Current Issue : January - March Volume : 2013 Issue Number : 1 Articles : 6 Articles
Automatic authentication systems, using biometric technology, are becoming increasingly important with the increased need\r\nfor person verification in our daily life. A few years back, fingerprint verification was done only in criminal investigations.\r\nNow fingerprints and face images are widely used in bank tellers, airports, and building entrances. Face images are easy to\r\nobtain, but successful recognition depends on proper orientation and illumination of the image, compared to the one taken at\r\nregistration time. Facial features heavily change with illumination and orientation angle, leading to increased false rejection as\r\nwell as false acceptance. Registering face images for all possible angles and illumination is impossible. In this work, we proposed a\r\nmemory efficient way to register (store) multiple angle and changing illumination face image data, and a computationally efficient\r\nauthentication technique, using multilayer perceptron (MLP). Though MLP is trained using a few registered images with different\r\norientation, due to generalization property of MLP, interpolation of features for intermediate orientation angles was possible. The\r\nalgorithm is further extended to include illumination robust authentication system. Results of extensive experiments verify the\r\neffectiveness of the proposed algorithm....
Mental image directed semantic theory (MIDST) has proposed an omnisensory mental image model and its description language\r\nLmd. This language is designed to represent and compute human intuitive knowledge of space and can provide multimedia\r\nexpressions with intermediate semantic descriptions in predicate logic. It is hypothesized that such knowledge and semantic\r\ndescriptions are controlled by human attention toward the world and therefore subjective to each human individual. This paper\r\ndescribes Lmd expression of human subjective knowledge of space and its application to aware computing in cross-media operation\r\nbetween linguistic and pictorial expressions as spatial language understanding....
Mental care has become crucial with the rapid growth of economy and technology. However, recent movements, such as\r\ngreen technologies, place more emphasis on environmental issues than on mental care. Therefore, this study presents an\r\nemerging technology called orange computing for mental care applications. Orange computing refers to health, happiness, and\r\nphysiopsychological care computing, which focuses on designing algorithms and systems for enhancing body and mind balance.\r\nThe representative color of orange computing originates from a harmonic fusion of passion, love, happiness, and warmth. A case\r\nstudy on a human-machine interactive and assistive system for emotion care was conducted in this study to demonstrate the\r\nconcept of orange computing. The system can detect emotional states of users by analyzing their facial expressions, emotional\r\nspeech, and laughter in a ubiquitous environment. In addition, the system can provide corresponding feedback to users according\r\nto the results. Experimental results show that the system can achieve an accurate audiovisual recognition rate of 81.8% on average,\r\nthereby demonstrating the feasibility of the system. Compared with traditional questionnaire-based approaches, the proposed\r\nsystem can offer real-time analysis of emotional status more efficiently....
Stock market prediction is an important area of financial forecasting, which attracts great interest to stock buyers and sellers, stock\r\ninvestors, policy makers, applied researchers, and many others who are involved in the capital market. In this paper, a comparative\r\nstudy has been conducted to predict stock index values using soft computing models and time series model. Paying attention to\r\nthe applied econometric noises because our considered series are time series, we predict Chittagong stock indices for the period\r\nfrom January 1, 2005 to May 5, 2011. We have used well-known models such as, the genetic algorithm (GA) model and the\r\nadaptive network fuzzy integrated system (ANFIS) model as soft computing forecasting models. Very widely used forecasting\r\nmodels in applied time series econometrics, namely, the generalized autoregressive conditional heteroscedastic (GARCH) model\r\nis considered as time series model. Our findings have revealed that the use of soft computing models is more successful than the\r\nconsidered time series model....
As part of ââ?¬Å?intelligence,ââ?¬Â the ââ?¬Å?awarenessââ?¬Â is the state or ability to perceive, feel, or be mindful of events, objects, or sensory\r\npatterns: in other words, to be conscious of the surrounding environment and its interactions. Inspired by early-ages human\r\nskills developments and especially by early-ages awareness maturation, the present paper accosts the robots intelligence from a\r\ndifferent slant directing the attention to combining both ââ?¬Å?cognitiveââ?¬Â and ââ?¬Å?perceptualââ?¬Â abilities. Within such a slant, the machine\r\n(robot) shrewdness is constructed on the basis of a multilevel cognitive concept attempting to handle complex artificial behaviors.\r\nThe intended complex behavior is the autonomous discovering of objects by robot exploring an unknown environment: in other\r\nwords, proffering the robot autonomy and awareness in and about unknown backdrop....
Entropy, as a complexity measure, is a fundamental concept for time series analysis. Among many methods, sample entropy\r\n(SampEn) has emerged as a robust, powerful measure for quantifying complexity of time series due to its insensitivity to data\r\nlength and its immunity to noise. Despite its popular use, SampEn is based on the standardized data where the variance is\r\nroutinely discarded, which may nonetheless provide additional information for discriminant analysis. Here we designed a simple,\r\nyet efficient, complexity measure, namely variance entropy (VarEn), to integrate SampEn with variance to achieve effective\r\ndiscriminant analysis. We applied VarEn to analyze local field potential (LFP) collected from visual cortex of macaque monkey\r\nwhile performing a generalized flash suppression task, in which a visual stimulus was dissociated from perceptual experience, to\r\nstudy neural complexity of perceptual awareness. We evaluated the performance of VarEn in comparison with SampEn on LFP,\r\nat both single and multiple scales, in discriminating different perceptual conditions. Our results showed that perceptual visibility\r\ncould be differentiated by VarEn, with significantly better discriminative performance than SampEn. Our findings demonstrate\r\nthat VarEn is a sensitive measure of perceptual visibility, and thus can be used to probe perceptual awareness of a stimulus....
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