Multiple-input-multiple-output (MIMO) wireless systems can not always have full spatial multiplexing gain due to the channel correlation problem caused by various factors such as the coupled antenna elements, and the key-hole effect of the propagation environment. In this paper, we proposed a channel reconfiguration technique to combat the rank deficiency problem of the involved MIMO wireless channels that can not afford high-order multiplexing gains. In the proposed approach, each mobile station can simultaneously receive several independent data streams from multiple base stations through a set of MMSE-based receive beamformers to suppress the multiple access interferences. Making use of the receive beamforming, which virtually produce the effect of a single antenna at each receive mobile, makes the transmit base station possible to reconfigure the MIMO downlink channel and then pre-cancel the co-channel interferences. The proposed signal processing mechanism that iteratively optimized the MMSE receive weights and the transmit precoders, which brings the reconfigured MIMO system about the high data throughput seen only with indoor MIMO systems having rich wireless channels. It is shown that as compared to the conventional MIMO system, the M4 system can achieve a significantly higher capacity which is proportional to the number of the linked base stations.

In this paper, throughput and error performance analysis is conducted on the proposed space-time resource management (STRM) scheme to realize a multi-Gbps millimeter-wave wireless personal area network (WPAN) system. The proposed STRM allows multiple peer-to-peer communication links to occupy the same time-division-multiple-access (TDMA) time slot, in contrary to the conventional TDMA system that allocates only one time slot to one communication link. Theoretical analysis is performed to investigate the achievable system throughput in the presence of co-channel interference (CCI) generated by communication links co-sharing the same time slot. To increase accuracy, the analysis results are validated by Monte Carlo simulations. Firstly, it is found that the upper bound of the achievable throughput increases linearly with the number of communication links sharing the same time slot. However, optimum throughput exists corresponding to the CCI present in the system. Secondly, by manipulating a parameter that controls the allowable CCI in the network, the system throughput can be optimized. Lastly, it is also found that in a millimeter-wave band system, a victim system with transmitter-receiver separation of 1-meter can achieve bit error rate (BER) of 10^-6 provided that the interferer is at least 6-meters away.

We present an attractive approach for OFDM transmission using an adaptive pre-FFT equalizer, which can select ICI reduction mode according to channel condition, and a degenerated-inverse-matrix-based channel estimator (DIME), which uses a cyclic sinc-function matrix uniquely determined by transmitted subcarriers. In addition to simulation results, the proposed system with an adaptive pre-FFT equalizer and DIME has been laboratory tested by using a software defined radio (SDR)-based test bed. The simulation and experimental results demonstrated that the system at a rate of more than 100 Mbps can provide a bit error rate of less than 10^-3 for a fast multi-path fading channel that has a moving velocity of more than 200 km/h with a delay spread of 1.9 µs (a maximum delay path of 7.3 µs) in the 5-GHz band.

An exact expression of error rate is developed for maximal ratio combining (MRC) in an independent but not necessarily identically distributed frequency selective Nakagami fading channel taking into account inter-symbol, co-channel and adjacent channel interferences (ISI, CCI and ACI respectively). The characteristic function (CF) method is adopted. While accurate analysis of MRC performance cannot be seen in frequency selective channel taking ISI (and CCI) into account, such analysis for ACI has not been addressed yet. The general analysis presented in this paper solves a problem of past and present interest, which has so far been studied either approximately or in simulations. The exact method presented also lets us obtain an approximate error rate expression based on Gaussian approximation (GA) of the interferences. It is shown, especially while the channel is lightly faded, has fewer multipath components and a decaying delay profile, the GA may be substantially inaccurate at high signal-to-noise ratio. However, the exact results also reveal an important finding that there is a range of parameters where the simpler GA is reasonably accurate and hence, we don't have to go for more involved exact expression.

Direct conversion receivers in orthogonal frequency division multiplexing (OFDM) systems suffer from direct current (DC) offset, frequency offset, and IQ imbalance. We have proposed an IQ imbalance estimation scheme in the presence of DC offset and frequency offset, which uses preamble signals in the time domain. In this scheme, the DC offset is eliminated by a differential filter. However, the accuracy of IQ imbalance estimation is deteriorated when the frequency offset is small. To overcome this problem, a new IQ imbalance estimation scheme in the frequency domain with the differential filter has been proposed in this paper. The IQ imbalance is estimated with pilot subcarriers. Numerical results obtained through computer simulation show that estimation accuracy and bit error rate (BER) performance can be improved even if the frequency offset is small.

