A new blind adaptive equalization method for constant modulus signals based on minimizing the approximate negentropy of the estimation error for a finite-length equalizer is presented. We consider the approximate negentropy using nonpolynomial expansions of the estimation error as a new performance criterion to improve the performance of a linear equalizer using the conventional constant modulus algorithm (CMA). Negentropy includes higher order statistical information and its minimization provides improved convergence, performance, and accuracy compared to traditional methods, such as the CMA, in terms of the bit error rate (BER). Also, the proposed equalizer shows faster convergence characteristics than the CMA equalizer and is more robust to nonlinear distortion than the CMA equalizer.

This letter proposes an efficient transmit power allocation using partial channel information feedback for the closed-loop sorted QR decomposition (SQRD) based V-BLAST systems. For the feedback information, the positive real-valued diagonal elements of R are forwarded to the transmitter. With the proposed transmit power allocation that is numerically derived by the Lagrange optimization method, the bit error rate performance of the system can be remarkably improved compare to the conventional open-loop SQRD based V-BLAST systems without increasing the receiver complexity.

Video surveillance systems have a huge market, as indicated by the number of installed cameras, particularly for low-power systems. In this paper, we propose a low-power quadtree video encoder for video surveillance systems. It features a low-complexity motion estimation algorithm, an application-specific ME-MC processor, a dedicated quadtree encoder engine and a processor control-based clock-gating technique. A chip capable of encoding 30 fps VGA (640480) at 80 MHz is fabricated using 0.18 µm CMOS technology. A total of 153 K gates with 558 kbits SRAM have been integrated into a 5.0 mm3.5 mm die. The power consumption is 40.87 mW at 80 MHz for VGA at 30 fps and 1.97 mW at 3.3 MHz for QCIF at 15 fps.

Orthogonal multi-carrier direct sequence code division multiple access (orthogonal MC DS-CDMA) is a combination of orthogonal frequency division multiplexing (OFDM) and time-domain spreading, while multi-carrier code division multiple access (MC-CDMA) is a combination of OFDM and frequency-domain spreading. In MC-CDMA, a good bit error rate (BER) performance can be achieved by using frequency-domain equalization (FDE), since the frequency diversity gain is obtained. On the other hand, the conventional orthogonal MC DS-CDMA fails to achieve any frequency diversity gain. In this paper, we propose a new orthogonal MC DS-CDMA that can obtain the frequency diversity gain by applying FDE. The conditional BER analysis is presented. The theoretical average BER performance in a frequency-selective Rayleigh fading channel is evaluated by the Monte-Carlo numerical computation method using the derived conditional BER and is confirmed by computer simulation of the orthogonal MC DS-CDMA signal transmission.

MIMO (Multiple-Input Multiple-Output) technologies have attracted much interest for high-rate and high-capacity wireless communications. MIMO technologies under frequency-selective fading environments (wideband MIMO technologies) have also been studied. A wideband MIMO system is affected by ISI (Inter Symbol Interference) and CCI (Co-Channel Interference). Hence, we need a MIMO signal detection technique that simultaneously suppresses ISI and CCI. The OFDM system and SC-FDE (Single Carrier-Frequency Domain Equalization) techniques are often used for suppressing ISI. By employing these techniques with the ZF (Zero Forcing) or the MMSE (Minimum Mean Square Error) spatial filtering technique, we can cancel both ISI and CCI. To use ZF or MMSE, we need channel state information for calculating the receive weights. Although an LS (Least Square) channel estimation technique has been proposed for MIMO-OFDM systems, it needs a large estimation matrix at the receiver side to obtain sufficient estimation performance in heavy multipath environments. However, the use of a large matrix increases computational complexity and the circuit size. We use frequency domain channel estimation to solve these problems and propose an iterative method for achieving better estimation performance. In this paper, we assume the use of a MIMO-UWB system that employs a UWB-IR (Ultra-Wideband Impulse Radio) scheme with the FDE technique as the wideband wireless transmission scheme for heavy multipath environments, and we evaluate the iterative frequency domain channel estimation through computer simulations and computational complexity calculations.

We propose the P-SLM (Partitioned-SeLected Mapping) scheme with low complexity for PAPR reduction of OFDM signals. In the proposed scheme, a symbol sequence in the frequency domain is partitioned into several sub-blocks which are multiplied by different orthogonal phase sequences whose length and number are shorter and smaller than those used in the conventional SLM. Then, among various sequences in the time domain generated after the IFFT for the SLM sub-blocks, the sub-block combination with the lowest PAPR is selected and transmitted. Simulation results show that the proposed P-SLM scheme significantly reduces the number of IFFT calculation and multiplication than the conventional SLM without loss of PAPR reduction performance.

