A two-dimensional filter for photonic label recognition system using time-to-space conversion and delay compensation was designed using Genetic-Algorithms (GA). For four-bit Binary Phase Shift Keying (BPSK) labels at 160 Gbit/s, contrast ratio of the output for eight different labels was improved by optimization of two-dimentional filtering. The contrast ratio of auto-correlation to cross-correlation larger than 2.16 was obtained by computer simulation. This value is 22% larger than the value of 1.77 with the previously reported system using matched filters.

The wireless robotic sensor network (WRSN) is a combination of a mobile robot and wireless sensor networks. In WRSN, robots perform high-level missions such as human rescue, exploration in dangerous areas, and maintenance and repair of unmanned networks in cooperation with surrounding sensor nodes. In such a network, robots should move to the accident site as soon as possible. This paper proposes a distance-aware robot routing (DAR) algorithm, which focuses on how to pick the shortest path for the mobile robot by considering characteristics different from packet routing. Simulations are performed to demonstrate the benefits of using the proposed algorithm.

This letter presents integrating algorithms for affine constraints defined on a manifold. We first explain definition and geometric representation of affine constraints. Next, we derive integrating algorithms to calculate independent first integrals of affine constraints for the two cases where the they are completely integrable and partially nonintegrable. Moreover, we prove the existence of inverse functions in the algorithms. Some examples are also shown to verify our results.

Base station (BS) cooperation is a promising technique to suppress co-channel interference for cellular networks. However, practical limitations constrain the scale of cooperation, thus the network is divided into small disjoint BS cooperation groups, namely clusters. A decentralized scheme for BS cluster formation is proposed based on efficient BS negotiations, of which the feedback overhead per user is nearly irrelevant to the network size, and the number of iteration rounds scales very slowly with the network size. Simulations show that our decentralized scheme provides significant sum-rate gain over static clustering and performs almost the same as the existing centralized approach. The proposed scheme is well suited for large-scale cellular networks due to its low overhead and complexity.

Accurate registration is crucial for medical image analysis. In this letter, we proposed an improved Demons technique (IDT) for medical image registration. The IDT improves registration quality using orthogonal gradient information. The advantage of the proposed IDT is assessed using 14 medical image pairs. Experimental results show that the proposed technique provides about 8% improvement over existing Demons-based techniques in terms of registration accuracy.

To reduce the manufacturing cost of SoCs with many embedded SRAMs, we propose a scheme to reduce the area per good die for the SoC memory built-in self-test (MBIST). We first propose BIST hardware overhead reduction by application of an encoder-based comparator. For the repair of a faulty SRAM module with 2-D redundancy, we propose spare assignement algorithm. Based on an existing range-cheking-first algorithm (RCFA), we propose assign-all-row-RCFA (A-RCFA) which assign unused spare rows to faulty ones, in order to suppress the degradation of repair rate due to compressed fail location information output from the encoder-based comparator. Then, considering that an SoC has many SRAM modules, we propose a heuristic algorithm based on iterative improvement algorithm (IIA), which determines whether each SRAM should have a spare row or not, in order to minimize area per a good die. Experimental results on practical scale benchmark SoCs with more than 1,000 SRAM modules indicate that encoder-based comparators reduce hardware overhead by about 50% compared to traditional ones, and that combining the IIA-based algorithm for determining redundancy architecture with the encoder-based comparator effectively reduces the area per good die.

In this paper, we discuss the parallel degree of well-structured workflow nets, WF-nets, for short. First, we give the definition of parallel degree, PARAdeg, for WF-nets. Second, we show it is intractable to compute the value of PARAdeg for acyclic well-structured WF-nets. Next we construct two heuristic algorithms to compute the value. The first algorithm is focused on nest structure and the second one is focused on the longest path. Finally, we perform an experiment to compare the two algorithms and the result is that the accuracy of the first algorithm based on nest structure was higher than that of the second one based on the longest path for most well-structured WF-nets and the accuracy of the second one is better than that of first one only when the well-structured workflow nets are mainly composed by the parallel structures.