With the increase of communication demand and the emergence of new services, various innovative wireless technologies have been deployed recently. Free Space Optics (FSO) links combined with Radio over Fiber (RoF) technology can realize a cost-effective heterogeneous wireless access system for both urban and rural areas. In this paper, we introduce a newly developed advanced DWDM Radio-on-FSO (RoFSO) system capable of simultaneously transmitting multiple Radio Frequency (RF) signals carrying various wireless services including W-CDMA, WLAN IEEE802.11g and ISDB-T signals over FSO link. We present an experimental performance evaluation of transmitting RF signals using the RoFSO system over a 1 km link under different deployment environment conditions. This work represents a pioneering attempt, based on a realistic operational scenario, aiming at demonstrating the RoFSO system can be conveniently used as a reliable alternative broadband wireless technology for complementing optical fiber networks in areas where the deployment of optical fiber is not feasible.

Invariants of Petri nets are fundamental algebraic characteristics of Petri nets, and are used in various situations, such as checking (as necessity of) liveness, boundedness, periodicity and so on. Any given Petri net N has two kinds of invariants: a P-invariant is a |P|-dimensional vector Y with Y^t A = and a T-invariant is a |T|-dimensional vector X with A X = for the place-transition incidence matrix A of N. T-invariants are nonnegative integer vectors, while this is not always the case with P-invariants. This paper deals only with nonnegative integer invariants (invariants that are nonnegative vectors) and shows results common to the two invariants. For simplicity of discussion, only P-invariants are treated. The Fourier-Motzkin method is well-known for computing all minimal support integer invariants. This method, however, has a critical deficiency such that, even if a given Perti net N has any invariant, it is likely that no invariants are obtained because of an overflow in storing intermediate vectors as candidates for invariants. The subject of the paper is to give an overview and results known to us for efficiently computing minimal-support nonnegative integer invariants of a given Petri net by means of the Fourier-Motzkin method. Also included are algorithms for efficiently extracting siphon-traps of a Petri net.

Petri net is a powerful modeling tool for concurrent systems. Liveness, which is a problem to verify there exists no local deadlock, is one of the most important properties of Petri net to analyze. Computational complexity of liveness of a general Petri net is deterministic exponential space. Liveness is studied for subclasses of Petri nets to obtain necessary and sufficient conditions that need less computational cost. These are mainly done using a subset of places called siphons. CS-property, which denotes that every siphon has token(s) in every reachable marking, in one of key properties in liveness analysis. On the other hand, normal Petri net is a subclass of Petri net whose reachability set can be effectively calculated. This paper studies computational complexity of liveness problem of normal Petri nets. First, it is shown that liveness of a normal Petri net is equivalent to cs-property. Then we show this problem is co-NP complete by deriving a nondeterministic algorithm for non-liveness which is similar to the algorithm for liveness suggested by Howell et al. Lastly, we study structural feature of bounded Petri net where liveness and cs-property are equivalent. From this consideration, liveness problem of bounded normal Petri net is shown to be deterministic polynomial time complexity.

Workflow nets (WF-nets) are Petri nets which represent workflows. Soundness is a criterion of logical correctness defined for WF-nets. It is known that soundness verification is intractable. In this paper, we propose a method to verify soundness using a Linear Temporal Logic (LTL) model checking tool, SPIN. We give an LTL necessary and sufficient condition to verify soundness for WF-nets without livelock. Acyclic WF-nets have no livelock, but cyclic WF-nets may have livelock. We also give a necessary and sufficient condition to verify livelock. Meanwhile, we show that any LTL model checking tool cannot verify soundness for WF-nets with livelock. We give necessary conditions to verify soundness for them. Those conditions enable us to use SPIN even if a given WF-net has livelock. We also develop a tool to verify soundness based on our method. We show effectiveness of our method by comparing our tool with existing soundness verification tools on verification time for 200 cyclic ACWF-nets.

