International Journal of Computational Intelligence and Applications

This paper presents an investigation into the detection and quantification of persons in real-world beach scenes for the automated monitoring of public recreation areas. Aside from the obvious use of video and digital imagery for surveillance applications, this research focuses on the analysis of images for the purpose of predicting trends in the intensity of public usage at beach sites in Australia. The proposed system uses image enhancement and segmentation techniques to detect objects in cluttered scenes. Following these steps, a newly proposed feature extraction technique is used to represent salient information in the extracted objects for training of a neural network. The neural classifier is used to distinguish the extracted objects between "person" and "non-person" categories to facilitate analysis of tourist activity. Encouraging results are presented for person classification on a database of real-word beach scene images.

This paper presents the enhanced frame-based video coding scheme. The input source video to the enhanced frame-based video encoder consists of a rectangular-sized video and shapes of arbitrarily shaped objects on video frames. The rectangular frame texture is encoded by the conventional frame-based coding technique and the video object's shape is encoded using the contour-based vertex coding. It is possible to achieve several useful content-based functionalities by utilizing the shape information in the bitstream at the cost of a very small overhead to the bit-rate.

Medical image segmentation plays an instrumental role in clinical diagnosis. An ideal medical image segmentation scheme should possess some preferred properties such as minimum user interaction, fast computation, and accurate and robust segmentation results. In this paper, an automated algorithm is proposed to enable the doctors to detect the presence of abnormal tissues in brain magnetic resonance images (MRIs). The merged image of different weighted images of each slice is obtained by averaging the intensities of pixels and is enhanced based on their local information by variance mapping. The abnormal regions are segmented by using minimum error thresholding method by formulating a criterion function. The segmentation is performed on the real data of MRI images for different abnormalities and the results are compared with radiologist labeled ground truth. Quantitative analysis between ground truth and segmented abnormal region is presented in terms of Percent Match and Correspondence Ratio. A maximum average percent match of 98.56% and correspondence ratio of 0.8892 of an MRI data is obtained.

In this paper we present a novel technique for determining term importance by exploiting concept-based information found in ontologies. Calculating term importance is a significant and fundamental aspect of most information retrieval approaches, and it is traditionally determined through inverse document frequency (IDF). We propose concept-based term weighting (CBW), a technique that is fundamentally different to IDF in that it calculates term importance by intuitively interpreting the conceptual information in ontologies. We show that when CBW is used in an approach for web information retrieval on benchmark data, it performs comparatively to IDF, with only a 3.5% degradation in retrieval accuracy. While this small degradation has been observed, the significance of this technique is that (1) unlike IDF, CBW is independent of document collection statistics, (2) it presents a new way of interpreting ontologies for retrieval, and (3) it introduces an additional source of term importance information that can be used for term weighting.

The artificial mouth is a robotic device that simulates a human mouth. It consists of moveable lips and an adjustable air supply. In this study the use of a genetic algorithm to compute air pressure and lip pressure values so that the artificial mouth can correctly play five notes on a brass instrument is investigated. In order to properly play a brass instrument, a player must apply proper tension between the lips and apply proper airflow so that the lips vibrate at the proper frequency. A fast search algorithm was needed because it takes about 4 seconds to measure the frequency produced by each combination of pressure parameters. This measurement is slow because of the slow moving mechanics of the system and a delay produced while the notes are measured for pitch. The efficiency of this algorithm was compared to an exhaustive search. Experimentation was performed using various combinations of genetic parameters, including population size, crossover rate, and mutation rate. The genetic search was shown to be about 10 times faster than the exhaustive search because the genetic algorithm searches only very small portions of the search space.

Data availability is not a major issue at present times in view of the widespread use of Internet; however, information and knowledge availability are the issues. Due to data overload and time-critical nature of information need, automatic summarization of documents plays a significant role in information retrieval and text data mining. This paper discusses the design of a multi-document summarizer that uses Katz's K-mixture model for term distribution. The model helps in ranking the sentences by a modified term weight assignment. Highly ranked sentences are selected for the final summary. The sentences that are repetitive in nature are eliminated, and a tiled summary is produced. Our method avoids redundancy and produces a readable (even browsable) summary, which we refer to as an event-specific tiled summary. The system has been evaluated against the frequently occurring sentences in the summaries generated by a set of human subjects. Our system outperforms other auto-summarizers at different extraction levels of summarization with respect to the ideal summary, and is close to the ideal summary at 40% extraction level.

This paper proposes an effective lecture allocation method based on users' profiles and utilities in elective subjects. In many universities and colleges, elective subject systems are employed as a curriculum in which students make their own learning experiences. Students select multiple subjects based on their interests and preferences. Generally, each university determines members of elective subjects based on simple rules, such as the order of grade, random selection, and first arrival. Current determination systems have strong limitations in terms of reflecting users' multiple preferences. We propose a new determination algorithm and a user support system based on users' profiles and multi-attribute preferences. When users' preferences are not reflected in algorithm-based determination, a coordination agent decides lecture allocation cooperatively with each user's agent. Main advantage of our system is mainly that lectures are allocated effectively that reflected users' multiple preferences.

This paper presents a Finite State Machine (FSM) implementation method based on symbolic functional decomposition. This novel approach to multilevel logic synthesis of FSMs targets Field Programmable Gate Array (FPGA) architectures. Traditional methods consist of two steps: internal state encoding and then mapping the encoded state transition table into target architecture. In the case of FPGAs, functional decomposition is recognized as the most efficient method of implementing digital circuits. However, none of the known state encoding algorithms can be considered as a good method to be used with functional decomposition. In this paper, the concept of symbolic functional decomposition is applied to obtain a multilevel structure that is suitable for implementation in FPGA architectures. The symbolic functional decomposition does not require a separate encoding step. It accepts FSM description with symbolic states and performs decomposition, producing such a state encoding that guarantees the optimal or near-optimal solution.

In this article, we introduce a new function visualization method and demonstrate that numerical integration and visualization of multi-dimensional functions are closely related. Adaptive numerical integration is utilized to reduce the number of function evaluations, and generate time series data. The integration region is partitioned into a uniform grid. A grid cell can be sampled many times, or is not sampled at all, depending on the function properties and the integration rule. Function properties are extracted during the process of function evaluation. An aging technique helps visualize functions by retaining the most recently sampled areas and making the older ones transparent. This also results in giving the non-smooth areas more attention than the smooth areas. The new function visualization method gives a view of the whole function while elaborating on important areas such as ridges and troughs, which are critical in many fields, including numerical integration. A Grid service, called Integration Service, is used to solve computationally intensive integration problems. Remote visualization based on the adaptive method helps monitor the progress of a computation, and can be utilized for computational steering. The data are filtered by the server and transferred to the client, which is responsible for visualization mapping and rendering.

The realization problem for positive, continuous-time linear single-input, single-output systems with delays is formulated and solved. Sufficient conditions for the existence of positive realizations of a given proper transfer function are established. A procedure for computation of positive minimal realizations is presented and illustrated by an example.