We propose an "estimation of distribution algorithm" incorporating switching. The algorithm enables switching from the standard estimation of distribution algorithm (EDA) to the genetic algorithm (GA), or vice versa, on the basis of switching criteria. The algorithm shows better performance than GA and EDA in deceptive problems.

Vision sensors provide rich sources of information, but sensing images and processing them in real time would be a challenging task. This paper introduces a vision system using SoCBase platform and presents heuristic designs of SAD correlation algorithm as a component of the vision system. Simulation results show that the vision system is suitable for real-time applications and that the heuristic designs of SAD algorithm are worth utilizing since they save a considerable amount of space with little sacrificing in quality.

In this work, a high performance LDPC decoder architecture is presented. It is a partially-parallel architecture for low-complexity consideration. In order to eliminate the idling time and hardware complexity in conventional partially-parallel decoders, the decoding process, decoder architecture and memory structure are optimized. Particularly, the parity-check matrix is optimally partitioned into four unequal sub-matrices that lead to high efficiency in hardware sharing. As a result, it can handle two different codewords simultaneously with 100% hardware utilization. Furthermore, for minimizing the performance loss due to round-off errors in fixed-point implementations, the well-known modified min-sum decoding algorithm is enhanced by our recently proposed high-performance CMVP decoding algorithm. Overall, the proposed decoder has high throughput, low complexity, and good BER performances. In the circuit implementation example of the (576,288) parity check matrix for IEEE 802.16e standard, the decoder achieves a data rate of 5.5 Gbps assuming 10 decoding iterations and 7 quantization bits, with a small area of 653 K gates, based on UMC 90 nm process technology.

In this paper we propose an unsupervised method of optimizing structuring elements (SEs) used for impulse noise reduction in texture images through the opening operation which is one of the morphological operations. In this method, a genetic algorithm (GA), which can effectively search wide search spaces, is applied and the size of the shape of the SE is included in the design variables. Through experiments, it is shown that our new approach generally outperforms the conventional method.

We consider the backlight calculation of local dimming as an optimization problem. The luminance produced by many LEDs at each pixel considered is calculated which should cover the gray value of each pixel, while the sum of LED currents is to be minimized. For this purpose a specific approach called as "Sorted Sector Covering" (SSC) was developed and is described in this paper. In our pre-processing unit called condenser the source image is reduced to a matrix of much lower resolution so that the computation effort of the SSC algorithm is drastically reduced. During this preprocessing phase, filter functions can be integrated so that a further reduction of the power consumption is achieved. Our processing system allows high power saving and high visual quality at low processor cost. We approach the local dimming problem in the physical viewing direction -- from LED to pixel. The luminance for the pixel is based on the light spread function (LSF) and the PWM values of the LEDs. As the physical viewing direction is chosen, this method is universal and can be applied for any kind of LED arrangement -- direct-lit as well as edge-lit. It is validated on prototypes, e.g., a locally dimmed edge-lit TV.

Image registration is an important topic in medical image analysis. It is usually used in 2D mosaics to construct the whole image of a biological specimen or in 3D reconstruction to build up the structure of an examined specimen from a series of microscopic images. Nevertheless, owing to a variety of factors, including microscopic optics, mechanisms, sensors, and manipulation, there may be great differences between the acquired image slices even if they are adjacent. The common differences include the chromatic aberration as well as the geometry discrepancy that is caused by cuts, tears, folds, and deformation. They usually make the registration problem a difficult challenge to achieve. In this paper, we propose an efficient registration method, which consists of a feature-based registration approach based on analytic robust point matching (ARPM) and a refinement procedure of the feature-based Levenberg-Marquardt algorithm (FLM), to automatically reconstruct 3D vessels of the rat brains from a series of microscopic images. The registration algorithm could speedily evaluate the spatial correspondence and geometric transformation between two point sets with different sizes. In addition, to achieve subpixel accuracy, an FLM method is used to refine the registered results. Due to the nonlinear characteristic of FLM method, it converges much faster than most other methods. We evaluate the performance of proposed method by comparing it with well-known thin-plate spline robust point matching (TPS-RPM) algorithm. The results indicate that the ARPM algorithm together with the FLM method is not only a robust but efficient method in image registration.