The minimum initial marking problem of Petri nets (MIM) is defined as follows: "Given a Petri net and a firing count vector X, find an initial marking M₀, with the minimum total token number, for which there is a sequence δ of transitions such that each transition t appears exactly X(t) times in δ, the first transition is enabled at M₀ and the rest can be fired one by one subsequently." In a production system like factory automation, economical distribution of initial resources, from which a schedule of job-processings is executable, can be formulated as MIM. AAD is known to produce best solutions among existing algorithms. Although solutions by AMIM+ is worse than those by AAD, it is known that AMIM+ is very fast. This paper proposes new heuristic algorithms AADO and AMDLO, improved versions of existing algorithms AAD and AMIM+, respectively. Sharpness of solutions or short CPU time is the main target of AADO or AMDLO, respectively. It is shown, based on computing experiment, that the average total number of tokens in initial markings by AADO is about 5.15% less than that by AAD, and the average CPU time by AADO is about 17.3% of that by AAD. AMDLO produces solutions that are slightly worse than those by AAD, while they are about 10.4% better than those by AMIM+. Although CPU time of AMDLO is about 180 times that of AMIM+, it is still fast: average CPU time of AMDLO is about 2.33% of that of AAD. Generally it is observed that solutions get worse as the sizes of input instances increase, and this is the case with AAD and AMIM+. This undesirable tendency is greatly improved in AADO and AMDLO.

In this paper, we propose a new modeling method to express discrete-time hybrid systems with parameter uncertainty as a mixed logical dynamical (MLD) model. In analysis and control of hybrid systems, there are problem formulations in which convex polyhedra are computed, but for high-dimensional systems, it is difficult to solve these problems within a practical computation time. The key idea of this paper is to use an interval method, which is one of the classical methods in verified numerical computation, and to regard an interval as an over-approximation of a convex polyhedron. By using the obtained MLD model, analysis and synthesis of robust control systems are formulated.

To realize the harmonious cooperation with the operator, the man-machine cooperative system must be designed so as to accommodate with the characteristics of the operator's skill. One of the important considerations in the skill analysis is to investigate the switching mechanism underlying the skill dynamics. On the other hand, the combination of the feedforward and feedback schemes has been proved to work successfully in the modeling of human skill. In this paper, a new stochastic switched skill model for the sliding task, wherein a minimum jerk motion and feedback schemes are embedded in the different discrete states, is proposed. Then, the parameter estimation algorithm for the proposed switched skill model is derived. Finally, some advantages and applications of the proposed model are discussed.

This paper presents a new hierarchical mode segmentation of the observed driving behavioral data based on the multi-level abstraction of the underlying dynamics. By synthesizing the ideas of a feature vector definition revealing the dynamical characteristics and an unsupervised clustering technique, the hierarchical mode segmentation is achieved. The identified mode can be regarded as a kind of symbol in the abstract model of the behavior. Second, the grammatical inference technique is introduced to develop the context-dependent grammar of the behavior, i.e., the symbolic dynamics of the human behavior. In addition, the behavior prediction based on the obtained symbolic model is performed. The proposed framework enables us to make a bridge between the signal space and the symbolic space in the understanding of the human behavior.

This paper proposes a framework for modular verification of evolving component-based software. This framework includes two stages: modular conformance testing for updating inaccurate models of the evolved components and modular verification for evolving component-based software. When a component is evolved after adapting some refinements, the proposed framework focuses on this component and its model in order to update the model and recheck the whole evolved system. The framework also reuses the previous verification results and the previous models of the evolved components to reduce the number of steps required in the model update and modular verification processes. An implementation and some experimental results are presented.

The algebra of communicating shared resources (ACSR) is a timed process algebra which extends classical process algebras with the notion of a resource. In analyzing ACSR models, the existing techniques such as bisimulation checking and Hennessy-Milner Logic (HML) model checking are very important in theory of ACSR, but they are difficult to use for large complex system models in practice. In this paper, we suggest a framework to verify ACSR models against their requirements described in an expressive timed temporal logic. We demonstrate the usefulness of our approach with a real world case study.

