Fusion of soft computing and hard computing in industrial applications: an overview

Soft computing (SC) is an emerging collection of methodologies which aims to exploit tolerance for imprecision, uncertainty, and partial truth to achieve robustness, tractability, and low total cost. It differs from conventional hard computing (HC) in the sense that, unlike hard computing, it is strongly based on intuition or subjectivity. Therefore, soft computing provides an attractive opportunity to represent the ambiguity in human thinking with real life uncertainty. Fuzzy logic (FL), neural networks (NN), and genetic algorithms (GA) are the core methodologies of soft computing. However, FL, NN, and GA should not be viewed as competing with each other, but synergistic and complementary instead. Considering the number of available journal and conference papers on various combinations of these three methods, it is easy to conclude that the fusion of individual soft computing methodologies has already been advantageous in numerous applications. On the other hand, hard computing solutions are usually more straightforward to analyze; their behavior and stability are more predictable; and, the computational burden of algorithms is typically either low or moderate. These characteristics. are particularly important in real-time applications. Thus, it is natural to see SC and HC as potentially complementary methodologies. Novel combinations of different methods are needed when developing high-performance, cost-effective, and safe products for the demanding global market. We present an overview of applications in which the fusion of soft computing and hard computing has provided innovative solutions for challenging real-world problems. A carefully selected list of references is considered with evaluative discussions and conclusions.     

Network computing: a tutorial review

Network computing is generally considered to be an unsuccessful initiative. It is strongly associated in the minds of many with the overhyped network computer that failed to capture a significant market share from PCs. However, network computing and network computers are not synonymous. In fact, one of the major benefits of network computing is the ability to tailor applications to the capabilities of heterogeneous client devices. Given the very fast growing mobile computing market, with its numerous and diverse terminal types, network computing could at last realise its full potential. This tutorial paper provides an overview of computing from the early mainframes to today's multiplicity of computing devices. The advantages of network computing are discussed and an overview is provided of some of the underpinning technologies. To provide an insight into the potential of network computing, two applications are described. Some overall conclusions are also given     

MobiSoC: a middleware for mobile social computing applications

Recently, we started to experience a shift from physical communities to virtual communities, which leads to missed social opportunities in our daily routine. For instance, we are not aware of neighbors with common interests or nearby events. Mobile social computing applications (MSCAs) promise to improve social connectivity in physical communities by leveraging information about people, social relationships, and places. This article presents MobiSoC, a middleware that enables MSCA development and provides a common platform for capturing, managing, and sharing the social state of physical communities. Additionally, it incorporates algorithms that discover previously unknown emergent geo-social patterns to augment this state. To demonstrate MobiSoC's feasibility, we implemented and tested on smart phones two MSCAs for location-based mobile social matching and place-based ad hoc social collaboration. Experimental results showed that MobiSoC can provide good response time for 1,000 users. We also demonstrated that an adaptive localization scheme and carefully chosen cryptographic methods can significantly reduce the resource consumption associated with the location engine and security on smart phones. A user study of the mobile social matching application proved that geo-social patterns can double the quality of social matches and that people are willing to share their location with MobiSoC in order to benefit from MSCAs.

Bioinformatics with soft computing

Soft computing is gradually opening up several possibilities in bioinformatics, especially by generating low-cost, low-precision (approximate), good solutions. In this paper, we survey the role of different soft computing paradigms, like fuzzy sets (FSs), artificial neural networks (ANNs), evolutionary computation, rough sets (RSes), and support vector machines (SVMs), in this direction. The major pattern-recognition and data-mining tasks considered here are clustering, classification, feature selection, and rule generation. Genomic sequence, protein structure, gene expression microarrays, and gene regulatory networks are some of the application areas described. Since the work entails processing huge amounts of incomplete or ambiguous biological data, we can utilize the learning ability of neural networks for adapting, uncertainty handling capacity of FSs and RSes for modeling ambiguity, searching potential of genetic algorithms for efficiently traversing large search spaces, and the generalization capability of SVMs for minimizing errors.     

Grid computing for electromagnetics: a beginner's guide with applications

The demand for computing power in computational electromagnetics (CEM) is continuously increasing. Meanwhile, cooperative engineering is becoming more and more present in daily research and development workflows. Projects are often developed by teams, which interact remotely, and need tighter and tighter connectivity. Grid computing (GC), from the perspective of progress in computer networks, seems a promising way to satisfy both the need of high-performance computing platforms, and the requirements for effective cooperative computing. In this paper, researchers involved in CEM are introduced to grid computing, and to the use of grid computing for CEM. Two real applications are proposed, with a critical discussion on potential benefits and drawbacks with respect to alternative strategies.     