Recently, Mooij et al. proposed new sufficient conditions for convergence of the sum-product algorithm, and it was also shown that if the factor graph is a tree, Mooij's sufficient condition for convergence is always activated. In this letter, we show that the converse of the above statement is also true under some assumption, and that the assumption holds for the sum-product decoding. These newly obtained fact implies that Mooij's sufficient condition for convergence of the sum-product decoding is activated if and only if the factor graph of the a posteriori probability of the transmitted codeword is a tree.

Random scattering of sensors may cause some location not to be covered. In such a case, it is useful to make use of mobile sensors that can move to eliminate the coverage holes. Wang et al [1]. proposed self-deployment schemes of mobile sensors by using Voronoi polygon. However, some coverage holes still remain after the execution of the schemes. We propose a new self-deployment scheme using the centroid (geometric center) of each sensor's Voronoi polygon as the moving target position. The performance evaluation shows that the proposed scheme achieves better results than the existing schemes in terms of fast coverage expansion.

Let T be a tree with n nodes, in which each edge is associated with a length and a weight. The density-constrained longest (heaviest) path problem is to find a path of T with maximum path length (weight) whose path density is bounded by an upper bound and a lower bound. The path density is the path weight divided by the path length. We show that both problems can be solved in optimal O(n log n) time.

We propose a new parameter-extraction approach based on a mixed-mode genetic algorithm (GA), including the efficient search-space separation and local-minima-convergence prevention process. The technique, substantially extended from our previous work, allows the designed figures-of-merit, such as internal quantum efficiency (ηi) as well as transparency current density (Jtr) of lasers and minimum noise figure (NFmin) as well as associated available gain (GA,assoc) of low-noise amplifiers (LNAs), extracted by an analytical equation-based methodology combined with an evolutionary numerical tool. Extraction results, which agree well with actually measured data, for both state-of-the-art InGaAs quantum-well lasers and advanced SiGe LNAs are presented for the first time to demonstrate this multi-parameter analysis and high-accuracy optimization.

Estimating the ratio of two probability density functions (a.k.a. the importance) has recently gathered a great deal of attention since importance estimators can be used for solving various machine learning and data mining problems. In this paper, we propose a new importance estimation method using a mixture of probabilistic principal component analyzers. The proposed method is more flexible than existing approaches, and is expected to work well when the target importance function is correlated and rank-deficient. Through experiments, we illustrate the validity of the proposed approach.

For optimization problems with irregular and complex multimodal landscapes, Estimation of Distribution Algorithms (EDAs) suffer from the drawback of premature convergence similar to other evolutionary algorithms. In this paper, we propose an adaptive niching EDA based on Affinity Propagation (AP) clustering analysis. The AP clustering is used to adaptively partition the niches and mine the searching information from the evolution process. The obtained information is successfully utilized to improve the EDA performance by using a balance niching searching strategy. Two different categories of optimization problems are used to evaluate the proposed adaptive niching EDA. The first one is solving three benchmark functional multimodal optimization problems by a continuous EDA based on single Gaussian probabilistic model; the other one is solving a real complicated discrete EDA optimization problem, the HP model protein folding based on k-order Markov probabilistic model. Simulation results show that the proposed adaptive niching EDA is an efficient method.

This paper presents an approach for improving proximity and diversity in multiobjective evolutionary algorithms (MOEAs). The idea is to discover new nondominated solutions in the promising area of search space. It can be achieved by applying mutation only to the most converged and the least crowded individuals. In other words, the proximity and diversity can be improved because new nondominated solutions are found in the vicinity of the individuals highly converged and less crowded. Empirical results on multiobjective knapsack problems (MKPs) demonstrate that the proposed approach discovers a set of nondominated solutions much closer to the global Pareto front while maintaining a better distribution of the solutions.