Recently multi-car elevator (MCE) consisting of several elevator cars in a single elevator shaft received great interest as transportation systems for high-rise buildings. Algorithms for efficiently controlling elevator cars are necessary to put MCEs to practical use. We propose an algorithm for controlling MCEs to reduce passenger-waiting time. A feature of our algorithm is the introduction of a simple function estimating efficiency of passenger transport and a procedure checking assignability of a car. We evaluate the performance of our algorithm using a simulation and show that it performs better compared with a previous algorithm.

This research proposes efficient calculation methods for the transition matrices in discrete event systems, where the adjacency matrices are represented by directed acyclic graphs. The essence of the research focuses on obtaining the Kleene Star of an adjacency matrix. Previous studies have proposed methods for calculating the longest paths focusing on destination nodes. However, in these methods the chosen algorithm depends on whether the adjacency matrix is sparse or dense. In contrast, this research calculates the longest paths focusing on source nodes. The proposed methods are more efficient than the previous ones, and are attractive in that the efficiency is not affected by the density of the adjacency matrix.

Recently, many researchers tackle accurate object recognition algorithms and many algorithms are proposed. However, these algorithms have some problems caused by variety of real environments such as a direction change of the object or its shading change. The new tracking algorithm, Cascade Particle Filter, is proposed to fill such demands in real environments by constructing the object model while tracking the objects. We have been investigating to implement accurate object recognition on embedded systems in real-time. In order to apply the Cascade Particle Filter to embedded applications such as surveillance, automotives, and robotics, a hardware accelerator is indispensable because of limitations in power consumption. In this paper we propose a hardware implementation of the Discrete AdaBoost algorithm that is the most computationally intensive part of the Cascade Particle Filter. To implement the proposed hardware, we use PICO Express, a high level synthesis tool provided by Synfora, for rapid prototyping. Implementation result shows that the synthesized hardware has 1,132,038 transistors and the die area is 2,195 µm 1,985 µm under a 0.180 µm library. The simulation result shows that total processing time is about 8.2 milliseconds at 65 MHz operation frequency.

In this study, we introduce shift-invariant sparse image representations using tree-structured dictionaries. Sparse coding is a generative signal model that approximates signals by the linear combinations of atoms in a dictionary. Since a sparsity penalty is introduced during signal approximation and dictionary learning, the dictionary represents the primal structures of the signals. Under the shift-invariance constraint, the dictionary comprises translated structuring elements (SEs). The computational cost and number of atoms in the dictionary increase along with the increasing number of SEs. In this paper, we propose an algorithm for shift-invariant sparse image representation, in which SEs are learnt with a tree-structured approach. By using a tree-structured dictionary, we can reduce the computational cost of the image decomposition to the logarithmic order of the number of SEs. We also present the results of our experiments on the SE learning and the use of our algorithm in image recovery applications.

This paper presents a reconfigurable SAD Tree (RSADT) architecture based on adaptive sub-sampling algorithm for HDTV application. Firstly, to obtain the the feature of HDTV picture, pixel difference analysis is applied on each macroblock (MB). Three hardware friendly sub-sampling patterns are selected adaptively to release complexity of homogeneous MB and keep video quality for texture MB. Secondly, since two pipeline stages are inserted, the whole clock speed of RSADT structure is enhanced. Thirdly, to solve data reuse and hardware utilization problem of adaptive algorithm, the RSADT structure adopts pixel data organization in both memory and architecture level, which leads to full data reuse and hardware utilization. Additionally, a cross reuse structure is proposed to efficiently generate 16 pixel scaled configurable SAD (sum of absolute difference). Experimental results show that, our RSADT architecture can averagely save 61.71% processing cycles for integer motion estimation engine and accomplish twice or four times processing capability for homogeneous MBs. The maximum clock frequency of our design is 208 MHz under TSMC 0.18 µm technology in worst work conditions(1.62 V, 125C). Furthermore, the proposed algorithm and reconfigurable structure are favorable to power aware real-time encoding system.

