A constant-time dynamic storage allocator for real-time systems

Dynamic memory allocation has been used for decades. However, it has seldom been used in real-time systems since the worst case of spatial and temporal requirements for allocation and deallocation operations is either unbounded or bounded but with a very large bound. In this paper, a new allocator called TLSF (Two Level Segregated Fit) is presented. TLSF is designed and implemented to accommodate real-time constraints. The proposed allocator exhibits time-bounded behaviour, O(1), and maintains a very good execution time. This paper describes in detail the data structures and functions provided by TLSF. We also compare TLSF with a representative set of allocators regarding their temporal cost and fragmentation. Although the paper is mainly focused on timing analysis, a brief study and comparative analysis of fragmentation incurred by the allocators has been also included in order to provide a global view of the behaviour of the allocators. The temporal and spatial results showed that TLSF is also a fast allocator and produces a fragmentation close to that caused by the best existing allocators.

Supervised and Reinforcement Evolutionary Learning for Wavelet-based Neuro-fuzzy Networks

This study presents a wavelet-based neuro-fuzzy network (WNFN). The proposed WNFN model combines the traditional Takagi–Sugeno–Kang (TSK) fuzzy model and the wavelet neural networks (WNN). This study adopts the non-orthogonal and compactly supported functions as wavelet neural network bases. A novel supervised evolutionary learning, called WNFN-S, is proposed to tune the adjustable parameters of the WNFN model. The proposed WNFN-S learning scheme is based on dynamic symbiotic evolution (DSE). The proposed DSE uses the sequential-search-based dynamic evolutionary (SSDE) method. In some real-world applications, exact training data may be expensive or even impossible to obtain. To solve this problem, the reinforcement evolutionary learning, called WNFN-R, is proposed. Computer simulations have been conducted to illustrate the performance and applicability of the proposed WNFN-S and WNFN-R learning algorithms.     

Topological Repairing of 3D Digital Images

We present here a new randomized algorithm for repairing the topology of objects represented by 3D binary digital images. By “repairing the topology”, we mean a systematic way of modifying a given binary image in order to produce a similar binary image which is guaranteed to be well-composed. A 3D binary digital image is said to be well-composed if, and only if, the square faces shared by background and foreground voxels form a 2D manifold. Well-composed images enjoy some special properties which can make such images very desirable in practical applications. For instance, well-known algorithms for extracting surfaces from and thinning binary images can be simplified and optimized for speed if the input image is assumed to be well-composed. Furthermore, some algorithms for computing surface curvature and extracting adaptive triangulated surfaces, directly from the binary data, can only be applied to well-composed images. Finally, we introduce an extension of the aforementioned algorithm to repairing 3D digital multivalued images. Such an algorithm finds application in repairing segmented images resulting from multi-object segmentations of other 3D digital multivalued images.

An asynchronous recurrent linear threshold network approach to solving the traveling salesman problem

In this paper, an approach to solving the classical Traveling Salesman Problem (TSP) using a recurrent network of linear threshold (LT) neurons is proposed. It maps the classical TSP onto a single-layered recurrent neural network by embedding the constraints of the problem directly into the dynamics of the network. The proposed method differs from the classical Hopfield network in the update of state dynamics as well as the use of network activation function. Furthermore, parameter settings for the proposed network are obtained using a genetic algorithm, which ensure a stable convergence of the network for different problems. Simulation results illustrate that the proposed network performs better than the classical Hopfield network for optimization.     

Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network

Rainfall forecasting plays many important role in water resources studies such as river training works and design of flood warning systems. Recent advancement in artificial intelligence and in particular techniques aimed at converting input to output for highly nonlinear, non-convex and dimensionalized processes such as rainfall field, provide an alternative approach for developing rainfall forecasting model. Artificial neural networks (ANNs), which perform a nonlinear mapping between inputs and outputs, are such a technique. Current literatures on artificial neural networks show that the selection of network architecture and its efficient training procedure are major obstacles for their daily usage. In this paper, feed-forward type networks will be developed to simulate the rainfall field and a so-called back propagation (BP) algorithm coupled with genetic algorithm (GA) will be used to train and optimize the networks. The technique will be implemented to forecast rainfall for a number of times using rainfall hyetograph of recording rain gauges in the Upper Parramatta catchment in the western suburbs of Sydney, Australia. Results of the study showed the structuring of ANN network with the input parameter selection, when coupled with GA, performed better compared to similar work of using ANN alone.     

An integrated multi-objective immune algorithm for optimizing the wire bonding process of integrated circuits

Optimization of the wire bonding process of an integrated circuit (IC) is a multi-objective optimization problem (MOOP). In this research, an integrated multi-objective immune algorithm (MOIA) that combines an artificial immune algorithm (IA) with an artificial neural network (ANN) and a generalized Pareto-based scale-independent fitness function (GPSIFF) is developed to find the optimal process parameters for the first bond of an IC wire bonding. The back-propagation ANN is used to establish the nonlinear multivariate relationships between the wire boning parameters and the multi-responses, and is applied to generate the multiple response values for each antibody generated by the IA. The GPSIFF is then used to evaluate the affinity for each antibody and to find the non-dominated solutions. The “Error Ratio” is then applied to measure the convergence of the integrated approach. The “Spread Metric” is used to measure the diversity of the proposed approach. Implementation results show that the integrated MOIA approach does generate the Pareto-optimal solutions for the decision maker, and the Pareto-optimal solutions have good convergence and diversity performance.     

Tissue segmentation in ultrasound images by using genetic algorithms

This paper presents a genetic based incremental neural network (GINeN) for the segmentation of tissues in ultrasound images. Performances of the GINeN and the Kohonen network are investigated for tissue segmentation in ultrasound images. Feature extraction is carried out by using continuous wavelet transform. Pixel intensities at the same spatial location on 12 wavelet planes and on the original image are considered as features, leading to 13-dimensional feature vectors. The same training set is used for the training of the Kohonen network and the GINeN.

This paper proposes the use of wavelet transform and genetic based incremental neural network together in order to increase the segmentation performance. It is observed that genetic based incremental neural network gives satisfactory segmentation performance for ultrasound images.     

一种有腿机器人步态轨迹生成算法

目前存在一类有腿机器人,由于其底层动力学控制参数难于获取,行走设计无法使用已有的控制方法,现有的基于建模的步态存在缺乏完善规划,固定规划步态与实际步态相差较大的问题,针对这个问题,提出一种使用曲线拟合生成步态轨迹的想法,通过引入遗传算法,让机器人能自主的搜索良好的行走步态轨迹,在四足步行机器人平台上取得了良好的实验结果.     

上下文感知服务器中基于预测的负载均衡算法

在上下文感知服务器中,需要收集大量的上下文信息并对其进行处理。必须通过服务器集群并采用负载均衡技术才能保证服务器的正常运行。该文提出基于预测负载进行自适应负载均衡的算法,采用支持向量机预测系统运行下一个阶段的负载量,结合当前系统各节点处理情况进行动态负载分配过程,在任务执行过程中,不进行任务的迁移以减少迁移所带来的系统消耗。通过比较表明,在负载变化较大的情况下,算法能够有效地提高服务器性能。     

智能搜索算法求解最大团问题研究

最大团问题MCP（Maximum Clique Problem）在国外得到了广泛的研究,在国内刚起步,是一类NP完全问题。传统的确定性算法不能有效地进行求解。定义了MCP;介绍了使用启发式算法求解MCP的研究进展;综述了几种典型的智能搜索算法;分析了使用这些典型算法求解MCP的基本思想;研究了这些智能算法在求解MCP时的特点及性能。