Adaptive transmission methods improve the performance of wireless communication system by adjusting parameters like modulation, code-rate, and power depending on the channel state adaptively. In this letter, we consider the adaptive code-rate OFDM system in which code-rate of each subcarrier is adapted optimally. RCPC code is used to obtain different code-rate for each subcarrier. Performance analysis shows that 3-6 dB SNR gain or up to 30-50% data rate increase is achieved at bit error rate 10-6.

We have proposed a neural network called the Lagrange programming neural network with polarized high-order connections (LPPH) for solving the satisfiability problem (SAT) of propositional calculus. The LPPH has gradient descent dynamics for variables and gradient ascent dynamics for Lagrange multipliers, which represent the weights of the clauses of the SAT. Each weight wr increases according to the degree of unsatisfaction of clause Cr. This causes changes in the energy landscape of the Lagrangian function, on which the values of the variables change in the gradient descent direction. It was proved that the LPPH is not trapped by any point that is not a solution of the SAT. Experimental results showed that the LPPH can find solutions faster than existing methods. In the LPPH dynamics, a function hr(x) calculates the degree of unsatisfaction of clause Cr via multiplication. However, this definition of hr(x) has a disadvantage when the number of literals in a clause is large. In the present paper, we propose a new definition of hr(x) in order to overcome this disadvantage using the "min" operator. In addition, we extend the LPPH to solve the constraint satisfaction problem (CSP). Our neural network can update all neurons simultaneously to solve the CSP. In contrast, conventional discrete methods for solving the CSP must update variables sequentially. This is advantageous for VLSI implementation.

Incorporating sensor nodes with data aggregation capability to transmit less data flow in wireless sensor networks could reduce the total energy consumption. This calls for the efficient and effective data-centric routing algorithm to facilitate this advantage. In the first part of this paper, we model the data-centric routing problem by rigorous mixed integer and linear mathematical formulation, where the objective function is to minimize the total transmission cost subject to multicast tree constraints. With the advancement of sensor network technology, sensor nodes with configurable transmission radius capability could further reduce energy consumption. The second part of this paper considers the transmission radius assignment of each sensor node and the data-centric routing assignment jointly. The objective function is to minimize the total power consumption together with consideration of construction of a data aggregation tree and sensor node transmission radius assignment. The solution approach is based on Lagrangean relaxation in conjunction with the novel optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithms calculate better solution than other existing heuristics with improvement ratio up to 169% and 59% with respect to fixed transmission radius and configurable transmission radius for network with 300 random generated nodes.

This paper presents a fuzzy optimization based scheduling method for the manufacturing systems with uncertain production capacities. To address the uncertainties efficiently, the fuzzy optimization technique is used in defining the scheduling problem. Based on the symmetric approach of fuzzy optimization and Lagrangian relaxation technique, a practical fuzzy-optimization based algorithm is developed. The computational experiments based on the real factory data demonstrate that the proposed method provides robust scheduling to hedge against uncertainties.

An essential issue in designing, operating and managing a modern network is to assure end-to-end QoS from users perspective, and in the meantime to optimize a certain average performance objective from the systems perspective. So in the first part of this paper, we address the above issue by using the rerouting approach, where the objective is to minimize the average cross-network packet delay in virtual circuit networks with the consideration of an end-to-end delay constraint (DCR) for each O-D pair. The problem is formulated as a multicommodity network flow problem with integer routing decision variables, where additional end-to-end delay constraints are considered. As the traffic demands increases over time, the rerouting approach may not be applicable, which results in the necessity of capacity augmentation. Henceforth, the second part of this paper is to jointly consider the link capacity assignment and the routing problem (JCR) at the same time where the objective is to minimize the total link installation cost with considering the average and end-to-end delay constraints. Unlike previous research tackling this problem with a two-phase approach, we propose an integrated approach to considering the routing and capacity assignment at the same time. The difficulties of DCR and JCR result from the integrality nature and particularly the nonconvexity property associated with the end-to-end delay constraints. We propose novel Lagrangean relaxation based algorithms to solve the DCR and the JCR problems. Through computational experiments, we show that the proposed algorithms calculate near-optimal solutions for the DCR problem and outperform previous two-phase approach for the JCR problem under all tested cases.

This paper discusses the least-squares linear filtering and fixed-lag smoothing problems of discrete-time signals from uncertain observations when the random interruptions in the observation process are modelled by a sequence of not necessarily independent Bernoulli variables. It is assumed that the observations are perturbed by white noise and the autocovariance function of the signal is factorizable. Using an innovation approach we obtain the filtering and fixed-lag smoothing recursive algorithms, which do not require the knowledge of the state-space model generating the signal. Besides the observed values, they use only the matrix functions defining the factorizable autocovariance function of the signal, the noise autocovariance function, the marginal probabilities and the (2,2)-element of the conditional probability matrices of the Bernoulli variables. The algorithms are applied to estimate a scalar signal which may be transmitted through one of two channels.