In this paper, we propose a new PAPR reduction by using the hybrid of partial transmit sequences (PTS) and cascade adaptive peak power reduction (CAPPR) methods with side information (SI) technique coded by genetic algorithm (GA). These methods are used in an Orthogonal Frequency Division Multiplexing (OFDM) system. The OFDM employs orthogonal sub-carriers for data modulation. These sub-carriers unexpectedly present a large peak to average power ratio (PAPR) in some cases. A proposed reduction method realizes both the advantages of PTS and CAPPR at the same time. In order to obtain the optimum condition on PTS for PAPR reduction, a quite large calculation cost is demanded and thus it is impossible to obtain the optimum PTS in a short time. In the proposed method, by using the pseudo-optimum condition based on a GA coded SI technique, the total calculation cost becomes drastically reduced. In simulation results, the proposed method shows the improvement on PAPR and also reveals the high performance on bit error rate (BER) of an OFDM system.

A Generalized Symbol-rate-increased (GSRI) Pragmatic Adaptive Trellis Coded Modulation (ATCM) is applied to a Multi-carrier CDMA (MC-CDMA) system with bi-orthogonal keying is analyzed. The MC-CDMA considered in this paper is that the input sequence of a bi-orthogonal modulator has code selection bit sequence and sign bit sequence. In, an efficient error correction code using Reed-Solomon (RS) code for the code selection bit sequence has been proposed. However, since BPSK is employed for the sign bit modulation, no error correction code is applied to it. In order to realize a high speed wireless system, a multi-level modulation scheme (e.g. MPSK, MQAM, etc.) is desired. In this paper, we investigate the performance of the MC-CDMA with bi-orthogonal keying employing GSRI ATCM. GSRI TC-MPSK can arbitrarily set the bandwidth expansion ratio keeping higher coding gain than the conventional pragmatic TCM scheme. By changing the modulation scheme and the bandwidth expansion ratio (coding rate), this scheme can optimize the performance according to the channel conditions. The performance evaluations by simulations on an AWGN channel and multi-path fading channels are presented. It is shown that the proposed scheme has remarkable throughput performance than that of the conventional scheme.

Alamouti's orthogonal space-time block code (OSTBC) is a simple yet important technique to take advantage of transmit diversity to mitigate fading channel effects. In this paper, we analyze the effects of time-selective channels and channel estimation errors on the bit error rate (BER) performance of Alamouti's OSTBC. We develop an analytical expression of the BER performance for the linear decoding with minimum mean squared error (MMSE) channel estimates in place of the true channel. Based on the expression, we derive a BER performance limit in decision-directed mode where the channel is tracked with Kalman filtering. Numerical examples are provided to validate our analysis and to see the impact of time-selective fading and channel estimation errors on the BER performance.

A 2.45-GHz-band small passive radio-frequency identification (RFID) tag consists of a small loop antenna and chip, and its size is several millimeters. Because of the tag's poor impedance-matching characteristic and radiation efficiency, an ordinary reader has difficulty reading it. We propose a new technique for reading the tag that involves installing a square half-wavelength meander-line conductor on the reader as an adapter and placing the adapter in the vicinity of the tag, and verify the effectiveness of the technique by simulation and experiment. Moreover, characteristics of simultaneous read of the small RFID tags by the proposed reading technique are revealed by simulation and experimental results.

On the use of a surveillance camera, there is a case where privacy protection should be considered. This paper proposes a new privacy protection method by automatically degrading the face region in surveillance images. The proposed method consists of ROI coding of JPEG2000 and a face detection method based on template matching. The experimental result shows that the face region can be detected and hidden correctly.

In orthogonal frequency division multiplexing (OFDM) system, a guard interval (GI) is used to remove the inter-symbol interference (ISI) due to a multipath channel. It is difficult to set an optimal GI length in the environment whose multipath varies. In this paper, we propose a variable guard interval based on the estimated maximum delay of a multipath channel. The maximum delay is estimated from a channel impulse response (CIR), which is estimated by a preamble symbol. However, since the estimated CIR includes the noise, it is difficult to decide the optimal GI. In order to solve the problem, we introduce the method which selects the path whose signal to noise ratio is high. Additionally, the information of the optimal GI length is required to be transmitted from a receiver to a transmitter. In this paper, we use an acknowledgment (ACK) frame for the feedback of the GI information.

