We investigated the influence of the thickness of the AlN interlayer for InAlN/GaN and InAlN/AlGaN/GaN heterostructures. The AlN thickness strongly affects the surface morphology and electron mobility of the InAlN/GaN structures. The rms roughness of the surface increases from 0.35 to 1.2 nm with increasing AlN thickness from 0 to 1.5 nm. Large pits are generated when the AlN is thicker than 1 nm. The highest electron mobility of 1470 cm2/VS is obtained for a 0.75-nm-thick AlN interlayer. The mobility, however, becomes lower with increasing deviation from 0.75 nm. It is only 200 cm2/VS for the 0-nm thick AlN. Inserting AlGaN between AlN and InAlN suppresses the influence of the AlN interlayer thickness. A smooth surface with rms roughness of 0.35 nm is obtained for all samples with 0-1.5-nm-thick AlN. The electron mobility ranges from 1000 to 1690 cm2/VS. The variation is smaller than that for InAlN/GaN. We fabricated field effect transistors (FETs) with gate length of 2 µm. The electron mobility in the access region affects the transconductance (gm) of FETs. As a results, the influence of the AlN thickness for InAlN/GaN FETs is larger than that for InAlN/AlGaN/GaN FETs, which reduces gate leakage current. The transconductance varies from 93 to 235 mS/mm for InAlN/GaN FETs. In contrast, it varies from 180 to 230 mS/mm for InAlN/AlGaN/GaN FETs. These results indicate that the InAlN/AlGaN/GaN heterostructures could lead to the development of GaN-based FETs.

A new adaptive algorithm is proposed by introducing some modifications to the recursive least squares (RLS) algorithm. Except for the noise variance, the proposed algorithm does not require any statistics or knowledge of the desired signal, thus, it is suitable for adaptive filtering for channel estimation in code division multiple access (CDMA) systems in cases where the standard RLS approach cannot be used. A theoretical analysis demonstrates the convergence of the proposed algorithm, and simulation results for CDMA channel estimation show that the proposed algorithm outperforms existing channel estimation schemes.

In this paper, a modified Belief Propagation (BP) decoding algorithm for low-density parity check (LDPC) codes based on minimum mean square error (MMSE) criterion is proposed. This modified algorithm uses linear equation to replace the hyperbolic function in the original BP algorithm and optimizes the linear approximation error based on MMSE criterion. As a result, compared with the standard BP algorithm the computational complexity is reduced significantly as the modified algorithm requires only addition operations to implement. Besides that simulation results show our modified algorithm can achieve an error performance very close to the BP algorithm on the additive white Gaussian noise channel.

Graph matching is a NP-Hard problem. In this paper, we relax the admissible set of permutation matrices and meantime incorporate a barrier function into the objective function. The resulted model is equivalent to the original model. Alternate iteration algorithm is designed to solve it. It is proven that the algorithm proposed is locally convergent. Our experimental results reveal that the proposed algorithm outperforms the algorithm in .

The proportionate normalized least mean square algorithm (PNLMS) greatly improves the convergence of the sparse impulse response. It exploits the shape of the impulse response to decide the proportionate step gain for each coefficient. This is not always suitable. Actually, the proportionate step gain should be determined according to the difference between the current estimate of the coefficient and its optimal value. Based on this idea, an approach is proposed to determine the proportionate step gain. The proposed approach can improve the convergence of proportionate adaptive algorithms after a fast initial period. It even behaves well for the non-sparse impulse response. Simulations verify the effectiveness of the proposed approach.

Recent advances of superconducting single-flux-quantum (SFQ) circuit technology make it attractive to investigate computing systems using SFQ circuits, where arithmetic circuits play important roles. In order to develop excellent SFQ arithmetic circuits, we have to design or select their underlying algorithms, called hardware algorithms, from different point of view than CMOS circuits, because SFQ circuits work by pulse logic while CMOS circuits work by level logic. In this paper, we compare implementations of hardware algorithms for addition by synchronous-clocking SFQ circuits. We show that a set of individual bit-serial adders and Kogge-Stone adder are superior to others.

