We extend the adaptive-order rational Arnoldi algorithm for multiple-inputs and multiple-outputs (MIMO) interconnect model order reductions. Instead of using the standard Arnoldi algorithm for the SISO adaptive-order reduction algorithm (AORA), we study the adaptive-order rational global Arnoldi (AORGA) algorithm for MIMO model reductions. In this new algorithm, the input matrix is treated as a vector form. A new matrix Krylov subspace, generated by the global Arnoldi algorithm, will be developed by a Frobenius-orthonormal basis. By employing congruence transformation with the matrix Krylov subspace, the one-sided projection method can be used to construct a reduced-order system. It will be shown that the system moment matching can be preserved. In addition, we also show that the transfer matrix residual error of the reduced system can be derived analytically. This error information will provide a guideline for the order selection scheme. The algorithm can also be applied to the classical multiple point MIMO Pade approximation by the rational Arnoldi algorithm for multiple expansion points. Experimental results demonstrate the feasibility and the effectiveness of the proposed method.

This paper proposes a low power scan test scheme and formulates a problem based on this scheme. In this scheme the flip-flops are grouped into N scan chains. At any time, only one scan chain is active during scan test. Therefore, both average power and peak power are reduced compared with conventional full scan test methodology. This paper also proposes a tabu search-based approach to minimize test application time. In this approach we handle the information during deterministic test efficiently. Experimental results demonstrate that this approach drastically reduces both average power and peak power dissipation at a little longer test application time on various benchmark circuits.

This paper proposes the combination of adjustable architecture and parameter optimization software, employing a method based on artificial intelligence (AI), to realize an image rejection mixer (IRM) that can enhance its image rejection ratio within a short period of time. The main components of the IRM are 6 Gilbert-cell multipliers. The tail current of each multiplier is adjusted by the optimization software, and the gain and phase characteristics are optimized. This adjustment is conventionally extremely difficult because the 6 tail currents to be adjusted simultaneously are mutually interdependent. In order to execute this adjustment efficiently, we employed a Genetic Algorithm (GA) that is a robust search algorithm that can find optimal parameter settings in a short time. We have successfully developed an IRM chip that has a performance of 71 dB and is suitable for single-chip integration with WCDMA applications.

In this paper, a new dimming algorithm for the electronic ballast of an electrodeless fluorescent lamp is proposed. The proposed method is based on the burst dimming method that controls the duty ratio for the two switches of the electronic ballast by intermittently modulated pulse signal. This paper presents a fully digital circuit using an erasable programmable logic device (EPLD). To verify the validity of the proposed method, the implemented control circuit was applied to the electronic ballast for a 100 W electrodeless fluorescent lamp. As a result, a dimming method with a wide illumination range from 5 to 100% was obtained.

We propose an optimal algorithm for the real-time scheduling of parallel tasks on multiprocessors, where the tasks have the properties of flexible preemption, linear speedup, bounded parallelism, and arbitrary deadline. The proposed algorithm is optimal in the sense that it always finds out a feasible schedule if one exists. Furthermore, the algorithm delivers the best schedule consuming the fewest processors among feasible schedules. In this letter, we prove the optimality of the proposed algorithm. Also, we show that the time complexity of the algorithm is O(M2N2) in the worst case, where M and N are the number of tasks and the number of processors, respectively.

Electrical capacitance tomography (ECT) is used to obtain information about the distribution of a mixture of dielectric materials inside a vessel or pipe. ECT has several advantages over other reconstruction algorithms and has found many applications in the industrial fields. However, there are some difficulties with image reconstruction in ECT: The relationship between the permittivity distribution and measured capacitance is nonlinear. And inverse problem is ill-posed so that the inverse solution is sensitive to measurement error. To cope with these difficulties iterative image reconstruction algorithms have been developed. In general, the iterative reconstruction algorithms in ECT have comparatively good-quality in reconstructed images but result in intensive computational burden. This paper presents the iterative image reconstruction algorithm for ECT that can enhance the speed of image reconstruction without degradation in the quality of reconstructed image. The main contribution of the proposed algorithm is new weighting matrices, which are obtained by the interpolation of the grouped electrical field centre lines (EFCLs). Extensive simulation results have demonstrated that proposed algorithm provides improved reconstruction performance in terms of computational time and image quality.

Recent work has shown that the usage of multiple antennas at the transmitter and receiver in a flat fading environment results in a linear increase in channel capacity. But increasing the number of antennas induces the higher hardware costs and computational burden. To overcome those problems, it is effective to select antennas appropriately among all available ones. In this paper, a new antenna selection method is proposed. The transmit antennas are selected so as to maximize the channel capacity using the genetic algorithm (GA) which is the one of the general random search algorithm. The results show that the proposed GA achieves almost the same performance as the optimal selection method with less computational amount.