We present a method to identify stakeholders and their preferences about non-functional requirements (NFR) by using use case diagrams of existing systems. We focus on the changes about NFR because such changes help stakeholders to identify their preferences. Comparing different use case diagrams of the same domain helps us to find changes to be occurred. We utilize Goal-Question-Metrics (GQM) method for identifying variables that characterize NFR, and we can systematically represent changes about NFR using the variables. Use cases that represent system interactions help us to bridge the gap between goals and metrics (variables), and we can easily construct measurable NFR. For validating and evaluating our method, we applied our method to an application domain of Mail User Agent (MUA) system.

This paper presents a knowledge acquisition method using sentence topics for question answering. We define templates for information extraction by the Korean concept network semi-automatically. Moreover, we propose the two-phase information extraction model by the hybrid machine learning such as maximum entropy and conditional random fields. In our experiments, we examined the role of sentence topics in the template-filling task for information extraction. Our experimental result shows the improvement of 18% in F-score and 434% in training speed over the plain CRF-based method for the extraction task. In addition, our result shows the improvement of 8% in F-score for the subsequent QA task.

This paper investigates the design curves of the class DE amplifier with the nonlinear shunt capacitances for any output Q and any grading coefficient m of the diode junction in the MOSFET. The design curves are derived by the numerical calculation using Spice. The results of this paper have two important meanings. Firstly, it is clarified that the nonlinearities of the shunt capacitances affect the design curves of the class DE amplifier, especially, for low output Q. Moreover, the supply voltage is a quite important parameter to design the class DE amplifier with the nonlinear shunt capacitances. Secondly, it is also clarified that the numerical design tool using Spice, which is proposed by authors, can be applied to the derivation of the design curves. This shows the possibility of the algorithm to be a powerful tool for the analysis of the class E switching circuits. The waveforms from Spice simulations denote the validity of the design curves.

JPEG2000 still image coding standard has a feature called Region of Interest (ROI) coding. This feature can encode a restricted region in an image prior to than its background (BG) region. In low bit rate compression, the code of the ROI region occupies the most of the bit stream in the whole image and it causes the serious deterioration of the image quality in the BG region. This paper proposes a new image quality control method between the ROI region and the BG one by the one time encoding process and it can achieve more detailed image quality control. The use of ROI masks in the encoder makes it possible. The standard decoder of JPEG2000 part1 can decode the encoded data in the proposed method.

The present paper describes a method for the construction of a zero-correlation zone sequence set from a perfect sequence. Both the cross-correlation function and the side-lobe of the auto-correlation function of the proposed sequence sets are zero for phase shifts within the zero-correlation zone. These sets can be generated from an arbitrary perfect sequence, the length of which is the product of a pair of odd integers ((2n+1)(2k+1) for k ≥ 1 and n ≥ 0). The proposed sequence construction method can generate an optimal zero-correlation zone sequence set that achieves the theoretical bounds of the sequence member size given the size of the zero-correlation zone and the sequence period. The peak in the out-of-phase correlation function of the constructed sequences is restricted to be lower than the half of the power of the sequence itself. The proposed sequence sets could successfully provide CDMA communication without co-channel interference, or, in an ultrasonic synthetic aperture imaging system, improve the signal-to-noise ratio of the acquired image.

We analyze the MAC performance of the IEEE 802.11 DCF and 802.11e EDCA in non-saturation condition where device does not have packets to transmit sometimes. We assume that a flow is not generated while the previous flow is in service and the number of packets in a flow is geometrically distributed. In this paper, we take into account the feature of non-saturation condition in standards: possibility of transmission performed without preceding backoff procedure for the first packet arriving at the idle station. Our approach is to model a stochastic behavior of one station as a discrete time Markov chain. We obtain four performance measures: normalized channel throughput, average packet HoL (head of line) delay, expected time to complete transmission of a flow and packet loss probability. Our results can be used for admission control to find the optimal number of stations with some constraints on these measures.

P2P DHT (Peer-to-Peer Distributed Hash Table) is one of typical techniques for realizing an efficient management of shared resources distributed over a network and a keyword search over such networks in a fully distributed manner. In this paper, we propose a new method for supporting conjunctive queries in P2P DHT. The basic idea of the proposed technique is to share a global information on past trials by conducting a local caching of search results for conjunctive queries and by registering the fact to the global DHT. Such a result caching is expected to significantly reduce the amount of transmitted data compared with conventional schemes. The effect of the proposed method is experimentally evaluated by simulation. The result of experiments indicates that by using the proposed method, the amount of returned data is reduced by 60% compared with conventional P2P DHT which does not support conjunctive queries.

The single-hidden-layer feedforward neural networks (SLFNs) are frequently used in machine learning due to their ability which can form boundaries with arbitrary shapes if the activation function of hidden units is chosen properly. Most learning algorithms for the neural networks based on gradient descent are still slow because of the many learning steps. Recently, a learning algorithm called extreme learning machine (ELM) has been proposed for training SLFNs to overcome this problem. It randomly chooses the input weights and hidden-layer biases, and analytically determines the output weights by the matrix inverse operation. This algorithm can achieve good generalization performance with high learning speed in many applications. However, this algorithm often requires a large number of hidden units and takes long time for classification of new observations. In this paper, a new approach for training SLFNs called least-squares extreme learning machine (LS-ELM) is proposed. Unlike the gradient descent-based algorithms and the ELM, our approach analytically determines the input weights, hidden-layer biases and output weights based on linear models. For training with a large number of input patterns, an online training scheme with sub-blocks of the training set is also introduced. Experimental results for real applications show that our proposed algorithm offers high classification accuracy with a smaller number of hidden units and extremely high speed in both learning and testing.