External interferences can severely degrade the performance of an Over-the-horizon radar (OTHR), so suppression of external interferences in strong clutter environment is the prerequisite for the target detection. The traditional suppression solutions usually began with clutter suppression in either time or frequency domain, followed by the interference detection and suppression. Based on this traditional solution, this paper proposes a method characterized by joint clutter suppression and interference detection: by analyzing eigenvalues in a short-time moving window centered at different time position, clutter is suppressed by discarding the maximum three eigenvalues at every time position and meanwhile detection is achieved by analyzing the remained eigenvalues at different position. Then, restoration is achieved by forward-backward linear prediction using interference-free data surrounding the interference position. In the numeric computation, the eigenvalue decomposition (EVD) is replaced by singular values decomposition (SVD) based on the equivalence of these two processing. Data processing and experimental results show its efficiency of noise floor falling down about 10-20 dB.

This paper addresses linear quadratic control with state-dependent noise for singularly perturbed stochastic systems (SPSS). First, the asymptotic structure of the stochastic algebraic Riccati equation (SARE) is established for two cases. Second, a new iterative algorithm that combines Newton's method with the fixed point algorithm is established. As a result, the quadratic convergence and the reduced-order computation in the same dimension of the subsystem are attained. As another important feature, a high-order state feedback controller that uses the obtained iterative solution is given and the degradation of the cost performance is investigated for the stochastic case for the first time. Furthermore, the parameter independent controller is also given in case the singular perturbation is unknown. Finally, in order to demonstrate the efficiency of the proposed algorithm, a numerical example is given for the practical megawatt-frequency control problem.

Incremental Redundancy Hybrid ARQ (IR-HARQ) based on rate-compatible punctured low-density parity-check (LDPC) codes can achieve high throughput over a wide range of SNRs. One drawback of such IR-HARQ schemes is high computational complexity of decoding for early transmission at high rates. In order to overcome this problem, a HARQ scheme based on rate-compatible LDPC codes by shortening and extending is presented in this paper. In the HARQ scheme, a high-rate mother code is transmitted at first, and parity-bits of a shortened code are transmitted for early retransmission requests. With a low-complexity decoder of the high-rate mother code, this shortened-code approach would result in low computational complexity of decoding, but it causes smaller length and larger number of shortened codes to be decoded as retransmission repeats. To prevent the resultant degradation of performance and complexity, extending is efficiently applied to the shortened codes after predetermined retransmission-times. A multi-edge type code-design is employed to construct irregular LDPC codes that meet the requirement of the HARQ scheme. Simulation results show that the HARQ scheme can achieve lower computational complexity of decoding than a conventional IR-HARQ scheme with good throughput over a wide range of SNRs.

In this paper, we investigate the smallest value of p for which a (J,L,p)-QC LDPC code with girth 6 exists for J=3 and J=4. For J=3, we determine the smallest value of p for any L. For J=4, we determine the smallest value of p for L ≤ 301. Furthermore we provide examples of specific constructions meeting these smallest values of p.

By using multiple repeated signal replicas to formulate the accumulative observed noisy signal sequence (AONSS) or the differential observed noisy signal sequence (DONSS) in the hybrid ARQ system, a novel data-aided maximum likelihood (DA ML) SNR estimation and a blind ML SNR estimation technique are proposed for the AWGN channel. It is revealed that the conventional DA ML estimate is a special case of the novel DA ML estimate, and both the proposed DA ML and the proposed blind ML SNR estimation techniques can offer satisfactory SNR estimation without introducing significant additional complexity to the existing hybrid ARQ scheme. Based on the AONSS, both the generalized deterministic and the random Cramer-Rao lower bounds (GCRLBs), which include the traditional Cramer-Rao lower bounds (CRLBs) as special cases, are also derived. Finally, the applicability of the proposed SNR estimation techniques based on the AONSS and the DONSS are validated through numerical analysis and simulation results.

This paper focuses on fusion estimation algorithms weighted by matrices and scalars, and relationship between them is considered. We present new algorithms that address the computation of matrix weights arising from multidimensional estimation problems. The first algorithm is based on the Cholesky factorization of a cross-covariance block-matrix. This algorithm is equivalent to the standard composite fusion estimation algorithm however it is low-complexity. The second fusion algorithm is based on an approximation scheme which uses special steady-state approximation for local cross-covariances. Such approximation is useful for computing matrix weights in real-time. Subsequent analysis of the proposed fusion algorithms is presented, in which examples demonstrate the low-computational complexity of the new fusion estimation algorithms.