Fusion of soft computing and hard computing: computational structures and characteristic features

Soft computing (SC) and hard computing (HC) methodologies are fused together successfully in numerous industrial applications. The principal aim is to develop computationally intelligent hybrid systems that are straightforward to analyze, with highly predictable behavior and stability, and with computational burden that is no more than moderate. All these goals are particularly important in embedded real-time applications. This paper is intended to clarify the present vagueness related to the fusion of SC and HC methodologies. We classify the different fusion schemes to 12 core categories and six supplementary categories, and discuss the characteristic features of SC and HC constituents in practical fusion implementations. The emerging fusion approach offers a natural evolution path from pure hard computing toward dominating soft computing.     

Hybrid soft computing systems: industrial and commercialapplications

Soft computing (SC) is an association of computing methodologies that includes as its principal members fuzzy logic, neurocomputing, evolutionary computing and probabilistic computing. We present a collection of methods and tools that can be used to perform diagnostics, estimation, and control. These tools are a great match for real-world applications that are characterized by imprecise, uncertain data and incomplete domain knowledge. We outline the advantages of applying SC techniques and in particular the synergy derived from the use of hybrid SC systems. We illustrate some combinations of hybrid SC systems, such as fuzzy logic controllers (FLCs) tuned by neural networks (NNs) and evolutionary computing (EC), NNs tuned by EC or FLCs, and EC controlled by FLCs. We discuss three successful real-world examples of SC applications to industrial equipment diagnostics, freight train control, and residential property valuation     

Incorporating soft computing techniques into a probabilistic intrusion detection system

There are a lot of industrial applications that can be solved competitively by hard computing, while still requiring the tolerance for imprecision and uncertainty that can be exploited by soft computing. This paper presents a novel intrusion detection system (IDS) that models normal behaviors with hidden Markov models (HMM) and attempts to detect intrusions by noting significant deviations from the models. Among several soft computing techniques neural network and fuzzy logic are incorporated into the system to achieve robustness and flexibility. The self-organizing map (SOM) determines the optimal measures of audit data and reduces them into appropriate size for efficient modeling by HMM. Based on several models with different measures, fuzzy logic makes the final decision of whether current behavior is abnormal or not. Experimental results with some real audit data show that the proposed fusion produces a viable intrusion detection system. Fuzzy rules that utilize the models based on the measures of system call, file access, and the combination of them produce more reliable performance.     

Staging of cervical cancer with soft computing

This paper describes a way of designing a hybrid decision support system in soft computing paradigm for detecting the different stages of cervical cancer. Hybridization includes the evolution of knowledge-based subnetwork modules with genetic algorithms (GA's) using rough set theory and the Interactive Dichotomizer 3 (ID3) algorithm. Crude subnetworks obtained via rough set theory and the ID3 algorithm are evolved using GA's. The evolution uses a restricted mutation operator which utilizes the knowledge of the modular structure, already generated, for faster convergence. The GA tunes the network weights and structure simultaneously. The aforesaid integration enhances the performance in terms of classification score, network size and training time, as compared to the conventional multilayer perceptron. This methodology also helps in imposing a structure on the weights, which results in a network more suitable for extraction of logical rules and human interpretation of the inferencing procedure.     

A taxonomy of ubiquitous computing applications

Ubiquitous computing is emerging as a new paradigm in next-generation information technology. This new paradigm has been embodied into tremendous business models and applications through lots of ubiquity-related technologies. In this study, a new taxonomy for these business applications and technologies is suggested. In order to prove the practical values, two case applications of the taxonomy are conducted. In the cases, 24 ubiquitous computing services and 19 ubiquitous computing projects are classified so that the status quo of ubiquitous computing is analyzed.     

并行计算技术综述

针对目前并行计算技术发展现状,对并行计算技术进行了介绍。分析比较目前常用的并行架构,常用的并行架构分为SMP(多处理系统)、NUMA(非统一内存存取)、MPP(巨型并行处理)及CLUSTER(集群)。介绍了多种并行编程模型,如MPI、PVM、OpenMP、TBB及Cilk++等模型,对它们进行了比较。介绍了目前流行的并行计算技术、云计算技术、网格计算技术以及分布式计算技术,对它们目前所采用的技术手段、开发可行性、商业价值、开发效率以及市场应用前景都进行了详细介绍。     

Optimized intrusion detection mechanism using soft computing techniques

Intrusion detection is an important technique in computer and network security. A variety of intrusion detection approaches be present to resolve this severe issue but the main problem is performance. It is important to increase the detection rates and reduce false alarm rates in the area of intrusion detection. Therefore, in this research, an optimized intrusion detection mechanism using soft computing techniques is proposed to overcome performance issues. The KDD-cup dataset is used that is a benchmark for evaluating the security detection mechanisms. The Principal Component Analysis (PCA) is applied to transform the input samples into a new feature space. The selecting of an appropriate number of principal components is a critical problem. So, Genetic Algorithm (GA) is used in the optimum selection of principal components instead of using traditional method. The Support Vector Machine (SVM) is used for classification purpose. The performance of this approach is addresses. Further, a comparative analysis is made with existing approaches. Consequently, this method provides optimal intrusion detection mechanism which is capable to minimize amount of features and maximize the detection rates.     