The multi-channel Hybrid Fiber Coaxial (HFC) network is essentially a shared medium with multi-channels. Its operation requires the use of a scheduling algorithm to manage the data transmission within each channel. The Data-Over-Cable Service Interface Specification (DOCSIS) protocol is an important standard for HFC networks. Since this protocol does not explicitly specify the scheduling algorithm to be used, many alternative algorithms have been proposed. However, none of these algorithms are applicable to the scheduling of non-Unsolicited Grant Service (UGS) data in multi-channel HFC networks. Accordingly, the present study develops a multi-channel scheduling algorithm which optimizes the scheduling delay time of each transmitted non-UGS request. This algorithm manages the amount of data transmission in each upstream channel according to the overall network load and the bandwidth available in each channel. This study constructs a mathematical model of the algorithm and then uses this model as the basis for a series of simulations in which the performance of the scheduling algorithm is evaluated.

This paper treats the least-squares linear filtering and smoothing problems of discrete-time signals from uncertain observations when the random interruptions in the observation process are modelled by a sequence of independent Bernoulli random variables. Using an innovation approach we obtain the filtering algorithm and a general expression for the smoother which leads to fixed-point, fixed-interval and fixed-lag smoothing recursive algorithms. The proposed algorithms do not require the knowledge of the state-space model generating the signal, but only the covariance information of the signal and the observation noise, as well as the probability that the signal exists in the observed values.

In this paper, we propose an optimal bit rate control algorithm which is fully compatible with MPEG-4 or H.263+. The proposed algorithm is designed to identify the optimal quantizer set through Lagrangian optimization when used for optimal bit allocation. To find the optimal quantizer set, we make use of the Viterbi algorithm in order to solve the dependency between quantization parameters of each macroblock due to the unique characteristics of MPEG-4 or H.263+. We set the Lagrangian cost function as a cost function of the Viterbi algorithm. We implement the proposed algorithm in MPEG-4 coders and compare its performance to the VM8 and optimal bit rate control algorithm, using independent quantization parameters in the circumstance of a low bit rate.

Structural learning algorithms are obtained by adding a penalty criterion (usually comes from the network structure) to the conventional criterion of the sum of squared errors and applying the backpropagation (BP) algorithm. This problem can be viewed as a constrained minimization problem. In this paper, we apply the Lagrangian differential gradient method to the structural learning based on the backpropagation-like algorithm. Computational experiments for both artificial and real data show that the improvement of generalization performance and the network optimization are obtained applying the proposed method.

A real-time frame-layer rate control algorithm using sliding window method is proposed for low bit rate video coding over the Internet. The proposed rate control method performs bit allocation at the frame level to minimize the average distortion over an entire sequence as well as variations in distortion between frames. A new frame-layer rate-distortion model is derived, and a non-iterative optimization method is used for low computational complexity. In order to reduce the quality fluctuation, we use a sliding window scheme which does not require the pre-analysis process. Therefore, the proposed algorithm does not produce time delay from encoding, and is suitable for real-time low-complexity video encoder. Experimental results indicate that the proposed control method provides better visual and PSNR performance than the existing TMN8 rate control method.

The development of efficient quality of service (QoS) routing algorithms in a high-speed network environment is a very important and at the same time very difficult task due to the need to provide divergent services with multiple QoS requirements. Recently heuristic algorithms based on Lagrange relaxation techniques have been proposed to resolve the contradiction between the time complexity and the quality of solution. In this paper, we investigate the performance of two heuristic algorithms, LR_DCLC and NR_DCLC, for the delay-constrained least-cost (DCLC) routing problem. Algorithm LR_DCLC is based on linear relaxation, while algorithm NR_DCLC, which is proposed in this paper, is based on nonlinear relaxation. A large number of simulations demonstrate that even though both algorithms have very good performance, NR_DCLC can obtain much better solutions than LR_DCLC by running Dijkstra's algorithm on average a few more times, especially in the case when the optimal solutions are hard to find.

In this paper, we present an image compression algorithm using two concepts, subdividing an image matrix and stratifying submatrices into FD-submatrices (feature distribution submatrices). According to the feature distribution and the view that an image can be decomposed into some feature layers, we generate a compression tree by setting up a logic process of decomposition and stratification. To get better compression ratios, the set of submatrices having one and zero as elements, including logic flag sequences is compressed by vector space theory.