In this letter, a new addressing scheme for hexagonal networks is proposed. Using the new addressing scheme, many routing algorithms designed for networks using square-based topologies such as mesh and torus can also be applied to hexagonal networks. Methods of applying the turn model to hexagonal networks are derived, with some new minimal and partial adaptive routing algorithms obtained. Simulations of the new routing algorithms under different working conditions are carried on hexagonal networks of various sizes. The results show that the proposed algorithms can offer lower packet delay and loss rate than the popular dimension order routing algorithm.

Let H be a graph with a designated vertex s, where edges are weighted by nonnegative reals. Splitting edges e={u,s} and e'={s,v} at s is an operation that reduces the weight of each of e and e' by a real δ>0 while increasing the weight of edge {u,v} by δ. It is known that all edges incident to s can be split off while preserving the edge-connectivity of H and that such a complete splitting is used to solve many connectivity problems. In this paper, we give an O(mn+n2log n) time algorithm for finding a complete splitting in a graph with n vertices and m edges.

The medium access control (MAC) protocol is the main determiner of the system throughput in Wireless Local Area Networks (WLANs). The MAC technique of the IEEE 802.11 protocol is called Distributed Coordination Function (DCF). DCF is based on a carrier sense multiple access with collision avoidance (CSMA/CA) scheme with binary slotted exponential backoff. Each station generates a random backoff interval before transmitting a packet to minimize the probability of collision with packets being transmitted by other stations. However, when the number of stations increases, the system throughput decreases. This paper proposes a new backoff algorithm that uses finish tags. The proposed algorithm uses the finish tag of each station to control the backoff intervals so as to improve system throughput. The finish tag is updated when a packet reaches the front of its flow, and it is attached to the packet just prior to transmission. When a station receives packets with older finish tags, its backoff time interval is increased. For this reason, the more the stations there are, the larger the backoff time becomes. Simulations confirm that the proposal improves system throughput of a IEEE 802.11 network under saturation conditions.

In this paper, a 2-D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares solution in the theory of 1-D robust regression is extended to 2-D. Then a new algorithm is derived which combines 2-D robust regression concepts with the 2-D recursive least squares lattice algorithm. With this approach, whatever the probability distribution of the prediction error may be, small weights are assigned to the outliers so that the least squares algorithm will be less sensitive to the outliers. Implementation of the proposed iteratively reweighted least squares lattice algorithm to the problem of defect detection in textured images is then considered. The performance evaluation, in terms of defect detection rate, demonstrates the importance of the proposed algorithm in reducing the effect of the outliers that generally correspond to false alarms in classification of textures as defective or nondefective.

We consider the classification problem to construct a classifier c:{0,1}n{0,1} from a given set of examples (training set), which (approximately) realizes the hidden oracle y:{0,1}n{0,1} describing the phenomenon under consideration. For this problem, a number of approaches are already known in computational learning theory; e.g., decision trees, support vector machines (SVM), and iteratively composed features (ICF). The last one, ICF, was proposed in our previous work (Haraguchi et al., (2004)). A feature, composed of a nonempty subset S of other features (including the original data attributes), is a Boolean function fS:{0,1}S{0,1} and is constructed according to the proposed rule. The ICF algorithm iterates generation and selection processes of features, and finally adopts one of the generated features as the classifier, where the generation process may be considered as embodying the idea of boosting, since new features are generated from the available features. In this paper, we generalize a feature to an extended Boolean function fS:{0,1,*}S{0,1,*} to allow partial knowledge, where * denotes the state of uncertainty. We then propose the algorithm ICF* to generate such generalized features. The selection process of ICF* is also different from that of ICF, in that features are selected so as to cover the entire training set. Our computational experiments indicate that ICF* is better than ICF in terms of both classification performance and computation time. Also, it is competitive with other representative learning algorithms such as decision trees and SVM.

Given a graph G=(V,E), a set of vertices S ⊆ V covers v ∈ V if the edge connectivity between S and v is at least a given number k. Vertices in S are called sources. The source location problem is a problem of finding a minimum-size source set covering all vertices of a given graph. This paper presents a new variation of the problem, called maximum-cover source-location problem, which finds a source set S with a given size p, maximizing the sum of the weight of vertices covered by S. It presents an O(np + m + nlog n)-time algorithm for k=2, where n=|V| and m=|E|. Especially it runs linear time if G is connected. This algorithm uses a subroutine for finding a subtree with the maximum weight among p-leaf trees of a given vertex-weighted tree. For the problem we give a greedy-based linear-time algorithm, which is an extension of the linear-time algorithm for finding a longest path of a given tree presented by E. W. Dijkstra around 1960. Moreover, we show some polynomial solvable cases, e.g., a given graph is a tree or (k-1)-edge-connected, and NP-hard cases, e.g., a vertex-cost function is given or G is a digraph.

