Towards a variable size sliding window model for frequent itemset mining over data streams

Sliding window is a widely used model for data stream mining due to its emphasis on recent data and its bounded memory requirement. The main idea behind a transactional sliding window is to keep a fixed size window over a data stream. The window size is kept constant by removing old transactions from the window, when new transactions arrive. Older transactions of window are removed irrespective to whether a significant change has occurred or not. Another challenge of sliding window model is determining window size. The classic approach for determining the window size is to obtain it from the user. In order to determine the precise size of the window, the user must have prior knowledge about the time and scale of changes within the data stream. However, due to the unpredictable changing nature of data streams, this prior knowledge cannot be easily determined. Moreover, by using a fixed window size during a data stream mining, the performance of this model is degraded in terms of reflecting recent changes. Based on these observations, this study relaxes the notion of window size and proposes a new algorithm named VSW (Variable Size sliding Window frequent itemset mining) which is suitable for observing recent changes in the set of frequent itemsets over data streams. The window size is determined dynamically based on amounts of concept change that occurs within the arriving data stream. The window expands as the concept becomes stable and shrinks when a concept change occurs. In this study, it is shown that if stale transactions are removed from the window after a concept change, updated frequent itemsets always belong to the most recent concept. Experimental evaluations on both synthetic and real data show that our algorithm effectively detects the concept change, adjust the window size, and adapts itself to the new concepts along the data stream.     

A target-based color space for sea target detection

Sea target detection is a vital application for military and navigation purposes. A new supervised clustering method based on the combination of the PSO and FCM techniques is presented for the sea target detection problem. The color components of the target and non-target pixels in the RGB color space are used as features to train the classification algorithm. The new classifier is presented in the form of a new color space which we call the Target-based Color Space (TCS); in fact the RGB color space is converted to this new space through a 3×3 matrix. The Particle Swarm Optimization (PSO) algorithm is then used to search for the optimum weights of the conversion matrix which results in a more discriminating clustering space between the target and non-target pixels. In other words, solving the optimization problem, minimization of the objective function of the FCM clustering technique in linear and quadratic transform domain (with a NP-hard problem in quadratic conversion), is done using the PSO algorithm. The main objective of this work is to demonstrate the efficiency of using just color features, as well as color space conversion in the classification domain. Experimental results show the efficiency of new method in finding sea targets in color images.     

Research on group search optimizers for a reconfigurable flow shop sequencing problem

A reconfigurable manufacturing system is usually designed for quick re-adjusting of production capacity in response to market changes. In this paper, we study a flow shop sequencing problem (FSSP) with controllable processing times as a special case of reconfigurable manufacturing system. It is possible to speed up the processing times through assigning additional resources or control of machine speed. After formulating this problem mathematically, a novel evolutionary procedure, entitled group search optimizer (GSO), is devised as solution method. The adapted GSO is a population-based search tool which is devised based on the producer and scrounger behavior. GSO emphasizes on imitating searching model of real-world animals. The basic GSO with four promising improvements is elaborated and discussed for addressing the FSSP with controllable processing times. A set of computational experiments is also conducted to demonstrate the applicability of proposed FSSP and performance of improved GSOs.     

Multi-focus image fusion for visual sensor networks in DCT domain

The objective of image fusion is to combine relevant information from multiple images into a single image. The discrete cosine transform (DCT) based methods of image fusion are more efficient and time-saving in real-time systems using DCT based standards of still image or video. Existing DCT based methods are suffering from some undesirable side effects like blurring or blocking artifacts which reduce the quality of the output image. Furthermore, some of these methods are rather complex and this contradicts the concept of the simplicity of DCT based algorithms. In this paper, an efficient approach for fusion of multi-focus images based on variance calculated in DCT domain is presented. Due to simplicity of our proposed method, it can be easily used in real-time applications. The experimental results verify the efficiency improvement of our method both in output quality and complexity reduction in comparison with several recent proposed techniques.     

Production capacity planning and scheduling in a no-wait environment with controllable processing times: An integrated modeling approach

This paper develops an integrated model between a production capacity planning and an operational scheduling decision making process in which a no-wait job shop (NWJS) scheduling problem is considered incorporating with controllable processing times. The duration of any operations are assumed to be controllable variables based on the amount of capacity allocated to them, whereas in classical NWJS it is assumed that the machine capacity and hence processing times are fixed and known in advance. The suggested problem which is entitled no-wait job shop crashing (NWJSC) problem is decomposed into the crashing, sequencing and timetabling subproblems. To tackle the addressed NWJSC problem, an improved hybrid timetabling procedure is suggested by employing the concept of both non-delay and enhanced algorithms which provides better solution than each one separately. Furthermore, an effective two-phase genetic algorithm approach is devised integrating with hybrid timetabling to deal with the crashing and sequencing components. The results obtained from experimental evaluations support the outstanding performance of the proposed approach.     