The Real-time VLBI Correlator (RVC) is a new type processor for the Very-Long-Baseline Interferometry (VLBI). This correlator was primarily designed for supporting the VLBI Space Observatory Programme (VSOP). Two particular techniques, the fringe rotator after correlation and the lag-time extension technique, are newly developed for the RVC. The correlation circuit size of VLBI correlator is reduced to half by introducing the new fringe rotator, and it makes possible to realize a large delay window being essential in finding a cross correlation in real-time. The delay window can be changed flexibly with the lag-time extension technique, and its technique is useful to detect the fringe peak in a VSOP observation. The new correlator was installed at the Usuda Deep Space Center in Japan, and is used in VSOP and other domestic VLBI observations. In this paper, the key features of the Real-time VLBI Correlator (RVC) focusing on these advanced techniques are presented, and the results of its performance test are shown.

This paper proposes a new simple method for network measurement. It extracts 6-bit control flags of TCP (Transmission Control Protocol) packets. The idea is based on the unique feature of flag ratios which is discovered by our exhaustive search for the new indexes of network traffic. By the use of flag ratios, one can tell if the network is really congested. It is much simpler than the conventional network monitoring by a network analyzer. The well-known monitoring method is based on the utilization parameter of a communication circuit which ranges from 0% to 100%. One cannot tell the line is congested even if the factor is 100%. 100% means full utilization and does not give any further information. To calculate the real performance of the network, one should estimate the throughput or effective speed of each user. The estimation needs much calculation. Our new method tries to correlate ratios of TCP control flags and network congestion. The result shows the usefulness of this new method. This paper analyzes the reason why the flag ratios show the unique feature.

We consider the performance of hyperelliptic curve cryptosystems over the fields Fp vs. F2n. We analyze the complexity of the group law of the jacobians JC(Fp) and JC(F2n) and compare their performance taking into consideration the effectiveness of the word size (32-bit or 64-bit) of the applied CPU (Alpha and Pentium) on the arithmetic of the definition field. Our experimental results show that JC(F2n) is faster than JC(Fp) on an Alpha, whereas JC(Fp) is faster than JC(F2n) on a Pentium. Moreover, we investigate the algorithm of the jacobian and the definition-field arithmetic to clarify our results from a practical point of view, with theoretical analysis.

In this paper, we consider the working VP and backup VP routing problems jointly and employ the integer programming based approach to maximize the system resource utilization and the network survivability. The VP planning problem is formulated as a nonlinear combinatorial optimization problem. The objective function minimizes the resource usage while maximizing the network survivability. By proper transformation of the objective function and applying cutting plane method, the original formulation is transformed into an integer linear programing formulation which is suitable for applying Lagrangian relaxation techniques. After Lagrangian relaxation, the problem is further decomposed into several tractable subproblems. Unlike others' work, the candidate path set does not need to be prepared in advance and the best paths are generated while solving subproblems in our approach. Heuristic algorithms based on the solving procedure of the Lagrangian relaxation are developed. Closely examining the gap between the heuristic upper bounds and the Lagrangian lower bounds reveals that the proposed algorithm can efficiently provide a nearly optimal solution for the survivable VP layout design in ATM networks.

Secret sharing schemes are good for protecting the important secrets. They are, however, inefficient if the secret shadow held by the shadowholder cannot be reused after recovering the shared secret. Traditionally, the (t, n) secret sharing scheme can be used only once, where t is the threshold value and n is the number of participants. To improve the efficiency, we propose an efficient dynamic secret sharing scheme. In the new scheme, each shadowholder holds a secret key and the corresponding public key. The secret shadow is constructed from the secret key in our scheme, while in previously proposed secret sharing schemes the secret key is the shadow. In addition, the shadow is not constructed by the shadowholder unless it is necessary, and no secure delivery channel is needed. Morever, this paper will further discuss how to change the shared secret, the threshold policy and cheater detection. Therefore, this scheme provides an efficient way to maintain important secrets.

In this paper we present an algorithm to solve an as-yet untreated knapsack problem, the Multidimensional Multiple-choice Knapsack Problem (MMKP). Since our specific application occurs in the real-time domain, a solution for the MMKP with a small upper bound on the runtime is desirable. Thus, the Lagrange multiplier method is chosen, and a heuristic with a worst-case runtime behavior better than O(n2m) is developed, n being the number of elements and m the number of dimensions. Extensive testing against an exact algorithm based on partial enumeration is used to establish the accuracy and efficiency of the heuristic.

This paper describes the results of tests that measured the allowable delay between images and tactile information via a force feedback device. In order to investigate the allowable delay, two experiments were performed: 1) subjective evaluation in real space and 2) subjective evaluation in virtual space using a force feedback device.