Efficient tag detection in RFID systems

Recent technological advances have motivated large-scale deployment of RFID systems. However, a number of critical design issues relating to efficient detection of tags remain unresolved. In this paper, we address three important problems associated with tag detection in RFID systems: (i) accurately detecting RFID tags in the presence of reader interference (reader collision avoidance problem); (ii) eliminating redundant tag reports by multiple readers (optimal tag reporting problem); and (iii) minimizing redundant reports from multiple readers by identifying a minimal set of readers that cover all tags present in the system (optimal tag coverage problem). The underlying difficulties associated with these problems arise from the lack of collision detection mechanisms, the potential inability of RFID readers to relay packets generated by other readers, and severe resource constraints on RFID tags. In this paper we present a randomized, distributed and localized Reader Collision Avoidance (RCA) algorithm and provide detailed probabilistic analysis to establish the accuracy and the efficiency of this algorithm. Then, we prove that the optimal tag coverage problem is NP-hard even with global knowledge of reader and tag locations. We develop a distributed and localized Redundant Reader Elimination (RRE) algorithm, that efficiently identifies redundant readers and avoids redundant reporting by multiple readers. In addition to rigorous analysis of performance and accuracy, we provide results from elaborate simulations for a wide range of system parameters, demonstrating the correctness and efficiency of the proposed algorithms under various scenarios.     

An artificial immune system approach to CNC tool path generation

Reduced machining time and increased accuracy for a sculptured surface are both very important when producing complicated parts, so, the step-size and tool-path interval are essential components in high-speed and high-resolution machining. If they are too small, the machining time will increase, whereas if they are too large, rough surfaces will result. In particular, the machining time, which is a key factor in high-speed machining, is affected by the tool-path interval more than the step size. The present paper introduces a ‘system software’ developed to reduce machining time and increased accuracy for a sculptured surface with Non-Uniform Rational B-Spline (NURBS) patches. The system is mainly based on a new and a powerful artificial intelligence (AI) tool, called artificial immune systems (AIS). It is implemented using C programming language on a PC. It can be used as stand alone system or as the integrated module of a CNC machine tool. With the use of AIS, the impact and power of AI techniques have been reflected on the performance of the tool path optimization system. The methodology of the developed tool path optimization system is illustrated with practical examples in this paper.     

Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes

This paper introduces different classification systems based on artificial neural networks for the automatic detection of epileptic spikes in electroencephalogram records. Different multilayer perceptron networks are constructed and trained with different algorithms. The inputs of the networks consist of either raw data or extracted features. To improve the generalization performance of the classifiers, “training with noise” method is used whereby new training data is constructed by adding uncorrelated Gaussian noise to real data. The performances of the constructed classifiers are examined and compared both with each other and with other similar systems found in literature based on sensitivity, specificity and selectivity measures.     

Online mining of fuzzy multidimensional weighted association rules

This paper addresses the integration of fuzziness with On-Line Analytical Processing (OLAP) based association rules mining. It contributes to the ongoing research on multidimensional online association rules mining by proposing a general architecture that utilizes a fuzzy data cube for knowledge discovery. A data cube is mainly constructed to provide users with the flexibility to view data from different perspectives as some dimensions of the cube contain multiple levels of abstraction. The first step of the process described in this paper involves introducing fuzzy data cube as a remedy to the problem of handling quantitative values of dimensional attributes in a cube. This facilitates the online mining of fuzzy association rules at different levels within the constructed fuzzy data cube. Then, we investigate combining the concepts of weight and multiple-level to mine fuzzy weighted multi-cross-level association rules from the constructed fuzzy data cube. For this purpose, three different methods are introduced for single dimension, multidimensional and hybrid (integrates the other two methods) fuzzy weighted association rules mining. Each of the three methods utilizes a fuzzy data cube constructed to suite the particular method. To the best of our knowledge, this is the first effort in this direction. We compared the proposed approach to an existing approach that does not utilize fuzziness. Experimental results obtained for each of the three methods on a synthetic dataset and on the adult data of the United States census in year 2000 demonstrate the effectiveness and applicability of the proposed fuzzy OLAP based mining approach. OLAP is one of the most popular tools for on-line, fast and effective multidimensional data analysis. In the OLAP framework, data is mainly stored in data hypercubes (simply called cubes).     

Single machine scheduling problem with stochastic sequence-dependent setup times

In this study, we consider stochastic single machine scheduling problem. We assume that setup times are both sequence dependent and uncertain while processing times and due dates are deterministic. In the literature, most of the studies consider the uncertainty on processing times or due dates. However, in the real-world applications (i.e. plastic moulding industry, appliance assembly, etc.), it is common to see varying setup times due to labour or setup tools availability. In order to cover this fact in machine scheduling, we set our objective as to minimise the total expected tardiness under uncertain sequence-dependent setup times. For the solution of this NP-hard problem, several heuristics and some dynamic programming algorithms have been developed. However, none of these approaches provide an exact solution for the problem. In this study, a two-stage stochastic-programming method is utilised for the optimal solution of the problem. In addition, a Genetic Algorithm approach is proposed to solve the large-size problems approximately. Finally, the results of the stochastic approach are compared with the deterministic one to demonstrate the value of the stochastic solution.     

