Scene pathfinder: unsupervised clustering techniques for movie scenes extraction

The need for watching movies is in perpetual increase due to the widespread of the internet and the increasing popularity of the video on demand service. The important mass of movies stored in the Internet or in VOD servers need to be structured to accelerate the browsing operation. In this paper, we propose a new system called "The Scene Pathfinder" that aims at segmenting the movies into scenes to give users the opportunity to have a non- sequential access and to watch particular scenes of the movie. This helps them to judge quickly the movie and decide if they have to buy or to download it and avoiding waste of time and money. The proposed approach is multimodal. We use both of visual and auditory information to accomplish the segmentation. We base on the assumption that every movie scene is either action or non- action scene. Non-action scenes are generally characterized by static backgrounds and occur in the same place. For this reason, we base on the content information and on the Kohonen map to extract these kinds of scenes (shots agglomerations). Action scenes are characterized by high tempo and motion. For this reason, we base on tempo features and on the Fuzzy CMeans to classify shots and to localize the action zones. The two processes are complementary. Indeed, the over segmentation that may occur in the extraction of action scenes by basing on the content information is repaired by the Fuzzy clustering. Our system is tested on a varied database and obtained results show the merit of our approach and that our assumptions are well-founded.     

Task space control of a welding robot using a fuzzy coordinator

This paper introduces a fuzzy coordinator as a novel application of fuzzy controller. A control transformation from the task space to the joint space is required to control a robot manipulator in the task space. Because the actuators operate in the joint space while the manipulator is controlled in the task space. A conflict between two spaces is produced due to using an imprecise transformation. Fuzzy coordinator coordinates two spaces by modifying the control transformation affected by uncertainties. The fuzzy coordinator is designed simply and operates as a robust controller. The role of fuzzy coordinator is analyzed and illustrated in the robust control of a welding robot in the task space. A circular trajectory is planned for a welding task performed by a SCARA robot. The fuzzy coordinator is then used to improve the performance of control system affected by imprecise transformations including the imprecise path transformation and the approximated feedback linearization.     

Nanofluid thin film flow and heat transfer over an unsteady stretching elastic sheet by LSM

This study is carried out on the unsteady flow and heat transfer of a nanofluid in a stretching flat plate. Least square method is implemented for solving the governing equations. It also attempts to demonstrate the accuracy of the aforementioned method compared with a numerical one, Runge-Kutta fourth order. Furthermore, the impact of some physical parameters like unsteadiness parameter (S), Prandtl number (Pr) and the nanoparticles volume fraction (?) on the temperature and velocity profiles is scrutinized carefully. Accordingly, the results obtained from this study reveal that the temperature enhances by means of augmenting the nanoparticles volume fraction. At η ∈ {0, 0.5}, the velocity decreases as a result of a rise in nanoparticles volume fraction and at η ∈ {0.5, 1}, an opposite treatment takes place. Moreover, velocity distribution augments by raising the S value, however an inverse trend is observed in temperature values. Moreover, the local skin friction coefficient indicated a notable rise by increasing the S parameter as well as a steady decrease by rising ?. Finally, water-Alumina nanofluid demonstrated better heat transfer enhancement compared to other types of nanofluids.     

Chattering-free Fault-tolerant Attitude Control with Fast Fixed-time Convergence for Flexible Spacecraft

International Journal of Control, Automation and Systems - This paper is mainly dedicated to the challenging issue of fixed-time attitude control for a flexible spacecraft in the presence of...     

Removal of noise patterns in handwritten images using expectation maximization and fuzzy inference systems

The removal of noise patterns in handwritten images requires careful processing. A noise pattern belongs to a class that we have either seen or not seen before. In the former case, the difficulty lies in the fact that some types of noise patterns look similar to certain characters or parts of characters. In the latter case, we do not know the class of noise in advance which excludes the possibility of using parametric learning methods. In order to address these difficulties, we formulate the noise removal and recognition as a single optimization problem, which can be solved by expectation maximization given that we have a recognition engine that is trained for clean images. We show that the processing time for a noisy input is higher than that of a clean input by a factor of two times the number of connected components of the input image in each iteration of the optimization process. Therefore, in order to speed up the convergence, we propose to use fuzzy inference systems in the initialization step of the optimization process. Fuzzy inference systems are based on linguistic rules that facilitate the definition of some common classes of noise patterns in handwritten images such as impulsive noise and background lines. We analyze the performance of our approach both in terms of recognition rate and speed. Our experimental results on a database of real-world handwritten images corroborate the effectiveness and feasibility of our approach in removing noise patterns and thus improving the recognition performance for noisy images.     