Theoretical 14N nuclear quadrupole resonance parameters for sulfa drugs: sulfamerazine and sulfathiazole

A density functional theory investigation was carried out to characterize (14)N electric field gradient tensors, EFG, in crystalline sulfamerazine and sulfathiazole. To include hydrogen-bonding effects in the calculations, the most probable interacting molecules with the target were considered as tetrameric and pentameric clusters, respectively. The calculated EFG tensors were used to evaluate nuclear quadrupole coupling tensors (chi(ii)) and asymmetry parameters (eta(Q)) for the target molecule in the clusters. Results are in satisfactory agreement with the experimental data. The EFG calculations reveal different contributions of nitrogen atoms in hydrogen-bonding network of the sulfamerazine and sulfathiazole. Moreover, based on the results obtained via atoms in molecules (AIM) analyses, an acceptable linear relation between (14)N nuclear quadrupole coupling constants and charge density values at N-H...N and N-H...O bond critical points, rho(b)(r(cp)), is observed.     

A general audio classifier based on human perception motivated model

The audio channel conveys rich clues for content-based multimedia indexing. Interesting audio analysis includes, besides widely known speech recognition and speaker identification problems, speech/music segmentation, speaker gender detection, special effect recognition such as gun shots or car pursuit, and so on. All these problems can be considered as an audio classification problem which needs to generate a label from low audio signal analysis. While most audio analysis techniques in the literature are problem specific, we propose in this paper a general framework for audio classification. The proposed technique uses a perceptually motivated model of the human perception of audio classes in the sense that it makes a judicious use of certain psychophysical results and relies on a neural network for classification. In order to assess the effectiveness of the proposed approach, large experiments on several audio classification problems have been carried out, including speech/music discrimination in Radio/TV programs, gender recognition on a subset of the switchboard database, highlights detection in sports videos, and musical genre recognition. The classification accuracies of the proposed technique are comparable to those obtained by problem specific techniques while offering the basis of a general approach for audio classification.

Triple-wavelength passively Q-switched ytterbium-doped fibre laser using zinc oxide nanoparticles film as a saturable absorber

We report on the generation of a triple-wavelength passively Q-switched ytterbium-doped fibre laser using a saturable absorber (SA) based on zinc oxide nanoparticles (ZnO NPs) film. The SA was fabricated by embedding ZnO NPs powder into a polyvinyl alcohol as a host polymer. By properly adjusting the pump power and the polarization state, single-, dual- and triple-wavelength Q-switching are stably generated without additional components (such as optical filter, or fibre grating). For the triple wavelength operation, the fibre laser generates a maximum pulse repetition of 87.9 kHz with the shortest pulse duration of 2.7 μs. To the best of authors’ knowledge, it’s the first demonstration of triple-wavelength passively Q-switching fibre laser using ZnO NPs as a SA. Our results suggest that ZnO is a promising SA for multi-wavelength laser operation.     

A multi-objective military system of systems architecting problem with inflexible and flexible systems: formulation and solution methods

System of systems (SoS) architecting is the process of bringing together and connecting a set of systems so that the collection of the systems, i.e., the SoS is equipped with a set of required capabilities. A system is defined as inflexible in case it contributes to the SoS with all of the capabilities it can provide. On the other hand, a flexible system can collaborate with the SoS architect in the capabilities it will provide. In this study, we formulate and analyze a SoS architecting problem representing a military mission planning problem with inflexible and flexible systems as a multi-objective mixed-integer-linear optimization model. We discuss applications of an exact and an evolutionary method for generating and approximating the Pareto front of this model, respectively. Furthermore, we propose a decomposition approach, which decomposes the problem into smaller sub-problems by adding equality constraints, to improve both the exact and the evolutionary methods. Results from a set of numerical studies suggest that the proposed decomposition approach reduces the computational time for generating the exact Pareto front as well as it reduces the computational time for approximating the Pareto front while not resulting in a worse approximated Pareto front. The proposed decomposition approach can be easily used for different problems with different exact and heuristic methods and thus is a promising tool to improve the computational time of solving multi-objective combinatorial problems. Furthermore, a sample scenario is presented to illustrate the effects of system flexibility.