Vibes: A visual language for specifying behavioral requirements of algorithms

Manually verifying the behavior of software systems with respect to a set of requirements is a time-consuming and error-prone task. If the verification is automatically performed by a model checker however, time can be saved, and errors can be prevented. To be able to use a model checker, requirements need to be specified using a formal language. Although temporal logic languages are frequently used for this purpose, they are neither commonly considered to have sufficient usability, nor always naturally suited for specifying behavioral requirements of algorithms. Such requirements can be naturally specified as regular language recognizers such as deterministic finite accepters, which however suffer from poor evolvability: the necessity to re-compute the recognizer whenever the alphabet of the underlying model changes. In this paper, we present the visual language Vibes that both is naturally suited for specifying behavioral requirements of algorithms, and enables the creation of highly evolvable specifications. Based on our observations from controlled experiments with 23 professional software engineers and 21 M.Sc. computer science students, we evaluate the usability of Vibes in terms of its understandability, learnability, and operability. This evaluation suggests that Vibes is an easy-to-use language.     

Spatio-Temporal Video Motion Detection Using Marked-Watershed and Morphological Operations

In this article, we describe a moving object detection method developed based on spatio-temporal information and marked-watershed for extracting the moving objects from a video sequence. The algorithm begins with difference image between two adjacent frames and, using the Canny operator on the difference image, determines the initial edge mask for the object in motion Morphological operators are applied to the initial edge map to obtain a temporal segmentation mask of the moving object and binary marker image of the foreground and background, which is subject to the watershed thresholding. The markers are used to modify multi-scale morphological gradient image of the current frame. Finally, the watershed algorithm is performed on the modified gradients to locate the non-stationary objects accurately in the spatial domain of motion frames. Experimental results show that the proposed technique can overcome the shortcoming of over-segmentation of the watershed algorithm. In computationally efficient way, it segments and extracts semantically meaningful objects, which are in slow or fast motion from the video frames of scenes involving complex background. Performance evaluation yields that the detection accuracy can be as high as 98% to 99% for different video sequences.     

A software tool for interactive generation, representation, and systematical storage of transfer functions for 3D medical images

As being a tool that assigns optical parameters, i.e. color, transparency, used in interactive visualization, transfer functions have very important effects on the quality of volume rendered medical images. However, finding accurate transfer functions is a very difficult, tedious, and time consuming task because of the variety of all possibilities. By addressing this problem, a software module, which can be easily plugged into any visualization program, is developed based on the specific expectations of medical experts. Its design includes both a new user interface to ease the interactive generation of the volume rendered medical images and a volumetric histogram based method for initial generation of transfer functions. In addition, a novel file system has been implemented to represent 3D medical images using transfer functions based on the DICOM standard. For evaluation of the system by various medical experts, the software is installed into a DICOM viewer. Based on the feedback obtained from the medical experts, several improvements are made, especially to increase the flexibility of the program. The final version of the implemented system shortens the transfer function design process and is applicable to various application areas.     

Mechanical properties of soft liner–poly(methyl methacrylate)‐based denture material

In this study, the mechanical properties of two different permanent soft lining materials and their bonding to poly(methyl methacrylate) (PMMA) were compared. Both of the soft liners were heat‐cured commercial materials. The polymerization was carried out by conventional methods suggested by manufacturer, and the curing was done at the temperature of boiling water for 5, 15, 25, and 35 min. The sample groups were tested in the computer‐aided tensile‐testing machine at a rate of 2 mm/min. The slow rate helps the collection of more and more reliable data. At this time, the stress–strain curves were used to calculate ultimate tensile strength, elastic modulus, resilience, and toughness. The measurements were carried for PMMA, Molloplast B, Flexor, and a combination of PMMA/soft liner. After introducing the soft lining material on PMMA of the same thickness, the new material structure was more elastic than the original PMMA. Flexor showed adhesive failure at studied curing periods, but Molloplast B gave larger tear strength values and cohesive rather than adhesive failure at the 25‐min and 35‐min curing times. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 467–474, 2002     

Biomass gasification in supercritical water: Part 1. Effect of the nature of biomass

In this study, biomass feedstocks, including lignocellulosic materials and the tannery wastes, were gasified in supercritical water. Gasification experiments were performed in a batch autoclave at 500 °C. The amount of gases, the gas compositions and the amount of water soluble compounds from gasification were determined. The hydrogen yields ranging between 4.05 and 4.65 mol H₂/kg biomass have been obtained. The results showed that the yields and composition of gases depend also on the organic materials other than cellulose and lignin in lignocellulosic material. In addition to this, it was concluded that the kind of lignin may also have an effect on gasification products. In the case of tannery wastes, the type of tannen agent used in leather production considerably effected the gasification results.