bits for characterizing the quantized channel vector. Simulations verify that the proposed scheme can provide effective capacity improvement.

The TFT-LCD image has non-uniform brightness that is the major difficulty of finding the visible defect called Mura in the field. To facilitate Mura detection, background signal shading should level off and Mura signal must be amplified. In this paper, Mura signal amplification and background signal flattening method is proposed based on human visual system (HVS). The proposed DC normalized contrast sensitivity function (CSF) is used for the Mura signal amplification and polynomial regression (PR) is used to level off the background signal. In the enhanced image, tri-modal thresholding segmentation technique is used for finding Dark and White Mura at the same time. To select reliable defect, falsely detected invisible region is eliminated based on Weber's Law. By the experimental results of artificially generated 1-d signal and TFT-LCD image, proposed algorithm has novel enhancement results and can be applied to real automated inspection system.

A site-general type prediction formula is created based on the measurement results in an urban area in Japan assuming that the prediction frequency range required for Fourth -- Generation (4G) Mobile Communication Systems is from 3 to 6 GHz, the distance range is 0.1 to 3 km, and the base station (BS) height range is from 10 to 100 m. Based on the measurement results, the path loss (dB) is found to be proportional to the logarithm of the distance (m), the logarithm of the BS height (m), and the logarithm of the frequency (GHz). Furthermore, we examine the extension of existing formulae such as the Okumura -- Hata, Walfisch -- Ikegami, and Sakagami formulae for 4G systems and propose a prediction formula based on the Extended Sakagami formula.

In this paper, we present a new three-dimensional (3D) virtual environment (3DVE) system named "QuViE/P", which can enhance quality of service (QoS), that users actually feel, as good as possible when resources of computers and networks are limited. To realize this, we focus on characteristics of user's perceptual quality evaluation on 3D objects. We propose an effective QoS control scheme for QuViE/P by introducing relationships between system's internal quality parameters and user's perceptual quality parameters. This scheme can appropriately maintain the QoS of the 3DVE system and it is expected to improve convenience when using 3DVE system where resources are insufficient. We designed and implemented a prototype of QuViE/P using a multiagent framework. The experiment results show that even when the computer resource is reduced to 20% of the required amount, the proposed scheme can maintain the quality of important objects to a certain level.

To improve the computation efficiency of the application specific instruction-set processor (ASIP), a strategy of hardware/software collaborative design is usually utilized. In this process, the auto-customization of specific instruction set has always been a key part to support the automated design of ASIP. The key issue of this problem is how to effectively reduce the huge exponential exploration space in the instruction identification process. To address this issue, we first formulate it as a feasible sub-graph enumeration problem under multiple constraints, and then propose a fast instruction identification algorithm based on a new model called basic convex pattern (BCP). The kernel technique in this algorithm is the transformation from the graph exploration to the formula-based computations. The experimental results have indicated that the proposed algorithm has a distinct reduction in the execution time.

In this paper, we evaluate BER (bit error rate) performance and diversity gain when employing a transmission technique utilizing LC (Linear Combination) diversity using 2 time slots with QPSK channels in 2 2 MIMO (Multiple-Input Multiple-Output) spatial multiplexing systems by comparing it with the upper and lower bound on BER. This evaluation shows that this transmission technique realizes high diversity gain and high transmission rate in LOS (line-of-sight) and NLOS (non line-of-sight) environments.

This study is to investigate new schemes for distributing cryptographic keys in sensor networks. Sharing a key is the very first step to realize secure communication over an untrusted network infrastructure, but commonly used cryptographic techniques cannot be employed for sensor networks due to the restriction of computational resources of sensor nodes. A practical solution to this issue is to predistribute cryptographic keys in sensor nodes before they are deployed. A focal point in this solution is the choice of keys that are assigned to a sensor node. Eschenauer et al. considered to choose keys randomly, and Chan et al. also followed the random choice approach. We consider in this paper a new approach in which keys are assigned according to a basic algebraic geometry. The performance of the proposed scheme is investigated analytically.

We address reliable key distribution scheme for lossy channels such as wireless or power line. In the key distribution over these lossy channels, if key information is lost, there is critical issue that the subsequent communication is disabled. In this paper, we show that our proposal has more reliable property than the related works and has the reliable property equivalent to the dedicated communication channels such as Ethernet.

This paper will propose a wolf attack probability (WAP) as a new measure for evaluating security of biometric authentication systems. The wolf attack is an attempt to impersonate a victim by feeding "wolves" into the system to be attacked. The "wolf" means an input value which can be falsely accepted as a match with multiple templates. WAP is defined as a maximum success probability of the wolf attack with one wolf sample. In this paper, we give a rigorous definition of the new security measure which gives strength estimation of an individual biometric authentication system against impersonation attacks. We show that if one reestimates using our WAP measure, a typical fingerprint algorithm turns out to be much weaker than theoretically estimated by Ratha et al. Moreover, we apply the wolf attack to a finger-vein-pattern based algorithm. Surprisingly, we show that there exists an extremely strong wolf which falsely matches all templates for any threshold value.