Large area Ag-TiO2 UV radiation sensor fabricated on a thermally oxidized titanium chip

A Schottky-type ultraviolet (UV) light sensor is fabricated on a thermally oxidized titanium chip. The device is of Ag-TiO₂-Ti structure, and the Schottky junction between silver and rutile is formed subsequent to the vacuum deposition of silver on the thermally grown rutile layer by a controlled thermal annealing in air. The device operates at −300 mV biasing established between silver and titanium electrodes. The dark reverse current of this diode increases four orders of magnitude when illuminated with UV light (355 nm) of 10 μW/mm² intensity. The device is almost insensitive to visible light and requires no filtering when used for ambient UV level detection. The operation mechanism of the device is described by photonic electron-hole pair generation in the carrier depleted titanium dioxide layer adjacent to the silver electrode. The electrode-to-electrode distance is 1 μm only affording much faster performance compared to photoconductive UV detectors fabricated based on titanium dioxide; the response and recovery times of the device are 10 ms and 17 ms, respectively. At its standby mode, a 1 mm² active area device consumes less than 10 pW of electric power. Sensors with sensitive areas as large as ∼10 mm² are easy to fabricate. The fabricated devices are rugged, resistant to UV degradation, and cost effective.     

Three new fuzzy neural networks learning algorithms based on clustering, training error and genetic algorithm

Three new learning algorithms for Takagi-Sugeno-Kang fuzzy system based on training error and genetic algorithm are proposed. The first two algorithms are consisted of two phases. In the first phase, the initial structure of neuro-fuzzy network is created by estimating the optimum points of training data in input-output space using KNN (for the first algorithm) and Mean-Shift methods (for the second algorithm) and keeps adding new neurons based on an error-based algorithm. Then in the second phase, redundant neurons are recognized and removed using a genetic algorithm. The third algorithm then builds the network in one phase using a modified version of error algorithm used in the first two methods. The KNN method is shown to be invariant to parameter K in KNN algorithm and in two simulated examples outperforms other neuro-fuzzy approaches in both performance and network compactness.     

Mining anomalous events against frequent sequences in surveillance videos from commercial environments

In the UK alone there are currently over 4.2 million operational CCTV cameras, that is virtually one camera for every 14th person, and this figure is increasing at a fast rate throughout the world (especially after the tragic events of 9/11 and 7/7) (Norris, McCahill, & Wood, 2004). Security concerns are not the only factor driving the rapid growth of CCTV cameras. Another important reason is the access of hidden knowledge extracted from CCTV footage to be used for effective business decision making, such as store designing, customer services, product marketing, reducing store shrinkage, etc.Events occurring in observed scenes are one of the most important semantic entities that can be extracted from videos (Anwar & Naftel, 2008). Most of the work presented in the past is based upon finding frequent event patterns or deals with discovering already known abnormal events. In contrast, in this paper we present a framework to discover unknown anomalous events associated with a frequent sequence of events (A_EASP); that is to discover events, which are unlikely to follow a frequent sequence of events. This information can be very useful for discovering unknown abnormal events and can provide early actionable intelligence to redeploy resources to specific areas of view (such as PTZ camera or attention of a CCTV user). Discovery of anomalous events against a sequential pattern can also provide business intelligence for store management in the retail sector. The proposed event mining framework is an extension to our previous research work presented in Anwar et al. (2010) and also takes the temporal aspect of anomalous events against frequent sequence of events into consideration, that is to discover anomalous events which are true for a specific time interval only and might not be an anomalous events against frequent sequence of events over a whole time spectrum and vice versa. To confront the memory expensive process of searching all the instances of multiple sequential patterns in each data sequence an efficient dynamic sequential pattern search mechanism is introduced. Different experiments are conducted to evaluate the proposed anomalous events against frequent sequence of events mining algorithm’s accuracy and performance.     

An iterative approach to determine the complexity of local models for robust identification of nonlinear systems

In this paper, a new multi-model approach is proposed for identification of nonlinear systems. In similar identification methods, the operating space is partitioned and a local model is suggested for each partition. In such approaches, since the same linear structure is often used for all local models; huge number of local linear models is usually required to reasonably model an operating region with severely nonlinear dynamics. Therefore the size of the global model may exponentially increase; and as a result model robustness may decrease. In the proposed approach the best model structure is selected for the particular nonlinear study system in an iterative approach. At each iteration, a choice is made to increase number of local models and/or increase the local model complexity. Furthermore, it determines the complexity of local models based on increasing the model accuracy and ensuring the model robustness. In order to optimize the model approximation capability and model robustness, a model term selection approach based on a forward orthogonal least squares algorithm and a criterion that minimizes the sum of the variance of the parameter estimates is applied. Simulation results show that the proposed method results in an excellent validation performance with fewer parameters.