Amita Chatterjee

Emotion Recognition From Facial Expressions and Its Control Using Fuzzy Logic

This paper presents a fuzzy relational approach to human emotion recognition from facial expressions and its control. The proposed scheme uses external stimulus to excite specific emotions in human subjects whose facial expressions are analyzed by segmenting and localizing the individual frames into regions of interest. Selected facial features such as eye opening, mouth opening, and the length of eyebrow constriction are extracted from the localized regions, fuzzified, and mapped onto an emotion space by employing Mamdani-type relational models. A scheme for the validation of the system parameters is also presented. This paper also provides a fuzzy scheme for controlling the transition of emotion dynamics toward a desired state. Experimental results and computer simulations indicate that the proposed scheme for emotion recognition and control is simple and robust, with good accuracy.

EEG Analysis for Olfactory Perceptual-Ability Measurement Using a Recurrent Neural Classifier

A recurrent neural network model is designed to classify (pretrained) aromatic stimuli and discriminate noisy stimuli of both similar and different genres, using EEG analysis of the experimental subjects. The design involves determining the weights of the selected recurrent dynamics so that for a given base stimulus, the dynamics converges to one of several optima (local attractors) on the given Lyapunov energy surface. Experiments undertaken reveal that for small noise amplitude below a selected threshold, the dynamics essentially converges to fixed stable attractor. However, with a slight increase in noise amplitude above the selected threshold, the local attractor of the dynamics shifts in the neighborhood of the attractor obtained for the noise-free standard stimuli. The other important issues undertaken in this paper include a novel algorithm for evolutionary feature selection and data-point reduction from multiple experimental EEG trials using principal component analysis. The confusion matrices constructed from experimental results show a marked improvement in classification accuracy in the presence of data point reduction algorithm. Statistical tests undertaken indicate that the proposed recurrent classifier outperforms its competitors with classification accuracy as the comparator. The importance of this paper is illustrated with a tea-taster selection problem, where an olfactory perceptual-ability measure is used to rank the tasters.

An evolutionary SPDE breeding-based hybrid particle swarm optimizer: application in coordination of robot ants for camera coverage area optimization

In this paper we propose a new Hybrid Particle Swarm Optimizer model based on particle swarm, with breeding concepts from novel evolutionary algorithms. The hybrid PSO combines traditional velocity and position update rules of RANDIW-PSO and ideas from Self Adaptive Pareto Differential Evolution Algorithm (SPDE). The hybrid model is tested and compared with some high quality PSO models like the RANDIW-PSO and TVIW-PSO. The results indicate two good prospects of our proposed hybrid PSO model: potential to achieve faster convergence as well as potential to find a better solution. The hybrid PSO model, with the abovementioned features, is then efficiently utilized to coordinate robot ants in order to help them to probe as much camera coverage area of some planetary surface or working field as possible with minimum common area coverage.

Brajendra Nath Seal: A Disenchanted Hegelian

Brajendra Nath Seal, one of the greatest savants of the nineteenth/twentieth century Bengal, set on his philosophical journey following in the footprints of Hegel. However, he discovered the flaws and biases in the Hegelian system of thought quite early. Having imbibed the wisdom of the East and the West, he developed his own philosophy characterized by syncretism, internationalism and interdisciplinarity. He drew the attention of the Western world to the scientific temper of the Indian mind garnering evidence from the ancient Indian philosophical treatises. He was the architect of the subject ‘Indian philosophy’ as we study it today. His philosophy of education and academic administration are still relevant.

Diṅnāga and Mental Models: A Reconstruction

In the fifth century c.e. , Diṇnāga introduced a distinction between inferencefor-oneself (svarthānumāna) and inference-for-others (parārthānumāna), which was adopted by all later pramāṇa theorists. A reevaluation of this well-known distinction has led us to some philosophically significant theses, which we propose to discuss here. Many scholars have already pointed out that (a) the aim of the Buddhists in developing a theory of inference was different from that of a formal logician; (b) svarthānumāna falls in the domain of psychology of reasoning, while parārthānumāna falls in the domain of logic proper; and (c) parārthānumāna should be considered a model-theoretic as opposed to a prooftheoretic enterprise. In consonance with these views, it is aimed to show that (1) Diṇnāgas account of good inferential process leading to sound inference as laid down in the Hetucakaraḍamaru is very similar to the Mental Model Theory proposed by P. N. Johnson-Laird and others, and (2) although Jonardon Ganeris reinterpretation of the early Nyāya inference as a type of case-based reasoning may be extended to the Buddhist parārthānumāna, the most plausible reinterpretation of svārthānumāna can be given in terms of mental models.