This paper introduces a coordinate calculation method for a real-time locating system. A ToA algorithm is used to obtain the target node coordinates, but a conventional DC method, which incurs heavy calculation time, is not suitable for embedded systems. This paper proposes the use of a P-control in the PID control algorithm to resolve real-time locating system issues. Performance measures of the accumulated operator number and position error are evaluated. It is shown that the PID method has less calculation and more robust performance than the DC method.

We present a congestion control algorithm for the Internet and assess its stability. The algorithm has low operation complexity and exercises control over sources without keeping per-flow information. Given the lack of support for explicit-rate feedback in the Internet, we discuss an implementation where feedback is based on explicit binary indications. We assess the stability through a discrete-time model and present simulation results showing the efficacy of the algorithm. The obtained results indicate that when the algorithm is used to control sources that support explicit binary feedback, its stability is not affected and its performance is close to that obtained with sources that support explicit-rate feedback.

MIMO-OFDM systems aim to improve transmission quality and/or throughput but require significant signal processing capability and flexibility at reasonable cost. This paper proposes a reconfigurable architecture and associated algorithm optimizations for these types of systems based on the IEEE 802.11n and IEEE 802.16e standards. In particular, we describe the implementation of two key computations onto this architecture, namely Fast Fourier Transform (FFT) and Space-Time Block Decoding (STBD). The design is post-layout using a UMC 0.18 micron technology at a clock rate of 100 MHz. Performance comparisons with other optimization methods and hardware implementations are given.

It is a well-known fact that the BMS algorithm with majority voting can decode up to half the Feng-Rao designed distance dFR. Since dFR is not smaller than the Goppa designed distance dG, that algorithm can correct up to errors. On the other hand, it has been considered to be evident that the original BMS algorithm (without voting) can correct up to errors similarly to the basic algorithm by Skorobogatov-Vladut. But, is it true? In this short paper, we show that it is true, although we need a few remarks and some additional procedures for determining the Groebner basis of the error locator ideal exactly. In fact, as the basic algorithm gives a set of polynomials whose zero set contains the error locators as a subset, it cannot always give the exact error locators, unless the syndrome equation is solved to find the error values in addition.

We consider the minimum cost edge installation problem (MCEI) in a graph G=(V,E) with edge weight w(e)≥ 0, e∈ E. We are given a vertex s∈ V designated as a sink, an edge capacity λ>0, and a source set S⊆ V with demand q(v)∈ [0,λ], v∈ S. For each edge e∈ E, we are allowed to install an integer number h(e) of copies of e. MCEI asks to send demand q(v) from each source v∈ S along a single path Pv to the sink s without splitting the demand of any source v∈ S. For each edge e∈ E, a set of such paths can pass through a single copy of e in G as long as the total demand along the paths does not exceed the edge capacity λ. The objective is to find a set P={Pv| v∈ S∈ of paths of G that minimizes the installing cost ∑e∈ E h(e)w(e). In this paper, we propose a (15/8+ρST)-approximation algorithm to MCEI, where ρST is any approximation ratio achievable for the Steiner tree problem.

Wireless mesh networks have been extensively studied as expandable, flexible, and inexpensive access networks to the Internet. This paper focuses on one composed of multiple access points (APs) connected through multihop wireless communications mainly by the wireless distribution system (WDS). For scalability, the proper partition of APs into multiple WDS clusters is essential, because the number of APs in one cluster is limited due to the increasing radio interference and control packets. In this paper, we formulate this WDS clustering problem and prove the NP-completeness of its decision version through reduction from a known NP-complete problem. Then, we propose its heuristic algorithm, using a greedy method and a variable depth search method, to satisfy the complex constraints while optimizing the cost function. We verify the effectiveness of our algorithm through extensive simulations, where the results confirm its superiority to the existing algorithm in terms of throughput.

In wireless sensor networks, preserving location privacy under successive inference attacks is extremely critical. Although this problem is NP-complete in general cases, we propose a dynamic programming based algorithm and prove it is optimal in special cases where the correlation only exists between p immediate adjacent observations.