Fair queueing is a service scheduling discipline to pursue the fairness among users in packet communication networks. Many fair queueing algorithms, however, have problems of computational overhead since the central scheduler has to maintain a certain performance counter for each flow of user packets based on the global virtual time. Moreover, they are not suitable for wireless networks with high probability of input channel errors due to the lack or complexity in the compensation mechanism for the recovery from the error state. In this paper, we propose a new, computationally efficient, distributed fair queueing scheme, which we call Channel-Aware Throughput Fair Queueing (CATFQ), that is applicable to both wired and wireless packet networks. In our CATFQ scheme, each flow is equipped with a counter that measures the weighted throughput achievement while it has a backlog of packets. At the end of every service to a packet, the scheduler simply selects a flow with the minimum counter value as the one from which a packet is served next. We show that the difference between any two throughput counters is bounded. Our scheme significantly reduces the scheduler's computational overhead and guarantees fair throughput for all flows. For wireless networks with error-prone channels, the service chance lost in bad channel condition is compensated quickly as the channel recovers. Our scheme suppresses the service for leading flows, brings short-term fairness for flows without channel errors, and achieves long-term fairness for all flows. These merits are verified by simulation.

A power-aware compiler controllable chip multiprocessor (CMP) is presented and its performance and power consumption are evaluated with the optimally scheduled advanced multiprocessor (OSCAR) parallelizing compiler. The CMP is equipped with power control registers that change clock frequency and power supply voltage to functional units including processor cores, memories, and an interconnection network. The OSCAR compiler carries out coarse-grain task parallelization of programs and reduces power consumption using architectural power control support and the compiler's power saving scheme. The performance evaluation shows that MPEG-2 encoding on the proposed CMP with four CPUs results in 82.6% power reduction in real-time execution mode with a deadline constraint on its sequential execution time. Furthermore, MP3 encoding on a heterogeneous CMP with four CPUs and four accelerators results in 53.9% power reduction at 21.1-fold speed-up in performance against its sequential execution in the fastest execution mode.

A packet detection method for zero-padded orthogonal frequency division multiplexing (OFDM) transmission is presented. The proposed algorithm effectively conducts packet detection by employing both an M-sample time delayed cross correlation value, and a received signal power calculated by using the received input samples corresponding to the zero padding (ZP) intervals or less.

The overall performance of P2P-based file sharing applications is becoming increasingly important. Based on the Adaptive Resource-based Probabilistic Search algorithm (ARPS), which was previously proposed by the authors, a novel probabilistic search algorithm with QoS guarantees is proposed in this letter. The algorithm relies on generating functions to satisfy the user's constraints and to exploit the power-law distribution in the node degree. Simulation results demonstrate that it performs well under various P2P scenarios. The proposed algorithm provides guarantees on the search performance perceived by the user while minimizing the search cost. Furthermore, it allows different QoS levels, resulting in greater flexibility and scalability.

Orthogonal frequency-division multiplexing (OFDM) systems often use a cyclic prefix (CP) to simplify the equalization design at the cost of bandwidth efficiency. To increase the bandwidth efficiency, we study the blind equalization with linear smoothing [1] for single-input multiple-output (SIMO) OFDM systems without CP insertion in this paper. Due to the block Toeplitz structure of channel matrix, the block matrix scheme is applied to the linear smoothing channel estimation, which equivalently increases the number of sample vectors and thus reduces the perturbation of sample autocorrelation matrix. Compared with the linear smoothing and subspace methods, the proposed block linear smoothing requires the lowest computational complexity. Computer simulations show that the block linear smoothing yields a channel estimation error smaller than that from linear smoothing, and close to that of the subspace method. Evaluating by the minimum mean-square error (MMSE) equalizer, the block linear smoothing and subspace methods have nearly the same bit-error-rates (BERs).

We present a model checking approach to the rationale, implementation, and applications of a query language for location-based services. Such query mechanisms are necessary so that users, objects, and/or services can effectively benefit from the location-awareness of their surrounding environment. The underlying data model is founded on a symbolic model of space organized in a tree structure. Once extended to a semantic model for modal logic, we regard location query processing as a model checking problem, and thus define location queries as hybrid logic-based formulas. Our approach is unique to existing research because it explores the connection between location models and query processing in ubiquitous computing systems, relies on a sound theoretical basis, and provides modal logic-based query mechanisms for expressive searches over a decentralized data structure. A prototype implementation is also presented and will be discussed.