Code acquisition performance in the Direct-Sequence Code-Division Multiple-Access (DS/CDMA) communication system is strongly related to the quality of the communication systems. The performance is assessed by (i) code acquisition time; (ii) precision; and (iii) complexity for implementation. This paper applies the method of maximum likelihood (ML) to estimation of propagation delay in DS/CDMA communications, and proposes a low-complexity method for code acquisition. First, a DS/CDMA system model and properties of outputs with a passive matched-filter receiver are reviewed, and a statistical problem in code acquisition is mentioned. Second, an error-controllable code acquisition method based on the maximum likelihood is discussed. Third, a low-complexity ML code acquisition method is proposed. It is shown that the code acquisition time with the low-complexity method is about 1.5 times longer than that with the original ML method, e.g. 13 data periods under 4.96 dB.

Pairing based cryptosystems can accomplish novel security applications such as ID-based cryptosystems, which have not been constructed efficiently without the pairing. The processing speed of the pairing based cryptosystems is relatively slow compared with the other conventional public key cryptosystems. However, several efficient algorithms for computing the pairing have been proposed, namely Duursma-Lee algorithm and its variant ηT pairing. In this paper, we present an efficient implementation of the pairing over some mobilephones. Moreover, we compare the processing speed of the pairing with that of the other standard public key cryptosystems, i.e. RSA cryptosystem and elliptic curve cryptosystem. Indeed the processing speed of our implementation in ARM9 processors on BREW achieves under 100 milliseconds using the supersingular curve over F397. In addition, the pairing is more efficient than the other public key cryptosystems, and the pairing can be achieved enough also on BREW mobilephones. It has become efficient enough to implement security applications, such as short signature, ID-based cryptosystems or broadcast encryption, using the pairing on BREW mobilephones.

Intrusion detection system (IDS) has played a central role as an appliance to effectively defend our crucial computer systems or networks against attackers on the Internet. The most widely deployed and commercially available methods for intrusion detection employ signature-based detection. However, they cannot detect unknown intrusions intrinsically which are not matched to the signatures, and their methods consume huge amounts of cost and time to acquire the signatures. In order to cope with the problems, many researchers have proposed various kinds of methods that are based on unsupervised learning techniques. Although they enable one to construct intrusion detection model with low cost and effort, and have capability to detect unforeseen attacks, they still have mainly two problems in intrusion detection: a low detection rate and a high false positive rate. In this paper, we present a new clustering method to improve the detection rate while maintaining a low false positive rate. We evaluated our method using KDD Cup 1999 data set. Evaluation results show that superiority of our approach to other existing algorithms reported in the literature.

In this paper, we revisit a generally accepted opinion: implementing Elliptic Curve Cryptosystem (ECC) over GF(2m) on sensor motes using small word size is not appropriate because XOR multiplication over GF(2m) is not efficiently supported by current low-powered microprocessors. Although there are some implementations over GF(2m) on sensor motes, their performances are not satisfactory enough to be used for wireless sensor networks (WSNs). We have found that a field multiplication over GF(2m) are involved in a number of redundant memory accesses and its inefficiency is originated from this problem. Moreover, the field reduction process also requires many redundant memory accesses. Therefore, we propose some techniques for reducing unnecessary memory accesses. With the proposed strategies, the running time of field multiplication and reduction over GF(2163) can be decreased by 21.1% and 24.7%, respectively. These savings noticeably decrease execution times spent in Elliptic Curve Digital Signature Algorithm (ECDSA) operations (signing and verification) by around 15-19%. We present TinyECCK (Tiny Elliptic Curve Cryptosystem with Koblitz curve - a kind of TinyOS package supporting elliptic curve operations) which is the first implementation of Koblitz curve on sensor motes as far as we know. Through comparisons with existing software implementations of ECC built in C or hybrid of C and inline assembly on sensor motes, we show that TinyECCK outperforms them in terms of running time, code size and supporting services. Furthermore, we show that a field multiplication over GF(2m) can be faster than that over GF(p) on 8-bit Atmega128 processor by comparing TinyECCK with TinyECC, a well-known ECC implementation over GF(p). TinyECCK with sect163k1 can generate a signature and verify it in 1.37 and 2.32 secs on a Micaz mote with 13,748-byte of ROM and 1,004-byte of RAM.

In this paper, we present an efficient fingerprint classification algorithm which is an essential component in many critical security application systems e.g. systems in the e-government and e-finance domains. Fingerprint identification is one of the most important security requirements in homeland security systems such as personnel screening and anti-money laundering. The problem of fingerprint identification involves searching (matching) the fingerprint of a person against each of the fingerprints of all registered persons. To enhance performance and reliability, a common approach is to reduce the search space by firstly classifying the fingerprints and then performing the search in the respective class. Jain et al. proposed a fingerprint classification algorithm based on a two-stage classifier, which uses a K-nearest neighbor classifier in its first stage. The fingerprint classification algorithm is based on the fingercode representation which is an encoding of fingerprints that has been demonstrated to be an effective fingerprint biometric scheme because of its ability to capture both local and global details in a fingerprint image. We enhance this approach by improving the efficiency of the K-nearest neighbor classifier for fingercode-based fingerprint classification. Our research firstly investigates the various fast search algorithms in vector quantization (VQ) and the potential application in fingerprint classification, and then proposes two efficient algorithms based on the pyramid-based search algorithms in VQ. Experimental results on DB1 of FVC 2004 demonstrate that our algorithms can outperform the full search algorithm and the original pyramid-based search algorithms in terms of computational efficiency without sacrificing accuracy.