St. Thomas' College of Engineering and Technology

About: St. Thomas' College of Engineering and Technology is a based out in . It is known for research contribution in the topics: Steganography & Image segmentation. The organization has 274 authors who have published 406 publications receiving 1924 citations.

General and Interval Type-2 Fuzzy Face-Space Approach to Emotion Recognition

Abstract: Facial expressions of a person representing similar emotion are not always unique. Naturally, the facial features of a subject taken from different instances of the same emotion have wide variations. In the presence of two or more facial features, the variation of the attributes together makes the emotion recognition problem more complicated. This variation is the main source of uncertainty in the emotion recognition problem, which has been addressed here in two steps using type-2 fuzzy sets. First a type-2 fuzzy face space is constructed with the background knowledge of facial features of different subjects for different emotions. Second, the emotion of an unknown facial expression is determined based on the consensus of the measured facial features with the fuzzy face space. Both interval and general type-2 fuzzy sets (GT2FS) have been used separately to model the fuzzy face space. The interval type-2 fuzzy set (IT2FS) involves primary membership functions for m facial features obtained from n-subjects, each having l-instances of facial expressions for a given emotion. The GT2FS in addition to employing the primary membership functions mentioned above also involves the secondary memberships for individual primary membership curve, which has been obtained here by formulating and solving an optimization problem. The optimization problem here attempts to minimize the difference between two decoded signals: the first one being the type-1 defuzzification of the average primary membership functions obtained from the n-subjects, while the second one refers to the type-2 defuzzified signal for a given primary membership function with secondary memberships as unknown. The uncertainty management policy adopted using GT2FS has resulted in a classification accuracy of 98.333% in comparison to 91.667% obtained by its interval type-2 counterpart. A small improvement (approximately 2.5%) in classification accuracy by IT2FS has been attained by pre-processing measurements using the well-known interval approach.

A cryptanalytic attack on the knapsack cryptosystem using binary Firefly algorithm

Abstract: This paper presents a binary Firefly Algorithm (FA), for cryptanalysis of knapsack cipher algorithm so as to deduce the meaning of an encrypted message (i.e. to determine a plaintext from the cipher text). The implemented algorithm has been characterized, in this paper, by a number of properties and operations that build up and evolve the fireflies' positions. These include light intensity, distances, attractiveness, and position updating, fitness evaluation. The results of the Firefly algorithm are compared with the results shown by Genetic Algorithm (GA), to discover the plaintext from the cipher text. Experimental results show that binary firefly algorithm is capable of finding correct results more efficiently than GA.

Patient health management system using e-health monitoring architecture

Abstract: This paper illustrates the design and implementation of an e-health monitoring networked system. The architecture for this system is based on smart devices and wireless sensor networks for real time analysis of various parameters of patients. This system is aimed at developing a set of modules which can facilitate the diagnosis for the doctors through tele-monitoring of patients. It also facilitates continuous investigation of the patient for emergencies looked over by attendees and caregivers. A set of medical and environmental sensors are used to monitor the health as well as the surrounding of the patient. This sensor data is then relayed to the server using a smart device or a base station in close proximity. The doctors and caregivers monitor the patient in real time through the data received through the server. The medical history of each patient including medications and medical reports are stored on cloud for easy access and processing for logistics and prognosis of future complications. The architecture is so designed for monitoring a unitary patient privately at home as well as multiple patients in hospitals and public health care units. Use of smartphones to relay data over internet reduces the total cost of the system. We have also considered the privacy and security aspects of the system keeping the provision for selective authority for patients and their relatives to access the cloud storage as well as the possible threats to the system. We have also introduced a novel set of value added services through this paper which include Real Time Health Advice and Action (ReTiHA) and Parent monitoring for people with their family living abroad.

Autocorrelation Aided Random Forest Classifier-Based Bearing Fault Detection Framework

Abstract: Rolling bearing defects in induction motors are usually diagnosed using vibration signal analysis. For accurate detection of rolling bearing defects, appropriate feature extraction from vibration signals is necessary, failure of which may lead to incorrect interpretation. Considering the above fact, this article presents an autocorrelation aided feature extraction method for diagnosis of rolling bearing defects. To this end, the vibration signals of healthy as well as different faulty bearings were recorded using accelerometers and autocorrelation of the respective vibration signals were done to examine their self-similarity in time scale. Following this, several statistical, hjorth as well as non-linear features were extracted from the respective vibration correlograms and were subjected to feature reduction using recursive feature elimination technique. The dimensionally reduced top ranked feature vectors were subsequently fed to a random forest classifier for classification of vibration signals. A large number of experiments were carried out for (i) three different fault diameters at (ii) four different shaft speeds and also at (iii) two different sampling frequencies. Besides, for each condition, six binary class and one multiclass classification problem is also addressed in this paper, resulting in a total 112 different classification tasks. It was observed that the proposed method has yielded very high accuracy in identifying different bearing defects which can be practically implemented for automated bearing fault detection of induction motors.