We provide an efficient transmission scheme which embeds a pilot signal in the data signal for channel state information (CSI) based on the configuration of a multiple-input multiple-output (MIMO) system using space-time block coding (STBC) with an adaptive array (AA). A computer simulation and analysis show that the proposed scheme, which combines the advantage of an Alamouti-like STBC scheme and the pilot-based AA, can suppress the irreducible error due to random FM noise. The proposed scheme using a pilot minimizes the decoding delay, and is highly robust against fast fading. We show that the proposed scheme can significantly increase the data transmission rate by using the transmitter diversity based on STBC, and the accuracy of the proposed technique is exemplified by a computer simulation.

The algebraic path problem (APP) is a general framework which unifies several solution procedures for a number of well-known matrix and graph problems. In this paper, we present a new 3-dimensional (3-D) orbital algebraic path algorithm and corresponding 2-D toroidal array processors which solve the nn APP in the theoretically minimal number of 3n time-steps. The coordinated time-space scheduling of the computing and data movement in this 3-D algorithm is based on the modular function which preserves the main technological advantages of systolic processing: simplicity, regularity, locality of communications, pipelining, etc. Our design of the 2-D systolic array processors is based on a classical 3-D2-D space transformation. We have also shown how a data manipulation (copying and alignment) can be effectively implemented in these array processors in a massively-parallel fashion by using a matrix-matrix multiply-add operation.

This letter proposes a novel mobile sensor deployment scheme for maximizing coverage. The basic idea is to force mobile sensors to move to predetermined target points that are the optimal layout in a distributed manner using Voronoi diagram data structure. A simulation shows that the result of the proposed scheme is quite close to the optimal result and outperforms previous works.

A method to construct Boolean functions with maximum algebraic immunity have been proposed in . Based on that method, we propose a different method to construct Boolean functions on even variables with maximum algebraic immunity in this letter. By counting on our construction, a lower bound of the number of such Boolean functions is derived, which is the best among all the existing lower bounds.

In network tomography, most work to date is based on exploiting probe packet level correlations to infer the link loss rates and delay distributions. Some other work focuses on identifying the congested links using uncorrelated end-to-end measurements and link prior probability of being congested. In their work, the prior probabilities are identified by the matrix inversion with a number of measurement snapshots, and the algorithm to find the congested links is heuristic and not optimal. In this letter, we present a new estimator for the prior probabilities that is computationally simple, being an explicit function of the measurement snapshots. With these prior probabilities, the identification of the congested link set is equivalent to finding the solution for a probability maximization problem. We propose a fast bottom-up approach named FBA to find the solution for this problem. The FBA optimizes the solution step by step from the bottom up. We prove that the solution by the FBA is optimal.

In this paper, we propose a novel noncoherent maximum likelihood detection (NMLD) method for differential spatial multiplexing (SM) multiple-input multiple-output (MIMO) systems. Unlike the conventional maximum likelihood detection (MLD) method which needs the knowledge of channel state information (CSI) at the receiver, NMLD method has no need of CSI at either the transmitter or receiver. After repartitioning the observation block of multiple-symbol differential detection (MSDD) and following a decision feedback process, the decision metric of NMLD is derived by reforming that of MSDD. Since the maximum Doppler frequency and noise power are included in the derived decision metric, estimations of both maximum Doppler frequency and noise power are needed at the receiver for NMLD. A fast calculation algorithm (FCA) is applied to reduce the computational complexity of NMLD. The feasibility of the proposed NMLD is demonstrated by computer simulations in both slow and fast fading environments. Simulation results show that the proposed NMLD has good bit error rate (BER) performance, approaching that of the conventional coherent MLD with the extension of reference symbols interval. It is also proved that the BER performance is not sensitive to the estimation errors in maximum Doppler frequency and noise power.

We propose the multi-domain adaptive learning that enables us to find a point meeting possibly time-varying specifications simultaneously in multiple domains, e.g. space, time, frequency, etc. The novel concept is based on the idea of feasibility splitting -- dealing with feasibility in each individual domain. We show that the adaptive projected subgradient method (Yamada, 2003) realizes the multi-domain adaptive learning by employing (i) a projected gradient operator with respect to a ‘fixed’ proximity function reflecting the time-invariant specifications and (ii) a subgradient projection with respect to ‘time-varying’ objective functions reflecting the time-varying specifications. The resulting algorithm is suitable for real-time implementation, because it requires no more than metric projections onto closed convex sets each of which accommodates the specification in each domain. A convergence analysis and numerical examples are presented.