Signal processing and pattern recognition with soft computing

We describe the overall role of soft computing (SC) in signal processing and pattern recognition (SPPR) with specific applications to biomedical engineering, geoscience for mining and civil engineering human interfaces, and image processing. Detection of characteristic points in an electrocardiogram to implement an advanced ECG analyzer is presented which is carried out using both conventional SPPR techniques and self-organizing neural networks. Successful technologies for monitoring a geostructure by supervised and self-organizing neural networks are described. Identification of a freehand drawing by a combination of fuzzy logic and neural networks is also described. Moreover, application of fuzzy logic to image segmentation is presented. Finally, innovation of SPPR using SC technologies is discussed     

Solid and soft nanostructured materials: Fundamentals and applications

The scientific work worldwide on nanostructured materials is extensive as well as the work on the applications of nanostructured materials. We will review quasi two-, one- and zero-dimensional solid and soft materials and their applications. We will restrict ourselves to a few examples from partly fundamental aspects and partly from application aspects. We will start with trapping of excitons in semiconductor nanostructures. The subjects are: physical realizations, phase diagrams, traps, local density approximations, and mesoscopic condensates. From these fundamental questions in solid nanomaterials we will move to trapping of molecules in water using nanostructured electrodes. We will also discuss how to manipulate water (create vortices) by nanostructure materials.The second part deals with nanorods (nano-wires). Particularly we will exemplify with ZnO nanorods. The reason for this is that ZnO has: a very strong excitons binding energy (60 meV) and strong photon-excitons coupling energy, a strong tendency to create nanostructures, and properties which make the material of interest for both optoelectronics and for medical applications. We start with the growth of crystalline ZnO nanorods on different substrates, both crystalline (silicon, silicon carbide, sapphire, etc) and amorphous substrates (silicon dioxide, plastic materials, etc) for temperatures from 50 °C up to 900 °C. The optical properties and crystalline properties of the nanorods will be analyzed. Applications from optoelectronics (lasers, LEDs, lamps, and detectors) are analyzed and also medical applications like photodynamic cancer therapy are taken up.The third part deals with nano-particles in ZnO for sun screening. Skin cancer due to the exposure from the sun can be prevented by ZnO particles in a paste put on the exposed skin.     

OTRA,its implementations and applications: a state-of-the-art review

During the last three decades, a large number of new analog circuit building blocks have emerged beyond the well-known operational amplifier, operational transconductance amplifier, Current Conveyors and Current feedback operational amplifier. Among the new building blocks, the operational transresistance amplifier (OTRA) has received considerable attention in the literature. This paper presents a state-of-the-art review of the OTRAs, their bipolar and CMOS implementations and applications in linear and nonlinear analog signal processing/generation along with a comprehensive list of references covering the period from 1992 till date.     

Perfect sampling: a review and applications to signal processing

Markov chain Monte Carlo (MCMC) sampling methods have gained much popularity among researchers in signal processing. The Gibbs and the Metropolis-Hastings (1954, 1970) algorithms, which are the two most popular MCMC methods, have already been employed in resolving a wide variety of signal processing problems. A drawback of these algorithms is that in general, they cannot guarantee that the samples are drawn exactly from a target distribution. New Markov chain-based methods have been proposed, and they produce samples that are guaranteed to come from the desired distribution. They are referred to as perfect samplers. We review some of them, with the emphasis being given to the algorithm coupling from the past (CFTP). We also provide two signal processing examples where we apply perfect sampling. In the first, we use perfect sampling for restoration of binary images and, in the second, for multiuser detection of CDMA signals     

Fault detection using thermal image based on soft computing methods: Comparative study

This paper presents Integrated Circuit (IC) fault detection of a Printed Circuit Board (PCB) model using thermal image processing. The thermal image is captured and processed from the PCB model by the finite element method (FEM). The histogram features are extracted from the ICs hotspots which are used as inputs in a classifier model. The effective features are minimized by the principal component analysis method. In this work, a comparative study for image classification and detection is performed based on three soft computing techniques: multilayer perceptron, support vector machine, and adaptive neuron-fuzzy inference system. The effectiveness of the models is evaluated by comparing the performance and accuracy of the classification. To validate the model, the experimental evaluation is performed on Arduino UNO in order to detect the fault condition on the real time operating PCB.