A radio network (RN for short) is a distributed system with no central arbiter, consisting of n radio transceivers, henceforth referred to as stations. We assume that the stations run on batteries and expends power while broadcasting/receiving a data packet. Thus, the most important measure to evaluate protocols on the radio network is the number of awake time slots, in which a station is broadcasting/receiving a data packet. We also assume that the stations are identical and have no unique ID number, and no station knows the number n of the stations. For given n keys one for each station, the ranking problem asks each station to determine the number of keys in the RN smaller than its own key. The main contribution of this paper is to present an optimal randomized ranking protocol on the k-channel RN. Our protocol solves the ranking problem, with high probability, in O(+log n) time slots with every station being awake for at most O(log n) time slots. We also prove that any randomized ranking protocol is required to run in expected Ω(+log n) time slots with at least one station being awake for expected Ω(log n) time slots. Therefore, our ranking protocol is optimal.

Suppose that we have a timetable of a round-robin tournament with a number of teams, and distances among their homes. The home-away assignment problem is to find a home-away assignment that minimizes the total traveling distance of the teams. We propose a formulation of the home-away assignment problem as an integer program, and a rounding algorithm based on Bertsimas, Teo and Vohra's dependent randomized rounding method [2]. Computational experiments show that our method quickly generates feasible solutions close to optimal.

In this paper, we will report practical modifications of the side-channel analysis to (EC)DSA [1],[2],[5],[34] that Leadbitter et al. have proposed in [16]. To apply the analyses, we assume that the window method is used in the exponentiation or elliptic curve (EC) scalar multiplication and the side-channel information described in Sect. 3.2 can be collected. So far, the method in [16] hasn't been effective when the size q of a cyclic group used in (EC)DSA is 160 bit long and the window size w < 9. We show that the modified method we propose in this paper is effective even when q is 160 bit long and w=4. This shows that our method is effective for various practical implementations, e.g., that in resource restricted environment like IC card devises. First, we estimate the window size w necessary for the proposed analyses (attacks) to succeed. Then by experiment of the new method, we show that private keys of (EC)DSA can be obtained under the above assumptions, in practical time and with sufficient success rate. The result raises the necessity of countermeasures against the analyses (attacks) in the window method based implementation of (EC)DSA.

An improved genetic algorithm for solving the graph planarization problem is presented. The improved genetic algorithm which is designed to embed a graph on a plane, performs crossover and mutation conditionally instead of probability. The improved genetic algorithm is verified by a large number of simulation runs and compared with other algorithms. The experimental results show that the improved genetic algorithm performs remarkably well and outperforms its competitors.

In this work we present an energy efficient leader election protocol for anonymous radio network populated with n mobile stations. Previously, Nakano and Olariu have presented a leader election protocol that terminates, with probability exceeding 1- (f ≥ 1), in log log n+o(log log n)+O(log f) time slots [14]. As the above protocol works under the assumption that every station has the ability to transmit and monitor the channel at the same time, it requires every station to be equipped with two transceivers. This assumption, however, is unrealistic for most mobile stations due to constraints in cost, size, and energy dissipation. Our main contribution is to show that it is possible to elect a leader in an anonymous radio network where each station is equipped with a single transceiver. Quite surprisingly, although every station has only one transceiver, our leader election protocol still runs, with probability exceeding 1- (f ≥ 1), in log log n+o(log log n)+O(log f) time slots. Moreover, our leader election protocol needs only expected O(n) total awake time slots, while Nakano and Olariu's protocol needs expected O(nlog log n) total awake time slots. Since every leader election protocol needs at least Ω(n) awake time slots, our leader election protocol is optimal in terms of the expected awake time slots.

A naive coding of floorplans needs 2m bits for each floorplan. In this paper we give a new simple coding of floorplans, which needs only 5m/3 bits for each floorplan.

The concept of M-convex functions has recently been generalized for functions defined on constant-parity jump systems. The b-matching problem and its generalization provide canonical examples of M-convex functions on jump systems. In this paper, we propose a steepest descent algorithm for minimizing an M-convex function on a constant-parity jump system.