The Experts below are selected from a list of 18645 Experts worldwide ranked by ideXlab platform
P. Rangarajan - One of the best experts on this subject based on the ideXlab platform.
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Novel effective X-path particle swarm optimization based deprived video data Retrieval for smart city
Cluster Computing, 2019Co-Authors: S. Thanga ramya, Bhuvaneshwari Arunagiri, P. RangarajanAbstract:With the tremendous increase in low resolution videos on video sharing websites, Retrieval of a correct video becomes a tougher task. The existing methods provide Retrieval approaches based on minimum number of features comparison. It leads to an inefficient video Retrieval. Most researches had concentrated on tracking ability and conversion of low resolution to high resolution videos. These methods failed to provide fast Retrieval of videos from large databases. The proposed work is concentrated mostly on riot videos from large video repositories to identify the previous criminal records in a particular region of the smart city (Cocchia in Smart and digital city: a systematic literature review, Springer International Publishing, Switzerland, 2014 ; Pardo and Taewoo in Proceedings of the 12th annual international conference on digital government research, ACM, New York, 2011 ). It uses certain combination ofobject oriented features like object and camera motion feature, color histogram and edge detection technique. In the proposed Retrieval Process, the key frames are extracted from the original video instead of using the whole video information for Retrieval Process. Object Oriented features were then extracted from these key frames and saved in database. Then, the Retrieval Process is done by searching the availability of relevant Object Oriented values based on the query submitted by the user. Thus the combination of four different features provides an efficient Retrieval of low resolution videos from the database. The retrieved video may include redundant information in the projected work. To avoid such redundancy, particle swarm optimization (PSO) is used. The result of query video is compared with database video using degree of closeness measurement. Consequently, low resolution video Retrieval based on PSO seems to be encouraging in terms of its performance in extracting videos than existing Retrieval approaches.
Masaki Fukunaga - One of the best experts on this subject based on the ideXlab platform.
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respiratory modulation of cognitive performance during the Retrieval Process
PLOS ONE, 2018Co-Authors: Nozomu H Nakamura, Masaki FukunagaAbstract:Recent research suggests that cognitive performance might be altered by the respiratory-synchronized activity generated in the brain. Previous human studies, however, have yielded inconsistent results when assessing task performance during distinct respiratory phases (inspiratory phase vs. expiratory phase). We therefore tested whether cognitive performance was regulated based on the timing of breathing components (e.g., expiratory-to-inspiratory (EI) phase transition) during the Retrieval Process. To determine the role of respiration in performance, the present study employed healthy subjects (n = 18) in a delayed matching-to-sample visual recognition task where a test cue was given in the respiratory phase-locked (Phased) or regularly paced (Non-phased) presentation paradigm. During the Phased session but not during the Non-phased session, the response time (RT) of the task increased by 466 ms (p = 0.003), and accuracy decreased by 21.4% (p = 0.004) when the Retrieval Process encompassed the EI transition. Breathing-dependent changes were particularly prominent when the EI transition occurred during the middle step of the Retrieval Process. Meanwhile, changes in the RT and accuracy were not observed when the Retrieval Process encompassed the inspiratory-to-expiratory phase transition. This is the first time that a certain phase transition in the respiratory cycle has been shown to modulate performance on a time scale of several seconds in a cognitive task. We propose that attenuation of these breathing-dependent cognitive fluctuations might be crucial for the maintenance and stability of successful performance in daily life and sports.
S. Tripathi - One of the best experts on this subject based on the ideXlab platform.
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Multi-server optimal bandwidth monitoring for collaborative distributed Retrieval
2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512), 2004Co-Authors: Lihang Ying, A. Basu, S. TripathiAbstract:Accurate bandwidth monitoring is critical for multimedia delivery applications that guarantee QoS parameters like time limit for transmission. In our past work we monitored the bandwidth at several servers independently, then chose the most reliable links to transmit scalable multimedia information. The assumption behind our past work was that each server retrieves a fixed part of a multimedia object, and this part is determined after the bandwidth monitoring Process and cannot be changed during the Retrieval Process. In this paper, we consider a Retrieval strategy where the different servers collaborate in the Retrieval Process; i.e., if a server finishes its share of the Retrieval task it helps with the Retrieval task of other servers. Given this "collaborative" Retrieval scenario, a new distributed monitoring algorithm is proposed. Simulation results show that the proposed algorithm is more effective than our past work in distributed Retrieval.
Jose Aguilar - One of the best experts on this subject based on the ideXlab platform.
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learning algorithm and Retrieval Process for the multiple classes random neural network model
Neural Processing Letters, 2001Co-Authors: Jose AguilarAbstract:Gelenbe has modeled neural networks using an analogy with queuing theory. This model (called Random Neural Network) calculates the probability of activation of the neurons in the network. Recently, Fourneau and Gelenbe have proposed an extension of this model, called multiple classes random neural network model. The purpose of this paper is to describe the use of the multiple classes random neural network model to learn patterns having different colors. We propose a learning algorithm for the recognition of color patterns based upon non-linear equations of the multiple classes random neural network model using gradient descent of a quadratic error function. In addition, we propose a progressive Retrieval Process with adaptive threshold values. The experimental evaluation shows that the learning algorithm provides good results.
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Multiple classes random neural network model and color pattern recognition problems
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for, 2000Co-Authors: Jose Aguilar, V. RossellAbstract:The purpose of the paper is to describe the use of the multiple classes random neural network model to learn patterns having different colors. We propose a learning algorithm for the recognition of color patterns based upon the nonlinear equations of the multiple classes random neural network model using gradient descent of a quadratic error function. In addition, we propose a progressive Retrieval Process with adaptive threshold value.
Thumrongrat Amornraksa - One of the best experts on this subject based on the ideXlab platform.
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Efficiency Improvement in Watermark Retrieval Process
Engineering and Applied Science Research, 2020Co-Authors: Rapee Puertpau, Thumrongrat AmornraksaAbstract:Presently, digital watermarking has been employed as a means for indicating the ownership of coppyrighted multimedia data. While many existing watermarking methods focused on the embedding method in order to achieve the imperceptibility and robustness characteristics, in this paper, a method for improving the efficiency in the watermark Retrieval Process by estimating the value of the original image is considered. The propossed method is based on a digital image averaging technique, which is widely applied in various frequency filters. By comparing different Retrieval techniques, the experimental results showed that the improved performances could be obtained when applying the mean filter in the watermark Retrieval Process.
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Reducing prediction error in watermark Retrieval Process by multiple non-linear filtering
The 8th Electrical Engineering Electronics Computer Telecommunications and Information Technology (ECTI) Association of Thailand - Conference 2011, 2011Co-Authors: Narong Mettripun, Nwe Ni Hlaing, Thitiporn Pramoun, Thumrongrat AmornraksaAbstract:This paper presents an improving method used in the watermark Retrieval Process of digital watermarking for color image based on the modification of image pixels. Based on the prior knowledge of the embedded watermark pattern, some errors from the watermark bit prediction Process can be reduced simply by changing the predicted bit value in accordance with all predicted bits value within the prediction area. To achieve this, we simply implement a non-linear filter with different characteristics to the predicted watermark bits, and output the result as the retrieved watermark. With the proposed method, the performance in both accuracy of the retrieved watermark and robustness of the embedded watermark can be improved. The experimental results show the improvements in term of NC at the equivalent PSNR after implementing our Retrieval method with the existing watermarking scheme, compared to the previous methods. The improved robustness of the embedded watermark against common image Processing based attacks and geometrical attacks are also evaluated and compared.