This paper presents a new feature extraction method for robust speech recognition based on the autocorrelation mel frequency cepstral coefficients (AMFCCs) and a variable window. While the AMFCC feature extraction method uses the fixed double-dynamic-range (DDR) Hamming window for higher-lag autocorrelation coefficients, which are least affected by noise, the proposed method applies a variable window, depending on the frame energy and periodicity. The performance of the proposed method is verified using an Aurora-2 task, and the results confirm a significantly improved performance under noisy conditions.

Based on the theoretical analysis of literature, saturation in measured signal of active noise control (ANC) systems will degrade the convergence speed. However, the experiments show that the saturated input signal can speed up the convergence of the narrow-band ANC systems. This paper intends to remodel the saturation effects for feedforward and feedback ANC systems. Combining the action of analog-to-digital converters (ADC), the mathematical expression and block diagrams are proposed to model the saturation effects in the practical ANC systems. The derivation and simulation results show that since the saturation is able to amplify the principle component of signal, the convergence would be speeded up.

This paper considers the problem of target location estimation in heterogeneous wireless sensor networks and proposes a novel algorithm using a factor graph to fuse the heterogeneous measured data. In the proposed algorithm, we map the problem of target location estimation to a factor graph framework and then use the sum-product algorithm to fuse the heterogeneous measured data so that heterogeneous sensors can collaborate to improve the accuracy of target location estimation. Simulation results indicate that the proposed algorithm provides high location estimation accuracy.

We consider a problem of global asymptotic stabilization of a class of feedforward nonlinear systems that have the unknown linear growth rate and unknown input delay. The proposed output feedback controller employs a dynamic gain which is tuned adaptively by monitoring the output value. As a result, a priori knowledge on the linear growth rate and delay size are not required in controller design, which is a clear benefit over the existing results.

The joint source-channel coding problem is considered. The joint source-channel coding theorem reveals the existence of a code for the pair of the source and the channel under the condition that the error probability is smaller than or equal to ε asymptotically. The separation theorem guarantees that we can achieve the optimal coding performance by using the two-stage coding. In the case that ε = 0, Han showed the joint source-channel coding theorem and the separation theorem for general sources and channels. Furthermore the ε-coding theorem (0 ≤ ε <1) in the similar setting was studied. However, the separation theorem was not revealed since it is difficult in general. So we consider the separation theorem in this setting.

In this paper, we propose a low-complexity, high-efficiency two-dimensional look-up table (2D LUT) for carrying out the sum-product algorithm in the decoding of low-density parity-check (LDPC) codes. Instead of employing adders for the core operation when updating check node messages, in the proposed scheme, the main term and correction factor of the core operation are successfully merged into a compact 2D LUT. Simulation results indicate that the proposed 2D LUT not only attains close-to-optimal bit error rate performance but also enjoys a low complexity advantage that is suitable for hardware implementation.

In this letter, an interference suppression scheme by MMSE combining in OFDM-antenna array with time shifted sampling (TSS) is proposed. An array antenna at a base station has been thoroughly investigated to increase the uplink capacity. The performance of the uplink is not only limited by the correlation between the antenna elements, which strongly depends on the spatial aspects of the channel, as well as the interference from the terminals. Numerical results through computer simulation show that the proposed scheme improves bit error rate performance because of interference suppression by MMSE combining and diversity reception by TSS at the same time.

It is an almost classical problem to develop an efficient broadcast mechanism in multi-hop networks. However, the problem is far from solved though a lot of research till recently has been focused on it. In this letter, we analyze the optimal distribution of forwarding nodes that can doubly cover the network with the minimum transmission overlapped area in a multi-hop wireless network where nodes are ideally distributed. On this basis we propose a simple and effective broadcast method to significantly increase the transmission efficiency and deliverability. In this method, the total number of retransmissions is quite low since each node only needs to select three forwarding nodes, and high deliverability can be achieved for the set of forwarding nodes provide nearly double coverage to the whole network. In addition, the method has excellent scalability in large scale networks and highly dynamic environment.